Artículos de revistas sobre el tema "Machine Learning, Deep Learning, Quantum Computing, Network Theory"

In recent years, deep learning has had a profound impact on machine learning and artificial intelligence. At the same time, algorithms for quantum computers have been shown to efficiently solve some problems that are intractable on conventional, classical computers. We show that quantum computing not only reduces the time required to train a deep restricted Boltzmann machine, but also provides a richer and more comprehensive framework for deep learning than classical computing and leads to significant improvements in the optimization of the underlying objective function. Our quantum methods also permit efficient training of multilayer and fully connected models.

We investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks. We associate a transverse field Ising spin Hamiltonian with a layout of qubits similar to that of a deep Boltzmann machine (DBM) and use simulated quantum annealing (SQA) to numerically simulate quantum sampling from this system. We design a reinforcement learning algorithm in which the set of visible nodes representing the states and actions of an optimal policy are the first and last layers of the deep network. In absence of a transverse field, our simulations show that DBMs are trained more effectively than restricted Boltzmann machines (RBM) with the same number of nodes. We then develop a framework for training the network as a quantum Boltzmann machine (QBM) in the presence of a significant transverse field for reinforcement learning. This method also outperforms the reinforcement learning method that uses RBMs.

Through the years, the market for mobile devices has been rapidly increasing, and as a result of this trend, mobile malware has become sophisticated. Researchers are focused on the design and development of malware detection systems to strengthen the security and integrity of sensitive and private information. In this context, deep learning is exploited, also in cybersecurity, showing the ability to build models aimed at detecting whether an application is Trusted or malicious. Recently, with the introduction of quantum computing, we have been witnessing the introduction of quantum algorithms in Machine Learning. In this paper, we provide a comparison between five state-of-the-art Convolutional Neural Network models (i.e., AlexNet, MobileNet, EfficientNet, VGG16, and VGG19), one network developed by the authors (called Standard-CNN), and two quantum models (i.e., a hybrid quantum model and a fully quantum neural network) to classify malware. In addition to the classification, we provide explainability behind the model predictions, by adopting the Gradient-weighted Class Activation Mapping to highlight the areas of the image obtained from the application symptomatic of a certain prediction, to the convolutional and to the quantum models obtaining the best performances in Android malware detection. Real-world experiments were performed on a dataset composed of 8446 Android malicious and legitimate applications, obtaining interesting results.

The proliferation of the Internet of Things (IoT) and wireless sensor networks enhances data communication. The demand for data communication rapidly increases, which calls the emerging edge computing paradigm. Edge computing plays a major role in IoT networks and provides computing resources close to the users. Moving the services from the cloud to users increases the communication, storage, and network features of the users. However, massive IoT networks require a large spectrum of resources for their computations. In order to attain this, resource scheduling algorithms are employed in edge computing. Statistical and machine learning-based resource scheduling algorithms have evolved in the past decade, but the performance can be improved if resource requirements are analyzed further. A deep learning-based resource scheduling in edge computing IoT networks is presented in this research work using deep bidirectional recurrent neural network (BRNN) and convolutional neural network algorithms. Before scheduling, the IoT users are categorized into clusters using a spectral clustering algorithm. The proposed model simulation analysis verifies the performance in terms of delay, response time, execution time, and resource utilization. Existing resource scheduling algorithms like a genetic algorithm (GA), Improved Particle Swarm Optimization (IPSO), and LSTM-based models are compared with the proposed model to validate the superior performances.

The characterization of the Hamiltonian parameters defining a quantum walk is of paramount importance when performing a variety of tasks, from quantum communication to computation. When dealing with physical implementations of quantum walks, the parameters themselves may not be directly accessible, and, thus, it is necessary to find alternative estimation strategies exploiting other observables. Here, we perform the multiparameter estimation of the Hamiltonian parameters characterizing a continuous-time quantum walk over a line graph with n-neighbor interactions using a deep neural network model fed with experimental probabilities at a given evolution time. We compare our results with the bounds derived from estimation theory and find that the neural network acts as a nearly optimal estimator both when the estimation of two or three parameters is performed.

Solving for quantum error correction remains one of the key challenges of quantum computing. Traditional decoding methods are limited by computing power and data scale, which restrict the decoding efficiency of color codes. There are many decoding methods that have been suggested to solve this problem. Machine learning is considered one of the most suitable solutions for decoding task of color code. We project the color code onto the surface code, use the deep Q network to iteratively train the decoding process of the color code and obtain the relationship between the inversion error rate and the logical error rate of the trained model and the performance of error correction. Our results show that through unsupervised learning, when iterative training is at least 300 times, a self-trained model can improve the error correction accuracy to 96.5%, and the error correction speed is about 13.8% higher than that of the traditional algorithm. We numerically show that our decoding method can achieve a fast prediction speed after training and a better error correction threshold.

Abstract In the last two decades, network science has proven to be an invaluable tool for the analysis of empirical systems across a wide spectrum of disciplines, with applications to data structures admitting a representation in terms of complex networks. On the one hand, especially in the last decade, an increasing number of applications based on geometric deep learning have been developed to exploit, at the same time, the rich information content of a complex network and the learning power of deep architectures, highlighting the potential of techniques at the edge between applied math and computer science. On the other hand, studies at the edge of network science and quantum physics are gaining increasing attention, e.g., because of the potential applications to quantum networks for communications, such as the quantum Internet. In this work, we briefly review a novel framework grounded on statistical physics and techniques inspired by quantum statistical mechanics which have been successfully used for the analysis of a variety of complex systems. The advantage of this framework is that it allows one to define a set of information-theoretic tools which find widely used counterparts in machine learning and quantum information science, while providing a grounded physical interpretation in terms of a statistical field theory of information dynamics. We discuss the most salient theoretical features of this framework and selected applications to protein–protein interaction networks, neuronal systems, social and transportation networks, as well as potential novel applications for quantum network science and machine learning.

In next-generation networks, including the sixth generation (6G), a large number of computing devices can communicate with ultra-low latency. By implication, 6G capabilities present a massive benefit for the Internet of Things (IoT), considering a wide range of application domains. However, some security concerns in the IoT involving authentication and encryption protocols are currently under investigation. Thus, mechanisms implementing quantum communications in IoT devices have been explored to offer improved security. Algorithmic solutions that enable better quantum key distribution (QKD) selection for authentication and encryption have been developed, but having limited performance considering time requirements. Therefore, a new approach for selecting the best QKD protocol based on a Deep Convolutional Neural Network model, called Tree-CNN, is proposed using the Tanh Exponential Activation Function (TanhExp) that enables IoT devices to handle more secure quantum communications using the 6G network infrastructure. The proposed model is developed, and its performance is compared with classical Convolutional Neural Networks (CNN) and other machine learning methods. The results obtained are superior to the related works, with an Area Under the Curve (AUC) of 99.89% during testing and a time-cost performance of 0.65 s for predicting the best QKD protocol. In addition, we tested our proposal using different transmission distances and three QKD protocols to demonstrate that the prediction and actual results reached similar values. Hence, our proposed model obtained a fast, reliable, and precise solution to solve the challenges of performance and time consumption in selecting the best QKD protocol.

The conventional dermatology practice of performing noninvasive screening tests to detect skin diseases is a source of escapable diagnostic inaccuracies. Literature suggests that automated diagnosis is essential for improving diagnostic accuracies in medical fields such as dermatology, mammography, and colonography. Classification is an essential component of an assisted automation process that is rapidly gaining attention in the discipline of artificial intelligence for successful diagnosis, treatment, and recovery of patients. However, classifying skin lesions into multiple classes is challenging for most machine learning algorithms, especially for extremely imbalanced training datasets. This study proposes a novel ensemble deep learning algorithm based on the residual network with the next dimension and the dual path network with confidence preservation to improve the classification performance of skin lesions. The distributed computing paradigm was applied in the proposed algorithm to speed up the inference process by a factor of 0.25 for a faster classification of skin lesions. The algorithm was experimentally compared with 16 deep learning and 12 ensemble deep learning algorithms to establish its discriminating prowess. The experimental comparison was based on dermoscopic images congregated from the publicly available international skin imaging collaboration databases. We propitiously recorded up to 82.52% average sensitivity, 99.00% average specificity, 98.54% average balanced accuracy, and 92.84% multiclass accuracy without prior segmentation of skin lesions to outstrip numerous state-of-the-art deep learning algorithms investigated.

