Reviewerly/exHarmony huggingface dataset:
Copyright and Joint Authorship as a Disruption of the Video Game Streaming Industry

&#x0D; &#x0D; &#x0D; Video game streaming on sites like YouTube and Twitch is now a billion-dollar industry. Top streaming personalities make tens of millions of dollars annually, as viewership of video game play continues to expand. While video game companies’ control over intellectual property embodied in video games is largely accepted, streamers’ rights in their recorded gameplay have yet to be settled.&#x0D; Game companies likely maintain the right to stop unauthorized streaming of gameplay, but most do not exercise that right, as streaming represents free advertising. This raises the related question of what rights streamers have against unauthorized use of their gameplay. It also raises the question, unexplored in the literature, of what rights gameplayers maintain when competitors in their online games stream their matches.&#x0D; We find that copyright can provide protection to streamers over the audiovisual recordings of their play, subject to contractual limitations imposed by game companies. Our analysis likewise establishes that gamers whose play is streamed by another party may qualify as joint authors of the streamed recording. This co-authorship could result in multi- millionaire streamers owing an accounting to other players appearing in their streams. The Article then explores the potential business implications associated with these findings and discusses potential strategies to protect the interests of game companies and streamers.&#x0D; &#x0D; &#x0D;
Beyond the CPU: Side–Channel Attacks on GPUs

This article demonstrates that architectural side-channel attacks are possible also on applications that use the GPUs. -Rosario Cammarota, Intel Labs -Francesco Regazzoni, University of Amsterdam and Università della Svizzera Italiana
Note-on-Watch: Live Scribing from Board-works to Class-notes

The COVID-19 pandemic directly impacts educational systems worldwide. Although the online mode of education is the most viable solution under this scenario, it introduces new challenges to the course instructors. Primarily in the low and middle-economy countries, the majority of instructors do not have access to touch-enabled devices like tablets to mimic board-works, from where students can generate the class-notes. Pertaining to these constraints, in this paper, we propose an online note-generation system – Note-on-Watch, using community-off-the-shelf smartwatches and smartphones, leveraging the rapid and huge penetration of these devices across these countries. In Note-on-Watch, the instructor writes a text over a vertical board while wearing the smartwatch, and the locomotive data from the smartwatch is captured over a smartphone to regenerate the text to mimic on-device writing. We implement a prototype of Note-on-Watch using a Moto-360 smartwatch and Android smartphone and observe that the system nicely captures English alphanumerics and words.
When Oracles Go Wrong: Using Preferences as a Means to Explore

When a robot is deployed to learn a new task in a "real-word" environment, there may be multiple teachers and therefore multiple sources of feedback. Furthermore, there may be multiple optimal solutions for a given task and teachers may have preferences among those various solutions. We present an Interactive Reinforcement Learning (I-RL) algorithm, Multi-Teacher Activated Policy Shaping (M-TAPS), which addresses the problem of learning from multiple teachers and leverages differences between them as a means to explore the environment. We show that this algorithm can significantly increase an agent's robustness to the environment and quickly adopt to a teacher's preferences. Finally, we present a formal model for comparing human teachers and constructed oracle teachers and the way that they provide feedback to a robot.
A Digital Laser Speckle Technique for Generating Slope, Curvature, and Deflection Contours of Bent Plates

A digital method is proposed whereby spatial speckles created when an optically rough surface is illuminated by a diffuse coherent laser beam are used to generate slope contour fringes. This is done by photographing the speckles contained in a parallel plane in front of the plate before and after deformation. The resulting photographs are then fast Fourier transformed twice and numerically compared with the digital image correlation (DIC) software computer-aided speckle interferometry (CASI) to produce slope contours of the plate. The slope contour data is then stored in an array. The curvature contours are generated by numeric differentiation of the slope data; similarly the deflection contour is generated by numeric integration of the slope data. The curvature data can then be further used in conjunction with plate theory to obtain the bending and twisting moments, the plate’s experiences, as well as the fiber stress on the surface of the plate.
MENINGKATKAN HASIL BELAJAR MELALUI METODE PEMBERIAN TUGAS PADA SISWA KELAS II SEKOLAH DASAR

