Reviewerly/exHarmony huggingface dataset:
GEOLOGICAL INVESTIGATIONS IN THE UKRAINIAN ANTARCTIC STATION REGION BEFORE 1996

The author submits a chronological account on the history of geological exploration on the Graham Coast and the Wilhelm Archipelago of West Antarctica within the time interval from 1819 to 1996. The state of geological exploration of this area before the creation of the Ukrainian Antarctic Station (UAS) here is determined. A number of conclusions were made regarding: the temporal sequence of individual studies and the geological specifics of the territories covered by them; conditions for carrying out and features of the organization of field works; the reliability of the results of individual geological surveys; the presence of unexplored territories. It was found out that all reconnaissance and geological survey work in the study area was carried out in the 30–50s of the XXth century. Medium-scale geological surveys of individual parts of the Graham Coast and adjacent islands were carried out under unfavorable conditions. This led to the fragmentariness of the created geological maps and their heterogeneous support by field observations. Due to poor ice conditions and local features of the organization of geological routes, numerous bays on the Graham Coast, as well as its hard-to-reach inland territories, have remained completely unexplored. Also, many of the islands of the Wilhelm Archipelago were not covered by geological survey. In particular, there was no way to explore those islands that are remote from the navigable Lemaire Channel, Penola Strait and Grandidier Channel. The Argentine Islands are the only site where a large-scale geological survey has been carried out. But the available large-scale geological map of the Argentine Islands has a number of significant disadvantages. The map contains only petrographic data. There are no elements of bedding of rocks, tectonic deformations, zones of postmagmatic changes, manifestations of ore mineralization. The spatial distribution of the main petrographic representatives does not always correspond to the true one. The relative ages of individual geological bodies are insufficiently substantiated, and sometimes, in general, are determined incorrectly. All of the above-mentioned explains why the level of geological knowledge about the area at the time of the creation of the UAS here remained much worse than in the adjacent territories.
The possibilistic horn non-clausal knowledge bases

Non-clausal deduction in classical logic is one of the oldest areas in artificial intelligence. It first appeared in the sixties and consequently a large body of research has been devoted to it. Within the last decades, computing with non-clausal formulas has been considered in several fields, and in particular, in answer set programming, wherein non-clausal or nested logic programs were conceived in 1999. Possibilistic logic is the most extended approach to handle uncertain and partially inconsistent information. Here, we generalize some well-known clausal outcomes in possibilistic reasoning to the non-clausal setting, concretely the objective of our proposal is: (i) to extend available insights from clausal to non-clausal form; (ii) to show that possibilistic reasoning admits feasible classes also at the non-clausal level; (iii) to combine the high expressiveness of non-clausal possibilistic logic with the highest efficient (polynomial) reasoning mechanisms; and (iii) to suggest that some meaningful subclasses of possibilistic nested programs can be efficiently processed. Firstly, we define the class of Possibilistic Horn Non-Clausal formulas, or H‾Σ, which covers the classes: possibilistic Horn and propositional Horn-NC. H‾Σ is shown to be non-clausal, analogous to the standard Horn class. Secondly, we define Possibilistic Non-Clausal Unit-Resolution, or URΣ, and prove that URΣ correctly computes the inconsistency degree of Horn-NC bases. URΣ is formulated in a clausal-like manner, which eases its understanding, formal proofs and future extension towards full non-clausal resolution. Thirdly, we prove that computing the inconsistency degree of Horn-NC bases takes polynomial time. Although there already exist tractable classes in possibilistic logic, all of them are clausal, and thus, H‾Σ turns out to be the first characterized polynomial non-clausal class within possibilistic reasoning. We discuss that our approach serves as a starting point to developing uncertain non-clausal reasoning on the basis of both methodologies: DPLL and resolution.
Impact of Deep Learning Models On Hate Speech Detection

Internet one of the mediums of connectivity that is available at the doorstep, with access to the internet one gets access to many web-based platforms. An increase in the use of these platforms gives us some benefits as well as some drawbacks. One of such drawbacks is hate speech. Hate speech is a topic of concern for social media platforms. With dynamically increasing datasets manual intervention of posts is quite impossible or will be time-consuming. Hate speech detection is an automated task to detect hate speech from the input. In this paper, we have compared some deep learning models like Convolution Neural Network (CNN), Recurrence Neural Network (RNN), Long Gated Recurrent Unit (GRU), and Long-Short Term Memory. The datasets used here are publicly available. The result of our analysis shows us that GRU performed better than other basic deep learning models. The model achieved an accuracy of 92.60% with an F1 score of 81.84% for dataset (D1) and the respective values for dataset (D2) are 96.15% and 83.06%.
Fast Knowledge Discovery in Social Media Data using Clustering via Ranking

