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Journal articles on the topic 'Input-aware design'
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1
Samadi, Mehrzad, Amir Hormati, Mojtaba Mehrara, Janghaeng Lee, and Scott Mahlke. "Adaptive input-aware compilation for graphics engines." ACM SIGPLAN Notices 47, no. 6 (August 6, 2012): 13–22. http://dx.doi.org/10.1145/2345156.2254067.
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Dworczak, Piotr. "Inequality and Market Design." ACM SIGecom Exchanges 22, no. 1 (June 2024): 83–92. http://dx.doi.org/10.1145/3699824.3699831.
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Policymakers are often concerned about inequalities in the markets they control. In this letter, I argue that mechanism design has not responded sufficiently to the need for a comprehensive theory of inequality-aware market design. I review some of my recent work trying to fill this gap and identify research directions where input from computer scientists would be particularly useful.
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Ling, Zhen, Melanie Borgeest, Chuta Sano, Jazmyn Fuller, Anthony Cuomo, Sirong Lin, Wei Yu, Xinwen Fu, and Wei Zhao. "Privacy Enhancing Keyboard: Design, Implementation, and Usability Testing." Wireless Communications and Mobile Computing 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/3928261.
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To protect users from numerous password inference attacks, we invent a novel context aware privacy enhancing keyboard (PEK) for Android touch-based devices. Usually PEK would show a QWERTY keyboard when users input text like an email or a message. Nevertheless, whenever users enter a password in the input box on his or her touch-enabled device, a keyboard will be shown to them with the positions of the characters shuffled at random. PEK has been released on the Google Play since 2014. However, the number of installations has not lived up to our expectation. For the purpose of usable security and privacy, we designed a two-stage usability test and performed two rounds of iterative usability testing in 2016 and 2017 summer with continuous improvements of PEK. The observations from the usability testing are educational: (1) convenience plays a critical role when users select an input method; (2) people think those attacks that PEK prevents are remote from them.
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Hu, Xiao, and Paraschos Koutris. "Topology-aware Parallel Joins." Proceedings of the ACM on Management of Data 2, no. 2 (May 10, 2024): 1–25. http://dx.doi.org/10.1145/3651598.
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We study the design and analysis of parallel join algorithms in a topology-aware computational model. In this model, the network is modeled as a directed graph, where each edge is associated with a cost function that depends on the data transferred between the two endpoints and the link bandwidth. The computation proceeds in synchronous rounds and the cost of each round is measured as the maximum cost over all the edges in the network. Our main result is an asymptotically optimal join algorithm over symmetric tree topologies. The algorithm generalizes prior topology-aware protocols for set intersection and cartesian product to a binary join over an arbitrary input distribution with possible data skew.
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Duy Nhat Vien, Nguyen. "MMSE Beamforming Design for IoT MIMO SWIPT System." Journal of Science and Technology: Issue on Information and Communications Technology 4, no. 1 (September 30, 2018): 28. http://dx.doi.org/10.31130/jst.2018.69.
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Internet of Things (IoT) is a smart infrastructure of the unique identification device capable of wireless communication with each other, and human services on a large scale through the Internet. The IoT devices themselves must self-aware and harvest the energy they need from ambient sources. Simultaneous wireless information and power transfer (SWIPT) is a promising new solution to provide an opportunity for energy-restrained wireless devices to operate uninterruptedly. In this paper, we propose a beamforming approach for Internet of Things (IoT) multi-input multi-output (MIMO) SWIPT downlink systems, which minimizes the mean square error (MSE) of the information decode (ID) device while satisfying the energy constraint of the energy harvesting (EH) device. Simulation results are provided to evaluate the performance and confirm the efficiency of the proposed algorithm.
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McGeorge, Nicolette M., Susan Latiff, Christopher Muller, Lucas Dong, Ceara Chewning, Daniela Friedson-Trujillo, and Stephanie Kane. "Design and Development of a Prototype Heads-Up Display: Supporting Context-Aware, Semi-Automated, Hands-Free Medical Documentation." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 10, no. 1 (June 2021): 18–22. http://dx.doi.org/10.1177/2327857921101066.
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Military and civilian medical personnel across all echelons of medical care play a critical role in evaluating, caring for, and treating casualties. Accurate medical documentation is critical to effective, coordinated care and positive patient outcomes. We describe our prototype, Context-Aware Procedure Support Tools and User Interfaces for Rapid and Effective Workflows (CAPTURE). Leveraging human factors and usercentered design methods, and advanced artificial intelligence and computer vision capabilities, CAPTURE was designed to enable Tactical Combat Causality Care (TCCC) providers to more efficiently and effectively input critical medical information through hands-free interaction techniques and semiautomated data capture methods. We designed and prototyped a heads-up display that incorporates: multimodal interfaces, including augmented reality-based methods for input and information display to support visual image capture and heads-up interaction; post-care documentation support (e.g., artifacts to support post-care review and documentation); context-aware active and passive data capture methods, specifically natural language interpretation using systemic functional grammars; and computer vision technologies for semi-automated data capture capabilities. During the course of this project we encountered challenges towards effective design which fall into three main categories: (1) challenges related to designing novel multimodal interfaces; (2) technical challenges related to software and hardware development to meet design needs; and (3) challenges as a result of domain characteristics and operational constraints. We discuss how we addressed some of these challenges and provide additional considerations necessary for future research regarding next generation technology design for medical documentation in the field.
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Khanfir, Leïla, and Jaouhar Mouïne. "Systematic Hysteresis Analysis for Dynamic Comparators." Journal of Circuits, Systems and Computers 28, no. 06 (June 12, 2019): 1950100. http://dx.doi.org/10.1142/s0218126619501007.
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Comparator hysteresis is a memory phenomenon allowing outputs maintaining their past stable states until the input difference overcomes a given threshold voltage. In some applications, such as ADCs and memories, hysteresis is a deterministic error that should be minimized. In others, it can be considered as one of the design parameters, such as in implementing hysteresis control-based systems such as peak detectors and spectrum analyzers. In any case, the designer should be aware of how to estimate hysteresis to achieve the desired performances. This paper presents a mathematical approach to estimate hysteresis in clocked latch comparators. It has been demonstrated that hysteresis is not only sensitive to the clock frequency, but also to several design parameters including the transistors sizes, the common mode input voltage and the tracked input frequencies. The analysis results are validated through electrical simulations using a commercially available 0.18[Formula: see text][Formula: see text]m CMOS technology showing a maximum error of 8.6%.
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Peng, Jinlong, Zekun Luo, Liang Liu, and Boshen Zhang. "FRIH: Fine-Grained Region-Aware Image Harmonization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 5 (March 24, 2024): 4478–86. http://dx.doi.org/10.1609/aaai.v38i5.28246.
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Image harmonization aims to generate a more realistic appearance of foreground and background for a composite image. All the existing methods perform the same harmonization process for the whole foreground. However, the implanted foreground always contains different appearance patterns. Existing solutions ignore the difference of each color block and lose some specific details. Therefore, we propose a novel global-local two stages framework for Fine-grained Region-aware Image Harmonization (FRIH). In the first stage, the whole input foreground mask is used to make a global coarse-grained harmonization. In the second stage, we adaptively cluster the input foreground mask into several submasks. Each submask and the coarsely adjusted image are concatenated respectively and fed into a lightweight cascaded module, refining the global harmonization result. Moreover, we further design a fusion prediction module to generate the final result, utilizing the different degrees of harmonization results comprehensively. Without bells and whistles, our FRIH achieves a competitive performance on iHarmony4 dataset with a lightweight model.
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Wang, Qi, Yiming Ouyang, Zhengfeng Huang, and Huaguo Liang. "Workload-Aware WiNoC Design with Intelligent Reconfigurable Wireless Interface." Security and Communication Networks 2023 (May 9, 2023): 1–14. http://dx.doi.org/10.1155/2023/9519044.
