Reviewerly/exHarmony huggingface dataset:
Comparative analysis of performance of ASP.NET Core MVC and Symfony 4 programming frameworks

The article presents a comparative analysis of popular ASP.NET Core MVC and Symfony 4 frameworks. Two web applications, containing the same functionalities and acting as a simple system for managing articles, were implemented in these technologies. The applications underwent time performance tests during typical operations performed by means of a simple form such as entering, editing, viewing and deleting data. These actions were performed automatically using commands from the Puppeteer library. The listed operations were repeated 10, 100 and 1,000 times in order to obtain precise mean times. On the basis of the obtained results, it was difficult to clearly state which of the compared programming tools is better. The ASP.NET Core MVC framework coped much better with two time-consuming operations, i.e. entering and editing data. Its results in this regard (the average from 1,000 repetitions) were respectively approximately 28% and 25% better compared to the Symphony 4 framework. However, for the two less time-consuming operations, i.e. displaying and deleting articles, the Symphony 4 framework proved to be considerably better. Its results with regard to displaying and deleting articles (the average for 1,000 measurements) were respectively 15% and 36% lower compared to the other of the tested frameworks.
Research on Trajectory Clustering Optimization Algorithm Based on Sparse Representation

Privacy issues in the trajectory play an significant role with the popularization of intelligent terminals in the age of big data. Personal information about users is easily exposed to the attackers in LBS. A big number of solutions are presented in the literature to better protect privacy. The scheme based on k-anonymity model has been widely used. However, the traditional methods have some limitations due to their long convergence time and subjective factors. In this paper, we propose a scheme based on sparse representation, which has high computational efficiency and simple solution. Firstly, we process the data set with certain techniques. Then we introduce the expression of sparse representation between trajectories and add regularization for continuous training. Experiments show that the optimization model achieves better clustering results, and the validity of the algorithm is testified by different evaluation indexes.
Analytical Study of Virtual Machine Migration Techniques in Cloud Computing

Cloud Computing is pegged to be the future of supercomputing. The day is not far when there will be huge amount of adoption for cloud network, and that is when it would be essential for cloud network to perform seamlessly. The live Virtual Machine Migrations (VMM) help to efficaciously cope up the ever-rising need for storage, communication, and computing. Keeping in mind the optimum utilization of energy units and reduction of environmental effects like CO2 emissions, it is essential to have precise placement of instruction sets or jobs with the help of computing. The same calls for scientific architectural design and techniques to cope up with the job scheduling process from start to end. This paper presents and analyzes the various techniques and approaches to virtual machine (VM) migration for the cloud data centres (DCs). Finally, the concluding remarks and future research areas are also specified.
Anomaly Detection by Robust Feature Reconstruction

Autoencoders have become increasingly popular in anomaly detection tasks over the years. Nevertheless, it remains a challenge to train autoencoders for anomaly detection tasks properly. A key contributing factor to this problem in many applications is the absence of a clean dataset from which the normal case can be learned. Instead, autoencoders must be trained based on a contaminated dataset containing an unknown amount of anomalies that potentially harm the training process. In this paper, we address this problem by studying the impact of the loss function on the robustness of an autoencoder. It is common practice to train an autoencoder by minimizing a loss function (e.g. squared error loss) under the assumption that all features are equally important to be reconstructed well. We relax this assumption and introduce a new loss function that adapts its robustness to anomalies based on the characteristics of data and on a per feature basis. Experimental results show that an autoencoder can be trained by this loss function robustly even when the training process is subject to many anomalies.
Data-Enabled Futuring: Designing for and with the Invisible

In a world where data is everywhere, designers are working to find ways in which to design with this intangible and invisible new material. Data-enabled design is becoming an increasingly accepted practice to use for user research around data and for the development of data products and systems. As a methodology, it offers designers and researchers the possibility to rapidly react to user insights and to iterate even during product or system deployment. However, as the practice is reactive, it is more difficult to consider long-term consequences while performing data-enabled studies. In my PhD research, I investigate different ways to design for the future of data practices, by making futuring practices part of data-enabled design processes. My research is situated in a clinical context, where the use of more personal informatics is up and coming but where also the long-term user well-being is a primary concern.
BALM: QoS-Aware Memory Bandwidth Partitioning for Multi-Socket Cloud Nodes

