Reviewerly/exHarmony huggingface dataset:
The Department as a Subject of professional self-determination of pedagogical university students

Introduction, problem statement. In modern conditions of changes in the labor market and the system of vocational education, the process of professional self-determination of young people and the resources for its activation, intensification, and optimization are of particular relevance. The authors consider the subjects of the university system of promoting the professional self-determination of students, within which a significant place is determined by the Department of Social Pedagogy. The purpose is to analyze and substantiate the significance of the subject position of the Department of Social Pedagogy in the intra-university system of promoting students’ professional self-determination. Methodology and research methods. Theoretical and empirical methods were used: the analysis of scientific psychological and pedagogical literature and the analysis of the experience of the Department of Social Pedagogy of the TSPU, demonstrating the importance of the subject position of the elements of the intra-university system of promoting professional self-determination of students. Research results. On the basis of the analysis and generalization of the empirical experience of the Department of Social Pedagogy of the TSPU, the main directions in the context of promoting the professional self-determination of students are presented: lined up interaction with social partners – social organizations; organization of pedagogical intensives for TSPU students; support of industrial pedagogical practice of students of pedagogical areas of training; assistance in organizing employment (occupation) of students and graduates. Conclusion. It is determined that the subjective position of the Department of Social Pedagogy affects the “entry” of students of pedagogical areas of training in professional pedagogical activities, their con scious employment in teaching positions (even during their studies at the university) and is considered as one of the conditions for intra-university assistance to professional self-determination of students.
Management of the Procedural (Technological) Component of the Engineer's Training System

The article is devoted to the problem of quality management of education at a technical university on the basis of criteria-based assessment and monitoring of a student’s professional development using digital resources. The mechanism of education quality management includes monitoring and evaluation of students’ professional development, analysis of the educational situation and decision-making on adjusting the content and methods of forming the readiness of future specialists for engineering activities. This work is a continuation of the author’s research on education quality management. The purpose of this article is to consider the impact of university policies in the main areas of activity on the procedural (technological) component of the engineer training system and to propose a set of measures aimed at improving the quality of education. The methodology of the study consisted of: a systematic approach, an activity-based approach, a personality-oriented approach, a competence-based approach, and a cybernetic approach. In conclusion, on the basis of the reviewed university policies on the main areas of activity for the procedural (technological) component of the engineer training system, complexes of measures aimed at improving the quality of education are proposed.
Stravinsky’s Path to Serialism

It was inevitable that Igor Stravinsky would experiment with serialism, given his penchant for interval-based composition, even making a comment to Milton Babbitt that he had always composed with intervals. Stravinsky’s intrigue with intervallic patterns is significant in some of his earlier works – particularly the motivic networks supporting the narratives of Firebird (1910) and Perséphone (1934). In fact, examining the interval ordering in the motives from these works, Stravinsky, perhaps unwittingly, retains the exact order of intervals while producing twelve different pitch classes. In retrospect, this seems to have foreshadowed the development of his own brand of serialism in his later years, beginning with Cantata in 1952, and maturing over the fourteen years through works such as Septet, In Memoriam Dylan Thomas, Canticum Sacrum, Threni, Agon, Movements for Piano and Orchestra, A Sermon, a Narrative, and a Prayer, Variations: Aldous Huxley in memoriam, Abraham and Isaac, Elegy for J.F.K., Introitus: T.S. Eliot in memoriam, and his last major work, Requiem Canticles, in 1966.
Predicting individual brain MRIs at any age using style encoding generative adversarial networks

Brain structural changes in older adults over time can help identify and predict which individuals are at risk for neurodegenerative disorders and dementias. These trajectories are traditionally calculated by assessing localized rates of brain tissue atrophy from a longitudinal series of brain MRIs as compared to group averages. However, these methods do not preserve individual differences in brain structure, which may provide added information regarding risk. A map of how an individual’s brain may look at a given age - in the case of a normal, healthy, aging trajectory - may help to identify deviations and abnormalities when presented with a true scan at that age. Here, we consider estimating the age-related brain changes as a domain transfer problem. We develop a fully unsupervised generative adversarial network (GAN) with cycle consistency reconstruction losses, trained on cross-sectional brain MRI data from participants of the UK Biobank aged 45 to 81. We show that brain MRIs for males and females at a given age can be predicted by converting the content information encoded in a T1-weighted MRI (i.e., the individual’s identifying anatomical features), accompanied by adding the style (age/sex) information from a reference group. Results on the PREVENT-AD cohort demonstrated that our style-encoding domain transfer model can predict follow-up brain MRIs, successfully, without relying on longitudinal data from the subjects. We show how deviations from the predicted images are indicative of factors related to neurodegenerative disease risk.
The Teaching of Machine Translation

