Reviewerly/exHarmony huggingface dataset:
The Use of Virtual Reality Controllers and Comparison Between Vive, Leap Motion and Senso Gloves Applied in The Anatomy Learning System

Abstract The availability of control devices that use hand and finger movements in virtual reality systems has an essential role in increasing the level of interaction in the virtual reality system. Hand and finger movements in a virtual reality system can be displayed either in the form of a controller that resembles its original shape or in the form of a virtual hand. The translation motion and rotation of the virtual reality controller can be used as input to the system. In addition, the virtual reality controllers allow the use of buttons or gestures as triggers on the system. Input in the form of translational and rotational movements on the virtual reality controller and its triggers can be applied to anatomical learning systems in a virtual reality environment. Virtual reality controller has a role as a system control tools. Different virtual reality controllers have different levels of feasibility to the system, similarity when used, and ease of learning. Therefore a comparison was performed on three virtual reality controllers that have different usage methods and features. The three tools chosen in this study are the VIVE Controller, Leap Motion Controller, and Senso Glove. These three tools have different ways of using and feature, including differences in touching the user or not, triggers in the form of buttons or gestures, and whether or not haptic feedback is available. The analysis of the use and its comparison between these three virtual reality controllers are the outcome of this study.
An approach to analyse and Forecast Social media data using Machine Learning and Data Analysis

Twitter is one of the most used social network platforms with more than 321 million active users, sending a daily average of 500 million Tweets. Twitter basically reaches a broad audience, connects many users, and also provides a useful point of view on an ongoing topic based on their interests. This research paper includes works of Machine Learning, NLP, and sentiment analysis on twitter data which is related to Goods and Service Tax (GST) collected from twitter by web scraping technology. Basically here we have found out how many people are against or for about the GST. Here we calculated the sentiments of positive, negative and neutral according to the twitter user. Here Data visualization also used to visualize the data can include cleaning checking transforming and finding the pattern. Machine learning and NLP are used on the data-driven model from the prediction purpose. Machine learning techniques applied to corresponding twitter dataset that contain reviews of GST users to find out the relevant information and inference. Machine learning used to extract the sentiments of the user and find future trends based on current uses.
The Marios and Luigis of Design

Designers increasingly need to collect data to design personalized, contextualized experiences. The act of collecting such data--often in context--is hard, and collecting the right quality and quantity data is even harder. By applying approaches such as Data-Enabled Design (DED) we can remotely collect data. The DED process involves operational, behind-the-scenes activities to facilitate and scale data collection and designing with data. These activities show similar characteristics to plumbing, channeling the flow of data in design. In this provocation, we describe six main "design plumbing'' tasks throughout three main phases of a DED study that are seemingly out of scope for design, yet crucial to design. We posit that knowing about the pipes and drains of your design process is as essential as concepts, prototypes and qualitative studies are.
Strategic Interaction Multi-Agent Deep Reinforcement Learning

Despite the proliferation of multi-agent deep reinforcement learning (MADRL), most existing typical methods do not scale well to the dynamics of agent populations. And as the population increases, the dimensional explosion of joint state-action and the complex interaction between agents make learning extremely cumbersome, which poses the scalability challenge for MADRL. This paper focuses on the scalability issue of MADRL with homogeneous agents. In a natural population, local interaction is a more feasible mode of interplay rather than global interaction. And inspired by the strategic interaction model in economics, we decompose the value function of each agent into the sum of the expected cumulative rewards of the interaction between the agent and each neighbor. This novel value function is decentralized and decomposable, which enables it to scale well to the dynamic changes in the number of large-scale agents. Hereby, the corresponding strategic interaction reinforcement learning algorithm (SIQ), is proposed to learn the optimal policy of each agent, wherein a neural network is employed to estimate the expected cumulative reward for the interaction between the agent and one of its neighbors. We test the validity of the proposed method in a mixed cooperative-competitive confrontation game through numerical experiments. Furthermore, the scalability comparison experiments illustrate that the scalability of the SIQ algorithm outperforms the independent learning and mean field reinforcement learning algorithms in multiple scenarios with different and dynamically changing numbers.
Integration of an intelligent tutoring system in a magnetic resonance simulator for education: Technical feasibility and user experience

