Reviewerly/exHarmony huggingface dataset:
Content Strategies of Belarusian Media for Children in Social Networks

The article identifies the main types of content strategies used by children's media in Belarus on public Internet sites. The advertising, engaging, developing and combined content strategies of Belarusian media for children in social networks are distinguished through differentiating the target nature of the broadcast content, the formats used and the presence of a direct reference to the main media platform (magazine, newspaper, website). The most effective content strategies of Belarusian children's media are established based on the calculated index of Internet subscribers' engagement demonstrated by three indicators of user reactions (the number of "like" marks (likes), reprints (reposts) and comments). The factors influencing the degree of loyalty of the target audience to social children's media and their content strategies are determined. In a comparative aspect, the article describes multifunctional specialized media projects of the Children's Runet, which offer a variety of interactive multimedia formats aimed at development, education and entertainment of children of all age groups in the web environment
An Implementation of Voice Assistant for Hospitality

Voice user interface has gained popularity in the recent years. A chatbot is a machine with the ability to answer automatically through a conversational interface. Instead of using mouse and keyboards as input and screen as output, a chatbot with extra voice user interface feature improve the system and enhance the user experience. A chatbot is considered as one of the most exceptional and promising expressions of human computer interaction. Voice-based chatbots or artificial intelligence (AI) devices transform humancomputer bidirectional interactions that allow users to navigate an interactive voice response (IVR) system with their voice generally using natural language. In this paper, we focus on voice based chatbots for mediating interactions between hotels and guests from both the hospitality technology providers’ and guests’ perspectives. A hotel web application with voice user interface was implemented which provides voice input/output interface to enhance the user experience. Speech recognition component was used to dictate the user voice input to text. Speech synthesis API was used for text to voice conversion. A closed domain question answering (cdQA) Natural Language Processing (NLP) solution was used for processingof query and return the best answer possible.
SwarMED: A High-Throughput Interoperability Architecture Over Ethereum and Swarm for Big Biomedical Data

In this paper, we introduce SwarMED, a decentralized yet high throughput interoperability system for big biomedical data. SwarMED uses Etehreum blockchain for trustless security and Swarm p2p storage to handle high throughput transaction of big data. In SwarMED, we developed an indexing mechanism over the immutable storage of Swarm to achieve high-throughput while sharing millions of patient records and images among multiple parties. SwarMED achieved a high throughput of 250K medical records per second over a private network constructed over LSU-HPC cluster. This high throughput is 9x more comparing to conventional way of using p2p storage in conjunction with blockchain. This high throughput enables the patients to get realtime access to his comprehensive medical history and scientists to gain real-time access to different medical data for collaborative research complying to the constraints posed by existing laws. Our system-level analysis over different design alternatives over different transfer and storage architectures shows that, p2p storage platforms automatically provide significantly better scalability over traditional HTTP with increasing number of clients. Swarm provides 2x more I/O throughput and 10x less latency than IPFS, another p2p storage system making it a better choice for decentralized big data transaction.
Generating Expert Systems to Detect Specific Bacteria Types and Extract Handwriting from Banck Checks

Detecting specific types of bacteria in a microscopic Bacteria Colony Image (BCI) is a tedious and timeconsuming task. Based on the previous experience in the field of computerized signature and handwriting analysis, we generated two expert systems to detect specific types of bacteria in BCI and to extract handwriting from binary bank checks images using the same software platform developed for this purpose. Using the Domain Expert Guided Heuristic Search (DEGHS) and the platform, we generated an expert system that succeeded in cases where no algorithmic approach can be applied. We exploited the fact that the function of an expert system is determined mainly by its knowledge base to generate different expert systems by modifying the content of the knowledge base. The mechanism used expedite very much the development of the expert system to reach its best performance.
Domain Engineering for Applied Monocular Reconstruction of Parametric Faces

Many modern online 3D applications and videogames rely on parametric models of human faces for creating believable avatars. However, manually reproducing someone's facial likeness with a parametric model is difficult and time-consuming. Machine Learning solution for that task is highly desirable but is also challenging. The paper proposes a novel approach to the so-called Face-to-Parameters problem (F2P for short), aiming to reconstruct a parametric face from a single image. The proposed method utilizes synthetic data, domain decomposition, and domain adaptation for addressing multifaceted challenges in solving the F2P. The open-sourced codebase illustrates our key observations and provides means for quantitative evaluation. The presented approach proves practical in an industrial application; it improves accuracy and allows for more efficient models training. The techniques have the potential to extend to other types of parametric models.
E-Health Preventing Pediatric Home Accidents

