Reviewerly/exHarmony huggingface dataset:
A Statistical Frame Work to Identify Type and Level of Image Distortion

Growth and usage of digital images have been tremendous in recent years as they are the source for representing and communicating information. Various algorithms are developed to improve the performance of an image when it is subjected to distortions viz., acquisition, transmission, compression. It is highly undesirable to evaluate the quality score or quantitative index based on Human Visual System (HVS) in practical systems. A large number of restoration algorithms are available to enhance quality. So, a quality metric needs to be deployed to determine which algorithm automatically predicts the quality score of an image based on the availability of reference(original) image and without availability, and it should provide the best results. Blind Image Quality Assessment (BIQA) aims to identify the distortion level and type blindly without prior knowledge about the reference image. Contrast features convey most of the skeletal information about an image. Work is to extract the features based on the joint statistics of adaptive normalization of Gradient Magnitude (GM) and Laplacian of Gaussian (LOG). These normalized responses are quantized into different levels P and Q, respectively. For the quantized levels of GM and LOG, estimate the Marginal and conditional probabilities, i.e., "P" _"GM" and "P" _"log", "c" _"g" and "c" _"l". When an image is unnatural, the shapes of "P" _"GM" and "P" _"log", "c" _"g" and "c" _"l" will be different when compared to the natural image. To estimate the quality score, the correlation between probabilities using Spearman and Pearson methods is used and the classical tool SSIM. The Proposed model is evaluated on the CSIQ 2013 database for different levels of P and Q, the correlation coefficients will follow specific relationship to identify the distortion type, and its performance is highly competitive when compared with some of the NR-IQA algorithms.
An Integrated Non-linear Deep Learning Method for Sentiment Classification of Online Reviews

This paper presents an integrated non-linear method for sentiment classification of online reviews. Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs) utilizing Bidirectional LSTM (BiLSTM) are adopted in the research as they have demonstrated great information displaying abilities when testing large datasets from a wide scope of application regions. CNNs offer points of interest in choosing good level features and LSTM have demonstrated great capacities of learning sequential (non-linear) information score encoded via BiLSTM. Random Forest is then utilized to perform the learning process for large sample of 23K and small sample size of 11K tweets. The results show that the proposed integrated non-linear deep learning method has a better accuracy as compared to other existing methods.
A diachronic study of Korean corresponding to Japanese formal noun "-koto", amd "-no" from the late 19th century to the early 20th century

This study examines the diachronic aspect of the Japanese formal nouns "koto" and "no" and their corresponding expressions in Korean. The data analyzed in this study are Japanese and Korean text books from the end of the 19th century to the beginning of the 20th century. Results are as follows. Firstly, reparding the distribution of semantic characteristics, "koto" was used as a real noun, ‘to-iu-kotoha’ patterns and modality usages. In particular, the formal noun use represents frequency of 72%. In particular, "no" shows, 37.2% of use as a formal noun, 31% as a substitute for a real noun, and 24.6% for a modality usage. This shows that ‘no’ was used evenly in a variety of usages. Next, in terms of corresponding expressiions in Korean, Japanese "koto" corresponds to "il",‘ki","um" noun clause,’ and ‘kes". Firstly, ‘il’ shows frequency of 38% and is used as a corresponding word in almost all usages. Secondly, the ‘ki, um’ noun clause shows frequency of 19.3%, mostly in nominal sentences. Thirdly, ‘kes’ is used when it is interchangeable with the ‘ki, um" noun clause or ‘il’. On the other hand, Japanese "no" mostly corresponds to the Korean ‘kes’. It"s frequency is 60%. This means that the meaning of the Korean "kes" is more diverse than that of the Japanese "no". In addition, in 21.3% of cases, verbs have been frequently used as corresponding words to "no"modality usages and ‘to-iu-noha’ patterns since the 1910s. The ‘ki, um’noun clause rarely appears with frequency of 6%. 本稿は19世紀末から20世紀初期にかけての日本語における形式名詞「こと、の」がどのように韓国語に対訳されているのか、その対応の様子と史的変化を考察したものである。分析資料は年代別に選んだ日本語と韓国語の学習書そして当時の朝鮮總督府発刊、国定教科書である。 考察の結果、明らかになったこを簡単に要約すると、以下の通りである。 まず、‘こと、の’の意味別分布を察してみると、「こと」の場合、形式名詞の用法が72%、実質名詞の用法が11.5%、‘ということ(は/だ)’そしてモダリティの用法が各々4%の出現頻度を表した。つまり、「こと」は一つの用法で高い頻度律を表しているが、それに対し「の」は形式名詞の用法で37.2%、実質名詞の代用法で31%、モダリティの用法で24.6%の頻度律をみせており、「こと」よりは多様な意味で用いられたことがわかった。特に句または節を連体化させる用法または動作より状態を表す場合が多かった。 次に、日本語に対応する韓国語の対応様子を察してみると、ます、「こと」に対応する韓国語として「일」に対訳される場合が38%で実質名詞の用法、形式名詞、体言化用法など、ほとんどの意味で対応語として用いられていた。「기,음」名詞節に対訳される場合は19.3%の出現頻度をみせて文の体言化用法で大部用いられていた。「것」に対応される場合は「기,음」名詞節または「일」に交換可能な場合がおおく、「것」の意味の領域が広がっていることがわかった。反面、「の」の対応語としては「것」が用例凡ての60%を占めしており、日本語「の」に比べ韓国語「것」の意味領域が広いことがわかった。21.3%の頻度律を見せた動詞への対応は1910年代以後、モダリティの用法または‘というのは’文型の対応語としてよく用いられた。「기,음」名詞節は6%の出現頻度を見せるのみであまり用いられてはなった。
METHODOLOGICAL ASPECTS OF ORGANIZING COMMUNICATION BETWEEN TEACHER AND STUDENTS IN DISTANCE LEARNING