Background and objective: In this paper we propose to include an intelligent tutoring system (ITS) within a magnetic resonance (MR) simulator that has been developed in house. With this, we intend to measure the impact, in terms of user experience, of including an ITS in our simulator. Methods: We thoroughly describe the integration procedure and we have tested the benefits of this integration by means of two actual educational experiences, with one of them using the simulator as a standalone tool, and the other with the joint use of simulator+ITS. The experiences have consisted of two online courses with a number of students around 180 in both of them, where measurements of usability, perceived utility and likelihood to recommend were collected. Results: We have observed that the three measurements improved noticeably in the second course with respect to the first one; specifically, overall usability improved by 22.3%, perceived utility by an average of 55.1% and likelihood to recommend by 13.7%. In addition, quantitative measurements are complemented with comments in free text format directly provided by the students. Results show evidence on the benefits of integrating an ITS in terms of quantitative user experience, as well as qualitative comparative comments directly by students of both courses. Conclusions: This is the first time that an ITS is used within the scope of MR simulation for training purposes. Benefits of integrating an ITS within an MR simulator have been evaluated in terms of user experience, with satisfactory comparative results.
Error-Correction for AI Safety

The complex socio-technological debate underlying safety-critical and ethically relevant issues pertaining to AI development and deployment extends across heterogeneous research subfields and involves in part conflicting positions. In this context, it seems expedient to generate a minimalistic joint transdisciplinary basis disambiguating the references to specific subtypes of AI properties and risks for an error-correction in the transmission of ideas. In this paper, we introduce a high-level transdisciplinary system clustering of ethical distinction between antithetical clusters of Type I and Type II systems which extends a cybersecurity-oriented AI safety taxonomy with considerations from psychology. Moreover, we review relevant Type I AI risks, reflect upon possible epistemological origins of hypothetical Type II AI from a cognitive sciences perspective and discuss the related human moral perception. Strikingly, our nuanced transdisciplinary analysis yields the figurative formulation of the so-called AI safety paradox identifying AI control and value alignment as conjugate requirements in AI safety. Against this backdrop, we craft versatile multidisciplinary recommendations with ethical dimensions tailored to Type II AI safety. Overall, we suggest proactive and importantly corrective instead of prohibitive methods as common basis for both Type I and Type II AI safety.
Reversible Data Hiding Using Multi-Layer Perceptron Based Pixel Prediction

Reversible data hiding is a branch of data hiding in which cover image can additionally be restored along with the extraction of hidden data. In prediction error expansion based reversible data hiding techniques, pixel values are predicted using the neighborhood pixels. Data bits are hidden in the expanded prediction error. Prediction error expansion based techniques have become popular due to their superior performances over other categories of techniques. In this paper, a novel reversible data hiding technique is proposed based on a multi-layer perceptron based pixel prediction. The multi-layer perceptron is trained to predict a pixel value based on its eight neighbors. Experiments have been conducted by varying the number of hidden layers and the number of neurons in each hidden layer in the multi-layer perceptron. The best architecture for the multi-layer perceptron is chosen for the proposed reversible data hiding technique. Finally, an adaptive embedding strategy is used to insert either one bit or two bits of data in the expanded prediction error of the pixel based on the standard deviation of the pixel values in the local neighborhood.
Integrating Induction and Coinduction via Closure Operators and Proof Cycles

Coinductive reasoning about infinitary data structures has many applications in computer science. Nonetheless developing natural proof systems (especially ones amenable to automation) for reasoning about coinductive data remains a challenge. This paper presents a minimal, generic formal framework that uniformly captures applicable (i.e. finitary) forms of inductive and coinductive reasoning in an intuitive manner. The logic extends transitive closure logic, a general purpose logic for inductive reasoning based on the transitive closure operator, with a dual 'co-closure' operator that similarly captures applicable coinductive reasoning in a natural, effective manner. We develop a sound and complete non-well-founded proof system for the extended logic, whose cyclic subsystem provides the basis for an effective system for automated inductive and coinductive reasoning. To demonstrate the adequacy of the framework we show that it captures the canonical coinductive data type: streams.
A density-based approach for querying informative constraints for clustering

During the last years, constrained clustering has emerged as an interesting direction in machine learning research. With constrained clustering, the quality of results can be improved by using constraints if a high-quality set of constraints is selected. Querying beneficial constraints is a challenging task because there is no metric for measuring the quality of constraints before clustering. A new method is proposed in this study that estimates density and impurity of data points on different adjacency distances and calculates centrality for each data point by applying a density tracking approach on the obtained densities. The obtained information is then used to select a set of high-quality constraints. Multi-resolution density analysis to more accurately estimate the point-point relationship of data, data density tracking in order to estimate the impurity and centrality of data, and selection of constraints from skeleton of clusters in order to discover the intrinsic structure of data can be mentioned as the most important contributions of this study. To verify the effectiveness of the proposed method, we conducted a series of experiments on real data sets. The obtained results show that the proposed algorithm can improve the clustering process compare with some recent reference algorithms.
Development of a Virtual Scientific and Educational Center for Personnel Advanced Training in the Energy Sector of Ukraine