The recent emergence of novel hardware-based resource partitioning mechanisms has unveiled the opportunity for a new generation of QoS-aware resource allocation approaches for workload consolidation. Still, to the best of our knowledge, existing proposals are, by design, not tailored to the growing prevalence of multi-socket systems in contemporary warehouse-scale data centers. We propose BALM, a QoS-aware memory bandwidth allocation technique for multi-socket architectures that combines commodity bandwidth allocation mechanisms with a novel adaptive cross-socket page migration scheme. Our experimental evaluation with real applications on a dual-socket machine shows that BALM can overcome the efficiency limitations of state-of-the-art. BALM can ensure marginal SLO violation windows while delivering up to 87% throughput gains to bandwidth-intensive best-effort applications when compared to state-of-the-art alternatives.
Surrogate-Assisted Evolutionary Neural Architecture Search

With the recent booming development of deep neural networks, the demand for automated design of efficient deep neural architectures has been increasing. This chapter introduces the basics of automated neural architecture search and discusses the current remaining challenges, focusing on scalability and flexibility of network architecture representation, effectiveness of search strategies and reduction of computational complexity in performance evaluations. Then, Bayesian optimization techniques used in neural architecture search are discussed. Finally, a random forest assisted neural architecture search algorithm is described and applied to convolutional neural network design, showing the effectiveness and efficiency of data-driven evolutionary optimization for deep neural architecture search.
FCSIT WhatsApp Chatbot

WhatsApp is currently one of the most widely used communication mediums among students in tertiary education in the form of both personal messages and group messaging chats. Currently, the practice in most Malaysian Universities is that students need to consult the faculty staff or search through the faculty’s website to obtain information related to their academic studies. However, the process of obtaining needed information can take some time ranging from minutes to hours and at times are not consistent as the queries are answered by different staff members. There are WhatsApp chat groups for students to interact with faculty staff, but the chat group can be overwhelmed by questions due to the huge number of group members hence causing important messages to be overlooked. In an attempt to overcome these challenges, we developed a FCSIT (Faculty of Computer Science and Information Technology) WhatsApp chatbot to solve these problems by offering a centralized platform of communication for both faculty staff and students to carry out information sharing through WhatsApp messages. Communication is in the form of personalized messages between the chatbot and the user thus solving the problem of overwhelming messages posed by group chats. Agile Kanban methodology was used to develop the system which promotes the use of a Kanban board to visualize the development stages. A questionnaire was distributed to lecturers, faculty staff and students to test the system and to gather opinions and suggestions from targeted users, which is the FCSIT community, in order to assess whether the system fulfils its objectives.
AI in the Financial Industries: Between Apathy and Hysteria

Financial industries are key players and enablers in the world economies. AI, together with climate change, poses a paradigm shift in any business decision-making, as well in the financial industries. The extreme positions—apathy on the one side, and hysteria on the other—are illustrated and declassified as non-options. The sweet spot to strive for is being worked out by reflecting on the current stage of financial services. The opportunities derived from the digital transformation are explained as well as the challenges posed by requirements for new skills, changing roles, and cultural new thinking. Shortcomings on biased AI applications originating from flawed data, responsibility and involvement of a very small group of programmers, and the problem of opaque algorithms are demonstrated using specific examples. Their ethical consequences, in particular discrimination, are elaborated. The sweet spot between apathy and hysteria envisaged by de-biasing approaches is explained and an appeal for virtuous handling of AI is developed.
Research on the Algorithm of Intelligent Review of Image Content

With the development and popularization of mobile products, people's demand for content information continues to increase. In order to provide users with a better and cleaner reading environment as much as possible, the content of the published image messages is reviewed and the images containing low-quality information are eliminated. However, for the huge amount of audit data, using traditional manual auditing methods can no longer achieve ideal results while ensuring the timeliness of information. Therefore, intelligent review of image messages by means of machine review is the key method to solve the above problems. The image message content intelligent review system designed in this paper uses deep learning related algorithms to review information. And it mainly includes two parts. They are vulgar porn review module and politically sensitive review module. The image message intelligent review system reduces the number of various types of illegal images, improves the reading environment and achieves the design purpose of this paper.
Performance Analysis of Residential PV Systems in Tripoli

The main objective of this study is to discuss the performance of residential photovoltaic systems in Tripoli, Libya, by the analysis of the operational data of three systems with different specifications and circumstances. The consumption behavior of these three households is discussed in order to determine the peaks and averages of consumption and generation during different months. Moreover, the independency from the grid of these systems is discussed for different months with direct consumption and battery discharging patterns to determine the self-consumption rates.
A Redundancy Based Unsupervised Feature Selection Method for High-Dimensional Data

