Reviewerly/exHarmony huggingface dataset:
Performance Evaluation in Arabic Sentiment Analysis during the Covid-19 Pandemic

This paper classifies sentiment analysis in Arabic language and mining sentiment in relation to the COVID-19 pandemic in the period (2019 - 2021). Three large data sets are collected from tweets, hotel and restaurant reviews for building the proposed sentiment analysis model. We compared eight machine learning algorithms ,Multinomial Naïve Bayes (MNB), Bernoulli Naïve Bayes (BNB), Decision Tree (DT) ,K-nearest neighbor classifier (KNN), Support Vector Machines (SVM), Linear Support Vector Classifier (LSVC), Random Forest Classifier (RFC) and Stochastic Gradient Descent Classifier (SGD) on three cases: n-gram unigram, bigram, and trigram for each algorithm. The performance evaluations are compared according to precision, recall, and F-measure. The polarity prediction results in sentiment analysis models was achieved by linear SVC using hotel datasets with bigram case , with the accuracy of 0.966, precision of 0.967, recall of 0.966 and F-measure of 0.966 . The rest algorithms give average performance on all datasets . It can be concluded that the machine learning algorithms need the right morphological features to enhance the classification accuracy when dealing with different words that play different roles in the sentence with the same letters.
Acute Leukemia Types and Sub Types Detection

Leukemia is the most prevalent type of blood cancer that affects youngsters. Blood cancers, also known as hematologic cancers, are a type of cancer that affects the blood. These cancers have an impact on the production and function of blood cells. Early detection of blood cancers is critical since it reduces mortality. In general, haematologists examine microscopic blood samples to diagnose and assess the severity of cancer. This process consumes more time and it does not have any standard accuracy. In the recent past, many methods are proposed to detect cancer using image processing. The main motive of this research work is to find the efficient method.
DIGITAL FORENSIC READINESS FRAMEWORK BASED ON HONEYPOT AND HONEYNET FOR BYOD

The utilization of the internet within organizations has surged over the past decade.Though, it has numerous benefits, the internet also comes with its own challenges such as intrusions and threats.Bring Your Own Device (BYOD) as a growing trend among organizations allow employees to connect their portable devices such as smart phones, tablets, laptops, to the organization's network to perform organizational duties.It has gained popularity over the years because of its flexibility and cost effectiveness.This adoption of BYOD has exposed organizations to security risks and demands proactive measures to mitigate such incidents.In this study, we propose a Digital Forensic Readiness (DFR) framework for BYOD using honeypot technology.The framework consists of the following components: BYOD devices, Management, People, Technology and DFR.It is designed to comply with ISO/IEC 27043, detect security incidents/threats and collect potential digital evidence using low-and high-level interaction honeypots.Besides, the framework proffers adequate security support to the organization through space isolation, device management, crypto operations, and policies database.This framework would ensure and improve information security as well as securely preserve digital evidence.Embedding DFR into BYOD will improve security and enable an organization to stay abreast when handling a security incident.
n-qubit operations on sphere and queueing scaling limits for programmable quantum computer

We study n-qubit operation rules on (n+1)-sphere with the target to help developing a (photon or other technique) based programmable quantum computer. In the meanwhile, we derive the scaling limits (called reflecting Gaussian random fields on a (n+1)-sphere) for n-qubit quantum computer based queueing systems under two different heavy traffic regimes. The queueing systems are with multiple classes of users and batch quantum random walks over the $(n+1)$-sphere as arrival inputs. In the first regime, the qubit number $n$ is fixed and the scaling is in terms of both time and space. Under this regime, performance modeling during deriving the scaling limit in terms of balancing the arrival and service rates under first-in first-out and work conserving service policy is conducted. In the second regime, besides the time and space scaling parameters, the qubit number $n$ itself is also considered as a varying scaling parameter with the additional aim to find a suitable number of qubits for the design of a quantum computer. This regime is in contrast to the well-known Halfin-Whitt regime.
Analysis of Methods and Techniques Used for Speaker Identification, Recognition, and Verification: A Study on Quarter-Century Research Outcomes