Pediatric home accidents still a nightmare for parents, especially for who don’t know how to act in such situations. With the digital health advancements, it will be possible to avoid the disasters of these accidents, especially falls. In this paper, we will present the definition of each of home accidents and ehealth, the motivations and the challenges of this work, related works and propose a prototype to avoid falls disasters with a discussion of the positive and negative points of this prototype and finally make a comparison between our approach and the related works.
A HUMAN RIGHTS-BASED APPROACH TO THE USE AND REGULATION OF ARTIFICIAL INTELLIGENCE – AN AFRICAN PERSPECTIVE

This article aims to describe the impact of artificial intelligence (AI) on human rights, analyze the use and regulation of AI in some African countries and propose a human rights-based approach to such use and regulation, among other recommendations. Whereas there is no doubt that artificial intelligence (AI) has enhanced technological innovation and contributed tremendously to addressing societal and global challenges, it has however, also disrupted certain legal, social and economic systems. In many ways, it has also had a negative impact on the enjoyment of human rights. This article begins with a conceptual context that focuses on the term artificial intelligence. It then proceeds to analyze how artificial intelligence impacts on the protection and enjoyment of several human rights, including civil and political rights on the one hand and socio-economic rights on the other. This is followed by a brief discussion of the various artificial intelligence strategies, policies and relevant legislation in some African countries. It is argued that because of the importance of human rights, such strategies, policies and regulation must incorporate human rights norms and standards. This calls for a human rights-based approach. The novelty of this study lies in the fact that artificial intelligence is a new technological development. Many people are not familiar with this, especially in Africa. There are not many studies conducted on its impact on human rights. Therefore, the paper concludes with recommendations on how best African countries can use and regulate artificial intelligence, including adopting a human based-approach.
How to represent uncertainty via qudits: Probability distributions, regular, intuitionistic and picture fuzzy sets, F-transforms, etc.

While modern computers are fast, there are still many important practical situations in which we need even faster computations. It turns out that, due to the fact that the speed of all communications is limited by the speed of light, the only way to make computers drastically faster is to drastically decrease the size of computer’s components. When we decrease their size to sizes comparable with micro-sizes of individual molecules, it becomes necessary to take into account specific physics of the micro-world – known as quantum physics. Traditional approach to designing quantum computers – i.e., computers that take effect of quantum physics into account – was based on using quantum analogies of bits (2-state systems). However, it has recently been shown that the use of multi-state quantum systems – called qudits – can make quantum computers even more efficient. When processing data, it is important to take into account that in practice, data always comes with uncertainty. In this paper, we analyze how to represent different types of uncertainty by qudits.
The effect of government credit intervention in banking system on growth of iran economy sectors (FMOLS Co-Integrating Approach)

The effect of government credit intervention in banking system on growth of iran economy sectors (FMOLS Co-Integrating Approach)
Sensing Image Regions for Enhancing Accuracy in People Re-identification

Video surveillance systems are widely used in the public and private sectors for maintaining security and healthcare purposes. Performance of surveillance systems directly depends on their accuracy in re-identification. There are three regions in a camera view, including person’s body, background, and possible carried object by the person. Background, in existing approaches, is either overlooked or treated like a person’s body in re-identification. In this paper, these three regions are considered in re-identification but with different importance. In our proposed technique, first, the input image is semantically segmented into the three regions using a deep semantic segmentation approach. Then, the effect of each region on characteristic features of people is tuned depending on the region’s importance in re-identification. The proposed technique, leveraging robust descriptors, such as the Gaussian of Gaussian (GOG) and Hierarchical Gaussian Descriptors (HGD), can enhance existing methods in dealing with the challenging issues such as partial occlusion caused by carried objects and background in re-identification. Experimental results on commonly used people re-identification datasets demonstrate effectiveness of the proposed technique in improving performance of existing re-identification methods.
Study of Systemic risk in the banking sector of Tehran Stock Exchange: Graph theory approach and ARMA-gjrGARCH-DCC

Study of Systemic risk in the banking sector of Tehran Stock Exchange: Graph theory approach and ARMA-gjrGARCH-DCC
Investigating the Effects of Financial Risks with Central Bank Policy Intervention and Foreign Exchange Market Pressure on the Stability of Banking sector Based on Gerton and Ruper Model: Nonlinear Smooth Transition Regression Approach

Investigating the Effects of Financial Risks with Central Bank Policy Intervention and Foreign Exchange Market Pressure on the Stability of Banking sector Based on Gerton and Ruper Model: Nonlinear Smooth Transition Regression Approach
The Best Practices of Commercialized Security on National Security in Nairobi County, Kenya

