Reviewerly/exHarmony huggingface dataset:
Face Recognition from Correspondence Maps

This chapter presents several algorithms that have proved useful in the task of recognizing human faces from gray-value images without additional information. Their common feature is the extraction of correspondence maps between an image to be analyzed and several stored views of known objects, which are called models. The processing of a retinal gray-level image in simple cells of the primary visual cortex can be modeled by a wavelet transform based on complex-valued Gabor functions. The major drawback of correspondence-based recognition systems is that the computationally expensive procedure of creating a correspondence map must be carried out for each of the stored models. Humans do very poorly in recognizing faces from photographic negatives. One recognition system has no problem with this, because Gabor amplitudes are identical for positive and negative images. There is now increasing evidence that the human brain employs different modules for the recognition of faces and general objects.
Editors-in-the-loop News Article Summarization Framework with Sentence Selection and Compression

This paper proposes a human-in-the-loop framework to summarize news articles by sentence selection and compression. The system enumerates summary candidates by selecting N sentences representing the article based on a quantitative metric and then compressing each sentence by syntactic analysis. Experiments showed that the proposed system was able to extract the same topics as the human editor's work at the rate of 26 %. Even though the rate was not high enough, the proposed framework has the advantage that it is easy to incorporate the editor's intentions in the sentence selection and compression by giving weights. This approach not only has the potential to reduce the burden on editors, but can also contribute to giving them a new perspective in creating summaries.
STUDY OF PERIODIC PROCESSES TAKING INTO ACCOUNT THE UNCERTAINTY STATE FACTOR OF THE ANALYZED PARAMETER

The paper describes the approach and methodology for the numerical assessment of the quality of periodic processes. With regard to their particular case the processes of selfoscillations, on the basis of the proposed approach, the problem of optimization of the control system is formulated and possible ways of solving it are shown. The practical implementation of the approach is possible when synthesizing a control system, as well as in the variant of automatic adaptive control during its operation. The proposed solutions are based on the use of methods of the theory of entropy potentials. The implementation of the proposed solutions will improve the quality of monitoring of periodic processes and the efficiency of regulation systems in selfoscillation modes.
The Disintegration of Intersubjectivity: Madness as the Failure of Love (according to L. Binswanger)

The article considers the problem of love and madness as opposite modes of human existence. Using the example of human interaction with artificial intelligence it has been analyzed from the point of view of the phenomenology of love and Ludwig Binswanger's Dasein analysis the reciprocal influence of madness and disintegration of intersubjectivity in the context of an anthropocrisis. It has been demonstrated that the full realization of a person is impossible in isolation from others, it is possible only in relationships with others, the highest form of which is love. A human being is a born possibility of a "generalized We", his task is to realize this possibility in a direct meeting of "We". Only through "We" can Dasein achieve its fullest realization. Love is defined as the essential structure of the human, the "fundamental form" that structures the human being and its failure causes madness. It has been considered the hypothesis of L. Binswanger about the failure of love in a psychopathological state, in connection with the perception of others as objects and the consequent impossibility of creating "We" as being-in-the-world-together, and about the form of treatment, which consists in opening the possibility of "We". A hypothesis has been put forward that the forced external limitation of the realization of intentionality to "We" can also affect the narrowing of the world-project and lead to madness.
Machine Learning Techniques for Speech Recognition using the Magnititudes

Speech is the most proficient method of correspondence between people groups. Discourse acknowledgment is an interdisciplinary subfield of computational phonetics that creates approaches and advances that empowers the acknowledgment and interpretation of communicated in language into content by PCs. It is otherwise called programmed discourse acknowledgment (ASR), PC discourse acknowledgment or discourse to content (STT). It consolidates information and research in the etymology, software engineering, and electrical building fields. This, being the best methodology of correspondence, could likewise be a helpful interface to speak with machines. Machine learning consists of supervised and unsupervised learning among which supervised learning is used for the speech recognition objectives. Supervised learning is that the data processing task of inferring a perform from labeled coaching information. Speech recognition is the current trend that has gained focus over the decades. Most automation technologies use speech and speech recognition for various perspectives. This paper offers a diagram of major innovative point of view and valuation for the fundamental advancement of speech recognition and offers review method created in each phase of discourse acknowledgment utilizing supervised learning. The project will use ANN to recognize speeches using magnitudes with large datasets.
Peer review declaration

