Reviewerly/exHarmony huggingface dataset:
Hybrid Model for Optimal Container Resource Allocation in Cloud

Most of the industries and fields reside on cloud computing based microservice owing to its capability with high-performance. The main constraint for cloud providers is the container resource allocation, as it impacts system performance and resource consumption directly. This paper presents a narrative hybrid approach, which hybrids the theory of particle swarm optimization (PSO) and grey wolf optimization (GWO), which is named as velocity updated GWO (VU-GWO) for optimal container resource allocation. Moreover, a new rescaled objective function is defined as the solution of optimized resource allocation. The considered rescaled objective function involves threshold distance, balanced cluster use, system failure, and total network distance. To the end, the presented scheme is evaluated over other classical schemes, and the betterment of the proposed model is proved.
Domain-Specific Allocation &amp; Load Balancing in Cloud Computing Using Virtual Machines

Domain specific allocation plays a major role in cloud computing. The recent trends in cloud computing attracts user to generate random requests to any processor which are not domain specific. Therefore load balancing is a significant aspect in distributing the workload evenly among all the nodes. Since the workload is not distributed uniformly, some processors are overloaded while others are under-loaded. This might result with the resources being overheated, sometimes might lead to carbon emission. Therefore in domain-specific storage text based and image based files are stored separately in two virtual machines for faster response. This paper presents the SJF scheduling algorithm for load balancing and AES algorithm for data encryption. The data is migrated into cloud called CloudMe for data recovery and data storage. This reduces the excessive work of the servers and provides efficient load balancing. The project handles both basic and premium users where premium user requests are prioritized and handled. The `n' number of requests that is generated using bots by brute force attack can also be prevented. Thus the project aims to maintain a domain-specific storage to avoid overloading of virtual machines and maintains system stability and reduce carbon emission.
Keeping up with the Influencers: Improving User Recommendation in Instagram using Visual Content

In the social media domain user-to-user recommendation is an important factor to suggest new content and to strengthen the user social circle. In this paper we investigate how to improve user-to-user recommendation exploiting a user similarity metric computed analysing the photos shared by users on their Instagram profile. We consider in particular users with an established credibility and audience, the so called "influencers". The main idea is that if two influencers publish photos containing similar content it is more likely that they share the same interests and are similar. Moreover, users that follow other users sharing related content are also more similar. Similarity between influencers' photo collections is estimated through neural network embeddings, using a network trained to classify photo collections in categories of interest. An hybrid recommendation approach, which combines collaborative filtering and results from this compact representation of visual content of photo collections, is proposed. Experiments on a large dataset of ~4.8M Instagram users show how our visual approach enhances the performance of a user-to-user recommender with respect to a baseline recommendation algorithm based on collaborative filtering.
Designing Technology, Developing Theory: Toward a Symmetrical Approach

We focus on collaborative activities that engage computer graphics designers and social scientists in systems design processes. Our conceptual symmetrical account of technology design and theory development is elaborated as a mode of mutual engagement occurring in an interdisciplinary trading zone, where neither discipline is placed at the service of the other and nor do disciplinary boundaries dissolve. To this end, we draw on analyses of mutual engagements between computer and social scientists stemming from the fields of computer-supported cooperative work, human−computer interaction, and science and technology studies (STS). We especially build on theoretical work in STS concerning information technology in health care and extend recent contributions from STS with respect to the modes of engagement and trading zones between computer and social sciences. We conceive participative digital systems design as a form of inquiry for the analysis of cooperative work settings, particularly when social science becomes part of design processes. We illustrate our conceptual approach using data from an interdisciplinary project involving computer graphics designers, sociologists, and neurosurgeons with the aim of developing patient-centered visualizations for clinical cooperation on a hospital ward.
Blockchain as a Platform for Collaborative Ontology Evolution

