Reviewerly/exHarmony huggingface dataset:
Combination of Classifiers with “Teacher” and “Without Teacher” in a Medical Diagnostic Task

At the moment, cardiovascular disease is the leading cause of death worldwide. Hypotension is one of the diseases related to the human cardiovascular system, which can manifest itself in different degrees of severity. Consequently, this problem requires different ways of study, including the use of neural networks and other ways of mathematical analysis of data. As the initial data for research we used information about the patients, characterizing the state of their cardiovascular system (CVS). The aim was to classify (cluster) hypotensive patients according to the state of different subsystems of the cardiovascular system. Taking into account the strong “noisiness” of the initial data, we used their functional preprocessing and a combination of Data Mining classification methods: neural networks (classifier with a “teacher”) and a number of clustering methods (k-means method, Ward’s method, full communication method) “without a teacher”. As a result of the “cross-sectional” study, patients with “noisy” data were identified, which were further excluded from the original data, which significantly improved the quality of classification.
Space-Angle Super-Resolution for Multi-View Images

The limited spatial and angular resolutions in multi-view multimedia applications restrict their visual experience in practical use. In this paper, we first argue the space-angle super-resolution (SASR) problem for irregular arranged multi-view images. It aims to increase the spatial resolution of source views and synthesize arbitrary virtual high resolution (HR) views between them jointly. One feasible solution is to perform super-resolution (SR) and view synthesis (VS) methods separately. However, it cannot fully exploit the intra-relationship between SR and VS tasks. Intuitively, multi-view images can provide more angular references, and higher resolution can provide more high-frequency details. Therefore, we propose a one-stage space-angle super-resolution network called SASRnet, which simultaneously synthesizes real and virtual HR views. Extensive experiments on several benchmarks demonstrate that our proposed method outperforms two-stage methods, meanwhile prove that SR and VS can promote each other. To our knowledge, this work is the first to address the SASR problem for unstructured multi-view images in an end-to-end learning-based manner.
OGrES Welcome! Toward a Systematic Theory for Serious Game Design

Despite the vast scholarship around serious games, there is still no consensus methodology for how to best go about designing them. This paper presents work-in-progress toward synthesizing an actionable, systematic serious game design theory. We introduce mIBO as a computational model to aid in thinking about game design processes and rationales, and OGrES as a method for defining the function of a serious game.
Selection of contour images based on alphabetical representation

The article is devoted to the actual problem of searching for contour images by given reference samples, due to the large amount of graphic scientific and technical information of the Contour type. Existing methods for image search do not allow taking into account the actual identity of images arising from affine transformations (stretching / compression, rotation) resulting from replication, which is important when analyzing graphic documentation. The paper proposes an alphabetical representation of contour images, which made it possible to construct a complex of affine-invariant equivalence relations. The relations built up served as the basis for the development of a set of selection procedures that can be considered as the initial stage of image search. The procedures included in the complex sequentially implement the reduction of the search scope up to a small set of contour images, allowing for the possibility of visual analysis. The complex is programmatically implemented in Python using the CUDA (NVIDIA GPU) software and hardware architecture for parallel computing.
ScaleReactor: A graceful performance isolation agent with interference detection and investigation for container‐based scale‐out workloads

Summary Striking a balance between improved cluster utilization and guaranteed application QoS is a long‐standing research problem in multi‐tenants shared cluster. The typical solution is to detect performance degradation and investigate the root cause to conduct performance isolation. Existing efforts rely on lots of prior knowledge of applications and the assumption of interference‐free workload placement is possible. Performance interference is usually mitigated through application‐level approaches such as centralized rescheduling, which is usually an hindsight and a waste of resources. In this article, we present ScaleReactor, a graceful runtime agent on a per node basis that decouples the performance isolation from centralized resource management, and migrates the performance interference of scale‐out workloads in container‐based cluster using a lightweight black‐box approach. ScaleReactor analyzes the degree of contention for multi‐dimensional resources among co‐located workloads to detect the performance degradation without additional prior information, and uses correlation analysis to locate the cause of contention, while isolating resources in a graceful manner to reduce system overhead and the performance degradation of intrusive workloads. Experiments have demonstrated that ScaleReactor effectively reduces the job completion time of scale‐out applications in shared clusters, with the maximum value up to 36% and low system overhead against the existing isolation mechanism.
INTERNET FRAUD: THE ATTITUDE OF YOUNG PEOPLE

