Reviewerly/exHarmony huggingface dataset:
Cyber Crime and Their Restriction Through Laws and Techniques for Protecting Security Issues and Privacy Threats

This chapter explains the privacy and security threats belonging to information technology and personal data. Cyber Security plays a significant part in the world of information development. Cyber protection has been one of the greatest problems of our period. Whenever we talk about information protection, the first aspect that falls to our minds is cyber-attacks that are rising exponentially every day. A range of countries and businesses are implementing a variety of steps to combat such cybercrimes. Besides numerous information protection initiatives, others are also really worried regarding cybersecurity. This paper focuses primarily on threats to information protection in terms of the new technology. It also reflects on the current cyber management strategies, principles, and developments that transform the landscape of computer security. This chapter explores law enforcement cybercrime cases from a range of angles, including regulatory authorities for disciplinary initiatives, preservation of privacy concerns, barriers to prosecution and best practices, relationships with law enforcement and the business sector, and preparation and capability for law enforcement. That illustrates the difficulty of cybercrime cases and the necessity for clear regulatory mechanisms, along with cybercrime law enforcement expertise and operational abilities. This chapter discusses the role of global, foreign, and regional laws and structures in the prevention and battle against cybercrime. This states that reform is important in all respects, including criminalization, administrative control, authority, and foreign cooperation. Although important advances have taken place over the last decade in the implementation of multilateral treaties aimed at fighting cybercrime, the chapter illustrates the increasing deterioration of law at the foreign and national level.
Practical Use of Machine Learning in the Minecraft Computer Game

This article investigates the use of the Minecraft computer game for the implementation and training of machine learning algorithms. In the introduction, an analysis of works on this topic is described. Similar experiments using Minecraft are also discussed. The experimental part describes the process of implementing a game modification, which adds a new AI-controlled character to the game. As the result, examples of work that reflect the possibilities of using machine learning are shown. In conclusion, the pros and cons of this work are given basing on the results of the experiment.
Automatic Exploration Process Adjustment for Safe Reinforcement Learning with Joint Chance Constraint Satisfaction

In reinforcement learning (RL) algorithms, exploratory control inputs are used during learning to acquire knowledge for decision making and control, while the true dynamics of a controlled object is unknown. However, this exploring property sometimes causes undesired situations by violating constraints regarding the state of the controlled object. In this paper, we propose an automatic exploration process adjustment method for safe RL in continuous state and action spaces utilizing a linear nominal model of the controlled object. Specifically, our proposed method automatically selects whether the exploratory input is used or not at each time depending on the state and its predicted value as well as adjusts the variance-covariance matrix used in the Gaussian policy for exploration. We also show that our exploration process adjustment method theoretically guarantees the satisfaction of the constraints with the pre-specified probability, that is, the satisfaction of a joint chance constraint at every time. Finally, we illustrate the validity and the effectiveness of our method through numerical simulation.
Performance Evaluation of VM Allocation Strategies on Heterogeneous Environments in Cloud Data Center

Cloud computing paradigm provides on demand utilities to the customers and involves high resource requirements. Virtual machine allocation plays a vital role in the optimization of resource usage in a data center. This approach consolidates workload on minimal number of servers to improve their resource utilization and thus contributes to energy efficiency. The literature evidences number of virtual machine allocation strategies that vary considerably in adopted approaches and evaluation environments. This research work aims to analyze performance of commonly used heuristic allocation policies. To ensure logistic comparison, the selection of policies is done that implements bin-packing approach in allocation. These algorithms are intensively compared to different workload data-sets and varying experimental setups. Performance of these algorithms with different threshold and virtual machine selection policies is also evaluated. The results conclude that the policies which consider power and computing capacity of the server perform better in almost all scenarios.
Helping Hand Device for Speech Impaired People

In this paper, we propose a well-organized system to bridge the gap of communication for the speech-impaired person and assist him in a smooth conversation with a normal person by providing an artificial mouth. This paper includes the method of easy conversion of the hand gestures and the intact conversion of these gestures into speech and text messages. A flex sensor is used to take the input from gestures and forward it to the microcontroller Arduino Uno where a template database is created to assign the desired message which is further processed to be displayed on the LCD display and the acquainted sound signal stored in the Secure Digital card is amplified before it is provided to the speaker to generate the audio signal and ensure better sound quality.
RETRACTED: Oil Painting Material Collection System Based on Artificial Intelligence

