Reviewerly/exHarmony huggingface dataset:
Cloud and Its Security Impacts on Managing a Workforce Remotely: A Reflection to Cover Remote Working Challenges

Attacks against remote workers who are working from home due to the global pandemic has significantly increased. Cyber criminals have realised this and are exploiting users for financial gain and for espionage motives. Criminals are aiming to exploit vulnerable smart homes through Internet of Things and leverage access into corporate networks. This means that home users need to be extra vigilant against this contemporary technique. This paper will address these challenges with robust protocols for organisations to absorb, train, and implement. Additionally, this paper will align organisations expectations with user vulnerabilities to increase organisation resilience.
Harnessing Psycho-lingual and Crowd-Sourced Dictionaries for Predicting Taboos in Written Emotional Disclosure in Anonymous Confession Boards

There have been many efforts in the last decade in the health informatics community to develop systems that can automatically recognize and predict disclosures on social media. However, a majority of such efforts have focused on simple topic prediction or sentiment classification. However, taboo disclosures on social media that people are not comfortable to talk with their friends represent an abstract theme dependent on context and background. Recent research has demonstrated the efficacy of injecting concept into the learning model to improve prediction. We present a vectorization scheme that combines corpus- and lexicon-based approaches for predicting taboo topics from anonymous social media datasets. The proposed vectorization scheme exploits two context-rich lexicons LIWC and Urban Dictionary. Our methodology achieves cross-validation accuracies of up to 78.1% for the supervised learning task on Facebook Confessions dataset, and 70.5% for the transfer learning task on the YikYak dataset. For both the tasks, supervised algorithms trained with features generated by the proposed vectorizer perform better than vanilla tf-idf representation. This work presents a novel methodology for predicting taboos from anonymous emotional disclosures on confession boards.
On CMMN Model for Software System Project Simulation

Software systems (SS) project failure has been a recurring problem for decades. One of the main factors of project success is appropriately chosen project management methodology and its adaption to the project type, company, and its employees. Although some methods have been proposed to address this problem, the number of failed projects shows that the problem is still relevant. Means, that new and more efficient solutions are needed. This article proposes a new approach to simulate a proj ect management flow and anticipate its shortcomings in the initial stages. It is based on dynamic case management model and notation (CMMN) SS project management process modelling and simulation. The proposed method is evaluated with industry case study.
Cybercrime Predicting in the Light of Police Statistics

Cybercrime is widely felt to be a huge and growing threat to individuals, companies and organizations and even entire countries. In view of the number of such incidents and their potential danger and the need to counteract them adequately, the development of cybercrime should be predict. The paper presents attempts to predict of different categories of cybercrimes in Poland in 2020 and checks whether the predicting of cybercrime gives satisfactory results, as well as to what extent the results of the predicting will indicate a trend and cyclicality in the light of random factors. For this purpose, police statistical data on cybercrime in Poland in the years 2000–2019 were used and the concept of cybercrime and its interpretation in the Polish Penal Code have been described. It also seems to be an important practical issue to determine the time horizon of predicting, based on statistical data obtained from police data, by various exploration methods, as well as the magnitude of predicting error resulting from accidental and cyclical factors. Linear trend and the Holt’s method were used for predicting.
METHODS OF TEACHING HTML AND CSS STYLES IN THE DISCIPLINE OF INFORMATICS FOR STUDENTS OF PEDAGOGICAL INSTITUTES

The paper analyzes the methods of teaching HTML, CSS, JavaScript. The expediency of learning these technologies by students of pedagogical universities is substantiated. The essence of the new method of teaching HTML and CSS is that the emphasis in the content component is shifted to the study of: 1 - visual objects of a hypertext document; 2 - rules of their unique identification; 3 - setting the basic properties and styling properties of these objects; 4 - modeling of hypertext page design based on instances of block objects; 5 - consolidation of knowledge of HTML, CSS languages through the use of these technologies in the formation of reporting sites for laboratory work; 6 - formation of the basis for mastering the technologies of dynamic adjustment of styles by means of JavaScript language. The new methodology involves the use of visual objects of a hypertext document to generate reports to the thematic sections of the course Computer Science such as Office Software, Mathematical Packages, Computer Graphics, etc. Internet hosting is a learning tool for new methods. It is recommended to use this tool to highlight the results of students’ laboratory work. The technique was experimentally implemented using distance learning tools. Analysis of the results of the pedagogical experiment confirmed the feasibility of introducing the new methodology. The conclusions of the work are that the introduction of this technique increases the effectiveness of training. The basis for the positive result is: 1) providing an individualized approach to teaching Computer Science; 2) creating conditions for mastering the concept of modeling through practical application; 3) the formation of understanding of the object-oriented essence of the language of HTML and CSS styles; 4) assistance in mastering modern network technologies; 5) increasing the importance of programming in mastering Informatics. This technique forms the basis for mastering the technologies of object-oriented programming in JavaScript and becomes the basis for further mastering of technologies for the development of Web-oriented information systems. The prospects of this study are seen in the formation of a holistic perception of computer science as a science through the study of Web-programming.
Fast and efficient lossless encoder in image compression with low computation and low memory

