Reviewerly/exHarmony huggingface dataset:
Segmentation of bone metastases based on attention mechanism

SPECT bone imaging is an important means to assist doctors in diagnosing diseases. The traditional processing method is that radiologists diagnose images. Manual diagnosis is not only cumbersome and time-consuming, but also different diagnosis results will be caused by the different diagnosis experience of doctors. In view of the above problems, this paper uses U-Net network as the basic model, and at the same time conducts model performance optimization research. Based on the U-Net network, the attention mechanism is integrated to segment the bone metastases in the pelvic area. Introducing the attention mechanism into the U-Net network can help improve the correlation of the pelvic region and reduce the interference caused by problems such as uneven brightness and low contrast to the model. Through multiple sets of experimental demonstrations, the U-Net network integrated with the attention mechanism can better segment bone metastases in the pelvic region based on SPECT images, and the model indicators have been significantly improved.
Handshape Classification in a Reverse Dictionary of Sign Languages for the Deaf

Abstract This paper showcases the work that aims at building a user-friendly mobile application of a reverse dictionary to translate sign languages to spoken languages. The concept behind the reverse dictionary is the ability to perform a video-based search by demonstrating a handshape in front of a mobile phone’s camera. The user would be able to use this feature in two ways. Firstly, the user would be able to search for a word by showing a handshape for the application to provide a list of signs that contain that handshape. Secondly, the user could fingerspell the word letter by letter in front of the camera for the application to return the sign that corresponds to that word. The user can then look through the suggested videos and see their written translations. To offer other functionalities, the application also has Search by Category and Search by Word options. Currently, the reverse dictionary supports translations from Russian Sign Language (RSL) to Russian language.
Application of Machine learning algorithms in diagnosis and detection of psychological disorders

A psychological disorder can be described as the disturbance of the natural state of the mind that affects the cognitive and social behaviour of the individual. The rapid modernization of society and the lack of social and personal interactions are further assisting in the increasing number new cases of psychological disorders. This paper intends to provides a brief overview of existing research being carried out in the field of machine learning and diagnosis, classification and prediction of psychological disorders and will present a sample framework which uses the data from the electronic health records to extract different text-based documents and reports to produce a tagged list of words relevant to disorder which is matched against the symptoms and signs of different psychological disorders to predict the disorder. To validate this prediction, it is further checked against the output of the machine learning models that will predict the psychological disorder based on the patient's fMRI image and PET images extracted from the patient's EHR. Through this paper, readers will be able to get an overview of the recent developments in the field of diagnosis of mental disorders by utilizing the machine learning algorithms and techniques to process the relevant unstructured data for improving the accuracy of the diagnosis to reduce the risk of misdiagnosis.
Regularities of ore gold placement in central kyzylkumakh (South Tyan-shan)

The article discusses the patterns of distribution of gold mineralization in the Central Kyzylkum desert of the Southern Tien Shan. As a result of applying the methods of statistical metallogenic analysis, quantitative characteristics of the localization of gold-bearing objects were obtained, explaining some of the features of their formation in connection with the stages of the geological development of the region. It was found that the formation of the gold source and the gold deposits of the Central Kyzyl Kum itself involved all stages, from the stage of formation of the oceanic crust in the spreading zones to the stage of collision and associated metamorphism.
Analyzing the User Navigation Pattern from Web Logs Using Maximum Frequent Pattern Approach

Web Usage Mining is the application, it automatically discovers the user access pattern from web servers i.e. logs. The role of the organization is to collect the data from a web server on daily basis. This research work helps to predict the user navigation pattern using the classification and clustering technique. In this first stage, this technique identifies the interested potential users from a weblog. In the second stage, the classification technique is applied to classify users having maximum similar interest. In the third stage, the clustering techniques utilize a classifier to identify and predict the user request based on classification. This experimental result will improve the navigation pattern based on user behavior. This result will be used for predicting the user request on web sites.
Scalable Generic Platform For Big Data Visualization

