Reviewerly/exHarmony huggingface dataset:
Using Collective Metrics to Assess Team Dynamics and Performance in eSports

A challenge posed by virtual teams is monitoring team interactions remotely. Research with field-based soccer teams provides evidence that measures of collective behavior can be used to assess the dynamics of sports teams. Collective behaviors calculated using the spatial characteristics of teammates as they moved across the field have been found to vary by the state of the soccer match, including ball possession and proximity to a goal. The present study examined whether similar effects were observed with collective metrics calculated from players of a car-soccer eSport video game. A set of matches were retrieved and used to calculate collective behavior metrics based on the placement of teammates within a virtual arena. A subset of metrics varied by team location and ball possession, aligning with and extending previous field-based soccer research, and correlated with team performance. This suggests that collective behaviors can be used to assess aspects of team dynamics within virtual environments.
An Overlap-and-Add Based Time Domain Acceleration of CNNs on FPGA-CPU Systems

Convolutional neural networks (CNNs) have become widespread in the area of image recognition and are widely implemented in modern facial recognition systems. With the increasing use of CNNs, their run-time speed becomes critical for faster real-world systems. Traditional FPGA-based acceleration requires either large on-chip memory or high bandwidth and memory access time. We present an algorithm and subsequent hardware design for computing CNN using an overlap-and-add-based technique in the time domain. In the proposed algorithm, the input image is broken into tiles which can be processed independently without involving the overheads of computing in the frequency domain. This also allows efficient concurrency of the convolution process which results in higher throughput and lower power consumption. At the same time, we maintain the low on-chip memory requirements necessary for the fabrication of faster and cheaper processor designs. We implement CNN VGG16 and AlexNet models with our design on the Xilinx Virtex 7 and Zynq boards. Performance analysis of our design provides a 0.48 $$\times $$ better throughput than the state-of-the-art AlexNet and uses 0.15 $$\times $$ lesser multipliers and other resources than the state-of-the-art VGG16.
Blockchain-Based Social Network Access Control Mechanism

With the development of society and the rapid increase of social networks, access control has become a big challenge. The current mainstream centralized access control methods have shortcomings such as single point of failure, low scalability, low availability and low non-repudiation. When using blockchain technology for access control, it is necessary to consider how to use the characteristics of blockchain to solve the problem of centralized access control and improve the performance limitations of the blockchain itself. Therefore, this paper proposes a blockchain-based social network access control mechanism, which uses feature extraction to simplify roles, and uses voting to allow users to participate in the authorization process of personal information. User identity authentication is more complete, and the process of information data circulation is effectively achieved. Transparent. Simulation experiments show that the scheme can provide a fast, comprehensive and scalable access control mechanism.
Optimal Value for Number of Clusters in a Dataset for Clustering Algorithm

It is essential to know the parameters required to clustering the dataset. One of the parameters is the number of clusters k, and it is very important to select the k value to get efficient results on clustering. There are few algorithms to find the k value for k-means algorithm, and it requires specifying a maximum value for k or a range of values for k as an input. This paper proposes a novel method Optimal cluster number estimation algorithm (OCNE) to find the optimal number of clusters without specifying the maximum or range of k values or knee point detection in the graph. In the experiment, this method is compared with the different existing methods with different real-world as well as synthetic datasets and provides good performance.
An open-source motion planning framework for mobile manipulators using constraint-based task space control with linear MPC

We present an open source motion planning framework for ROS, which uses constraint and optimization based task space control to generate trajectories for the whole body of mobile manipulators. Motion goals are defined as constraints which are enforced on task space functions. They map the controllable degrees of freedom of a system onto custom task spaces, which can, but do not have to be, the Cartesian space. We use this expressive tool from motion control to pre-compute trajectories in order to utilize the fact that most robots offer controllers to follow such trajectories. As a result, our framework only requires a kinematic model of the robot to control it. In addition, we extend the constraint-based motion control approach with linear MPC to explicitly optimize for velocity, acceleration and jerk simultaneously, which allows us to enforce constraints on all derivatives in both joint and task space at the same time. As a result, we can reuse predefined motion goals on any robot without modifications. Our framework was tested on four different robots to show its generality.
STUDENTS' MOTIVATION BECOMING ENGLISH TEACHER STUDENTS AT STKIP MUHAMMADIYAH MUARA BUNGO