All conference organisers/editors are required to declare details about their peer review. Therefore, please provide the following information: • Type of peer review: Double-blind • Conference submission management system: AI Scholar System • Number of submissions received: 91 • Number of submissions sent for review: 86 • Number of submissions accepted: 45 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 49.5% • Average number of reviews per paper: 2 • Total number of reviewers involved: 30 • Any additional info on review process: • Step 1. Each of selected paper will be reviewed by two/three professional experts in the related subject area. • Step 2. Review Reports received from the experts will be judged by one of the editors either Review Reports are logical or not? • Step 3. If not logical, then editor can assign new reviewer or can also judge at his/her own. • Step 4. If logical, then Review Reports will be sent to authors to modify the manuscript accordingly. • Step 5. Authors will be required to revise their papers according to the points raised. • Step 6. Revised version will then be evaluated by the editor for the incorporation of the points raised by the reviewers. • Step 7. Then the editor will send the revised manuscript to the reviewers again for re-evaluation. • Step 8. If the reviewers approve the revise version of the manuscript, then will be accepted for publication. • Contact person for queries: Xuexia Ye publication@keoaeic.org AEIC Academic Exchange Information Centre
Discovering Knowledge in Corporate Databases

Data mining (DM) is a particular activity of Knowledge discovery and databases (KDD) that applies a specific algorithm to extract patterns. DM combines techniques from machine learning, pattern recognition, statistics, databases, and visualization to extract concepts, concept interrelations, and interesting patterns automatically from large corporate databases. The availability of sufficient, quality data is another important criterion in choosing an appropriate domain. The DM operations can be classified as classification, regression, segmentation, link analysis, and deviation detection. The goal of data mining is to unearth information and knowledge useful for making decisions by building a real-world model from the data collected from various sources such as customer histories, transactions, and credit bureau information. The goal of classification is to develop a model that maps a data item into one of several predefined classes. Neural net methods can be used to develop classification, regression, association, and segmentation models.
Immutability and Mutation

Most software developers, for most of the history of programming, have been entirely comfortable with mutation. Code that uses mutation declares a variable (perhaps with some initial value, perhaps uninitialized) and then updates its value one or more times until some final result is achieved. It’s so natural of an approach (at least for those of us who came to programming via languages such as BASIC, JavaScript, and C) that we didn’t really feel the need to name it. Mutable programming was programming.
Feasibility Study Report on Cloud Technology of High-Performance Self-Service Database

Nowadays, cloud applications are extensively used with the comprehensive promotion and online application of integrated cloud services, and a large number of business systems migrate applications to the cloud. However, the progress of the database application in the cloud environment is slow. The research on “key cloud technologies of self-service database” proposed is aimed at studying the cloud service of database resources. Specifically, this research intends to design a database resource management software architecture that can not only guarantee the efficient and smooth operation of the business system, but also be applicable to the cloud environment. In addition, this research intends to form an architectural scheme for the database migration to cloud services and focus on the development of the backup service component of the cloud database and the highly available self-service database cloud component.
Question answering algorithm based on deep learning

With the rapid development of Internet technology, people are no longer limited to simply obtaining information from the Internet and then manually screening, but prefer to use high-tech to retrieve information quickly and accurately. Therefore, this paper designs an intelligent question answering system to solve and apply the above problems. At present, intelligent question answering system has become a hot direction in the field of natural language processing. The purpose of this paper is to build a deep learning-based question answering algorithm based on deep learning technology and lay a foundation for its future application.
Formulation of a Regulatory Regime for Cryptocurrencies in India

During the pandemic year 2020, cryptocurrencies have surfaced as a hedge against the financial uncertainty raised by COVID-19. Virtual currencies have emerged as a focus area for the investors to re-engage their interest and overcome the financial instability. Income tax department has been keeping a close vigil on these Virtual currencies. We have also seen a boost in the valuation of Bitcoin due to Global Liquidity injected by Central Banks. Cryptocurrencies still do not have any formulated appropriate legislation to provide clear vision for cryptocurrencies’ future in India. The present paper is an attempt to highlight the potential of cryptocurrencies and the need for the formulation of appropriate legislation so that investors can perform their transactions safely.
Controlled Experiment for Assessing the Contribution of Ontology Based Software Redocumentation Approach to Support Program Understanding

Redocumentation is an approach that is used to recover knowledge from raw software artifacts by using alternative presentations. Several legacy systems have been developed based on event-driven programming which require redocumentation. However, these existing repository and query techniques emphasize only on lexical and syntactical based queries which come with limitations in providing the semantic relationship for program understanding. We are using ontology based approach that uses both ontology reasoning and querying techniques to generate software documentation from the knowledge repository. We present a controlled experiment for the empirical evaluation on the proposed ontology based approach and implemented in a tool called Ontology Based Software Redocumentation (OBSR). In this experiment, two existing tools namely Universal Report (UR) and Microsoft Visual Studio specifically for Visual Basic (VB) programming environment have been selected to be compared with the OBSR tool. The goal is to provide experimental evidence of the viability of our approach in the context of program understanding using HTML based semantic software documentation. The experiment shows that the software maintainers are able to understand and provide significant improvement in program understanding to accomplish the maintenance task easily. We describe in detail the experiment performed, discuss its results and reflect the lesson learned from the experiment.
The Design and Application in Secure Communication Based on Quantum Technology

