Reviewerly/exHarmony huggingface dataset:
Cyber Threats Landscape Overview Under the New Normal

During the time of this pandemic, the dependency on Internet has increased manifolds. Employees are doing Work from Home, Students are Studying from Home, Teachers are teaching from Home, Everyone is shopping from Home and the list continues. All this has given rise to Cyber Security Vulnerability attacks like phishing attacks, malware attacks, ransomware attacks, social engineering attacks, identity theft, and a denial-of-service attack. This paper discusses the basic concept of cybersecurity and with a detailed review of recent cybersecurity attacks from January 2020 to March 2021 with a detailed analysis of motivation behind such attacks, attack techniques used, and targets of the attacks. This paper will also discuss cloud breaches, data breaches, and leaky buckets which have increased from 2021. The paper will discuss the types of cyber attacks, attack types, and targets of those attacks.
Document-Level Relation Extraction with Entity Enhancement and Context Refinement

Document-level Relation Extraction (DocRE) is the task of extracting relational facts mentioned in the entire document. Despite its popularity, there are still two major difficulties with this task: (i) How to learn more informative embeddings for entity pairs? (ii) How to capture the crucial context describing the relation between an entity pair from the document? To tackle the first challenge, we propose to encode the document with a task-specific pre-trained encoder, where three tasks are involved in pre-training. While one novel task is designed to learn the relation semantic from diverse expressions by utilizing relation-aware pre-training data, the other two tasks, Masked Language Modeling (MLM) and Mention Reference Prediction (MRP), are adopted to enhance the encoder’s capacity in text understanding and coreference capturing. For addressing the second challenge, we craft a hierarchical attention mechanism to refine the context for entity pairs, which considers the embeddings from the encoder as well as the sequential distance information of mentions in the given document. Extensive experimental study on the benchmark dataset DocRED verifies that our method achieves better performance than the baselines.
The effect of ISO/IEC 27001 standard over open-source intelligence

The Internet's emergence as a global communication medium has dramatically expanded the volume of content that is freely accessible. Through using this information, open-source intelligence (OSINT) seeks to meet basic intelligence requirements. Although open-source information has historically been synonymous with strategic intelligence, today's consumers range from governments to corporations to everyday people. This paper aimed to describe open-source intelligence and to show how to use a few OSINT resources. In this article, OSINT (a combination of public information, social engineering, open-source information, and internet information) was examined to define the present situation further, and suggestions were made as to what could happen in the future. OSINT is gaining prominence, and its application is spreading into different areas. The primary difficulty with OSINT is separating relevant bits from large volumes of details. Thus, this paper proposed and illustrated three OSINT alternatives, demonstrating their existence and distinguishing characteristics. The solution analysis took the form of a presentation evaluation, during which the usage and effects of selected OSINT solutions were reported and observed. The paper's results demonstrate the breadth and dispersion of OSINT solutions. The mechanism by which OSINT data searches are returned varies greatly between solutions. Combining data from numerous OSINT solutions to produce a detailed summary and interpretation involves work and the use of multiple disjointed solutions, both of which are manual. Visualization of results is anticipated to be a potential theme in the production of OSINT solutions. Individuals' data search and analysis abilities are another trend worth following, whether to optimize the productivity of currently accessible OSINT solutions or to create more advanced OSINT solutions in the future.
Developing of Middleware and Cross Platform Chat Application

The rapid development of technology has resulted in many new innovations on social media platforms.Now-a-days, there are many chat applications available, namely Whatsapp, Telegram, LINE, Viber, and many others.This in turn forces users to juggle between many chat applications as different applications can't communicate with each other.This research aims to develop a chat application which serves as a middleware to make communication between developed chat application and two conventional chat applications possible (Telegram and LINE).Several tests are done to ensure that the message exchange process (in text, picture, video, and file type) works well between the developed chat application as well as Telegram or LINE.
CS-BERT: a pretrained model for customer service dialogues

Large-scale pretrained transformer models have demonstrated state-of-the-art (SOTA) performance in a variety of NLP tasks.Nowadays, numerous pretrained models are available in different model flavors and different languages, and can be easily adapted to one's downstream task.However, only a limited number of models are available for dialogue tasks, and in particular, goal-oriented dialogue tasks.In addition, the available pretrained models are trained on general domain language, creating a mismatch between the pretraining language and the downstream domain launguage.In this contribution, we present CS-BERT, a BERT model pretrained on millions of dialogues in the customer service domain.We evaluate CS-BERT on several downstream customer service dialogue tasks, and demonstrate that our indomain pretraining is advantageous compared to other pretrained models in both zero-shot experiments as well as in finetuning experiments, especially in a low-resource data setting.
Yet another investigation on blockchain in smart healthcare