This research was conducted to find out the students' motivation and factor becoming English department students at STKIP Muhammadiyah Muara Bungo. This research was completed using the qualitative method, case study approach. The data of this research was collected through interview and documentation. This research was conducted in English education department of STKIP Muhammadiyah Muara, involving 20 students as the respondents. Those students were studying in different semesters, semester 2, 4, and 6. The results of this research were obtained, the motivation of students to become English education students at STKIP Muhammadiyah Muara Bungo is, there are indicators or supporting elements that bring a big role in the success of individuals in learning, especially in learning English. By learning English makes, respondents have a feeling of need for satisfaction, self-esteem needs, and self-actualization. Factor motivation of students to become Students of English education at STKIP Muhammadiyah Muara Bungo above is the encouragement of intrinsic and extrinsic factors in learning English. From the above also researcher concluded that students learning English is greater controlled by intrinsic factors than extrinsic.
Sentiment Analysis of Covid-19 Tweets using Twitter Database–A Global Scenario

Sentiment analysis falls within the category of Natural Language Processing (NLP) technology. Behaviour analysis and recommendation employ sentiment analysis. On the dataset of COVID-19 tweets, random forest Classifier, Decision Tree, Support Vector Machine (SVM), and Random Forest are the machine learning methods under consideration for sentiment analysis. The total number of tweets used for this research is 179108. Pre-Processing is used to clean and analyze these tweets. The sentiment analysis of COVID-19 tweets used in this study reveals the individual experiences of those affected by the epidemic. The primary goal of this survey is to analyze people’s experiences. It helps to better understand the emotions of people, especially during an epidemic period. Twitter, a microblogging platform, contains a sizable collection of datasets that illustrate a wide range of human emotions, including fear, happy, sad, anger, and joy etc. Sentiment analysis is crucial for gauging the consensus of the populace. This research can help us assess the potential impact of a pandemic on the general public’s decision-making.
A highly robust and secure digital image watermarking using modified optimal foraging algorithm - based SVM classifier and hyperchaotic Fibonacci Q-matrix

Transmission media is at rise over the internet, due to which the copyright risk is alarming. In such situation, enhanced security is the main concern. Digital image watermarking is the technique that ensures the copyright protection, security, and authenticity of the digital image. This research work recommends a highly secured and robust digital image watermarking system. In this method, the cover image is confused by arbitrarily generated numbers by a six-dimensional hyperchaotic technique, then this permuted image is scattered by applying the Fibonacci Q matrix to generate a scattered host image. This scattered image is decomposed up to 3 levels through DWT and the low pass sub-band caused by DWT are further decomposed by SVD. Singular values generated by SVD are used for watermark embedding as a slight change in the value does not affect the image quality. SVM classifier is used to classify the appropriate location to insert the scattered binary watermark. In this method SVM parameters are optimized by a modified optimal foraging algorithm, so that classification error can be reduced. Pixel rearrangement of the watermark image and host image makes the proposed method more secure, and it is highly robust as SVM is trained to classify locations that are less distorted by noise. Experimental outcomes depicts that the proposed method is accurate and better to the current cutting-edge methods in terms of security and robustness of digital image watermarking, as PSNR is approx. 72db and NC values are 1 after applying all the possible attacks.
Optimizing Performance through Stress and Induction Levels in Virtual Reality Using Autonomic Responses

Virtual reality (VR) is now a consumer technology, but individuals' experience with systems or different applications varies enormously. This means that most consumer applications need to consider how to train naive users in the application's operation. We examine three different ways of imparting on-boarding instructions to users: first-person audio guidance, second-person non-player character (NPC) diegetic guidance or written instruction. Our primary hypothesis is that the second-person condition will induce a higher stress level on the user, given the perceived presence of a supervising NPC. Our secondary hypothesis is that there is a correlation between stress and performance, meaning that participants with elevated stress levels within a certain margin will complete their tasks faster and more successfully. By extension, participants whose stress levels are either above or below this optimal margin will under-perform on the same tasks. The tasks in question are an interaction test (IT), designed to test participants' abilities to pick up and manipulate virtual objects, and a mental rotation test (MRT), designed to place them under cognitive load. During these tasks we measure the users' level of stress from their bio signals via a mobile wearable device that tracks their heart rate (HR), galvanic skin response (GSR) and body temperature in real-time. Statistical significance was not found in the stress or performance levels between the instruction conditions, but the secondary hypothesis was supported and a correlation was found between stress and performance levels across the conditions in both HR and GSR.
Static Analysis for Automated Identification of Valid Game Actions During Exploration

