Reviewerly/exHarmony huggingface dataset:
Topic Modeling and Sentiment Analysis of Twitter Discussions on COVID-19 from Spatial and Temporal Perspectives

The study reported in this paper aimed to evaluate the topics and opinions of COVID-19 discussion found on Twitter It performed topic modeling and sentiment analysis of tweets posted during the COVID-19 outbreak, and compared these results over space and time In addition, by covering a more recent and a longer period of the pandemic timeline, several patterns not previously reported in the literature were revealed Author-pooled Latent Dirichlet Allocation (LDA) was used to generate twenty topics that discuss different aspects related to the pandemic Time-series analysis of the distribution of tweets over topics was performed to explore how the discussion on each topic changed over time, and the potential reasons behind the change In addition, spatial analysis of topics was performed by comparing the percentage of tweets in each topic among top tweeting countries Afterward, sentiment analysis of tweets was performed at both temporal and spatial levels Our intention was to analyze how the sentiment differs between countries and in response to certain events The performance of the topic model was assessed by being compared with other alternative topic modeling techniques The topic coherence was measured for the different techniques while changing the number of topics Results showed that the pooling by author before performing LDA significantly improved the produced topic models © Iyad AlAgha, 2021
On the Two-Sample Randomisation Test for IR Evaluation

While previous work in comparing statistical significance tests for IR system evaluation have focused on paired data tests (e.g., for evaluating two systems using a common test collection), two-sample tests must be used when the reproducibility of IR experiments across different test collections must be examined. Using real runs and a test collection from the NTCIR-15 WWW-3 Task, the present study compares the properties of three two-sample significance tests for comparing two systems: Student's t-test (i.e., the classical parametric test), the Wilcoxon rank sum test (i.e., the classical nonparametric test), and the randomisation test (i.e., a population-free method that utilises modern computational power). In terms of the false positive rate (i.e., the chance of detecting a statistical significance even though the two samples of evaluation measure scores come from the same system), the three tests behave similarly, although the Wilcoxon rank sum test appears to be slightly more robust than the other two for very small topic set sizes (e.g., 10 topics each) with a large significance level (e.g., α=0.10). On the other hand, the t-test and the Wilcoxon rank sum test are very similar to each other from the following two viewpoints: "How often do they both detect a nonexistent difference?" and "How often do they both overlook a true difference?" Compared to the two classical significance tests, the randomisation test behaves markedly differently in terms of the above two viewpoints. Hence, we suggest that researchers should at least be aware of the above properties of the three two-sample tests when choosing from them.
Design and Application of Intelligent Classroom Teaching Mode under the Blended Curriculum Reform

With the progress of network technology, under the magical fusion of network technology and education, intelligent classroom, a new education mode and education industry, has become the focus of current education research.Under the monitoring of big data, educators can create a comprehensive monitoring network for the curriculum, which makes a three-dimensional learners' learning effects, and also greatly extends learners' learning space and time.Smart classroom is also the inevitable product of the development of science and technology and education.In this paper, the author analyzes the concept and characteristics of smart classroom in the new era, and makes a detailed distinction between smart classroom, traditional classroom and flipped classroom, so as to clarify the concept of smart classroom.Then, according to the theory of blended learning and the theory of the elements of teaching mode, a new type of Higher Vocational English smart classroom is designed.Finally, relying on the questionnaire survey of students' attitude towards the teaching mode reform, teachers could get the teaching feedback of this experiment.
N-gram and Neural Models for Uralic Language Identification: NRC at VarDial 2021.

We describe the systems developed by the National Research Council Canada for the Uralic language identification shared task at the 2021 VarDial evaluation campaign. We evaluated two different approaches to this task: a probabilistic classifier exploiting only character 5-grams as features, and a character-based neural network pre-trained through self-supervision, then fine-tuned on the language identification task. The former method turned out to perform better, which casts doubt on the usefulness of deep learning methods for language identification, where they have yet to convincingly and consistently outperform simpler and less costly classification algorithms exploiting n-gram features.
Empathetic BERT2BERT Conversational Model: Learning Arabic Language Generation with Little Data

Enabling empathetic behavior in Arabic dialogue agents is an important aspect of building human-like conversational models. While Arabic Natural Language Processing has seen significant advances in Natural Language Understanding (NLU) with language models such as AraBERT, Natural Language Generation (NLG) remains a challenge. The shortcomings of NLG encoder-decoder models are primarily due to the lack of Arabic datasets suitable to train NLG models such as conversational agents. To overcome this issue, we propose a transformer-based encoder-decoder initialized with AraBERT parameters. By initializing the weights of the encoder and decoder with AraBERT pre-trained weights, our model was able to leverage knowledge transfer and boost performance in response generation. To enable empathy in our conversational model, we train it using the ArabicEmpatheticDialogues dataset and achieve high performance in empathetic response generation. Specifically, our model achieved a low perplexity value of 17.0 and an increase in 5 BLEU points compared to the previous state-of-the-art model. Also, our proposed model was rated highly by 85 human evaluators, validating its high capability in exhibiting empathy while generating relevant and fluent responses in open-domain settings.
Design of communication power centralized remote monitoring system based on big data technology