The article focuses on the methodological aspects of organizing communication between participants in the educational process in the context of using distance technologies. The main forms of organizing educational activities in distance learning are considered, depending on the level of technical equipment of the educational organization, the availability of personal devices, communication facilities and communication software. The modern services for organizing the interactive interaction of subjects of the educational process are described, their functionality and features in organizing communication are highlighted, the advantages and disadvantages are revealed. The article touches on the organization of the pedagogical process online from the point of view of the teacher's readiness to use modern digital technologies and depending of the level of development of students' communicative skills. The article presents a block diagram of an online lesson with the organization of group work, designed in accordance with the requirements of the Federal State Educational Standard of Basic General Education from the position of systematizing the actions of the teacher and students. At each stage of the online lesson (organizational, motivational-target, design, organizational-activity, evaluative, reflexive-semantic), the tasks of educational activities and methods for organizing communication are highlighted. The preparatory work of a teacher for conducting an online lesson in distance learning mode is disclosed in detail and the features of interaction are described that allow the teacher to recognize the psychological readiness of students for learning. Online etiquette is designed to help learners build productive relationships with classmates. The use of verbal and non-verbal communication methods in an online lesson expands the opportunities for students to interact with each other and with the teacher. The proposed methodological approaches to the use of digital means make it possible to activate the activities of students in the process of distance learning and to develop communication skills in practice.
Automatic Facial Expression Neutralisation Using Generative Adversarial Network

Face recognition systems has reached a level of maturity which makes it applicable in many applications. However, despite these recent results, the precision of face recognition systems still needs to be improved, especially with regard to facial expression and pose variation. Unlike existing methods that mostly operate on existed databases for recognition and identification, we present a new technique of facial expression neutralisation based on synthesizing realistic neutral images using Generative Adversarial Networks (GANs). In this article, we want to propose a new optimization technique called Ranger to be applied in the GANnotation architecture. Promising results are obtained on the RaFD dataset leading to improvements in the facial recognition system.
Robust Neural Computation From Error Correcting Codes : An Invited Paper

Neural networks (NNs) are a driving force behind the ongoing information revolution, with a broad spectrum of applications affecting most aspects of science and technology. The interest in robust neural computation under adversarial noise has increased lately, due applications in sensitive tasks ranging from healthcare to finance and autonomous vehicles. This has ignited an influx of research on the topic, which for the most part focuses on obtaining robustness by altering the training process. In contrast, this paper surveys and develops a recently proposed novel approach to obtain robustness after training, by adding redundancy to the network and to the data in the form of error correcting codes.Since neural networks are essentially a concatenation of linear classifiers, we focus on obtaining robustness for a single linear classifier by coding the input and the classifier, and then apply the results on the network. We address two different types of adversaries, a worst-case one and an average-case one. For a worst-case adversary, that can choose the input to be attacked, we focus on binarized classifiers and show that the problem is related to construction of certain linear codes with restricted weight patterns. As a result, it is shown that the parity code can obtain robustness against any 1-erasure in any binarized NN, and no decoding is required. For an average-case adversary, that is given a uniformly random input to be attacked, it is shown that the optimal weights for any classifier and any code are given by the Fourier coefficients of that classifier. We demonstrate the latter experimentally, exposing improved accuracy-robustness tradeoff in neural classification of several popular datasets under state-of-the-art attacks.
Designing an embodied conversational agent for a learning space

