Reviewerly/exHarmony huggingface dataset:
Preliminary research on developing the methodology for predicting and estimation of productive sulfide mineralization of the Norilsk type using polarizing-optical and petrophysical methods (on the example of the Talnakh Deposit)

The quantity of the mineral composition of ore and parent rocks was carried out. Ore sections from the core samples of two wells of the Talnakh field were used for these studying. Also a number of their physical, mechanical, magnetic and electrical properties were determined. The content of the main ore minerals and useful components in the studied samples were calculated due to comparison of these data of their petrophysical properties. Additionally petrophysical anomalies in the external contact of ore horizons were identified. Using of this methodology may contribute to improving the efficiency of survey and evaluation work on Norilsk type sulfide mineralization.
Methods and forms of digital learning with distance education technologies: Opinions and preferences of students at the institutes of culture

The author continues to review the attitude of students in the speciality “Library information activities” toward methods and formats of digital learning through distance education technologies. The students of educational institutions of the RF Ministry of Culture were surveyed. Three hundred and eighty three (383) bachelor-program students and 65 masters-to-be participated in the survey. The respondents specified the methods and format of digital learning introduced during the restriction pandemic period. It is demonstrated that after these emasures had been introduced, the intensity of utilization of digital learning methods and forms increased, as well as the ratio of the so-called external methods and forms, i. e. messengers and videoconference systems. It is established that the learners prefer the systems and/or services that they know and are used to. At the same time, the dynamics of moving toward solutions integrated into digital information education environment is demonstrated. The author argues that almost every digital information education environment features some kinds of external methods and forms of digital learning with distance education technologies. The results of their application can be preserved and replicated. To make their digital learning with distance education technologies more efficient, educational institutions have to minimize external systems and look into possibility of introducing mass open online courses.
Sanal/Kripto Varlıkların/Paraların Vergilendirilmesi

The digital economy produces its elements. The most important elements are the coins/assets produced in digital or virtual environments, which cannot be fully conceptualised. In terms of taxation, the most important issues are the definition of these assets, the determination of a term that everyone generally agrees on, and the classification of these assets. This study aims to find the current crypto asset definition, classification, and taxation situation. For this purpose, Turkey and various country implementations and OECD recommendations were examined. A unity of terminology has yet to be reached on this issue, and the term virtual currency was used for these assets in the report published by the OECD in October 2020. As a result of this study, it is found that there is no still unity of implementation in terms of the taxation of these virtual assets, which are still considered illegal in some countries.
Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction

Rich user behavior information is of great importance for capturing and understanding user interest in click-through rate (CTR) prediction. To improve the richness, collecting long-term behaviors becomes a typical approach in academy and industry but at the cost of increasing online storage and latency. Recently, researchers have proposed several approaches to shorten long-term behavior sequence and then model user interests. These approaches reduce online cost efficiently but do not well handle the noisy information in long-term user behavior, which may deteriorate the performance of CTR prediction significantly. To obtain better cost/performance trade-off, we propose a novel Adversarial Filtering Model (ADFM) to model long-term user behavior. ADFM uses a hierarchical aggregation representation to compress raw behavior sequence and then learns to remove useless behavior information with an adversarial filtering mechanism. The selected user behaviors are fed into interest extraction module for CTR prediction. Experimental results on public datasets and industrial dataset demonstrate that our method achieves significant improvements over state-of-the-art models.
AIDE

Review imagery is crucial for helping online shoppers validate product information and garner context from unbiased reviewers. However, blind or low vision (BLV) shoppers, who routinely use screen readers, report challenges accessing review imagery, citing minimal to generic alt-text that often accompany these images. Contextual alt-text impacts the quality of a BLV shopper's browsing experience; our initial surveys additionally reveal that shoppers want product color and product features in alt-text descriptions for review imagery. We present AIDE, an Automatic Image Description Engine for review imagery, which leverages object detection and keyword extraction to automatically generate alt-text containing context-specific product feature information. Evaluative studies indicate that AIDE promotes accessibility by fostering a more inclusive shopping environment, increases the feeling of independence, and encourages online retail engagement.
AIDE