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By introducing wireless interfaces in conventional wired routers or hubs, wireless network-on-chip (WiNoC) is proposed to relieve congestion pressure from high volume inter-subnet data transmission. Generally, processing elements on chip receive input data and return feedback through network interface, and data transmission function in Network-on-Chip (NoC) is completed by routers. Hubs equipped with wireless interface are fixed to certain wired routers. While wireless channels may not be fully utilized due to unbalanced workload and constant hub-router connection, e.g., certain nodes processing excess inter-subnet data traffic are far away from hubs. In this paper, we proposed a workload-aware WiNoC design with intelligent reconfigurable wireless interface to improve wireless resources utilization and mitigate congestion. Through multidimensional analysis of traffic flow, a 4-layer neural network is trained offline and applied to analyze workload in each tile, and return three most potential tiles for wireless interface reconfiguration to fully utilize wireless channel and lowing latency. We also implement a historical traffic information-based reconfigurable scheme for comparation. Evaluation results show that in an 8 × 8 hybrid mesh topology, the proposed scheme can achieve 10%–16% reduction in network latency and 5%–11% increment in network throughput compared with fixed-link hub-node connection scheme under several mixed traffic patterns.
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Trevithick, Alex, Matthew Chan, Michael Stengel, Eric Chan, Chao Liu, Zhiding Yu, Sameh Khamis, Manmohan Chandraker, Ravi Ramamoorthi, and Koki Nagano. "Real-Time Radiance Fields for Single-Image Portrait View Synthesis." ACM Transactions on Graphics 42, no. 4 (July 26, 2023): 1–15. http://dx.doi.org/10.1145/3592460.
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We present a one-shot method to infer and render a photorealistic 3D representation from a single unposed image (e.g., face portrait) in real-time. Given a single RGB input, our image encoder directly predicts a canonical triplane representation of a neural radiance field for 3D-aware novel view synthesis via volume rendering. Our method is fast (24 fps) on consumer hardware, and produces higher quality results than strong GAN-inversion baselines that require test-time optimization. To train our triplane encoder pipeline, we use only synthetic data, showing how to distill the knowledge from a pretrained 3D GAN into a feedforward encoder. Technical contributions include a Vision Transformer-based triplane encoder, a camera data augmentation strategy, and a well-designed loss function for synthetic data training. We benchmark against the state-of-the-art methods, demonstrating significant improvements in robustness and image quality in challenging real-world settings. We showcase our results on portraits of faces (FFHQ) and cats (AFHQ), but our algorithm can also be applied in the future to other categories with a 3D-aware image generator.
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Chatterjee, Subarna, Meena Jagadeesan, Wilson Qin, and Stratos Idreos. "Cosine." Proceedings of the VLDB Endowment 15, no. 1 (September 2021): 112–26. http://dx.doi.org/10.14778/3485450.3485461.
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We present a self-designing key-value storage engine, Cosine, which can always take the shape of the close to "perfect" engine architecture given an input workload, a cloud budget, a target performance, and required cloud SLAs. By identifying and formalizing the first principles of storage engine layouts and core key-value algorithms, Cosine constructs a massive design space comprising of sextillion (10 36 ) possible storage engine designs over a diverse space of hardware and cloud pricing policies for three cloud providers - AWS, GCP, and Azure. Cosine spans across diverse designs such as Log-Structured Merge-trees, B-trees, Log-Structured Hash-tables, in-memory accelerators for filters and indexes as well as trillions of hybrid designs that do not appear in the literature or industry but emerge as valid combinations of the above. Cosine includes a unified distribution-aware I/O model and a learned concurrency-aware CPU model that with high accuracy can calculate the performance and cloud cost of any possible design on any workload and virtual machines. Cosine can then search through that space in a matter of seconds to find the best design and materializes the actual code of the resulting storage engine design using a templated Rust implementation. We demonstrate that on average Cosine outperforms state-of-the-art storage engines such as write-optimized RocksDB, read-optimized WiredTiger, and very write-optimized FASTER by 53x, 25x, and 20x, respectively, for diverse workloads, data sizes, and cloud budgets across all YCSB core workloads and many variants.
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Ahmadianshalchi, Alaleh, Syrine Belakaria, and Janardhan Rao Doppa. "Preference-Aware Constrained Multi-Objective Bayesian Optimization (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (March 24, 2024): 23436–38. http://dx.doi.org/10.1609/aaai.v38i21.30418.
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We consider the problem of constrained multi-objective optimization over black-box objectives, with user-defined preferences, with a largely infeasible input space. Our goal is to approximate the optimal Pareto set from the small fraction of feasible inputs. The main challenges include huge design space, multiple objectives, numerous constraints, and rare feasible inputs identified only through expensive experiments. We present PAC-MOO, a novel preference-aware multi-objective Bayesian optimization algorithm to solve this problem. It leverages surrogate models for objectives and constraints to intelligently select the sequence of inputs for evaluation to achieve the target goal.
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ER, Mahendrawathi, Carola Funke, Michael Rosemann, Franziska Goetz, and Tabitha Marie Wruck. "Trust-aware process design: the case of GoFood." Business Process Management Journal 28, no. 2 (February 8, 2022): 348–71. http://dx.doi.org/10.1108/bpmj-10-2021-0663.
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PurposeTrust is an increasingly important requirement for any business and as a result has become a contemporary design criterion for business processes. However, the literature to date is very much focused on the technical (security) aspects, which are provider centric, as opposed to trust that is customer centric. In this paper, the authors extended an initial meta-model of trust-aware process design by proposing a way to capture trust-intensity for four trust dimensions, i.e. input, people, process and output and an organizational trust position. The authors also investigate the deployment of the extended meta-model in practice.Design/methodology/approachAn extensive literature study is conducted to derive an understanding of the dimension's customer trust when interacting with an organization. Based on the findings of the literature review and a previously developed trust meta-model, the authors propose a way to describe an organizational trust position, i.e. the depiction of how much uncertainty is prevalent in the trust dimensions. Next, the authors conducted an exploratory case study using secondary data to validate the extended meta-model.FindingsThe case study demonstrated the applicability of the extended trust meta-model and derived actionable practices. In this case, the Indonesian food delivery company GoFood, the authors identified trust concerns in the input, process, resources and output of their business at the start of their operations. Since then, GoFood took specific actions to reduce their operational, behavioral and perceived uncertainty and these identified trust concerns. To a lesser degree, GoFood has managed vulnerability issues and invested in measures to increase customers' confidence. As a result of reduced uncertainties, GoFood's business has grown and became the number one in food service delivery in Indonesia.Research limitations/implicationsThe approach to capture trust (in the trust dimensions) is still a simplified version and a pre-step for a fully developed management tool or method. The use of a secondary data from a single case study also limits the validity and generalizability of the findings.Practical implicationsThe extended meta-model proposed in this paper has several implications related to the organization's BPM capabilities. The result also demonstrates how trust measures related to reducing uncertainty, reducing vulnerability and increasing confidence can be applied in practice. Strategies used by the case company presented here such as rating systems to increase confidence can be used by other firms within a similar context.Social implicationsHaving an empirically validated framework for the management of trust, allows organizations to execute an operational model for the development of trusted engagement with the main benefactor being the customer.Originality/valuePrevious trust-related studies focused on conceptual ideas only, relied on fictive examples or were very much focused on the technical (security) aspects of business processes. This study is the first empirical validation of a trust meta-model that serves managers to understand their trust position and to guide trust-building actions.
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Lin, Xuxin, and Yanyan Liang. "Region-Aware Deep Feature-Fused Network for Robust Facial Landmark Localization." Mathematics 11, no. 19 (September 22, 2023): 4026. http://dx.doi.org/10.3390/math11194026.
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In facial landmark localization, facial region initialization usually plays an important role in guiding the model to learn critical face features. Most facial landmark detectors assume a well-cropped face as input and may underperform in real applications if the input is unexpected. To alleviate this problem, we present a region-aware deep feature-fused network (RDFN). The RDFN consists of a region detection subnetwork and a region-wise landmark localization subnetwork to explicitly solve the input initialization problem and derive the landmark score maps, respectively. To exploit the association between tasks, we develop a cross-task feature fusion scheme to extract multi-semantic region features while trading off their importance in different dimensions via global channel attention and global spatial attention. Furthermore, we design a within-task feature fusion scheme to capture the multi-scale context and improve the gradient flow for the landmark localization subnetwork. At the inference stage, a location reweighting strategy is employed to transform the score maps into 2D landmark coordinates. Extensive experimental results demonstrate that our method has competitive performance compared to recent state-of-the-art methods, achieving NMEs of 3.28%, 1.48%, and 3.43% on the 300W, AFLW, and COFW datasets, respectively.