As the national economy has entered a stage of rapid development, the national economy and social development have also ushered in the “14th Five-Year Plan,” and the country has also issued support policies to encourage and guide college students to start their own businesses. Therefore, the establishment of an innovation and entrepreneurship platform has a significant impact on China’s economy. This gives college students great support and help in starting a business. The theory of deep learning algorithms originated from the development of artificial neural networks and is another important field of machine learning. As the computing power of computers has been greatly improved, especially the computing power of GPU can quickly train deep neural networks, deep learning algorithms have become an important research direction. The deep learning algorithm is a nonlinear network structure and a standard modeling method in the field of machine learning. After modeling various templates, they can be identified and implemented. This article uses a combination of theoretical research and empirical research, based on the views and research content of some scholars in recent years, and introduces the basic framework and research content of this article. Then, deep learning algorithms are used to analyze the experimental data. Data analysis is performed, and relevant concepts of deep learning algorithms are combined. This article focuses on exploring the construction of an IAE (innovation and entrepreneurship) education platform and making full use of the role of deep learning algorithms to realize the construction of innovation and entrepreneurship platforms. Traditional methods need to extract features through manual design, then perform feature classification, and finally realize the function of recognition. The deep learning algorithm has strong data image processing capabilities and can quickly process large-scale data. Research data show that 49.5% of college students and 35.2% of undergraduates expressed their interest in entrepreneurship. Entrepreneurship is a good choice to relieve employment pressure.

The convolutional neural network (CNN) is one of the most used deep learning models for image detection and classification, due to its high accuracy when compared to other machine learning algorithms. CNNs achieve better results at the cost of higher computing and memory requirements. Inference of convolutional neural networks is therefore usually done in centralized high-performance platforms. However, many applications based on CNNs are migrating to edge devices near the source of data due to the unreliability of a transmission channel in exchanging data with a central server, the uncertainty about channel latency not tolerated by many applications, security and data privacy, etc. While advantageous, deep learning on edge is quite challenging because edge devices are usually limited in terms of performance, cost, and energy. Reconfigurable computing is being considered for inference on edge due to its high performance and energy efficiency while keeping a high hardware flexibility that allows for the easy adaption of the target computing platform to the CNN model. In this paper, we described the features of the most common CNNs, the capabilities of reconfigurable computing for running CNNs, the state-of-the-art of reconfigurable computing implementations proposed to run CNN models, as well as the trends and challenges for future edge reconfigurable platforms.

Interest in quantum computing has increased significantly. Tensor network theory has become increasingly popular and widely used to simulate strongly entangled correlated systems. Matrix product state (MPS) is a well-designed class of tensor network states that plays an important role in processing quantum information. In this letter, we show that MPS, as a one-dimensional array of tensors, can be used to classify classical and quantum data. We have performed binary classification of the classical machine learning data set Iris encoded in a quantum state. We have also investigated its performance by considering different parameters on the ibmqx4 quantum computer and proved that MPS circuits can be used to attain better accuracy. Furthermore the learning ability of an MPS quantum classifier is tested to classify evapotranspiration (ET[Formula: see text]) for the Patiala meteorological station located in northern Punjab (India), using three years of a historical data set (Agri). We have used different performance metrics of classification to measure its capability. Finally, the results are plotted and the degree of correspondence among values of each sample is shown.

It is a fact that natural disasters often cause severe damage both to ecosystems and humans. Moreover, man-made disasters can have enormous moral and economic consequences for people. A typical example is the large deadly and catastrophic explosion in Beirut on 4 August 2020, which destroyed a very large area of the city. This research paper introduces a Geo-AI disaster response computer vision system, capable to map an area using material from Synthetic Aperture Radar (SAR). SAR is a unique form of radar that can penetrate the clouds and collect data day and night under any weather conditions. Specifically, the Memory-Augmented Deep Convolutional Echo State Network (MA/DCESN) is introduced for the first time in the literature, as an advanced Machine Vision (MAV) architecture. It uses a meta-learning technique, which is based on a memory-augmented approach. The target is the employment of Deep Reservoir Computing (DRC) for domain adaptation. The developed Deep Convolutional Echo State Network (DCESN) combines a classic Convolutional Neural Network (CNN), with a Deep Echo State Network (DESN), and analog neurons with sparse random connections. Its training is performed following the Recursive Least Square (RLS) method. In addition, the integration of external memory allows the storage of useful data from past processes, while facilitating the rapid integration of new information, without the need for retraining. The proposed DCESN implements a set of original modifications regarding training setting, memory retrieval mechanisms, addressing techniques, and ways of assigning attention weights to memory vectors. As it is experimentally shown, the whole approach produces remarkable stability, high generalization efficiency and significant classification accuracy, significantly extending the state-of-the-art Machine Vision methods.

In recent times, mankind is seeking for certain peculiar solutions to multiple facets containing an identically very fundamental philosophy i.e., certainly intend to have indeterminism as a primordial prerequisite; however, that indeterminism is itself like a void filled with determinism as analogous to the quantum computing as qubits and the corresponding complexity. In the meantime, there are algorithms and mathematical frameworks and those in general; yield the required distinctions in the underlying theories constructed upon principles which then give rise to respective objectifications. But, when it comes to the Artificial Intelligence and Machine Learning, then there find some mathematical gaps in order to connect other regimes in relation of one and the other. The proposed discovery in this paper is about quilting some of those gaps as like the whole structure of Artificial Intelligence is yet to be developed in the realm concerning with responsive analysis in betwixt to humans and machines or beyond to such analogy. Hence, the entire introduction & incitement of this theory is to mathematically determine the deep rationality as responsive manifestation of human brain with a designed computing and both with the highest potential degree of attributions or overlaps and both the conditions will be shown mathematically herewith as identifications that make each other separate and clear to persuade.

The quantum leap in mobile data traffic and high density of wireless electronic devices, coupled with the advancements in industrial radio monitoring and autonomous systems, have created great challenges for smooth wireless network operations. The fifth-generation and beyond (B5G) (also being referred to as sixth-generation (6G)) wireless communication technologies, due to their compatibility with the previous generations, are expected to overcome these unparalleled challenges. Accompanied by traditional and new techniques, the massive multiple input multiple output (mMIMO) approach is one of the evolving technologies for B5G/6G systems used to control the ever-increasing user stipulations and the emergence of new cases efficiently. However, the major challenges in deploying mMIMO systems are their high computational intricacy and high computing time latencies, as well as difficulties in fully exploiting the multi-antenna multi-frequency channels. Therefore, to optimize the current and B5G/6G wireless network elements proficiently, the use of the mMIMO approach in a HetNet structure with artificial intelligence (AI) techniques, e.g., machine learning (ML), distributed learning, federated learning, deep learning, and neural networks, has been considered as the prospective efficient solution. This work analyzes the observed problems and their AI/ML-enabled mitigation techniques in different mMIMO deployment scenarios for 5G/B5G networks. To provide a complete insight into the mMIMO systems with emerging antenna and propagation precoding techniques, we address and identify various relevant topics in each section that may help to make the future wireless systems robust. Overall, this work is designed to guide all B5G/6G stakeholders, including researchers and operators, aiming to understand the functional behavior and associated techniques to make such systems more agile for future communication purposes.