Quantum secure communication based on Quantum Key Distribution (QKD) has entered the practical stage, and will become one of the most reliable schemes to improve the network information security protection capability.Quantum key distribution (QKD), underpinned by the uncertainty, indivisibility and non-duplication nature of quantum, adopts coding techniques to ensure the security of keys with a series of protocols.
Fraud Identification Model of Profit and Loss Adjustment Financial Report Based on Lib SVM Algorithm

In recent years, the pace of world economic integration is speeding up, and the relations between countries are increasingly close. However, the fraud cases in various countries are gradually exposed to the public. This paper discusses the definition of financial fraud, introduces the relevant domestic and foreign background of financial fraud, and the methods of predicting financial fraud. Taking the annual financial report which was punished by the CSRC for adjusting the profit and loss in the early twentieth century as the research object, all samples are divided into training samples by using the Lib SVM classification algorithm Practice samples and test samples. Using the training samples, this paper constructs the identification model of financial reporting fraud in the profit and loss adjustment of Listed Companies in China, and tests the prediction accuracy of the model through test samples. The results show that the prediction accuracy is 88.89%, and the overall accuracy of the model is 85.55%.
Linguistic Concordances. Case study – father in 2 languages (Romanian – Italian) and 2 dialects (Dacoromanian – Calabrian)

The aim of the present research is to compare the same word in two different linguistical levels and to share through this example how the language and the dialect are different.Our lexical comparison is made in base of the Latin roots and takes into account the semantic branch, as well as the onomasiological and the etymological ones.Father, individualized in the kinship lexical field, represents our research champion for the analysis.By using linguistic atlases and etymological dictionaries, we traced the evolution/stagnation/involution of this specific lemma, across the time, having, as geographical observation field, Romania and Italy.Inside those two Romance territories, we chose to emphasize the border between language and dialect through Dacoromanian and Calabrian, interpreted in a triple perspective (regional variant, subdialect, language).We meant to work with modern means of investigation, as berlinian schema, semantic matrix or lexical tables, in order to make our research both interesting and connected to the present reality.
VIRTUAL REALITY SIMULATIONSERVICE ON SDN SOFTWARE UNDER A LIMITED RESOURCE 5G MOBILE TERMINAL

This article develops a hierarchical structure of data placement in the augmented reality service system and examines the advantages of using this structure. The article develops and investigates a set of models and methods for virtual reality, including models of VR services, the interaction of the main elements in the provision of VR services and a model of a mobile user of a VR service.
Fighting Media Hyper-partisanship with Modern Language Representation Models

The Internet has shaped how people gather knowledge, learn from their surroundings, form their individual opinions, and deal with socially relevant topics. In a time of polarization, when the news that we see is twisted according to one’s view, extremely one-sided views aim to conquer the internet. In such a case, it is of utmost importance to devise an algorithm that can outperform and overcome such biases. We propose to build a convolutional neural network by utilizing sentence embeddings from language representation models like BERT, RoBERTa, DistilBERT, and XLNet, which would be able to classify whether an article displays a hyper-partisan narrative or not. We analyze the writing style of the author rather than depending on fact verification to prove an article’s underlying bias. Our model gives an accuracy up to 88% with BERTweet-base. Such a model can actively prevent the spread of political propaganda through news outlets and can lead to the public consuming unbiased and accurate information.
ROBUST AND SECURE S-BOX DESIGN WITH GATED HYBRID ENERGY RECOVERY LOGIC (GHERL) FOR IOT APPLICATIONS

The smart Internet of Things (IoT) network relies heavily on data transmission over wireless channels. Hence, it should be designed to be robust against the attacks from hackers and antagonists. The confidentiality in IoT devices is directly proportional to the complexity and power consumption. To mitigate these issues, this paper proposes a secure Substitution Box (S-Box) design that is exploited in the IoT for cyber security applications. The S-Box is based on Gated Hybrid Energy Recovery Logic (GHERL) that is an amalgamation of two different techniques as adiabatic logic and power gating. Adiabatic logic is preferred to attain high energy efficiency in practical applications such as portable and handheld devices. Power gating technique is preferred to reduce the leakage power and energy consumption. The proposed GHERL XOR gate and S-Box are implemented with 125nm technology in Tanner EDA tool. The consequences of the experiments exhibits that the novel S-Box design with GHERL XOR decreases the power consumption by 1.76%, 35.26%, 36.81%, 41.01% and reduces the leakage power by 58.54%, 20.27%, 27.38%, 13.63% when compared with the existing techniques such as S-Box with sleep transistor, dual sleep transistor, dual-stack and sleepy keeper approach. Keywords: Adiabatic logic, Power Gating, Internet of Things, S-Box
Nonparametric Path Analysis on Consumer Satisfaction and Consumer Engagement in PT Pertamina