Feature selection is a process to select key features from the initial feature set. It is commonly used as a preprocessing step to improve the efficiency and accuracy of a classification model in artificial intelligence and machine learning domains. This paper proposes a redundancy based unsupervised feature selection method for high-dimensional data called as RUFS. Firstly, RUFS roughly descending order the features by the average SU with the other features. Secondly, RUFS orderly check each feature to decide whether it is redundant or not. Finally, it selects the proper feature subset by repeating the second step until all the features are checked. After key features are selected, the research implements classifiers to check the quality of the selected feature subset. Compared with the other existing methods, the proposed RUFS method improves the mean classification of 11 real datasets by 8.1 percent at least.
Not Tracking: The Antipolitics of Contact-Tracing Applications

Abstract In response to the COVID-19 pandemic, governments around the world turned to contact-tracing applications in an attempt to balance the reopening of the economy with keeping the virus at bay. But as this article demonstrates, contact-tracing applications not only fail to protect the most vulnerable among us; they also shift responsibility for failing to prepare public-health systems for a pandemic away from governments and onto the individual user struggling to contain its worst effects. In the process, contact-tracing applications change the definition of failure. They also reinforce existing inequalities. Technology in this case not only has politics; it prevents politics. By focusing on contact-tracing applications as an example, the article points to some of the deeper perils of accepting app-based solutions to structural problems.
Multi-label Classification Based on Label Hierarchical Compression

Large-scale multi-label learning has been widely applied in daily life, such as product recommendation and search ranking. It is an important and challenging research problem for its extremely high dimensional feature space and label space. To solve the problem, this paper presents a classification algorithm based on label hierarchical compression that combined spectral clustering with principal label space transformation or conditional principal label space transformation. The algorithm compresses labels on the basis of decentralized thoughts, which has the advantage of extracting multiple characteristics of the data, making the classification of data more accurate. Experimental results show that the algorithm has a better performance than principal label space transformation or conditional principal label space transformation.
fastText-based methods for Emotion Identification in Russian Internet Discourse

In this paper we tackle the problem of emotion detection and classification in Russian short text messages. We use such recent NLP development as fastText that produces state-of-the-art results for a variety of tasks. We put a special emphasis on the challenges that arise while using a dataset of text messages from the most popular Russian messaging/social networking services (Telegram, VK). We also provide an extensive quantitative prediction analysis along with suggestions of possible ways to improve the results. Finally, we discuss the prospects of developing and implementing discourse-specific emotion identification technologies for the Web.
Social Science for Natural Language Processing: A Hostile Narrative Analysis Prototype

We propose a new methodology for analysing hostile narratives by incorporating theories from Social Science into a Natural Language Processing (NLP) pipeline. Drawing upon Peace Research, we use the “Self-Other gradient” from the theory of cultural violence to develop a framework and methodology for analysing hostile narratives. As test data for this development, we contrast Hitler’s Mein Kampf and texts from the “War on Terror” era with non-violent speeches from Martin Luther King. Our experiments with this dataset question the explanatory value of numerical outputs generated by quantitative methods in NLP. In response, we draw upon narrative analysis techniques for the technical development of our pipeline. We experimentally show how analysing narrative clauses has the potential to generate outputs of improved explanatory value to quantitative methods. To the best of our knowledge, this work constitutes the first attempt to incorporate cultural violence into an NLP pipeline for the analysis of hostile narratives.
MENGKAJI KETERSEDIAAN SISTEM DC DI GAS INSULATED SUBSTATION (GIS) SENAYAN

DC resources on a GIS have a very important role for move the PMT when a disturbance occurs. DC system of 110 V in GIS Senayan there are 2 units that are supplied by rectifier 380V. The load supplied is an essential load that is 110 V DC load divided into 2 main DC. Performance on the battery is still in thecondition above 60%, where the battery is still said in good condition that is when the last battery capacity test in 2019 in a percentage condition 72% in units 1 and 64% in Unit 2. With a battery capacity of 220.5 Ah in units 1 and 197.8 Ah in Unit 2 can supply loads in the state of 61 A when the year 2019produce battery usage for 3.2-3.6 hours, while with a load of real units 1 and Unit 2 of 5 A to 10 A can generate longer battery usage for 22 hours to 39.5 hours. DC Ground disruption that occurs at GIS Senayan resulted in DC system working in short time or quickly depleted. Ground DC interference was found onthe control panel of the Trafo 3 Bay and the Danayasa Bay in Terminal B50. X1 No. 14, with voltage +0.7 V,-118 V. DC Ground in GIS Senayan done Repair is by opening the cable from the terminal and cut the wires that are too long andnot neat, after the neatly clipped and then reworked the part of the fibers with the meeting and plug it back into the terminal so that it does not open and not connected to the Ground again, and after repaired normal return voltage of +65 V, -52 V.
Shout about infection control