In modern conditions of information technologies constant integration into a person’s everyday life, the problem of the formation and manifestation of new risks while using the Internet is aggravated. According to international and all-Russian data, there is a significant increase in the number of cybercrimes, new forms of Internet fraud are constantly appearing and the actual damage from illegal actions in the virtual space is increasing. Young people, as the most active users of the global network, become targets of Internet fraudsters. The purpose of the study is to study the representatives attitude of modern youth to fraud on the Internet, as well as to various forms of its manifestation. The theoretical and methodological analysis of the study was carried out on the basis of the deviant behavior theories and the theory of manipulation. To obtain primary sociological data, the work used the method of young people online survey living in Tyumen. The survey was conducted in the summer of 2020. According to the results of the study, the attitude of modern young people representatives to Internet fraud was determined. It was recorded that the overwhelming majority (85%) of young people faced various manifestations of fraud on the Internet, while every fourth (26%) respondent suffered from this negative social phenomenon. Most often, fraud on the Internet by young people is recorded in virtual social networks using social engineering technologies. In addition, not all forms of online fraud are well known to young people, for example, just under half of the respondents are aware of online banking fraud. Overall in the sample, young people negatively assess Internet fraud as a phenomenon, although there are some different opinions depending on their personal life experiences. In the conclusion of the article, possible prospects for further research in this area are identified.
A Multi-layered Friend Recommendation System on Twitter

Nowadays, the number of active users on social media is growing. Therefore, the friend recommendation plays a critical role in building a substantial social network. Compared with previous recommendation systems in social networks, our research is not focused on a particular direction (e.g., geographic location, tag) but introduces another field of social media, common interests among users. In the proposed recommendation system on Twitter, the common interests between two users are determined by four features retrieved from a Twitter user account: user introduction, geographic distance between the target user and candidate users, keywords in tweets, and hashtags of tweets. These features are utilized to calculate the similarities between the candidate users and the target user. In the end, a candidate user with a high similarity score is recommended to the target user.
Physical training within the discipline "Plastic education"

The author demonstrates that plastic education (if we do not mean the name of the discipline, but the term that is very popular in the professional circle of teachers) is an abbreviated term that means “education of the plastic culture of actors”. Actor's plastic culture is the level of professional skill in the field of stage plastic; in this case, stage plastics mean the external, physical (bodily) side of stage action, understood in general as a single psychophysical process. In practice, the plastic culture of an actor is expressed in the ability, readiness and need to realize stage action in a figurative, artistically significant plastic form. If we are talking about plastic education as a discipline in an educational institution, the content of this discipline is quite extensive; it has many sections aimed at acquiring the necessary knowledge, skills and abilities by the future actor, as well as developing certain abilities and qualities necessary for working in acting profession. In this work, I will consider in detail physical training as one of the most important and basic section of the discipline "Plastic Education". The article shows the role of physical training in the plastic education of an actor.
FEATURES OF THE ORGANIZATION OF THE WORLDSKILLS COMPETITION IN THE PREPARATION OF A TRANSPROFESSIONAL IN A VOCATIONAL PEDAGOGICAL UNIVERSITY (THE «SKILLS-CASE» METHOD)

Представлены теоретико-методологические подходы к подготовке транспрофессионала в условиях профессионально-педагогического вуза на базе специально подготовленных площадках кафедры. Обоснована специфика подготовки к конкурсу WorldSkills на основе трансдисциплинарного подхода, конкретизирована целевая установка и результат каждого компонента деятельности структурно-содержательной модели. Компонентный состав модели включает примеры модульных заданий, разработанных с применением метода «скиллс-кейс» и направлен на транспрофессиональную подготовку конкурсантов в условиях имитационных площадок кафедры. Применение метода «скиллскейс» позволяет на качественно новом уровне подготовить большее количество конкурсантов чемпионата в процессе обучения, а также в условиях практик не только по выбранной профессии, но и в смежных транспрофессиональных областях. Theoretical and methodological approaches to the training of a transprofessional in the conditions of a professional pedagogical university on the basis of specially prepared sites of the department are presented. Analyzing the accumulated experience, the conclusion arises that it is necessary to study issues (concretization, clarification, updating) from the field of technologyoriented training due to the lack of a holistic view of the formation of the personality of a specialist capable of self-improvement and self-realization in the transdisciplinary areas of machine-building industries. Currently, in professional education, a system of training a specialist with a wide production profile is presented, but it does not sufficiently take into account the structural and content component of technology-oriented activities, the absence of which is compensated by the acquisition of experience already in the conditions of the enterprise (production). The specifics of preparing for the WorldSkills competition on the basis of a transdisciplinary approach are substantiated, the target setting and the result of each component of the activity of the structural and content model are specified. The component composition of the model includes examples of modular tasks developed using the «skills-case» method and are aimed at transprofessional training of contestants in the conditions of simulation sites of the department. The use of the «skills-case» method allows you to prepare a larger number of contestants of the championship at a qualitatively new level in the learning process, as well as in the conditions of practice not only in the chosen profession, but also in related transprofessional areas.
Clinical Medicine Retrieval System Oriented to Treatment Plan

