Reviewerly/exHarmony huggingface dataset:
Application of Gesture Data Recognition in a Human-Interactive Leap Motion Sensor Chair

Gestural interface has been the subject of research for more than 40 years. The implementation of a novel intuitive and adaptive manipulation scheme is proposed by developing a human-machine communication interface between the Leap Motion controller and the wheel chair. In addition, an algorithm is developed to allow an optimum mapping between the user hand movement, tracked by the Leap Motion controller, and the wheel chair. The system should allow for a more natural human-computer interaction and a smooth manipulation of the wheel chair, by constantly adapting to the user hand tremor or shake. The sensor detects motions and moves the wheelchair accordingly. It will track the movement of the fingers and hands in order to identify the gesture, and then the motors will perform the required action using Arduino. The implementation would specially enhance the quality of living, especially for people with lower limb problems, and would support them in performing some of the essential Activities of Daily Living “ADLs”.
Brain Tissue Segmentation via Deep Convolutional Neural Networks

Deep convolutional neural networks were used to successfully segment several important neural tissue classes in MRI brain images, and approaches for integrating prior information into the networks to increase their performance on this task were investigated. Regrettably, only the first of them is addressed in this paper. To make the implementation of nonstandard architectures, which was expected to be required for the second goal, it was determined to provide a framework for defining and training networks by only using fundamental components. While this was an educational experience, the amount of progress accomplished was far less than if a conventional network package had been utilized instead. The requirement to deal with all of the lowest level aspects of network construction, from initialization schemes to adaptive learning rates and all the other components of the optimizer pipeline has left no time for utilizing this infrastructure to do something which has not been accomplished by the existing frameworks, and of course in all other respects it is far more limited than they are. It would in-stead be focused on a clear and detailed analysis of the full pipeline, which is required to build a network for solving the first problem. Despite several difficulties tracking down bugs in the optimizer, GPU memory allocation, and the last-minute accidental deletion of a large portion of the experimental results, the software implementation made available at: achieves DICE results of 0:8, which, while not class leading, would still place well in many benchmarks.
Evaluation of language analysis to summarize the literature: a comparison to traditional meta-analysis in primary hip and knee surgery

Sentiment analysis, by evaluating written wording and its context, is a growing tool used in computer science that can determine the level of support expressed in a body of text using artificial intelligence methodologies. The application of sentiment analysis to biomedical literature is a growing field and offers the potential to rapidly and economically explore large amounts of published research and characterize treatment efficacy.We compared the results of sentiment analysis of 115 article abstracts analyzed in a recently published meta-analysis of peripheral nerve block usage in primary hip and knee arthroplasty to the conclusions drawn by the authors of the original meta-analysis.A moderately positive outlook supporting the utilization of regional anesthesia for hip and knee arthroplasty was found in the 115 articles that were included for analysis, with 46% expressing positive sentiment, 35% expressing neutral sentiment, and 19% of abstracts expressing negative sentiment. This was well aligned with the conclusions reached by a previous meta-analysis of the same articles.Sentiment analysis applied to the medical literature can rapidly evaluate large collections of published data and generate an impression of overall findings that are aligned with the findings of a traditional meta-analysis.
Multivariate online regression analysis with heterogeneous streaming data

New data collection and storage technologies have given rise to a new field of streaming data analytics, called real‐time statistical methodology for online data analyses. Most existing online learning methods are based on homogeneity assumptions, which require the samples in a sequence to be independent and identically distributed. However, inter‐data batch correlation and dynamically evolving batch‐specific effects are among the key defining features of real‐world streaming data such as electronic health records and mobile health data. This article is built under a state‐space mixed model framework in which the observed data stream is driven by a latent state process that follows a Markov process. In this setting, online maximum likelihood estimation is made challenging by high‐dimensional integrals and complex covariance structures. In this article, we develop a real‐time Kalman‐filter‐based regression analysis method that updates both point estimates and their standard errors for fixed population average effects while adjusting for dynamic hidden effects. Both theoretical justification and numerical experiments demonstrate that our proposed online method has statistical properties similar to those of its offline counterpart and enjoys great computational efficiency. We also apply this method to analyze an electronic health record dataset.
Towards Context-Aware Smart Contracts for Blockchain IoT Systems