Review imagery is a critical way to share products in authentic contexts. This visual method of showcasing a product can help sighted people govern their purchasing decisions, as it can include details not apparent in the product page and image. However, visually impaired people are not afforded the same opportunity due to lack of image descriptions. Previous research has found that context impacts the desired description; our studies have identified key features to extract from review imagery in the online retail context. In this paper, we present AIDE, an Automatic Image Description Engine that leverages computer vision and natural language processing to automatically generate image descriptions for user-generated imagery. Results from our evaluative studies show that AIDE encourages online retail engagement, fosters an inclusive shopping environment, and increases the feeling of independence.
Skew Cyclic Codes over the Non-Chain ring $\mathcal{R}_q=\mathbb{F}_q[v]/\langle v^{2}+1\rangle$

In this paper, we investigate the algebraic structure of the non-local ring $\mathcal{R}_q = \mathbb{F}_q[v]/\langle v^{2}+1\rangle$ and identify the automorphisms of this ring to study the algebraic structure of the skew cyclic codes and their duals over it.
Teaching Subtraction with the Think-Aloud Method to a Student with Mild Intellectual Disability

The research was conducted to teach subtraction skills through the think-aloud method. The research was carried out through action research, one of the qualitative research methods. The study group in the research consists of a mildly intellectually disabled student. Data collection tools used in the research are criterion-referenced measurement tools employed in pre-test and post-test following action research process, performance record charts, teacher and student journals, and the think-aloud method student interview form. The research was recorded through tape recording during the whole process until the preparation and evaluation phases of the research action plans. According to the research results, it was observed that the student was able to perform the steps of subtraction independently; she obtained the information regarding the meaning of subtraction and told this. It is thought that this research will contribute to the literature since it improved the student’s academic and self-expression skills.
sAuth: a hierarchical implicit authentication mechanism for service robots

Abstract With advances in networks, Artificial Intelligence (AI), and the Internet of Things, humanoid robots are rising in many areas, including elderly care, companion, education, and services in public sectors. Given their sensing and communication functionality, information leakage and unauthorized access will be of big concern. Very often, authentication techniques for service robots, especially those related to behavioral identification, have been developed, in which behavior models are created using raw data from sensors. However, behavioral-based authentication and re-authentication is still an open area for research, including cold start problems, accuracy, and uncertainty. This paper proposes a hierarchical implicit authentication system by joint built-in sensors and trust evaluation, coined sAuth, which exploits sensor data-based sliding window trust model to identify the service robot and its expected users. In order to mitigate the fluctuations of identification results in the real world environment, the trust evaluation is computed via combining the weighted intermediate identification probability of various small sliding windows. The performance of sAuth is evaluated under different scenarios where we show that (i) approximately 5–7% higher accuracy and 2–18% lower equal error rate can be achieved by our method compared to other works; and (ii) the hierarchical scheme with joint sensors and trust sliding windows improves the authentication accuracy significantly by comparing it with only sensor-based authentication.
The role of digital pathology in enabling remote operations during the CVID-19 pandemic and beyond a pathologist's perspective for the future opportunity

Significant progress has been made in the area of digital pathology during the past 20 years. With rapid scanners, whole slide imaging (WSI) can now create high-resolution digital duplicates of glass slides. Objective: To assess fundamental understanding of digital pathology among Pakistani pathologists practicing in labs throughout the country. Methods: The current study was cross sectional study carried out at the Department of Pathology, Gomal Medical College, Medical Teaching Institute, Dera Ismail Khan, Pakistan for a period of six months. This study was proforma based and pathologists across the country were included in the study. All the data from the questionnaire was entered and analyzed by employing IBM SPSS version 24. Results: A total of 210 pathologists were enrolled, amongst which 84 (40%) were males and 126 (60%) were females. Pathologist familiar with the Digital Pathology idea were 147 (70%) while 63 (30%) pathologists were not familiar with the digital pathology. Amongst 210 pathologists, 157 (74.77%) pathologists have idea about digital microscope. Pathologist having knowledge of pathology slide scanner idea were 147 (70%) while 63 (30%) pathologists were observed as having no knowledge of pathology slide scanner. Pathologist familiar with the idea of artificial intelligence were 96 (45.71%) while 114 (54.29%) participants were not familiar with the artificial intelligence. Conclusion: In spite of the fact that digital pathology is very promising and has resulted in a shift in the thought pattern of pathologists practicing in underdeveloped countries such as Pakistan, there are still challenges to overcome such as expense, technical assistance, regulation and validation needs.
Asian CHI Symposium: Decolonizing Technology Design in Asia

