Reviewerly/exHarmony huggingface dataset:
Data to Information: Computational Models and Analytic Methods

Computational models and analytic methods are increasingly important in the modeling and analyses of mental health and illness. The increased availability of data, the development of a wide range of analytic methods, and powerful and ubiquitous computing capability provide an unprecedented opportunity to develop computational models. Broadly speaking, two types of computational models are used in the context of mental health. Theory-based approaches generate explanatory models that describe the mechanisms of neural or psychological processes. Data-driven approaches typically extract predictive relations between variables and relevant outcomes or uncover patterns such as disease subtypes in data. Machine learning methods are increasingly used to develop models, especially data-driven models. This chapter will describe key methods and application examples, the workflow in machine learning, data preprocessing, feature selection methods, and the main categories of machine learning algorithms including supervised learning, unsupervised learning, semi-supervised learning, and deep learning. In addition, this chapter will briefly describe standards for reporting models and ethical and safety issues related to the development and use of computational models.
Technical Factors Affecting Augmented Reality User Experiences in Sports Spectating

The maturity of augmented reality (AR) technology and research now paves the way for dissemination of AR outside of the laboratory. However, it is still under-explored which factors are influencing the user experience of an AR application. In this poster, we describe some of the technical factors that could influence the user experience. We focus on a use-case in the field of on-site sports spectating with mobile AR. We present a study design which analyzes the influence of latency, registration accuracy, and jitter as factors on AR user experience.
Chronological Poor and Rich Tunicate Swarm Algorithm integrated Deep Maxout Network for human action and abnormality detection

Human behavior has always been a significant parameter in social interaction and human activity recognition is a major clue that eases the analysis of human character. In recent decades, researchers have gained more attention towards human activity detection that plays a huge part in different domains, like intelligent video surveillance, and human computer communication in order to improve security level. In order to recognize the human action from videos, effective features are required. However, extraction of effective features is still resembles as a challenging process due to changes occurred in human action. In order to counterpart such limitations, this article proposes an effective strategy for human action recognition and abnormality detection using Chronological Poor and Rich Tunicate Swarm Algorithm (CPRTSA)-based Deep Maxout Network. However, the human action recognition and abnormality detection is performed using same Deep Maxout Network, where the network classifier is trained using proposed CPRTSA. The proposed approach provides better accuracy in terms of human action recognition with higher efficiency and the proposed CPRTSA achieved a maximum accuracy of 0.959, maximum sensitivity of 0.963, and a maximum specificity of 0.965.
Comparing Contextual Embeddings for Semantic Textual Similarity in Portuguese

Semantic textual similarity (STS) measures how semantically similar two sentences are. In the context of the Portuguese language, STS literature is still incipient but includes important initiatives like the ASSIN and ASSIN 2 shared tasks. The state-of-the-art for those datasets is a contextual embedding produced by a Portuguese pre-trained and fine-tuned BERT model. In this work, we investigate the application of Sentence-BERT (SBERT) contextual embeddings to these datasets. Compared to BERT, SBERT is a more computationally efficient approach, enabling its application to scalable unsupervised learning problems. Given the absence of SBERT models pre-trained in Portuguese and the computational cost for such training, we adopt multilingual models and also fine-tune them for Portuguese. Results showed that SBERT embeddings were competitive especially after fine-tuning, numerically surpassing the results of BERT on ASSIN 2 and the results observed during the shared tasks for all datasets considered.
Vertical Scaling of Resource for OpenMP Application

OpenMP applications have been mostly executed on high-performance devices. As problem size expands and users’ demands for performance increase, whether to purchase higher-performance computers has become a problem faced by the organizations. Cloud offers a new way to solve this problem, which can automatically allocate elastic resources to meet different workload demands. In this paper, a vertical elastic solution for OpenMP applications is proposed, which is a combination of exponential smoothing and fuzzy logic control. According to the solution, an elasticity controller ECOMP was implemented, and the experimental verification was conducted from performance and accuracy. The results show that the controller can complete vertical elasticity scaling of resources, shorten the execution time of the program and improve the resource utilisation efficiency.
Analysis of Hybrid Power Generation System on the Highway and Tracking Through IoT