Based on nine years’ teaching experience on computer translation courses in the master’s degree program of computer-aided translation (MACAT) at the Chinese University of Hong Kong, a case study of the essential components and teaching methodologies employed is introduced and discussed. The outcome-based strategy is also addressed.
Post-Editing of Machine Translation

This chapter starts with a justification of why post-editing MT has become more prevalent in recent years. The common practice of post-editing is then introduced, with demonstrations of post-editing guidelines at different levels and with different organizations. The role of source text in post-editing is explored in relation to the increase of translation productivity. Data from a recent experiment of post-editing MT texts with and without source text between English and Chinese will be analyzed in detail to show the impact of the source text on the time and quality of post-editing. Suggestions are provided to facilitate post-editing in a productive manner.
Investigating Group Distributionally Robust Optimization for Deep Imbalanced Learning: A Case Study of Binary Tabular Data Classification

One of the most studied machine learning challenges that recent studies have shown the susceptibility of deep neural networks to is the class imbalance problem.While concerted research efforts in this direction have been notable in recent years, findings have shown that the canonical learning objective, empirical risk minimization (ERM), is unable to achieve optimal imbalance learning in deep neural networks given its bias to the majority class.An alternative learning objective, group distributionally robust optimization (gDRO), is investigated in this study for imbalance learning, focusing on tabular imbalanced data as against image data that has dominated deep imbalance learning research.Contrary to minimizing average per instance loss as in ERM, gDRO seeks to minimize the worst group loss over the training data.Experimental findings in comparison with ERM and classical imbalance methods using four popularly used evaluation metrics in imbalance learning across several benchmark imbalance binary tabular data of varying imbalance ratios reveal impressive performance of gDRO, outperforming other compared methods in terms of g-mean and roc-auc.
Complex Online Harms and the Smart Home: A Scoping Review

Background: Technological advances in the smart home have created new opportunities for supporting digital citizens’ well-being and facilitating their empowerment but have enabled new types of complex online harms to develop. Recent statistics have indicated that ‘smart’ technology ownership increases yearly, driven by lower costs and increased accessibility. Research on smart homes has also developed, focusing on technology perspectives at the expense of a user-centric approach sensitive to the smart home’s harms, risks, and vulnerabilities.Objective: This scoping review addresses the information gap where there exists a paucity of information regarding the complex online harms and their relationship to the ‘smart home’ technologies and citizens’ agency.Design: Three online databases were utilised to identify papers published between 2017 and 2022, from which we selected 235 publications written in English that addressed harms, risks, vulnerabilities, and agency in the smart home context. By mapping contemporary literature to identify the existing dearth of knowledge to identify opportunities for further research.Results: This review identified emerging themes of ’risks’, ’vulnerabilities’, and ’harms’ in that order of frequency within the literature on smart homes. The usage of terms is skewed towards computing science and information security as that comprised the majority of the literature at 54.6%, human-computer interaction papers accounted for 24.4%, social sciences at 16.2%, and the minority being in law at 4.8%.Conclusion: Risks, harms and vulnerabilities within smart home ecosystems and IoTs are ongoing issues with complexities that necessitate research. Privacy, security, and well-being are key themes that embody the scope of complex harms affecting smart home devices in the broad literature. This review establishes disciplinary research gaps, especially in user-centred perspectives, due to a heavy technology focus in the existing literature. We argue that further research is needed to address emergent risks, harms and vulnerabilities of smart home devices and understand how user agency and autonomy can complement the design, interface and sociotechnical aspects of smart home systems.
Investigating Consumers’ Intent to Shop at Metaverse: A Proposed Agenda

The term <b>Metaverse</b> is comprised of the prefix “meta” and “universe” indicating the imaginary linkage between the physical and digital world where the users can interact with each other through digital avatars. Metaverse creates the opportunity to relate consumers’ increased demand for novel experiences with compelling retail experiences that have established a presence in a 3D virtual world. In the Metaverse, the development of “virtual avatars” creates the opportunity for different brands to connect with consumers by engaging them in a new shopping experience and facilitate businesses in a unique way. There are ample opportunities for selling digital goods inside the Metaverse. Studies related to the Metaverse in retail settings are limited. Therefore, this study was to explore the recent status of the Metaverse and proposed to borrow concepts of consumer experiences and engagement research in the physical world and adopt them in the Metaverse context for future research.&nbsp
COMPARISON OF BLEEDING TIME AND CLOTTING TIME IN WOMEN AGAINST ABO BLOOD TYPE