Blockchain technology is a digital tool to modernise the existing smart healthcare ecosystem that uses the internet of things (IoT) as one of its prerequisites. The growing demand for reliable and cost-effective remote healthcare services incorporated with blockchain technology amidst the pandemic COVID-19 particularly highlights the positive impact on the healthcare industry. Identifying and classifying the advantages of blockchain-based healthcare applications are required for bringing a revolutionary transformation. There has been a plethora of research and reviews on blockchain in healthcare, however, there is a lack of synergy among COVID-19, healthcare, blockchain platforms, interoperability, datasets, and challenges. Hence, this survey showcases these key areas of the blockchain-based healthcare ecosystem. Unlike others, our survey also indicated the requirement of explainable blockchain and appropriate future directions.
Visible Infrared Person Re-Identification via Global-Level and Local-Level Constraints

Visible infrared person re-identification (VI-ReID) is an extremely challenging task. VI-ReID suffers from two challenges. One is the cross-modality discrepancy due to different camera spectrums, the other is the intra-modality variation caused by the noise of background clutter or occlusion. We propose a global-level and local-level constraints network (GLoC-Net) to learn discriminative feature representations. It mainly contains two aspects. 1) We employ a non-local attention mechanism for extracting shared features to mitigate the cross-modality discrepancy, and present the division operation of local features to alleviate the problem that the non-local attention mechanism is less robust to noise. 2) We propose joint constraints of global-level and local-level to alleviate the intra-modality variation, which makes the algorithm more robust to noise. Experiments demonstrate that the superior performance of proposed method compared with the state-of-the-arts.
Visible Light Communication Channel Modeling by Experiment-Data-Driven Deep Learning

An experiment-data-driven channel-modeling method based on bidirectional long short-term memory is proposed for visible light communication channels. The resulting mean absolute error is stable in a small range, reaching 0.009 without occupying spectrum resources.
RecNN: A Deep Neural Network based Recommendation System

Deep learning’s breakthrough in speech recognition, image analysis and natural language processing has helped it gain a considerable amount of recognition in today’s highly modernized world. As it is known, collaborative filtering and content-based filtering are two incredibly desired memory-based methods used for recommending new products to the targeted users, but it does happen to have certain restrictions and it thus fails to provide the intended user with effectual recommendations as primarily required. In this paper, we evaluate the performance of a revised version of a deep learning-based recommender system for movies and books using multiple fully connected dense layered neural net embeddings-based structures that primarily ensembles deep neural networks integrated alongside embedding layers and their dot product values. We develop and test the recommendation systems using the data provided by Wikipedia book dataset and MovieLens 100k dataset for books and movies respectively. To recommend books or movies to a particular user, the inputs are converted to an embedding layer and then passed through dense layers for obtaining recommendations. The same process was applied to 2,3 and 4 layer architecture for obtaining recommendations for both books and movies. The results that we’ve obtained shows that our approach is a promising solution when compared with the independent memory-based collaborative filtering methods or content-based methods. It leads us to conclude that our comparative study of multiple layered architectures provides probable research directions for deep learning-based recommender systems in the near future.
A Comparative Study of the Performance of Load Balancing Algorithms Using Cloud Analyst

Cloud computing technology has grown and expanded widely in recent years. Cloud computing is a distributed system that may be on physical or virtual machines. Because of the ease of working in the cloud, its ease of use, it's being an interactive model and a highly profitable business model, many users have used it. This led to an increase in the load on it, and thus the overload has become a problem that must be solved. Many algorithms have been proposed to balance the load on cloud. The essential are three: Round robin, Equally Spread Current Execution (ESCE), and Throttled. This research investigates the performance of those algorithms with increasing of number of users. Previous researches did not take into account the increasing of users. CloudAnalyst simulation tool is used for this comparison. The comparison is done according to DC (Data Centre) processing time, overall total response time (ORT), processing cost, and hourly data center. It was found that initially with a certain settings and for a certain number of users, there may be no difference that can be mentioned in performance between the three algorithms. Then the improvement in performance increases with the increasing number of users. Throttled algorithm showed best performance in the average of ORT with increasing number of users.
Petrographic and Elemental Analysis of Pottery from the Hlyboke Ozero-2 Settlement (the First Archaeometric Data)