Cloud Data Computing (CDC) is conducive to precise energy-saving management of user data centers based on the real-time energy consumption monitoring of Information Technology equipment. This work aims to obtain the most suitable energy-saving strategies to achieve safe, intelligent, and visualized energy management. First, the theory of Convolutional Neural Network (CNN) is discussed. Besides, an intelligent energy-saving model based on CNN is designed to ameliorate the variable energy consumption, load, and power consumption of the CDC data center. Then, the core idea of the policy gradient (PG) algorithm is introduced. In addition, a CDC task scheduling model is designed based on the PG algorithm, aiming at the uncertainty and volatility of the CDC scheduling tasks. Finally, the performance of different neural network models in the training process is analyzed from the perspective of total energy consumption and load optimization of the CDC center. At the same time, simulation is performed on the CDC task scheduling model based on the PG algorithm to analyze the task scheduling demand. The results demonstrate that the energy consumption of the CNN algorithm in the CDC energy-saving model is better than that of the Elman algorithm and the ecoCloud algorithm. Besides, the CNN algorithm reduces the number of virtual machine migrations in the CDC energy-saving model by 9.30% compared with the Elman algorithm. The Deep Deterministic Policy Gradient (DDPG) algorithm performs the best in task scheduling of the cloud data center, and the average response time of the DDPG algorithm is 141. In contrast, the Deep Q Network algorithm performs poorly. This paper proves that Deep Reinforcement Learning (DRL) and neural networks can reduce the energy consumption of CDC and improve the completion time of CDC tasks, offering a research reference for CDC resource scheduling.

The 3-dimensional fold of an RNA molecule is largely determined by patterns of intramolecular hydrogen bonds between bases. Predicting the base pairing network from the sequence, also referred to as RNA secondary structure prediction or RNA folding, is a nondeterministic polynomial-time (NP)-complete computational problem. The structure of the molecule is strongly predictive of its functions and biochemical properties, and therefore the ability to accurately predict the structure is a crucial tool for biochemists. Many methods have been proposed to efficiently sample possible secondary structure patterns. Classic approaches employ dynamic programming, and recent studies have explored approaches inspired by evolutionary and machine learning algorithms. This work demonstrates leveraging quantum computing hardware to predict the secondary structure of RNA. A Hamiltonian written in the form of a Binary Quadratic Model (BQM) is derived to drive the system toward maximizing the number of consecutive base pairs while jointly maximizing the average length of the stems. A Quantum Annealer (QA) is compared to a Replica Exchange Monte Carlo (REMC) algorithm programmed with the same objective function, with the QA being shown to be highly competitive at rapidly identifying low energy solutions. The method proposed in this study was compared to three algorithms from literature and, despite its simplicity, was found to be competitive on a test set containing known structures with pseudoknots.

The Pyramidal Neural Networks (PNN) are an example of a successful recently proposed model inspired by the human visual system and deep learning theory. PNNs are applied to computer vision and based on the concept of receptive fields. This paper proposes a variation of PNN, named here as Structured Pyramidal Neural Network (SPNN). SPNN has self-adaptive variable receptive fields, while the original PNNs rely on the same size for the fields of all neurons, which limits the model since it is not possible to put more computing resources in a particular region of the image. Another limitation of the original approach is the need to define values for a reasonable number of parameters, which can turn difficult the application of PNNs in contexts in which the user does not have experience. On the other hand, SPNN has a fewer number of parameters. Its structure is determined using a novel method with Delaunay Triangulation and k-means clustering. SPNN achieved better results than PNNs and similar performance when compared to Convolutional Neural Network (CNN) and Support Vector Machine (SVM), but using lower memory capacity and processing time.

Machine learning models for the potential energy of multi-atomic systems, such as the deep potential (DP) model, make molecular simulations with the accuracy of quantum mechanical density functional theory possible at a cost only moderately higher than that of empirical force fields. However, the majority of these models lack explicit long-range interactions and fail to describe properties that derive from the Coulombic tail of the forces. To overcome this limitation, we extend the DP model by approximating the long-range electrostatic interaction between ions (nuclei + core electrons) and valence electrons with that of distributions of spherical Gaussian charges located at ionic and electronic sites. The latter are rigorously defined in terms of the centers of the maximally localized Wannier distributions, whose dependence on the local atomic environment is modeled accurately by a deep neural network. In the DP long-range (DPLR) model, the electrostatic energy of the Gaussian charge system is added to short-range interactions that are represented as in the standard DP model. The resulting potential energy surface is smooth and possesses analytical forces and virial. Missing effects in the standard DP scheme are recovered, improving on accuracy and predictive power. By including long-range electrostatics, DPLR correctly extrapolates to large systems the potential energy surface learned from quantum mechanical calculations on smaller systems. We illustrate the approach with three examples: the potential energy profile of the water dimer, the free energy of interaction of a water molecule with a liquid water slab, and the phonon dispersion curves of the NaCl crystal.

Changes in society related to the development of science, technology, computing power, cloud services, artificial intelligence, increasing general access to huge amounts of open data, lead to increased global investment in technology and services. Appropriate training is required by specialists to create a workforce to work with artificial intelligence. On the one hand, it puts forward new requirements for the training of young people, and educational content, on the other hand, provides opportunities for the use of cloud technologies during the educational process. Widespread use of AI in various fields and everyday life poses the task of understanding the basic terms related to Artificial intelligence (AI), such as Machine learning (ML), Neural network (NN), Artificial neural networks (ANN), Deep Learning, Data Science, Big Data, mastering the basic skills of using and understanding the AI principles, which is possible during the study in the school course of computer science. Cloud technologies allow you to use the power of a remote server (open information systems, digital resources, software, etc.) regardless of the location of the consumer and provide ample opportunities for the study of artificial intelligence. In this article we reveal the possibilities of cloud technologies as a means of studying artificial intelligence at school, consider the need for three stages of training and provide development of tasks and own experience of using cloud technologies to study artificial intelligence on the example of DALL-E, Google QuickDraw, cloud technologies Makeblock, PictoBlox, Teachable Machine at different stages of AI study.