Kelompok darah ABO dan jenis kelamin yang berbeda dapat memberikan hasil yang berbeda dari waktu perdarahan dan waktu pembekuan. Kebaruan penelitian ini karena meneliti tentang perbandingan bleeding time dan clotting time pada wanita terhadap golongan darah ABO. Penelitian ini bertujuan untuk membandingkan waktu perdarahan dan waktu pembekuan pada wanita berdasarkan golongan darah ABO. Penelitian ini merupakan penelitian deskriptif dengan desain cross sectional dan sampel total 115 subjek. Pengukuran waktu perdarahan menggunakan metode duke, waktu pembekuan menggunakan metode slide dan golongan darah menggunakan metode aglutinasi slide. Penelitian ini menggunakan uji komparatif numerik dengan one way ANOVA, dilanjutkan dengan analisis post hoc untuk membandingkan waktu perdarahan dan waktu pembekuan antara golongan darah ABO. Hasil penelitian ini menunjukkan waktu perdarahan pada golongan darah B yang secara signifikan lebih lama dibandingkan golongan darah lainnya (p=0,001). Sementara itu, waktu pembekuan pada golongan darah B secara signifikan lebih lama dibandingkan golongan darah lainnya (p= 0,001). Kesimpulan bahwa wanita dengan golongan darah B memiliki waktu perdarahan dan waktu pembekuan yang jauh lebih lama daripada golongan darah lainnya.Kata Kunci: Waktu pendarahan; Waktu pembekuan; Golongan darah ABO.Â AbstractDifferent ABO blood groups and sexes can give different results from bleeding and clotting times. This study's novelty is that it examines the comparison of bleeding time and clotting time in women to the ABO blood type. This study aimed to compare bleeding and clotting time in women based on ABO blood type. This study is a descriptive study with a cross-sectional design and a total sample of 115 subjects. Measure bleeding time using the duke method, clotting time using the slide method, and blood type using the slide agglutination method. This study used a comparative numerical test with a one-way ANOVA, followed by post hoc analysis to compare bleeding and clotting time between ABO blood types. This study showed that the bleeding time in blood type B was significantly longer than in other blood types (p=0.001). Meanwhile, the clotting time in blood type B is considerably longer than in different blood types (p= 0.001). In conclusion, women with blood type B have much longer bleeding and clotting times than other blood types.Keywords: Bleeding time; Freezing time; ABO Blood type.Â
Simulation Operation System of Civil Aviation Professional Electromechanical Equipment Based on Human-Computer Interaction

Traditional equipment simulation operating system adopts the principle of combining real object with simulation, which has the problems of poor feedback accuracy and slow operation feedback speed. In view of the above problems, this research designs a civil aviation professional electromechanical equipment simulation and operation system based on human-computer interaction. Taking FPGA and arm as the core, the hardware part of the system is designed by connecting Kinect sensor. After the electromechanical equipment simulation model is established by using 3D Max software, the key frames in the actual operation interactive action video are extracted by using k-means clustering algorithm, and then the simulation operation gestures are recognized by combining with the expert judgment module, so as to realize the electromechanical equipment simulation operation training under human-computer interaction. Experimental results show that the feedback accuracy of the system is higher than 95%, and the feedback is sensitive, interactive and the simulation scene is clearer.
Exploring Spatial-Temporal Instance Relationships in an Intermediate Domain for Image-to-Video Object Detection

Image-to-video object detection leverages annotated images to help detect objects in unannotated videos, so as to break the heavy dependency on the expensive annotation of large-scale video frames. This task is extremely challenging due to the serious domain discrepancy between images and video frames caused by appearance variance and motion blur. Previous methods perform both image-level and instance-level alignments to reduce the domain discrepancy, but the existing false instance alignments may limit their performance in real scenarios. We propose a novel spatial-temporal graph to model the contextual relationships between instances to alleviate the false alignments. Through message propagation over the graph, the visual information from the spatial and temporal neighboring object proposals are adaptively aggregated to enhance the current instance representation. Moreover, to adapt the source-biased decision boundary to the target data, we generate an intermediate domain between images and frames. It is worth mentioning that our method can be easily applied as a plug-and-play component to other image-to-video object detection models based on the instance alignment. Experiments on several datasets demonstrate the effectiveness of our method. Code will be available at: https://github.com/wenzihan/STMP .
Research on Unmanned System Task Coalition Problem Based on Shuffled Frog Leaping Algorithm

A mathematical model is developed and an improved Shuffled Frog Leaping Algorithm (SFLA) is proposed for solving the task coalitions formation problem in Autonomous Unmanned Systems (AUS). The single solution update in the traditional SFLA algorithm is improved to a complete solution update, the cross-variation operation of genetic algorithm is introduced, the differential evolution mechanism is designed, and different update strategies are adopted for different frog individuals, which effectively improves the convergence speed of the algorithm while avoiding the premature convergence of the algorithm into a local optimum. Finally, a simulation is carried out to verify the effectiveness of the improved SFLA algorithm in solving the task coalition problem.
Recovering and Reusing Historical Data for Science: Retrospective Curation Practices Across Disciplines