To gain insight on a large amount of text data collected by social media outlets is challenging, even availed with the latest advancement of big data technology. In addition to the curse of dimensionality and computational complexity, users expect (near) real-time solutions. Instant analytic values are needed for making fast decisions in social media applications where data is constantly growing. This paper proposes a unique solution of generating insight on a large amount of data using an approximation of clustering via ranking approach. The solution provides a comprehensive insight without the need to scan entire data in the collection. Using a novel method, it determines the most relevant objects in the data to be included in the clustering process. Incremental clustering is done via the relevant clusters concept, instead of using the conventional window-based model. Experiments from publicly available social media datasets show that this approach is able to produce near real-time and accurate clustering results by merely using a standard computer setup instead of a costly big data machine setup.
POSTER: Recovering Songs from a Hanging Light Bulb

In this paper, we introduce a novel side-channel attack for eavesdropping sound using an electro-optical sensor. We show how small vibrations of a hanging bulb (in response to sound hitting its surface), can be exploited by eavesdroppers to recover sound. We evaluate our method's performance in a realistic setup and show that our method can be used by eavesdroppers to recover songs from a target room containing the hanging light bulb.
Identification of Hate Tweets: Which Words Matter the Most?

For years, social media platforms have been used to incite violence towards minority groups using hate speech or writing that express pejorative or discriminatory language undermining gender, ethnicity, religion, or nationality. In this article, we assembled a multi-class dataset of 83,360 tweets, which include four hate classes and one non-hate class. We adopted three popular machine learning and deep learning models, such as SVM, long short-term memory model extended with an attention mechanism (LSTM-Attention), and BERT model for the classification of those hate tweets. Our experiments show that for the identification of religious-hate tweets, LSTM-Attention model consistently produced the best performance, while for other two types of hate tweets, such as sexist and racist, we observed that on average BERT produces the best result. Both the LSTM-Attention and BERT reached an overall F1-score of 90% and 91% respectively for multi-label classification. Our detail analysis reveals that since sexist or racist tweets make use of more hate or slang words than that of religious-hate tweets, BERT performs better in detecting the first two classes. However, since many of religious-hate tweets not necessarily include any disparaging words, BERT failed to identify those hate speeches. Thus, LSTM-Attention was used to extract the words with higher attention weights to identify which words matter the most for the classification of hate speech. We show that words that carry more predictive information for the hate tweets classification are not necessarily high frequency or hateful words.
Pedagogical Design of Digital Coursewith the use of LMS Moodle Tools

Introduction. The introduction of a competency-based approach in Ukrainian higher education made it possible to update educational standards that define a set of general and professional competencies and learning outcomes, as well as introduce new pedagogical technologies and systems for monitoring the quality of education. Changes in the organization of digital learning largely depend on a competency-based approach. LMS Moodle is a platform that provides standard tools for implementing a competency-based approach. Purpose. To research the possibilities of LMS Moodle for the pedagogical design of the digital course and implementation of a competency-based approach in digital learning. Results. At the first stage, the summary of pedagogical design of a specific digital course built upon flip peddles on sequence is provided and a scheme is formed in which competencies, learning outcomes, and forms of assessment are defined. At the second stage, the process of designing a digital course based on the methodology of pedagogical design and tuning is researched, there an attempt is made to implement the course based on the competence approach using LMS Moodle tools of version 2.7 and version 3.3. As a result, a developed curriculum and framework are added to it, which allows observing the implementation of the learning outcomes in a convenient format. Conclusion. The results of the research show that in order to implement a competency-based approach with the use of LMS Moodle, the following requirements are essential: the scheme with linked competencies, learning outcomes, learning activities, and assessment tools. each activity has clearly indicated form, result, support methods, and estimated time. use of rubrics with all criteria aligned to learning outcomes. According to the results of the study, there are differences between LMS Moodle 2.7 and 3.3. In versions of Moodle later than 3.1, it is more convenient to analyze the achievement of learning outcomes for multiple digital courses by creating a hierarchy of competencies and learning outcomes; it is easier to conduct monitoring of the progress made towards the achievement of learning outcomes.
Learning the Rules of the Game: An Interpretable AI for Learning How to Play

