Reviewerly/exHarmony huggingface dataset:
Swarm-Inspired Computing to Solve Binary Optimization Problems: A Backward Q-Learning Binarization Scheme Selector

In recent years, continuous metaheuristics have been a trend in solving binary-based combinatorial problems due to their good results. However, to use this type of metaheuristics, it is necessary to adapt them to work in binary environments, and in general, this adaptation is not trivial. The method proposed in this work evaluates the use of reinforcement learning techniques in the binarization process. Specifically, the backward Q-learning technique is explored to choose binarization schemes intelligently. This allows any continuous metaheuristic to be adapted to binary environments. The illustrated results are competitive, thus providing a novel option to address different complex problems in the industry.
The Transition Toward Merging Big Data Analytics, IoT, and Artificial Intelligence with Blockchain in Transactive Energy Markets

Blockchain is a special technology for peer-to-peer (P2P) trading platforms, which uses decentralized storage to record all transaction data. Blockchain was first used in 2009 in the infrastructure of the financial sector of the Bitcoin cryptocurrency. Recently, further applications have evolved in this area to provide decentralized transaction storage by employing innovative mechanisms that enable decentralized operation. This mechanism is called a smart contract that operates based on individually defined rules (e.g., quality, quantity, and price specifications) that enable an independent matching of market players at the distribution level on the supplier side and their potential customers and end-users as well as prosumers. In this article, in addition to introducing the concept of Blockchain, the way this cutting-edge technology deals with different agents in the energy sector and markets and its impact on the operation performance of the electricity and energy industry have been examined. Thus, merging other emerging technologies and platforms, such as big data analysis, the Internet of Things (IoT), and artificial intelligence (AI) with Blockchain, can solve many practical open challenges in smart electrical grids and the energy domain. This article delves into the impacts of integrating these platforms to boost the interoperability between different components and enhance the liquidity of markets.
Self-Ensembling GAN for Cross-Domain Semantic Segmentation

Deep neural networks (DNNs) have greatly contributed to the performance gains in semantic segmentation. Nevertheless, training DNNs generally requires large amounts of pixel-level labeled data, which is expensive and time-consuming to collect in practice. To mitigate the annotation burden, this paper proposes a self-ensembling generative adversarial network (SE-GAN) exploiting cross-domain data for semantic segmentation. In SE-GAN, a teacher network and a student network constitute a self-ensembling model for generating semantic segmentation maps, which together with a discriminator, forms a GAN. Despite its simplicity, we find SE-GAN can significantly boost the performance of adversarial training and enhance the stability of the model, the latter of which is a common barrier shared by most adversarial training-based methods. We theoretically analyze SE-GAN and provide an $\mathcal O(1/\sqrt{N})$ generalization bound ($N$ is the training sample size), which suggests controlling the discriminator's hypothesis complexity to enhance the generalizability. Accordingly, we choose a simple network as the discriminator. Extensive and systematic experiments in two standard settings demonstrate that the proposed method significantly outperforms current state-of-the-art approaches. The source code of our model is available online (https://github.com/YonghaoXu/SE-GAN).
Study of Urban Logistics Drone Path Planning Model Incorporating Service Benefit and Risk Cost

The application of drones provides a powerful solution for “the last-mile” logistics services, while the large-scale implementation of logistics drone services will threaten the safety of buildings, pedestrians, vehicles, and other elements in the urban environment. The balance of risk cost and service benefit is accordingly crucial to managing logistics drones. In this study, we proposed a cost-benefit assessment model for quantifying risk cost and service benefit in the urban environment. In addition, a global heuristic path search rule was developed to solve the path planning problem based on risk mitigation and customer service. The cost-benefit assessment model quantifies the risk cost from three environmental elements (buildings, pedestrians, and vehicles) threatened by drone operations based on the collision probability, and the service benefit based on the characteristics of logistics service customers. To explore the effectiveness of the model in this paper, we simulate and analyse the effects of different risk combinations, unknown risk zones, and risk-benefit preferences on the path planning results. The results show that compared with the traditional shortest-distance method, the drone path planning method proposed in this paper can accurately capture the distribution of risks and customers in the urban environment. It is highly reusable in ensuring service benefits while reducing risk costs and generating a cost-effective path for logistics drones. We also compare the algorithm in this paper with the A* algorithm and verify that our algorithm improves the solution quality in complex environments.
A Survey on Authorship Analysis Tasks and Techniques