The term data analytics is referred to as the study of raw data to gain knowledge. Visualizations are the best way to represent the analyzed data so that it could be consumed by anyone on the go. However, when the amount of data increases and use cases differ, creating these visualizations indulge difficulties and will become repetitive yet hectic tasks. The insights platform depicted here is a generic platform to ease the above said complex process. Here, a generalized platform is made using the internal data from the company and a full stack application is furnished. Since it's developed keeping the generic aspect in mind, the platform could be used for a wide range of use cases by making changes to the data engineering configurations and visualization queries. All others will remain the same and hence new and new applications could be developed in no time with much ease.
Features of the organisation of theatrical activities among rural schoolchildren

The definition is given, upbringing possibilities of theatre activities are identified; their main form is children's association. Theatre association is seen as an upbringing environment where pedagogues and children solve a set of tasks of children’s upbringing and socialising in the process of theatrical activity. Particular attention is paid to the fact that, when organising theatrical activities, one should take into account the peculiarities of the region, the societas and the specifics of rural school. Rural school often assumes the functions of additional education for children and adults; it is the centre of cultural and leisure activities for the whole population. Problems when organising theatre activities for rural children are considered; it is also noted that setting up a theatre group in a school helps to overcome the socialisation difficulties of rural children. A number of favourable factors, positively influencing the organisation and development of children’s theatrical activity, their socialisation, include close ties of school and societas, closeness of children to nature, development of interaction of children with parents, with fellow villagers, rural spiritual and moral environment, development of integrative connections. It is noted that theatrical activity in a number of rural schools is a system-forming one, i.e. it unites the activities of all actors, it contributes to the integration of general education and additional education of rural schoolchildren. Peculiarities of the content, forms, methods and technologies that can be used when organising theatrical activity of rural schoolchildren are considered.
Research on Blended Learning Methods of College Chinese Under the Background of “Internet+”

In the context of the “Internet+” education revolution, the hybrid teaching model has been gradually introduced into the education and teaching process of schools. School education staff independently meet the test of this teaching model, gradually change their thinking in teaching activities, optimize their own teaching ability, and create a relatively high-quality learning environment for students, which has become an inevitable requirement of education in the new era. As a general education subject of the school, college Chinese can enrich students’ humanistic literacy and comprehensive Chinese culture, which requires professional teachers to pay special attention to it and construct a mixed teaching classroom under the positive influence of Internet technology.
Learning Device-invariant And Location-invariant Embedding For Speaker Verification Using Adversarial Multi-task Training

Deep speaker embedding model has achieved the satisfactory performance in close-talking speaker verification. However, in the real home environment, the device used to record voice may be different and the distance between speaker and device is constantly changing. These will make performance degradation. In this paper, a novel approach of adversarial multitask training is proposed to solve the problem of device mismatch and location variation by learning device-invariant and location-invariant embeddings. A gradient reversal layer and a device-and-location classifier are added to the speaker validation model in order to build an auxiliary adversarial task. Experiments are conducted on the far-field text-dependent speaker verification database called HI-MIA, the proposed approach achieves 3.38% Equal Error Rate (EER) in close-talking enrollment task and achieves 3.36% EER in far-field enrollment task.
Learning Video Correspondence using Appearance Module for Target Tracking

We introduce a new method for self-supervised video correspondence matching to effectively track targets in battlefield situations. Specifically, we propose Appearance module that enforces to maintain a target appearance between two consecutive frames in a local window, where an affinity matrix is computed on high-resolution feature maps with a small search window. It has an advantage of mitigating an over-fitting problem of a conventional affinity matrix that only predicts motions by reducing ambiguity of labels in a pretest task. Also the proposed method preserves the detailed shape of an object by handling high resolution information while high computational costs due to the correlation filter is alleviated by a small search window. Our experimental results on the DAVIS2017 dataset showed the significant performance improvement over the baseline.
Experience in developing professional competences of social pedagogues in higher educational institutions of Kazakhstan

In the era of integration, the key resource of the country is the training of professionals in the social sphere. Global trends require the development of competences among University students. The scientific novelty and theoretical significance of the study, which consists in studying the experience of training social pedagogues at higher education institutions in Kazakhstan, taking into account the modern competency-oriented paradigm of education, is justified. The authors revealed that the competency-based approach at Kazakhstani universities is implemented in accordance with the principles of the Bologna Declaration. The relationship between educational programs and professional standards in Kazakhstan is consistent with modern international documents, primarily the European qualification framework, expressed in the Dublin descriptors. The researchers describe educational programs for training social pedagogues, the typology of competences implemented in the system of professional training of future specialists is presented. It is proved that an important element of educational programs is their modular structure and calculation of the volume of academic load in ECTS credits. The authors, based on the identification of the nature and trends in the training of social pedagogues at Kazakhstani universities as well as the analysis of regulatory documents, identified the general and special in the process of developing the competences of social pedagogues and made a forecast about the need to focus on professional competences accepted by the world community.
Deep Reinforcement Learning for Blockchain-Enabled Mobile Edge Computing Systems

