Reviewerly/exHarmony huggingface dataset:
An Adaptive Graph Cut Algorithm for Spammer Group Detection from Weighted One Mode Projection of Bipartite Graph

Now-a-days online reviews have become an indispensable resource for customers'decision making before purchasing any product. Product reviews provide valuable information that helps customers, retailers, and product owners in making decision to buy or sell product online. The rise of ecommerce platforms has led to an increase volume of customer review data available in the internet. Though most of the customers write review about the product solidness, quality, usability or else but it has also been also found that some people write intentional collusive review to mislead customer's decision-making process and damage the online business as well. These intentional fake reviews are written in order to rise or down a business and they are called spam reviews. The persons who writes fake review are called spammer. Sometimes review spammer works in group to control the overall sentiment or notion about a product, item or a business. When the review spammer works in collaboration they are called group spammer. Most of the research works found in the literature worked on individual spammer detection using the traditional machine learning or neural network-based algorithms. Very few researchers worked on group spammer detection. This paper focuses on group spammer detection from online reviews and proposed a weighted one mode bipartite projection graph-based algorithm that extract the structural, behavioral and time centric features of reviews to detect group spammer from online marketplace.
Cross-media Hash Retrieval Using Multi-head Attention Network

The cross-media hash retrieval method is to encode multimedia data into a common binary hash space, which can effectively measure the correlation between samples from different modalities. In order to further improve the retrieval accuracy, this paper proposes an unsupervised cross-media hash retrieval method based on multi-head attention network. First of all, we use a multi-head attention network to make better matching images and texts, which contains rich semantic information. At the same time, an auxiliary similarity matrix is constructed to integrate the original neighborhood information from different modalities. Therefore, this method can capture the potential correlations between different modalities and within the same modality, so as to make up for the differences between different modalities and within the same modality. Secondly, the method is unsupervised and does not require additional semantic labels, so it has the potential to achieve large-scale cross-media retrieval. In addition, batch normalization and replacement hash code generation functions are adopted to optimize the model, and two loss functions are designed, which make the performance of this method exceed many supervised deep cross-media hash methods. Experiments on three datasets show that the average performance of this method is about 5 to 6 percentage points higher than the state-of-the-art unsupervised method, which proves the effectiveness and superiority of this method.
GPU Accelerated Matrix Factorization for Recommender Systems

Matrix Factorization (MF) is a popular algorithm used to power many recommender systems. Efficient and scalable MF algorithms are essential in order to train on the massive datasets that large scale recommender systems utilize. Graphics Processing Unit (GPU) technology has become very popular in recent years and has become widely used in machine learning. The massive parallelism GPUs offer creates an opportunity to develop an accelerated MF algorithm. This paper presents cu2rec, a matrix factorization algorithm written in CUDA. cu2rec implements a parallel version of Stochastic Gradient Descent (SGD) to solve large scale MF problems. cu2rec utilizes multiple advanced techniques to harness better performance from a GPU. These include aggressive use of constant memory for hyper-parameters and registers for heavily reused values, a sparse matrix data structure, a reduction sum total loss kernel, a novel approach to parallel lock-free updating of feature weights with minimized global memory writes, and fairness across weight updates using user index striding. With a single NVIDIA GPU, cu2rec can be 10x faster than state of the art sequential algorithms while reaching similar error metrics.
Development of an Intelligent Control System for an Autonomous Hybrid Solar System

Purpose: The purpose of the work is to form an information model in UML notation, which can be used as a basis for developing an algorithm for the operation of an intelligent control module for an automated autonomous power supply system.Design/methodology/approach: The methodology for achieving the goal of work includes the development of a functional diagram containing the main groups of objects that make up the power supply system that supports the conversion of electromagnetic radiation into heat, according to the proposed scheme, the classification of objects over the system and subsystem of the control object with which it will work intelligent control module, a mathematical description of the interaction of classification objects, through parametric equations.Findings: As a result, an information model is presented containing a diagram of classes in the structure of which the parameters and interrelationships between objects of an autonomous power supply system and objects of the external environment are presented, which will allow the formation of algorithmic software aimed at optimizing and increasing the efficiency of local power supply systems that support work with electromagnetic radiation converters.Originality/value: In conclusion, an approach to the development of an algorithm for the operation of an intelligent control module of a modern solar system to increase its efficiency is presented, based on the proposed information model and assuming the use of a simulation model based on the connections proposed in the information model that describe interactions between objects of the process of converting electromagnetic radiation into warmly.
Strategies for Intelligent Registration of IoT Devices