Automated exploration has many important applications for testing and analysis of games. Techniques for automated exploration require the capability of identifying the set of available user actions at a given game state, then performing the action selected by the exploration logic. This has been traditionally supported by having the game developer provide an API for this purpose or randomly guessing inputs. In this paper we develop a program analysis based technique for performing an automated analysis of the input-handling logic of the game code, then using this information to provide the set of player actions available at a game state (as well as the device inputs that should be simulated to perform a chosen action). We focus on developing such a technique for games built with the Unity game engine. We implemented an automatic exploration tool based on our technique and evaluated its state exploration performance for six open-source Unity games. We found that our approach is competitive with manually specified actions and is fast enough to play the games in real time.
Encoder Decoder Based Deep Learning Architecture For Video Scene Parsing

Due to its use in a variety of fields, including autonomous driving, robot navigation, remote sensing, medical research, agriculture etc.,In recent years, video scene parsing (VSP) has become increasingly significant in the field of computer vision. the act of putting a semantic label on anything, as a tree, a person, the sky, a car, a road, etc., to each pixel of each frame in a video is known as video scene parsing. Semantic labelling of every pixel in a video is challenging because the labelling has to be consistent with the temporal aspect of the video. Additionally, the training process lacks labelled video data. To enhance the precision of the performance we have used encoder decoder architecture for semantic segmentation where VGGNet (VGG16 and VGG19) architecture is used as encoder and as a decoder the archite U Net is considered. For this work, experiment is performed on pre-trained encoder and non pre-trained encoder to analyze their results on CamVid video scene parsing dataset.
An Efficient OpenCL-Based Implementation of a SHA-3 Co-Processor on an FPGA-Centric Platform

This brief proposes and evaluates several OpenCL-based implementations of the Secure Hash Algorithm-3 (SHA-3) co-processor. These implementations are developed based on OpenCL optimization techniques and their impact on throughput and speedup are reported. The experimental results show that the proposed optimization techniques achieve a 310x speedup when compared to an unoptimized baseline implementation. Moreover, the best reported optimized SHA-3 co-processor achieves a 22.36 Gbps throughput, which is 2 times higher than the best previously published SHA-3 implemented using a high-level synthesis tool and even higher than the performance of most previously reported implementations developed using hardware description languages (HDLs). As a result, an efficient OpenCL-based SHA-3 co-processor suitable for FPGA platforms is proposed. To our knowledge, the reported SHA3 co-processor is the first OpenCL implementation targeting an FPGA-based edge computing platform.
Deciding Program Properties via Complete Abstractions on Bounded Domains

Abstract interpretation provides an over-approximation of program behaviours that is used to prove the absence of bugs. When the computed approximation in the chosen abstract domain is as precise as possible, we say the analysis is complete and false alarms cannot arise. Unfortunately for any non trivial abstract domain there is some program whose analysis is incomplete. In this paper we want to characterize the classes of complete programs on some non-trivial abstract domains for studying their expressiveness. To this aim we introduce the notion of bounded domains for posets with ascending chains of bounded length only. We show that any complete program on bounded domains can be rewritten in an equivalent canonical form without nontrivial loops. This result proves that program termination on the class of complete programs on bounded domain is decidable. Moreover, semantic equivalence between programs in the above class can be reduced to determining the equivalence of a set of guarded statements. We show how our approach can be applied to a quite large class of programs. Indeed, abstract domains defined on Boolean abstractions that are complete for the same functions can be composed by preserving boundedness and completeness also w.r.t. any expressible guard. This suggests that new complete bounded abstract domains can be tailored on the guards and functions appearing in the program. Their existence is sufficient to prove decidability of termination and program equivalence for such programs.
Extended Cultural Algorithms

Depending on the type of the optimisation problem and the features of the search space, the efficiency of CAs could be sensitive to the update and influence functions as well as the type of knowledge sources. The success of CAs remarkably depends on the information exchange between the belief and the population spaces. In some cases, the information provided by the belief space is not productive enough to guide the individuals to efficiently explore the search space. For example, the excessively narrow normative intervals can reduce the step size in the influence functions and cause the algorithm to trap into local optimums. This chapter discusses the extended versions of CAs, in which new influence, acceptance, and update functions have been devised.
Multitask Neuroevolution for Reinforcement Learning With Long and Short Episodes