Commercialised security has grown and developed in Kenya. This is attributed to citizens' many security demands on the state. Hence they turn to commercialised security to complement. Despite this development, few studies have studied the best practices of commercialised security on national security. The study took on a cross-sectional survey design. It was done in Nairobi involving adult residents. A multistage sampling technique was employed to obtain the wards to be studied. Those managing or purchasing the commercialised security services for their establishments and the managers of commercialised security firms were purposively sampled. The general public and the security guards were systematically sampled. Questionnaires and scheduled interviews were used to collect data. The SPSS version 25 was employed in data management and analysis. In the research, descriptive statistics included percentages and frequency distribution tables to summarise variables into thematic areas and convey the characteristics of critical variables. The study identified five best practices of commercialised security on national security: improvement in working conditions of the security personnel, adequate training and development, cooperation with the state security agencies, after-sale service and enforcement of regulations. The study concluded that the favourable mechanisms could only be realised through the consultation of the stakeholders: the commercial security industry, the citizens and the state. Thus the study recommended that The Private Security Regulation Authority needs to implement some of the requirements in the PSRA No. 13 of 2016.
Selecting pseudo supervision for unsupervised domain adaptive SAR target classification

Abstract In recent years, deep learning has brought significant progress for the problem of synthetic aperture radar (SAR) target classification. However, SAR image characteristics are highly sensitive to the change of imaging conditions. The inconsistency of imaging parameters (especially the depression angle) leads to the distribution shift between the training and test data and severely deteriorates the classification performance. To address this problem, in this paper we propose an unsupervised domain adaptation method based on selective pseudo-labelling for SAR target classification. Our method directly trains a deep model using the data from the target domain by generating pseudo-labels in the target domain. The key idea is to iteratively select valuable samples from the target domain and optimize the classifier. In each iteration, the breaking ties (BT) criterion is adopted to select the best samples with the highest scores of relative confidence. Besides, to avoid error accumulation in the iterative process, class confusion regularization is used to improve the accuracy of pseudo-labelling. Our method is compared with state-of-the-art methods, including supervised classification and unsupervised domain adaptation methods, over the moving and stationary target acquisition and recognition (MSTAR) dataset. The experimental results demonstrate that the proposed method can achieve better classification performance, especially when the difference of depression angles of the source and target domain images is large. Besides, our method also shows its superiority under limited-sample conditions.
Provenance Studies of Alluvial Tin Deposits in Parts of Ropp Younger Granite Complex, Northcentral Nigeria

The study area in this research is within the Ropp Complex, part of the Nigerian Mesozoic Younger Granite province. The study aims to interpret the depositional environment and establish the provenance of the alluvial cassiterite deposits in the study area. Boreholes/mining pits were logged for this study, and stanniferous sandstone samples were collected, which were used for textural and mineralogical studies. The mineral assemblages documented in the samples include ilmenite (3% to 27%). Cassiterite (2 to 14%), Zircon (2 to 16%), magnetite (0 to 17%), tourmaline (5 to 11%), rutile (2 to 8%) and monazite (2 to 7%).The ZTR Index calculated from the result of heavy minerals analysis for the selected pieces is 59%. Mineralogical studies revealed that quartz is the most dominant detrital mineral averaging about 93-99%, indicating that the stanniferous sandstones are compositionally matured and have experienced a high degree of chemical weathering. The quartz grains have grain sizes ranging from coarse to very coarse. They are poorly sorted, sub-angular to sub-rounded, with low sphericity. This also indicates a closeness to the source and textural immaturity. The occurrence of relatively very few feldspar grains suggests a slow sedimentation rate, very high rate of chemical weathering and composition maturity. The bivariate plots, univariate grain size parameters and probability plots, and the absence of fossils and trace fossils suggest deposition in a fluvial environment. The results of the granulometric analysis indicate that the study area's stanniferous sandstone was deposited in a fluvial environment by a low-energy fluvial (river) depositional system and the deposition in proximal (close to the source). This study suggests that the Basement complex and Younger Granite are the sources of the stanniferous placer deposits.
Numerically stable coded matrix computations via circulant and rotation matrix embeddings