The Asian CHI symposium is an annual event organized by researchers and practitioners in the Asia Pacific since the authors first co-initiated South East Asia Computer-Human Interaction (SEACHI) during CHI 2015 [1]. The symposium aims to bring together both early-career and senior HCI academia and UX practitioners from industries in the Asia Pacific and bring about cross-exchange of information and transfer of knowledge in a multidisciplinary environment and multi-socio-economic aspects of HCI research, foster social ties and collaboration in the field of HCI [2]. As the Asian CHI community grows to be more diverse than when we started in 2015, there is a need to address the issues of equity, justice, access, and transparency more strategically, especially with a historical link to colonialism in Asia. Beyond showcasing the latest Asian-inspired HCI work and those focusing on incorporating Asian sociocultural factors in their design, implementation, evaluation, and improvement, the Asian CHI Symposium 2022 is a sandbox for decolonizing yet academically rigorous discourse platform for both HCI academic and UX practitioners to present their latest research findings and solutions that reflect the expansion of HCI theory and applications towards culturally inclusive design for diverse audiences in Asia.
Automatic data volley: game data acquisition with temporal-spatial filters

Abstract Data Volley is one of the most widely used sports analysis software for professional volleyball statistics analysis. To develop the automatic data volley system, the vision-based game data acquisition is a key technology, which includes the 3D multiple objects tracking, event detection and quality evaluation. This paper combines temporal and spatial features of the game information to achieve the game data acquisition. First, the time-vary fission filter is proposed to generate the prior state distribution for tracker initialization. By using the temporal continuity of image features, the variance of team state distribution can be approximated so that the initial state of each player can be filtered out. Second, the team formation mapping with sequential motion feature is proposed to deal with the detection of event type, which represents the players’ distribution from the spatial concept and the temporal relationship. At last, to estimate the quality, the relative spatial filters are proposed by extracting and describing additional features of the subsequent condition in different situations. Experiments are conducted on game videos from the Semifinal and Final Game of 2014 Japan Inter High School Games of Mens Volleyball in Tokyo Metropolitan Gymnasium. The results show 94.1% rounds are successfully initialized, the event type detection result achieves the average accuracy of 98.72%, and the success rate of the events’ quality evaluation achieves 97.27% on average.
Mitigation Web Server for Cross-Site Scripting Attack Using Penetration Testing Method

The increasing number of user-oriented applications uploading all their information to the web is causing cyber-attacks and data theft. One of the most prevalent vulnerabilities is Cross-Site Scripting (XSS). Intruders take advantage of these attacks to access sensitive user data. This study aims to mitigate XSS attacks by using the penetration testing method as an official effort to improve web server security. The subject of this research uses the login form from the academic information system web server. This study offers a mitigation system prototype against XSS using the penetration test method and the secure code algorithm. This method plays a role in obtaining vulnerability data and security code as a prevention system. The results of this study indicate three categories of web server weaknesses: five at the high level, 164 at the medium level, and 52 vulnerabilities at the low level. Mitigation measures use secure code by denying repeated failed login attempts. These results provide a strategy for web managers to improve security and consider the risk of cyberattacks.
Particle Filter Design Based on Reinforcement Learning and Its Application to Mobile Robot Localization

Particle filters have been widely used for state estimation problems in nonlinear and non-Gaussian systems. Their performance depends on the given system and measurement models, which need to be designed by the user for each target system. This paper proposes a novel method to design these models for a particle filter. This is a numerical optimization method, where the particle filter design process is interpreted into the framework of reinforcement learning by assigning the randomnesses included in both models of the particle filter to the policy of reinforcement learning. In this method, estimation by the particle filter is repeatedly performed and the parameters that determine both models are gradually updated according to the estimation results. The advantage is that it can optimize various objective functions, such as the estimation accuracy of the particle filter, the variance of the particles, the likelihood of the parameters, and the regularization term of the parameters. We derive the conditions to guarantee that the optimization calculation converges with probability 1. Furthermore, in order to show that the proposed method can be applied to practical-scale problems, we design the particle filter for mobile robot localization, which is an essential technology for autonomous navigation. By numerical simulations, it is demonstrated that the proposed method further improves the localization accuracy compared to the conventional method.
A Semantic-Based Dual Location Privacy-Preserving Approach