Studies have shown evolution strategies (ES) to be a promising approach for reinforcement learning (RL) with deep neural networks. However, the issue of high sample complexity persists in applications of ES to deep RL over long horizons. This article is the first to address the shortcoming of today’s methods via a novel neuroevolutionary multitasking (NuEMT) algorithm, designed to transfer information from a set of auxiliary tasks (of short episode length) to the target (full length) RL task at hand. The auxiliary tasks, extracted from the target, allow an agent to update and quickly evaluate policies on shorter time horizons. The evolved skills are then transferred to guide the longer and harder task toward an optimal policy. We demonstrate that the NuEMT algorithm achieves data-efficient evolutionary RL, reducing expensive agent-environment interaction data requirements. Our key algorithmic contribution in this setting is to introduce a first multitask skills transfer mechanism based on the statistical importance sampling technique. In addition, an adaptive resource allocation strategy is utilized to assign computational resources to auxiliary tasks based on their gleaned usefulness. Experiments on a range of continuous control tasks from the OpenAI Gym confirm that our proposed algorithm is efficient compared to recent ES baselines.
Towards Resilient Execution of Adaptation in Decentralized Self-Adaptive Software Systems

In an environment of ever-growing numbers of IoT devices and distributed software systems, we face particular challenges regarding updates and maintenance of the underlying system. Generally, this maintenance of all nodes would require human interaction and is costly and time-consuming. A solution to that are self-adaptive software systems (SAS) [1], [2], which handle the growing complexity of distributed software systems by enabling run-time adaptation. Decentralized solutions [3] for SAS (dSAS) have been developed recently to eliminate single points of failures and bottlenecks compared to centralized approaches where the adaptation logic relies on a single node. However, dSAS require a sophisticated coordination mechanism to ensure a reliable adaptation process on multiple, decentralized devices. Existing approaches for the coordination of the adaptation execution [4]–[6] are based on concepts of the Two-Phase-Commit (2PC) protocol [7] to execute structural adaptations which modify the systems architecture depending on the current context. They ensure a consistent outcome of the adaptation on all nodes - either all adaptations are performed or rolled back - and thus, in any case, a consistent system configuration. In case of an error or disconnection during the adaptation, intermediate changes and modifications get reverted to their original state. This imposes a drawback for self-optimizing (i.e., increasing performance, efficiency, and quality) [8] adaptations, where the system should work even if one node fails to adapt. A rollback is non-essential when the whole system can continue working even if the execution failed for parts of the system. In our system model, this partial adaptation improves the overall task execution. Our work aims to tackle those issues of a strict transactional approach for adaptation by introducing a relaxed consistency constraint for dSAS. We present a protocol for coordinating adaptation operations performed by a set of distributed nodes that allows the different outcomes of participating nodes. Nodes detect inconsistencies and aim to recover when the failed node is ready again.
Analysis of students' conceptual understanding on the acid-base remedial program with multi-representation teaching material

The study aimed to analyze the conceptual understanding profile of students who had not passed the grade in the remedial program. The remedial program which used multi-representation teaching-material. The subject was 40 students of XI MIPA who had not passed the grade in the acid-base topic. The study was a case study which classified as descriptive research. Three-tier multiple-choice test was chosen as the instrument of the study. The data analysis technique was done by classifying students' combination answer. The combination can represent students' conceptual understanding. The N-gain test was also done to investigate the enhancement. The results of the N-gain test 0.3. It is classified as medium criteria. Students' conceptual understanding profile of acid-base topic could be described as follow: 1) students who deeply understood was improved from 49% to 61%, 2) Students who had misconception reduced from 41% to 36%, 3) Students who only guessed reduce from 4% to 1%, 4) Students who slightly understood dropped from 2 % to 1%, 5) Students who didn't understand was dropped from 4% to 1%. Based on the study, we could infer that multi-representation teaching-material in an acid-base topic is able to improve students' conceptual understanding and reduce the misconception.
A Study on the Cultivation System of Educational Informatization Ability of English Majors in Normal Schools under the Background of Information Technology

The educational informatization ability of English major normal school students is the endogenous variable that drives the reform of English education system, and it is also the key to drive the development of education modernization. Training and improving the educational informatization ability of English majors is a brand-new ability requirement under the information technology. In order to better adapt to the new teaching environment and improve the effectiveness of education, based on the characteristics of English major and the basis of students, the system of cultivating the educational informatization ability of English major normal school students should be constructed by integrating the information technology background. Based on the background of the application of information technology, this paper puts forward some strategies for the construction of the educational informatization competence training system for English majors.
NAS-based Auxiliary Learning for Fine-grained Vehicle Recognition

Fine grained vehicle recognition is a sub-category classification task, specifically for vehicles. It is faced with the problems of small inter-class differences and large intra-class variants, which is very challenging. In order to obtain better recognition accuracy, based on the network architecture searched by neural architecture search (NAS), this paper proposes a novel auxiliary learning method to help learn a more robust model for fine-grained vehicle recognition. Experimental results verify the effectiveness of the proposed method on the public Stanford-Cars and self-built FGVC_LAV datasets.