Abstract In recent years, with the rapid development of computer technology and artificial intelligence technology, the traditional oil painting creation field has also had a lot of influence. The most typical way to collect and manage materials in the future is to collect and manage materials through the Internet. Therefore, this paper puts forward the research of oil painting material collection system based on artificial intelligence. In this paper, the artificial intelligence technology and oil painting material collection work are deeply studied. The analysis shows that the traditional oil painting material collection work has obvious problems such as low efficiency, high cost and low accuracy. And through artificial intelligence technology, we can better solve these problems. According to the characteristics of oil painting material collection, combined with the latest artificial intelligence information collection technology, this paper establishes an intelligent system suitable for oil painting material collection. In order to further verify the actual effect of the collection system in this paper, the corresponding comparative experiments are established. The experimental data show that the highest accuracy rate of the artificial intelligence material collection system in this paper is as high as 99.6%, which is significantly improved compared with the 87.6% accuracy rate of the traditional artificial material collection method. Moreover, while ensuring the accuracy rate, the number of collected materials has been greatly improved. Analysis shows that this system effectively improves the efficiency of oil painting material collection.
Learning to Engage in Interactive Digital Art

The aim of this study was to determine whether reinforcement learning could increase user engagement in interactive art installations. Building on a physical interactive art installation called The Plants [1] by Playable Streets, reinforcement learning was integrated into a web application adapted from the physical interactive art piece. The original installation consisted of real plants that visitors could touch to produce sounds. A digital model and interface was developed to simulate the physical installation. A user study was conducted with 178 participants. Three modes were examined: the original settings of the installation as designed by the artist; a predetermined fixed schedule of consistently changing sound banks; and a reinforcement learning mode, where an agent changes the interactive behaviours to maximise user engagement. User engagement was estimated by comparing the number of touches by an individual over successive time intervals.
WITHDRAWN: A new lossless compression method using Direction Adaptive-Discrete wavelet transform and Modified SPIHT coding

In programmable hardware, the most commonly used Image Compression technique is the Set Partitioning In Hierarchical Trees (SPIHT). Based on the Discrete Wavelet Transformation (DWT) architecture the correlation is deployed in the midst position of the image pixels in the lifting. In addition to that, the wavelet coefficient is provided by the storage elements in which the requirement of the pixels remains the same. Encryption of the wavelet coefficients are offered with lieu of a Modified SPIHT algorithm. The wavelet coefficient is simplified by dint of the scanning process with a custom of 1-D addressing method as an alternative of the innovative 2-D arrangement for the wavelet coefficients. A static memory data lists are shared out in the lieu of the substitute by means of the dynamic allocation prerequisite in the innovative SPIHT. The proposed algorithm has been exemplified mutually the 2-Dimage and a 3-D MRI data set and is set up to achieve appreciable compression with a high peak-signal-to-noise ratio (PSNR) values.
RETRACTED: Comparative analysis of Iranian medical academic libraries websites the base Google SEO component

The main purpose of this study was to compare the websites of Iranian medical academic libraries from the perspective of SEO components or visibility in Google, in addition to identifying the differences between SEO rules in the two general search engines of Google and Watson Smart. The present study is of a practical study with webometric method and a cross-sectional descriptive-analytical approach. To achieve the purpose of this article, first a researcher-made checklist or checklist derived from the common components of Google SEO agreed between the two tools “SEO Site Check-up” and “SEO p timer” were designed, then members of the research sample were reviewed and compared based on the said checklist (analytical survey). Other components of Google SEO were obtained by documentation method (without online SEO analysis tools), and the research sample was compared according to them too, (Descriptive survey). Iranian medical academic libraries often are under-visited and under-used because few libraries are sufficiently active and strategic in search engine optimization. Library administrators can achieve worth information about their organizations via a good managed SEO program that ensures websites and digital object metadata are comprehensible and usable by search engine crawlers. Most of the existing studies on SEO have been confined to popular literature, outside of scholarly academic research in library and information science. Also, few studies have been done on library SEO in Iran, So, this study compared analysis of Iranian Medical Academic libraries website from the perspective of components affecting Google SEO.
Position Enhanced Mention Graph Attention Network for Dialogue Relation Extraction