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Wei, Xinyue, Minghua Liu, Zhan Ling, and Hao Su. "Approximate convex decomposition for 3D meshes with collision-aware concavity and tree search." ACM Transactions on Graphics 41, no. 4 (July 2022): 1–18. http://dx.doi.org/10.1145/3528223.3530103.
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Approximate convex decomposition aims to decompose a 3D shape into a set of almost convex components, whose convex hulls can then be used to represent the input shape. It thus enables efficient geometry processing algorithms specifically designed for convex shapes and has been widely used in game engines, physics simulations, and animation. While prior works can capture the global structure of input shapes, they may fail to preserve fine-grained details (e.g., filling a toaster's slots), which are critical for retaining the functionality of objects in interactive environments. In this paper, we propose a novel method that addresses the limitations of existing approaches from three perspectives: (a) We introduce a novel collision-aware concavity metric that examines the distance between a shape and its convex hull from both the boundary and the interior. The proposed concavity preserves collision conditions and is more robust to detect various approximation errors. (b) We decompose shapes by directly cutting meshes with 3D planes. It ensures generated convex hulls are intersection-free and avoids voxelization errors. (c) Instead of using a one-step greedy strategy, we propose employing a multi-step tree search to determine the cutting planes, which leads to a globally better solution and avoids unnecessary cuttings. Through extensive evaluation on a large-scale articulated object dataset, we show that our method generates decompositions closer to the original shape with fewer components. It thus supports delicate and efficient object interaction in downstream applications.
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Wu, Zongwei, Liangyu Chai, Nanxuan Zhao, Bailin Deng, Yongtuo Liu, Qiang Wen, Junle Wang, and Shengfeng He. "Make Your Own Sprites." ACM Transactions on Graphics 41, no. 6 (November 30, 2022): 1–16. http://dx.doi.org/10.1145/3550454.3555482.
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Pixel art is a unique art style with the appearance of low resolution images. In this paper, we propose a data-driven pixelization method that can produce sharp and crisp cell effects with controllable cell sizes. Our approach overcomes the limitation of existing learning-based methods in cell size control by introducing a reference pixel art to explicitly regularize the cell structure. In particular, the cell structure features of the reference pixel art are used as an auxiliary input for the pixelization process, and for measuring the style similarity between the generated result and the reference pixel art. Furthermore, we disentangle the pixelization process into specific cell-aware and aliasing-aware stages, mitigating the ambiguities in joint learning of cell size, aliasing effect, and color assignment. To train our model, we construct a dedicated pixel art dataset and augment it with different cell sizes and different degrees of anti-aliasing effects. Extensive experiments demonstrate its superior performance over state-of-the-arts in terms of cell sharpness and perceptual expressiveness. We also show promising results of video game pixelization for the first time. Code and dataset are available at https://github.com/WuZongWei6/Pixelization.
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Wu, Zizhang, Yunzhe Wu, Jian Pu, Xianzhi Li, and Xiaoquan Wang. "Attention-Based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (June 26, 2023): 2892–900. http://dx.doi.org/10.1609/aaai.v37i3.25391.
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Monocular 3D object detection is a low-cost but challenging task, as it requires generating accurate 3D localization solely from a single image input. Recent developed depth-assisted methods show promising results by using explicit depth maps as intermediate features, which are either precomputed by monocular depth estimation networks or jointly evaluated with 3D object detection. However, inevitable errors from estimated depth priors may lead to misaligned semantic information and 3D localization, hence resulting in feature smearing and suboptimal predictions. To mitigate this issue, we propose ADD, an Attention-based Depth knowledge Distillation framework with 3D-aware positional encoding. Unlike previous knowledge distillation frameworks that adopt stereo- or LiDAR-based teachers, we build up our teacher with identical architecture as the student but with extra ground-truth depth as input. Credit to our teacher design, our framework is seamless, domain-gap free, easily implementable, and is compatible with object-wise ground-truth depth. Specifically, we leverage intermediate features and responses for knowledge distillation. Considering long-range 3D dependencies, we propose 3D-aware self-attention and target-aware cross-attention modules for student adaptation. Extensive experiments are performed to verify the effectiveness of our framework on the challenging KITTI 3D object detection benchmark. We implement our framework on three representative monocular detectors, and we achieve state-of-the-art performance with no additional inference computational cost relative to baseline models. Our code is available at https://github.com/rockywind/ADD.
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Alderighi, Thomas, Luigi Malomo, Bernd Bickel, Paolo Cignoni, and Nico Pietroni. "Volume decomposition for two-piece rigid casting." ACM Transactions on Graphics 40, no. 6 (December 2021): 1–14. http://dx.doi.org/10.1145/3478513.3480555.
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We introduce a novel technique to automatically decompose an input object's volume into a set of parts that can be represented by two opposite height fields. Such decomposition enables the manufacturing of individual parts using two-piece reusable rigid molds. Our decomposition strategy relies on a new energy formulation that utilizes a pre-computed signal on the mesh volume representing the accessibility for a predefined set of extraction directions. Thanks to this novel formulation, our method allows for efficient optimization of a fabrication-aware partitioning of volumes in a completely automatic way. We demonstrate the efficacy of our approach by generating valid volume partitionings for a wide range of complex objects and physically reproducing several of them.
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Li, Shuying, Muyi Han, Yuemei Qin, and Qiang Li. "Self-Attention Progressive Network for Infrared and Visible Image Fusion." Remote Sensing 16, no. 18 (September 11, 2024): 3370. http://dx.doi.org/10.3390/rs16183370.
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Visible and infrared image fusion is a strategy that effectively extracts and fuses information from different sources. However, most existing methods largely neglect the issue of lighting imbalance, which makes the same fusion models inapplicable to different scenes. Several methods obtain low-level features from visible and infrared images at an early stage of input or shallow feature extraction. However, these methods do not explore how low-level features provide a foundation for recognizing and utilizing the complementarity and common information between the two types of images. As a result, the complementarity and common information between the images is not fully analyzed and discussed. To address these issues, we propose a Self-Attention Progressive Network for the fusion of infrared and visible images in this paper. Firstly, we construct a Lighting-Aware Sub-Network to analyze lighting distribution, and introduce intensity loss to measure the probability of scene illumination. This approach enhances the model’s adaptability to lighting conditions. Secondly, we introduce self-attention learning to design a multi-state joint feature extraction module (MSJFEM) that fully utilizes the contextual information among input keys. It guides the learning of a dynamic attention matrix to strengthen the capacity for visual representation. Finally, we design a Difference-Aware Propagation Module (DAPM) to extract and integrate edge details from the source images while supplementing differential information. The experiments across three benchmark datasets reveal that the proposed approach exhibits satisfactory performance compared to existing methods.
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Elmeligy, Karim, and Hesham Omran. "Fast Design Space Exploration and Multi-Objective Optimization of Wide-Band Noise-Canceling LNAs." Electronics 11, no. 5 (March 5, 2022): 816. http://dx.doi.org/10.3390/electronics11050816.
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Design optimization of RF low-noise amplifiers (LNAs) remains a time-consuming and complex process. Iterations are needed to adjust impedance matching, gain, and noise figure (NF) simultaneously. The process can involve more iterations to adjust the non-linear behavior of the circuit which can be represented by the input-referred third-order intercept (IIP3). In this work, we present a variation-aware automated design and optimization flow for a wide-band noise-canceling LNA. We include the circuit non-linearity in the optimization flow without using a simulator in the loop. By describing the transistors using precomputed lookup tables (LUTs), a design database that contains 200,000 design points is generated in 3 s only without non-linearity computation and 10 s when non-linearity is taken into account. Using a gm/ID-based correct-by-construction design procedure, the generated design points automatically satisfy proper biasing, input matching, and gain matching requirements. The generated database enables the designer to visualize the design space and explore the design trade-offs. Moreover, multi-objective optimization across corners for a given set of specifications is applied to find the Pareto-optimal fronts of the design figures-of-merit. We demonstrate the presented flow using two design examples in a 65 nm process and the results are verified using Cadence Spectre.
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Hwang, Yongkeun, Yanghoon Kim, and Kyomin Jung. "Context-Aware Neural Machine Translation for Korean Honorific Expressions." Electronics 10, no. 13 (June 30, 2021): 1589. http://dx.doi.org/10.3390/electronics10131589.