Internet of Things (IoT) is regarded as a next-generation wave of Information Technology (IT) after the widespread emergence of the Internet and mobile communication technologies. IoT supports information exchange and networked interaction of appliances, vehicles and other objects, making sensing and actuation possible in a low-cost and smart manner. On the other hand, cyber-physical systems (CPS) are described as the engineered systems which are built upon the tight integration of the cyber entities (e.g., computation, communication, and control) and the physical things (natural and man-made systems governed by the laws of physics). The IoT and CPS are not isolated technologies. Rather it can be said that IoT is the base or enabling technology for CPS and CPS is considered as the grownup development of IoT, completing the IoT notion and vision. Both are merged into closed-loop, providing mechanisms for conceptualizing, and realizing all aspects of the networked composed systems that are monitored and controlled by computing algorithms and are tightly coupled among users and the Internet. That is, the hardware and the software entities are intertwined, and they typically function on different time and location-based scales. In fact, the linking between the cyber and the physical world is enabled by IoT (through sensors and actuators). CPS that includes traditional embedded and control systems are supposed to be transformed by the evolving and innovative methodologies and engineering of IoT. Several applications areas of IoT and CPS are smart building, smart transport, automated vehicles, smart cities, smart grid, smart manufacturing, smart agriculture, smart healthcare, smart supply chain and logistics, etc. Though CPS and IoT have significant overlaps, they differ in terms of engineering aspects. Engineering IoT systems revolves around the uniquely identifiable and internet-connected devices and embedded systems; whereas engineering CPS requires a strong emphasis on the relationship between computation aspects (complex software) and the physical entities (hardware). Engineering CPS is challenging because there is no defined and fixed boundary and relationship between the cyber and physical worlds. In CPS, diverse constituent parts are composed and collaborated together to create unified systems with global behaviour. These systems need to be ensured in terms of dependability, safety, security, efficiency, and adherence to real‐time constraints. Hence, designing CPS requires knowledge of multidisciplinary areas such as sensing technologies, distributed systems, pervasive and ubiquitous computing, real-time computing, computer networking, control theory, signal processing, embedded systems, etc. CPS, along with the continuous evolving IoT, has posed several challenges. For example, the enormous amount of data collected from the physical things makes it difficult for Big Data management and analytics that includes data normalization, data aggregation, data mining, pattern extraction and information visualization. Similarly, the future IoT and CPS need standardized abstraction and architecture that will allow modular designing and engineering of IoT and CPS in global and synergetic applications. Another challenging concern of IoT and CPS is the security and reliability of the components and systems. Although IoT and CPS have attracted the attention of the research communities and several ideas and solutions are proposed, there are still huge possibilities for innovative propositions to make IoT and CPS vision successful. The major challenges and research scopes include system design and implementation, computing and communication, system architecture and integration, application-based implementations, fault tolerance, designing efficient algorithms and protocols, availability and reliability, security and privacy, energy-efficiency and sustainability, etc. It is our great privilege to present Volume 21, Issue 3 of Scalable Computing: Practice and Experience. We had received 30 research papers and out of which 14 papers are selected for publication. The objective of this special issue is to explore and report recent advances and disseminate state-of-the-art research related to IoT, CPS and the enabling and associated technologies. The special issue will present new dimensions of research to researchers and industry professionals with regard to IoT and CPS. Vivek Kumar Prasad and Madhuri D Bhavsar in the paper titled "Monitoring and Prediction of SLA for IoT based Cloud described the mechanisms for monitoring by using the concept of reinforcement learning and prediction of the cloud resources, which forms the critical parts of cloud expertise in support of controlling and evolution of the IT resources and has been implemented using LSTM. The proper utilization of the resources will generate revenues to the provider and also increases the trust factor of the provider of cloud services. For experimental analysis, four parameters have been used i.e. CPU utilization, disk read/write throughput and memory utilization. Kasture et al. in the paper titled "Comparative Study of Speaker Recognition Techniques in IoT Devices for Text Independent Negative Recognition" compared the performance of features which are used in state of art speaker recognition models and analyse variants of Mel frequency cepstrum coefficients (MFCC) predominantly used in feature extraction which can be further incorporated and used in various smart devices. Mahesh Kumar Singh and Om Prakash Rishi in the paper titled "Event Driven Recommendation System for E-Commerce using Knowledge based Collaborative Filtering Technique" proposed a novel system that uses a knowledge base generated from knowledge graph to identify the domain knowledge of users, items, and relationships among these, knowledge graph is a labelled multidimensional directed graph that represents the relationship among the users and the items. The proposed approach uses about 100 percent of users' participation in the form of activities during navigation of the web site. Thus, the system expects under the users' interest that is beneficial for both seller and buyer. The proposed system is compared with baseline methods in area of recommendation system using three parameters: precision, recall and NDGA through online and offline evaluation studies with user data and it is observed that proposed system is better as compared to other baseline systems. Benbrahim et al. in the paper titled "Deep Convolutional Neural Network with TensorFlow and Keras to Classify Skin Cancer" proposed a novel classification model to classify skin tumours in images using Deep Learning methodology and the proposed system was tested on HAM10000 dataset comprising of 10,015 dermatoscopic images and the results observed that the proposed system is accurate in order of 94.06\% in validation set and 93.93\% in the test set. Devi B et al. in the paper titled "Deadlock Free Resource Management Technique for IoT-Based Post Disaster Recovery Systems" proposed a new class of techniques that do not perform stringent testing before allocating the resources but still ensure that the system is deadlock-free and the overhead is also minimal. The proposed technique suggests reserving a portion of the resources to ensure no deadlock would occur. The correctness of the technique is proved in the form of theorems. The average turnaround time is approximately 18\% lower for the proposed technique over Banker's algorithm and also an optimal overhead of O(m). Deep et al. in the paper titled "Access Management of User and Cyber-Physical Device in DBAAS According to Indian IT Laws Using Blockchain" proposed a novel blockchain solution to track the activities of employees managing cloud. Employee authentication and authorization are managed through the blockchain server. User authentication related data is stored in blockchain. The proposed work assists cloud companies to have better control over their employee's activities, thus help in preventing insider attack on User and Cyber-Physical Devices. Sumit Kumar and Jaspreet Singh in paper titled "Internet of Vehicles (IoV) over VANETS: Smart and Secure Communication using IoT" highlighted a detailed description of Internet of Vehicles (IoV) with current applications, architectures, communication technologies, routing protocols and different issues. The researchers also elaborated research challenges and trade-off between security and privacy in area of IoV. Deore et al. in the paper titled "A New Approach for Navigation and Traffic Signs Indication Using Map Integrated Augmented Reality for Self-Driving Cars" proposed a new approach to supplement the technology used in self-driving cards for perception. The proposed approach uses Augmented Reality to create and augment artificial objects of navigational signs and traffic signals based on vehicles location to reality. This approach help navigate the vehicle even if the road infrastructure does not have very good sign indications and marking. The approach was tested locally by creating a local navigational system and a smartphone based augmented reality app. The approach performed better than the conventional method as the objects were clearer in the frame which made it each for the object detection to detect them. Bhardwaj et al. in the paper titled "A Framework to Systematically Analyse the Trustworthiness of Nodes for Securing IoV Interactions" performed literature on IoV and Trust and proposed a Hybrid Trust model that seperates the malicious and trusted nodes to secure the interaction of vehicle in IoV. To test the model, simulation was conducted on varied threshold values. And results observed that PDR of trusted node is 0.63 which is higher as compared to PDR of malicious node which is 0.15. And on the basis of PDR, number of available hops and Trust Dynamics the malicious nodes are identified and discarded. Saniya Zahoor and Roohie Naaz Mir in the paper titled "A Parallelization Based Data Management Framework for Pervasive IoT Applications" highlighted the recent studies and related information in data management for pervasive IoT applications having limited resources. The paper also proposes a parallelization-based data management framework for resource-constrained pervasive applications of IoT. The comparison of the proposed framework is done with the sequential approach through simulations and empirical data analysis. The results show an improvement in energy, processing, and storage requirements for the processing of data on the IoT device in the proposed framework as compared to the sequential approach. Patel et al. in the paper titled "Performance Analysis of Video ON-Demand and Live Video Streaming Using Cloud Based Services" presented a review of video analysis over the LVS \& VoDS video application. The researchers compared different messaging brokers which helps to deliver each frame in a distributed pipeline to analyze the impact on two message brokers for video analysis to achieve LVS & VoS using AWS elemental services. In addition, the researchers also analysed the Kafka configuration parameter for reliability on full-service-mode. Saniya Zahoor and Roohie Naaz Mir in the paper titled "Design and Modeling of Resource-Constrained IoT Based Body Area Networks" presented the design and modeling of a resource-constrained BAN System and also discussed the various scenarios of BAN in context of resource constraints. The Researchers also proposed an Advanced Edge Clustering (AEC) approach to manage the resources such as energy, storage, and processing of BAN devices while performing real-time data capture of critical health parameters and detection of abnormal patterns. The comparison of the AEC approach is done with the Stable Election Protocol (SEP) through simulations and empirical data analysis. The results show an improvement in energy, processing time and storage requirements for the processing of data on BAN devices in AEC as compared to SEP. Neelam Saleem Khan and Mohammad Ahsan Chishti in the paper titled "Security Challenges in Fog and IoT, Blockchain Technology and Cell Tree Solutions: A Review" outlined major authentication issues in IoT, map their existing solutions and further tabulate Fog and IoT security loopholes. Furthermore, this paper presents Blockchain, a decentralized distributed technology as one of the solutions for authentication issues in IoT. In addition, the researchers discussed the strength of Blockchain technology, work done in this field, its adoption in COVID-19 fight and tabulate various challenges in Blockchain technology. The researchers also proposed Cell Tree architecture as another solution to address some of the security issues in IoT, outlined its advantages over Blockchain technology and tabulated some future course to stir some attempts in this area. Bhadwal et al. in the paper titled "A Machine Translation System from Hindi to Sanskrit Language Using Rule Based Approach" proposed a rule-based machine translation system to bridge the language barrier between Hindi and Sanskrit Language by converting any test in Hindi to Sanskrit. The results are produced in the form of two confusion matrices wherein a total of 50 random sentences and 100 tokens (Hindi words or phrases) were taken for system evaluation. The semantic evaluation of 100 tokens produce an accuracy of 94\% while the pragmatic analysis of 50 sentences produce an accuracy of around 86\%. Hence, the proposed system can be used to understand the whole translation process and can further be employed as a tool for learning as well as teaching. Further, this application can be embedded in local communication based assisting Internet of Things (IoT) devices like Alexa or Google Assistant. Anshu Kumar Dwivedi and A.K. Sharma in the paper titled "NEEF: A Novel Energy Efficient Fuzzy Logic Based Clustering Protocol for Wireless Sensor Network" proposed a a deterministic novel energy efficient fuzzy logic-based clustering protocol (NEEF) which considers primary and secondary factors in fuzzy logic system while selecting cluster heads. After selection of cluster heads, non-cluster head nodes use fuzzy logic for prudent selection of their cluster head for cluster formation. NEEF is simulated and compared with two recent state of the art protocols, namely SCHFTL and DFCR under two scenarios. Simulation results unveil better performance by balancing the load and improvement in terms of stability period, packets forwarded to the base station, improved average energy and extended lifetime.