The Semantic Web is an incomplete dream so far, but a revolutionary platform as Blockchain could be the solution to this, not optimal reality. There is still little understanding of, and support for, the evolutionary aspects of ontologies. This is particularly crucial in distributed and collaborative settings such as the Semantic Web, where ontologies naturally co-evolve with their communities of use. In this setting, different organizations and teams collaboratively build a common ground of the domain. In this "complex" and dynamic setting, a collaborative change process model requires more powerful engineering, argumentation and negotiation methodologies. Blockchain offers a robust framework for teams' collaboration and ontology versioning globally between an infinite number of teams. Blockchain is an example of a distributed computing system with high Byzantine fault tolerance. This makes blockchains potentially suitable for the recording of evolution events, ontology records, and other records management activities, such as ontology evolution, transaction processing and ontology documenting provenance. In this paper, after briefly summarizing the significant features of Blockchain, we describe blockchain-empowered solutions for building an evolution model based on blockchain technology and its artifacts.
Big Fish, Little Fish, Critical Infrastructure: An Analysis of Phineas Fisher and the ‘Hacktivist’ Threat to Critical Infrastructure

The hacktivist threat actor is listed in many risk decision documents. Yet their tactics and techniques often remain a mystery. We create a MITRE ATT&CK <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> (ATT&CK) model of a well known hacktivist who goes under the pseudonym of Phineas Fisher, and map that threat to critical infrastructure. The analysis is derived from hacker manifestos, journalist reporting, and official government documentation. This analysis fills a gap in current threat models, to better define what skills and methods a determined hacker might employ. This paper also identifies seven essential mitigations which can be deployed by critical infrastructure operations and asset owners, to prevent such intrusions by hacktivists. We are in the process of contributing this threat actor into the MITRE ATT&CK <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> knowledge base.
Design of Networked and Digital Teaching Platform based on Big Data

Abstract In the traditional network-learning, we have produced the massive data, but they do not play a corresponding role in the actual teaching, therefore, large data technology is used for improving the existing teaching platform, so that it can dig the hidden knowledge in teaching, this issue has become a hot topic that many educators continue to explore. We deeply study the big data technology, take MapReduce as the data analysis model, introduce clustering technology and association rule as the main models of data analysis, which are used for building the teaching platform based on big data. In the process of designing the large data teaching platform, we have built the digital teaching platform and the networked teaching platform respectively, the emphasis of the two platforms is different, among them, the networked teaching platform is used to analyze the students’ academic performance, that is, we take a certain student as the research object, analyze the students’ different academic achievements, adopt association rules technology, analyze the relationship of different disciplines, to develop a set of knowledge points to improve the performance of students’ weak disciplines, and generate a networked large data teaching platform; digital teaching platform can be used for the recommendation of students’ suitable curriculum combination, clustering technology will be introduced, analyze the effective combination of disciplines, which can help students improve academic performance, at the same time, students are willing to spend more time on learning, and take the subject combination module as a main function to generate digital large data teaching platform. By the design of digital and network large data teaching platform, they help educators make personalized learning plans for students, therefor, it help more students to achieve their learning goals.
Tracking Ball in Soccer Game Video using Extended Kalman Filter

The detection of the ball is the first step for tracking in soccer broadcasted video. In some cases, it is difficult to detect the ball by shape and color. Especially, when it overlaps with other objects (line or players). Therefore, we have been suggested a new technique of real-time ball tracking. First, reducing the rate of a missing ball through determining the candidate position of balls rather than attempting to identify the position of ball, computing the distance between the ball and candidate balls to delete the false candidate position of the balls by the threshold. At last, estimating the ball position via Extended Kalman filter. The proposed work has achieved higher accuracy and speed than other methods which are used Kalman filter and template matching or used only a Kalman filter for tracking the ball.
A Personalized Location Recommendation based on Convolutional Neural Network

Location recommendation is an important research content of recommendation system, but it often faces the problems of sparse data and low degree of personalization. The top-k recommendation is selected as the research objective to model users' rating behavior of explicit feedback behavior. A personalized location recommendation algorithm LRA-CNN based on convolutional neural network (CNN) is designed and implemented. The LRA-CNN combines various features between locations and study their joint influence between users. More concretely, co-appearing and geography effects in locations are used to alleviate check-in data sparse matter in location recommendation, and converted into the feature vector representation of users and locations by feature embedding. Besides, the embedding users and locations are fed into CNN for learning high-order interactions among various features adaptively. Experimental results show that compared with several traditional methods, the proposed algorithm can effectively improve the accuracy of location recommendation.
A Prototype Model for Semantic Segmentation of Curvilinear Meandering Regions by Deconvolutional Neural Networks