With the popularity and development of Location-Based Services (LBS), location privacy-preservation has become a hot research topic in recent years, especially research on k-anonymity. Although previous studies have done a lot of work on anonymity-based privacy protection, there are still several challenges far from being perfectly solved, such as the negative impact on the security of anonymity by the semantic information, which from anonymous locations and query content. To address these semantic challenges, we propose a dual privacy preservation scheme based on the architecture of multi-anonymizers in this paper. Different from existing approaches, our method enhanced location privacy by integrating location anonymity and the encrypted query. First, the query encryption method that combines improved shamir mechanism and multi-anonymizers is proposed to enhance query safety. Second, we design an anonymity method that enhances semantic location privacy through anonymous locations that satisfy personal semantic diversity and replace sensitive semantic locations. Finally, the experiment on the real dataset shows that our algorithms provide much better privacy and use than previous solutions.
Toward Generating Robot-Robot Natural Counseling Dialogue

In this study, we generate dialogue contents in which two systems discuss their distress with each other. The user inputs sentences that include environment and feelings of distress. The system generates the dialogue content from the input. In this study, we created dialogue data about distress in order to generate them using deep learning. The generative model fine-tunes the GPT of the pre-trained model using the TransferTransfo method. The contribution of this study is the creation of a conversational dataset using publicly available data. This study used EmpatheticDialogues, an existing empathetic dialogue dataset, and Reddit r/offmychest, a public data set of distress. The models fine-tuned with each data were evaluated both automatically (such as by the BLEU and ROUGE scores) and manually (such as by relevance and empathy) by human assessors.
Localization of Pointed-At Word in Printed Documents via a Single Neural Network

Most existing object detection methods and text detection methods are mainly designed to detect either text or objects. In some scenarios where the task is to find the target word pointed-at by an object, results of existing methods are far from satisfying. However, such scenarios happen often in human-computer interaction, when the computer needs to figure out which word the user is pointing at. Comparing with object detection, pointed-at word localization (PAWL) requires higher accuracy, especially in dense text scenarios. Moreover, in printed document, characters are much smaller than those in scene text detection datasets such as ICDAR-2013, ICDAR-2015 and ICPR-2018 etc. To address these problems, the authors propose a novel target word localization network (TWLN) to detect the pointed-at word in printed documents. In this work, a single deep neural network is trained to extract the features of markers and text sequentially. For each image, the location of the marker is predicted firstly, according to the predicted location, a smaller image is cropped from the original image and put into the same network, then the location of pointed-at word is predicted. To train and test the networks, an efficient approach is proposed to generate the dataset from PDF format documents by inserting markers pointing at the words in the documents, which avoids laborious labeling work. Experiments on the proposed dataset demonstrate that TWLN outperforms the compared object detection method and optical character recognition method on every category of targets, especially when the target is a single character that only occupies several pixels in the image. TWLN is also tested with real photographs, and the accuracy shows no significant differences, which proves the validity of the generating method to construct the dataset.
LICENSEE’S PERCEPTION IN THE INTRODUCTION OF THE PEDAGOGIA RESIDENCE PROGRAM IN THEIR TRAINING AT THE FACULTY OF EDUCATION OF CRATEÚS

The main objective of this work is to analyze the perceptions that the members of the Pedagogical Residency Program have in relation to the program in which they are linked. The work deals with a bibliographical research which emphasizes important points of the professional teaching career, such as the importance of the Pedagogical Residency program in the lives of undergraduates, theory in relation to practice, a brief summary of the history of the formation of initial teachers and the job market for teachers. In order to carry out this monographic work, a qualitative research was carried out through Google forms, in which eleven fellows from the group of undergraduate students of the Chemistry course at the Faculty of Education of Crateús participated, the research had five questions in which all were about obligatory objective questions. As a result, it can be concluded that the program is very important in the lives of future teachers, mainly because it allows teachers to have a broad view of what their chosen profession really is.
Consortia as an Organizational Form of Cooperation in Library and Bibliographic Activities: Foreign Experience

Cooperation at the national and international levels is a promising direction of libraries’ activity. One of the modern forms of such cooperation is library consortia that have been created around the world since the 1960s. The activities of such library associations have been successfully carried out in Russia for many years, but for the more complete understanding of the such organizations’ work character, it is necessary to examine the foreign experience of library consortia creating. Considering the activities of the largest national, regional and international library consortia since the 1960s, one can find out the transformation of such institutions from organizations performing strictly defined functions, such as automating routine library processes or facilitating access to information and exchange of it, into a significant tool for strengthening cooperation in the field of librarianship and bibliography. The article presents the most common definitions of the term «library consortium», defines the main goals and objectives of such organizations, and considers the activities of the largest library consortia of the following types: consortia that unite libraries according to the technological principle and consortia uniting libraries of a certain type. The author pays attention to the International Coalition of Library Consortia (ICOLC), which is a special type of consortium – an association of associations.