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1
Oettershagen, Lutz, and Petra Mutzel. "Computing top-k temporal closeness in temporal networks." Knowledge and Information Systems 64, no. 2 (January 8, 2022): 507–35. http://dx.doi.org/10.1007/s10115-021-01639-4.
Повний текст джерелаАнотація:
AbstractThe closeness centrality of a vertex in a classical static graph is the reciprocal of the sum of the distances to all other vertices. However, networks are often dynamic and change over time. Temporal distances take these dynamics into account. In this work, we consider the harmonic temporal closeness with respect to the shortest duration distance. We introduce an efficient algorithm for computing the exact top-k temporal closeness values and the corresponding vertices. The algorithm can be generalized to the task of computing all closeness values. Furthermore, we derive heuristic modifications that perform well on real-world data sets and drastically reduce the running times. For the case that edge traversal takes an equal amount of time for all edges, we lift two approximation algorithms to the temporal domain. The algorithms approximate the transitive closure of a temporal graph (which is an essential ingredient for the top-k algorithm) and the temporal closeness for all vertices, respectively, with high probability. We experimentally evaluate all our new approaches on real-world data sets and show that they lead to drastically reduced running times while keeping high quality in many cases. Moreover, we demonstrate that the top-k temporal and static closeness vertex sets differ quite largely in the considered temporal networks.
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Van Beek, P., and D. W. Manchak. "The Design and Experimental Analysis of Algorithms for Temporal Reasoning." Journal of Artificial Intelligence Research 4 (January 1, 1996): 1–18. http://dx.doi.org/10.1613/jair.232.
Повний текст джерелаАнотація:
Many applications -- from planning and scheduling to problems in molecular biology -- rely heavily on a temporal reasoning component. In this paper, we discuss the design and empirical analysis of algorithms for a temporal reasoning system based on Allen's influential interval-based framework for representing temporal information. At the core of the system are algorithms for determining whether the temporal information is consistent, and, if so, finding one or more scenarios that are consistent with the temporal information. Two important algorithms for these tasks are a path consistency algorithm and a backtracking algorithm. For the path consistency algorithm, we develop techniques that can result in up to a ten-fold speedup over an already highly optimized implementation. For the backtracking algorithm, we develop variable and value ordering heuristics that are shown empirically to dramatically improve the performance of the algorithm. As well, we show that a previously suggested reformulation of the backtracking search problem can reduce the time and space requirements of the backtracking search. Taken together, the techniques we develop allow a temporal reasoning component to solve problems that are of practical size.
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Li, Meng He, Chuan Lin, Jing Bei Tian, and Sheng Hui Pan. "An Algorithms for Super-Resolution Reconstruction of Video Based on Spatio-Temporal Adaptive." Advanced Materials Research 532-533 (June 2012): 1680–84. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1680.
Повний текст джерелаАнотація:
For the weakness of conventional POCS algorithms, a novel spatio-temporal adaptive super-resolution reconstruction algorithm of video is proposed in this paper. The spatio-temporal adaptive mechanism, which is based on POCS super-resolution reconstruction algorithm, can effectively prevent reconstructed image from the influence of inaccuracy of motion information and avoid the impact of noise amplification, which exist in using conventional POCS algorithms to reconstruct image sequences in dramatic motion. Experimental results show that the spatio-temporal adaptive algorithm not only effectively alleviate amplification noise but is better than the traditional POCS algorithms in signal to noise ration.
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Sun, Xiaoli, Yusong Tan, Qingbo Wu, Jing Wang, and Changxiang Shen. "New Algorithms for Counting Temporal Graph Pattern." Symmetry 11, no. 10 (September 20, 2019): 1188. http://dx.doi.org/10.3390/sym11101188.
Повний текст джерелаАнотація:
Temporal networks can describe multiple types of complex systems with temporal information in the real world. As an effective method for analyzing such network, temporal graph pattern (TGP) counting has received extensive attention and has been applied in diverse domains. In this paper, we study the problem of counting the TGP in the temporal network. Then, an exact algorithm is proposed based on the time first search (TFS) algorithm. This algorithm can reduce the intermediate results generated in the graph isomorphism and has high computational efficiency. To further improve the algorithm performance, we design an estimation algorithm by applying the edge sampling strategy to the exact algorithm. Finally, we evaluate the performances of the two algorithms by counting both the symmetric and asymmetric TGP. Extensive experiments on real datasets demonstrated that the exact algorithm is faster than the existing algorithm and the estimation algorithm can greatly reduce the running time while guaranteeing the accuracy.
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Li, Xin, Huayan Yu, Ligang Yuan, and Xiaolin Qin. "Query Optimization for Distributed Spatio-Temporal Sensing Data Processing." Sensors 22, no. 5 (February 23, 2022): 1748. http://dx.doi.org/10.3390/s22051748.
Повний текст джерелаАнотація:
The unprecedented development of Internet of Things (IoT) technology produces humongous amounts of spatio-temporal sensing data with various geometry types. However, processing such datasets is often challenging due to high-dimensional sensor data geometry characteristics, complex anomalistic spatial regions, unique query patterns, and so on. Timely and efficient spatio-temporal querying significantly improves the accuracy and intelligence of processing sensing data. Most existing query algorithms show their lack of supporting spatio-temporal queries and irregular spatial areas. In this paper, we propose two spatio-temporal query optimization algorithms based on SpatialHadoop to improve the efficiency of query spatio-temporal sensing data: (1) spatio-temporal polygon range query (STPRQ), which aims to find all records from a polygonal location in a time interval; (2) spatio-temporal k nearest neighbors query (STkNNQ), which directly searches the query point’s k closest neighbors. To optimize the STkNNQ algorithm, we further propose an adaptive iterative range optimization algorithm (AIRO), which can optimize the iterative range of the algorithm according to the query time range and avoid querying irrelevant data partitions. Finally, extensive experiments based on trajectory datasets demonstrate that our proposed query algorithms can significantly improve query performance over baseline algorithms and shorten response time by 81% and 35.6%, respectively.
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Ahmed, Nesreen K., Nick Duffield, and Ryan A. Rossi. "Online Sampling of Temporal Networks." ACM Transactions on Knowledge Discovery from Data 15, no. 4 (June 2021): 1–27. http://dx.doi.org/10.1145/3442202.
Повний текст джерелаАнотація:
Temporal networks representing a stream of timestamped edges are seemingly ubiquitous in the real world. However, the massive size and continuous nature of these networks make them fundamentally challenging to analyze and leverage for descriptive and predictive modeling tasks. In this work, we propose a general framework for temporal network sampling with unbiased estimation. We develop online, single-pass sampling algorithms, and unbiased estimators for temporal network sampling. The proposed algorithms enable fast, accurate, and memory-efficient statistical estimation of temporal network patterns and properties. In addition, we propose a temporally decaying sampling algorithm with unbiased estimators for studying networks that evolve in continuous time, where the strength of links is a function of time, and the motif patterns are temporally weighted. In contrast to the prior notion of a △ t -temporal motif, the proposed formulation and algorithms for counting temporally weighted motifs are useful for forecasting tasks in networks such as predicting future links, or a future time-series variable of nodes and links. Finally, extensive experiments on a variety of temporal networks from different domains demonstrate the effectiveness of the proposed algorithms. A detailed ablation study is provided to understand the impact of the various components of the proposed framework.