We investigate the analogy between the renormalization group (RG) and deep neural networks, wherein subsequent layers of neurons are analogous to successive steps along the RG. In particular, we quantify the flow of information by explicitly computing the relative entropy or Kullback-Leibler divergence in both the one- and two-dimensional Ising models under decimation RG, as well as in a feedforward neural network as a function of depth. We observe qualitatively identical behavior characterized by the monotonic increase to a parameter-dependent asymptotic value. On the quantum field theory side, the monotonic increase confirms the connection between the relative entropy and the c-theorem. For the neural networks, the asymptotic behavior may have implications for various information maximization methods in machine learning, as well as for disentangling compactness and generalizability. Furthermore, while both the two-dimensional Ising model and the random neural networks we consider exhibit non-trivial critical points, the relative entropy appears insensitive to the phase structure of either system. In this sense, more refined probes are required in order to fully elucidate the flow of information in these models.

Abstract Background Stochastic processes leading voltage-gated ion channel dynamics on the nerve cell membrane are a sufficient condition to describe membrane conductance through statistical mechanics of disordered and complex systems. Results Voltage-gated ion channels in the nerve cell membrane are described by the Ising model. Stochastic circuit elements called “Ising Neural Machines” are introduced. Action potentials are described as quasi-particles of a statistical field theory for the Ising system. Conclusions The particle description of action potentials is a new point of view and a powerful tool to describe the generation and propagation of nerve impulses, especially when classical electrophysiological models break down. The particle description of action potentials allows us to develop a new generation of devices to study neurodegenerative and demyelinating diseases as Multiple Sclerosis and Alzheimer’s disease, even integrated by connectomes. It is also suitable for the study of complex networks, quantum computing, artificial intelligence, machine and deep learning, cryptography, ultra-fast lines for entanglement experiments and many other applications of medical, physical and engineering interest.

AbstractWe present studies of quantum algorithms exploiting machine learning to classify events of interest from background events, one of the most representative machine learning applications in high-energy physics. We focus on variational quantum approach to learn the properties of input data and evaluate the performance of the event classification using both simulators and quantum computing devices. Comparison of the performance with standard multi-variate classification techniques based on a boosted-decision tree and a deep neural network using classical computers shows that the quantum algorithm has comparable performance with the standard techniques at the considered ranges of the number of input variables and the size of training samples. The variational quantum algorithm is tested with quantum computers, demonstrating that the discrimination of interesting events from background is feasible. Characteristic behaviors observed during a learning process using quantum circuits with extended gate structures are discussed, as well as the implications of the current performance to the application in high-energy physics experiments.

Predicting electronic energies, densities, and related chemical properties can facilitate the discovery of novel catalysts, medicines, and battery materials. However, existing machine learning techniques are challenged by the scarcity of training data when exploring unknown chemical spaces. We overcome this barrier by systematically incorporating knowledge of molecular electronic structure into deep learning. By developing a physics-inspired equivariant neural network, we introduce a method to learn molecular representations based on the electronic interactions among atomic orbitals. Our method, OrbNet-Equi, leverages efficient tight-binding simulations and learned mappings to recover high-fidelity physical quantities. OrbNet-Equi accurately models a wide spectrum of target properties while being several orders of magnitude faster than density functional theory. Despite only using training samples collected from readily available small-molecule libraries, OrbNet-Equi outperforms traditional semiempirical and machine learning–based methods on comprehensive downstream benchmarks that encompass diverse main-group chemical processes. Our method also describes interactions in challenging charge-transfer complexes and open-shell systems. We anticipate that the strategy presented here will help to expand opportunities for studies in chemistry and materials science, where the acquisition of experimental or reference training data is costly.

AbstractEngineering simulation accelerates the development of reliable and repeatable design processes in various domains. However, the computing resource consumption is dramatically raised in the whole development processes. Making the most of these simulation data becomes more and more important in modern industrial product design. In the present study, we proposed a workflow comprised of a series of machine learning algorithms (mainly deep neuron networks) to be an alternative to the numerical simulation. We have applied the workflow to the field of dental implant design process. The process is based on a complex, time-dependent, multi-physical biomechanical theory, known as mechano-regulatory method. It has been used to evaluate the performance of dental implants and to assess the tissue recovery after the oral surgery procedures. We provided a deep learning network (DLN) with calibrated simulation data that came from different simulation conditions with experimental verification. The DLN achieves nearly exact result of simulated bone healing history around implants. The correlation of the predicted essential physical properties of surrounding bones (e.g. strain and fluid velocity) and performance indexes of implants (e.g. bone area and bone-implant contact) were greater than 0.980 and 0.947, respectively. The testing AUC values for the classification of each tissue phenotype were ranging from 0.90 to 0.99. The DLN reduced hours of simulation time to seconds. Moreover, our DLN is explainable via Deep Taylor decomposition, suggesting that the transverse fluid velocity, upper and lower parts of dental implants are the keys that influence bone healing and the distribution of tissue phenotypes the most. Many examples of commercial dental implants with designs which follow these design strategies can be found. This work demonstrates that DLN with proper network design is capable to replace complex, time-dependent, multi-physical models/theories, as well as to reveal the underlying features without prior professional knowledge.

Redundant robotic arm models as a control object discussed. Background of computational intelligence IT based on soft computing optimizer of knowledge base in smart robotic manipulators introduced. Soft computing optimizer is the toolkit of deep machine learning SW platform with optimal fuzzy neural network structure. The methods for development and design technology of intelligent control systems based on the soft computing optimizer presented in this Part 1 allow one to implement the principle of design an optimal intelligent control systems with a maximum reliability and controllability level of a complex control object under conditions of uncertainty in the source data, and in the presence of stochastic noises of various physical and statistical characters. The knowledge bases formed with the application of a soft computing optimizer produce robust control laws for the schedule of time dependent coefficient gains of conventional PID controllers for a wide range of external perturbations and are maximally insensitive to random variations of the structure of control object. The robustness of control laws is achieved by application a vector fitness function for genetic algorithm, whose one component describes the physical principle of minimum production of generalized entropy both in the control object and the control system, and the other components describe conventional control objective functionals such as minimum control error, etc. The application of soft computing technologies (Part I) for the development a robust intelligent control system that solving the problem of precision positioning redundant (3DOF and 7 DOF) manipulators considered. Application of quantum soft computing in robust intelligent control of smart manipulators in Part II described.