The communication power supply is an important basis for each hardware to realize the basic functions. Therefore, a centralized remote monitoring system is proposed to master the working state of the communication power supply in real time. However, due to the poor communication effect of system software under the traditional design, the ability of centralized remote monitoring system to track abnormal data is poor, so the centralized remote monitoring system of communication power supply is designed based on big data technology. In terms of hardware, the watchdog circuit is set, and the battery access interface circuit, communication interface circuit and voltage conversion circuit are designed. In terms of software, intelligent monitoring program is designed; remote centralized monitoring target is tracked based on big data technology; monitoring unit software is optimized. Experimental results: compared with the traditional design of remote monitoring system, in 20 times of abnormal node monitoring of communication power supply, the design system obtained all abnormal nodes, while the traditional system only got all abnormal nodes three times. It can be seen that the process monitoring system based on big data technology has better performance.
Nature Inspired Optimizers

Many important real world problems are computationally “hard” and one of those is cloud scheduling. There are various approaches to cloud scheduling, but in recent time scheduling strategies based on heuristics and metaheuristics are gaining popularity because of their performance. Especially important metaheuristics are nature inspired metaheuristics which have been proved to be very efficient in solving hard problems. These metaheuristics are inspired by natural phenomenon and simulate them in an algorithmic manner. In this paper, we describe those methods and present their successful applications in cloud scheduling problem. We will also describe the formal statement of the problem so that a reader can directly correlate the algorithms with applications. Moreover we had also implemented promising, nature inspired algorithms to perform the research for binary support vector machine optimization.
SOCIO-HUMANITARIAN KNOWLEDGE IN THE DIGITAL ENVIRONMENT

The article presents an overview of the speeches of the participants of the All-Russian Scientific Conference "From idea to practice: Socio-humanitarian Knowledge in the digital environment", held in Novosibirsk Akademgorodok on March 24-25, 2021. The leitmotif of the speeches of the conference participants was the idea of cooperation and interdisciplinary interaction of representatives of natural science and socio-humanitarian areas in the conditions of the formation of the digital environment.
A View From the Crowd: Evaluation Challenges for Time-Offset Interaction Applications

Dialogue systems like chatbots, and tasks like question-answering (QA) have gained traction in recent years; yet evaluating such systems remains difficult. Reasons include the great variety in contexts and use cases for these systems as well as the high cost of human evaluation. In this paper, we focus on a specific type of dialogue systems: Time-Offset Interaction Applications (TOIAs) are intelligent, conversational software that simulates face-to-face conversations between humans and pre-recorded human avatars. Under the constraint that a TOIA is a single output system interacting with users with different expectations, we identify two challenges: first, how do we define a ‘good’ answer? and second, what’s an appropriate metric to use? We explore both challenges through the creation of a novel dataset that identifies multiple good answers to specific TOIA questions through the help of Amazon Mechanical Turk workers. This ‘view from the crowd’ allows us to study the variations of how TOIA interrogators perceive its answers. Our contributions include the annotated dataset that we make publicly available and the proposal of Success Rate @k as an evaluation metric that is more appropriate than the traditional QA’s and information retrieval’s metrics.
The campus cloud platform setup based on virtualization technology

In the new era, with the increase of users’ demand for Internet content, Software and resources have been released and accessed as services. The service mode "IT resources should be treated as water and electricity" has put forward higher requirements for the data center construction, which determines that the traditional data center construction model and programs are no longer able to meet the needs of innovative applications of the new era. Therefore, the integration and innovation of new and old technologies is required. The high reliability, scalability, security, flexibility, greenness and resource customization of cloud computing technology enable more and more campuses to use cloud computing platforms to build campus networks. This paper mainly elaborates the use of virtualization technology and cloud computing technology to build campus cloud platform.
Identifying and Mitigating Against XSS Attacks in Web Applications

