Reviewerly/exHarmony huggingface dataset:
PressTapFlick: Exploring a gaze and foot-based multimodal approach to gaze typing

Text entry is extremely difficult or sometimes impossible in the scenarios of situationally-induced impairments and disabilities, and for individuals with motor impairments (physical impairments and disabilities) by birth or due to an injury. As a remedy, many rely on gaze typing with dwell-based selection as it allows for hands-free text entry. However, dwell-based gaze typing could be limited by usability issues, reduced typing speed, high error rate, steep learning curve, and visual fatigue with prolonged usage. Addressing these issues is crucial for improving the usability and performance of gaze typing. In our work, we present a dwell-free, multimodal approach to gaze typing where the gaze input is supplemented with a foot input modality. Our combined gaze and foot-based typing system comprises of an enhanced virtual QWERTY keyboard (VKB), and a footwear augmenting wearable device that provides the foot input. In this multi-modal setup, the user points her gaze at the desired character, and selects it with the foot input. We further investigated two approaches to foot-based selection, a foot gesture-based selection and a foot press-based selection, which are compared against the standard dwell-based selection. We evaluated our gaze typing system through a comparative study involving three experiments (51 participants), where each experiment used one of the three target selection methods, and had 17 participants in it. In the first experiment the participants used dwell-based selection, second, foot gesture-based selection, and third, foot press-based selection for gaze typing. We found that with dwell-based selection the highest mean typing speed of 11.65 WPM (max 14.83 WPM) was achieved when using a dwell time of 400 ms. Similarly, among foot-based selection methods the highest mean typing speed of 14.98 WPM (max 18.18 WPM) was achieved with foot press-based selection. Furthermore, ANOVA tests revealed that the difference in the typing speeds between the three selection methods is significant, however, no significant difference was found in the error rate. Overall, based on both typing performance and qualitative feedback the results suggest that gaze and foot-based typing is convenient, easy to learn, and addresses the usability issues associated with dwell-based typing. Furthermore, toe tapping is the most preferred foot gesture of all the four gestures (toe tapping, heel tapping, right flick and left flick) we used in the study. Also, we found that when using foot-based selection users quickly develop a rhythm in focusing at a character with gaze and selecting it with the foot, and this familiarity reduces the errors significantly. We believe, our findings would encourage further research in leveraging a supplemental foot input in gaze typing, or in general, would assist in the development of rich foot-based interactions.
Multiclass Classification of UML Diagrams from Images Using Deep Learning

Unified Modeling Language (UML) diagrams are a recognized standard modeling language for representing design of software systems. For academic research, large cases containing UML diagrams are needed. One of the challenges in collecting such datasets is automatically determining whether an image is a UML diagram or not and what type of UML diagram an image contains. In this work, we collected UML diagrams from open datasets and manually labeled them into 10 types of UML diagrams (i.e. class diagrams, activity diagrams, use case diagrams, sequence diagrams, communication diagrams, component diagrams, deployment diagrams, object diagrams, package diagrams, and state machine diagrams) and non-UML images. We evaluated the performance of seven popular neural network architectures using transfer learning on the dataset of 4706 images, including 700 class diagrams, 454 activity diagrams, 651 use case diagrams, 706 sequence diagrams, 204 communication diagrams, 208 component diagrams, 287 deployment diagrams, 207 object diagrams, 246 package diagrams, 323 state machine diagrams, and 720 non-UML images, respectively. We also proposed our neural network architecture for multiclass classification of UML diagrams. The experiment results show that Xception achieved the best performance amongst the algorithms we evaluated with a precision of 93.03%, a recall of 92.44%, and an F1-score of 92.73%. Moreover, it is possible to develop small and almost the same efficient neural network architectures, that our proposed architecture has the least parameters (around 2.4 millions) and spends the least time per image (0.0135[Formula: see text]s per image using graphics processing unit) for classifying UML diagrams with a precision of 91.25%, a recall of 90.34%, and an F1-score of 90.79%.
Synchronization competencies provided by traditional educational system with real-life required competencies in conditions of digital sociality

In terms of development of digital sociality, the role of IT skills is growth for both, IT and non-IT-specialists. However, it is not taken into account in current educational programs. The paper aims to describe the system that will provide corresponding between providing the request by employers and by delivering that request to methodists that developing educational programs. The main actors in the proposed systems are Employer, Job Seeker and Ministry specialist (Methodist). The proposed concept may be realized as well using simple basic tools such as MS Excel and as well by specialized tools such as KIT Polyhedron and as by using Python. In further studies all, MS Excel-, Ontology- and Python-based tools to make concept proof test.
Stochastic Models in the Problems of Predicting the Epidemiological Situation