The paper highlights the first results of compositional (raw materials and technological) investigation of ceramic pastes from the Hlyboke Ozero–2 settlement of the Late Timber-Grave Culture — Post-Timber Grave Culture type in the middle flow of the Siverskyi Donets river. Petrographic analysis and energy-dispersive X-ray spectroscopy have been applied. This allowed to characterize traditions of ceramic pastes preparation within the site and to start developing the database of petrographic and chemical composition of the Late Bronze Age ceramics of the studied region. Obtained petrographic data (allocation of two petrogroups) have been confirmed by elemental analysis of clay matrix. Elemental analysis showed chemical and mineralogical similarity of the samples of the two first layers of the site and allowed to provide distinction of the third layer samples. Supposedly, this could be explained by the change of the place of raw clay procurement on that stage.
Music Against the Challenges of Digital Society

The aim of the article is to separate the concepts of musicology, music anthropology, music, and instrumentalism. This very division and reflection of the interrelations of concepts provide a starting point for a detailed study of the problems of musical instrumentalism. The article is methodological in nature. It contains a number of important settings that are necessary for the anthropological analysis of music. The Central theoretical explanation of the article is the separation of musical anthropology and the anthropology of music. For this purpose, a number of tasks, namely, the definition of the theoretical fields of musical anthropology and the anthropology of music, and the disclosure of the role of music in traditional and modern society are proposed. The concept of musical action is introduced. Solving these problems allows to give a more complete analysis of the relationship between a person and music. It is this problem that becomes the main problem in the music media mainstream, where popular music is used as a way of producing moods and experiences. Music becomes a particularly valuable commodity in a networked society, which is made not so much for the purpose of incorporating into the values of high culture but to control the behavior of people in the music market. In addition to commercialization, music is becoming an effective political technology that provides consensus among voters. In this regard, there is a cultural problem, how high art in general and music, in particular, can preserve its traditional purpose – to promote humanization
Robust dynamic sliding mode control design for interval type-2 fuzzy systems

&lt;p style='text-indent:20px;'&gt;This paper discusses the problem of stabilization of interval type-2 fuzzy systems with uncertainties, time delay and external disturbance using a dynamic sliding mode controller. The sliding surface function, which is based on both the system's state and control input vectors, is used during the control design process. The sliding mode dynamics are presented by defining a new vector that augments the system state and control vectors. First, the reachability of the addressed sliding mode surface is demonstrated. Second, the required sufficient conditions for the system's stability and the proposed control design are derived by using extended dissipative theory and an asymmetric Lyapunov-Krasovskii functional approach. Unlike some existing sliding mode control designs, the one proposed in this paper does not require the control coefficient matrices of all linear subsystems to be the same, reducing the method's conservatism. Finally, numerical examples are provided to demonstrate the viability and superiority of the proposed design method.&lt;/p&gt;
Data Feature Extraction Method of Wearable Sensor Based on Convolutional Neural Network

With the rapid development of society and science technology, human health issues have attracted much attention due to wearable devices’ ability to provide high-quality sports, health, and activity monitoring services. This paper proposes a method for feature extraction of wearable sensor data based on a convolutional neural network (CNN). First, it uses the Kalman filter to fuse the data to obtain a preliminary state estimation, and then it uses CNN to recognize human behavior, thereby obtaining the corresponding behavior set. Moreover, this paper conducts experiments on 5 datasets. The experimental results show that the method in this paper extracts data features at multiple scales while fully maintaining data independence, can effectively extract corresponding feature data, and has strong generalization ability, which can adapt to different learning tasks.
Recognition of arm and body postures as social cues for proactive HRI