The heterogeneity of the underlying devices introduced significant technical challenges for interoperability in the Internet of Things (IoT), as a rapidly increasing landscape of IoT devices. A unified form of device registration is an important method to shield the heterogeneity. However, most solutions were to focus on the abstraction of device data. Notably, device description information is also necessary. For example, we can deploy the operation temperature of the device in its range to ensure normal operation of the device. However, the device description information registration methods mainly rely on developers entering manually, which causes inefficiency and high information error rate under the cases of many devices. Therefore, it is necessary to develop an excellent strategy for the intelligent registration of IoT devices.In this paper, we proposed the device intelligent registration strategy by a semantic ontology-based IoT device registration middleware (IDRM): (1) It achieves automatic registration by using automatic recognition and ontology matching of banner information from device application protocol. (2) It realizes registration with converting manual submitted by device vendor users into ontology-based device information. Our experimental results show that IDRM implements Plug-and-Play (PnP) for devices by providing an intelligent registration strategy to shield the heterogeneity of underlying devices.
Analyzing the Concept of Priority Queue for Scheduling in Cloud Scenario

Nowadays, cloud computing is rigorously used by academicians as well as by researchers due to its excellent applicability. Most significant feature of cloud computing is Live Migration of Virtual Machines (VMs). However, it demands large amount of migration time to transfer data from source to destination. Migration time of VMs can be mitigated by several techniques available in study. In migration, scheduling also plays a vital role which handles the processes, task, resources, etc. FCFS (first come, first serve), RR (round robin), and SJF (shortest job first) are among the several scheduling algorithms in CPU scheduling, utilized for minimization of makespan. In this paper, a more optimized scheduling algorithm known as MLFQ (multilevel feedback queue) is utilized. MLFQ helps in minimizing the makespan and overall workload with the help of feedback scheduler and by categorizing the load as under load and overload, resulting in effective migration.
Data Management Plans in Horizon 2020: what beneficiaries think and what we can learn from their experience

<ns4:p><ns4:bold>Background: </ns4:bold>Data Management Plans (DMPs) are at the heart of many research funder requirements for data management and open data, including the EU’s Framework Programme for Research and Innovation, Horizon 2020. This article provides a summary of the findings of the DMP Use Case study, conducted as part of OpenAIRE Advance.</ns4:p><ns4:p> <ns4:bold>Methods:</ns4:bold> As part of the study we created a vetted collection of over 800 Horizon 2020 DMPs. Primarily, however, we report the results of qualitative interviews and a quantitative survey on the experience of Horizon 2020 projects with DMPs.</ns4:p><ns4:p> <ns4:bold>Results &amp; Conclusions: </ns4:bold>We find that a significant number of projects had to develop a DMP for the first time in the context of Horizon 2020, which points to the importance of funder requirements in spreading good data management practices. In total, 82% of survey respondents found DMPs useful or partially useful, beyond them being “just” an European Commission (EC) requirement. DMPs are most prominently developed within a project’s Management Work Package. Templates were considered important, with 40% of respondents using the EC/European Research Council template. However, some argue for a more tailor-made approach. The most frequent source for support with DMPs were other project partners, but many beneficiaries did not receive any support at all. A number of survey respondents and interviewees therefore ask for a dedicated contact point at the EC, which could take the form of an EC Data Management Helpdesk, akin to the IP helpdesk. If DMPs are published, they are most often made available on the project website, which, however, is often taken offline after the project ends. There is therefore a need to further raise awareness on the importance of using repositories to ensure preservation and curation of DMPs. The study identifies IP and licensing arrangements for DMPs as promising areas for further research.</ns4:p>
Research on Informatics for Learning and Education

The key to developing learning support using technology is understanding learning from an informatics point of view. Research on informatics for learning and education applies learning theories or views to model learning and develop various methods and systems for learning support. The Japanese Society for Information and Systems in Education (JSiSE) has demonstrated leadership in this research field in Japan. JSiSE particularly encourages theoretical development, as well as practical use of models, methods, and systems, which involves circular interaction between them. In this paper, we introduce the history, research aim and approach, and academic activities of JSiSE. The paper also highlights the research being conducted by JSiSE on theoretical modeling of learning and development of methods and systems, and its transition owing to the influence of learning theory and learning media.
English Linguistic Term Extraction and Classification Strategies under The Influence of Network Language