This chapter presents an approach to mitigate cross-site scripting (XSS) vulnerabilities in web-based environments using static and dynamic analysis. In order to identify and mitigate XSS attack vectors in web-based environments, a novel approach which successfully identifies and prevents XSS attack strings from injection points has been presented. Static analysis is the process of identifying whether a given web page is vulnerable to cross-site scripting. In addition, dynamic analysis approach is able to verify whether a given web application with vulnerable page assessed by static analysis approach is actually vulnerable. To identify XSS vulnerability of a web page, we make use of the Control-Flow Graph suggested by Lucca et al. The proposed approach involves a secure XSS interception layer placed in the client’s browser. To defend against XSS attacks, the proposed approach makes use of training phase, where every benevolent script is promoted by an identifier.
Extraction of Common-Sense Relations from Procedural Task Instructions using BERT

Manipulation-relevant common-sense knowledge is crucial to support action-planning for complex tasks. In particular, instrumentality information of what can be done with certain tools can be used to limit the search space which is growing exponentially with the number of viable options. Typical sources for such knowledge, structured common-sense knowledge bases such as ConceptNet or WebChild, provide a limited amount of information which also varies drastically across different domains. Considering the recent success of pre-trained language models such as BERT, we investigate whether common-sense information can directly be extracted from semi-structured text with an acceptable annotation effort. Concretely, we compare the common-sense relations obtained from ConceptNet versus those extracted with BERT from large recipe databases. In this context, we propose a scoring function, based on the WordNet taxonomy to match specific terms to more general ones, enabling a rich evaluation against a set of ground-truth relations.
Discriminating Between Similar Nordic Languages.

Automatic language identification is a challenging problem. Discriminating between closely related languages is especially difficult. This paper presents a machine learning approach for automatic language identification for the Nordic languages, which often suffer miscategorisation by existing state-of-the-art tools. Concretely we will focus on discrimination between six Nordic languages: Danish, Swedish, Norwegian (Nynorsk), Norwegian (Bokmal), Faroese and Icelandic.
UL2C: Mapping User Locations to Countries on Arabic Twitter

Mapping user locations to countries can be useful for many applications such as dialect identification, author profiling, recommendation system, etc. Twitter allows users to declare their locations as free text, and these user-declared locations are often noisy and hard to decipher automatically. In this paper, we present the largest manually labeled dataset for mapping user locations on Arabic Twitter to their corresponding countries. We build effective machine learning models that can automate this mapping with significantly better efficiency compared to libraries such as geopy. We also show that our dataset is more effective than data extracted from GeoNames geographical database in this task as the latter covers only locations written in formal ways.
The Impact of the University in Increasing the Book Reading on Students: Case Study Soran University

Reading is perusing through texts to understand concepts. In the event that you battle to centre, reading can improve your attention span. Books with better structures encourage us to think in sequence the more we read, the more our brains are able to link cause and effect. The main aim of this study is to investigate the effective factors that the role of the university in reading books. The sample of the study includes all the students at Soran University. The focused on populace size for the investigation is 400 understudies who chose haphazardly. The acquired information were broke down with the SPSS program by using methods containing descriptive statistics, Chi-square test. The results indicated some factors such as good communication between reading a book and students, offering university and professional development courses with a variety of skills for students, The research includes “Individual Reasons”, “age group”, “Perusing material during recreation time”, “Department”, play a significant role in reading book students in the university.
AraELECTRA: Pre-Training Text Discriminators for Arabic Language Understanding.

Advances in English language representation enabled a more sample-efficient pre-training task by Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA). Which, instead of training a model to recover masked tokens, it trains a discriminator model to distinguish true input tokens from corrupted tokens that were replaced by a generator network. On the other hand, current Arabic language representation approaches rely only on pretraining via masked language modeling. In this paper, we develop an Arabic language representation model, which we name AraELECTRA. Our model is pretrained using the replaced token detection objective on large Arabic text corpora. We evaluate our model on multiple Arabic NLP tasks, including reading comprehension, sentiment analysis, and named-entity recognition and we show that AraELECTRA outperforms current state-of-the-art Arabic language representation models, given the same pretraining data and with even a smaller model size.
Akurasi Pemetaan Kelompok Belajar Siswa Menuju Prestasi Menggunakan Metode K-Means (Studi Kasus SMP Pembangunan Laboratorium UNP)

The quality of students in school has a lot of diversity, this makes students have different levels of understanding. This can be seen from the variety of student scores obtained on report card scores, this needs to be a concern for the school, especially teachers. One of them is by forming effective study groups so that every student has the opportunity to excel. So this research was carried out with the aim of helping schools, especially teachers, to map student study groups evenly based on student report cards obtained in Semester I to Semester IV. The method used was clustering with the K-Means algorithm on the report card scores of Class IX.C students at SMP Pembangunan Laboratorium UNP. The results in this study obtained 3 clusters of students, namely students with High Achievement, Achievement and Less Achievement. This research can be used as a guide for teaching teachers in making decisions about the formation of student study groups in Class IX.C.
Networked Protests &amp;amp; State Responses: The Case of Hong Kong 2019-20