Abstract Artificial agents can uplift the living standards of domestic population considerably. One hindrance for this is that the robot is less competent to perceive complex human behaviors. With such perceptive skills in the robot, nonexpert users will find it easier to cope with their robot companion with less and less instructions to follow. Perception of the internal state of a user or “user situation” before interaction is crucial in this regard. There are a variety of factors that affect this user situation. Out of these, posture becomes prominent in displaying the emotional state of a person. This article presents a novel approach to identify diverse human postures often encountered in domestic environments and how a robot could assess its user’s emotional state of mind before an interaction based on postures. Therefore, the robot evaluates posture and the overall postural behavior of its user throughout the period of observation before initiating an interaction with its user. Aforementioned user evaluation is nonverbal and decisions are made through observation as well. We introduced a variable called “valence” to measure how “relaxed” or “stressed” a user is, in a certain encounter. The robot decides upon an appropriate approach behavior accordingly. Furthermore, the proposed concept was capable of recognizing both arm and body postures and both postural behaviors over time. This leads to an interaction initiated by robot itself in a favorable situation so that the scenario looks more intelligent. Hence more humanlike. The system has been implemented, and experiments have been conducted on an assistive robot placed in an artificially created domestic environment. Results of the experiments have been used to validate the proposed concept and critical observations are discussed.
Cybercrime and Underground Attack Technologies: Perspectives from the Nigerian Banking Sector.

Banks all over the world are taking advantage and opportunities brought about by e-banking which happened as a result of Internet. As the security level in this sector becomes stronger, the strength and tactics of these fraudsters also increases. Various lucrative attacks have been launched and unfortunately, many have succeeded. This paper addressed different types of cybercrimes in banking sector, factors contributing to cybercrime, tools used by cybercriminals, effects of cybercrime on Nigeria banking, cyber security technology for preventing cybercrimes and duties of individual and organizations on protecting our banks from cybercrime. Keywords: Cybercrime, Banking Sector, Internet, Cyber security.
Performance study on IOTA Chrysalis and Coordicide in the Industrial Internet of Things

Due to the big advantages like immutable, decentralized data record and smart contracts (SC), distributed ledger technologies (DLT) gain strongly in importance in a wide variety of areas. With the fourth industrial revolution and the industrial internet of things (IIoT), new business models and technology fields based on data-driven approaches evolve and lead to the demand for trustworthy data and secure data exchange in a decentralized system. While a comprehensive number of possible industrial use cases exists, there is a lack of experimental studies on their applicability on IIoT devices. Available performance tests mostly focus on the performance of the DLT but not on their influence on the resources of the IIoT device. Furthermore, one very important DLT, namely IOTA, which is explicitly designed for application in IoT environments, did not receive much attention yet. This was due to two major drawbacks, namely the need for a centralized instance and the lack of SC functionality in the first IOTA version compared to other DLTs such as Hyperledger. The recently published version IOTA Coordicide improved in both. This paper presents detailed results from two industrial use cases and experiments on a private DLT network based on IOTA in IIoT, focusing on the resource demands for the IIoT devices with different network setups. The results confirm the suitability of IOTA for IIoT devices. Furthermore, an overview of the required resources of the IIoT devices with different transaction rates and networks sizes is given.
The Rating of the Journal in the Bibliographic Database

The tool for building ratings of scientific journals is one of the popular services of bibliographic databases. The task of building a rating is usually divided into two main subtasks: determining the reference group of journals and calculating the rating indicator for journals of this reference group. Practice shows that for the correct comparison of journals, a necessary condition is to limit the reference group to exclusively journals of a certain subject. In the case of methodological errors made at the stage of selecting a reference group, the values of the journal index in the rating may differ greatly from the expected ones.&#x0D; For example, in the ranking of journals in the Russian Science Citation Index (RSCI) according to the two-year impact factor in the thematic area “Mathematics”, classical fundamental mathematical journals, contrary to expectations, do not reach the first positions of the rating. The first positions were taken by journals for which mathematics is not the dominant profile discipline. Analysis of statistical data on the subject of published articles and citations in journals that occupy leading positions in the RSCI rating shows that the multidisciplinary nature of these journals significantly influenced the rating indicators.&#x0D; The noted misunderstanding leads to the idea that in this case, not all the articles of the journal should have been involved in the calculation of the rating, but only those related to this thematic area. At the same time, the existing scheme of thematic classification of directions also raises questions. The "bottom-up" classification, which is gaining popularity and works on a representative array of articles, seems to be more promising. Here thematic clusters are isolated on the basis of the concept of proximity of articles, interpreted as the proximity of their bibliographic links. And further, the thematic affiliation of the article is not assigned by the volitional decision of the author or the editorial board, but is strictly formally calculated on the basis of its bibliographic list.