This paper aims to justify the characteristics of the hybrid power generation on the highway using vertical axis wind turbine (VAWT) and photovoltaic panels whose individual electrical parameters are monitored through the IoT from the base station. Solar and wind both are an unpredictable energy source, to get an uninterrupted power supply without disrupting the stability in the ecosystem with the utilization of few parts of the land, this hybrid power generation system is very feasible comparatively based on the economical setup and outcome of the power generation. The characteristic study of power-producing capacity from low-speed wind energy which comes from vehicles on the highway along with the integration of automatic solar radiation tracking PV panel has created interest toward highway lighting system in which Internet of Things (IoT) is envisioned to play a pivotal role in controlling and exchanging the data. For tracking and controlling the sources of energy, manually and remotely IoT technology plays a vital role. Through the help of an open source of IoT platform like the “Thingspeak” website using the NODEMCU ESP-32 WiFi module, the data is transmitted wirelessly which helps to control the power mechanism remotely by using a smartphone or personal computers very efficiently.
A Clustering Ensemble Algorithm of Computing Stability of Sample Points Based on Neighborhood

In the process of clustering ensemble, different sample points play different roles in the ensemble results, and the certainty of each sample point in the distribution to each cluster is also different. In order to reduce the impact of this uncertainty on clustering results, some scholars proposed the concept of sample stability. In this paper, we propose to calculate the stability of sample points by calculating the probability of the occurrence of sample points and sample points in their neighborhood in the same cluster of different base clusters, and propose an algorithm framework based on this calculation method. In this paper, the original data are first clustered to calculate the Mahalanobis distance between the sample points. Then, the co-occurrence probability of the target sample point and its nearest K sample points is calculated. According to the cooccurrence probability, the stability of each sample point is calculated. First, the stable sample points are hard clustered, and then the unstable sample points are assigned to the nearest cluster. The effectiveness of the proposed clustering ensemble algorithm is verified on benchmark datasets.
Development of Data Set for Automatic News Telecast System for Deaf Using ISL Videos

Sign Deaf people use sign language to communicate with others. There are numerous languages because of countries and their cultural variations. So, every country carries its sign language to serve the impaired people. In this paper, we have outlined data collections and research challenges for our system. The data is collected related to “DD news with hearing-impaired people” from YouTube. To get the news data into script format, we used the Downsub site. This site supports script format into different languages script but according to our requirements, we downloaded news scripts into English. Then scripts are converted into unique unigram words by using the wordlist online tool. We have analyzed each word one by one and eliminated unwanted (articles, helping verb, inflections, etc.) words and finally, unique words are converted into Sign Language with the help of ISL experts.
Quadrotor Mapless Navigation in Static and Dynamic Environments based on Deep Reinforcement Learning

In this paper, we propose a mapless autonomous navigation planner which plans a collision-free trajectory for quadrotor without any manual operations. Deep Reinforcement Learning (DRL) can optimize the policy by trial and error without knowing the prior information of the environment. The designed reward function has better convergence which compares to the benchmark method. The learned policy makes a real time collision free trajectory which can cope with the dynamic obstacles under different scenarios. The evaluation result shows that the trained model can be applied directly to the unknown environment without retraining the agent.
Improved Chinese Word Segmentation Algorithm of Quantitative Units in Elementary Mathematics Application Problems

Aiming at the problems of Chinese word segmentation (CWS) and part-of-speech (POS) tagging of quantitative units in elementary mathematics application problems, an improved CWS algorithm is proposed. By compiling the revised dictionary about the quantitative units, designing a segmentation algorithm and a merging algorithm to segment and then merge the matched target words, the problems of CWS and POS tagging of quantitative units are corrected. The experimental results show that the improved CWS algorithm can improve the accuracy of CWS and POS tagging of quantitative units to 98%.
Semantic Annotation Framework For Intelligent Information Retrieval Using KIM Architecture