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Jain, Anuj, and Sartaj Sahni. "Foremost Walks and Paths in Interval Temporal Graphs." Algorithms 15, no. 10 (September 29, 2022): 361. http://dx.doi.org/10.3390/a15100361.
Повний текст джерелаАнотація:
The min-wait foremost, min-hop foremost and min-cost foremost paths and walks problems in interval temporal graphs are considered. We prove that finding min-wait foremost and min-cost foremost walks and paths in interval temporal graphs is NP-hard. We develop a polynomial time algorithm for the single-source all-destinations min-hop foremost paths problem and a pseudopolynomial time algorithm for the single-source all-destinations min-wait foremost walks problem in interval temporal graphs. We benchmark our algorithms against algorithms presented by Bentert et al. for contact sequence graphs and show, experimentally, that our algorithms perform up to 207.5 times faster for finding min-hop foremost paths and up to 23.3 times faster for finding min-wait foremost walks.
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Deb, Rohan, and Shalabh Bhatnagar. "Gradient Temporal Difference with Momentum: Stability and Convergence." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6488–96. http://dx.doi.org/10.1609/aaai.v36i6.20601.
Повний текст джерелаАнотація:
Gradient temporal difference (Gradient TD) algorithms are a popular class of stochastic approximation (SA) algorithms used for policy evaluation in reinforcement learning. Here, we consider Gradient TD algorithms with an additional heavy ball momentum term and provide choice of step size and momentum parameter that ensures almost sure convergence of these algorithms asymptotically. In doing so, we decompose the heavy ball Gradient TD iterates into three separate iterates with different step sizes. We first analyze these iterates under one-timescale SA setting using results from current literature. However, the one-timescale case is restrictive and a more general analysis can be provided by looking at a three-timescale decomposition of the iterates. In the process we provide the first conditions for stability and convergence of general three-timescale SA. We then prove that the heavy ball Gradient TD algorithm is convergent using our three-timescale SA analysis. Finally, we evaluate these algorithms on standard RL problems and report improvement in performance over the vanilla algorithms.
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Guo, Yangnan, Cangjiao Wang, Shaogang Lei, Junzhe Yang, and Yibo Zhao. "A Framework of Spatio-Temporal Fusion Algorithm Selection for Landsat NDVI Time Series Construction." ISPRS International Journal of Geo-Information 9, no. 11 (November 4, 2020): 665. http://dx.doi.org/10.3390/ijgi9110665.
Повний текст джерелаАнотація:
Spatio-temporal fusion algorithms dramatically enhance the application of the Landsat time series. However, each spatio-temporal fusion algorithm has its pros and cons of heterogeneous land cover performance, the minimal number of input image pairs, and its efficiency. This study aimed to answer: (1) how to determine the adaptability of the spatio-temporal fusion algorithm for predicting images in prediction date and (2) whether the Landsat normalized difference vegetation index (NDVI) time series would benefit from the interpolation with images fused from multiple spatio-temporal fusion algorithms. Thus, we supposed a linear relationship existed between the fusion accuracy and spatial and temporal variance. Taking the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM) as basic algorithms, a framework was designed to screen a spatio-temporal fusion algorithm for the Landsat NDVI time series construction. The screening rule was designed by fitting the linear relationship between the spatial and temporal variance and fusion algorithm accuracy, and then the fitted relationship was combined with the graded accuracy selecting rule (R2) to select the fusion algorithm. The results indicated that the constructed Landsat NDVI time series by this paper proposed framework exhibited the highest overall accuracy (88.18%), and lowest omission (1.82%) and commission errors (10.00%) in land cover change detection compared with the moderate resolution imaging spectroradiometer (MODIS) NDVI time series and the NDVI time series constructed by a single STARFM or ESTARFM. Phenological stability analysis demonstrated that the Landsat NDVI time series established by multiple spatio-temporal algorithms could effectively avoid phenological fluctuations in the time series constructed by a single fusion algorithm. We believe that this framework can help improve the quality of the Landsat NDVI time series and fulfill the gap between near real-time environmental monitoring mandates and data-scarcity reality.
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Visca, Jorge, and Javier Baliosian. "rl4dtn: Q-Learning for Opportunistic Networks." Future Internet 14, no. 12 (November 23, 2022): 348. http://dx.doi.org/10.3390/fi14120348.
Повний текст джерелаАнотація:
Opportunistic networks are highly stochastic networks supported by sporadic encounters between mobile devices. To route data efficiently, opportunistic-routing algorithms must capitalize on devices’ movement and data transmission patterns. This work proposes a routing method based on reinforcement learning, specifically Q-learning. As usual in routing algorithms, the objective is to select the best candidate devices to put forward once an encounter occurs. However, there is also the possibility of not forwarding if we know that a better candidate might be encountered in the future. This decision is not usually considered in learning schemes because there is no obvious way to represent the temporal evolution of the network. We propose a novel, distributed, and online method that allows learning both the network’s connectivity and its temporal evolution with the help of a temporal graph. This algorithm allows learning to skip forwarding opportunities to capitalize on future encounters. We show that explicitly representing the action for deferring forwarding increases the algorithm’s performance. The algorithm’s scalability is discussed and shown to perform well in a network of considerable size.
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Bani Abdelrahman, Ra’ed, Rafat Alshorman, Walter Hussak, and Amitabh Trehan. "Specification of Synchronous Network Flooding in Temporal Logic." International Arab Journal of Information Technology 17, no. 6 (November 1, 2020): 867–74. http://dx.doi.org/10.34028/iajit/17/6/5.
Повний текст джерелаАнотація:
In distributed network algorithms, network flooding algorithm is considered one of the simplest and most fundamental algorithms. This research specifies the basic synchronous memory-less network flooding algorithm where nodes on the network don’t have memory, for any fixed size of network, in Linear Temporal Logic. The specification can be customized to any single network topology or class of topologies. A specification of the termination problem is formulated and used to compare different topologies for earlier termination. This research gives a worked example of one topology resulting in earlier termination than another, for which we perform a formal verification using the model checker NuSMV
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Zhong, Yanling, Jinling Kong, Juqing Zhang, Yizhu Jiang, Xiao Fan, and Zhuoyue Wang. "A trajectory data compression algorithm based on spatio-temporal characteristics." PeerJ Computer Science 8 (October 3, 2022): e1112. http://dx.doi.org/10.7717/peerj-cs.1112.