Abstract Deconvolutional neural networks are a very accurate tool for semantic image segmentation. Segmenting curvilinear meandering regions is a typical task in computer vision applied to navigational, civil engineering, and defence problems. In the study, such regions of interest are modelled as meandering transparent stripes whose width is not constant. The stripe on the white background is formed by the upper and lower non-parallel black curves so that the upper and lower image parts are completely separated. An algorithm of generating datasets of such regions is developed. It is revealed that deeper networks segment the regions more accurately. However, the segmentation is harder when the regions become bigger. This is why an alternative method of the region segmentation consisting in segmenting the upper and lower image parts by subsequently unifying the results is not effective. If the region of interest becomes bigger, it must be squeezed in order to avoid segmenting the empty image. Once the squeezed region is segmented, the image is conversely rescaled to the original view. To control the accuracy, the mean BF score having the least value among the other accuracy indicators should be maximised first.
The impact of language and systemic factors on tweeted countries of the world

This study is intended to unveil the difference of social mediated world via major languages and investigates the volume of tweets individual countries received during 2015–2016 in nine languages –Arabic, Chinese, English, French, German, Japanese, Portuguese, Russian, and Spanish. Shared language, country attributes, economic power, and communication resources were used in predicting country mention. The salient countries on Twitter overall are vastly diverse and vary from language to language. Based on cluster analysis, English and Japanese tweets distinguish themselves from other languages; yet the result from rank-order correlation test shows Arabic and French tweets treat countries differently from the rest. Core nations are still covered more in English- and French-language tweets. Shared language factor is found to predict well for tweets in Chinese, Arabic, Spanish, French, and German but not in English and Portuguese.
Pragmatics Research and Non-task Dialog Technology

Interest is growing in dialog systems which engage users in conversations that are not entirely focussed on the immediate performance of clearly defined practical tasks. Interest is also growing in data-driven methods for dialog system design, with increasing focus on sequence-to-sequence deep learning models, inspired by success in the machine translation sphere. In this position paper, we discuss research on casual conversation or social talk and current methods in design of systems, highlighting areas which need reconciliation.
PSO-Based Cooperative Learning Using Chunking

Bio-inspired optimization consists of drawing inspiration from the behavior and internal functioning of physical, biological and social systems to design enhanced optimization algorithms. Our aim in this paper is to enhance the performance of particle swarm optimization (PSO), when optimizing the feature selection (FS) problem, by combining two bio-inspired approaches which are chunking and cooperative learning. The experimental results show that our hybrid wrapper-filter approach gives competitive results in terms of the predictive accuracy on the training set.
An O(1) time complexity sorting network for small number of inputs with hardware implementation

The sorting algorithms have found applications in various research fields including image and video processing. High speed sorting is important in real-time applications, such as real-time image processing. Two properties are criterion for sorting networks: the number of steps (speed) and the number of comparators (efficiency). In this paper, we have proposed a method to decrease the number of steps. We introduce a new method for sorting an unordered sequence, which is based on the graph theory concept,and contrary to the conventional sorting networks, it consists of graph edge calculators rather than compare-and-swaps. An outstanding feature of the proposed method is that for small number of inputs, its number of steps is independent of the number of inputs that denotes it orders N inputs in O(1) steps. Moreover, the proposed method surpasses the previous ones in term of speed; it just needs two steps to sort a sequence. In the first step, all the inputs are compared with each other, and in the second step, the results of comparisons direct the inputs to the right outputs. To justify the functionality, the proposed method is simulated with the Modelsim-Altera and implemented on a Cyclone IV FPGA platform. Simulation results indicate that in comparison with the Bather's Bitonic mergesort, which is known to be the fastest algorithm, the proposed method while requires 80% more LEs, consumes 52% less memory and achieves 73% more throughput. Furthermore, the Bitonic mergesort can only accept a power of 2 number of inputs, while the proposed method does not hold such a limitation.
Smart Approach to ESP Instruction

The paper deals with the smart approach to teaching and learning English for Specific Purposes (ESP). First, the theoretical background is introduced providing the definition of the term “smart” and theories examining motivation to learning. Second, in the research part, results of didactic pretests and posttests are compared reflecting learners’ types of motivation defined by Plaminek’s Inventory, i.e. Coordinators, Explorers, Accurators, Directors. Totally, 119 probands, prospective teachers from the Faculty of Education and experts from the Faculty of Science, participated in this comparative study conducted via quasi-experimental ex-post-facto method for the period of 12 weeks. The smart approach includes the exploitation of smart devices and technologies (applications) in face to face instruction at school and in home preparation for lessons. The findings discovered that the smart approach as applied within this study suited to maximum extent to Coordinators whose increase in knowledge was significantly higher compared to Explorers, Accurators, or Directors.
Digitally Driven Micro‐display System Based on Bit‐plane Compression