In order to overcome the problem of low retrieval efficiency in the field of clinical medicine. According to the TREC evaluation task, the paper proposes a method for constructing a clinical medical retrieval system for treatment plan to improve retrieval efficiency, aiming at being able to obtain effective treatment options by entering patient-related information to facilitate the solution given by physicians or biomedical scientists. For the specificity of the dataset, this method considers genetic and demographic characteristics to preprocess documents and topics, and improve the query using the query expansion technique based on Medical Subject Headings (MeSH); Set up a Lucene-based retrieval system to read in the converted files, and get the primary feedback results by inputting the processed query into the retrieval, and then filter the result document to get the final result document. Experimental results show that this method can obtain documents with high relevance, which is of great significance to the field of clinical medicine information retrieval.
Cost effective processing of detection-driven video analytics at the edge

We demonstrate a real-time video analytics system for applications that use objection detection models on incoming frames as part of their computation pipeline. Through edge-cloud collaboration, we show how a reinforcement learning based agent can skip successive video frames while keeping the object detection results almost intact for end applications.
I will stand by you: Measuring the perceived Social Presence towards Semi-Autonomous Companions in a 2D game

This paper investigates the players’ perceived social presence and willingness to support pet-like companions within a computer game. In detail, we raised the question of whether subjects will experience a higher degree of social presence towards a companion when this companion acts more autonomously than a companion that does not show any autonomous behavior? We created an experimental setting with two conditions realized via a puzzle game prototype to investigate this question. Ten subjects took part in a study (within-subject design). Results show that, in general, companions with a higher degree of autonomy lead to a higher degree of social presence. However, this applies only to basic forms of social presence. Some players noted that the social bond did not go beyond the game. With our discoveries, we want to provide researchers insights into pet companions’ design in games and help game designers create social bonds between the players and their companions.
Towards Post-Quantum Enhanced Identity-Based Encryption

Identity-based encryption (IBE) is a type of public-key encryption (PKE) that employs an identifier as the basis for the encryption mechanism. Thus, the communication parties are able to encrypt messages (or verify signatures) without any prior setup between users or distribution of user certificates. This is especially relevant in many mission critical applications, usually characterized by constrained end-points. MIKEY-SAKKE uses this concept to build a highly scalable protocol able to secure cross-platform multimedia communications. However, MIKEY-SAKKE is based on cryptographic primitives that will be no longer secure when sufficiently powerful quantum computers are built. To this end, this paper presents three contributions. First, it evaluates the performance of MIKEY-SAKKE in constrained embedded devices. Second, it extracts the requirements that post-quantum cryptographic primitives should meet in order to allow a plug-and-play replacement of the threatened security primitives with quantum-secure primitives. Third, it benchmarks the different post-quantum primitives running in the NIST standardization process and analyses their impact on the quantum-secure MIKEY-SAKKE. The results show that none of the NIST finalists perfectly meet all the specified requirements to achieve a post-quantum plug-and-play approach. However, the different combinations of post-quantum KEMs and signature schemes have a range of trade-offs compared to SAKKE and ECCSI, either having slower computation or larger keys and ciphertexts/signatures, or both.
Multi-camera tracking technique of a speaker based on Arduino

In this aera of smart applications, multi-camera tracking technique has established itself as an emerging technology that is being popular day by day. Addition identification of the active speaker using camera assisted microphone array. The possibility of identifying the active speaker component and the camera automatically turns towards the sound source. The PZT cameras work not limited to the human voice, but it is sensitive to all the vibration of sound and works on the camera and audio recording is not observed, but it can record video conference. It has been programmed to save the information about the speaker or the sound source and the ability of displaying it at the anther time if the need arises. According to the option of night vision by adding an infrared sensor. This type of sound sensitive surveillance cameras is of a great important and urgent need, because of its benefit commensurate with all requirement. The camera tracking has developed pervious research on surveillance cameras, with an external cover suitable for environmental changes, and a few very high efficiencies. Assessment by user and independent codes suggests promising initial results and future work. We associate the projected 3D tracker against anther procedures on the all database and on an anew collected data group with sensors, which we variety presented to the investigation public. A speaker position combined with camera image protects effective and accurate tracking structure. The scheme was applied Arduino microcontroller for image processing and development of tracking system and increasing the accuracy of the tracking of living organisms.
Predicting Categorial Sememe for English-Chinese Word Pairs via Representations in Explainable Sememe Space