This research focuses on frequency-based lossless new encoding technique with low computation and low memory scheme. This encoding technique doesn't require, any table similar to Huffman and Golomb Rice encoder, and doesn't take high computation time like an arithmetic encoder. For better comparative results, nearly 200 standard images were tested with the proposed encoder and their results were compared with the standard encoders. Based on the analysis of results reported in the experimental part, the proposed scheme in Discrete Cosine Transformation (DCT) achieves a high Compression Ratio (CR) of 97.68 %, Bits Per Pixel (BPP) of 0.19, Peak Signal To Noise Ratio (PSNR) of 27.62 dB for pepper image, CR of 98.65%, BPP of 0.11 and PSNR of 29.24 dB for house image, CR of 98.58 %, BPP of 0.11, PSNR of 30.59 dB for pepper image, CR of 98.37%, BPP of 0.13, PSNR of 31.37dB for house image in Discrete Wavelet Transformation (DWT) than compared to other encoders. It consumes less computation time to attain superior CR with a better quality of reconstructed picture than DCT based Huffman, Golomb- Rice (GR) coder and Low Complexity, Low memory Entropy Coder (LLEC), DWT based Arithmetic, GR code, LLEC.
Regional Language Code-Switching for Natural Language Understanding and Intelligent Digital Assistants

Due to their widespread use around the globe, especially in a multilingual country like India, digital assistants are programmed to be capable of understanding speech and conversing for an array of languages. The objective is to understand user intent and respond with human expression, achieving a great degree of accuracy and efficiency. However, a multilingual society such as India has grown quite comfortable using a combination of two or more languages while communicating, commonly referenced as code-switching. Code-switching is not inherently taken care of by today’s intelligent assistants. Due to the lack of standardized dataset, the presented work includes corpus generation of code-switching frequently asked queries was generated. It includes a study of intent classification of code-switching queries with various vectorizers and classifier combinations. This work also comprises a pipeline to identify the user intent and provide a desired response using keyword extraction and named entity recognition models on code-switching queries. Standardized code-switching metrics applied on the generated corpus to estimate the strength of code-switching yielded high intensity. The results of the intent classification study on code-switching queries obtained by evaluating the models on standardized classification metrics have yielded high accuracy. Named entity recognition in code-switching queries also have yielded high precision and recall scores.
Role of Byzantine Fault Tolerance (BFT) in Maintaining Patient Health Records Using Block Chain Technology

This research paper is primarily focused on analyzing Byzantine Fault Tolerance (BFT) and its essential in maintaining the patient data or information using blockchain technology. One of the best features of BFT is to operate on a distributed network to make an agreement or consensus even when the few nodes in the network fail to respond or respond with incorrect information. The Electronic Health Record (EHR) or Medical Health Record (MHR) is one of the blooming online health care service that is used to store the patient health record in protected manner. Hyperledger Fabric is used to provide security and privacy and also promotes the globalization of world-class treatment that can be given to any patient with appropriate cost and medicine. According to HIPAA guidelines and policies, every patient has the right to proper treatment and medicine. It not only promotes health to patients but also the right to know the treatment charges around the globe and disease is cured with less cost and time. Every patient feels comfort and ease as the treatments are given properly because no doctor gives an inappropriate treatment or collects more than what is taken as the medical charge. Now-a-days more than the doctors or the hospital management the medical representatives or medical insurance people make unnecessary charges and make the patient claim more medical insurance. When the BFT is used in maintaining the health records of patients it provides overall reliability and trustworthiness in making efficient access to the data.
Quantifying clause chains in Nungon texts