The theories and applications of speaker identification, recognition, and verification are among the well-established fields. Many publications and advances in the relevant products are still emerging. In this paper, research-related publications of the past 25 years (from 1996 to 2020) were studied and analysed. Our main focus was on speaker identification, speaker recognition, and speaker verification. The study was carried out using the Science Direct databases. Several references, such as review articles, research articles, encyclopaedia, book chapters, conference abstracts, and others, were categorized and investigated. Summary of these kinds of literature is presented in this paper, together with statistical analyses to represent the publications and their categories over the mentioned period. Important information, including the dataset used, the size of the data adopted, the implemented methods, and the accuracy of the obtained results in the analysed research, are extracted from the explored publications and tabulated. The results show that the sum of published research articles is outnumbering other categories of publications. The number of researches in speech and speaker identification, recognition, and verification shows an increasing trend. Based on the normalized comparative factors of research publications, we found that many of them reached a high level of accuracy in their findings; hence the significantly superior techniques were derived and discussed for future researches. This survey paper would be beneficial for all those who wish to enhance their researches in the area of voice identification, recognition, and verification.
The Next Wave in Cloud Systems Architecture

Twenty-five years ago, no one expected services to be seamlessly and consistently delivered across campuses and edge. It is now possible to envision machine learning-powered services capable of scaling to the reliability, performance, and security requirements of billions of users worldwide.
Markers of emotionality in Russian news coverage of the 75-th anniversary of WWII Victory

The article explores how emotionality is manifested in the news texts covering politically sensitive topics, using as the case study coverage of the 75th anniversary of WWII Victory in news programmes of the Russian TV Channel One, Russia TV and RT. Proceeding from the key theoretical assumption of medialinguistics defining media texts as an integral unity of verbal and media components, the author singles out and analyzes markers of emotionality at both - language and media levels, paying special attention to lexis and the way it is supplemented by illustrations and video footing. The analysis of the news flow is based on the information model, which allows to structure the process of news formation according to the following stages - selection of events for news coverage, interpretation of facts, shaping images, forming stereotypes and cultural-ideological context. The goal of the study was to identify markers of emotionality and analyze how emotionality affects the interpretation and the perception of facts, paying special attention to realization of the category of broadcasting style defined as the tone of voice, or tonality news media use when addressing their audience. Conceiving emotionality as both explicitly manifested and implicitly present quality, the study singles out its three types as represented in the analyzed media texts - 1) specific pretentious style, used by newsreaders and commentators to stress the dignity and solemnity of the event; 2) deliberate affectation disguised as emotionality on the part of news anchors while presenting topically sensitive news items; 3) emotionality as spontaneous display of sincere emotions observed in interviews, dispatches of correspondents and commentaries of the participants of the events. The results of the study could serve as a basis for further analysis of emotionality markers in different types of media discourse, including news, commentary and debate on politically sensitive issues in traditional media and social networks.
Graph-Based Hand-Object Meshes and Poses Reconstruction With Multi-Modal Input

Estimating the hand-object meshes and poses is a challenging computer vision problem with many practical applications. In this paper, we introduce a simple yet efficient hand-object reconstruction algorithm. To this end, we exploit the fact that both the poses and the meshes are graphs-based representations of the hand-object with different levels of details. This allows taking advantage of the powerful Graph Convolution networks (GCNs) to build a coarse-to-fine Graph-based hand-object reconstruction algorithm. Thus, we start by estimating a coarse graph that represents the 2D hand-object poses. Then, more details (e.g. third dimension and mesh vertices) are gradually added to the graph until it represents the dense 3D hand-object meshes. This paper also explores the problem of representing the RGBD input in different modalities (e.g. voxelized RGBD). Hence, we adopted a multi-modal representation of the input by combining 3D representation (i.e. voxelized RGBD) and 2D representation (i.e. RGB only). We include intensive experimental evaluations that measure the ability of our simple algorithm to achieve state-of-the-art accuracy on the most challenging datasets (i.e. HO-3D and FPHAB).
Design-Based of FBFRP-Technique in Writing Computerized English Essays in the Light of CTL Method

Students and test takers of iBT faced difficult problem in solving how to write a full English Essay of different genres in 30 minutes with minimum 250 words.The designed technique facilitates students and test-takers how to write such essay in 30 minutes, but the result meets the standard ideal iBT TOEFL test model.The technique is called Forward-Back Forward Revising and Publishing (FBFRP) Technique.The designed is under the learning principles of CTL: critical creative and self-regulative thought.This study is 1) to provide a technique that can help students and test takers to develop their computerized-based English Essay effectively in 30 minutes, 2) to describe how the procedure of FBFRP technique work in writing computerized -based English Essay, 3) to shows readers how FBFRP technique was developed and validated.This is a research and development adopted by Borg and Gale (2003), Formative Evaluation taken by Tesmer (2013) self-evaluation, expert review, one-to-one, small group, and field test.The outcomes are 1) a technique that can help students and test takers called "FBFRP Technique, 2) a description or underlying principles of how FBFRP technique work in writing computerized-based English Essay, 3) the process of how FBFRP technique was validated.The product is applicable across disciplines in ELT or ESL context as an instructional tool, particularly for Academic writing course, and real test of internet-based TOEFL test takers.
Using Data Compression to Build a Method for Statistically Verified Attribution of Literary Texts