The paper investigates some stochastic models with discrete and continuous time to solve important problems of predicting the spread of epidemiological diseases in the population. Various factors of epidemic spread and the main parameters influencing the forecast assessment are taken into account. Some test calculations based on the proposed methods have been performed.
Reversing and Fuzzing the Google Titan M Chip

Google recently introduced a secure chip called Titan M in its Pixel smartphones, enabling the implementation of a Trusted Execution Environment (TEE) in Tamper Resistant Hardware. TEEs have been proven effective in reducing the attack surface exposed by smartphones, by protecting specific security-sensitive operations. However, studies have shown that TEE code and execution can also be targeted and exploited by attackers, therefore, studying their security lays the basis of the trust we have in their features.
IPv6CC: IPv6 covert channels for testing networks against stegomalware and data exfiltration

<h2>Abstract</h2> IPv6CC is a suite of network covert channels targeting the IPv6 protocol. Its main scope is supporting penetration test campaigns to evaluate the security of a system against emerging information-hiding-capable attacks or steganographic malware. This paper presents the techniques used to inject data within IPv6 packets, the reference use case and the software architecture of the suite. It also showcases a performance evaluation of the different covert channels offered by IPv6CC, as well as an analysis of their ability to bypass some de-facto standard security tools.
Research on E-Commerce Personalized Recommendation System based on Big Data Technology

Regarding to the recommendation systems for most e-commerce platforms at this stage, there are some issues such as the single personalized recommendation method and no deeper data analysis. By researching the development background of recommendation systems, domestic and international related research results, recommendation algorithm and its application, and analysis and summary of many e-commerce user behavior data, we propose a personalized recommendation system architecture based on a combination of offline mining, real-time mining, and deep learning technology. After determining the architecture and functions of the system, we started to design the personalized recommendation system in detail. The core idea is to collect and pre-process data in real-time from multiple e-commerce platforms which generate user data and gather all the personalized data of the users to prepare for the next data mining, then use the data mining technology in the big data to recommend personalizing products automatically, so that it can satisfy the personalized requirements of users.
Query-aware video encoder for video moment retrieval

Given an untrimmed video and a sentence query, video moment retrieval is to locate a target video moment that semantically corresponds to the query. It is a challenging task that requires a joint understanding of natural language queries and video contents. However, video contains complex contents, including query-related and query-irrelevant contents, which brings difficulty for the joint understanding. To this end, we propose a query-aware video encoder to capture the query-related visual contents. Specifically, we design a query-guided block following each encoder layer to recalibrate the encoded visual features according to the query semantics. The core of query-guided block is a channel-level attention gating mechanism, which could selectively emphasize query-related visual contents and suppress query-irrelevant ones. Besides, to fully match with different levels of contents in videos, we learn hierarchical and structural query clues to guide the visual content capturing. We disentangle sentence query into a semantics graph and capture the local contexts inside the graph via a trilinear model as query clues. Extensive experiments on Charades-STA and TACoS datasets demonstrate the effectiveness of our approach, and we achieve the state-of-the-art on the two datasets.
Linex-RSVM: Ramp Linex Support Vector Machine

We propose a novel support vector machine named Ramp Linex Support Vector Machine (Linex-RSVM) for classification problems. By introducing the ramp Linex loss function, the Linex-RSVM has several advantages compared with existing SVMs. First of all, the Ramp Linex loss function is bounded so that the Linex-RSVM can avoid the influence of outliers while trading. Secondly, Linex-RSVM has the sparseness, which is the most essential characteristic of C-SVM, because of the introduction of ∈-insensitive region. Lastly, the Linex-RSVM treats instances differently based on the location of each point, it gives heavier penalty to the instances between the two support hyperplanes and gives lighter penalty to other instances. We adapt the Concave-Convex Procedure (CCCP) to solve the non-convex and non-differential optimization problem in Linex-RSVM and demonstrate effectiveness on numerical experiments.
A Novel Deep Learning Model for Understanding Two-Person Interactions Using Depth Sensors