The network language used in online chat has its commonness and characteristics. However, the level of word segmentation in the network text is not satisfactory, which puts forward a new topic for the current research on term extraction. This paper summarizes and analyzes the characteristics of the network text. Based on the conditional random field model and the combination of word tagging, the tagging set is improved, and the feature template suitable for the network text is adopted to improve the classification ability of the network text. We use the method of combining statistics and rules to recognize the Internet text named entity, normalize the text, and use CRF model to train and predict the text. Integrated with the matching rules, we modify the recognition results to achieve better results. The experimental results show that compared with the traditional text regularization and SVM classification methods, the performance of the proposed scheme is significantly improved.
Accessibility needs of extended reality hardware

research-article Open AccessAccessibility needs of extended reality hardware: a mixed academic-industry reflection Share on Authors: Aviv Elor University of California, Santa Cruz University of California, Santa CruzView Profile , Joel Ward Booz Allen Hamilton Booz Allen HamiltonView Profile Authors Info & Claims InteractionsVolume 28Issue 3May - June 2021 pp 42–46https://doi.org/10.1145/3457877Online:27 April 2021Publication History 0citation6,862DownloadsMetricsTotal Citations0Total Downloads6,862Last 12 Months6,862Last 6 weeks62 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Diffusion + Confusion: Chaos-assisted Twofold Cryptosystem for Secure Video Transmission

Video broadcasting has attained high importance due to the popularization of networks. Video communication has lot of advantages but not limited to e-learning, surveillance, etc. Video encryption is necessary for securing video information from unauthorized users. To achieve this, an enhanced video encryption scheme has been proposed in this work which adopts two techniques such as confusion and diffusion. Sundarapandian–Pehlivan chaotic system (Akgul et al. Optik (Stuttg). 127:5491–5499 (2016), []) has been adopted as a keystream generator to accomplish the encryption process. This 3D chaotic system has been realized by RungeKutta fourth-order method through which the differential system has been modelled as a discrete system. LabVIEW 2013 environment has been used to develop the proposed cryptosystem in which the camera module has been interfaced to capture the real-time video. This scheme achieves maximum entropy of 7.94627514 and near-zero correlation for a test video which has 141 encrypted frames. Further, NIST SP 800-22 batteries of test has been carried out to ensure the statistical efficiency.
What’s Inside Unreal Engine? - A Curious Gaze!

Game developers often use game engines to efficiently develop games and reduce effort by reusing predefined components. However, game engines are complex and difficult to comprehend to extend and integrate with other components. Unreal Engine is one of the most commonly used open-source game engine which is quite complex and involves thousands of APIs. In this paper, we aim to comprehend Unreal Engine by analyzing its underlying software architecture. We accomplish this by considering the architectural changes across various releases of the GitHub repository of Unreal Engine by using an in-house tool called AC2 which uses call graphs and collaboration graphs. We run this tool on three initial releases of two components of the Unreal Engine and observe two kinds of architectural patterns, namely Event-driven and Hierarchical MVC (Model-View-Controller). We also observe the changes in components across releases. We believe that this preliminary study on Unreal Engine may unveil its architectural design and modules and may aid in better comprehension of this complex and widely used game engine for researchers and practitioners.
Uniform and monotone line sum optimization

The line sum optimization problem asks for a (0,1)-matrix minimizing the sum of given functions evaluated at its row and column sums. We show that the uniform problem, with identical row functions and identical column functions, and the monotone problem, over matrices with nonincreasing row and column sums, are polynomial time solvable.
Facial Expression Neutralization With StoicNet

Expression neutralization is the process of synthetically altering an image of a face so as to remove any facial expression from it without changing the face's identity. Facial expression neutralization could have a variety of applications, particularly in the realms of facial recognition, in action unit analysis, or even improving the quality of identification pictures for various types of documents. Our proposed model, StoicNet, combines the robust encoding capacity of variational autoencoders, the generative power of generative adversarial networks, and the enhancing capabilities of super resolution networks with a learned encoding transformation to achieve compelling expression neutralization, while preserving the identity of the input face. Objective experiments demonstrate that StoicNet successfully generates realistic, identity-preserved faces with neutral expressions, regardless of the emotion or expression intensity of the input face.
Thermal performance of reused shipping containers as building components

