Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence (AI) and Its Associated Ethical Issues

The adoption of AI in many industries has been regarded by some as a threat to low- and middle-skill workers, as it will drastically cut down reliance on the human workforce. Besides unemployment, there are also concerns about rising economic inequality caused by AI-driven companies. With fewer employees, these companies could gain a disproportionate advantage over conventional companies that still depend on normal, shift-based systems. There is also the issue that some AI bots have achieved the capability to interact with humans and build relationships through conversations. This influential communication could eventually enable these bots to affect human behaviour and possibly trigger certain actions. Significantly, therefore, such intelligent machines are not immune to mistakes and confusion since not all possible examples of real world interaction are covered during their training; this weakness could be manipulated to fulfil certain ends. Also, Al cannot be expected to be entirely fair and neutral, since it is dependent on human programmers, who have their own interests and whims.
Constraint-Driven Explanations for Black-Box ML Models

The need to understand the inner workings of opaque Machine Learning models has prompted researchers to devise various types of post-hoc explanations. A large class of such explainers proceed in two phases: first perturb an input instance whose explanation is sought, and then generate an interpretable artifact to explain the prediction of the opaque model on that instance. Recently, Deutch and Frost proposed to use an additional input from the user: a set of constraints over the input space to guide the perturbation phase. While this approach affords the user the ability to tailor the explanation to their needs, striking a balance between flexibility, theoretical rigor and computational cost has remained an open challenge. We propose a novel constraint-driven explanation generation approach which simultaneously addresses these issues in a modular fashion. Our framework supports the use of expressive Boolean constraints giving the user more flexibility to specify the subspace to generate perturbations from. Leveraging advances in Formal Methods, we can theoretically guarantee strict adherence of the samples to the desired distribution. This also allows us to compute fidelity in a rigorous way, while scaling much better in practice. Our empirical study demonstrates concrete uses of our tool CLIME in obtaining more meaningful explanations with high fidelity.
Multilingual Sentiment Recommendation System based on Multilayer Convolutional Neural Networks (MCNN) and Collaborative Filtering based Multistage Deep Neural Network Models (CFMDNN)

Recommendation systems referred to synonymously as Recommender systems as well, are one of the most important computer-based online computational platforms, used in retail and business sector extensively. They play an important role in providing filtered and selective information that is useful to customers and can help in both enhancing the user experience with the platform as well as increase the prospect of purchase of goods or provision of services. Traditional approaches for recommendation systems are based on either collaborative filtering technique, or content-based filtering technique or a hybrid combination of both these techniques. However, these are certain limitations to these traditional approaches, including the cold start problems, and the need for prior user history, and interaction with items or service provided by the platform. In this paper we propose a novel computation framework based on use of deep learning models, along with inclusion of both explicit feedback in terms of ratings score and sentiments expressed in textual comments, for addressing the limitations of traditional recommender system approaches. The proposed deep learning framework allows joint modelling between users, items, and multiple channels of explicit feedback, with two novel deep neural models, the Collaborative Filtering based Deep Neural Network architecture (CFMDNN) model and the novel Multichannel Convolutional Neural Network (MCNN) model, for aiding the traditional Collaborative Filtering based system, and improve the performance of recommendation systems' performance. The evaluation of the proposed deep learning-based framework, in a multilingual context, based on two publicly available English and Arabic languages datasets, has shown promising outcomes in terms of ratings prediction accuracy used for assessing the performance.
Optimize the Cost of Resources in Federated Cloud by Collaborated Resource Provisioning and Most Cost-effective Collated Providers Resource First Algorithm

Cloud Computing works as the best solution for providing many of its services for cloud consumer agents with different requests for huge computational VM's with large storage capacity.The instance requests of cloud consumers will dynamically change as per their usage of application requirements with the demand for business growth, and singlevendor cloud becomes a constraint to satisfy these needs of the cloud consumers.Federated Cloud can contribute its solution approaches to meet these dynamic needs of cloud consumer requests of resource instances.The interoperability of clouds was made realistic with cloud federation.This paper provides an optimized solution approach where a set of collaborated cloud providers will provide services to satisfy consumer agents' multiple requests.It presents the two-phase collaborated resource provisioning (CCRP) approach and Most Cost-Effective Collated Providers Resources First (MCECPRF) algorithm.The algorithm's efficiency has been tested with specific data set for optimizing the cost for cloud consumer agents and analyzes the cancellation of requests, decision time for provisioning for different VM configurations within specific time slots.
Design of Uniform Scalar Quantizer for Discrete Input Signals