Polynomial based methods have recently been used in several works for mitigating the effect of stragglers (slow or failed nodes) in distributed matrix computations. For a system with $n$ worker nodes where $s$ can be stragglers, these approaches allow for an optimal recovery threshold, whereby the intended result can be decoded as long as any $(n-s)$ worker nodes complete their tasks. However, they suffer from serious numerical issues owing to the condition number of the corresponding real Vandermonde-structured recovery matrices; this condition number grows exponentially in $n$. We present a novel approach that leverages the properties of circulant permutation matrices and rotation matrices for coded matrix computation. In addition to having an optimal recovery threshold, we demonstrate an upper bound on the worst-case condition number of our recovery matrices which grows as $\approx O(n^{s+5.5})$; in the practical scenario where $s$ is a constant, this grows polynomially in $n$. Our schemes leverage the well-behaved conditioning of complex Vandermonde matrices with parameters on the complex unit circle, while still working with computation over the reals. Exhaustive experimental results demonstrate that our proposed method has condition numbers that are orders of magnitude lower than prior work.
LOCALIZATION PECULIARITIES OF GOLD PLACERS IN THE BURIED FILLED KARST OF INTERMOUNTAIN DEPRESSIONS OF THE SOUTHERN URALS

Large megablocks of the South Urals are separated by longitudinal suture zones presented by long-lived negative morphostructures, which are inherited by intermountain erosional-karst depressions: Belskaya , Mindyakskaya, and Miasskaya. In intermountain depressions, the main interest for placer gold prospecting is related to the areas of paleovalleys of the Belaya, Mindyak, and Shartymka rivers especially to the karst placer gold traps associated with zones of increased fracturing and permeability at the junctions of submeridian and intersecting neotectonically active transverse faults. In the ore placer clusters of the Mindyakskaya and Miasskaya (southern part) depressions, manifestations of superimposed low-temperature gold-argillisite-jasperoid mineralization associated with one of the stages of the Mesozoic tectonic magmatic activation of the region have been established
The Effect of Software Instruction on EFL Students Pronunciation Skills

The main purpose of the research aims to explore the effectiveness of students using software to learn English pronunciation skills in particular. Nowadays, English has become a bridge of communication around the world. Whether in work or society, good language skills can further expand social and communicative abilities. In recent years, the ability to communicate in English has been regarded as an important part of life, and many channels and methods have been used in learning to improve English ability, using software to learn independently, and learning easily without pressure. The researcher in this study adopted MyET, a professional listening and speaking training software, to explore the impact on students' English pronunciation. MyET can score oral ability, analyze and provide diagnosis toward pronunciation, intonation, fluency, etc. MyET allows students to learn English on the one hand, and on the other hand, students can continue to learn independently when they are out of the classroom. In order to better understand the effect of MyET on students’ English pronunciation, SPSS was conducted through pre- and post-test results to achieve statistical analysis. The result reflects that our assumption is established that software instructions employed in authentic classroom improves students’ willingness and effectiveness in English learning.
Ditch the Gold Standard: Re-evaluating Conversational Question Answering

Conversational question answering aims to provide natural-language answers to users in information-seeking conversations. Existing conversational QA benchmarks compare models with pre-collected human-human conversations, using ground-truth answers provided in conversational history. It remains unclear whether we can rely on this static evaluation for model development and whether current systems can well generalize to real-world human-machine conversations. In this work, we conduct the first large-scale human evaluation of state-of-the-art conversational QA systems, where human evaluators converse with models and judge the correctness of their answers. We find that the distribution of human machine conversations differs drastically from that of human-human conversations, and there is a disagreement between human and gold-history evaluation in terms of model ranking. We further investigate how to improve automatic evaluations, and propose a question rewriting mechanism based on predicted history, which better correlates with human judgments. Finally, we analyze the impact of various modeling strategies and discuss future directions towards building better conversational question answering systems.
Match the Script, Adapt if Multilingual: Analyzing the Effect of Multilingual Pretraining on Cross-lingual Transferability

Pretrained multilingual models enable zero-shot learning even for unseen languages, and that performance can be further improved via adaptation prior to finetuning. However, it is unclear how the number of pretraining languages influences a model's zero-shot learning for languages unseen during pretraining. To fill this gap, we ask the following research questions: (1) How does the number of pretraining languages influence zero-shot performance on unseen target languages? (2) Does the answer to that question change with model adaptation? (3) Do the findings for our first question change if the languages used for pretraining are all related? Our experiments on pretraining with related languages indicate that choosing a diverse set of languages is crucial. Without model adaptation, surprisingly, increasing the number of pretraining languages yields better results up to adding related languages, after which performance plateaus. In contrast, with model adaptation via continued pretraining, pretraining on a larger number of languages often gives further improvement, suggesting that model adaptation is crucial to exploit additional pretraining languages.