Jenny Nixon explains why informing your patients about infection control procedures will reassure them it's safe to visit
An Improved Track Before Detect Algorithm with Local Operator

Aiming at the problem that the Track Before Detect (TBD) algorithm based on Hough Transform (HT) cannot start the weak target effectively when there is a strong target, this paper proposes an HT-TBD algorithm that improves the accumulation method through a local operator. Using the peak clustering phenomenon that appears in the traditional accumulation method, the relative accumulation value of strong and weak targets is used as the new accumulation matrix to reduce the difference in accumulation results. Monte Carlo simulation shows that the algorithm in this paper can effectively eliminate the influence of strong targets on weak targets.
Super-resolution-based GAN for image processing: Recent advances and future trends

Humans can analyze the relationship of data collected from different domains without any supervision, but automatic learning of discovering data is a challenging task. Image processing is one of the examples where conversion from day to night and night to day is difficult. To avoid drawbacks in image processing, Generative adversarial network (GAN) came into existence, which helps to generate networks without losing properties of attributes such as face identity and orientation. Again, super-resolution generative adversarial network (SRGAN) is a formative task that is capable of producing realistic textures in single image super-resolution. Still, hallucinated descriptions are generally attained with no desirable artifacts. Furthermore, to improve visual quality, we significantly studied the prime components of SRGAN—architecture, perceptual loss, adversarial loss, and training algorithm. Moreover, various potential open challenges, along with the future scope, are also discussed.
Development of professional adaptation of employees of internal affairs bodies at the stage of vocational training

The article examines the content of professional adaptation of employees of internal affairs bodies, as well as the reasons that complicate their adaptation. Using a systematic approach to the study of professional activity, a number of features of adaptation of employees at the stage of training in the main vocational training programs are revealed. In particular, it emphasizes the importance of studying the formation of a student's own ideas in the educational organization of the Ministry of Internal Affairs of Russia about the profession of an employee of internal affairs bodies at the early stages of professional development. By them, the article proposes to un-derstand: 1) the very first stage of recruitment (including during the period of the beginning of training in the system of professional training in the main vocational training programs); 2) the process of vocational training (including prior to its completion); 3) completion of vocational training and independent performance of official duties by employees of the internal affairs bodies who have undergone professional training. The article proposes specific ways of creating conditions for professional adaptation of students of vocational training. It is concluded that the study of the indicated issue will strengthen the human resources of the internal affairs bodies, form professionally significant qualities of the employees of the internal affairs bodies, and also increase the efficiency of their activities.
A Survey of Deep Neural Network Compression

Deep neural networks have achieved great success in many fields, including computer vision, natural language processing, and speech recognition. But these deep neural networks require huge computational overhead and memory storage, making them difficult to deploy in resource-constrained embedded devices or mobile devices. Therefore, deep neural network compression has become a hot research direction in recent years. This paper reviews and summarizes current mainstream methods of compressing deep neural networks. We divide these methods into three categories: weight compression, local compression, and global compression. In addition, we compare and analyze the results of different compression methods on the dataset. Finally, we discuss how to choose different compression methods and the future development trend on this topic.
A Framework and Techniques for Image-based Search Application with an E-commerce Domain

Traditionally, most end-users use retrieval systems to write questions or queries and get text results. But end-users continuously expect search engines to be “intelligent” and to be able to manage more than just written queries. End-users want a retrieval system to explore cyberspace using an image, as with the built-in camera on a mobile phone to send relevant resemblance, videos, and other formats. The primary aim of this research work is to address more recent tools and techniques. The study found that convolutional neural networks can use to break down divisions and retrieval issues. Image details are much bigger than text data, and ocular particular cannot be identified by using old techniques, that are designed to identify text details. Therefore, Content-Based Image Retrieval [CBIR] has acquired significant benefits from research society. The paper discusses various CBIR systems that work with specific attributes at a minor plane of an old user input image, making it difficult for users to edit input and provide adequate retrieval results. Additionally, this paper explores the flutter framework with dart language along with the aim to mitigate the fundamental issue that app developers have been facing for so long-maintaining multiple apps for multiple platforms.