The article substantiates the importance of using modern web-oriented technologies advanced training of the energy sector experts, in particular, the operational and dispatching personnel. As a result, the conducted research allowed substantiating the academic framework, structure, and functions of the virtual scientific and educational centre for the personnel professional development in the energy sector of Ukraine, including the knowledge assessment, training, and development of the personnel key competences. The advantages of using the distributed environment for the organization of operational personnel education and training using the simulation tools for the operating modes of electric energy systems in the virtual center are considered. An important practical aspect of the designed distance learning courses is the software application for the continuous development of key competencies of the energy sector personnel for rapid recognition of emergencies and, if necessary, their rapid elimination. This will allow them to acquire knowledge and practical skills to solve the problems of analysis, simulation, forecasting, and visualization of the operation modes monitoring data of large electric energy systems. Therefore, to improve the qualification of the personnel in the energy sector of Ukraine, a virtual scientific and educational center was designed, developed and implemented, and its scientific assistance was provided. Besides, a full-featured web mode simulator was developed and implemented. We believe that the use of a virtual scientific and educational center for the advanced training of energy industry specialists is an important component of the digitalization of the society and the energy sector of Ukraine.
Adaptive Asynchronous Parallelization of Graph Algorithms

This article proposes an Adaptive Asynchronous Parallel (AAP) model for graph computations. As opposed to Bulk Synchronous Parallel (BSP) and Asynchronous Parallel (AP) models, AAP reduces both stragglers and stale computations by dynamically adjusting relative progress of workers. We show that BSP, AP, and Stale Synchronous Parallel model (SSP) are special cases of AAP. Better yet, AAP optimizes parallel processing by adaptively switching among these models at different stages of a single execution. Moreover, employing the programming model of GRAPE, AAP aims to parallelize existing sequential algorithms based on simultaneous fixpoint computation with partial and incremental evaluation. Under a monotone condition, AAP guarantees to converge at correct answers if the sequential algorithms are correct. Furthermore, we show that AAP can optimally simulate MapReduce, PRAM, BSP, AP, and SSP. Using real-life and synthetic graphs, we experimentally verify that AAP outperforms BSP, AP, and SSP for a variety of graph computations.
Stateful DRF: Considering the Past in a Multi-Resource Allocation

The multi-resource allocation problem arises in different scenarios. Different mechanisms have been proposed to fairly divide multiple resources, most notably, Dominant Resource Fairness (DRF). Even though DRF satisfies several desirable properties, it considers fairness only in the static setting. We propose Stateful DRF (SDRF), an extension of DRF that looks at past allocations and enforces fairness in the long run while keeping the fundamental properties of DRF. We prove that SDRF is strategyproof, since users cannot manipulate the system by misreporting their demands; incentivizes sharing, because no user is better off if resources are equally partitioned; and is efficient, as no allocation can be improved without decreasing another. In SDRF, users' priorities change over time. To avoid recalculating priorities at every task scheduling decision, we also propose Live Tree, a data structure that keeps elements with predictable time-varying priorities ordered. We implement SDRF on Mesos and run it in a real cluster. Moreover, we conduct large-scale simulations based on Google cluster traces of 30 million tasks over one month. Results show that SDRF reduces users' waiting time on average. This improves fairness, by increasing the number of completed tasks for users with lower demands, with negligible impact on high-demand users.
Is the Simplest Chatbot Effective in English Writing Learning Assistance?

While writing plays a central role in writing learning, non-native learners often find difficulty in writing English, which hinders them from engaging in writing exercises. This paper examines the hypothesis that even the simplest chatbot (such as ELIZA) has a positive effect on assisting learners in writing more. We empirically show such a tendency by comparing words that learners produce by using a standard editor and a chatbot-based writing system. We further look into the writing results, showing that the chatbot-based system has good effects on word usage and self-revision. Finally, we propose a new writing exercise combining the chatbot-based system with the conventional method.
Parsing Protocol Standards to Parse Standard Protocols

Internet protocol standards have been slow to adopt formal protocol description languages and methodologies, and are still largely written as English prose. This makes it hard to check them for correctness, or to automatically derive implementations from standards. Reasons for this are both technical and social. Some methodologies effectively describe complex communication patterns, but cannot model protocol data. Others are unnecessarily tied to particular description formats, or use unfamiliar concepts and terminology, and don't address usability by standards developers.
Development of an mHealth Behavior Change Communication Strategy