Abstract Clause chains are sequences of clauses with under-specified verbal predicates, plus a single clause with a fully-specified verbal predicate. Clause chains represent the morpho-syntactic demarcation of a speech unit greater than a single clause, but the precise length of this unit is rarely assessed. Clause chain length, distribution, bridging linkage, and non-canonical forms are evaluated in a sample of 64 texts, containing over 1742 clause chains, in the Papuan language Nungon. In the 49 narrative texts, unlike in other genres, total clause chains per text increases as text duration increases. The average clause chain in the sample is 3–4 clauses long; the longest has 22 clauses. Average clause chain length decreases as text length increases. The longest narrative texts have similar average clause chain length, proportion of bridging clauses, and proportion of non-canonical clause chains, and feature a leisurely rhythm: half of their clause chains are minimal, two-clause chains.
Mobile learning: esempi di buona pratica basata sull’uso dello smartphone in classe

This paper aims to illustrate how the use of features of smartphones, such as the use of video-recordings and of a specific application (Kahoot!), can motivate learners within Italian middle school. When smartphones are integrated into a pedagogical framework, they provide an opportunity to explore forms of language and expression creatively and facilitate a training process centred on learning and on learners, favouring different intelligences and skills. Reference is made to the international and national context of regulations on the didactic use of mobile devices at school. Some examples of good practices are included too, and reference is made to a class-based activity in which an activity was carried out using the Kahoot! application.
Cross-lingual Adaptation Using Universal Dependencies

We describe a cross-lingual adaptation method based on syntactic parse trees obtained from the Universal Dependencies (UD), which are consistent across languages, to develop classifiers in low-resource languages. The idea of UD parsing is to capture similarities as well as idiosyncrasies among typologically different languages. In this article, we show that models trained using UD parse trees for complex NLP tasks can characterize very different languages. We study two tasks of paraphrase identification and relation extraction as case studies. Based on UD parse trees, we develop several models using tree kernels and show that these models trained on the English dataset can correctly classify data of other languages, e.g., French, Farsi, and Arabic. The proposed approach opens up avenues for exploiting UD parsing in solving similar cross-lingual tasks, which is very useful for languages for which no labeled data is available.
Reimagining Rights &amp;amp; Responsibilities in the United States: Freedom of Speech and Media

The First Amendment guarantees some of the most fundamental rights provided to Americans under the Constitution. The right to free expression is a foundational tenet of American values. In fact, it was the First Amendment rights to freedom of speech and the press that provided much of the basis for the revolution that led to Americaâ€™s founding. The First Amendment provides broad protection from government censure of speech, although limitations on some forms of published or broadcast speech, such as obscenity and hate speech, have been allowed. As the traditional public square governed and protected by federal regulation moves online to spaces governed by private corporations, the rules for how speech is both expressed and censored are also changing. How should legal protections for speech adapt to these new tech-powered, private forums? This chapter will explore the current landscape of free speech and the associated information landscape as well as the threats that they face.
Enhancing the precision of remote eye-tracking using iris velocity estimation

Most of the previous work on eye-tracking has focused on positional information of the eye features. Recent advances in camera technology such as high-resolution and event cameras allow consideration of the velocity estimate for eye tracking. Some previous work on velocity-based estimates has demonstrated high-precision gaze estimation by tracking the motion of iris features on high-resolution images rather than by exploiting pupil edges. While these methods provide high precision, the bottleneck for velocity-based methods are temporal drift and the inability to track across blinks. In this work, we present a new theoretical methodology (πt) to address these issues by optimally combining low-temporal frequency components of the pupil edges with the high-temporal frequency components from the iris textures. We show improved precision with this method while fixating a series of small targets and following a smoothly moving target. Further, we demonstrate the capability to reliably identify microsaccades between targets separated by 0.2°.
New educational technologies in the educational and professional motivation of undergraduate students in the field of ecology

The article presents a study of the influence of the specifics of the formation of the motivational sphere of undergraduate students on their readiness and ability to perform their labor functions for obtaining a quality education. High-quality education is the basis for a decent life and sustainable personal and social development, which contributes to changing the labor market in all countries of the world, blurring the boundaries between the physical, digital, and psychological and biological spheres. The study of this topic is extremely important for Russia, which seeks to overcome the negative consequences on the labor market and lag behind global trends in development through the implementation of the strategy 2016-2030. As a result of the work, a cluster of pedagogical technologies “UnInd of motivations” was created, which has wide opportunities and allows to implement the educational process in a new way in the modern conditions of the university, namely: to create conditions for improving the training of a university student, increasing the motivation of learning by solving case problems of a practice-oriented nature, to create conditions for further self-improvement in the professional sphere.
A Novel Random Vector Functional Link Neural Network for Identification of Nonlinear Dynamic Systems