Context-awareness is very essential for blockchain-based IoT systems to guarantee successful system operations, even in presence of frequent external environment changes. However, current blockchain-based IoT solutions lack the adaptive ability required to accommodate external environment context changes during the system runtime. In this paper, we propose a context-aware smart contract system using rule-based reasoning for efficient energy consumption and environmental sustainability in blockchain-based IoT systems. Using smart contracts, the system collects contextual information, analyzes it by considering suitable control rules to perform required actions. To guarantee data authenticity and integrity, all transaction history and logs are stored in the blockchain-based distributed ledger for immutable data provenance evidence, accountability, and traceability. A smart grid home area network-based prototype was implemented on top of the Quorum blockchain to prove the feasibility of our concept. Besides, the performance evaluation of the experimental results shows that our system efficiently reduced energy consumption while maintaining environmental sustainability.
An Ethereum-based solution for energy trading in smart grids

The need for a flexible, dynamic, and decentralized energy market has rapidly grown in recent years. As a matter of fact, Industry 4.0 and Smart Grids are pursuing a path of automation of operations to insure all the steps among consumers and producers are getting closer. This leads towards solutions that exploit the paradigm of public blockchain, which represents the best platform to design flat and liquid markets for which providing trust and accountability to mutual interactions becomes crucial. On the other hand, one of the risks arising in this situation is that personal information is exposed to the network, with intolerable threats to privacy. In this paper, we propose a solution for energy trading, based on the blockchain Ethereum and Smart Contracts.The solution aims to be a concrete proposal to satisfy the needs of energy trading in smart grids, including the important feature that no information about the identity of the peers of the network is disclosed in advance.
Personalized Movie Hybrid Recommendation Model Based on GRU

the video platform is rich in movie resources, and the huge amount of data has obvious information overload problem. It is a difficult problem for users to quickly find interesting movie resources in the massive data, and the movie recommendation model is an effective way to solve this problem. It is considered to use the deep neural network DNN and factor decomposition machine FM to process the characteristics of users' demands for different movie types, and use GRU (Gate Recurrent Unit) to mine the laws and characteristics of their existence from the text data of movies. In order to make the recommendation result more accurate, a movie recommendation model based on GRU, DNN and FM is proposed by combining the relationship between users and movies. Finally, a large number of experiments are carried out on the Movielens-1m dataset. The experimental results show that compared with LSTM (Long-term and Short-term Memory Network) and CNN (Convolutional Neural Network) recommendation models, the model has better performance and higher recommendation accuracy.
Using Machine Learning Methods in Early Diagnosis of Breast Cancer

Breast cancer is one of the most important health diseases to be treated in the world, and it is a subject that has a wide place in research subjects. In this study, results are provided by using seven different machine learning techniques for the classification of breast cancer. In order to obtain better results, the preprocessing method was applied. As a result, when compared with some studies in the literature, it was observed that the general performance of some of the methods improved. In the experimental results, BayesNet was found to be the best classification method with an accuracy rate of 97.13%.
Controlling Media Player with Hand Gestures using Convolutional Neural Network

In today's world, everyone opts for fast interaction with complex systems that ensure a quick response. Thus, with increasing improvement in technology, response time and ease of operations are the concerns. Here is where human-computer interaction comes into play. This interaction is unrestricted and challenges the used devices such as the keyboard and mouse for input. Gesture recognition has been gaining much attention. Gestures are instinctive and are frequently used in day-to-day interactions. Therefore, communicating using gestures with computers creates a whole new standard of interaction. In this project, with the help of computer vision and deep learning techniques, user hand movements (gestures) are used in real-time to control the media player. In this project, seven gestures are defined to control the media players using hand gestures. The proposed web application enables the user to use their local device camera to identify their gesture and execute the control over the media player and similar applications (without any additional hardware). It increases efficiency and makes interaction effortless by letting the user control his/her laptop/desktop from a distance.
HAVS: Human action-based video summarization, Taxonomy, Challenges, and Future Perspectives

In computer vision, video summarization is a critical research problem as it is related to a more condensed and engaging portrayal of the video's original content. Deep learning models have lately been employed for various approaches to human action recognition. In this paper, we examine the most up-to-date methodologies for summarizing human behaviors in videos, as well as numerous deep learning and hybrid algorithms. We provide an in-depth analysis of the many forms of human activities, including gesture-based, interaction-based, human action-based, and group activity-based activities. Our study goes over the most recent benchmark datasets for recognizing human motion in video sequences. It also discusses the strengths and limitations of the existing methods, open research issues, and future directions for human action-based video summarization (HAVS). This work clearly reveals that majority of HAVS approaches rely upon key-frames selection using Convolution neural network (CNN), which direct research community to explore sequence learning such as Long short-term neural network (LSTM). Furthermore, inadequate datasets for learning HAVS models are an additional challenge. An improvement in existing deep learning models for HAVS may be oriented towards the notion of transfer learning, which results in lower training overhead and higher accuracy.
Sparse CapsNet with explicit regularizer