Повний текст джерелаАнотація:
Background With the growth of trajectory data, the large amount of data causes a lot of problems with storage, analysis, mining, etc. Most of the traditional trajectory data compression methods are focused on preserving spatial characteristic information and pay little attention to other temporal information on trajectory data, such as speed change points or stop points. Methods A data compression algorithm based on the spatio-temporal characteristics (CASC) of the trajectory data is proposed to solve this problem. This algorithm compresses trajectory data by taking the azimuth difference, velocity difference and time interval as parameters in order to preserve spatial-temporal characteristics. Microsoft’s Geolife1.3 data set was used for a compression test to verify the validity of the algorithm. The compression results were compared with the traditional Douglas-Peucker (DP), Top-Down Time Ratio (TD-TR) and Opening Window (OPW) algorithms. Compression rate, the direction information of trajectory points, vertical synchronization distance, and algorithm type (online/offline) were used to evaluate the above algorithms. Results The experimental results show that with the same compression rate, the ability of the CASC to retain the forward direction trajectory is optimal, followed by TD-TR, DP, and then OPW. The velocity characteristics of the trajectories are also stably retained when the speed threshold value is not more than 100%. Unlike the DP and TD-TR algorithms, CASC is an online algorithm. Compared with OPW, which is also an online algorithm, CASC has better compression quality. The error distributions of the four algorithms have been compared, and CASC is the most stable algorithm. Taken together, CASC outperforms DP, TD-TR and OPW in trajectory compression.
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Geist, M., and O. Pietquin. "Kalman Temporal Differences." Journal of Artificial Intelligence Research 39 (October 29, 2010): 483–532. http://dx.doi.org/10.1613/jair.3077.
Повний текст джерелаАнотація:
Because reinforcement learning suffers from a lack of scalability, online value (and Q-) function approximation has received increasing interest this last decade. This contribution introduces a novel approximation scheme, namely the Kalman Temporal Differences (KTD) framework, that exhibits the following features: sample-efficiency, non-linear approximation, non-stationarity handling and uncertainty management. A first KTD-based algorithm is provided for deterministic Markov Decision Processes (MDP) which produces biased estimates in the case of stochastic transitions. Than the eXtended KTD framework (XKTD), solving stochastic MDP, is described. Convergence is analyzed for special cases for both deterministic and stochastic transitions. Related algorithms are experimented on classical benchmarks. They compare favorably to the state of the art while exhibiting the announced features.
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Wu, Huanhuan, James Cheng, Yiping Ke, Silu Huang, Yuzhen Huang, and Hejun Wu. "Efficient Algorithms for Temporal Path Computation." IEEE Transactions on Knowledge and Data Engineering 28, no. 11 (November 1, 2016): 2927–42. http://dx.doi.org/10.1109/tkde.2016.2594065.
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Madhavan, Advait, Matthew W. Daniels, and Mark D. Stiles. "Temporal State Machines: Using Temporal Memory to Stitch Time-based Graph Computations." ACM Journal on Emerging Technologies in Computing Systems 17, no. 3 (May 11, 2021): 1–27. http://dx.doi.org/10.1145/3451214.
Повний текст джерелаАнотація:
Race logic, an arrival-time-coded logic family, has demonstrated energy and performance improvements for applications ranging from dynamic programming to machine learning. However, the various ad hoc mappings of algorithms into hardware rely on researcher ingenuity and result in custom architectures that are difficult to systematize. We propose to associate race logic with the mathematical field of tropical algebra, enabling a more methodical approach toward building temporal circuits. This association between the mathematical primitives of tropical algebra and generalized race logic computations guides the design of temporally coded tropical circuits. It also serves as a framework for expressing high-level timing-based algorithms. This abstraction, when combined with temporal memory, allows for the systematic exploration of race logic–based temporal architectures by making it possible to partition feed-forward computations into stages and organize them into a state machine. We leverage analog memristor-based temporal memories to design such a state machine that operates purely on time-coded wavefronts. We implement a version of Dijkstra’s algorithm to evaluate this temporal state machine. This demonstration shows the promise of expanding the expressibility of temporal computing to enable it to deliver significant energy and throughput advantages.
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Nguyen Hung An. "AN APPROACH FOR IMPROVING ACCURACY OF CHANGE DETECTION IN MULTI-TEMOPRAL SAR IMAGES." Journal of Military Science and Technology, no. 66A (May 6, 2020): 47–54. http://dx.doi.org/10.54939/1859-1043.j.mst.66a.2020.47-54.
Повний текст джерелаАнотація:
Algorithms of change detection in multi-temporal SAR images have received great interests for recent decades, and been widely applied in natural resource supervision activities. However, these algorithms still expose the limitation of detection accuracy due to inhenrent presence of speckle noise in SAR images. This paper developed a novel approach of change detection in multi-temporal SAR images of sea surface. The algorithm has increased accuracy of change detection in multi-temporal SAR images of sea surface compared with recent other methods.
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Boerkoel Jr., James, Léon Planken, Ronald Wilcox, and Julie Shah. "Distributed Algorithms for Incrementally Maintaining Multiagent Simple Temporal Networks." Proceedings of the International Conference on Automated Planning and Scheduling 23 (June 2, 2013): 11–19. http://dx.doi.org/10.1609/icaps.v23i1.13551.
Повний текст джерелаАнотація:
When multiple agents want to maintain temporal information, they can employ a Multiagent Simple Temporal Network (MaSTN). Recent work has shown that the constraints in a MaSTN can be efficiently propagated by enforcing partial path consistency (PPC) with a distributed algorithm. However, new temporal constraints may arise continually due to ongoing plan construction or execution, the decisions of other agents, and other exogenous events. For these new constraints, propagation is again required to re-establish PPC. Because the affected part of the network may be small, one typically wants to exploit the similarities between the new and previous version of the MaSTN. To this end, we propose two new distributed algorithms for incrementally maintaining PPC. The first is inspired by TriSTP, the seminal PPC algorithm for STNs; the second is a distributed version of IPPC, which represents the current state of the art for incrementally enforcing PPC in a centralized setting. The worst-case time performance of these algorithms is similar to their centralized counterparts. We empirically compare our distributed algorithms, analyzing their performance under various assumptions, and demonstrate significant speedup over their centralized counterparts.
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Chu, Wesley W., and Patrick H. Ngai. "Embedding temporal constraint propagation in machine sequencing for job shop scheduling." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 7, no. 1 (February 1993): 37–52. http://dx.doi.org/10.1017/s0890060400000056.
Повний текст джерелаАнотація:
In this paper, we show how a temporal constraint propagation technique can be embedded in the machine sequencing approach for solving the job shop scheduling problem. The temporal constraint propagation algorithm propagates the precedence constraints and machine interference constraints to reduce the search space generated by the machine sequencing approach. Further, by making use of the temporal nature of the job shop scheduling, efficient algorithms to propagate precedence constraints and machine interference constraints are developed. Experimental results reveal that embedding constraint propagation in the machine sequencing approach significantly reduces the computation time more than by just using the machine sequencing approach alone. Further, the proposed temporal constraint propagation algorithms provide an order of magnitude improvement on the computation time over the conventional constraint propagation algorithm.
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Hunsberger, Luke, and Roberto Posenato. "Faster Dynamic-Consistency Checking for Conditional Simple Temporal Networks." Proceedings of the International Conference on Automated Planning and Scheduling 30 (June 1, 2020): 152–60. http://dx.doi.org/10.1609/icaps.v30i1.6656.