The background of this research was that the classroom atmosphere was very calm, no one responded to the teacher's explanation, even though the teacher had repeatedly given the opportunity to ask questions. The questions given were sufficient, but in the final results of the formative test given by the teacher, the results were still far from what was expected. This study aims to describe the learning achievement of the second semester students of SDN 88 / II Sungai Mengkuang, Bungo Regency, academic year 2016/2017 in Indonesian through the assignment method. This type of research is a classroom action research (PTK) with a qualitative descriptive approach carried out in three cycles. The results showed that the use of the assignment method in class II students can improve student achievement in pre-cycle only 38.7%, 38.7% first cycle, 61.29% cycle, and 87.09% third cycle
THE USE OF THE MOZAIK INTERACTIVE PLATFORM AS A SOCIAL OBJECT FOR EDUCATIONAL INTERACTION BETWEEN THE PARTICIPANTS OF THE LEARNING PROCESS

The article characterizes the Mozaik interactive platform and its main components – mozaBook and mozaWeb, which are used by lecturers-philologists to give classes on the Ukrainian language to undergraduate students whose specialization is "Secondary Education" (educational program "Ukrainian Language and Literature. World Literature"). The possibilities of use of the software at different stages of educational process have been described. The didactic functions of the use of the mentioned resources in the process of teaching the disciplines “Practicum on the Ukrainian language” and “Methodology of teaching the Ukrainian language” have been outlined. The mozaBook software has been analyzed as a social object which provides pedagogical interaction of scientific methodology of rational-logical thinking with emotional and imaginative presentation and perception of information. The possibilities of mozaBook and mozaWeb have been used to improve the perception and acquisition of scientific information and to increase the level of educational achievements. The authors have analyzed the expediency of using e-textbooks, media library, software for attaching information of various types and use of mozaBook tools (“Spelling and grammar”, “Parts of speech”, “Dictionary cards”, “Language games”, “Questionnaire”, “Decoding”, “Tests”) while explaining and illustrating theoretical material, summarizing and systematizing material that has already been learned. It has been shown that the Mozaik interactive platform and its components make the classes more informative, compact, emotionally attractive, mobile and multifunctional.
A Vision Based Face Tracking using Camshift with BLBP Algorithm in Head Gesture Recognition System

This paper describes the face tracking using Camshift with BLBP Algorithm in head gesture recognition system. The major constraints of the head gesture recognition system such as face detection, feature extraction, tracking, and recognition are explained. Adaboost algorithm is used for detection and Camshift algorithm for tracking with different feature extraction methods. Performed extensive experimentations and presented a face tracking analysis in a head gesture recognition system under cluttered backgrounds, shadow, and sunshine conditions. Experimental results show the robustness in face detection, tracking, and direct recognition of the proposed method.
Direct 3D model extraction method for color volume images

BACKGROUND: There is a great demand for the extraction of organ models from three-dimensional (3D) medical images in clinical medicine diagnosis and treatment. OBJECTIVE: We aimed to aid doctors in seeing the real shape of human organs more clearly and vividly. METHODS: The method uses the minimum eigenvectors of Laplacian matrix to automatically calculate a group of basic matting components that can properly define the volume image. These matting components can then be used to build foreground images with the help of a few user marks. RESULTS: We propose a direct 3D model segmentation method for volume images. This is a process of extracting foreground objects from volume images and estimating the opacity of the voxels covered by the objects. CONCLUSIONS: The results of segmentation experiments on different parts of human body prove the applicability of this method.
Finding Linearly Generated Subsequences

We develop a new algorithm to compute determinants of all possible Hankel matrices made up from a given finite length sequence over a finite field. Our algorithm fits within the dynamic programming paradigm by exploiting new recursive relations on the determinants of Hankel matrices together with new observations concerning the distribution of zero determinants among the possible matrix sizes allowed by the length of the original sequence. The algorithm can be used to isolate \emph{very} efficiently linear shift feedback registers hidden in strings with random prefix and random postfix for instance and, therefore, recovering the shortest generating vector. Our new mathematical identities can be used also in any other situations involving determinants of Hankel matrices. We also implement a parallel version of our algorithm. We compare our results empirically with the trivial algorithm which consists of computing determinants for each possible Hankel matrices made up from a given finite length sequence. Our new accelerated approach on a single processor is faster than the trivial algorithm on 160 processors for input sequences of length 16384 for instance.
Effective Version Space Reduction for Convolutional Neural Networks

In active learning, sampling bias could pose a serious inconsistency problem and hinder the algorithm from finding the optimal hypothesis. However, many methods for neural networks are hypothesis space agnostic and do not address this problem. We examine active learning with convolutional neural networks through the principled lens of version space reduction. We identify the connection between two approaches – prior mass reduction and diameter reduction – and propose a new diameter-based querying method – the minimum Gibbs-vote disagreement. By estimating version space diameter and bias, we illustrate how version space of neural networks evolves and examine the realizability assumption. With experiments on MNIST, Fashion-MNIST, SVHN and STL-10 datasets, we demonstrate that diameter reduction methods reduce the version space more effectively and perform better than prior mass reduction and other baselines, and that the Gibbs vote disagreement is on par with the best query method.
Hadith Authenticity Prediction using Sentiment Analysis and Machine Learning