The purpose of this research is to apply nonparametric path analysis on consumer satisfaction and consumer engagement of PT Pertamina. The results of the analysis are expected to be able to provide an estimate of the function in determining consumer satisfaction and consumer engagement of PT Pertamina. This study uses primary data involving five variables, namely Digitalization (X1), Consumer Needs (X2), Consumer Service (X3), Consumer Satisfaction (Y1), Consumer Engagement (Y3). Variable measurement technique is done by calculating the average score on the items. Sampling in this study used a purposive sampling technique with the respondent's criteria being company leaders. The result of this research is the estimation of nonparametric Path function using MARS approach on various interactions. The best estimate of the function of obedient behavior in paying credit is when it involves 3 variables, namely the digitization variable (X1), Consumer Needs (X2), Consumer Service (X3) with a value ofgeneralized cross-validation The smallest (GCV) obtained is 0.2833. The originality of this research is that the variables used are the results of DNA analysis (Discourse Network Analysis), where the analysis extracts information from cyberspace which is then formed as the main issue and becomes a variable. In addition, there is no previous research that examines nonparametric path analysis on PT Pertamina's consumer satisfaction and engagement.The purpose of this research is to apply nonparametric path analysis on consumer satisfaction and consumer engagement of PT Pertamina. The results of the analysis are expected to be able to provide an estimate of the function in determining consumer satisfaction and consumer engagement of PT Pertamina. This study uses primary data involving five variables, namely Digitalization (X1), Consumer Needs (X2), Consumer Service (X3), Consumer Satisfaction (Y1), Consumer Engagement (Y3). Variable measurement technique is done by calculating the average score on the items. Sampling in this study used a purposive sampling technique with the respondent's criteria being company leaders. The result of this research is the estimation of nonparametric Path function using MARS approach on various interactions. The best estimate of the function of obedient behavior in paying credit is when it involves 3 variables, namely the digitization variable (X1), Consumer Needs (X2), Consumer Service (X3) with a value ofgeneralized cross-validation The smallest (GCV) obtained is 0.2833. The originality of this research is that the variables used are the results of DNA analysis (Discourse Network Analysis), where the analysis extracts information from cyberspace which is then formed as the main issue and becomes a variable. In addition, there is no previous research that examines nonparametric path analysis on PT Pertamina's consumer satisfaction and engagement.
Designing An Information Framework For Semantic Search

New generation information retrieval procedures provide complex tools to remodel the design of search engines.Even though semantic analysis is gradually adopted by corporations, complex behavior of knowledge behind the information entails subsequent data learning models.Text models are currently in use through lexical features.Search engines with lexical methods lack contextual and semantic information.This barrier has been overcome with the development of deep learning methods.More accurate results can be retrieved by obtaining contextual information of different types of content such as text, image, video with neural models.In this study, a broad perspective of search engines was considered through lexical and semantic features.Semantic search methods were experimented then compared with lexical methods in data sets consisting of scientific documents.Since scientific documents are relatively well-formatted datasets and do not contain irrelevant content, the focus was on comparing semantic search methods and neural models throughout the study, without dealing with out-of-context data and semantic conflicts.As a result, semantic search methods performed better than lexical search.We conclude that current search-retrieval tasks require new perspectives in semantics where multimodal information is handled with deep learning strategies.
Radioactive heat production characterization of Ar-Rassafeh Badyieh area (area-2), Syria by using aerial gamma ray spectrometric and fractal modeling techniques

Aerial gamma-ray spectrometric technique is used herein to evaluate the radioactive heat production (HP) of Ar-Rassafeh Badyieh Area (Area-2), Syria. The nine already established lithological scored units of Area-2 have been separately characterized for the heat production HP parameter. The Concentration-number (C-N) model and the log-log plots associated with fractal technique are proposed and applied as a new approach to map the measured equivalent uranium (eU), the equivalent thorium (eTh), and potassium (K%) and the computed heat production (HP) of Area-2. The HP of Area-2 varies between a minimum of 0.06 and a maximum of 4.28 ?w/m3 with an average of 0.548 ?w/m3 and a standard deviation of 0.27?w/m3. The highest observed HP values are related to the phosphatic environments represented by two lithological scored units A and B.