Повний текст джерелаАнотація:
A Conditional Simple Temporal Network (CSTN) is a structure for representing and reasoning about time in domains where temporal constraints may be conditioned on outcomes of observations made in real time. A CSTN is dynamically consistent (DC) if there is a strategy for executing its time-points such that all relevant constraints will necessarily be satisfied no matter which outcomes happen to be observed. The literature on CSTNs contains only one sound-and-complete DC-checking algorithm that has been implemented and empirically evaluated. It is a graph-based algorithm that propagates labeled constraints/edges. A second algorithm has been proposed, but not evaluated. It aims to speed up DC checking by more efficiently dealing with so-called negative q-loops.This paper presents a new two-phase approach to DC-checking for CSTNs. The first phase focuses on identifying negative q-loops and labeling key time-points within them. The second phase focuses on computing (labeled) distances from each time-point to a single sink node. The new algorithm, which is also sound and complete for DC-checking, is then empirically evaluated against both pre-existing algorithms and shown to be much faster across not only previously published benchmark problems, but also a new set of benchmark problems. The results show that, on DC instances, the new algorithm tends to be an order of magnitude faster than both existing algorithms. On all other benchmark cases, the new algorithm performs better than or equivalently to the existing algorithms.
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OUNI, BOURAOUI, and ABDELLATIF MTIBAA. "ONLINE SCHEDULING AND PLACEMENT OF HARDWARE MODULES ON PARTIALLY DYNAMIC ARCHITECTURES." Journal of Circuits, Systems and Computers 22, no. 03 (March 2013): 1350005. http://dx.doi.org/10.1142/s0218126613500059.
Повний текст джерелаАнотація:
New FPGA families can be reconfigured partially, meaning that only a certain portion of the chip area is reprogrammed. Partial reconfiguration makes possible to exchange function blocks on the FPGA and adapt it to a changing environment. It is used to have only a subset of necessary applications running on chip at each time, and replacing them by those needed. One of the challenging problems is the scheduling and the placement of modules on reconfigurable resources, this problem is called temporal placement. Several modules placement techniques have been introduced in the literature to solve the temporal placement problem. In this paper, we examine the temporal placement, showing how it can be decomposed into a number of distinct but not independent subtasks. Furthermore, in this paper, we have classified the temporal placement algorithms into the following classes: (1) Algorithms without design optimization (2) Routing cost optimization algorithms (3) Reconfiguration time optimization algorithms (4) Modules interfering optimization algorithms (5) Device resources optimization algorithms After that, experiments are conducted in order to evaluate the complexity and performances in term of traditional design metrics, like latency, area, etc., of each algorithm.
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Yu, Peng, Cheng Fang, and Brian Williams. "Resolving Uncontrollable Conditional Temporal Problems Using Continuous Relaxations." Proceedings of the International Conference on Automated Planning and Scheduling 24 (May 11, 2014): 341–48. http://dx.doi.org/10.1609/icaps.v24i1.13623.
Повний текст джерелаАнотація:
Uncertainty is commonly encountered in temporal scheduling and planning problems, and can often lead to over-constrained situations. Previous relaxation algorithms for over-constrained temporal problems only work with requirement constraints, whose outcomes can be controlled by the agents. When applied to uncontrollable durations, these algorithms may only satisfy a subset of the random outcomes and hence their relaxations may fail during execution. In this paper, we present a new relaxation algorithm, Conflict-Directed Relaxation with Uncertainty (CDRU), which generates relaxations that restore the controllability of conditional temporal problems with uncontrollable durations. CDRU extends the Best-first Conflict-Directed Relaxation (BCDR) algorithm to uncontrollable temporal problems. It generalizes the conflict-learning process to extract conflicts from strong and dynamic controllability checking algorithms, and resolves the conflicts by both relaxing constraints and tightening uncontrollable durations. Empirical test results on a range of trip scheduling problems show that CDRU is efficient in resolving large scale uncontrollable problems: computing strongly controllable relaxations takes the same order of magnitude in time compared to consistent relaxations that do not account for uncontrollable durations. While computing dynamically controllable relaxations takes two orders of magnitude more time, it provides significant improvements in solution quality when compared to strongly controllable relaxations.
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Wu, X., R. Zurita-Milla, M. J. Kraak, and E. Izquierdo-Verdiguier. "CLUSTERING-BASED APPROACHES TO THE EXPLORATION OF SPATIO-TEMPORAL DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 14, 2017): 1387–91. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-1387-2017.
Повний текст джерелаАнотація:
As one spatio-temporal data mining task, clustering helps the exploration of patterns in the data by grouping similar elements together. However, previous studies on spatial or temporal clustering are incapable of analysing complex patterns in spatio-temporal data. For instance, concurrent spatio-temporal patterns in 2D or 3D datasets. In this study we present two clustering algorithms for complex pattern analysis: (1) the Bregman block average co-clustering algorithm with I-divergence (BBAC_I) which enables the concurrent analysis of spatio-temporal patterns in 2D data matrix, and (2) the Bregman cube average tri-clustering algorithm with I-divergence (BCAT_I) which enables the complete partitional analysis in 3D data cube. Here the use of the two clustering algorithms is illustrated by Dutch daily average temperature dataset from 28 weather stations from 1992 to 2011. For BBAC_I, it is applied to the averaged yearly dataset to identify station-year co-clusters which contain similar temperatures along stations and years, thus revealing patterns along both spatial and temporal dimensions. For BCAT_I, it is applied to the temperature dataset organized in a data cube with one spatial (stations) and two nested temporal dimensions (years and days). By partitioning the whole dataset into clusters of stations and years with similar within-year temperature similarity, BCAT_I explores the spatio-temporal patterns of intra-annual variability in the daily temperature dataset. As such, both BBAC_I and BCAT_I algorithms, combined with suitable geovisualization techniques, allow the exploration of complex spatial and temporal patterns, which contributes to a better understanding of complex patterns in spatio-temporal data.
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Amirgaliyev, Yedilkhan, Kuanyshbay Kuanyshbay, and Aisultan Shoiynbek. "COMPARISON OF OPTIMIZATION ALGORITHMS OF CONNECTIONIST TEMPORAL CLASSIFIER FOR SPEECH RECOGNITION SYSTEM." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 9, no. 3 (September 26, 2019): 54–57. http://dx.doi.org/10.35784/iapgos.234.
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This paper evaluates and compares the performances of three well-known optimization algorithms (Adagrad, Adam, Momentum) for faster training the neural network of CTC algorithm for speech recognition. For CTC algorithms recurrent neural network has been used, specifically Long-Short-Term memory. LSTM is effective and often used model. Data has been downloaded from VCTK corpus of Edinburgh University. The results of optimization algorithms have been evaluated by the Label error rate and CTC loss.
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Brooks, Evan, Randolph Wynne, and Valerie Thomas. "Using Window Regression to Gap-Fill Landsat ETM+ Post SLC-Off Data." Remote Sensing 10, no. 10 (September 20, 2018): 1502. http://dx.doi.org/10.3390/rs10101502.