Abstract Authorship Analysis (AA) is a natural language processing field that examines the previous works of writers to identify the author of a text based on its features. Studies in authorship analysis include authorship identification, authorship profiling, and authorship verification. Due to its relevance, to many applications in this field attention has been paid. It is widely used in the attribution of historical literature. Other applications include legal linguistics, criminal law, forensic investigations, and computer forensics. This paper aims to provide an overview of the work done and the techniques applied in the authorship analysis domain. The examination of recent developments in this field is the principal focus. Many different criteria can be used to define a writer’s style. This paper investigates stylometric features in different author-related tasks, including lexical, syntactic, semantic, structural, and content-specific ones. A lot of classification methods have been applied to authorship analysis tasks. We examine many research studies that use different machine learning and deep learning techniques. As a means of pointing the direction for future studies, we present the most relevant methods recently proposed. The reviewed studies include documents of different types and different languages. In summary, due to the fact that each natural language has its own set of features, there is no standard technique generically applicable for solving the AA problem.
Depth-Aware Multi-Person 3D Pose Estimation With Multi-Scale Waterfall Representations

Estimating absolute 3D poses of multiple people from monocular image is challenging due to the presence of occlusions and the scale variation among different persons. Among the existing methods, the top-down paradigms are highly dependent on human detection which is prone to the influence from inter-person occlusions, while the bottom-up paradigms suffer from the difficulties in keypoint feature extraction caused by scale variation and unreliable joint grouping caused by occlusions. To address these challenges, we introduce a novel multi-person 3D pose estimation framework, aided by multi-scale feature representations and human depth perceiving. Firstly, a waterfall-based architecture is incorporated for multi-scale feature representations to achieve a more accurate estimation of occluded joints with a better detection of human shapes. Then the global and local representations are fused for handling the effects of inter-person occlusion and scale variation in depth perceiving and keypoint feature extraction. Finally, with the guidance of the fused multi-scale representations, a depth-aware model is exploited for better 2D joint grouping and 3D pose recovering. Quantitative and qualitative evaluations on benchmark datasets of MuCo-3DHP and MuPoTS-3D prove the effectiveness of our proposed method. Furthermore, we produce an occluded MuPoTS-3D dataset and the experiments on it validate the superiority of our method for overcoming the occlusions.
Progressive Motion Boosting for Video Frame Interpolation

Video frame interpolation has made great progress in estimating advanced optical flow and synthesizing in-between frames sequentially. However, frame interpolation involving various resolutions and motions remains challenging due to limited or fixed pre-trained networks. Inspired by the success of the coarse-to-fine scheme for video frame interpolation, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , gradually interpolating frames of different resolutions, we propose a progressive boosting network (ProBoost-Net) based on a multi-scale framework to achieve flexible recurrent scales and then gradually optimize optical flow estimation and frame interpolation. Specifically, we designed a dense motion boosting (DMB) module to transfer features close to real motion to the decoded features from the later scales, which provides complementary information to refine the motion further. Furthermore, to ensure the accuracy of the estimated motion features at each scale, we propose a motion adaptive fusion (MAF) module that adaptively deals with motions with different receptive fields according to the motion conditions. Thanks to the framework's flexible recurrent scales, we can customize the number of scales and make trade-offs between computation and quality depending on the application scenario. Extensive experiments with various datasets demonstrated the superiority of our proposed method over state-of-the-art approaches in various scenarios.
Accurate and Real-Time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network

Efficiently and accurately detecting people from 3D point cloud data is of great importance in many robotic and autonomous driving applications. This fundamental perception task is still very challenging due to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(i)</i> significant deformations of human body pose and gesture over time and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(ii)</i> point cloud sparsity and scarcity for pedestrian objects. Recent efficient 3D object detection approaches rely on pillar features. However, these pillar features do not carry sufficient expressive representations to deal with all the aforementioned challenges in detecting people. To address this shortcoming, we first introduce a stackable Pillar Aware Attention (PAA) module to enhance pillar feature extraction while suppressing noises in point clouds. By integrating multi-point-channel-pooling, point-wise, channel-wise, and task-aware attention into a simple module, representation capabilities of pillar features are boosted while only requiring little additional computational resources. We also present Mini-BiFPN, a small yet effective feature network that creates bidirectional information flow and multi-level cross-scale feature fusion to better integrate multi-resolution features. Our proposed framework, namely PiFeNet, has been evaluated on three popular large-scale datasets for 3D pedestrian Detection, i.e. KITTI, JRDB, and nuScenes. It achieves state-of-the-art performance on KITTI Bird-eye-view (BEV) as well as JRDB, and competitive performance on nuScenes. Our approach is a real-time detector with 26 frame-per-second (FPS).
Toward Attack Modeling Technique Addressing Resilience in Self-Driving Car