Dialogue Relation Extraction (DRE) is a new kind of relation extraction task from multi-turn dialogues. Different from the previous tasks, speaker specific relations are implicitly mixed together in both a local utterance window and a speaker context. To tackle both local and speaker dependency challenges, we explicitly construct a unified mention co-occurrence graph within a local utterance window or all utterances of a speaker from different entities. For each dialogue, a position enhanced graph attention network over this graph is proposed to obtain position aware mention representations in terms of both contexts. A gate function is utilized to help obtain a discriminative representation enough for each relation from original and position aware mention representations. For each entity pair in this dialogue, a pairwise attention mechanism is deployed to aggregate those discriminative mention representations as pair representation, which is fed into a standard multi-label classifier for relation label prediction. Experimental results on two benchmarks show the performance improvement of the proposed method is at least 1.6% and 3.2% compared with SOTA.
Design and Optimization of Reversible Logic Based Magnitude Comparator Using Gate Diffusion Input Technique

The power optimization is one of the biggest challenging issues for designing of VLSI circuits within the advanced technology. The reversible logic is one among the best approaches for low power application. This logic has wide application in the communication, nanotechnology, digital signal processing, computer graphics, optical computing, etc. In this paper, some proposed reversible magnitude comparator has been designed using the prevailing reversible gates and implemented using gate diffusion input (GDI) technique. The design methodologies are also proposed for the designing of N-bit comparator. The main objective of this paper is to design and implementation of reversible magnitude comparator using some proposed methods and compares them with the existing circuits in terms of constant input, garbage output, number of reversible gates, and quantum cost. The transistor implementations of the proposed comparators are done by the combination of CMOS and GDI technique in EDA Tanner tools. After the design and analysis of proposed comparators it has been found that the proposed-2 logic based 4-bit comparator has lowest quantum cost which is equal to 38 and the proposed-3 logic based 4-bit comparator has lowest constant input and garbage output which is equal to 8 and 13.
Novel QR Code Tagging System for Campus Vegetation to Promote Ecorestoration

This work is an effort to create a means to establish environment sustainability through QR code tagging to trees. In this digital era, with increased usage of smartphones, tablets and the availability of these devices at a lower cost motivated us to adopt QR code tagging for obtaining detailed information about the flora collection available in our college. The objective of this work is to tag each tree in the campus with QR Code and enable the students aware of all the facts and data about the tree from its scientific name to its medicinal and pharmaceutic potential along with other interesting information, by scanning the QR code put up on the tree using their smartphone. These codes can be easily accessed by anyone in the college so that it will help the student, faculty, and others to gain knowledge about the plants available in the college campus.
Development of an Information System for Managing User Requests Using Bitrix

In the report, the process of developing an information system for managing user requests based on the Russian customer relationship management system Bitrix was introduced. The system is considered to use in Gazprom Pererabotka LLC. The features of systems of this kind and the stability of their operation were studied, as a result of which a conclusion was made about the applicability of such an information system for a help desk team work organization.
Heuristic Path Planning Approach for a Granular-fill Insulation Distributing Robot

In this paper, a heuristic path planning approach for the robotic distribution of granular-fill insulation material is presented. The initial coarse manual distribution of the material leads to an uneven surface with areas of excessive or insufficient material. In order to distribute the granular-fill insulation uniformly with a robot, first the worked area is captured as point cloud with an RGB-D camera, and afterwards these irregularities are located via agglomerative hierarchical clustering. Subsequently, their volumes are estimated providing weights for the path calculation. A path planning method, inspired by the usual working method of human construction workers, is developed and applied. In a test scenario, the total path length and the processing sequence are analysed, varying the blade size and the weight of the distance-to-goal parameter. This analysis yields, that the presented path planning algorithm is well suited for the described application, showing the best results with a larger blade size and a quadratic distance-to-goal behavior.
Evaluating the Deep Convolutional Neural Network for Thyroid Nodule Detection on Vietnamese Ultrasound Dataset

Nowadays, computer-aided diagnosis (CAD) systems play an important role in supporting doctors and improving the performance of thyroid nodule detection which able to reduce the physician’s examination burden. In addition, medical data have certain characteristics that make their application very challenging on the existing system. In this paper, we propose an ultrasound image-based of thyroid nodule detection method using state-of-the-art object detectors. We employed radiologist knowledge from clinical studies such as size and aspect ratio distributions of real nodules to generate better region proposals and improve the detection accuracy. Additionally, we contribute a new Vietnamese thyroid nodule dataset, which is collected from Vietnamese Hospitals and validated by experienced radiologists. With this dataset, we expect to provide an essential medical imaging research resource for CAD development and validation. Through experimental results, our detection network FasterIncResnet** that was optimized with radiologist’s knowledge yielded the highest performance compared with original-based methods. Our optimized detection sensitivity, specificity, and AUC were 0.87, 0.86, and 0.89, respectively, on the Vietnamese dataset. The overall performance improves ~2.4% compared with the original FasterIncResnet method. The result proved the effectiveness of the proposed method on a new Vietnamese dataset in order to build an accurate system for thyroid nodule detection.
A Regression-based Hybrid Machine Learning Technique to Enhance the Database Migration in Cloud Environment