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The continued development of algorithms using multitemporal Landsat data creates opportunities to develop and adapt imputation algorithms to improve the quality of that data as part of preprocessing. One example is de-striping Enhanced Thematic Mapper Plus (ETM+, Landsat 7) images acquired after the Scan Line Corrector failure in 2003. In this study, we apply window regression, an algorithm that was originally designed to impute low-quality Moderate Resolution Imaging Spectroradiometer (MODIS) data, to Landsat Analysis Ready Data from 2014–2016. We mask Operational Land Imager (OLI; Landsat 8) image stacks from five study areas with corresponding ETM+ missing data layers, using these modified OLI stacks as inputs. We explored the algorithm’s parameter space, particularly window size in the spatial and temporal dimensions. Window regression yielded the best accuracy (and moderately long computation time) with a large spatial radius (a 7 × 7 pixel window) and a moderate temporal radius (here, five layers). In this case, root mean square error for deviations from the observed reflectance ranged from 3.7–7.6% over all study areas, depending on the band. Second-order response surface analysis suggested that a 15 × 15 pixel window, in conjunction with a 9-layer temporal window, may produce the best accuracy. Compared to the neighborhood similar pixel interpolator gap-filling algorithm, window regression yielded slightly better accuracy on average. Because it relies on no ancillary data, window regression may be used to conveniently preprocess stacks for other data-intensive algorithms.
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Li, Zhaoxu, Qiang Ling, Jing Wu, Zhengyan Wang, and Zaiping Lin. "A Constrained Sparse-Representation-Based Spatio-Temporal Anomaly Detector for Moving Targets in Hyperspectral Imagery Sequences." Remote Sensing 12, no. 17 (August 27, 2020): 2783. http://dx.doi.org/10.3390/rs12172783.
Повний текст джерелаАнотація:
At present, small dim moving target detection in hyperspectral imagery sequences is mainly based on anomaly detection (AD). However, most conventional detection algorithms only utilize the spatial spectral information and rarely employ the temporal spectral information. Besides, multiple targets in complex motion situations, such as multiple targets at different velocities and dense targets on the same trajectory, are still challenges for moving target detection. To address these problems, we propose a novel constrained sparse representation-based spatio-temporal anomaly detection algorithm that extends AD from the spatial domain to the spatio-temporal domain. Our algorithm includes a spatial detector and a temporal detector, which play different roles in moving target detection. The former can suppress moving background regions, and the latter can suppress non-homogeneous background and stationary objects. Two temporal background purification procedures maintain the effectiveness of the temporal detector for multiple targets in complex motion situations. Moreover, the smoothing and fusion of the spatial and temporal detection maps can adequately suppress background clutter and false alarms on the maps. Experiments conducted on a real dataset and a synthetic dataset show that the proposed algorithm can accurately detect multiple targets with different velocities and dense targets with the same trajectory and outperforms other state-of-the-art algorithms in high-noise scenarios.
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Bongki Moon, I. F. Vega Lopez, and V. Immanuel. "Efficient algorithms for large-scale temporal aggregation." IEEE Transactions on Knowledge and Data Engineering 15, no. 3 (May 2003): 744–59. http://dx.doi.org/10.1109/tkde.2003.1198403.
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Olstad, Bjoern. "Adaptive temporal decimation for video compression algorithms." Journal of Electronic Imaging 2, no. 1 (January 1, 1993): 5. http://dx.doi.org/10.1117/12.130194.
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Stergiou, Kostas, and Manolis Koubarakis. "Backtracking algorithms for disjunctions of temporal constraints." Artificial Intelligence 120, no. 1 (June 2000): 81–117. http://dx.doi.org/10.1016/s0004-3702(00)00019-9.
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Ranjan, D., E. Pontelli, and G. Gupta. "Efficient algorithms for the temporal precedence problem." Information Processing Letters 68, no. 2 (October 1998): 71–78. http://dx.doi.org/10.1016/s0020-0190(98)00141-0.
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Gao, Zhongpai, Guangtao Zhai, and Jiantao Zhou. "Factorization Algorithms for Temporal Psychovisual Modulation Display." IEEE Transactions on Multimedia 18, no. 4 (April 2016): 614–26. http://dx.doi.org/10.1109/tmm.2016.2523425.
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Schockaert, S., and M. De Cock. "Efficient Algorithms for Fuzzy Qualitative Temporal Reasoning." IEEE Transactions on Fuzzy Systems 17, no. 4 (August 2009): 794–808. http://dx.doi.org/10.1109/tfuzz.2008.924333.
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Thati, Prasanna, and Grigore Roşu. "Monitoring Algorithms for Metric Temporal Logic Specifications." Electronic Notes in Theoretical Computer Science 113 (January 2005): 145–62. http://dx.doi.org/10.1016/j.entcs.2004.01.029.
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Planken, Léon, Mathijs De Weerdt, and Roman Van der Krogt. "Computing All-Pairs Shortest Paths by Leveraging Low Treewidth." Proceedings of the International Conference on Automated Planning and Scheduling 21 (March 22, 2011): 170–77. http://dx.doi.org/10.1609/icaps.v21i1.13465.
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Considering directed graphs on n vertices and m edges with real (possibly negative) weights, we present two new, efficient algorithms for computing all-pairs shortest paths (APSP). These algorithms make use of directed path consistency (DPC) along a vertex ordering d. The algorithms run in O(n2wd) time, where wd is the graph width induced by this vertex ordering. For graphs of constant treewidth, this yields O(n2) time, which is optimal. On chordal graphs, the algorithms run in O(nm) time. We show empirically that also in many general cases, both constructed and from realistic benchmarks, the algorithms often outperform Johnson's algorithm, which represents the current state of the art with a run time of O(nm + n2log n). These algorithms can be used for temporal and spatial reasoning, e.g. for the Simple Temporal Problem (STP), which underlines its relevance to the planning and scheduling community.
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Jiménez, Sergio, Anders Jonsson, and Héctor Palacios. "Temporal Planning With Required Concurrency Using Classical Planning." Proceedings of the International Conference on Automated Planning and Scheduling 25 (April 8, 2015): 129–37. http://dx.doi.org/10.1609/icaps.v25i1.13731.
Повний текст джерелаАнотація:
In this paper we describe two novel algorithms for temporal planning. The first algorithm, TP, is an adaptation of the TEMPO algorithm. It compiles each temporal action into two classical actions, corresponding to the start and end of the temporal action, but handles the temporal constraints on actions through a modification of the Fast Downward planning system. The second algorithm, TPSHE, is a pure compilation from temporal to classical planning for the case in which required concurrency only appears in the form of single hard envelopes. We describe novel classes of temporal planning instances for which TPSHE is provably sound and complete. Compiling a temporal instance into a classical one gives a lot of freedom in terms of the planner or heuristic used to solve the instance. In experiments TPSHE significantly outperforms all planners from the temporal track of the International Planning Competition.