This paper considers the design and performance of the uniform quantizer with discrete input, for the Laplacian source. Differences in the design process and performance, compared to the uniform quantizer with continual analog input are highlighted. Uniform quantizers with discrete input signals are largely used in practice, for quantization of the previously discretized signals, with the aim of compression. Especially important is possible application in DNNs (deep neural networks) for compression of DNN parameters.
Quality of Accounting System and Internal Control: How is the Implementation at PT. Nusantara Jaya Sentosa

This study aims to determine the quality of accounting information systems and internal controls at PT Nusantara Jaya Sentosa. The method used is descriptive qualitative. The data used are primary data and secondary data. Data analysis techniques are data reduction, data presentation and data verification. The results showed that the quality of PT Nusantara Jaya Sentosa's administrative system has changed from a manual system to a computerized system that produces useful output for each user of the information. This is evidenced by all the components needed to support a valid systems are owned by PT Nusantara Jaya Sentosa, and there is an element of internal control. The leadership of PT Nusantara Jaya Sentosa gave instructions that it was better to implement structured internal controls and develop access controls that were restricted by the use of a User and Password so that only specific units could perform internal access.
Web-based document certification system with advanced encryption standard digital signature

&lt;span&gt;Web-based degree document certification system with a digital signature in Semarang University has a purpose to support academic to do online document certification through a system. The main problem which occurs in academic administration is a long document certification process that causes an ineffective and inefficient certification process. To solve the problem, a system that can encrypt a document for better security is required. This system is built with the advanced encryption standard algorithm with a 128-bit sized key to encrypt confidential information inside the document. During the encryption process, this algorithm operates using 4x4 bit array blocks and passing many encryption processes for at least 10 (ten) times. The application is analyzed with object-oriented analysis and modeled with Unified modeling language. The result of this research is a system which can secure document with AES algorithm with a 256-bit sized key. The security element in this algorithm will make easier to identify the owner of the document. The secured document is easily accessible through PHP-based web or available QR code. When decrypting the document, the application will activate the camera function and decrypt the information document.&lt;/span&gt;
Pengembangan E-Book Mata Kuliah Drama sebagai Media Pembelajaran Mandiri di Masa Pandemi

This research aims to develop an eBook of Drama courses that can be used independently by students by linking learning videos that have been uploaded by lecturers in youtube media. The development of drama e-book courses aims to help and support distance learning, especially during this pandemic. This research follows the steps of ADDIE (analize, design, develop, implement, and evaluate) which focuses on development research. Based on the results of data analysis, the average value of each aspect tested in the eBook of this Drama course is 83.85. This figure shows the teaching book on Drama Subjects is feasible and valid to use. The effectiveness and practicality of the e-book drama courses that have been used obtained an average score of 83.25. This figure shows the Drama course eBook is practical and effective for use in learning.
Kashmiri to English Machine Translation: A Study in Translation Divergence Issues of Personal and Possessive Pronouns

Machine translation (MT) as a sub-field of computational linguistics represents one of the most advanced and applied translation dimensions as a research field. Translation divergence occurs when structurally similar sentences of the source language do not translate into sentences that are similar in structure in the target language" (Dorr, 1993). The sophistication in the domain of MT depends mainly on the identification of divergence patterns in a language pair. Many researchers in MT field including Dorr (1990, 1994) have emphasized that the best quality in MT can be achieved when an individual language pair in a particular context is described in detail. This paper attempts to explore the divergence patterns that characterize the translation of Kashmiri pronouns into English. The analysis in this paper has been restricted to the class of personal and possessive pronouns. Kashmiri has rich inflections and pronouns are marked for case, number, tense and gender and show complex agreement patterns. The paper identifies and outlines a wide variety of divergence patterns that characterize the Kashmiri English language pair. These divergence patterns are identified and summarized in order to improve the quality of the MT system that may be developed for Kashmiri English language pair in the near future and can also be utilized for other language pairs that are similar in terms of their structure and typological features.
State Aviation Risk Assessment Level Determination Using Hierarchical Fuzzy Inference System Based on Cognitive Maps