Self-driving cars are going to be the main future mode of transportation. However, such systems like, any other cyber-physical system, are vulnerable to attack vectors and uncertainties. As a response, resilience-based approaches are being developed. However, the approaches lack a sound attack model that recognizes the attack vectors and vulnerabilities such a system would have and that does a proper severity analysis of such attacks. Moreover, the existing attack models are too generic. Currently, the domain lacks such specific work pertaining to self-driving cars. Given the technology and architecture of self-driving cars, the field requires a domain-specific attack model. This paper gives a review of the attack models and proposes a domain-specific attack model for self-driving cars. The proposed attack model, severity-based analytical attack model for resilience (SAAMR), provides attack analysis based on existing models. Also, a domain-based severity score for attacks is calculated. Further, the attacks are classified using the decision-tree method and predictions of the type of attacks are given using long short-term memory network.
Managing Policing Demand for Digital Forensics through Risk Assessment and Prioritization in England and Wales

Abstract Digital forensic helps to unlock the evidential opportunities presented by digital devices. Ninety per cent of all cases in England and Wales carry a digital element and identifying the devices with the most evidential value is central to dealing effectively and efficiently with the demand for digital forensic (DF) analysis. This article offers an ethnographically informed, empirical analysis of how police requests for in-house DF examinations are organized in 35 forces in England and Wales, and the mechanisms, contexts, and challenges of managing-related internal demand, which have been rarely examined systematically in both policing and DF practitioner literature. The analysis illustrates how an updated understanding of risk and prioritization is needed to address the growing demand for DF analysis. The findings call for updated, systematic approaches that encourage the forecasting of internal policing demand collaboratively and bridge between the operational and the technical sides of investigations.
A Continuous Real-time Hand Gesture Recognition Method based on Skeleton

While isolated hand gesture recognition methods aims to determine the type of gestures for a given sequence, continuous hand gesture recognition methods have to perform one more task: determining the starting point and ending point of the hand gesture. This task becomes challenging as the starting point and ending points of the gestures are not usually obvious even for human being. This paper presents a method for continuous hand gesture recognition based on skeleton information that consists of two phases: gesture detection and gesture recognition. In our method, to leverage the lightweight and the robustness of recognition models, TD-Net (Triple Feature Double Motion) model is employed in both gesture detection and recognition phases. Experimental results on IPN dataset have shown that the proposed method outperforms different state-of-the-art methods with 40.10% of Levenshtein accuracy and 0.1ms of inference time.
Fundamental Recognition of ADL Assessments Using Machine Learning Engineering

This paper describes an RGBD (Red Green Blue Depth) image daily life activity identification system that can monitor and detect human activities without the need of optical markers or motion sensors. In this paper, we have developed a practical methodology for detecting human activity in the daily environment. For feature extraction technique, we suggested a noble method for recognition of ADL activities based on symmetry principle. By extracting silhouette from depth images and performing mapping operations over RGB images, we have been able to extract the skeleton information of the human body in RGBD images and identify ADL activities using four critical parameters: angle formation between hand and upper half of the body, angle between center body point and hand, angle formation between hand and lower half of the body, and angle between two hands of the single silhouette. We employed the linearly dependent concept (LDC) and long short-term memory-recurrent neural networks (LSTM-RNN) for feature selection and classification, compared the results to existing approaches. The objective of our research is to not only find an effective and useful collection of features from the silhouette, but also to outperform current approaches. Finally, the suggested method’s testing results showed a 2.5 percent increase in accuracy with a 92.83 percent success rate, as well as a reduction in relative error to 2.47 percent of the original dataset.
Intelligent protection of Internet of things systems

The rapid development of information technology has allowed the creation, use and storage of all kinds of information in digital form and on the Internet. The concept of the Internet of Things (IoT) is one of the most popular topics for discussion. Intelligent protection of systems from the Internet of Things is a level more complex, as it merges the virtual world with the physical. This report aims to create a model for protecting the security of Internet of Things systems.
Elementary Number Theory Problems. Part IV

Summary In this paper problems 17, 18, 26, 27, 28, and 98 from [9] are formalized, using the Mizar formalism [8], [2], [3], [6].
A user-friendly attribute-based data access control scheme for smart grids