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Xie, Zixi, Weiguo Song, Rui Ba, Xiaolian Li, and Long Xia. "A Spatiotemporal Contextual Model for Forest Fire Detection Using Himawari-8 Satellite Data." Remote Sensing 10, no. 12 (December 8, 2018): 1992. http://dx.doi.org/10.3390/rs10121992.
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Two of the main remote sensing data resources for forest fire detection have significant drawbacks: geostationary Earth Observation (EO) satellites have high temporal resolution but low spatial resolution, whereas Polar-orbiting systems have high spatial resolution but low temporal resolution. Therefore, the existing forest fire detection algorithms that are based on a single one of these two systems have only exploited temporal or spatial information independently. There are no approaches yet that have effectively merged spatial and temporal characteristics to detect forest fires. This paper fills this gap by presenting a spatiotemporal contextual model (STCM) that fully exploits geostationary data’s spatial and temporal dimensions based on the data from Himawari-8 Satellite. We used an improved robust fitting algorithm to model each pixel’s diurnal temperature cycles (DTC) in the middle and long infrared bands. For each pixel, a Kalman filter was used to blend the DTC to estimate the true background brightness temperature. Subsequently, we utilized the Otsu method to identify the fire after using an MVC (maximum value month composite of NDVI) threshold to test which areas have enough fuel to support such events. Finally, we used a continuous timeslot test to correct the fire detection results. The proposed algorithm was applied to four fire cases in East Asia and Australia in 2016. A comparison of detection results between MODIS Terra and Aqua active fire products (MOD14 and MYD14) demonstrated that the proposed algorithm from this paper effectively analyzed the spatiotemporal information contained in multi-temporal remotely sensed data. In addition, this new forest fire detection method can lead to higher detection accuracy than the traditional contextual and temporal algorithms. By developing algorithms that are based on AHI measurements to meet the requirement to detect forest fires promptly and accurately, this paper assists both emergency responders and the general public to mitigate the damage of forest fires.
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Belazi, Akram, Héctor Migallón, Daniel Gónzalez-Sánchez, Jorge Gónzalez-García, Antonio Jimeno-Morenilla, and José-Luis Sánchez-Romero. "Enhanced Parallel Sine Cosine Algorithm for Constrained and Unconstrained Optimization." Mathematics 10, no. 7 (April 3, 2022): 1166. http://dx.doi.org/10.3390/math10071166.
Повний текст джерелаАнотація:
The sine cosine algorithm’s main idea is the sine and cosine-based vacillation outwards or towards the best solution. The first main contribution of this paper proposes an enhanced version of the SCA algorithm called as ESCA algorithm. The supremacy of the proposed algorithm over a set of state-of-the-art algorithms in terms of solution accuracy and convergence speed will be demonstrated by experimental tests. When these algorithms are transferred to the business sector, they must meet time requirements dependent on the industrial process. If these temporal requirements are not met, an efficient solution is to speed them up by designing parallel algorithms. The second major contribution of this work is the design of several parallel algorithms for efficiently exploiting current multicore processor architectures. First, one-level synchronous and asynchronous parallel ESCA algorithms are designed. They have two favors; retain the proposed algorithm’s behavior and provide excellent parallel performance by combining coarse-grained parallelism with fine-grained parallelism. Moreover, the parallel scalability of the proposed algorithms is further improved by employing a two-level parallel strategy. Indeed, the experimental results suggest that the one-level parallel ESCA algorithms reduce the computing time, on average, by 87.4% and 90.8%, respectively, using 12 physical processing cores. The two-level parallel algorithms provide extra reductions of the computing time by 91.4%, 93.1%, and 94.5% with 16, 20, and 24 processing cores, including physical and logical cores. Comparison analysis is carried out on 30 unconstrained benchmark functions and three challenging engineering design problems. The experimental outcomes show that the proposed ESCA algorithm behaves outstandingly well in terms of exploration and exploitation behaviors, local optima avoidance, and convergence speed toward the optimum. The overall performance of the proposed algorithm is statistically validated using three non-parametric statistical tests, namely Friedman, Friedman aligned, and Quade tests.
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Singh Oberoi, Kamaldeep, and Géraldine Del Mondo. "Spatio-temporal pattern detection in spatio-temporal graphs." Revue Internationale de Géomatique 31, no. 3-4 (July 2022): 377–400. http://dx.doi.org/10.3166/rig31.377-400.
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Spatio-temporal (ST) graphs have been used in many application domains to model evolving ST phenomenon. Such models represent the underlying structure of the phenomenon in terms of its entities and different types of spatial interactions between them. The reason behind using graph-based models to represent ST phenomenon is due to the existing well-established graph analysis tools and algorithms which can be directly applied to analyze the phenomenon under consideration. In this paper, considering the use case of two distinct, highly dynamic phenomena - invasive team sports, with a focus on handball and urban road traffic, we propose a spatio-temporal graph model applicable to both these phenomena. Different types of entities and spatial relations which make up these phenomena are highlighted to formalize the graph. Furthermore, the idea of graph-based pattern detection in both these phenomena is explored. Different types of ST patterns for both ST phenomena are discussed and the problem of pattern detection is formalized as the problem of subgraph isomorphism for dynamic graphs. Finally, the results of our algorithm to detect random ST patterns in random ST graphs are presented. The ideas discussed in this paper are applicable to other ST phenomena as well.
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Ravikumar, Penugonda, Palla Likhitha, Bathala Venus Vikranth Raj, Rage Uday Kiran, Yutaka Watanobe, and Koji Zettsu. "Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases." Electronics 10, no. 12 (June 19, 2021): 1478. http://dx.doi.org/10.3390/electronics10121478.
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Discovering periodic-frequent patterns in temporal databases is a challenging problem of great importance in many real-world applications. Though several algorithms were described in the literature to tackle the problem of periodic-frequent pattern mining, most of these algorithms use the traditional horizontal (or row) database layout, that is, either they need to scan the database several times or do not allow asynchronous computation of periodic-frequent patterns. As a result, this kind of database layout makes the algorithms for discovering periodic-frequent patterns both time and memory inefficient. One cannot ignore the importance of mining the data stored in a vertical (or columnar) database layout. It is because real-world big data is widely stored in columnar database layout. With this motivation, this paper proposes an efficient algorithm, Periodic Frequent-Equivalence CLass Transformation (PF-ECLAT), to find periodic-frequent patterns in a columnar temporal database. Experimental results on sparse and dense real-world and synthetic databases demonstrate that PF-ECLAT is memory and runtime efficient and highly scalable. Finally, we demonstrate the usefulness of PF-ECLAT with two case studies. In the first case study, we have employed our algorithm to identify the geographical areas in which people were periodically exposed to harmful levels of air pollution in Japan. In the second case study, we have utilized our algorithm to discover the set of road segments in which congestion was regularly observed in a transportation network.
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Zhu, Aihua, Zhiqing Meng, and Rui Shen. "Research on Fuzzy Temporal Event Association Mining Model and Algorithm." Axioms 12, no. 2 (January 23, 2023): 117. http://dx.doi.org/10.3390/axioms12020117.