The report of cloud computing in recent years has prompted circumstances that usually has lead to numerous advancements & novel mechanisms. The technologies available in the cloud have been prevalent for businesses as well as people who understand that cloud computing is a significant problem, even though they don't know why. We present a methodology that accurately assesses the migration cost, relocation length with cloud operating cost of the social databases, and upgraded the execution. The first step in our approach is to acquire workloads and structure models for moving the database from the database logs as well as from schemes. The second step uses these models to perform a discrete form of event simulation for estimated costs and time. We have implemented the software tools that simplify our approach in both phases. A comprehensive review contrasts our approach to the effects of real-world cloud data migration.
Perspectives of the use of ICT for the teaching-learning process among Peruvian university students and lecturers during the COVID-19 pandemic

Universities in Peru were unexpectedly physically closed due to the COVID-19 pandemic from March 2020. This forced university to transition from face-to-face education to virtual distance education. This was prior to them having any information on the digital tools that lecturers and students have access to, for the teaching-learning process. Faced with this situation, this study seeks to identify the knowledge, use and ways of the learning of Information and Communication Technologies (ICT), before and during the emergency. A total of 126 students and 48 lecturers from a private university in Lima were surveyed virtually. The results showed that there were no substantial changes in the knowledge and use of ICT, in both groups, except for the videoconference platform, which was established by the university for the development of the classes. Likewise, it was found that lecturers improved their self-evaluation during the emergency. On the other hand, ICT learning occurred basically on the lecturers and students' own initiative and in a self-taught way, especially for students, with a low incidence of formal courses within the university. These results show that both lecturers and university students were not prepared to face the use of digital tools for the teaching-learning environment which was required for them in the context of this health emergency.
Twitter Spam Detection Using Natural Language Processing by Encoder Decoder Model

With the increasing demand of social life in today's world one famous platform known to be as Twitter plays important role for every citizen to connect socially, whether in the form of tweeting a tweet for other person or exploring various fields in the running world. But this platform, now a days gets infected by some spammers, with the intention of increasing traffic in their spam web sites they connect their URL with the informative tweet where there is no relation between the content present in URL and the tweet message, these are called spammed tweets. This paper provides a unique approach to detect whether the tweet mentioned by the user is spam or not spam using the encoder decoder technique used with vectorizer converter on the mentioned tweets and on its linked URL, which leads to the prediction of similarity score between them.
Distributed Multidimensional Data Optimization Model in Big Data Environment

With the gradual transformation of spatial information service to spatio-temporal big data service, the quality and timeliness of spatio-temporal data are guaranteed. Efficient query, mining and analysis of spatio-temporal big data provide decision response support for complex scenes. This paper mainly studies the distributed multidimensional data optimization model under the big data environment. Based on the architecture of distributed computing and the characteristics of data storage, this paper analyzes the characteristics of spatio-temporal data and the key problems of realizing efficient storage and parallel processing in distributed environment. Build distributed space-time data storage organization model based on HBase, based on the S2-Geometry algorithm’s coding and operation power in geographic space, a generalized hierarchical grid was constructed to manage the spatio-temporal objects, and the Compact Hilbert Index algorithm was introduced to optimize the generation method of spatio-temporal mixed index value, reduce the structure and the index of the coupling between the spatio-temporal data, combined with the characteristics of data distribution and logical organization of hierarchical block, to design a table, a data storage model of multiple index of mixed management, to provide efficient query of spatiotemporal data high availability data storage structure.
PA-GAN: Graph Attention Network for Preference-Aware Social Recommendation

Abstract Social recommendation has been recently proposed by incorporating trust relationship to alleviate data-sparsity and cold-start problems. However, most of existing works only focus on friends different contribution to model user representation. They ignore users have different preference on items, and share different preference with friends. To address these problems, in this paper, we propose a novel Preference-Aware Graph Attention Network (PA-GAN) for trust recommendation. And we design three modules: item aggregation for user, friend-preference aggregation for user and user aggregation for item to model users’ local and global preference. Experiments on two publicly available datasets shows the proposed model PA-GAN outperforms the state-of-the-art recommendation models, and improves performance greatly.