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Neural machine translation (NMT) is one of the text generation tasks which has achieved significant improvement with the rise of deep neural networks. However, language-specific problems such as handling the translation of honorifics received little attention. In this paper, we propose a context-aware NMT to promote translation improvements of Korean honorifics. By exploiting the information such as the relationship between speakers from the surrounding sentences, our proposed model effectively manages the use of honorific expressions. Specifically, we utilize a novel encoder architecture that can represent the contextual information of the given input sentences. Furthermore, a context-aware post-editing (CAPE) technique is adopted to refine a set of inconsistent sentence-level honorific translations. To demonstrate the efficacy of the proposed method, honorific-labeled test data is required. Thus, we also design a heuristic that labels Korean sentences to distinguish between honorific and non-honorific styles. Experimental results show that our proposed method outperforms sentence-level NMT baselines both in overall translation quality and honorific translations.
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Chen, Hongming, Xiang Chen, Jiyang Lu, and Yufeng Li. "Rethinking Multi-Scale Representations in Deep Deraining Transformer." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 2 (March 24, 2024): 1046–53. http://dx.doi.org/10.1609/aaai.v38i2.27865.
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Existing Transformer-based image deraining methods depend mostly on fixed single-input single-output U-Net architecture. In fact, this not only neglects the potentially explicit information from multiple image scales, but also lacks the capability of exploring the complementary implicit information across different scales. In this work, we rethink the multi-scale representations and design an effective multi-input multi-output framework that constructs intra- and inter-scale hierarchical modulation to better facilitate rain removal and help image restoration. We observe that rain levels reduce dramatically in coarser image scales, thus proposing to restore rain-free results from the coarsest scale to the finest scale in image pyramid inputs, which also alleviates the difficulty of model learning. Specifically, we integrate a sparsity-compensated Transformer block and a frequency-enhanced convolutional block into a coupled representation module, in order to jointly learn the intra-scale content-aware features. To facilitate representations learned at different scales to communicate with each other, we leverage a gated fusion module to adaptively aggregate the inter-scale spatial-aware features, which are rich in correlated information of rain appearances, leading to high-quality results. Extensive experiments demonstrate that our model achieves consistent gains on five benchmarks.
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Lee, Jong-whi, and Jinhong Jung. "Time-Aware Random Walk Diffusion to Improve Dynamic Graph Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (June 26, 2023): 8473–81. http://dx.doi.org/10.1609/aaai.v37i7.26021.
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How can we augment a dynamic graph for improving the performance of dynamic graph neural networks? Graph augmentation has been widely utilized to boost the learning performance of GNN-based models. However, most existing approaches only enhance spatial structure within an input static graph by transforming the graph, and do not consider dynamics caused by time such as temporal locality, i.e., recent edges are more influential than earlier ones, which remains challenging for dynamic graph augmentation. In this work, we propose TiaRa (Time-aware Random Walk Diffusion), a novel diffusion-based method for augmenting a dynamic graph represented as a discrete-time sequence of graph snapshots. For this purpose, we first design a time-aware random walk proximity so that a surfer can walk along the time dimension as well as edges, resulting in spatially and temporally localized scores. We then derive our diffusion matrices based on the time-aware random walk, and show they become enhanced adjacency matrices that both spatial and temporal localities are augmented. Throughout extensive experiments, we demonstrate that TiaRa effectively augments a given dynamic graph, and leads to significant improvements in dynamic GNN models for various graph datasets and tasks.
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Shi, Jialiang, Rigui Zhou, Pengju Ren, and Zhengyu Long. "Multi-Dimensional Fusion Attention Mechanism with Vim-like Structure for Mobile Network Design." Applied Sciences 14, no. 15 (July 31, 2024): 6670. http://dx.doi.org/10.3390/app14156670.
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Recent advancements in mobile neural networks, such as the squeeze-and-excitation (SE) attention mechanism, have significantly improved model performance. However, they often overlook the crucial interaction between location information and channels. The interaction of multiple dimensions in feature engineering is of paramount importance for achieving high-quality results. The Transformer model and its successors, such as Mamba and Vision Mamba, have effectively combined features and linked location information. This approach has transitioned from NLP (natural language processing) to CV (computer vision). This paper introduces a novel attention mechanism for mobile neural networks inspired by the structure of Vim (Vision Mamba). It adopts a “1 + 3” architecture to embed multi-dimensional information into channel attention, termed ”Multi-Dimensional Vim-like Attention Mechanism”. The proposed method splits the input into two major branches: the left branch retains the original information for subsequent feature screening, while the right branch divides the channel attention into three one-dimensional feature encoding processes. These processes aggregate features along one channel direction and two spatial directions, simultaneously capturing remote dependencies and preserving precise location information. The resulting feature maps are then combined with the left branch to produce direction-aware, location-sensitive, and channel-aware attention maps. The multi-dimensional Vim-like attention module is simple and can be seamlessly integrated into classical mobile neural networks such as MobileNetV2 and ShuffleNetV2 with minimal computational overhead. Experimental results demonstrate that this attention module adapts well to mobile neural networks with a low parameter count, delivering excellent performance on the CIFAR-100 and MS COCO datasets.
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Walls, Richard, Celeste Viljoen, and Hennie de Clercq. "Parametric investigation into the cross-sectional stress-strain behaviour, stiffness and thermal forces of steel, concrete and composite beams exposed to fire." Journal of Structural Fire Engineering 11, no. 1 (August 24, 2019): 100–117. http://dx.doi.org/10.1108/jsfe-10-2018-0031.
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Purpose This paper aims to provide a parametric investigation into the behaviour of steel, concrete and composite beams exposed to fire. This investigation gives insight into the structural behaviour of elements experiencing thermal and mechanical loading illustrating reasons for observed global structural behaviour, and identifying how selected design parameters influence results obtained. Non-linear heating/thermal bowing behaviour is specifically considered. Design/methodology/approach Cross-sectional stresses and strains, resultant thermal forces, bending stiffness, axial stiffness and deflections are plotted for beams subjected to different fire regimes or input values. The impact of changes in input parameters on beam section properties is illustrated. Unusual structural responses, localised effects and general trends are identified in relation to variations in thermal gradients, concrete tensile capacity, standard fire exposure time and the assumed concrete flange widths of composite beams. Findings Stress-strain plots highlighting cross-sectional structural behaviour, trends in beam properties and the influence of design parameters are provided. Some counter-intuitive behaviour is explained, such as increased member stiffness being offset by increased thermal effects, leading to this parameter having negligible impact on global behaviour but a significant effect on local stresses and strains. Increased concrete strengths may lead to increased thermal deformations, whilst the inclusion of concrete tensile capacity typically has a minimal influence. Research limitations/implications The research focusses on cross-sectional properties, although results generated illustrate how global behaviour is affected. Practical implications Design engineers are made aware of how selected input values influence predicted structural response. Also, localised stress and strain behaviour relative to imposed loads and thermal effects can be identified. Originality/value This paper provides novel insight into the (sometimes counter-intuitive) behaviour of beams exposed to fire, highlighting trends and the influence of important input parameters on predicted response.
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Zhang, Yuxin, Weiming Dong, Fan Tang, Nisha Huang, Haibin Huang, Chongyang Ma, Tong-Yee Lee, Oliver Deussen, and Changsheng Xu. "ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models." ACM Transactions on Graphics 42, no. 6 (December 5, 2023): 1–14. http://dx.doi.org/10.1145/3618342.
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Personalizing generative models offers a way to guide image generation with user-provided references. Current personalization methods can invert an object or concept into the textual conditioning space and compose new natural sentences for text-to-image diffusion models. However, representing and editing specific visual attributes such as material, style, and layout remains a challenge, leading to a lack of disentanglement and editability. To address this problem, we propose a novel approach that leverages the step-by-step generation process of diffusion models, which generate images from low to high frequency information, providing a new perspective on representing, generating, and editing images. We develop the Prompt Spectrum Space P*, an expanded textual conditioning space, and a new image representation method called ProSpect. ProSpect represents an image as a collection of inverted textual token embeddings encoded from per-stage prompts, where each prompt corresponds to a specific generation stage (i.e., a group of consecutive steps) of the diffusion model. Experimental results demonstrate that P* and ProSpect offer better disentanglement and controllability compared to existing methods. We apply ProSpect in various personalized attribute-aware image generation applications, such as image-guided or text-driven manipulations of materials, style, and layout, achieving previously unattainable results from a single image input without fine-tuning the diffusion models. Our source code is available at https://github.com/zyxElsa/ProSpect.