Digital technologies and Internet connectivity are enabling rapid mobilization of large groups of individuals around a common cause. The defining feature of such a Radically Networked Society (RNS) is the scale and pace of its operations. Consequently, RNS movements pose a serious challenge for the hierarchically ordered state structures, which tend to lack the dexterity and speed to respond.In this paper, we apply the RNS framework to the 2019-20 Hong Kong protests. We conclude that the protests were the product of underlying fissures over issues of identity and political autonomy. The region’s thriving Internet ecosystem and hyper-connected society enabled the development and expansion of networked communities around these issues. This fueled sustained, leaderless mobilization, resulting in large-scale disruption and electoral advances for pro-democracy activists.Meanwhile, the state’s response was rooted in a strategy of attrition. This minimized costs and proved somewhat effective in that the movement failed to achieve the broader objective of earning universal suffrage for Hong Kongers. Yet, the protests have managed to fundamentally reshape state-society relations and shift the narrative around the region’s future.
Are robots perceived as good decision makers? A study investigating trust and preference of robotic and human linesman-referees in football

Abstract Increasingly, robots are decision makers in manufacturing, finance, medicine, and other areas, but the technology may not be trusted enough for reasons such as gaps between expectation and competency, challenges in explainable AI, users’ exposure level to the technology, etc. To investigate the trust issues between users and robots, the authors employed in this study, the case of robots making decisions in football (or “soccer” as it is known in the US) games as referees. More specifically, we presented a study on how the appearance of a human and three robotic linesmen (as presented in a study by Malle et al.) impacts fans’ trust and preference for them. Our online study with 104 participants finds a positive correlation between “Trust” and “Preference” for humanoid and human linesmen, but not for “AI” and “mechanical” linesmen. Although no significant trust differences were observed for different types of linesmen, participants do prefer human linesman to mechanical and humanoid linesmen. Our qualitative study further validated these quantitative findings by probing possible reasons for people’s preference: when the appearance of a linesman is not humanlike, people focus less on the trust issues but more on other reasons for their linesman preference such as efficiency, stability, and minimal robot design. These findings provide important insights for the design of trustworthy decision-making robots which are increasingly integrated to more and more aspects of our everyday lives.
A Lightweight Privacy Preserving Keyword Search Over Encrypted Data in Cloud Computing

With the emerging development of cloud computing services, data owners outsource their documents to Cloud Service Providers (CSP) which could lead to threats related to security and privacy. Hence, protecting the privacy of user data and providing queries privacy becomes one of the main concerns of the data owner. One of the solutions for providing privacy and confidentiality of the outsourced data is encrypting it before sending it to the cloud. Although this solution satisfies data confidentiality and prevents the CSP from reading or modifying the data without the data owner's permission, it prevents the data owner to search the outsourced documents directly. Symmetric encryption algorithms e.g. AES have a searching limitation, in which the whole encrypted document needs to be retrieved from the CSP and then decrypt before performing the search procedure. To overcome this limitation, a lot of keyword-based search approaches have been done. These approaches allow users to retrieve just those documents contain special keywords. However, most of these approaches suffer from privacy and security problems and are based on high overhead asymmetric encryption algorithms. This paper proposes a privacy-preserving keyword search scheme for searching over encrypted data. To avoids the high computational cost of asymmetric encryption, the proposed scheme employs symmetric encryption and Bloom filter. Experimental results demonstrate that the proposed searchable encryption algorithm is secure and lightweight, and it has the ability to perform a keyword search over encrypted data without decrypting them.
Cybersecurity Ontologies: A Systematic LiteratureReview

Cybersecurity is a young discipline that has gained relevance in our modern society.Thisresearchreportsthefindingsofasystematicreviewoftheliteratureonontologiesinthefieldofcybersecurity. From an initial set of 214 papers on the subject, 50 relevant papers were selected forthis SLR. With these documents we answered research questions related to the domains in whichontologies are reported, the methodologies, tools and languages used, and the verification andvalidation mechanisms reported. As results, we observed that the largest number of ontologies areclassified in the domains of infrastructure and networking, software and human factor. Regardingthe papers that report the use of a methodology for developing the ontologies (12%), Methontologyis the commonly used one. Protégé, in conjunction with the OWL language, are the preferred toolsfor ontology development. Regarding verification and validation (V&amp;V) mechanisms, we observethat morethanhalf(62%) reporttheapplicationofV&amp;Vmechanisms totheir ontologies.