Despite the ever-increasing efforts made in the field of data science and artificial intelligence, the task of automatic human interaction recognition remains challenging. Advanced computer vision sensors like depth sensors have made it easier to achieve the goal of accurate recognition of human interactions in complex situations. The reason for their success is that they are robust against lighting and illumination variation and are insensitive to color and texture changes. Therefore, the proposed system combines both depth and RGB images to train a Convolutional Neural Network (CNN). The robust features extracted from CNN have been classified using a Softmax classifier. Two publically available large RGB-D datasets have been used for training and evaluating the proposed method. During the experiments, the proposed method achieved an accuracy of 87.03% with the NTU RGB+D dataset and 86.21% with the UoL3D Social Interaction dataset.
Batch and Online Variational Learning of Hierarchical Pitman-Yor Mixtures of Multivariate Beta Distributions

In this paper, we propose hierarchical Pitman-Yor process mixtures of multivariate Beta distributions and learn this novel clustering method by online variational inference. The flexibility of this mixture model and its non-parametric hierarchical structure help in fitting our data. Also, the model complexity and model parameters are estimated simultaneously. We apply our proposed model to real medical applications. Our motivation is that labelling healthcare data is sensitive and expensive. Also, interpretability and evidence-based decision-making are some basic needs of medicine. These conditions led us to focus on clustering as it doesn’t need labelling. Another driving reason is that the amount of publicly available data in medicine is less compared to other fields due to the confidential regulations. To evaluate our proposed model, we compare its performance with other similar alternatives. The experimental results indicate the potential of our proposed model.
Subspace-based decision trees integration

The ensemble methods are meta-algorithms that combine several base machine learning techniques to produce one optimal predictive model. Many existing committees of classifiers use feature space partitioning to determine the decision regions in which the selection of base classifiers occur or to learn a diverse ensemble of base classifiers. Furthermore, the division of the feature space into subspaces does not depend on the predictive model that defines the decision boundaries. Therefore, we propose a novel ensemble learning algorithm based on the feature space partitioning defined by decision tree boundaries. In addition, in a proposed framework, the feature subspace selection occurs taking class label imbalance ratio into account. The proposed method defines the ensemble class label based on selected previously feature subspaces and a neighborhood of feature subspaces defined by its reference point. Experimental results indicate that our proposed method is more effective than state-of-the-art ensemble methods on twenty-seven benchmark datasets regarding seven representative classification performance measures.
An Introduction to Neural Network Analysis via Semidefinite Programming

Neural networks have become increasingly effective at many difficult machine learning tasks. However, the nonlinear and large-scale nature of neural networks makes them hard to analyze, and, therefore, they are mostly used as blackbox models without formal guarantees. This issue becomes even more complicated when neural networks are used in learning-enabled closed-loop systems, where a small perturbation can substantially impact the system being controlled. Therefore, it is of utmost importance to develop tools that can provide useful certificates of stability, safety, and robustness for neural network-driven systems.In this overview, we present a convex optimization framework for the analysis of neural networks. The main idea is to abstract hard-to-analyze components of a neural network (e.g., the nonlinear activation functions) with the formalism of quadratic constraints. This abstraction allows us to reason about various properties of neural networks (safety, robustness, generalization, stability in closed-loop settings, etc.) via semidefinite programming.
Dirichlet Matrix Factorization: A Reliable Classification-Based Recommender System

Traditionally, recommender systems have been approached as regression models aiming to predict the score that a user would give to a particular item. In this work, we propose a recommender system that tackles the problem as a classification task instead of as a regression. The new model, Dirichlet Matrix Factorization (DirMF), provides not only a prediction but also its reliability, hence achieving a better balance between the quality and quantity of the predictions (i.e., reducing the prediction error by limiting the model’s coverage). The experimental results conducted show that the proposed model outperforms other models due to its ability to discard unreliable predictions. Compared to our previous model, which uses the same classification approach, DirMF shows a similar efficiency, outperforming the former on some of the datasets included in the experimental setup.
Are Emoji Processed Like Words? An Eye‐Tracking Study

In this study, we investigate the processing of object-denoting emoji in sentences using eye tracking. We hypothesize that (a) such emoji are more difficult to process when used as word replacement; and (b) their processing is subject to ambiguity constraints similarly to what happens with words. We conduct two experiments in which participants have to read sentences in which an emoji either follows or replaces a word. Control stimuli not containing emoji are also tested. In the second experiment, the emoji are presented in two different disambiguating contexts. First fixation duration, total visit duration, and total revisit duration are modeled in the various conditions using linear mixed models. Both our hypotheses are supported. We observe longer total visit time for non-redundant emoji, and higher values for all three measures for ambiguous emoji. We conclude that lexical access may be more difficult for emoji especially when they are used as word replacements and their meaning is not immediately clear. Furthermore, we also conclude that non-redundant emoji are more difficult to integrate in the processing of the sentence than the equivalent words, or emoji used in a redundant way. In turn, our results indicate that emoji may not always be as immediate and easy to process for readers in spite of their popularity.
Wearable Sensor and Machine Learning Model-Based Fall Detection System for Safety of Elders and Movement Disorders