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Das, Apangshu, Yallapragada C. Hareesh, and Sambhu Nath Pradhan. "NSGA-II Based Thermal-Aware Mixed Polarity Dual Reed–Muller Network Synthesis Using Parallel Tabular Technique." Journal of Circuits, Systems and Computers 29, no. 15 (July 2, 2020): 2020008. http://dx.doi.org/10.1142/s021812662020008x.
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Proposed work presents an OR-XNOR-based thermal-aware synthesis approach to reduce peak temperature by eliminating local hotspots within a densely packed integrated circuit. Tremendous increase in package density at sub-nanometer technology leads to high power-density that generates high temperature and creates hotspots. A nonexhaustive meta-heuristic algorithm named nondominated sorting genetic algorithm-II (NSGA-II) has been employed for selecting suitable input polarity of mixed polarity dual Reed–Muller (MPDRM) expansion function to reduce the power-density. A parallel tabular technique is used for input polarity conversion from Product-of-Sum (POS) to MPDRM function. Without performance degradation, the proposed MPDRM approach shows more than 50% improvement in the area and power savings and around 6% peak temperature reduction for the MCNC benchmark circuits than that of earlier literature at the logic level. Algorithmic optimized circuit decompositions are implemented in physical design domain using CADENCE INNOVUS and HotSpot tool and silicon area, power consumption and absolute temperature are reported to validate the proposed technique.
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Kensek, Karen, Ye Ding, and Travis Longcore. "GREEN BUILDING AND BIODIVERSITY: FACILITATING BIRD FRIENDLY DESIGN WITH BUILDING INFORMATION MODELS." Journal of Green Building 11, no. 2 (March 2016): 116–30. http://dx.doi.org/10.3992/jgb.11.2.116.1.
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Green buildings should respect nature and endeavor to mitigate harmful effects to the environment and occupants. This is often interpreted as creating sustainable sites, consuming less energy and water, reusing materials, and providing excellent indoor environmental quality. Environmentally friendly buildings should also consider literally the impact that they have on birds, millions of them. A major factor in bird collisions with buildings is the choice of building materials. These choices are usually made by the architect who may not be aware of the issue or may be looking for guidance from certification programs such as LEED. As a proof of concept for an educational tool, we developed a software-assisted approach to characterize whether a proposed building design would earn a point for the LEED Pilot Credit 55: Avoiding Bird Collisions. Using the visual programming language Dynamo with the common building information modeling software Revit, we automated the assessment of designs. The approach depends on parameters that incorporate assessments of bird threat for façade materials, analyzes building geometry relative to materials, and processes user input on building operation to produce the assessment.
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Diaz, Kristian, and Ying-Khai Teh. "Design and Power Management of a Secured Wireless Sensor System for Salton Sea Environmental Monitoring." Electronics 9, no. 4 (March 25, 2020): 544. http://dx.doi.org/10.3390/electronics9040544.
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An embedded system composed of commercial off the shelf (COTS) peripherals and microcontroller. The system will collect environmental data for Salton Sea, Imperial Valley, California in order to understand the development of environmental and health hazards. Power analysis of each system features (i.e. Central Processing Unit (CPU) core, Input/Output (I/O) buses, and peripheral (temperature, humidity, and optical dust sensor) are studied. Software-based power optimization utilizes the power information with hardware-assisted power gating to control system features. The control of these features extends system uptime in a field deployed finite energy scenario. The proposed power optimization algorithm can collect more data by increasing system up time when compared to a Low Power Energy Aware Processing (LEAP) approach. Lastly, the 128 bit Advanced Encryption Standard (AES) algorithm is applied on the collected data using various parameters. A hidden peripheral requirement that must be considered during design are also noted to impact the efficacy of this method.
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Hayashi, Yusuke, Yoshikatsu Matsugaki, and Tamotsu Ninomiya. "Design Consideration for High Step-Up Nonisolated Multicellular dc-dc Converter for PV Micro Converters." Journal of Engineering 2018 (2018): 1–16. http://dx.doi.org/10.1155/2018/5098083.
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High step-up nonisolated multicellular dc-dc converter has been newly proposed for PV microconverters. The multicellular converter consists of the nonisolated step-up cell converters using bidirectional semiconductor switches, and these cell converters are connected in Input Parallel Output Series (IPOS). The voltage transformation ratio of the step-up converter is N/(1-D) in case all the transistors in N cell converters are operated at the duty ratio of D. The proposed multicellular dc-dc converter also accomplishes high efficiency because of no magnetic coupling such as the high frequency transformer and the coupled inductor. Laboratory prototype has been fabricated to show the feasibility of the proposed converter. Design consideration for the 20 V–40 V to 384 V, 240 W nonisolated multicellular dc-dc converter has been also conducted, and the potential to achieve the efficiency of 98% has been shown. The proposed multicellular converter contributes to realizing the environmentally aware data centers for future low carbon society.
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G.S., Thyagaraju, and U. P. Kulkarni. "Rough Set Theory Based User Aware TV Program and Settings Recommender." International Journal of Advanced Pervasive and Ubiquitous Computing 4, no. 2 (April 2012): 48–64. http://dx.doi.org/10.4018/japuc.2012040105.
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In this paper the authors are proposing a design of TV program and settings recommendation engine utilizing contextual parameters like personal, social, temporal, mood, and activity. In addition to the contextual parameters the system utilizes the explicit or implicit user ratings and watching history to resolve the conflict if any while recommending the services. The System is implemented exploiting AI techniques like fuzzy logic and Rough Sets Based Decision Rules. The motivation behind the proposed work is i) to improve the user’s satisfaction level and ii) to improve the social relationship between user and TV. The context aware recommender utilizes social context data as an additional input to the recommendation task alongside information of users and TV programs. They have analyzed the recommendation process and performed a subjective test to show the usefulness of the proposed system for small families.
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Li, Linfeng, Licheng Zhang, Chiwei Zhu, and Zhendong Mao. "QGAE: an End-to-end Answer-Agnostic Question Generation Model for Generating Question-Answer Pairs." JUSTC 53 (2023): 1. http://dx.doi.org/10.52396/justc-2023-0002.
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Question generation aims to generate meaningful and fluent questions, which can address the lack of question-answer type annotated corpus by augmenting the available data. Using unannotated text with optional answers as input contents, question generation can be divided into two types based on whether answers are provided: answer-aware and answer-agnostic. While generating questions with providing answers is challenging, generating high-quality questions without providing answers is even more difficult, for both humans and machines. In order to address this issue, we proposed a novel end-to-end model called QGAE, which is able to transform answer-agnostic question generation into answer-aware question generation by directly extracting candidate answers. This approach effectively utilizes unlabeled data for generating high-quality question-answer pairs, and its end-to-end design makes it more convenient compared to a multi-stage method that requires at least two pre-trained models. Moreover, our model achieves better average scores and greater diversity. Our experiments show that QGAE achieves significant improvements in generating question-answer pairs, making it a promising approach for question generation.
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Li, Xinchen, Yuan Hong, Yang Xu, and Mu Hu. "VerFormer: Vertebrae-Aware Transformer for Automatic Spine Segmentation from CT Images." Diagnostics 14, no. 17 (August 25, 2024): 1859. http://dx.doi.org/10.3390/diagnostics14171859.