Starting around 815AD/200AH scholars have put immense effort towards gathering and sifting authentic hadiths, which are prophetic traditions of the Muslim community. The authenticity of a hadith solely depends on the reliability of its reporters and narrators. Till now scholars have had to do this task manually by precisely anatomizing each hadith's chain of narrators or the list of people related to the transmission of a particular hadith. The evolution of modern computer science techniques has enabled new methods and introduced a potential paradigm shift in the science of hadith authentication. Focusing on the chain of narrators (also known as "Isnad") of a hadith, we have used a technique called `Sentiment Analysis' from Natural Language Processing (NLP) to build a text classifier which tries to predict the authenticity of a hadith. It learns from our custom-made dataset of Isnads and predicts an unknown hadith to be either authentic or fabricated based upon its Isnad. Our classifier was 86% accurate when tested on the test hadith dataset.
Network-based structure flow estimation

Structure flow is a novel three-dimensional motion representation that differs from scene flow in that it is directly associated with image change. Due to its close connection with both optical flow and divergence in images, it is well suited to estimation from monocular vision. To acquire an accurate measurement of structure flow, we design a method that employs the spatial pyramid structure and the network-based method. We investigate the current motion field datasets and validate the performance of our method by comparing its two-dimensional component of motion field with the previous works. In general, we experimentally show two conclusions: 1. Our motion estimator employs only RGB images and outperforms the previous work that utilizes RGB-D images. 2. The estimated structure flow map is a more effective representation for demonstrating the motion field compared with the widely-accepted scene flow via monocular vision.
Learning Assistant to Acquire the Fundamental Language Skills for Non-Native Learners using AI

The ability to speak and learn a language properly requires good practice, experience and good learning strategies but the existing solutions do not provide proper guidance to learn a language with instant feedback. This research is an approach to devise an improved language learning assistant with practices that will help to improve the fundamental language skills for non-native learners and children who are in the early stage of their education. The four main skills focused on this application will be conversation, pronunciation, listening and grammatical skills. The implementation of this research is done by using technologies like natural language processing, machine learning, and deep learning approaches to come up with components to train the learner. The solution of this research is delivered by using a cross-platform application called GLIB which facilities to improve all the English language skills mentioned above along with guides, tips, practices, and feedback based on an evaluation to improve the English language.
A Comparative Study of Image Steganography Based on Edge Detection

Abstract Information security has become the main concern of the most famous researchers and the focus of their attention, as they are constantly trying to find the best and safest ways to transfer information through a secure tunnel to protect it from hacking attempts and common attacks on the Internet and in this research, we tried to embody one of the methods of security in protecting data. In this study, a new approach to masking information based on the incorporation of LSB replacement mechanism and edge detection is proposed. To avoid HVS mining when more covert bits are included in pixels, we classify cover pixels into edge regions and non-edge regions. Then, the pixels belonging to the edge region are used to transmit more secret bits. In addition, to increase the load as well as maintain good image quality, we adopt a clever method in which the edge information is identified by the most important bits (MSBs) of the cover image so that it does not need to be stored. At the extraction stage, the same edge information is obtained. Therefore, confidential data can be extracted correctly without confusion. Experimental results prove that our scheme achieves a much higher payload and better optical quality than that of the latest technology.
Order-aware Pairwise Intoxication Detection

Alcoholic intoxication has always been and still is known as one of the major causes leading to traffic accidents and in-car conflicts. A system of intoxication detection is established to detect whether a person is intoxicated through the means of machine learning. The system would be able to provide significant assistance in the enforcement of traffic laws, which would ultimately save lives. However, most of the existing systems mainly attach great importance to the tested speaker's characteristics of current speech, and ignore the existence of personalized differences in speech. To deal with this problem, we focus on modeling the measurable acousic change between the current state and the sober state of a speaker, instead of the current state in the existing scheme only. Furthermore, we are inspired by our discovery that the order-related cues (e.g. gender, time, location) on speaker and trip is largely relevant to alcoholic intoxication. Therefore, we incorporate order-related cues into the speechbased system in order to obtain better performance. Finally, it is demonstrated by extensive experimental results on DiDi Drunk Dataset in real scene that our proposed system achieved a significant improvement from 74.1% to 84.9% in terms of AUC.
Identifying Sensor Node Behavior Using Temporal Correlation for Data Modifier Attack