We consider the problems of the authorship of literary texts in the framework of the quantitative study of literature. This article proposes a methodology for authorship attribution of literary texts based on the use of data compressors. Unlike other methods, the suggested one gives a possibility to make statistically verified results. This method is used to solve two problems of attribution in Russian literature.
FACTORS AFFECTING MOTIVATION OF ENGLISH-MAJORED STUDENTS TOWARDS LEARNING ENGLISH AT A UNIVERSITY IN THE MEKONG DELTA, VIETNAM

It could be broadly accepted that motivation plays its fundamental role as one of the most important elements in foreign language learning. The present research investigated factors affecting motivation in learning English of freshmen at Tay Do University. The participants were 84 freshmen studying in Faculty of Linguistics and Literature. In the study process, these participants were given questionnaires which consisted of several statements related to the mentioned factors and the interview to get information. The collected data from the two instruments mentioned above were all analyzed afterward. The current study showed that there were elements that motivate freshmen towards English learning. Parental, environmental, teacher’s and intrinsic (personal) factors were examined. It is hoped that this research can be helpful for not only students but also teachers in learning and teaching English. &#x0D; &#x0D; &lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0894/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Language and Culture in Intercultural Communication

Language is probably the best way of conveying a culture, both oral and written, in human societies. Language, written or oral, plays an essential role in developing a form of social knowledge, which is common sense thought, socially developed and shared by members of the same social or cultural characteristics. This common knowledge is sometimes called a social representation. Through language, man assimilates culture, perpetuates it or transforms it. Nevertheless, like every language, each culture implements a specific apparatus of symbols with which each society identifies. Culture is defined as the body of knowledge and behavior that characterizes a human society or a human group within a society. Different languages are necessary in order to preserve things such as culture; heritage and getting people from different cultures to dialogue may require intercultural mediation. These intercultural communications can be regarded as translation. Therefore, the relationship between language and culture is rather complex. Our article will discuss the relation between language and culture in intercultural communication which is translation in our case. Many ideas will be presented with examples to prove that language and culture are two faces to one coin. This research shows that language and culture are not competitors and not interdependent but complete each other.
Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings

Semantic sentence embeddings are usually supervisedly built minimizing distances between pairs of embeddings of sentences labelled as semantically similar by annotators.Since big labelled datasets are rare, in particular for non-English languages, and expensive, recent studies focus on unsupervised approaches that require not-paired input sentences.We instead propose a language-independent approach to build large datasets of pairs of informal texts weakly similar, without manual human effort, exploiting Twitter's intrinsic powerful signals of relatedness: replies and quotes of tweets.We use the collected pairs to train a Transformer model with triplet-like structures, and we test the generated embeddings on Twitter NLP similarity tasks (PIT and TURL) and STSb.We also introduce four new sentence ranking evaluation benchmarks of informal texts, carefully extracted from the initial collections of tweets, proving not only that our best model learns classical Semantic Textual Similarity, but also excels on tasks where pairs of sentences are not exact paraphrases.Ablation studies reveal how increasing the corpus size influences positively the results, even at 2M samples, suggesting that bigger collections of Tweets still do not contain redundant information about semantic similarities. 1
Spatial Role Labeling based on Improved Pre-trained Word Embeddings and Transfer Learning

In several real-world applications, extracting spatial semantics from text is critical. Spatial Role Labeling (SpRL) introduces a language-independent annotation scheme used in these applications, particularly for reasoning purposes. This paper proposes, first of all, a transfer learning method with a word embeddings-based approach for SpRL. Then, we enhance the word vectors with POS tags and CNN-based character-level representations. Finally, we propose a Residual BiLSTM CRF deep learning model to identify the spatial roles. The experimental results on two datasets: SemEval-2012 and SemEval-2013 Task 3, show that the proposed model outperforms other machine learning approaches.
RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification

The relationships that exist between entities can be a reliable indicator for classifying sensitive information, such as commercially sensitive information.For example, the relation person-IsDirectorOf-company can indicate whether an individual's salary should be considered as sensitive personal information.Representations of such relations are often learned using a knowledge graph to produce embeddings for relation types, generalised across different entity-pairs.However, a relation type may or may not correspond to a sensitivity depending on the entities that participate to the relation.Therefore, generalised relation embeddings are typically insufficient for classifying sensitive information.In this work, we propose a novel method for representing entities and relations within a single embedding to better capture the relationship between the entities.Moreover, we show that our proposed entity-relation-entity embedding approach can significantly improve (McNemar's test, p < 0.05) the effectiveness of sensitivity classification, compared to classification approaches that leverage relation embedding approaches from the literature (0.426 F 1 vs 0.413 F 1 ).
Credit Card Fraud Detection System Using Machine Learning

The security of any system is a key factor toward its acceptability by the general public. We propose an intuitive approach to fraud detection in financial institutions using machine learning by designing a Hybrid Credit Card Fraud Detection (HCCFD) system which uses the technique of anomaly detection by applying genetic algorithm and multivariate normal distribution to identify fraudulent transactions on credit cards. An imbalance dataset of credit card transactions was used to the HCCFD and a target variable which indicates whether a transaction is deceitful or otherwise. Using F-score as performance metrics, the model was tested and it gave a prediction accuracy of 93.5%, as against artificial neural network, decision tree and support vector machine, which scored 84.2%, 80.0% and 68.5% respectively, when trained on the same data set. The results obtained showed a significant improvement as compared with the other widely used algorithms.
Supervised Learning Enhanced Quantum Circuit Transformation

A quantum circuit transformation (QCT) is required when executing a quantum program in a real quantum processing unit (QPU). Through inserting auxiliary SWAP gates, a QCT algorithm transforms a quantum circuit to one that satisfies the connectivity constraint imposed by the QPU. Due to the non-negligible gate error and the limited qubit coherence time of the QPU, QCT algorithms which minimize gate number or circuit depth or maximize the fidelity of output circuits are in urgent need. Unfortunately, finding optimized transformations often involves exhaustive searches, which are extremely time-consuming and not practical for most circuits. In this paper, we propose a framework that uses a policy artificial neural network (ANN) trained by supervised learning on shallow circuits to help existing QCT algorithms select the most promising SWAP gate. ANNs can be trained off-line in a distributed way. The trained ANN can be easily incorporated into QCT algorithms without bringing too much overhead in time complexity. Exemplary embeddings of the trained ANNs into target QCT algorithms demonstrate that the transformation performance can be consistently improved on QPUs with various connectivity structures and random or realistic quantum circuits.
TEACHER PRESENCE AS MEDIATOR IN ONLINE LEARNING: THE CASE FOR FRENCH AS A FOREIGN LANGUAGE

Fortunately, technology and long entered the education sectors when the world is faced with the pandemic. Whether teachers and learners are ready or not, online classes are here to stay. Online learning is a unique way of learning that allow flexibility at many levels. Language educators once felt that the learning of language can only be done face-to-face because of the needs of some language skills. This study is done to explore the portrayal of presence during online learning of the French as a foreign language. This quantitative study is done to investigate how learners cope learning French online. 170 respondents were purposely chosen from learners who took French as a foreign language. They attended the course in a few public and private universities. The instrument used is a questionnaire. Findings revealed interesting influence of the teacher as mediator in online learning. &#x0D; &#x0D; &lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0893/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Comparative Analysis of Automatic Sign Language Generation Systems

More than 466 million people suffer from hearing disabilities comprising of 34 million children and 432 million adults worldwide.These people face so many problems at public places due to their hearing disability for the communication of their messages.The development of automatic sign language generation systems, which are very rarely available, is the need of the hour to solve their communication problem.Some researchers have tried to develop such systems for various public places using a smaller corpus rendering their works limited to a specific domain.In this paper, we tried to perform a study of available research studies from 2001 to 2020 for sign language generation systems for various public domains using different machine translation approaches.Seventy-two research papers on sign language generation were retrieved from our search.Afterward, these papers were shortlisted to 44 based on their titles, then 30 research papers were removed based on abstracts and conclusions, and finally, 14 research papers were selected based on full text.These 14 research papers were comparatively examined based on six comparison parameters.It is evident from our study that the lack of proper grammar rules of sign language and the non-availability of large bilingual corpora are the main hurdles in developing sign language generation systems for public places.A hybrid approach based announcement system prototype is developed for deaf people.The proposed system prototype produced 82% accuracy on translation of announcements into sign language.