Smart grids are envisioned as the next-generation electricity grids and can contribute to the overall economic and environmental health of our society. It is evident that effective use of the data measured from the grid is what makes the grid "smart". Because the smart grid is a major issue of people's livelihood, the data measured from it is very sensitive. It is thus highly necessary to adopt data access control in smart grids to guarantee the security and privacy of the measured data. Due to its flexibility and scalability, attribute-based encryption (ABE) is widely utilized to realize data access control in smart grids. However, most existing ABE solutions impose a heavy decryption overhead on their users, which hinders the widespread use of data and often causes the data to be left to "sleep" in the repository. To this end, we propose a user-friendly attribute-based data access control scheme for smart grids by adopting the idea of computation outsourcing. Under our proposed scheme, users can outsource a large amount of computation to a server during the decryption phase while still guaranteeing the security and privacy of the data. Theoretical analysis and experimental results demonstrate that our scheme outperforms the existing schemes by achieving a very low decryption cost. We hope that our proposed scheme will facilitate the use of data measured in smart grids.
Security Analyses of Random Number Generation with Image Encryption Using Improved Chaotic Map

A chaotic system can have a series of values for the developing variable that completely repeat themselves, giving periodic behavior beginning from any point in that series. It is very sensitive to initial conditions and control parameters which make them suitable for image encryption. The two maps Duffing and Logistic maps are combined to generate a random sequence. The Logistic map is 1 Dimensional and contains one control parameter (r). Duffing map contains 2 dimensions X and Y, and two control parameters A and B. Both are XOR'ed to get the random numbers. The control parameters of Duffing map are controlled by the control parameter r. X and Y are looped to get repetitive iterations. The random sequences are analyzed for statistical tests for randomness, keyspace, key sensitivity (or) cross-correlation, periodicity, auto-correlation, information entropy, equilibrium, linear complexity, and speed are the analyses to verify their conformity for use in cryptographic applications. Image encryption is the process of encoding a grayscale image with the help of a series of random numbers created by using a combination of chaotic maps, in such a way that uncertified users can't access it. NPCR, UACI, Histogram, and Information Entropy are the analyses performed. Image encryption and secret key generation are presented based on the Duffing map and logistic chaotic map. The series of random numbers and the greyscale image pixels are XOR'ed to shuffle the pixels and it turned the original image into a cipher image.
Deep Learning Models for Multiple Face Mask Detection under a Complex Big Data Environment

The Covid-19 (coronavirus) pandemic creates a worldwide health crisis. According to the WHO, the effective protection system is wearing a face mask in public places. Many studies proved that carrying a face mask is also one of the precautions to decrease the possibility of viral transmission. Strict monitoring of face mask being worn by people is now enforced in many countries. Manual observation and monitoring is quite tedious. Hence, automated systems have been researched using well-kwown face mask detection methods. However, this research paper, deals with some deep learning models which can be effectively used to detect multiple face masks in a crowded environment when the amount of incoming data from sensors is huge or in otherwise stated to a Big data problem. Hence, standalone face detection models are not quite suited. Deep learning models are required in such Big data scenario which forms the essence of this study.
Swarm Intelligence in Data Science: Challenges, Opportunities and Applications

The Swarm Intelligence (SI) algorithms have been useful in solving multifaceted optimization problems. SI Algorithms as the name suggests work on the simulation principle of behaviour of biological swarms. Today, there is lot of advancement in the field of Data Science; which comprises of large amount of Big Data that needs to managed as well as analysed. Traditional methods may only be applied to differentiable and continuous functions. For population-based approaches, Swarm Intelligence proves to be effective and efficient. In order to have better insight into applications of SI Algorithms in Data Science, and to accumulate a further reference for the future researchers in this field, this paper discusses scope of Swarm Intelligence in Data Science. On the basis of existing work; the following paper also focusses on the Challenges and Opportunities of SI in Data Science and also encourage to design more efficient algorithms in future that is capable to solve Data Science related problems in real world.
Implementation of Classical Path Planning Algorithms for Mobile Robot Navigation: A Comprehensive Comparison

Path planning for a mobile robot is the process of generating a suitable collision-free path to traverse between any two positions operating in challenging and cluttered environments. For efficient navigation and autonomous operations of mobile robots, intelligent and efficient algorithms of path planning play an important role. In this work, four classical path planning algorithms, namely, Dijkstra's, A-star <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathbf{A}^{\ast})$</tex> , rapidly exploring random tree (RRT) and artificial potential fields (APF) algorithms, have been explored. These algorithms are evaluated, simulated and implemented with a mobile robot with non-holonomic constraints. to test the merit of these algorithms, a suitable test environment has been set up with static obstacles. Based on the obtained map, the path planning algorithms are implemented and tested where the desired path was tracked by the mobile robot. to make a comparative study, standard performance indices have been evaluated for understanding the trade-off between these algorithms.