Recent technical advancements facilitated the application of wireless sensor network (WSN) to solve challenging real-world problems. Security in wireless sensor network is a major concern. The ability of anomaly detection is to detect new abnormal behavior of sensor nodes and new attacks. This paper presents node-level intrusion detection systems; temporal correlations of sensor readings were exploited to help in detecting the data modifier attack. Data anomaly is computed, updated and compared with the threshold value to detect the deviation in the sensed value. Threshold value is selected based on the false positive and false negative value. Data modifier attack is detected in the level intrusion detection system. The performance of the intrusion detection system is analyzed through false positive rate, detection rate and accuracy.
Simultaneous control algorithm for mobile manipulator using MMPPE

This paper proposes the simultaneous control algorithm using Mobile Manipulator Pre-Pose Estimator (MMPPE). A conventional method is a sequential method in which the manipulator is controlled after the mobile platform moves. However, the proposed method recognizes the target pose in advance and predicts the posture of the manipulator and the mobile robot. It has more time efficiency than conventional sequential control method. By integrating this based on ROS and verify the algorithm using simulation.
Application of Computer Network Programming Technology in Applied Mathematical Algorithm

Since the 1940s, with the rapid development of computer science and technology, Mathematics and Other Industries Are Increasingly Closely Linked, The Application of Mathematics Has becomes an important task of mathematics education. Mathematical modeling is a bridge that combines pure mathematical knowledge with real life, and is an important way to cultivate mathematical ability. The cognitive mechanism and teaching strategy of mathematical modeling have not been deeply studied at present. The research on this problem is helpful to enrich the theory of mathematical learning psychology, develop the theory of mathematical problem-solving, deepen the theory of mathematics teaching, provide theoretical basis and practical guidance for solving problems in mathematical modeling and applied mathematics teaching, and improve the effect of mathematics and teaching. This has important theoretical significance and practical value. How to apply computer network programming technology to applied mathematics has become a hot topic in recent two years. This paper uses the method of literature review and questionnaire survey to find that many people do not have a good understanding of mathematics. Modeling. It seems that although the course may have the requirements of mathematical modeling, more than half of the students in each class still don’t know what mathematical modeling is. Some students understand mathematical modeling as a way to turn mathematical problems into life, putting the cart before the horse. The unknown proportion of high school students is as high as 71%. Even many 19th graders have never heard of mathematical models.
Line loss prediction based on particle swarm optimization combined with extreme learning machine

Abstract As a key economic index to measure the planning and management level of power grid company, line loss is not only the main content of completing the construction and transformation task of power grid company, but also an important assessment standard for the operation level of power grid company. In order to improve the generalization ability and prediction accuracy of extreme learning machine (ELM), particle swarm optimization (PSO) algorithm is applied to extreme learning machine (ELM), and a line loss prediction method based on PSO combined with ELM is proposed. The particle swarm optimization algorithm is used to select the optimal hidden layer deviation and input weight matrix to calculate the output weight matrix, so as to improve the accuracy and stability of the extreme learning machine. Experimental results show that the proposed method can effectively improve the accuracy of line loss prediction.
Edgeless Graph: A New Graph-Based Information Visualization Technique

Information visualization is the study of visual representations of abstract data in order to strengthen the human understanding of the data. Graph visualization is one of the subfields of information visualization. It is used for the visualization of structured data, i.e. for inherently related data elements. In the traditional graph visualization techniques, nodes are used to represent data elements, whereas edges are used to represent relations. In this paper, our focus is on the representation of structured data. According to us, key challenges for any graph-based visualization technique are related to edges. Some of them are planar representation, minimization of edge crossing, minimizing the number of bends, distinguish between the vertices and the edges, etc. In this paper, we propose two methods that use the same underlying idea: Assignment of unique label to each vertex of the graph and remove all the edges. Nodes are adjacent if and only if their corresponding labels are disjoint. Our proposed representations do not have edges so we do not need to consider the challenges related to edges that is the biggest advantage. The algorithm for obtaining valid labelling as well as procedures related to dynamic changes (addition/removal of edges and/or vertices) is explained in detail. The space complexities of the proposed methods are O(\(n^ 2\)) and O(\(n^3\)) where n denotes the number of nodes. Application of our proposed methods in the analysis of a social network site is also demonstrated. Characteristics of these methods are highlighted along with possible future modifications.