Abstract Motivation While many quantum computing (QC) methods promise theoretical advantages over classical counterparts, quantum hardware remains limited. Exploiting near-term QC in computer-aided drug design (CADD) thus requires judicious partitioning between classical and quantum calculations. Results We present HypaCADD, a hybrid classical-quantum workflow for finding ligands binding to proteins, while accounting for genetic mutations. We explicitly identify modules of our drug design workflow currently amenable to replacement by QC: non-intuitively, we identify the mutation-impact predictor as the best candidate. HypaCADD thus combines classical docking and molecular dynamics with quantum machine learning (QML) to infer the impact of mutations. We present a case study with the SARS-CoV-2 protease and associated mutants. We map a classical machine-learning module onto QC, using a neural network constructed from qubit-rotation gates. We have implemented this in simulation and on two commercial quantum computers. We find that the QML models can perform on par with, if not better than, classical baselines. In summary, HypaCADD offers a successful strategy for leveraging QC for CADD. Availability Jupyter Notebooks with Python code are freely available for academic use on GitHub: https://www.github.com/hypahub/hypacadd_notebook Supplementary information Supplementary data are available at Bioinformatics online.

AbstractWe present a generalized potential theory for conservative as well as nonconservative forces for the Landau-Lifshitz magnetization dynamics. Importantly, this formulation makes possible an elegant generalization of the Behler-Parrinello machine learning (ML) approach, which is a cornerstone of ML-based quantum molecular dynamics methods, to the modeling of force fields in adiabatic spin dynamics of out-of-equilibrium itinerant magnetic systems. We demonstrate our approach by developing a deep-learning neural network that successfully learns the electron-mediated exchange fields in a driven s-d model computed from the nonequilibrium Green’s function method. We show that dynamical simulations with forces predicted from the neural network accurately reproduce the voltage-driven domain-wall propagation. Our work also lays the foundation for ML modeling of spin transfer torques and opens a avenue for ML-based multi-scale modeling of nonequilibrium dynamical phenomena in itinerant magnets and spintronics.

AbstractSurface ion traps are among the most promising technologies for scaling up quantum computing machines, but their complicated multi-electrode geometry can make some tasks, including compensation for stray electric fields, challenging both at the level of modeling and of practical implementation. Here we demonstrate the compensation of stray electric fields using a gradient descent algorithm and a machine learning technique, which trained a deep learning network. We show automated dynamical compensation tested against induced electric charging from UV laser light hitting the chip trap surface. The results show improvement in compensation using gradient descent and the machine learner over manual compensation. This improvement is inferred from an increase of the fluorescence rate of 78% and 96% respectively, for a trapped $$^{171}$$ 171 Yb$$^+$$ + ion driven by a laser tuned to $$-7.8$$ - 7.8 MHz of the $$^2$$ 2 S$$_{1/2}\leftrightarrow ^2$$ 1 / 2 ↔ 2 P$$_{1/2}$$ 1 / 2 Doppler cooling transition at 369.5 nm.

AbstractForce field-based classical molecular dynamics (CMD) is efficient but its potential energy surface (PES) prediction error can be very large. Density functional theory (DFT)-based ab-initio molecular dynamics (AIMD) is accurate but computational cost limits its applications to small systems. Here, we propose a molecular dynamics (MD) methodology which can simultaneously achieve both AIMD-level high accuracy and CMD-level high efficiency. The high accuracy is achieved by exploiting deep neural network (DNN)’s arbitrarily-high precision to fit PES. The high efficiency is achieved by deploying multiplication-less DNN on a carefully-optimized special-purpose non von Neumann (NvN) computer to mitigate the performance-limiting data shuttling (i.e., ‘memory wall bottleneck’). By testing on different molecules and bulk systems, we show that the proposed MD methodology is generally-applicable to various MD tasks. The proposed MD methodology has been deployed on an in-house computing server based on reconfigurable field programmable gate array (FPGA), which is freely available at http://nvnmd.picp.vip.

AbstractInteratomic potentials derived with Machine Learning algorithms such as Deep-Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations. Most DNN potentials were parametrized for neutral molecules or closed-shell ions due to architectural limitations. In this work, we propose an improved machine learning framework for simulating open-shell anions and cations. We introduce the AIMNet-NSE (Neural Spin Equilibration) architecture, which can predict molecular energies for an arbitrary combination of molecular charge and spin multiplicity with errors of about 2–3 kcal/mol and spin-charges with error errors ~0.01e for small and medium-sized organic molecules, compared to the reference QM simulations. The AIMNet-NSE model allows to fully bypass QM calculations and derive the ionization potential, electron affinity, and conceptual Density Functional Theory quantities like electronegativity, hardness, and condensed Fukui functions. We show that these descriptors, along with learned atomic representations, could be used to model chemical reactivity through an example of regioselectivity in electrophilic aromatic substitution reactions.

Reinforcement Learning (RL) based machine trading attracts a rich profusion of interest. However, in the existing research, RL in the day-trade task suffers from the noisy financial movement in the short time scale, difficulty in order settlement, and expensive action search in a continuous-value space. This paper introduced an end-to-end RL intraday trading agent, namely QF-TraderNet, based on the quantum finance theory (QFT) and deep reinforcement learning. We proposed a novel design for the intraday RL trader’s action space, inspired by the Quantum Price Levels (QPLs). Our action space design also brings the model a learnable profit-and-loss control strategy. QF-TraderNet composes two neural networks: 1) A long short term memory networks for the feature learning of financial time series; 2) a policy generator network (PGN) for generating the distribution of actions. The profitability and robustness of QF-TraderNet have been verified in multi-type financial datasets, including FOREX, metals, crude oil, and financial indices. The experimental results demonstrate that QF-TraderNet outperforms other baselines in terms of cumulative price returns and Sharpe Ratio, and the robustness in the acceidential market shift.

Abstract A myriad of phenomena in materials science and chemistry rely on quantum-level simulations of the electronic structure in matter. While moving to larger length and time scales has been a pressing issue for decades, such large-scale electronic structure calculations are still challenging despite modern software approaches and advances in high-performance computing. The silver lining in this regard is the use of machine learning to accelerate electronic structure calculations -- this line of research has recently gained growing attention. The grand challenge therein is finding a suitable machine-learning model during a process called hyperparameter optimization. This, however, causes a massive computational overhead in addition to that of data generation. We accelerate the construction of neural network models by roughly two orders of magnitude by circumventing excessive training during the hyperparameter optimization phase. We demonstrate our workflow for Kohn-Sham density functional theory, the most popular computational method in materials science and chemistry.