Abstract The era of disruption occurred influenced by innovations that emerged according to the needs and developments of the times. This occurs in various sectors, including the construction industry sector. One of the growing innovations in the construction industry sector is the reuse of shipping containers (SC) into buildings with various functions. The trend of reusing SC as buildings is quite significant. Several sectors have used a lot of SC as buildings, including: Offices, educational, commercial and residential. However, the reuse of SC as a building has several weaknesses, one of which is the thermal aspect. The structure of the container, which is generally composed of steel, should be considered in achieving indoor thermal comfort. The case study of the paper was SC buildings at Institut Teknologi Sains Bandung (ITSB). The aims of this study were to determine the contribution of design parameters of SC to indoor thermal comfort and its effect on energy efficiency. The method was model simulation on the thermal aspect using Sefaira software. The results showed that setting of design parameters considerably contributed to thermal comfort and energy efficiency.
Experience of automation of colleges in the Chuvash Republic

The article presents the stages of implementation of complex automation of colleges in the Chuvash Republic. The regulatory framework for the use of information technologies and automation processes in educational institutions is described, in particular, the “Concept of regional automation”, approved by the Government of the Russian Federation on December 29, 2014, which sets the main goals and areas of activity for the use of information and communication technologies in the subjects of the Russian Federation. The article provides information on the inclusion of the Chuvash Republic in the all-Russian process of automation of organizations of secondary vocational education, which was determined by the order of the Ministry of Education and Youth Policy of Chuvashia “On the implementation of a set of measures for the development and implementation of an automated information system in state professional educational organizations of the Chuvash Republic” dated September 11, 2014. The process of automation of the educational process in the system of secondary vocational education of the Chuvash Republic based on 1C is traced. Provides information on the implementation of the software 1C:College in educational institutions of the republic; creation of an information and analytical system of secondary vocational education of the Ministry of Education and Youth Policy of the Chuvash Republic, through which reporting information is collected and aggregated; development of the module “Personal web account of an applicant for 1C:College” for receiving documents from applicants on the basis of the Novocheboksarsk Chemical and Mechanical College.
A Self-Scaling G-transformation for weighted least squares problems

The G-transformation is an efficient method for solving the weighted least squares problems. However, the underflows were not considered in the original G-transformation. In order to keep its stability, some authors proposed three specified scaling strategies for guarding against the underflows. Note that these three specific strategies are not easy to be implemented in actual operations, in this paper we present a self-scaling G-transformation (SSGT) which not only avoids these specified scaling strategies, but maintain the stability of operations. Complexity analysis of our self-scaling G-transformation shows that its cost of computation is less than that of the G-transformation, which implies the high efficiency of our proposed SSGT. The stability of the SSGT was theoretically confirmed by a detailed error analysis. Some numerical experiments are performed to illustrate the effects of the self-scaling strategy and show the attractiveness of the SSGT method when solving sparse weighted least squares problems.
Obstetric Diagnosis Assistant via Knowledge Powered Attention and Information-Enhanced Strategy

The obstetric Electronic Medical Records (EMRs) contain a large amount of medical data and health information. The obstetric EMRs play a vital role in improving the quality of the diagnosis assistant service. In this paper, we treat the diagnosis assistant as a multi-label classification task and propose a Knowledge powered Attention and Information-Enhanced (KAIE) model for the obstetric diagnosis assistant. In order to make most of the information in EMRs, we propose to utilize the numerical information and chief complaint information to enhance the text representation. In addition to the use of information in EMRs, we integrate external knowledge from the COKG medical knowledge graph into the model. Specifically, we propose a multi-way attention mechanism for the generation of knowledge-aware representations based on text representations. Experiment results show that our approach is able to bring about +1.37 F1 score improvements upon the strong BERT baseline in the diagnosis assistant task.
Android-Based Assistance System for Visually Impaired Person Using Deep Learning and Augmented Reality

With the appearance of technology, the present age has changed significantly. We see a colossal amount of advancement in all fields and spheres of life, starting right from making life more convenient than ever to making the impossible possible. So, we came up with an idea to develop an application which would help blind people recognize objects around them and help them walk around freely avoiding obstacles around them. This proposed support system would help the blind people perceive the surroundings and help them interact with the system about the objects around them. This application is not only for blind people. It has many other applications too. The same concept can be used for autonomous driving, to locate objects in the surroundings and to avoid them and move in a clear path. The proposed system uses the concept of augmented reality and deep learning to detect objects and their distances from the system. Apart from this, the system would help to locate text and differentiate between similar-looking objects like currencies. The android environment is used for the development of the system. The system is validated with rigorous real-time testing.