During 2018, ICAO (International Civil Aviation Organization, a specialized UN organization) made available the results of its USOAP (Universal Safety Oversight Audit Program): the ratio of compliance for each ICAO member State to 1047 aviation safety-related protocol questions, divided into eight audit areas. Numbers itself has little meaning, even for aviation personnel. Using Cognitive Mapping (CogMap), a Problem Structuring Method tool, this paper develops a framework to extract and organize information from aviation specialists, allowing define Risk Assessment Level for each State, and for each Aviation Safety Branch defined. Using Fuzzy Inference Systems (FIS), helpful supporting decision making, Big Data available from ICAO is converted to Risk Levels for each State and audit area, what may be used to make informed better Safety decisions on the World Aviation Market. Up to the moment, there’s no evidence on the literature of using CogMap to establish a FIS.
METHOD OF PARADIGMATIC ANALYSIS OF PROGRAMMING LANGUAGES

The purpose of the article is to describe the method of comparison of programming languages, convenient for assessing the expressive power of languages and the complexity of the programming systems. The method is adapted to substantiate practical, objective criteria of program decomposition, which can be considered as an approach to solving the problem of factorization of very complicated definitions of programming languages and their support systems. In addition, the article presents the results of the analysis of the most well-known programming paradigms and outlines an approach to navigation in the modern expanding space of programming languages, based on the classification of paradigms on the peculiarities of problem statements and semantic characteristics of programming languages and systems with an emphasis on the criteria for the quality of programs and priorities in decision-making in their implementation. The concept of "programming paradigm" is manifested as the way of thinking in the programming process. The author thanks the organizers and participants of the conferences "Scientific Service in the Internet Environment" (http://agora.guru.ru/display.php?conf=abrau2020&amp;page=subjects&amp;PHPSESSID=qbn3kbhgnk8b6a9g21qi1nkkq2 ), discussions with which made it possible to understand the main provisions of this article.
A Recurrent Neural Network for Attenuating Non-cognitive Components of Pupil Dynamics

There is increasing interest in how the pupil dynamics of the eye reflect underlying cognitive processes and brain states. Problematic, however, is that pupil changes can be due to non-cognitive factors, for example luminance changes in the environment, accommodation and movement. In this paper we consider how by modeling the response of the pupil in real-world environments we can capture the non-cognitive related changes and remove these to extract a residual signal which is a better index of cognition and performance. Specifically, we utilize sequence measures such as fixation position, duration, saccades, and blink-related information as inputs to a deep recurrent neural network (RNN) model for predicting subsequent pupil diameter. We build and evaluate the model for a task where subjects are watching educational videos and subsequently asked questions based on the content. Compared to commonly-used models for this task, the RNN had the lowest errors rates in predicting subsequent pupil dilation given sequence data. Most importantly was how the model output related to subjects' cognitive performance as assessed by a post-viewing test. Consistent with our hypothesis that the model captures non-cognitive pupil dynamics, we found (1) the model's root-mean square error was less for lower performing subjects than for those having better performance on the post-viewing test, (2) the residuals of the RNN (LSTM) model had the highest correlation with subject post-viewing test scores and (3) the residuals had the highest discriminability (assessed via area under the ROC curve, AUC) for classifying high and low test performers, compared to the true pupil size or the RNN model predictions. This suggests that deep learning sequence models may be good for separating components of pupil responses that are linked to luminance and accommodation from those that are linked to cognition and arousal.
Reservoir memory machines.

In recent years, Neural Turing Machines have gathered attention by joining the flexibility of neural networks with the computational capabilities of Turing machines. However, Neural Turing Machines are notoriously hard to train, which limits their applicability. We propose reservoir memory machines, which are still able to solve some of the benchmark tests for Neural Turing Machines, but are much faster to train, requiring only an alignment algorithm and linear regression. Our model can also be seen as an extension of echo state networks with an external memory, enabling arbitrarily long storage without interference.
Intelligent immunity based security defense system for multi-access edge computing network

In this paper, the security problem for the multi-access edge computing (MEC) network is researched, and an intelligent immunity-based security defense system is proposed to identify the unauthorized mobile users and to protect the security of whole system. In the proposed security defense system, the security is protected by the intelligent immunity through three functions, identification function, learning function, and regulation function, respectively. Meanwhile, a three process-based intelligent algorithm is proposed for the intelligent immunity system. Numerical simulations are given to prove the effeteness of the proposed approach.
Penerapan Algoritma K-Means Dalam Pengelompokan Data Penduduk Miskin Menurut Provinsi