Introduction This article is a study in anxiety with regard to social online spaces (SOS) conceived of as dark. There are two possible ways to define ‘dark’ in this context. The first is that communication is dark because it either has limited distribution, is not open to all users (closed groups are a case example) or hidden. The second definition, linked as a result of the first, is the way that communication via these means is interpreted and understood. Dark social spaces disrupt the accepted top-down flow by the ‘gazing elite’ (data aggregators including social media), but anxious users might need to strain to notice what is out there, and this in turn destabilises one’s reception of the scene. In an environment where surveillance technologies are proliferating, this article examines contemporary, dark, interconnected, and interactive communications for the entangled affordances that might be brought to bear. A provocation is that resistance through counterveillance or “sousveillance” is one possibility. An alternative (or addition) is retreating to or building ‘dark’ spaces that are less surveilled and (perhaps counterintuitively) less fearful. This article considers critically the notion of dark social online spaces via four broad socio-technical concerns connected to the big social media services that have helped increase a tendency for fearful anxiety produced by surveillance and the perceived implications for personal privacy. It also shines light on the aspect of darkness where some users are spurred to actively seek alternative, dark social online spaces. Since the 1970s, public-key cryptosystems typically preserved security for websites, emails, and sensitive health, government, and military data, but this is now reduced (Williams). We have seen such systems exploited via cyberattacks and misappropriated data acquired by affiliations such as Facebook-Cambridge Analytica for targeted political advertising during the 2016 US elections. Via the notion of “parasitic strategies”, such events can be described as news/information hacks “whose attack vectors target a system’s weak points with the help of specific strategies” (von Nordheim and Kleinen-von Königslöw, 88). In accord with Wilson and Serisier’s arguments (178), emerging technologies facilitate rapid data sharing, collection, storage, and processing wherein subsequent “outcomes are unpredictable”. This would also include the effect of acquiescence. In regard to our digital devices, for some, being watched overtly—through cameras encased in toys, computers, and closed-circuit television (CCTV) to digital street ads that determine the resonance of human emotions in public places including bus stops, malls, and train stations—is becoming normalised (McStay, Emotional AI). It might appear that consumers immersed within this Internet of Things (IoT) are themselves comfortable interacting with devices that record sound and capture images for easy analysis and distribution across the communications networks. A counter-claim is that mainstream social media corporations have cultivated a sense of digital resignation “produced when people desire to control the information digital entities have about them but feel unable to do so” (Draper and Turow, 1824). Careful consumers’ trust in mainstream media is waning, with readers observing a strong presence of big media players in the industry and are carefully picking their publications and public intellectuals to follow (Mahmood, 6). A number now also avoid the mainstream internet in favour of alternate dark sites. This is done by users with “varying backgrounds, motivations and participation behaviours that may be idiosyncratic (as they are rooted in the respective person’s biography and circumstance)” (Quandt, 42). By way of connection with dark internet studies via Biddle et al. (1; see also Lasica), the “darknet” is a collection of networks and technologies used to share digital content … not a separate physical network but an application and protocol layer riding on existing networks. Examples of darknets are peer-to-peer file sharing, CD and DVD copying, and key or password sharing on email and newsgroups. As we note from the quote above, the “dark web” uses existing public and private networks that facilitate communication via the Internet. Gehl (1220; see also Gehl and McKelvey) has detailed that this includes “hidden sites that end in ‘.onion’ or ‘.i2p’ or other Top-Level Domain names only available through modified browsers or special software. Accessing I2P sites requires a special routing program ... . Accessing .onion sites requires Tor [The Onion Router]”. For some, this gives rise to social anxiety, read here as stemming from that which is not known, and an exaggerated sense of danger, which makes fight or flight seem the only options. This is often justified or exacerbated by the changing media and communication landscape and depicted in popular documentaries such as The Social Dilemma or The Great Hack, which affect public opinion on the unknown aspects of internet spaces and the uses of personal data. The question for this article remains whether the fear of the dark is justified. Consider that most often one will choose to make one’s intimate bedroom space dark in order to have a good night’s rest. We might pleasurably escape into a cinema’s darkness for the stories told therein, or walk along a beach at night enjoying unseen breezes. Most do not avoid these experiences, choosing to actively seek them out. Drawing this thread, then, is the case made here that agency can also be found in the dark by resisting socio-political structural harms. 1. Digital Futures and Anxiety of the Dark Fear of the darkI have a constant fear that something's always nearFear of the darkFear of the darkI have a phobia that someone's always there In the lyrics to the song “Fear of the Dark” (1992) by British heavy metal group Iron Maiden is a sense that that which is unknown and unseen causes fear and anxiety. Holding a fear of the dark is not unusual and varies in degree for adults as it does for children (Fellous and Arbib). Such anxiety connected to the dark does not always concern darkness itself. It can also be a concern for the possible or imagined dangers that are concealed by the darkness itself as a result of cognitive-emotional interactions (McDonald, 16). Extending this claim is this article’s non-binary assertion that while for some technology and what it can do is frequently misunderstood and shunned as a result, for others who embrace the possibilities and actively take it on it is learning by attentively partaking. Mistakes, solecism, and frustrations are part of the process. Such conceptual theorising falls along a continuum of thinking. Global interconnectivity of communications networks has certainly led to consequent concerns (Turkle Alone Together). Much focus for anxiety has been on the impact upon social and individual inner lives, levels of media concentration, and power over and commercialisation of the internet. Of specific note is that increasing commercial media influence—such as Facebook and its acquisition of WhatsApp, Oculus VR, Instagram, CRTL-labs (translating movements and neural impulses into digital signals), LiveRail (video advertising technology), Chainspace (Blockchain)—regularly changes the overall dynamics of the online environment (Turow and Kavanaugh). This provocation was born out recently when Facebook disrupted the delivery of news to Australian audiences via its service. Mainstream social online spaces (SOS) are platforms which provide more than the delivery of media alone and have been conceptualised predominantly in a binary light. On the one hand, they can be depicted as tools for the common good of society through notional widespread access and as places for civic participation and discussion, identity expression, education, and community formation (Turkle; Bruns; Cinque and Brown; Jenkins). This end of the continuum of thinking about SOS seems set hard against the view that SOS are operating as businesses with strategies that manipulate consumers to generate revenue through advertising, data, venture capital for advanced research and development, and company profit, on the other hand. In between the two polar ends of this continuum are the range of other possibilities, the shades of grey, that add contemporary nuance to understanding SOS in regard to what they facilitate, what the various implications might be, and for whom. By way of a brief summary, anxiety of the dark is steeped in the practices of privacy-invasive social media giants such as Facebook and its ancillary companies. Second are the advertising technology companies, surveillance contractors, and intelligence agencies that collect and monitor our actions and related data; as well as the increased ease of use and interoperability brought about by Web 2.0 that has seen a disconnection between technological infrastructure and social connection that acts to limit user permissions and online affordances. Third are concerns for the negative effects associated with depressed mental health and wellbeing caused by “psychologically damaging social networks”, through sleep loss, anxiety, poor body image, real world relationships, and the fear of missing out (FOMO; Royal Society for Public Health (UK) and the Young Health Movement). Here the harms are both individual and societal. Fourth is the intended acceleration toward post-quantum IoT (Fernández-Caramés), as quantum computing’s digital components are continually being miniaturised. This is coupled with advances in electrical battery capacity and interconnected telecommunications infrastructures. The result of such is that the ontogenetic capacity of the powerfully advanced network/s affords supralevel surveillance. What this means is that through devices and the services that they provide, individuals’ data is commodified (Neff and Nafus; Nissenbaum and Patterson). Personal data is enmeshed in ‘things’ requiring that the decisions that are both overt, subtle, and/or hidden (dark) are scrutinised for the various ways they shape social norms and create consequences for public discourse, cultural production, and the fabric of society (Gillespie). Data and personal information are retrievable from devices, sharable in SOS, and potentially exposed across networks. For these reasons, some have chosen to go dark by being “off the grid”, judiciously selecting their means of communications and their ‘friends’ carefully. 