In this article, we present an interpretable artificial intelligence, and its associated machine learning algorithm, that is capable of automatically learning the rules of a game whenever the rules—the relationship between a player’s current state and their corresponding set of legal moves—can be represented as a set of low degree Zhegalkin polynomials, a special class of Boolean functions. This is true for many popular games including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Spanish Dominó</i> and the card game <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">President</i> . Our method takes advantage of such low polynomial degree to compute an exact representation of the rules in polynomial time instead of the required exponential time for generic Boolean functions. The rules can also be represented using significantly less storage than in the generic case which, for many games, leads to a representation that is easy to interpret.
A Study of Social Media Analytics

Millions of people around the world make use of smartphones to be connected to the internet and use social media. The use of social media sites like Facebook, Instagram, WhatsApp, YouTube, Snapchat, Twitter, Linked-in, Tik-Tok, etc., And social networks connect up to trillions of data are collected and marketing agents are using that data to promote their market. Well, we analyze these data for this paper and the challenges and difficulty involving in the specific data analysis network. Online media offers an enormous pool of purchasers ready for brand correspondence. however, web-based media is not always about brands. It's about individuals offering their lives to others they know dependent on regular interests and they despise interferences, especially when somebody is attempting to sell them something. Thus, a brand-driven center can be perilous. Social media plays a vital role for youth. In this paper, we also discuss the influence of social media on young people.
A Usability Testing of COVID-19 Vaccine Appointment Websites

While COVID-19 caused a huge number of casualties, many people rushed to register for a vaccine appointment when the vaccines were available. However, the existing vaccination appointment websites were confusing and hard to use. To help alleviate this problem, this usability study was conducted to understand the major challenges users faced in order to provide possible design solutions. Three U.S. government vaccine appointment websites were chosen: one state government, and two local government websites. In order to properly evaluate the usability of these three websites, a triangular approach with Heuristic Evaluation, Persona Design, and Cognitive Walkthrough was used. The study revealed many usability problems for each chosen site. 1) For the New York State (NYS) site, the clustering of information was not organized well. Vaccine status was displayed inconsistently between parent and child pages. 2) For the New York City (NYC) site, there was a major lack of error prevention in many different aspects. Vaccine brands were not attached to vaccine sites, which can lead to people who have conflicting medical issues with one vaccine but not another. 3) For the Nassau County site, there was a poor organization of information, as everything was linearly displayed with a lack of visual organizations to facilitate navigation. The most fatal problem was there was no local vaccine system to book an appointment at Nassau County site. Corresponding design solutions were discussed based on these findings.
Efficacy of ADDIE Model in Peer-to-Peer Networks: Digital Evidence Investigation

While the need for content distribution proliferates - becoming more mammoth and complex on the Internet - the P2P network perseveres as one of the best avenues to service the demand for content distribution. It enjoys a wide range of clients that transport data in bits securely, making it susceptible to moving dubious contents, hence becoming exposed to varying security threats that require credible digital investigation to address. The tools and techniques used in performing digital investigations are still mostly lagging, successfully slowing down law enforcement agencies in general. The acquisition of digital evidence over the Internet is still elusive in the battle against cybercrime. This paper considers a new technique for detecting passive peers that participate in a P2P network. As part of our study, we crawled the µTorrent P2P client over 10 days while logging all participating peers. We then employed digital forensic techniques to analyze the popular users and generate evidence within them with high accuracy. Finally, we evaluated our proposed approach against the standard Analysis, Design, Development, Implementation, and Evaluation, or ADDIE model for digital investigation to arrive at the credible digital evidence presented in this paper.
Design and Evaluation of an HPC-based Expert System to speed-up Retail Data Analysis using Residual Networks Combined with Parallel Association Rule Mining and Scalable Recommenders