With the rapid development of mobile edge computing (MEC), users’ data can be shared anywhere at any time. However, data privacy in MEC systems cannot be ensured, which impedes the growth of MEC. Blockchain is proposed as a promising technology, which is suitable for guaranteeing the security and traceability of data sharing. Nonetheless, existing blockchain schemes do not apply to MEC systems due to the dynamic characteristics of wireless channels and traffic loads. In this paper, we propose a secure data sharing framework for the blockchain-enabled MEC system. To meet the privacy requirements of different users, we present an adaptive privacy-preserving mechanism according to the available resources of the system and the level of users’ privacy. A secure data sharing scheme is introduced to maximize the performance of the system in terms of the decreased energy consumption of the MEC system and the increased throughput of the blockchain system by employing deep reinforcement learning. Numerical results demonstrate the advantages of the proposed secure data sharing scheme in MEC systems.
PREVENTING THE PRE-SICK CONDITIONS OF THOSE WHO PRACTICE LIFELONG LEARNING

The aim: Disclosure of the results of the study of the expediency of considering the fluidity of mental processes in the triad “organism – personality Disclosure of the results of the study of the expediency of considering the fluidity of mental processes in the triad “organism – personality – environment” and the alternate stages of the dynamics of performance to prevent the occurrence of pre-painful conditions in students, combining professional activity with formal education. Materials and methods: The complex of research methods is used in the work: general scientific (analysis, synthesis, comparison, systematization, generalization) and empirical (observations, discussions, questionnaires). The research was carried out within the framework of the international project “AHIA” (innovation in education; access mode: https://sites.google.com/view/project-axia/). The Spielberger-Hanin questionnaire was used to achieve the stated purpose of the study, the purpose of which was to assess reactive and personal anxiety. Results: The results of a study aimed at organizing a lifelong learning process based on mental processes and stages of performance dynamics showed that the likelihood of pre-painful conditions in those who combine full-time education in higher education with professional activity significantly decreases their self-actualization. Educational events taking place here and now, and recognizing the theoretical and practical significance of the educational material at the level of intrinsic motives. It is established that the likelihood of pre-morbid conditions in those who learn during life significantly increases in the case of reduced functional reserves of their body as a result of the intense and prolonged performance of educational activities with the simultaneous experience of “negatively” colored actual emotional states, generated by activities or events ) experiences of relationships in professional activity, family, family, etc. Conclusions: The proposed article describes the effectiveness and appropriateness of using mental processes and stages of performance dynamics as a means to prevent the onset of painful conditions in lifelong learners.
Calibration and Positioning of nonlinear wideband FMCW radar

The nonlinearity in the transmitted signal of the Frequency-modulated continuous-wave (FMCW) radar will greatly reduce the accuracy of target positioning. This paper establishes the third order signal model to quantify the nonlinearity for wideband FMCW radars. The signal measurement of reference object can estimate the parameters very well, then the nonlinear sampling of time domain greatly improves the accuracy of RF system measurement, and then a more accurate Positioning model of three-dimensional space target is proposed.
Research on Jiuqi Game Strategy Based on Chess Shape

Tibetan Jiuqi is a two-person chess type of Chinese ethnic minorities. In 2019, it was listed as one of the chess types of China Computer Game Championship. There are relatively few researches on Jiuqi's game strategy at home and abroad. The layout of the pieces of Jiuqi is generally divided into three stages: the opening, the middle game, and the endgame. This article is mainly based on the rules of the game to study the shape of the game in the opening and middle games. In the opening stage, the opening library is used to assist the game. In the middle stage, a strategy of forming a chess shape is proposed. This strategy is based on a single Dalian and further derives a more aggressive and flexible chess shape on this basis. The experimental results show that the strategy proposed in this article is effective.
A Context-independent Representation of Task