This piece of work proposes a novel random vector functional link neural network (RVFLNN) to determine the nonlinear systems. It can benefit many vivid real-world application domains like design and analysis of control and power systems, broadband communications, industrial sectors, etc. For identification in system modelling, chaotic particle swarm optimization (CPSO) has been used for the optimization of the parameters. An extensive investigation has been formalized to validate the performance of the novel RVFLNN-CPSO model with conventional FLANN in terms of the convergence rate, error parameters, and computational complexity. The results reveal that the novel identification system modelling has outperformed the state-of-the-art system with lower error, better convergence, and faster response algorithm.
Development of Rule-Based Software Risk Assessment and Management Method with Fuzzy Inference System

There is an enormous budget and financial plan in software development projects, and it is required that they take a huge investment to carry on. When looked at, the costs depend on the global substantial information about software development: in 1985, $150 billion; in 2010, $2 trillion; in 2015, $5 trillion; and in 2020, over $7 trillion. Additionally, on the first new days of 2021, a day-by-day Apple Store’s quantity has been approximately $500 million. In spite of the expenditures and the margins that are dramatically expanding and increasing each year, the phase of software development accomplishment is not high enough. In light of the “CHAOS” report arranged in 2015, just 17% of the software projects were finished in an opportune way, in the allotted financial plan, and as per the necessities. However, 53% of the software projects were finished in the long run or potentially over a spending plan as well as without satisfying the prerequisites precisely. In addition, software development projects were not completed and were dropped out as well in the ratio of 30%. Also, the “CHAOS” report published in 2020 has figured out that only 33% of the software projects were completed successfully all over the world. In order to cope with these unsuccessful and failure results, an effective method for software risk assessment and management has to be specified, designated, and applied. In this way, before causing trouble that has the power of preventing successful accomplishment of software development projects, software risks are able to be noticed and distinguished on time. In this study, a new and original rule set, which could be used and carried out effectively in software risk assessment and management, has been designed and developed based on the implementation of fuzzy approached technique integrated with machine learning algorithm—Adaptive Neuro-Fuzzy Inference System (ANFIS). By this approach and technique, machines (computers) are able to create several software risk rules not to be seen, not to be recognized, and not to be told by human beings. In addition, this fuzzy inference approach aims to decrease risks in the software development process in order to increase the success rate of the software projects. Also, the experimental results of this approach show that rule-based software risk assessment and management method has a valid and accurate model with a high accuracy rate and low average testing error.
Research Summary of Blockchain Fragmentation Propagation Mechanism Based on Merkel Tree

Abstract At present, when the blockchain system propagates blocks, it is a point-to-point propagation of complete blocks. This method has three shortcomings: the propagation load is large; the message can only be received from one node; only after the complete block is received, can it be propagated to other nodes. Therefore, this article considers changing the block’s Merkel tree data structure to minimize the data size during block propagation while ensuring that the receiving node can reconstruct the correct and complete block. On the other hand, in order to be able to receive blocks from multiple nodes at the same time and relay to other nodes when incompletely received, the blocks are fragmented and then reorganized. At the same time, the correctness of a single fragment can also be verified.
Implementation on Text Classification Using Bag of Words Model

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Performance Optimization on GPGPU &amp; Multicore CPU Using Roofline Model

Abstract The roofline model introduced in this paper to evaluate the best optimized platform for training the neural network that used to recognize handwritten digits under multicore CPU and general-purpose GPU (GPGPU) as hardware environment. The pattern parallel training technique for MNIST dataset is applied. The parallel network training of MNIST using different data layout of multicore CPU and GPGPU is presented. Different bottlenecks have been explained by applying the roofline model. The most suitable platform is selected according to layouts and constrains either for memory or computation bounds. The computational intensity of all rooflines is moved toward right, then the performance is increased. As a result of optimization, and with the diversity of the available data size, core number, operational strength, the most suitable hardware platform is selected.
Blockchain Medicine Administration Records (BMAR): Reflections and Modelling Blockchain with UML

Modelling blockchain should be easy? There are many figures and diagrams that are intuitive, however they are often unorthodox and do not comply with standard modelling techniques, e.g., UML. The enterprise blockchain system designed is for Medicines Administration Records (MARs) and requires audits for accreditation, that exposes vulnerabilities to unauthorised access and alteration. These alterations are made in order to pass inspection of the auditors and often completed to correct human errors unforeseen until the audit. The design stage has five stages and includes four stages from UML. The initial stage looks at how effective blockchain is as a solution for the problem domain, and then followed by Use Case, Sequence, State Machine and Deployment. The final design was implemented and the overall approach is evaluated.