Given the Covid-19 pandemic, the retail industry shifts many business models to enable more online purchases that produce large transaction data quantities (i.e., big data). Data science methods infer seasonal trends about products from this data and spikes in purchases, the effectiveness of advertising campaigns, or brand loyalty but require extensive processing power leveraging High-Performance Computing to deal with large transaction datasets. This paper proposes an High-Performance Computing-based expert system architectural design tailored for ‘big data analysis’ in the retail industry, providing data science methods and tools to speed up the data analysis with conceptual interoperability to commercial cloud-based services. Our expert system leverages an innovative Modular Supercomputer Architecture to enable the fast analysis by using parallel and distributed algorithms such as association rule mining (i.e., FP-Growth) and recommender methods (i.e., collaborative filtering). It enables the seamless use of accelerators of supercomputers or cloud-based systems to perform automated product tagging (i.e., residual deep learning networks for product image analysis) to obtain colour, shapes automatically, and other product features. We validate our expert system and its enhanced knowledge representation with commercial datasets obtained from our ON4OFF research project in a retail case study in the beauty sector.
Viral ‘Charlie bit my finger’ video set to be auctioned as an NFT

The iconic video of a young baby biting his older brother’s finger is set to be auctioned as a non-fungible token
Evolution of Scholarly Collaboration in ICIS: A Scientometric Analysis

As the Information Systems (IS) community grows, it is reasonable to expect that collaborations between scholars have increased. This paper studies the evolution and trend of collaboration networks in the International Conference on Information Systems (ICIS). Based on evolutionary analysis of papers published in ICIS proceedings over the 40 years from 1980 to 2020, various characteristics of the social network of IS researchers are identified and discussed. Our research shows that key characteristics of the research collaboration network as reflected by the co-authorship: 1) ICIS community has thrived that demonstrated frequent interactions among members as new members have joined and connections between members have improved. 2) The critical core of the community, the most productive authors and countries, were identified. 3) Collaboration patterns of the community were also studied. 4) how social networks evolved was revealed.
Action Recognition Model Based on Feature Interaction

In order to solve incomplete information expression and large amount of calculation, this paper proposes an action recognition model based on feature interaction. Firstly, high frequency and low frequency components are extracted from the video, then the high frequency and low frequency fusion algorithm proposed in this paper is used to compress the original video to solve incomplete expression of action information. Secondly, feature interaction network is designed to extract the features of the odd and even frame sequences, and interact with each level of the two-way network to extract the hidden information in the video sequence, the two-way features are fused by fusion network to extract the spatial temporal feature. Finally, the effectiveness of the model was verified, and the recognition accuracy of 96.15% and 78.51% was achieved on the UCF101 and HMDB51 datasets, respectively.
Corpus-Based Methodologies in the Study of Heritage Languages

Corpus linguistics has gained prominence in the study of language, both of standard and learner varieties, in the 1990, as technological advances allowed for quick and reliable analyses of large volumes of language data. Computer-aided analyses of large principled collections of authentic texts, known as language corpora, brought about new insights into the nature of language and afforded a more nuanced understanding of linguistic structure, language change, and language development. The chapter provides an overview of the key principles of corpus linguistics methods and of some frequently-used corpus instruments and procedures; it explores the potential benefits of application of corpus linguistics methods and instruments to the study of heritage languages, using the examples of few existing heritage language corpora. Overall, the chapter aims to engage the heritage language community in investing energy in the development of heritage language corpora and in making better use of the existing computational tools in the study of heritage languages.
Research on Evolutionary Game of Service Value Chain Considering the Supervision of Service Platform

For the service value chain composed of service provider, service contractor and cloud service platform, an evolutionary game model is developed to improve its service quality considering the supervision of service platform. Firstly, the profit function is built in different strategies of all the game players, and then the profit matrix is also established. In order to create every individual’s evolutionary process, their replication dynamic equations are presented as well. Secondly, this work analyzes both the influence of related parameters on the decision of the game entities such as the strategic investment of entities and the gradual stability of the evolutionary game model. Moreover, the evolutionary stability strategy (ESS) of the different game players is obtained subsequently under the influence of different factors. The research results show that cloud service platform supervision can promote the high-quality service of service provider and service contractor. It can also push service provider to provide higher quality service through properly reducing the minimum service investment and its basic cost. At the same time, service contractor can provide high level service while increasing its service commitment.
Working as a Health AI Specialist

Artificial intelligence and the sub-field of machine learning offer the potential to deliver data-driven healthcare solutions that can improve patient care and increase efficiency in healthcare services. Despite this, the methods and models are new and complicated, to those who work in healthcare. This chapter explores the implementation of such solutions in healthcare settings, through five real-world case studies of experts applying this technology in a variety of clinical settings and at different stages of implementation. These cases highlight the challenges and opportunities posed by implementing artificial intelligence and data-driven solutions, and the lessons learnt from colleagues pioneering its adoption in the healthcare sector.