While data curation research and practice have provided a growing body of guidance for and tools to support the curation, sharing, and reuse of recent and future scientific data, attention to retrospective data curation has been limited. The Recovering and Reusing Archival Data for Science project draws on semi-structured interviews with scientists and data curators to investigate data recovery and reuse efforts focused on historical data, or data drawn from legacy research materials, across a wide range of institutional, disciplinary, and research contexts. This paper describes selected findings related to (1) the perceived value of historical data for current and future scientific research; (2) challenges particular to recovering historical data; and (3) ethical quandaries that arise in historical data recovery and reuse. These findings shed light on the potential impact of historical data recovery and implications for retrospective data curation practices in support of active scientific research across disciplines.
A Constructive Feature Grouping Approach for Analyzing the Feature Dominance to Predict Cardiovascular Disease

Predicting the resistivity of an individual is essential for the optimal and prompt treatment against cardiovascular disease (CVD) in the earlier stage, which recommends the requirement for productive risk evaluation tools. The data-driven-based approach can predict every individual's risk by handling the crucial data patterns. To facilitate clinically applicable CVD prediction resolving the missing data patterns and interpretability issues using machine learning (ML) approaches. Here, a multi-tier model is proposed for mining missing data patterns. Initially, data fusion is adapted to describe the block-wise data patterns. It enables patient data (1) grouping-based feature learning and imputation of missing data and (2) prediction model considering the data availability. The feature selection process uses group characterization to uncover the risk factors. Then, the boosting model is generalized for identifying the patient's sub-group. The experimentation is done on an online available UCI ML dataset to demonstrate the significance of the model compared to various other approaches. The model attains 99% prediction accuracy, which is substantially higher than other approaches.
A SEMIOTIC SIGNS ANALYSIS FOUND IN HOTEL TRANSYLVANIA: TRANSFORMANIA MOVIE POSTER

Semiotics is a study that explains signs, and that all things in this world are signs. Even if some things have no inherent significance, we can infuse meaning into them to make them signs. This research aims to find the meaning of semiotic signs found in the Hotel Transylvania: Transformania movie poster using the trichotomy signs theory based on Charles Sanders Peirce’s. The source data in this research is the Hotel Transylvania: Transformania movie poster. Then, the data studied are to explain the meaning of the trichotomy signs, categorize the verbal and non-verbal signs, and understand the color concept. A descriptive-qualitative method was applied in the analysis of this research. The result of the research shows that there are 97 data. In the representamen, there are 22 data: qualisign 1 data, sinsign 14 data, and legisign 7 data. In the object, there are 38 data: icon 14 data, index 9 data, and symbol 15 data. And for the interpretant, there are 37 data: rheme 29 data, dicent sign 1 data, and argument 7 data. Furthermore, for the verbal signs, there are 7 data, and for the non-verbal signs, there are 90 data.
ProbFlow: Using Probabilistic Programming in Anonymous Communication Networks

We present ProbFlow , a probabilistic programming approach for estimating relay capacities in the Tor network.We refine previously derived probabilistic model of the network to take into account more of the complexity of the real-world Tor network.We use this model to perform inference in a probabilistic programming language called NumPyro which allows us to overcome the analytical barrier present in purely analytical approach.We integrate the implementation of ProbFlow to the current implementation of capacity estimation algorithms in the Tor network.We demonstrate the practical benefits of ProbFlow by simulating it in flow-based Python simulator and packetbased Shadow simulations, the highest fidelity simulator available for the Tor network.In both simulators, ProbFlow provides significantly more accurate estimates that results in improved user performance, with average download speeds increasing by 25% in the Shadow simulations.
Demystifying Threshold Elliptic Curve Digital Signature Algorithm for MultiParty Applications

Elliptic Curve Digital Signature Algorithm (ECDSA) is a crucial authentication component of many modern internet and communication systems. It is used in different domains such as cryptocurrency, IoT, e-health, e-banking, e-governance, etc. Due to the spread of open, distributed and collaborative systems brought by the sharing economy, the surge of interest in generating ECDSA in a distributed manner, i.e. Threshold Signature has grown in recent years. Threshold ECDSA based on multiparty computation (MPC) enables two or more entities of a communication system to generate a signature in a distributed manner. Whereas a subset of t + 1 parties of a group of size n can forge a signature. Any subset of t or less cannot. Threshold ECDSA requires considerable expertise. Thus, in this paper, we provide an overview on how to build a threshold ECDSA scheme in a unified structured way and we describe briefly the different cryptographic building boxes required to provide secure communication between participants, in presence of malicious parties. We also review the state of the art of Threshold ECDSA built by the most recent works and we provide a comparison between the most known schemes that assume a dishonest majority. We finally highlight some challenges and open issues.