Sememe is the minimum unambiguous semantic unit in human language. Sememe knowledge bases(SKB) have been proven to be effective in many NLP tasks. Categorial sememe, indicating the basic category of word sense to bridge the lexicon and semantics, is indispensable in SKB. However, manual categorial sememe annotation is costly. This paper proposes a new task to automatically build SKB: English-Chinese Word Pair Categorial Sememe Prediction. The bilingual information is utilized to resolve the ambiguity challenge. Our method proposes the sememe space, in which sememes, words, and word senses are represented as vectors with interpretable semantics, to bridge the semantic gap between sememes and words. Extensive experiments and analyses validate the effectiveness of the proposed method. Using this method, we predict categorial sememes for 113,014 new word senses, and the prediction MAP is 85.8%. Further we conduct expert annotations based on prediction results and increase HowNet nearly by 50%. We will publish all the data and code.
Fighting AI-synthesized Fake Media

Recent years have witnessed an unexpected and astonishing rise of AI-synthesized fake media (GAN synthesized faces, face-swap videos, and style transferred audios, commonly known as the DeepFakes), thanks to the rapid advancement of technology and the omnipresence of social media. Together with other forms of online disinformation, the AI-synthesized fake media are eroding our trust in online information and have already caused real damage. It is thus important to develop countermeasures to limit the negative impacts of AI-synthesized fake media. In this presentation, I will first provide a high level overview of the AI-synthesized fake media and their potential negative social impacts. I will then highlight recent technical developments to fight AI-synthesized fake media, including the signal level methods [1], and physical/physiological based methods [2,3], as well as our effort of making large datasets [4] and open-platforms for DeepFake detection [5]. I will also point out some current challenges, and discuss the future of AI-synthesized fake media and their counter technology.
Recognition of Nested Entity with Dependency Information

Named entity recognition (NER) is a basic task in natural language processing. However, most existing models are hard to detect entities with nested structure which means that an entity contains one or more entities. In this paper, we propose a boundary-aware approach for nested NER. First, word information is incorporated in the same dimension via Lexicon, in which characters are feed into LSTM to learn internal structure of words and obtain character representation. To augment word representation, Graph Convolutional Network (GCN) is applied to extract dependency information between entities. Second, our model can detect boundaries to locate entity by using Star-Transformer, which is suitable for small-scale corpus and unstructured texts because of its star structure. Based on predicted boundaries, our model utilizes boundary-aware regions to predict entity categorical labels, which can reduce the number of candidate entities and decrease computation cost. In our experiment, it shows an impressive improvement on forum corpus and that our model can perform well on a small-scale corpus.
A deep action-oriented video image classification system for text detection and recognition

Abstract For the video images with complex actions, achieving accurate text detection and recognition results is very challenging. This paper presents a hybrid model for classification of action-oriented video images which reduces the complexity of the problem to improve text detection and recognition performance. Here, we consider the following five categories of genres, namely concert, cooking, craft, teleshopping and yoga. For classifying action-oriented video images, we explore ResNet50 for learning the general pixel-distribution level information and the VGG16 network is implemented for learning the features of Maximally Stable Extremal Regions and again another VGG16 is used for learning facial components obtained by a multitask cascaded convolutional network. The approach integrates the outputs of the three above-mentioned models using a fully connected neural network for classification of five action-oriented image classes. We demonstrated the efficacy of the proposed method by testing on our dataset and two other standard datasets, namely, Scene Text Dataset dataset which contains 10 classes of scene images with text information, and the Stanford 40 Actions dataset which contains 40 action classes without text information. Our method outperforms the related existing work and enhances the class-specific performance of text detection and recognition, significantly. Article highlights The method uses pixel, stable-region and face-component information in a noble way for solving complex classification problems. The proposed work fuses different deep learning models for successful classification of action-oriented images. Experiments on our own dataset as well as standard datasets show that the proposed model outperforms related state-of-the-art (SOTA) methods.