We present an embodied conversational assistant (CA) designed specifically for a learning space. We first relate the attributes of our learning space and the needs and opportunities that led to the creation of our CA. We then describe its main qualities, including the mutual assistance designed between the CA and its users. We present a visual narrative of the process of designing and implementing the CA prototype, including scenario building, form making, interaction design, engineering, and textile design. We describe the working prototype and initial trials with students. Finally, we discuss the rich potential of designing embodied CAs for specific learning spaces.
Human-based Consensus for Trust Installation in Ontologies

In this paper, we propose a novel protocol to represent the human factor on a blockchain environment. Our approach allows single or groups of humans to propose data in blocks which cannot be validated automatically but need human knowledge and collaboration to be validated. Only if human-based consensus on the correctness and trustworthiness of the data is reached, the new block is appended to the blockchain. This human approach significantly extends the possibilities of blockchain applications on data types apart from financial transaction data.
Thinking and Reasoning

The study of thinking and reasoning in humans can accurately be described as the study of the nature of intelligence. The work described falls, however, into a quite different tradition from the psychometric study of individual differences in intelligent performance that is usually referred to as the psychology of intelligence. A person has a problem whenever he or she wishes to achieve a goal and is unable to proceed immediately to do so. Reasoning is the process of drawing conclusions or inferences from given information. An important distinction is that between deductive and inductive inference. Deductive reasoning involves drawing conclusions that are logically valid, that is, they necessarily follow from the premises on which they are based. If human thinking is rational - and the success of the species suggests that it should be - then that rationality is highly constrained by capacity to process information.
A Novel MVTLR-HCFS Algorithm for High Dimensional Data Clustering

With the rapid development of hardware and software technology, modern industry has produced a large amount of high dimensional unlabeled data, such as pictures and videos. As clusters of these data sets may exist in some subspaces, traditional algorithms are no longer applicable. The related algorithms based on sparse subspace could find clusters in the subspace, which solves the problem of high data dimension. However, these clustering process usually based on only one single view feature, which may affects their performance particularly sensitive to one single view. Inspired by the integrated algorithm, a large number of multi-view methods began to emerge. These methods could improve the clustering performance to some extent by integrating the subspace expressions of multiple views, conversely, the complementary information of multiple views is not fully considered. In addition, the problem of non-uniform distribution in clusters also exists in high dimensional data sets. Therefore, in this paper, on the basis of multi-view subspace method, a clustering algorithm based on tensor low rank expression is proposed to solve the problem of high dimensional datasets.
Management of Finger Pulse using Oximeter – A Case Study in Scientific and Marketing Viability

Abstract The original purpose of this device was to create a lightweight, simple, and easy-to-use pulse oximeter for seniors and others who wanted to check their oxygen saturation at home. A portable pulse oximeter has become a must-have for all Covid-19 patients, particularly those who are quarantined at home, since the virus’s effects on the body require them to constantly track their oxygen saturation levels. The pulse oximeter’s design may be more advantageous to the average hospital patient
Online studying instructional measures at the International Relations Department of the National University of Ostroh Academy

Aim. The paper’s aim is to present some current specific online studying instructional measures at the International Relations Department of the National University of Ostroh Academy in Ukraine.Methods. The study is devoted to the observation of some theoretical and methodological aspects of the peculiarities of online studying measures at the International Relations Department. The theoretical and comparative methods of analysing the peculiarities of online studying have been taken into account. In order to summarise the currentonline studying experiences at the International Relations Department, some specific instructional measures for both students and teachers are presented, for example: how to handle the lack of interaction, master self-learning, promote self-regulation strategies and become more strategic thinkers.. The necessity of studying the above-mentionedaspects has been caused by their impact on students’ learning activity, especially in the times of COVID-19 pandemic.Results and conclusions. The focal point of this research is to provide an insight into the background of online studying peculiarities through the prism of Ukrainian teachers and students. The analysis is focused on online studying of foreign languages, as well as the possible online teaching methods of professional disciplines (History of International Relations; Foreign Policy of the Countries of Asia, Africa, and Latin America; International Conflicts; Current Problems of International Relations in the Middle East) at the International Relations Department. What is more, the authors outline how to help students deal with the lack of interaction, master self-learning, promote self-regulation strategies and become more strategic thinkers. The authors also highlight possible challenges for the faculty and suggest ways to overcome those difficulties. Moreover, some major prerequisites of students’ self-regulatory online learning are described. Contribution. The results of the study are instrumental in mastering the peculiarities of online studying through the prism of learning at the International Relations Department of the National University of Ostroh Academy (Ukraine). The findings of this study may be considered important and contribute to future research.
Stacked Deep Learning LSTM Model for Daily Solar Power Time Series Forecasting