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The accurate and efficient segmentation of the spine is important in the diagnosis and treatment of spine malfunctions and fractures. However, it is still challenging because of large inter-vertebra variations in shape and cross-image localization of the spine. In previous methods, convolutional neural networks (CNNs) have been widely applied as a vision backbone to tackle this task. However, these methods are challenged in utilizing the global contextual information across the whole image for accurate spine segmentation because of the inherent locality of the convolution operation. Compared with CNNs, the Vision Transformer (ViT) has been proposed as another vision backbone with a high capacity to capture global contextual information. However, when the ViT is employed for spine segmentation, it treats all input tokens equally, including vertebrae-related tokens and non-vertebrae-related tokens. Additionally, it lacks the capability to locate regions of interest, thus lowering the accuracy of spine segmentation. To address this limitation, we propose a novel Vertebrae-aware Vision Transformer (VerFormer) for automatic spine segmentation from CT images. Our VerFormer is designed by incorporating a novel Vertebrae-aware Global (VG) block into the ViT backbone. In the VG block, the vertebrae-related global contextual information is extracted by a Vertebrae-aware Global Query (VGQ) module. Then, this information is incorporated into query tokens to highlight vertebrae-related tokens in the multi-head self-attention module. Thus, this VG block can leverage global contextual information to effectively and efficiently locate spines across the whole input, thus improving the segmentation accuracy of VerFormer. Driven by this design, the VerFormer demonstrates a solid capacity to capture more discriminative dependencies and vertebrae-related context in automatic spine segmentation. The experimental results on two spine CT segmentation tasks demonstrate the effectiveness of our VG block and the superiority of our VerFormer in spine segmentation. Compared with other popular CNN- or ViT-based segmentation models, our VerFormer shows superior segmentation accuracy and generalization.
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Thomas Craig, Kelly J., Laura C. Morgan, Ching-Hua Chen, Susan Michie, Nicole Fusco, Jane L. Snowdon, Elisabeth Scheufele, Thomas Gagliardi, and Stewart Sill. "Systematic review of context-aware digital behavior change interventions to improve health." Translational Behavioral Medicine 11, no. 5 (October 21, 2020): 1037–48. http://dx.doi.org/10.1093/tbm/ibaa099.
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Abstract Health risk behaviors are leading contributors to morbidity, premature mortality associated with chronic diseases, and escalating health costs. However, traditional interventions to change health behaviors often have modest effects, and limited applicability and scale. To better support health improvement goals across the care continuum, new approaches incorporating various smart technologies are being utilized to create more individualized digital behavior change interventions (DBCIs). The purpose of this study is to identify context-aware DBCIs that provide individualized interventions to improve health. A systematic review of published literature (2013–2020) was conducted from multiple databases and manual searches. All included DBCIs were context-aware, automated digital health technologies, whereby user input, activity, or location influenced the intervention. Included studies addressed explicit health behaviors and reported data of behavior change outcomes. Data extracted from studies included study design, type of intervention, including its functions and technologies used, behavior change techniques, and target health behavior and outcomes data. Thirty-three articles were included, comprising mobile health (mHealth) applications, Internet of Things wearables/sensors, and internet-based web applications. The most frequently adopted behavior change techniques were in the groupings of feedback and monitoring, shaping knowledge, associations, and goals and planning. Technologies used to apply these in a context-aware, automated fashion included analytic and artificial intelligence (e.g., machine learning and symbolic reasoning) methods requiring various degrees of access to data. Studies demonstrated improvements in physical activity, dietary behaviors, medication adherence, and sun protection practices. Context-aware DBCIs effectively supported behavior change to improve users’ health behaviors.
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Yang, Guo-Ye, Wen-Yang Zhou, Yun Cai, Song-Hai Zhang, and Fang-Lue Zhang. "Focusing on your subject: Deep subject-aware image composition recommendation networks." Computational Visual Media 9, no. 1 (October 18, 2022): 87–107. http://dx.doi.org/10.1007/s41095-021-0263-3.
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AbstractPhoto composition is one of the most important factors in the aesthetics of photographs. As a popular application, composition recommendation for a photo focusing on a specific subject has been ignored by recent deep-learning-based composition recommendation approaches. In this paper, we propose a subject-aware image composition recommendation method, SAC-Net, which takes an RGB image and a binary subject window mask as input, and returns good compositions as crops containing the subject. Our model first determines candidate scores for all possible coarse cropping windows. The crops with high candidate scores are selected and further refined by regressing their corner points to generate the output recommended cropping windows. The final scores of the refined crops are predicted by a final score regression module. Unlike existing methods that need to preset several cropping windows, our network is able to automatically regress cropping windows with arbitrary aspect ratios and sizes. We propose novel stability losses for maximizing smoothness when changing cropping windows along with view changes. Experimental results show that our method outperforms state-of-the-art methods not only on the subject-aware image composition recommendation task, but also for general purpose composition recommendation. We also have designed a multistage labeling scheme so that a large amount of ranked pairs can be produced economically. We use this scheme to propose the first subject-aware composition dataset SACD, which contains 2777 images, and more than 5 million composition ranked pairs. The SACD dataset is publicly available at https://cg.cs.tsinghua.edu.cn/SACD/.
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Di, Xinkai, Hai-Gang Yang, Yiping Jia, Zhihong Huang, and Ning Mao. "Exploring Efficient Acceleration Architecture for Winograd-Transformed Transposed Convolution of GANs on FPGAs." Electronics 9, no. 2 (February 7, 2020): 286. http://dx.doi.org/10.3390/electronics9020286.
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The acceleration architecture of transposed convolution layers is essential since transposed convolution operations, as critical components in the generative model of generative adversarial networks, are computationally intensive inherently. In addition, the pre-processing of inserting and padding with zeros for input feature maps causes many ineffective operations. Most of the already known FPGA (Field Programmable Gate Array) based architectures for convolution layers cannot tackle these issues. In this paper, we firstly propose a novel dataflow exploration through splitting the filters and its corresponding input feature maps into four sets and then applying the Winograd algorithm for fast processing with a high efficiency. Secondly, we present an underlying FPGA-based accelerator architecture that features owning processing units, with embedded parallel, pipelined, and buffered processing flow. At last, a parallelism-aware memory partition technique and the hardware-based design space are explored coordinating, respectively, for the required parallel operations and optimal design parameters. Experiments of several state-of-the-art GANs by our methods achieve an average performance of 639.2 GOPS on Xilinx ZCU102 and 162.5 GOPS on Xilinx VC706. In reference to a conventional optimized accelerator baseline, this work demonstrates an 8.6× (up to 11.7×) increase in processing performance, compared to below 2.2× improvement by the prior studies in the literature.
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Kong, Junhan, Mingyuan Zhong, James Fogarty, and Jacob O. Wobbrock. "The Ability-Based Design Mobile Toolkit (ABD-MT): Developer Support for Runtime Interface Adaptation Based on Users' Abilities." Proceedings of the ACM on Human-Computer Interaction 8, MHCI (September 24, 2024): 1–26. http://dx.doi.org/10.1145/3676524.
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Despite significant progress in the capabilities of mobile devices and applications, most apps remain oblivious to their users' abilities. To enable apps to respond to users' situated abilities, we created the Ability-Based Design Mobile Toolkit (ABD-MT). ABD-MT integrates with an app's user input and sensors to observe a user's touches, gestures, physical activities, and attention at runtime, to measure and model these abilities, and to adapt interfaces accordingly. Conceptually, ABD-MT enables developers to engage with a user's "ability profile,'' which is built up over time and inspectable through our API. As validation, we created example apps to demonstrate ABD-MT, enabling ability-aware functionality in 91.5% fewer lines of code compared to not using our toolkit. Further, in a study with 11 Android developers, we showed that ABD-MT is easy to learn and use, is welcomed for future use, and is applicable to a variety of end-user scenarios.
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Paul, Somnath, Subho Chatterjee, Saibal Mukhopadhyay, and Swarup Bhunia. "Energy-Efficient Reconfigurable Computing Using a Circuit-Architecture-Software Co-Design Approach." IEEE Journal on Emerging and Selected Topics in Circuits and Systems 1, no. 3 (September 2011): 369–80. http://dx.doi.org/10.1109/jetcas.2011.2165232.