Most of the existing search and recommender systems do not adequately support complex search tasks. However, according to the theories and empirical evidence from interactive information seeking and retrieval studies, in people's search behaviors, actions, and outcomes, tasks that trigger the information search process in the first place generate different search actions. Also, defined successes and failures to find information and accomplish tasks are relative to the motivating task end goals, rather than the fulfillment of subtasks and goals of query segments along the search process. Based on this idea, the primary purpose of the proposed research is to explore how to leverage users' search behaviors and actions in developing methods to construct task representations that can be applied in all search contexts by conducting field studies. Then based on the data collected from the user studies, evaluate the task representations in designing search and recommendation techniques in naturalistic settings to examine the extent to which the context-independent task approach can approximate users' actual tasks and provide task-specific recommendations. The research will adopt a multi-disciplinary, multi-method approach with human-centered ML-based techniques in the process. The outcomes can help us better understand users' tasks and thus have implications for the task, user modeling, and system recommendations design.
Port ship Detection Based on Visual Saliency model and Center Dark Channel Prior

Aiming at the problem that the image texture is complex, the grayscale feature is very close to the wharf, and the accurate segmentation is very difficult in the process of port ship detection in high-resolution optical remote sensing image, a port ship detection method combining visual significance model and center dark channel priori is proposed. The watershed segmentation algorithm based on spectral intensity and image texture information achieves land and sea segmentation. The land area is masked to suppress the interference of complex land background. Extracting fitting line and smoothing port contour line. According to the characteristics of the straight shape of the port and the straight shape of both sides with the ship, the center line point of the line is defined to locate the port, and the set of candidate areas of interest to the port is obtained. Combined with the visual model and the dark channel prior feature, the ship initial significance map and the central dark channel map were fused to complete the ship target detection. Finally, through the bow characteristics of the ship, the adjacent ship is separated to eliminate false alarm and improve the detection accuracy. Experiments show that the algorithm is robust, has a good detection effect, and its accuracy and recall rate are greatly improved.
The Concept of Reverse Clustering

The paradigm of the reverse clustering is presented, consisting in attepting to reconstruct, via some clustering procedure, of a definite partition of a given set of data. We give some examples of such real-life problems. The reverse clustering problem is formulated, and its component parts described, i.e. the clustering algorithms considered, their parameters, the weighing or selection of variables, characterising the data items, the definition of distance, and, finally, the measure of similarity of the given definite partition and the one obtained from the reverse clustering procedure. Notation used throughout the book is also introduced. Some comments are given on the applied search procedure.
A Virtual Data Service Method Based on Autoregressive Model Optimized by Particle Filter

Aiming at the problems of random loss and fragment loss of monitoring data during the perception and transmission process, a virtual data service method based on autoregressive model optimized by particle filter is proposed. Firstly, the construction of autoregressive model for monitoring data is studied. The historical data are used to estimate the model parameters, and the virtual data are generated by model extrapolating to recover the missing data. Secondly, aiming at the problem that autoregressive model cannot adapt to the change of data trend caused by the state evolution of the monitored equipment, the particle filter is used to optimize the model parameters to improve the adaptability and accuracy of virtual data estimation. Finally, the simulating analysis and experimental results validate the effectiveness of the proposed method.
TopicTracker: A platform for topic trajectory identification and visualisation

Topic trajectory information provides crucial insight into the dynamics of topics and their evolutionary relationships over a given time. Also, this information can help to improve our understanding on how new topics have emerged or formed through a sequential or interrelated events of emergence, modification and integration of prior topics. Nevertheless, the implementation of the existing methods for topic trajectory identification is rarely available as usable software. In this paper, we present TopicTracker, a platform for topic trajectory identification and visualisation. The key of Topic Tracker is that it can represent the three facets of information together, given two kinds of input: a time-stamped topic profile consisting of the set of the underlying topics over time, and the evolution strength matrix among them: evolutionary pathways of dynamic topics, evolution states of the topics, and topic importance. TopicTracker is a publicly available software implemented using the R software.
LibGuides: Research Introduction: Introduction to NEW Virgo

With the challenges presented by the Fall 2020 semester and the increased dependence on the UVA Library's online resources, this guide is intended to walk you through the basics of access to our online databases as well as tips and tricks for honing your