2. Is There Room for Privacy Any More When Everyone in SOS Is Watching? An interesting turn comes through counterarguments against overarching institutional surveillance that underscore the uses of technologies to watch the watchers. This involves a practice of counter-surveillance whereby technologies are tools of resistance to go ‘dark’ and are used by political activists in protest situations for both communication and avoiding surveillance. This is not new and has long existed in an increasingly dispersed media landscape (Cinque, Changing Media Landscapes). For example, counter-surveillance video footage has been accessed and made available via live-streaming channels, with commentary in SOS augmenting networking possibilities for niche interest groups or micropublics (Wilson and Serisier, 178). A further example is the Wordpress site Fitwatch, appealing for an end to what the site claims are issues associated with police surveillance (fitwatch.org.uk and endpolicesurveillance.wordpress.com). Users of these sites are called to post police officers’ identity numbers and photographs in an attempt to identify “cops” that might act to “misuse” UK Anti-terrorism legislation against activists during legitimate protests. Others that might be interested in doing their own “monitoring” are invited to reach out to identified personal email addresses or other private (dark) messaging software and application services such as Telegram (freeware and cross-platform). In their work on surveillance, Mann and Ferenbok (18) propose that there is an increase in “complex constructs between power and the practices of seeing, looking, and watching/sensing in a networked culture mediated by mobile/portable/wearable computing devices and technologies”. By way of critical definition, Mann and Ferenbok (25) clarify that “where the viewer is in a position of power over the subject, this is considered surveillance, but where the viewer is in a lower position of power, this is considered sousveillance”. It is the aspect of sousveillance that is empowering to those using dark SOS. One might consider that not all surveillance is “bad” nor institutionalised. It is neither overtly nor formally regulated—as yet. Like most technologies, many of the surveillant technologies are value-neutral until applied towards specific uses, according to Mann and Ferenbok (18). But this is part of the ‘grey area’ for understanding the impact of dark SOS in regard to which actors or what nations are developing tools for surveillance, where access and control lies, and with what effects into the future. 3. Big Brother Watches, So What Are the Alternatives: Whither the Gazing Elite in Dark SOS? By way of conceptual genealogy, consideration of contemporary perceptions of surveillance in a visually networked society (Cinque, Changing Media Landscapes) might be usefully explored through a revisitation of Jeremy Bentham’s panopticon, applied here as a metaphor for contemporary surveillance. Arguably, this is a foundational theoretical model for integrated methods of social control (Foucault, Surveiller et Punir, 192-211), realised in the “panopticon” (prison) in 1787 by Jeremy Bentham (Bentham and Božovič, 29-95) during a period of social reformation aimed at the improvement of the individual. Like the power for social control over the incarcerated in a panopticon, police power, in order that it be effectively exercised, “had to be given the instrument of permanent, exhaustive, omnipresent surveillance, capable of making all visible … like a faceless gaze that transformed the whole social body into a field of perception” (Foucault, Surveiller et Punir, 213–4). In grappling with the impact of SOS for the individual and the collective in post-digital times, we can trace out these early ruminations on the complex documentary organisation through state-controlled apparatuses (such as inspectors and paid observers including “secret agents”) via Foucault (Surveiller et Punir, 214; Subject and Power, 326-7) for comparison to commercial operators like Facebook. Today, artificial intelligence (AI), facial recognition technology (FRT), and closed-circuit television (CCTV) for video surveillance are used for social control of appropriate behaviours. Exemplified by governments and the private sector is the use of combined technologies to maintain social order, from ensuring citizens cross the street only on green lights, to putting rubbish in the correct recycling bin or be publicly shamed, to making cashless payments in stores. The actions see advantages for individual and collective safety, sustainability, and convenience, but also register forms of behaviour and attitudes with predictive capacities. This gives rise to suspicions about a permanent account of individuals’ behaviour over time. Returning to Foucault (Surveiller et Punir, 135), the impact of this finds a dissociation of power from the individual, whereby they become unwittingly impelled into pre-existing social structures, leading to a ‘normalisation’ and acceptance of such systems. If we are talking about the dark, anxiety is key for a Ministry of SOS. Following Foucault again (Subject and Power, 326-7), there is the potential for a crawling, creeping governance that was once distinct but is itself increasingly hidden and growing. A blanket call for some form of ongoing scrutiny of such proliferating powers might be warranted, but with it comes regulation that, while offering certain rights and protections, is not without consequences. For their part, a number of SOS platforms had little to no moderation for explicit content prior to December 2018, and in terms of power, notwithstanding important anxiety connected to arguments that children and the vulnerable need protections from those that would seek to take advantage, this was a crucial aspect of community building and self-expression that resulted in this freedom of expression. In unearthing the extent that individuals are empowered arising from the capacity to post sexual self-images, Tiidenberg ("Bringing Sexy Back") considered that through dark SOS (read here as unregulated) some users could work in opposition to the mainstream consumer culture that provides select and limited representations of bodies and their sexualities. This links directly to Mondin’s exploration of the abundance of queer and feminist pornography on dark SOS as a “counterpolitics of visibility” (288). This work resulted in a reasoned claim that the technological structure of dark SOS created a highly political and affective social space that users valued. What also needs to be underscored is that many users also believed that such a space could not be replicated on other mainstream SOS because of the differences in architecture and social norms. Cho (47) worked with this theory to claim that dark SOS are modern-day examples in a history of queer individuals having to rely on “underground economies of expression and relation”. Discussions such as these complicate what dark SOS might now become in the face of ‘adult’ content moderation and emerging tracking technologies to close sites or locate individuals that transgress social norms. Further, broader questions are raised about how content moderation fits in with the public space conceptualisations of SOS more generally. Increasingly, “there is an app for that” where being able to identify the poster of an image or an author of an unknown text is seen as crucial. While there is presently no standard approach, models for combining instance-based and profile-based features such as SVM for determining authorship attribution are in development, with the result that potentially far less content will remain hidden in the future (Bacciu et al.). 4. There’s Nothing New under the Sun (Ecclesiastes 1:9) For some, “[the] high hopes regarding the positive impact of the Internet and digital participation in civic society have faded” (Schwarzenegger, 99). My participant observation over some years in various SOS, however, finds that critical concern has always existed. Views move along the spectrum of thinking from deep scepticisms (Stoll, Silicon Snake Oil) to wondrous techo-utopian promises (Negroponte, Being Digital). Indeed, concerns about the (then) new technologies of wireless broadcasting can be compared with today’s anxiety over the possible effects of the internet and SOS. Inglis (7) recalls, here, too, were fears that humanity was tampering with some dangerous force; might wireless wave be causing thunderstorms, droughts, floods? Sterility or strokes? Such anxieties soon evaporated; but a sense of mystery might stay longer with evangelists for broadcasting than with a laity who soon took wireless for granted and settled down to enjoy the products of a process they need not understand. As the analogy above makes clear, just as audiences came to use ‘the wireless’ and later the internet regularly, it is reasonable to argue that dark SOS will also gain widespread understanding and find greater acceptance. Dark social spaces are simply the recent development of internet connectivity and communication more broadly. The dark SOS afford choice to be connected beyond mainstream offerings, which some users avoid for their perceived manipulation of content and user both. As part of the wider array of dark web services, the resilience of dark social spaces is reinforced by the proliferation of users as opposed to decentralised replication. Virtual Private Networks (VPNs) can be used for anonymity in parallel to TOR access, but they guarantee only anonymity to the client. A VPN cannot guarantee anonymity to the server or the internet service provider (ISP). While users may use pseudonyms rather than actual names as seen on Facebook and other SOS, users continue to take to the virtual spaces they inhabit their off-line, ‘real’ foibles, problems, and idiosyncrasies (Chenault). To varying degrees, however, people also take their best intentions to their interactions in the dark. The hyper-efficient tools now deployed can intensify this, which is the great advantage attracting some users. In balance, however, in regard to online information access and dissemination, critical examination of what is in the public’s interest, and whether content should be regulated or controlled versus allowing a free flow of information where users self-regulate their online behaviour, is fraught. O’Loughlin (604) was one of the first to claim that there will be voluntary loss through negative liberty or freedom from (freedom from unwanted information or influence) and an increase in positive liberty or freedom to (freedom to read or say anything); hence, freedom from surveillance and interference is a kind of negative liberty, consistent with both libertarianism and liberalism. Conclusion The early adopters of initial iterations of SOS were hopeful and liberal (utopian) in their beliefs about universality and ‘free’ spaces of open communication between like-minded others. This was a way of virtual networking using a visual motivation (led by images, text, and sounds) for consequent interaction with others (Cinque, Visual Networking). The structural transformation of the public sphere in a Habermasian sense—and now found in SOS and their darker, hidden or closed social spaces that might ensure a counterbalance to the power of those with influence—towards all having equal access to platforms for presenting their views, and doing so respectfully, is as ever problematised. Broadly, this is no more so, however, than for mainstream SOS or for communicating in the world. References Bacciu, Andrea, Massimo La Morgia, Alessandro Mei, Eugenio Nerio Nemmi, Valerio Neri, and Julinda Stefa. “Cross-Domain Authorship Attribution Combining Instance Based and Profile-Based Features.” CLEF (Working Notes). Lugano, Switzerland, 9-12 Sep. 2019. Bentham, Jeremy, and Miran Božovič. The Panopticon Writings. London: Verso Trade, 1995. 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