Capsule Network (CapsNet) achieves great improvements in recognizing pose and deformation through a novel encoding mode. However, it carries a large number of parameters, leading to the challenge of heavy memory and computational cost. To solve this problem, we propose sparse CapsNet with an explicit regularizer in this paper. To our knowledge, it’s the first work that utilizes sparse optimization to compress CapsNet. Specifically, to reduce unnecessary weight parameters, we first introduce the component-wise absolute value regularizer into the objective function of CapsNet based on zero-means Laplacian prior. Then, to reduce the computational cost and speed up CapsNet, the weight parameters are further grouped by 2D filters and sparsified by 1-norm regularization. To train our model efficiently, a new stochastic proximal gradient algorithm, which has analytical solutions at each iteration, is presented. Extensive numerical experiments on four commonly used datasets validate the effectiveness and efficiency of the proposed method.
Information security policy compliance-eliciting requirements for a computerized software to support value-based compliance analysis

When end users have to prioritize between different rationalities in organisations there is a risk of non-compliance with information security policies. Thus, in order for information security managers to align information security with the organisations’ core work practices, they need to understand the competing rationalities. The Value-based compliance (VBC) analysis method has been suggested to this end, however it has proven to be complex and time-consuming. Computerized software may aid this type of analysis and make it more efficient and executable. The purpose of this paper is to elicit a set of requirements for computerized software that support analysis of competing rationalities in relation to end users’ compliance and non-compliance with information security policies. We employed a design science research approach, drawing on design knowledge on VBC and elicited 17 user stories. These requirements can direct future research efforts to develop computerized software in this area.
Machine learning attack on copy detection patterns: are $1\times 1$ patterns cloneable?

Nowadays, the modern economy critically requires reliable yet cheap protection solutions against product counterfeiting for the mass market. Copy detection patterns (CDP) are considered as such a solution in several applications. It is assumed that being printed at the maximum achievable limit of a printing resolution of an industrial printer with the smallest symbol size <tex>$1\times 1$</tex>, the CDP cannot be copied with sufficient accuracy and thus are unclonable. In this paper, we challenge this hypothesis and consider a copy attack against the CDP based on machine learning. The experimental results based on samples produced on two industrial printers demonstrate that simple detection metrics used in the CDP authentication cannot reliably distinguish the original CDP from their fakes under certain printing conditions. Thus, the paper calls for a need of careful reconsideration of CDP cloneability and search for new authentication techniques and CDP optimization facing the current attack.
Trust and Relationship Development Among Users in Dark Web Child Sexual Exploitation and Abuse Networks: A Literature Review From a Psychological and Criminological Perspective

The increased potential and speed of the Internet has changed the nature of sexual crimes against children. It enables individuals with a sexual interest in children to meet, interact, and engage in illegal activities. The literature review presented here aims to provide an overview of the current knowledge and understanding of trust and relationship development among users of online networks that are dedicated to the sexual exploitation and abuse of children. A systematic search using six databases was conducted to identify relevant literature from a psychological and a criminological perspective. Twenty-one studies met the inclusion criteria that centered around the key aspects of the literature review's research question, namely, (i) child sexual exploitation and abuse, (ii) Dark Web platforms, (iii) online forums and networks, and (iv) trust and relationship development. Our findings reveal that the engagement in interpersonal communication and interactions with like-minded others serves various functions, including validation, normalization, and support, as well as access to expert advice, information, and material. Dark Web networks are high-stake and risky environments, where users have to manage a continuous flow of threats, with information about others and their trustworthiness being limited. The establishment and maintenance of trust is of social and technical relevance, and users have to navigate a number of demands and commitments. Findings are discussed in relation to theoretical and practical implications, as well as directions for future research.
Imperceptible–visible watermarking for copyright protection of digital videos based on temporal codes