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As traditional models and algorithms are less effective in dealing with complex and irregular temporal data streams, this work proposed a fuzzy temporal association model as well as an algorithm. The core idea is to granulate and fuzzify information from both the attribute state dimension and the temporal dimension. After restructuring temporal data and extracting fuzzy features out of information, a fuzzy temporal event association rule mining model as well as an algorithm was constructed. The proposed algorithm can fully extract the data features at each granularity level while preserving the original information and reducing the amount of computation. Furthermore, it is capable of efficiently mining the possible rules underlying different temporal data streams. In experiments, by comparing and analyzing stock trading data in different temporal granularities, the model and algorithm identify association events in disorder trading. This not only is valuable in identifying stock anomalies, but also provides a new theoretical tool for dealing with complex irregular temporal data.
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Viseras, Alberto, Zhe Xu, and Luis Merino. "Distributed Multi-Robot Information Gathering under Spatio-Temporal Inter-Robot Constraints." Sensors 20, no. 2 (January 15, 2020): 484. http://dx.doi.org/10.3390/s20020484.
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Information gathering (IG) algorithms aim to intelligently select the mobile robotic sensor actions required to efficiently obtain an accurate reconstruction of a physical process, such as an occupancy map, a wind field, or a magnetic field. Recently, multiple IG algorithms that benefit from multi-robot cooperation have been proposed in the literature. Most of these algorithms employ discretization of the state and action spaces, which makes them computationally intractable for robotic systems with complex dynamics. Moreover, they cannot deal with inter-robot restrictions such as collision avoidance or communication constraints. This paper presents a novel approach for multi-robot information gathering (MR-IG) that tackles the two aforementioned restrictions: (i) discretization of robot’s state space, and (ii) dealing with inter-robot constraints. Here we propose an algorithm that employs: (i) an underlying model of the physical process of interest, (ii) sampling-based planners to plan paths in a continuous domain, and (iii) a distributed decision-making algorithm to enable multi-robot coordination. In particular, we use the max-sum algorithm for distributed decision-making by defining an information-theoretic utility function. This function maximizes IG, while fulfilling inter-robot communication and collision avoidance constraints. We validate our proposed approach in simulations, and in a field experiment where three quadcopters explore a simulated wind field. Results demonstrate the effectiveness and scalability with respect to the number of robots of our approach.
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Boerkoel Jr., James, and Edmund Durfee. "Decoupling the Multiagent Disjunctive Temporal Problem." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 30, 2013): 123–29. http://dx.doi.org/10.1609/aaai.v27i1.8583.
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The Multiagent Disjunctive Temporal Problem (MaDTP) is a general constraint-based formulation for scheduling problems that involve interdependent agents. Decoupling agents' interdependent scheduling problems, so that each agent can manage its schedule independently, requires agents to adopt additional local constraints that effectively subsume their interdependencies. In this paper, we present the first algorithm for decoupling MaDTPs. Our distributed algorithm is provably sound and complete. Our experiments show that the relative efficiency of using temporal decoupling to find solution spaces for MaDTPs, compared to algorithms that find complete solution spaces, improves with the interconnectedness between agents schedules, leading to orders of magnitude relative speeedup. However, decoupling by its nature restricts agents' scheduling flexibility; we define novel flexibility metrics for MaDTPs, and show empirically how the flexibility sacrificed depends on the degree of coupling between agents' schedules.
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Shalini, Sheel, and Kanhaiya Lal. "Mining Changes in Temporal Patterns in Latest Time Window for Knowledge Discovery." Journal of Information & Knowledge Management 18, no. 03 (September 2019): 1950028. http://dx.doi.org/10.1142/s021964921950028x.
Повний текст джерелаАнотація:
Temporal Association Rule mining uncovers time integrated associations in a transactional database. However, in an environment where database is regularly updated, maintenance of rules is a challenging process. Earlier algorithms suggested for maintaining frequent patterns either suffered from the problem of repeated scanning or the problem of larger storage space. Therefore, this paper proposes an algorithm “Probabilistic Incremental Temporal Association Rule Mining (PITARM)” that uncovers the changed behaviour in an updated database to maintain the rules efficiently. The proposed algorithm defines two support measures to identify itemsets expected to be frequent in the successive segment in advance. It reduces unnecessary scanning of itemsets in the entire database through three-fold verification and avoids generating redundant supersets and power sets from infrequent itemsets. Implementation of pruning technique in incremental mining is a novel approach that makes it better than earlier incremental mining algorithms and consequently reduces search space to a great extent. It scans the entire database only once, thus reducing execution time. Experimental results confirm that it is an enhancement over earlier algorithms.
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Rawassizadeh, Reza, Chelsea Dobbins, Mohammad Akbari, and Michael Pazzani. "Indexing Multivariate Mobile Data through Spatio-Temporal Event Detection and Clustering." Sensors 19, no. 3 (January 22, 2019): 448. http://dx.doi.org/10.3390/s19030448.
Повний текст джерелаАнотація:
Mobile and wearable devices are capable of quantifying user behaviors based on their contextual sensor data. However, few indexing and annotation mechanisms are available, due to difficulties inherent in raw multivariate data types and the relative sparsity of sensor data. These issues have slowed the development of higher level human-centric searching and querying mechanisms. Here, we propose a pipeline of three algorithms. First, we introduce a spatio-temporal event detection algorithm. Then, we introduce a clustering algorithm based on mobile contextual data. Our spatio-temporal clustering approach can be used as an annotation on raw sensor data. It improves information retrieval by reducing the search space and is based on searching only the related clusters. To further improve behavior quantification, the third algorithm identifies contrasting events withina cluster content. Two large real-world smartphone datasets have been used to evaluate our algorithms and demonstrate the utility and resource efficiency of our approach to search.
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Li, Wen-Jun, Yuan-Yuan Xu, Qiang Dong, Jun-Lin Zhou, and Yan Fu. "TaDb: A time-aware diffusion-based recommender algorithm." International Journal of Modern Physics C 26, no. 09 (June 22, 2015): 1550102. http://dx.doi.org/10.1142/s0129183115501028.
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Traditional recommender algorithms usually employ the early and recent records indiscriminately, which overlooks the change of user interests over time. In this paper, we show that the interests of a user remain stable in a short-term interval and drift during a long-term period. Based on this observation, we propose a time-aware diffusion-based (TaDb) recommender algorithm, which assigns different temporal weights to the leading links existing before the target user's collection and the following links appearing after that in the diffusion process. Experiments on four real datasets, Netflix, MovieLens, FriendFeed and Delicious show that TaDb algorithm significantly improves the prediction accuracy compared with the algorithms not considering temporal effects.
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Saeidian, Bahram, Mohammad Saadi Mesgari, Biswajeet Pradhan, and Abdullah M. Alamri. "Irrigation Water Allocation at Farm Level Based on Temporal Cultivation-Related Data Using Meta-Heuristic Optimisation Algorithms." Water 11, no. 12 (December 11, 2019): 2611. http://dx.doi.org/10.3390/w11122611.