Proverty is one of the problems that inhibits national and regional growth. This research uses data mining techniques. In this study tha data used were sourced from the 2012-2018 statistical center. The research uses data mining techniques. In the data processing using k-means method. K-means method is a method of grouping existing data into several groups where the data in one group has the same characteristics with each other and has different characteristics from the data in other groups. The number of records used is 34 provinces which are divided into 2 clusters namely high and low clusters. The purpose of this study is divided into 2 parts, namely the provincial group with a high proverty rate and the provincial group with the lowest proverty level. From the result of grouping there were 8 provinces of high cluster and 26 low clusters. It is hoped that this research can provide input to the government so that it can give more attention to provinces that are categorized as high in proverty
HY-index: A new science-meter index

In academic societies, it is essential to measure the efficiency of the researchers, scientists, institutions, departments, and universities by using a standard method that depends on their publications and citations. This work is devoted to constructing a new index that can measure the quality of the publications of any source mentioned above. The main goal of this new index is to fix the shortcoming of the current indices, such as the well-known h-index. Thus, we assumed a new index that is very sensitive to any change in the total number of publications, the total number of citations, and citations of each publication. The new index is called HY-index, is more sensitive to any change in the publications or the citations. The suggested data of the two authors have been discussed with various situations by using HY-index. This index has been applied to the authors of the recent paper as a real study case by using their data from SCOPUS.
An Investigative Study of Cognitive and Metacognitive Paraphrasing Strategies in ESL Writing

Paraphrasing is not an easy task as it involves the writer using many levels of knowledge.A writer needs to use both reading and writing skills to write.He/she needs to go through the stages of before, during and after writing processes in order to reach the final draft.Paraphrasing is not an easy task as it requires the writer to use both cognitive and metacognitive strategies.This study explores the aspects of cognitive and metacognitive strategies that is used by the writer to paraphrase.A survey is use to elicit responses from purposely random chose ESL writers who went through a semester of academic writing class.Findings revealed that writers consciously and unconsciously use components of cognitive and metacognitive strategies to paraphrase.The results of this study bear interesting implications in the teaching and learning of academic writing for ESL learners.
The role of external stakeholders in ensuring the quality of educational services of Polotsk state university

In modern conditions, the task of integrating science, education and innovation activities is of great importance. The need for highly qualified and proactive employees leads to the natural integration of the University and employers, who are the main external stakeholders and consumers of educational services. The article discusses the main forms of interaction between Polotsk State University and the main enterprises and organizations of the region. The main results of comprehensive monitoring of employers ‘satisfaction with the quality of training specialists in the higher educational institution are presented. The assessment of consumer satisfaction with educational services in the framework of this monitoring was carried out on the basis of a competence campaign. This is primarily due to the fact that the quality of education is determined precisely by the competencies formed by the graduate, which allow applying the acquired knowledge, skills and abilities in solving specific professional tasks. The monitoring demonstrated a high level of development of a number of competencies, in particular the competence “ability to use information and communication technologies”, which highlights the fact that the educational programs of compulsory and additional education implemented at Polotsk State University provide the knowledge and practical skills necessary for using the latest ICT in professional activities. Employers have also identified the scarce skills of graduates, namely “Ability to take decisive action in case of uncertainty”, “Ability to anticipate a problem” and “Ability to use time effectively”. Based on the self-assessment of graduates, during their studies at Polotsk State University they have to a large extent developed the competences required for successful work in today’s economy. The success of Polotsk State University graduates is clear evidence of the high quality of their training.
Sup_Ont

A domain-independent conceptual model that aims to be highly reusable across specific domain applications is provided by upper-level ontologies which usually describe abstract concepts. In this paper, the authors proposed Sup_Ont, a fundamental upper ontology. In this ontology, the structure of the universe shows the concept of reality that is defined to have an existence which is known as truth. The devised super ontology and hence the domain ontologies can be reused across applications because of the generalized representation scheme used that is an EHCPR. An extended hierarchical censored production rules (EHCPRs) system is a knowledge representation system for reasoning with real-life problems and a step towards a generalized representation system. An EHCPR is a unit of knowledge resulting in a knowledge base that shows modularity and hierarchy. Extended hierarchical censored production rules (EHCPRs) have been used to represent the knowledge in intelligent systems.