Digital watermarking has emerged as an important tool for copyright protection of digital videos. In this paper, we introduce a robust and imperceptible video watermarking method performed in the base-band domain that is not computationally expensive. Two nonoverlapped watermarks are embedded in the host video frames. The first watermark is a grayscale image that represents the ownership of the video. First, this watermark is decomposed into a sequence of binary images called temporal codes by using a spatiotemporal masking model. Then, the temporal codes are embedded in the spatial domain of video frames by using the imperceptible–visible watermarking paradigm. Since it is well known that such a paradigm is computationally expensive, we employ a technique that considers the video scene as the basic processing unit to considerably increase the execution speed. The second watermark works as a supporting media to share data between embedding and exhibition stages and is embedded in the discrete cosine transform (DCT) with a very robust method based on quantization index modulation (QIM) under dither modulation and some spatiotemporal criteria of the human visual system (HVS). Computer simulations were conducted regarding robustness, imperceptibility, and time consumption to determine the feasibility of our proposal. Experimental results confirm that the combination of temporal codes and the imperceptible–visible watermarking paradigm is an innovation in the field that brings advantages such as simplicity, low computational complexity, and improved robustness and imperceptibility.
Use of superordinate labels yields more robust and human-like visual representations in convolutional neural networks

Human visual recognition is outstandingly robust. People can recognize thousands of object classes in the blink of an eye (50-200 ms) even when the objects vary in position, scale, viewpoint, and illumination. What aspects of human category learning facilitate the extraction of invariant visual features for object recognition? Here, we explore the possibility that a contributing factor to learning such robust visual representations may be a taxonomic hierarchy communicated in part by common labels to which people are exposed as part of natural language. We did this by manipulating the taxonomic level of labels (e.g., superordinate-level [mammal, fruit, vehicle] and basic-level [dog, banana, van]), and the order in which these training labels were used during learning by a Convolutional Neural Network. We found that training the model with hierarchical labels yields visual representations that are more robust to image transformations (e.g., position/scale, illumination, noise, and blur), especially when images were first trained with superordinate labels and then fine-tuned with basic labels. We also found that Superordinate-label followed by Basic-label training best predicts functional magnetic resonance imaging responses in visual cortex and behavioral similarity judgments recorded while viewing naturalistic images. The benefits of training with superordinate labels in the earlier stages of category learning is discussed in the context of representational efficiency and generalization.
VBNet: An end-to-end 3D neural network for vessel bifurcation point detection in mesoscopic brain images

Accurate detection of vessel bifurcation points from mesoscopic whole-brain images plays an important role in reconstructing cerebrovascular networks and understanding the pathogenesis of brain diseases. Existing detection methods are either less accurate or inefficient. In this paper, we propose VBNet, an end-to-end, one-stage neural network to detect vessel bifurcation points in 3D images.Firstly, we designed a 3D convolutional neural network (CNN), which input a 3D image and output the coordinates of bifurcation points in this image. The network contains a two-scale architecture to detect large bifurcation points and small bifurcation points, respectively, which takes into account the accuracy and efficiency of detection. Then, to solve the problem of low accuracy caused by the imbalance between the numbers of large bifurcations and small bifurcations, we designed a weighted loss function based on the radius distribution of blood vessels. Finally, we extended the method to detect bifurcation points in large-scale volumes.The proposed method was tested on two mouse cerebral vascular datasets and a synthetic dataset. In the synthetic dataset, the F1-score of the proposed method reached 96.37%. In two real datasets, the F1-score was 92.35% and 86.18%, respectively. The detection effect of the proposed method reached the state-of-the-art level.We proposed a novel method for detecting vessel bifurcation points in 3D images. It can be used to precisely locate vessel bifurcations from various cerebrovascular images. This method can be further used to reconstruct and analyze vascular networks, and also for researchers to design detection methods for other targets in 3D biomedical images.
Deep Federated Learning-Based Cyber-Attack Detection in Industrial Control Systems

Due to the differences between Information Technology (IT) and Industrial Control System (ICS) networks, current IT security solutions are not working effectively on ICS networks. Moreover, due to security and privacy issues, ICS owners usually do not share their network data with third parties to train specific machine learning-based ICS security solutions. To rectify the mentioned issues, a scalable deep federated learning-based method is presented in this paper. In the proposed method, each client trains an unsupervised deep neural network model using local data and shares its parameters with a server. The server aggregates the clients’ parameters, makes a generalized public model, and shares it with all clients. The proposed model is evaluated using a real-world ICS dataset in a water treatment system and compared with two non-federated learning-based methods. Findings show that the proposed method outperformed the other two methods with the same computational complexity as other deep neural network-based methods in the literature.