Mobile health interventions are an innovative way to improve health outcomes and may play a powerful role in mitigating health disparities. However, their use poses special challenges and few articles have reported specifically on digital technology interventions for vulnerable populations. This article shares our experience from the Tika Vaani ("vaccine voice") Intervention which uses a combined face-to-face and mHealth strategy to educate and empower beneficiaries to improve immunization uptake and child health for a poor, low-literate population in rural Uttar Pradesh, India. Based on the mERA checklist, a guide to improve the completeness of reporting mHealth interventions, we provide information about the process of development, implementation and lessons for scaling up the Tika Vaani intervention. This study contributes to the literature to improve reporting on mHealth interventions and provide researchers with key points and actions to take during intervention development to serve hard-to-reach communities and improve health outcomes.
Providing Email Privacy by Preventing Webmail from Loading Malicious XSS Payloads

With the development of internet technology, email has become the formal communication method in modern society. Email often contains a large amount of personal privacy information, possible business agreements, and sensitive attachments, which make emails a good target for hackers. One of the most common attack method used by hackers is email XSS (Cross-site scripting). Through exploiting XSS vulnerabilities, hackers can steal identities, logging into the victim’s mailbox and stealing content directly. Therefore, this paper proposes an email XSS detection model based on deep learning technology, which can identify whether the XSS payload is carried in the email or not. Firstly, the model could extract the Sender, Receiver, Subject, Content, Attachment field information from the original email. Secondly, the email XSS corpus is formed after data processing. The Word2Vec algorithm is introduced to train the corpus and extract features for each email sample. Finally, the model uses the Bidirectional-RNN algorithm and Attention mechanism to train the email XSS detection model. In the experiment, the AUC (area under curve) value of the Bidirectional-RNN model reached 0.9979. When the Attention mechanism was added, the accuracy upper limit of the Bidirectional-RNN model was raised to 0.9936, and the loss value was reduced to 0.03.
A Novel Drift Detection Algorithm Based on Features’ Importance Analysis in a Data Streams Environment

Abstract The training set consists of many features that influence the classifier in different degrees. Choosing the most important features and rejecting those that do not carry relevant information is of great importance to the operating of the learned model. In the case of data streams, the importance of the features may additionally change over time. Such changes affect the performance of the classifier but can also be an important indicator of occurring concept-drift. In this work, we propose a new algorithm for data streams classification, called Random Forest with Features Importance (RFFI), which uses the measure of features importance as a drift detector. The RFFT algorithm implements solutions inspired by the Random Forest algorithm to the data stream scenarios. The proposed algorithm combines the ability of ensemble methods for handling slow changes in a data stream with a new method for detecting concept drift occurrence. The work contains an experimental analysis of the proposed algorithm, carried out on synthetic and real data.
Practical Proof Search for Coq by Type Inhabitation

We present a practical proof search procedure for Coq based on a direct search for type inhabitants in an appropriate normal form. The procedure is more general than any of the automation tactics natively available in Coq. It aims to handle as large a part of the Calculus of Inductive Constructions as practically feasible. For efficiency, our procedure is not complete for the entire Calculus of Inductive Constructions, but we prove completeness for a first-order fragment. Even in pure intuitionistic first-order logic, our procedure performs competitively. We implemented the procedure in a Coq plugin and evaluated it on a collection of Coq libraries, on CompCert, and on the ILTP library of first-order intuitionistic problems. The results are promising and indicate the viablility of our approach to general automated proof search for the Calculus of Inductive Constructions.
Tuning in to non-adjacencies: Exposure to learnable patterns supports discovering otherwise difficult structures

Non-adjacent dependencies are ubiquitous in language, but difficult to learn in artificial language experiments in the lab. Previous research suggests that non-adjacent dependencies are more learnable given structural support in the input - for instance, in the presence of high variability between dependent items. However, not all non-adjacent dependencies occur in supportive contexts. How are such regularities learned? One possibility is that learning one set of non-adjacent dependencies can highlight similar structures in subsequent input, facilitating the acquisition of new non-adjacent dependencies that are otherwise difficult to learn. In three experiments, we show that prior exposure to learnable non-adjacent dependencies - i.e., dependencies presented in a learning context that has been shown to facilitate discovery - improves learning of novel non-adjacent regularities that are typically not detected. These findings demonstrate how the discovery of complex linguistic structures can build on past learning in supportive contexts.