Due to different working culture of people, elderly people’s health gets neglected as they live alone at home. With the rising population, there is a pressing demand for the evolution of fall identification systems. People with age greater than 65 are suffering from highest number of fatal falls. Some of the difficulties and challenges faced by the elders and mobility disordered people can be over passed by implementing algorithms able to anticipate falls. It is possible to have a good to great quality of life for the affected, by providing living assistance through automatic fall detection and alarming systems. We propose implementation of a fall recognition system for real-time tracking of elderly people. The proposed system has wearable sensor unit for detecting falls and alert mechanism to intimate the concerned and the care takers in case of falls by means of messages. The acceleration data are collected by the system using triaxial accelerometer and use machine learning algorithms to detect the falls upon various feature calculations. Extensive computations are carried out to compare the performance of different machine learning algorithms with varying features, and the algorithm giving the highest accuracy with optimal features is identified. The system gains an accuracy up to 99% by using random forest algorithm with tenfold cross validation. Thus, with a secure and reliable fall detection and alarming system, one could reduce the fatal falls, improving social integration, productivity, and quality of life.
Mapping (in)visibility and structural injustice in the digital space

This study aims to map digitally mediated injustice and to understand how judicial versus non-judicial bodies contextualize and translate such harm into human rights violations. This study surveys judicial and quasi-judicial cases and case reports by non-judicial bodies, mainly civil society organizations, international organizations, and media. It divides digitally mediated harms identified through the survey into three categories: direct, structural, and hybrid harm. It then examines how these three forms of harm are represented and articulated in judicial judgments and case reports. To differentiate between the three forms of digitally mediated harm, the study uses Iris Young's political philosophy of structural injustice and Johan Galtung's account of structural violence in peace studies. The focus of this study is understanding the forms of injustices that are present but rendered invisible because of how they are contextualized. Therefore, the epistemology of absence is applied as the theoretical approach, that is, interpretation of absence and invisibility. The epistemology of absence facilitates the identification of structural and intersectional injustices that are not articulated in the same way they are experienced in society. The assessment reveals four observations. (1) Structural injustice is rarely examined through a conventional adjudicatory process. (2) Harms of structural quality examined by courts are narrowly interpreted when translated into rights violations. (3) The right to privacy, often presented as a gateway right, addresses structural injustice only partially, as this right has a subject-centric narrow interpretation currently. (4) There are limitations to the mainstream way of seeing and representing risks and injustices in the digital space, and such a view yields metonymic reasoning when framing digitally produced harms. As a result, the conventional way of contextualization is blind to unconventional experiences of vulnerability, which renders structural and intersectional injustices experienced by marginalized communities invisible.
Analysis of facial recognition techniques

Facial recognition has become a hot spot for researchers this past few decades due to its relevance in privacy and security application. The various facial recognition techniques can be catalogued into Database Matching, Statistical Approach, Contour Mapping, Fiducial Mapping, and Feature Mapping. This paper aims to compile the various techniques proposed into the five subcategories mentioned above for facile discrimination. Each technique involves training a classifier with a large number of facial images, applying statistical algorithms to the pixel matrix of the facial images, capturing the distinct variations in the contour of the facial image, tracing distance between points that stand out and differentiate the person and the use of a neural network to extract feature that best represents a particular face. The final object of the paper is to compile features and the drawback of each of the five categories of facial recognition, as there is does not exists a singular facial recognition technology that can perform facial recognition under various external factors such as poor lighting, different facial condition, and distortion in facial images. The compiled set of features for each technique will help the reader decide the best techniques for a given condition in which facial recognition has to be performed.
Role of Blockchain Technology in Healthcare: A Systematic Review

Demand for revolution in technology is necessary. The Healthcare Industry needs to make out most of the changes and equip itself with the best possible solution it can provide. This blockchain enables patients to access medical data. They can connect themselves anywhere to collect their medical history automatically. This technology helps transform into viable health systems. This technology changes the way to how a medical expert accesses a patient&#x2019;s data. The current healthcare system relies on the interaction of a patient and medical expert on limited data. Currently, because the current framework does not allow for the quick and simple exchange of patient information from their primary care provider, they are obliged to create a new clinical record. In this article, we will be conducting a systematic literature review explaining the role of blockchain in the healthcare sector. The features and applications of blockchain healthcare that play a vital role in solving the problems are discussed.