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Reconfigurable computing frameworks such as field programmable gate array (FPGA) provide flexibility to map arbitrary applications. However, their intrinsic flexibility comes at the cost of significantly worse performance and power dissipation than their custom counterparts. Existing design solutions such as voltage scaling and multi-threshold assignment typically trade off energy for performance or vise versa. In this paper, we show that an integrated circuit-architecture-software co-design approach can be extremely effective to simultaneously improve the power and performance of a reconfigurable hardware framework, leading to large improvement in energy-delay product (EDP). First, we select a spatio-temporal reconfigurable computing architecture based on 2-threshold assignment-D memory-array. Applications are mapped to memory as multiple-input multiple-output lookup tables (LUTs) and are evaluated in temporal manner inside a computing element. Multiple such computing elements communicate spatially through programmable interconnects. Next, we exploit the read-dominant memory access pattern in reconfigurable hardware to design an asymmetric memory cell, which provides higher read performance and lower read power leading to improvement in the overall EDP during operation. We note that the proposed memory cell is also asymmetric in terms of its content, providing better read power for one of the logic states (logic “0” or “1”). Based on this observation, next we propose a content-aware application mapping approach, which tries to maximize the logic “0” or logic “1” content in the lookup tables. A design flow is presented to incorporate the proposed architecture, asymmetric memory cell design and content-aware mapping. We show that for both nanoscale complementary metal-oxide-semiconductor (CMOS) [static random access memory (SRAM)] as well as emerging non-CMOS [spin torque transfer random access memory (- TTRAM)] memory technologies, such a co-design solution can achieve significant improvement in system EDP over a conventional FPGA framework.
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Jeong, Young-Seob, Jiyoung Woo, and Ah Reum Kang. "Malware Detection on Byte Streams of PDF Files Using Convolutional Neural Networks." Security and Communication Networks 2019 (April 3, 2019): 1–9. http://dx.doi.org/10.1155/2019/8485365.
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With increasing amount of data, the threat of malware keeps growing recently. The malicious actions embedded in nonexecutable documents especially (e.g., PDF files) can be more dangerous, because it is difficult to detect and most users are not aware of such type of malicious attacks. In this paper, we design a convolutional neural network to tackle the malware detection on the PDF files. We collect malicious and benign PDF files and manually label the byte sequences within the files. We intensively examine the structure of the input data and illustrate how we design the proposed network based on the characteristics of data. The proposed network is designed to interpret high-level patterns among collectable spatial clues, thereby predicting whether the given byte sequence has malicious actions or not. By experimental results, we demonstrate that the proposed network outperform several representative machine-learning models as well as other networks with different settings.
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Chen, Xianyu, Jian Shen, Wei Xia, Jiarui Jin, Yakun Song, Weinan Zhang, Weiwen Liu, et al. "Set-to-Sequence Ranking-Based Concept-Aware Learning Path Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (June 26, 2023): 5027–35. http://dx.doi.org/10.1609/aaai.v37i4.25630.
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With the development of the online education system, personalized education recommendation has played an essential role. In this paper, we focus on developing path recommendation systems that aim to generating and recommending an entire learning path to the given user in each session. Noticing that existing approaches fail to consider the correlations of concepts in the path, we propose a novel framework named Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation (SRC), which formulates the recommendation task under a set-to-sequence paradigm. Specifically, we first design a concept-aware encoder module which can capture the correlations among the input learning concepts. The outputs are then fed into a decoder module that sequentially generates a path through an attention mechanism that handles correlations between the learning and target concepts. Our recommendation policy is optimized by policy gradient. In addition, we also introduce an auxiliary module based on knowledge tracing to enhance the model’s stability by evaluating students’ learning effects on learning concepts. We conduct extensive experiments on two real-world public datasets and one industrial dataset, and the experimental results demonstrate the superiority and effectiveness of SRC. Code now is available at https://gitee.com/mindspore/models/tree/master/research/recommend/SRC.
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Loewen, Shawn, and Solène Inceoglu. "The effectiveness of visual input enhancement on the noticing and L2 development of the Spanish past tense." Studies in Second Language Learning and Teaching 6, no. 1 (March 31, 2016): 89–110. http://dx.doi.org/10.14746/ssllt.2016.6.1.5.
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Textual manipulation is a common pedagogic tool used to emphasize specific features of a second language (L2) text, thereby facilitating noticing and, ideally, second language development. Visual input enhancement has been used to investigate the effects of highlighting specific grammatical structures in a text. The current study uses a quasi-experimental design to determine the extent to which textual manipulation increase (a) learners’ perception of targeted forms and (b) their knowledge of the forms. Input enhancement was used to highlight the Spanish preterit and imperfect verb forms and an eye tracker measured the frequency and duration of participants’ fixation on the targeted items. In addition, pretests and posttests of the Spanish past tense provided information about participants’ knowledge of the targeted forms. Results indicate that learners were aware of the highlighted grammatical forms in the text; however, there was no difference in the amount of attention between the enhanced and unenhanced groups. In addition, both groups improved in their knowledge of the L2 forms; however, again, there was no differential improvement between the two groups.
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YU, Fawen. "Study on Rural Eco-Governance in the Context of New-Type Urbanization." Chinese Journal of Urban and Environmental Studies 06, no. 02 (June 2018): 1850014. http://dx.doi.org/10.1142/s2345748118500148.
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During the process of new-type urbanization, industrial enterprises, as they move to the vast rural areas, have also brought pollution there. Rural eco-governance tackles not only the damage to natural resources and environment, industrial pollution, agricultural non-point source pollution and pollution caused by poultry and livestock raising, but also the aggravation of rural living environment. At present, governments at all levels usually focus solely on urban environment building and eco-governance in rural areas, as a result, is rarely aware of, insufficient in capital input, weak in technical and institutional support. To improve rural eco-governance substantially, governments at all levels should attach equal importance to rural and new urban ecological development and take effective measures in the following aspects: (1) improving top-level design and reinforce the leading position of the green development concept; (2) increasing capital input and improve rural eco-governance facilities; (3) making technological innovations and integration to support rural eco-governance; and (4) creating new governance mechanisms to enhance rural eco-governance.
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Kord Toudeshki, Leila, Mir Ali Seyyedi, and Afshin Salajegheh. "A Context-Aware Architecture for Realizing Business Process Adaptation Strategies Using Fuzzy Planning." International Journal of Software Engineering and Knowledge Engineering 32, no. 01 (January 2022): 37–70. http://dx.doi.org/10.1142/s0218194022500024.
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Business competency emerges in flexibility and reliability of services that an enterprise provides. To reach that, executing business processes on a context-aware business process management suite which is equipped with monitoring, modeling and adaptation mechanisms and smart enough to react properly using adaptation strategies at runtime, are a major requisite. In this paper, a context-aware architecture is described to bring adaptation to common business process execution software. The architecture comes with the how-to-apply methodology and is established based on process standards like business process modeling notation (BPMN), business process execution language (BPEL), etc. It follows MAPE-K adaptation cycle in which the knowledge, specifically contextual information and their related semantic rules — as the input of adaptation unit — is modeled in our innovative context ontology, which is also extensible for domain-specific purposes. Furthermore, to support separation of concerns, we took apart event-driven adaptation requirements from process instances; these requirements are triggered based on ontology reasoning. Also, the architecture supports fuzzy-based planning and extensible adaptation realization mechanisms to face new or changing situations adequately. We characterized our work in comparison with related studies based on five key adaptation metrics and also evaluated it using an online learning management system case study.
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Andrade, Roberto, Jenny Torres, and Iván Ortiz-Garcés. "Enhancing Security in Software Design Patterns and Antipatterns: A Framework for LLM-Based Detection." Electronics 14, no. 3 (February 1, 2025): 586. https://doi.org/10.3390/electronics14030586.
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The detection of security vulnerabilities in software design patterns and antipatterns is crucial for maintaining robust and maintainable systems, particularly in dynamic Continuous Integration/Continuous Deployment (CI/CD) environments. Traditional static analysis tools, while effective for identifying isolated issues, often lack contextual awareness, leading to missed vulnerabilities and high rates of false positives. This paper introduces a novel framework leveraging Large Language Models (LLMs) to detect and mitigate security risks in design patterns and antipatterns. By analyzing relationships and behavioral dynamics in code, LLMs provide a nuanced, context-aware approach to identifying issues such as unauthorized state changes, insecure communication, and improper data handling. The proposed framework integrates key security heuristics—such as the principles of least privilege and input validation—to enhance LLM performance. An evaluation of the framework demonstrates its potential to outperform traditional tools in terms of accuracy and efficiency, enabling the proactive detection and remediation of vulnerabilities in real time. This study contributes to the field of software engineering by offering an innovative methodology for securing software systems using LLMs, promoting both academic research and practical application in industry settings.