The requirements for the resolution and refresh rate of micro‐displays in virtual reality applications are getting higher and higher. High resolution and high refresh rates mean that massive amounts of data will be transmitted, which will undoubtedly put tremendous pressure on bandwidth. To solve this problem, this paper considers the application scenarios of digitally driven micro‐displays in the field of virtual reality and proposes a compression scheme based on bit‐plane. On the one hand, the traditional just noticeable difference model is improved. A just noticeable difference model suitable for bit‐plane and virtual reality applications is established. On the other hand, combined with the correlation of video data, white block skipping encoding is optimized to achieve bit‐plane compression. Based on the proposed scheme, this paper builds a digitally driven micro‐ display system based on bit‐plane compression with FPGA as the core. The effectiveness of the system is verified by lighting a digitally driven silicon‐based OLED micro‐display. The experimental results show that the digitally driven micro‐display system based on bit‐plane compression effectively improves the compression efficiency and reduces the transmission data. The display effect is good, which meets the needs of virtual reality applications.
Automated Essay Scoring Using Deep Learning Algorithms

The recent transition from paper to digitally based assessment has brought many positive changes in educational testing. For example, many high-stakes exams have started implementing essay-type questions because they allow students to creatively express their understanding with their own words. To reduce the burden of scoring these items, the implementation of automated essay scoring (AES) systems have gained more attention. However, despite some of the successful demonstrations, AES still encountered many criticisms from practitioners. Such concerns often include prediction accuracy and interpretability of the scoring algorithms. Hence, overcoming these challenges is critical for AES to be widely adopted in the field. The purpose of this chapter is to introduce deep learning AES models and to describe how certain aspects of the models can be used to overcome the challenges of prediction accuracy and interpretability of the scoring algorithms.
VR virtual reality technology and treatment progress

Fear is a basic emotion of human beings. Normal people will have a fearful stress response when they face dangerous and threatening stimuli.Normal fear helps individuals to mobilize their physical and psychological functions to respond, and is an individual's need for survival and development. But environmental objective things or irrational fear and tension and with marked autonomic disorder is a disease that phobia (Phobia). Acrophobia is a common phobia. At present, the treatment of phobia is mainly desensitization technology. VR technology provides new ideas for the treatment of phobia. This article will introduce its progress.
Fair trade in building digital knowledge repositories: the knowledge economy as if researchers mattered

Abstract Both a significant body of literature and the case study presented here show that digital knowledge repositories struggle to attract the needed level of data and knowledge contribution that they need to be successful. This happens also to high profile and prestigious initiatives. The paper argues that the reluctance of researchers to contribute can only be understood in light of the highly competitive context in which research careers need to be built nowadays and how this affects researchers’ quality of life. Competition and managerialism limit the discretion of researchers in sharing their results and in donating their working time. A growing corpus of research shows that academic researchers are increasingly overworked and highly stressed. This corroborates the point that the room for undertaking additional tasks with future and uncertain benefits is very limited. The paper thus recommends that promoters of digital knowledge repositories focus on the needs of the researchers who are expected to contribute their knowledge. In order to treat them fairly and to ensure the success of the repositories, knowledge sharing needs to be rewarded so as to improve the working conditions of contributors. In order to help implementing this researcher-centred approach, the paper proposes the idea of expediential trust: rewards for contributing should be such that rational, self-interested researchers would freely decide to contribute their knowledge and effort trusting that this would make them better off.
Analysis of video surveillance images using computer vision in a controlled security environment

Facial analysis using video camera images proves to be a useful tool in the field of both personal and industrial security. By means of the facial analysis it is possible to develop applications based on the use of algorithms developed in Python programming languages and even with the support of Haar Cascade from the OpenCV library it is possible to solve problems caused by unexpected factors when analyzing an image. These factors can greatly influence image quality, whether it be poor lighting, shadows or even hardware problems. This document details the development of a facial recognition application for images from surveillance cameras, with the aim of providing security in a controlled environment.