Повний текст джерелаАнотація:
The present water crisis necessitates a frugal water management strategy. Deficit irrigation can be regarded as an efficient strategy for agricultural water management. Optimal allocation of water to agricultural farms is a computationally complex problem because of many factors, including limitations and constraints related to irrigation, numerous allocation states, and non-linearity and complexity of the objective function. Meta-heuristic algorithms are typically used to solve complex problems. The main objective of this study is to represent water allocation at farm level using temporal cultivation data as an optimisation problem, solve this problem using various meta-heuristic algorithms, and compare the results. The objective of the optimisation is to maximise the total income of all considered lands. The criteria of objective function value, convergence trend, robustness, runtime, and complexity of use and modelling are used to compare the algorithms. Finally, the algorithms are ranked using the technique for order of preference by similarity to ideal solution (TOPSIS). The income resulting from the allocation of water by the imperialist competitive algorithm (ICA) was 1.006, 1.084, and 1.098 times that of particle swarm optimisation (PSO), bees algorithm (BA), and genetic algorithm (GA), respectively. The ICA and PSO were superior to the other algorithms in most evaluations. According to the results of TOPSIS, the algorithms, by order of priority, are ICA PSO, BA, and GA. In addition, the experience showed that using meta-heuristic algorithms, such as ICA, results in higher income (4.747 times) and improved management of water deficit than the commonly used area-based water allocation method.
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Uzan, Oriel, Reuth Dekel, Or Seri, and Ya’akov (Kobi) Gal. "Plan Recognition for Exploratory Learning Environments Using Interleaved Temporal Search." AI Magazine 36, no. 2 (June 21, 2015): 10–21. http://dx.doi.org/10.1609/aimag.v36i2.2579.
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This article presents new algorithms for inferring users’ activities in a class of flexible and open-ended educational software called exploratory learning environments (ELE). Such settings provide a rich educational environment for students, but challenge teachers to keep track of students’ progress and to assess their performance. This article presents techniques for recognizing students activities in ELEs and visualizing these activities to students. It describes a new plan recognition algorithm that takes into account repetition and interleaving of activities. This algorithm was evaluated empirically using two ELEs for teaching chemistry and statistics used by thousands of students in several countries. It was able to outperform the state-of-the-art plan recognition algorithms when compared to a gold-standard that was obtained by a domain-expert. We also show that visualizing students’ plans improves their performance on new problems when compared to an alternative visualization that consists of a step-by-step list of actions.
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Pralet, Cédric, and Gérard Verfaillie. "Time-Dependent Simple Temporal Networks: Properties and Algorithms." Proceedings of the International Conference on Automated Planning and Scheduling 24 (May 11, 2014): 536–39. http://dx.doi.org/10.1609/icaps.v24i1.13656.
Повний текст джерелаАнотація:
Simple Temporal Networks (STNs) allow minimum and maximum distance constraints between time-points to be represented. They are often used when tackling planning and scheduling problems that involve temporal aspects. This paper is a summary of the journal article "Time-dependent Simple Temporal Networks: Properties and Algorithms" published in RAIRO - Operations Research. This journal article introduces an extension of STN called Time-dependent STN (TSTN), which covers temporal constraints for which the temporal distance required between two time-points is not necessarily constant. Such constraints are useful to model time-dependent scheduling problems, in which the duration of an activity may depend on its starting time. The paper introduces the TSTN framework, its properties, resolution techniques, as well as examples of applications.
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Zhang, Huihui, Hugo A. Loáiciga, Da Ha, and Qingyun Du. "Spatial and Temporal Downscaling of TRMM Precipitation with Novel Algorithms." Journal of Hydrometeorology 21, no. 6 (June 2020): 1259–78. http://dx.doi.org/10.1175/jhm-d-19-0289.1.
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AbstractTropical Rainfall Measuring Mission (TRMM) satellite products constitute valuable precipitation datasets over regions with sparse rain gauge networks. Downscaling is an effective approach to estimating the precipitation over ungauged areas with high spatial resolution. However, a large bias and low resolution of original TRMM satellite images constitute constraints for practical hydrologic applications of TRMM precipitation products. This study contributes two precipitation downscaling algorithms by exploring the nonstationarity relations between precipitation and various environment factors [daytime surface temperature (LTD), terrain slope, normalized difference vegetation index (NDVI), altitude, longitude, and latitude] to overcome bias and low-resolution constraints of TRMM precipitation. Downscaling of precipitation is achieved with the geographically weighted regression model (GWR) and the backward-propagation artificial neural networks (BP_ANN). The probability density function (PDF) algorithm corrects the bias of satellite precipitation data with respect to spatial and temporal scales prior to downscaling. The principal component analysis algorithm (PCA) provides an alternative method of obtaining accurate monthly rainfall estimates during the wet rainfall season that minimizes the temporal uncertainties and upscaling effects introduced by direct accumulation (DA) of precipitation. The performances of the proposed downscaling algorithms are assessed by downscaling the latest version of TRMM3B42 V7 datasets within Hubei Province from 0.25° (about 25 km) to 1-km spatial resolution at the monthly scale. The downscaled datasets are systematically evaluated with in situ observations at 27 rain gauges from the years 2005 through 2010. This paper’s results demonstrate the bias correction is necessary before downscaling. The high-resolution precipitation datasets obtained with the proposed downscaling model with GWR relying on the NDVI and slope are shown to improve the accuracy of precipitation estimates. GWR exhibits more accurate downscaling results than BP_ANN coupled with the genetic algorithm (GA) in most dry and wet seasons.
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Matthews, Stephen G., Mario A. Gongora, and Adrian A. Hopgood. "Evolutionary algorithms and fuzzy sets for discovering temporal rules." International Journal of Applied Mathematics and Computer Science 23, no. 4 (December 1, 2013): 855–68. http://dx.doi.org/10.2478/amcs-2013-0064.
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Abstract A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns are augmented into a dataset to analyse the method’s ability in a controlled experiment. It is shown that the method is capable of discovering temporal patterns, and the effect of Boolean itemset support on the efficacy of discovering temporal fuzzy association rules is presented.
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Ostuni, Dario, Alice Raffaele, Romeo Rizzi, and Matteo Zavatteri. "Faster and Better Simple Temporal Problems." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 11913–20. http://dx.doi.org/10.1609/aaai.v35i13.17415.
Повний текст джерелаАнотація:
In this paper we give a structural characterization and extend the tractability frontier of the Simple Temporal Problem (STP) by defining the class of the Extended Simple Temporal Problem (ESTP), which augments STP with strict inequalities and monotone Boolean formulae on inequations (i.e., formulae involving the operations of conjunction, disjunction and parenthesization). A polynomial-time algorithm is provided to solve ESTP, faster than previous state-of-the-art algorithms for other extensions of STP that had been considered in the literature, all encompassed by ESTP. We show the practical competitiveness of our approach through a proof-of-concept implementation and an experimental evaluation involving also state-of-the-art SMT solvers.