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Radhika Reddy Kondam. "Design and Implementation of Road Rutting Detection using MAnet with Efficientb0 Architecture." Journal of Information Systems Engineering and Management 10, no. 4s (January 18, 2025): 365–74. https://doi.org/10.52783/jisem.v10i4s.529.
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Road rutting will become a serious problem in transportation infrastructure causing surface deterioration, safety concern, and increased maintenance expenses. The aim of this study is to establish an automatic and efficient detection model for discriminating road rutting, which can overcome the inconvenience of human-made reading with less errors. The present study develops the state-of-the-art knowledge in real-time and computationally efficient models for road rutting detection, focusing on effective operation of these universal tools under complex environments with different lighting conditions and surface material types. In the proposed approach to detect rutting with high accuracy, MAnet and efficientb0 architectures are used in combination. MAnet is an attention mechanism-aware network developed to extract more useful fine-grained features, by capturing the spatial and channel-wise dependencies between input images. Efficientb0: Efficientb0 which is the least size model and very computational efficient that allows our model to do inferences on real-time keeping accuracy unaltered. The experimental results confirm that the proposed model outperforms current state-of-the-art models (DeeplabV3 and U-Net) in performing semantic segmentation tasks for aerial images, obtaining a test set mIoU of 0.865. The experiment results indicate that the MAnet-Efficientb0 model is suitable for application in road maintenance system with high accuracy and computationally efficient.
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Fei, Hao, Fei Li, Bobo Li, and Donghong Ji. "Encoder-Decoder Based Unified Semantic Role Labeling with Label-Aware Syntax." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (May 18, 2021): 12794–802. http://dx.doi.org/10.1609/aaai.v35i14.17514.
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Currently the unified semantic role labeling (SRL) that achieves predicate identification and argument role labeling in an end-to-end manner has received growing interests. Recent works show that leveraging the syntax knowledge significantly enhances the SRL performances. In this paper, we investigate a novel unified SRL framework based on the sequence-to-sequence architecture with double enhancement in both the encoder and decoder sides. In the encoder side, we propose a novel label-aware graph convolutional network (LA-GCN) to encode both the syntactic dependent arcs and labels into BERT-based word representations. In the decoder side, we creatively design a pointer-network-based model for detecting predicates, arguments and roles jointly. Our pointer-net decoder is able to make decisions by consulting all the input elements in a global view, and meanwhile it is syntactic-aware by incorporating the syntax information from LA-GCN. Besides, a high-order interacted attention is introduced into the decoder for leveraging previously recognized triplets to help the current decision. Empirical experiments show that our framework significantly outperforms all existing graph-based methods on the CoNLL09 and Universal Proposition Bank datasets. In-depth analysis demonstrates that our model can effectively capture the correlations between syntactic and SRL structures.
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Bielby, Ray. "Commissioning the Ideal Perioperative Suite: Is Going it Alone Your Best Option?" Journal of Perioperative Practice 19, no. 5 (May 2009): 132–36. http://dx.doi.org/10.1177/175045890901900502.
Full textAbstract:
Commissioning the ideal perioperative suite, what does it really mean to the perioperative manager? Negotiating through the maze of design, furniture and equipment needs in healthcare building projects requires a great deal of time and input and there will be many questions the perioperative manager will need to ask if they are to undertake this role effectively. What do they need to know and how do they ensure that their department is represented fairly in the building and procurement selection process? What do they really know about project management and how to be a successful and valuable member of a project team? What are the fundamentals that every perioperative manager who is seconded onto a project team needs to be aware of when asked to assist with the fit out and design of a new perioperative suite? If going it alone isn't the best option where can they go to find the right professional help? Is engaging a medical equipment planner the answer?
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Salam, Abdul. "Subsurface MIMO: A Beamforming Design in Internet of Underground Things for Digital Agriculture Applications." Journal of Sensor and Actuator Networks 8, no. 3 (August 10, 2019): 41. http://dx.doi.org/10.3390/jsan8030041.
Full textAbstract:
In underground (UG) multiple-input and multiple-output (MIMO), transmit beamforming is used to focus energy in the desired direction. There are three different paths in the underground soil medium through which the waves propagate to reach the receiver. When the UG receiver receives a desired data stream only from the desired path, then the UG MIMO channel becomes a three-path (lateral, direct, and reflected) interference channel. Accordingly, the capacity region of the UG MIMO three-path interference channel, and the degrees of freedom (multiplexing gain of this MIMO channel) requires careful modeling. Therefore, expressions are required for the degrees of freedom of the UG MIMO interference channel. The underground receiver needs to perfectly cancel the interference from the three different components of the EM waves propagating in the soil medium. This concept is based upon reducing the interference of the undesired components to a minimum level at the UG receiver using the receive beamforming. In this paper, underground environment-aware MIMO using transmit and receive beamforming has been developed. The optimal transmit and receive beamforming, combining vectors under minimal intercomponent interference constraints, are derived. It is shown that UG MIMO performs best when all three components of the wireless UG channel are leveraged for beamforming. The environment-aware UG MIMO technique leads to three-fold performance improvements and paves the way for design and development of next-generation sensor-guided irrigation systems in the field of digital agriculture. Based on the analysis of underground radio-wave propagation in subsurface radio channels, a phased-array antenna design is presented that uses water content information and beam-steering mechanisms to improve efficiency and communication range of wireless underground communications. It is shown that the subsurface beamforming using phased-array antennas improves wireless underground communications by using the array element optimization and soil–air interface refraction adjustment schemes. This design is useful for subsurface communication system where sophisticated sensors and software systems are used as data collection tools that measure, record, and manage spatial and temporal data in the field of digital agriculture.
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Zela, Elsa, and Enkeleda Jata. "Employing Digital Tools in Esp, a Need Analysis for Bussines English Course Design." Mediterranean Journal of Social Sciences 15, no. 4 (July 8, 2024): 118. http://dx.doi.org/10.36941/mjss-2024-0035.
Full textAbstract:
The occurrence of pandemics showed the world the importance of integrating technology in daily life. In education, this necessity was even more crucial as whole education systems switched to remote learning almost overnight. It was also a time when many teaching/learning platforms saw birth or were even developed further. This paper aims at investigating the students’ perceptions about digital tools they deem necessary (and feel comfortable) to be included in a Business English course conducted at the Agricultural University of Tirana, the department of Economics and Rural Development Policies. Through quantitave and qualitative data gathered, the students perceived present needs and target needs in English language communications were identified. Additionally, the research methods collected sufficient data on the preferred digital tools to be used in classroom and outside, in order for the students to be more motivated in language acquisition and skill development. The study concluded that the student were skilled users of digital tools due to remote learning during pandemics and were aware of the BE topics and language skill they needed to possess in their future workplace, thus ensuring input for a successful BE course design. Received: 12 April 2024 / Accepted: 26 June 2024 / Published: 8 July 2024
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Zhang, Huanlong, Panyun Wang, Jie Zhang, Fengxian Wang, Xiaohui Song, and Hebin Zhou. "Siamese Tracking Network with Spatial-Semantic-Aware Attention and Flexible Spatiotemporal Constraint." Symmetry 16, no. 1 (January 3, 2024): 61. http://dx.doi.org/10.3390/sym16010061.
Full textAbstract:
Siamese trackers based on classification and regression have drawn extensive attention due to their appropriate balance between accuracy and efficiency. However, most of them are prone to failure in the face of abrupt motion or appearance changes. This paper proposes a Siamese-based tracker that incorporates spatial-semantic-aware attention and flexible spatiotemporal constraint. First, we develop a spatial-semantic-aware attention model, which identifies the importance of each feature region and channel to target representation through the single convolution attention network with a loss function and increases the corresponding weights in the spatial and channel dimensions to reinforce the target region and semantic information on the target feature map. Secondly, considering that the traditional method unreasonably weights the target response in abrupt motion, we design a flexible spatiotemporal constraint. This constraint adaptively adjusts the constraint weights on the response map by evaluating the tracking result. Finally, we propose a new template updating the strategy. This strategy adaptively adjusts the contribution weights of the tracking result to the new template using depth correlation assessment criteria, thereby enhancing the reliability of the template. The Siamese network used in this paper is a symmetric neural network with dual input branches sharing weights. The experimental results on five challenging datasets show that our method outperformed other advanced algorithms.