Several researches have been concentrated on predicting issues in many appl cation areas. In machine learning field, recurrent neural networks have been successfully employed. Such models were suggested to tackle time- dependent learning problems. The goal of this paper is to afford a credible forecasting methodology for solar power, which will reduce the inaccuracies involved in forecasting the near future generation. In this proposed methodology, the stacked LSTM models with different configurations are applied to track the dynamic performance of the solar energy. The second objective of this analysis is to assess the effect of the stacked LSTM hidden layers by comparing the predicted outcomes of each model. We evaluate the proposed approach with three forecasting algorithms. The simulation results show that the value of the MAPE, using stacked LSTM model, starts from 10.5% (one hidden layer) and decreases to 0.442% when four hidden layers are applied. Thus, the use of Stacked-LSTM with multiple layers provides a further decrease in the fore- casting error relative to other approaches.
Sentiment Progression Based Searching and Indexing of Literary Textual Artefacts

Literary artefacts are generally indexed and searched based on titles, meta data and keywords over the years. This searching and indexing works well when user/reader already knows about that particular creative textual artefact or document. This indexing and search hardly takes into account interest and emotional makeup of readers and its mapping to books. In case of literary artefacts, progression of emotions across the key events could prove to be the key for indexing and searching. In this paper, we establish clusters among literary artefacts based on computational relationships among sentiment progressions using intelligent text analysis. We have created a database of 1076 English titles + 20 Marathi titles and also used database http://www.cs.cmu.edu/~dbamman/booksummaries.html with 16559 titles and their summaries. We have proposed Sentiment Progression based Search and Indexing (SPbSI) for locating and recommending books. This can be used to create personalized clusters of book titles of interest to readers. The analysis clearly suggests better searching and indexing when we are targeting book lovers looking for a particular type of books or creative artefacts.
Application and Implementation of Big Data Visualization Technology in Network Security System

Abstract In order to improve the safety and reliability of the information system of the State Grid, platforms that apply technologies such as big data and artificial intelligence to realize the safety monitoring and management of information systems have been widely used. At the same time, the application of visualization technology can fully display the current security status and situation of the network, as well as the intrusion and attack process of various attacks and threats. This paper proposes to apply big data visualization technology to realize asset visualization, behavior visualization, and attack visualization, and to show the risk status, intrusion view and process of the entire network.
Law and software analysis: A step closer to resolving investment disputes through artificial intelligence?

Considering the possibility of using artificial intelligence in resolving legal disputes is becoming increasingly popular. The authors examine whether soft ware analysis can be applied to resolve a specific issue in investment disputes - to determine the applicable law to the substance of the dispute and highlight the application of artificial intelligence in the area of law, especially in predicting the outcome of a dispute. The starting point is a sample of 50 arbitral awards and the results of previously conducted research. It has been confirmed that soft ware analysis can be useful in decision-making processes, but not to the extent that arbitrators could exclusively rely on it. On the other hand, the development of an algorithm that would predict applicable law for different legal issues required a much larger sample. We also believe that the existence of different legal and factual circumstances in each case, as well as the personality of the arbitrator and arbitral/judicial discretion are limitations of the application of artificial intelligence in this area.
BiPS: Hotness-aware Bi-tier Parameter Synchronization for Recommendation Models

While current deep learning frameworks are mainly optimized for dense-accessed models, they show low throughput and poor scalability in training sparse-accessed recommendation models. Our investigation shows that the poor performance is due to the parameter synchronization bottleneck. We therefore propose BiPS, a bi-tier parameter synchronization system that alleviates the parameter update and the sparse-accessed parameters communication bottleneck. BiPS includes a bi-tier parameter server that accelerates the traditional CPU-based parameter update process, a hotness-aware parameter placement and communication policy to balance the workloads between CPU and GPU and optimize the communication of sparse-accessed parameters. BiPS overlaps the worker computation with the synchronization stage to enable parameter updates in advance. We implement BiPS and incorporate it into mainstream DL frameworks including TensorFlow, MXNet, and PyTorch. The experimental results based on various deep learning frameworks show that BiPS greatly speeds up the training of recommenders (5 - 9$\times$) as the model scale increases, without degrading the accuracy.
The Interactive Query Method with Clustering and Differential Privacy Protection Model Under Big Data Environment

To enhance the privacy protection as well as to improve data availability, a differential privacy data protection approach is proposed. Through big data cross-platform query, differential privacy method is performed on the results of ICMD (insensitive clustering method for mixed data) based on the insensitive clustering algorithm. The combination of clustering and differential privacy achieves the distinction of query sensitivity from a single record to a group of records. In addition, to meet the requirements of maintaining differential privacy for mixed data, this paper uses different methods to calculate the distance and centroid of classification and numerical attributes. Finally, the experiment shows the validity of the method, which reduces the risk of information loss and leakage.