Due to the explosion of information/knowledge on the web and wide use of search engines for desired information,the role of knowledge management(KM) is becoming more significant in an organization. Knowledge Management in an Organization is used to create ,capture, store, share, retrieve and manage information efficiently. The semantic web, an intelligent and meaningful web, tend to provide a promising platform for knowledge management systems and vice versa, since they have the potential to give each other the real substance for machine-understandable web resources which in turn will lead to an intelligent, meaningful and efficient information retrieval on web. Today,the challenge for web community is to integrate the distributed heterogeneous resources on web with an objective of an intelligent web environment focusing on data semantics and user requirements. Semantic Annotation(SA) is being widely used which is about assigning to the entities in the text and links to their semantic descriptions. Various tools like KIM, Amaya etc may be used for semantic Annotation.
Formation of sets of independent components of a multidimensional random variable based on a nonparametric pattern recognition algorithm

The possibility of circumventing the problem of decomposition of the range of values of random variables when testing various hypotheses is considered. A brief review of the literature on this problem is given. A method for forming sets of independent components of a multidimensional random variable is proposed, based on hypotheses testing about the independence of paired combinations of components of a multidimensional random variable. The method uses a two-dimensional non-parametric algorithm for pattern recognition of the kernel type, corresponding to the criterion of maximum likelihood. In contrast to the traditional method based on the application of the Pearson criterion, the proposed approach avoids the problem of decomposing the range of values of random variables into multidimensional intervals. The results of computational experiments performed according to the method of forming sets of independent random variables are presented. Using the information obtained, an information graph is constructed, the vertices of which correspond to the components of a multidimensional random variable, and the edges determine their independence. Then the vertices of the complete subgraphs correspond to groups of independent components of a random variable. The obtained results form the basis for the synthesis of a multi-level nonparametric large volume data processing system, each level of which corresponds to a specific set of independent random variables.
Capri: Achieving Predictable Performance in Cloud Spot Markets

Large cloud providers offer spot instances at attractive prices to improve resource utilization, resulting in a spot market where users bid for resources and providers alter prices dynamically. As prices surpass bid values, resources may be relinquished from users with low bids. Achieving predictable performance on spot markets is challenging for data analytics workloads because they are very sensitive to preemptions due to the excessive cost of recomputations.We introduce capri, a scheduling system for running cloud data analytics in spot markets in which users may experience periods of degraded performance. capri dynamically predicts the functional relationship between bid and performance, thus helping with managing expectations and bid advice. We propose a new spot market abstraction called the bribe scheduler which delivers differentiated service levels based on bids. capri uses a prediction mechanism built on a queueing approximation of the bribe scheduler. capri dynamically estimates parameters to adapt the queueing model and provide accurate performance predictions in the face of time-varying workloads.We collect measurements using capri running two realistic workloads, imdb and tpcds, and demonstrate the accuracy of our approximation and parameter estimation methodology. We show that capri achieves a median prediction error below 3% in bursty workloads. We find that capri‘s service level prediction is pessimistic as users are likely to experience better performance than they should receive for their bids.
Imaging Modeling and Camera Sensitivity Recovery

Imaging modeling refers to the modeling and simulation of the raw data generation and the processing process from raw data to sRGB images. In this chapter, we model the imaging process, the main steps of which are: white balance, color space conversion (camera color space to XYZ space conversion, XYZ space to sRGB space conversion), gamma correction, Gamut mapping, and Tone mapping. Besides, we also present a method to recover camera spectral sensitivity. The experimental results show that our method is accurate, simple, and practical.
Data Privacy in Wearable IoT Devices: Anonymization and Deanonymization

With the development of IoT devices, wearable devices are being used to record various types of information. Wearable IoT devices are attached to the user and can collect and transmit user data at all times along with a smartphone. In particular, sensitive information such as location information has an essential value in terms of privacy, and therefore some IoT devices implement data protection by introducing methods such as masking. However, masking can only protect privacy to a certain extent in logs having large numbers of recorded data. However, the effectiveness may decrease if we are linked with other information collected from within the device. Herein, a scenario-based case study on deanonymizing anonymized location information based on logs stored in wearable devices is described. As a result, we combined contextual and direct evidence from the collected information. It was possible to obtain the result in which the user could effectively identify the actual location. Through this study, not only can a deanonymized user location be identified but we can also confirm that cross-validation is possible even when dealing with modified GPS coordinates.
Scientific and theoretical bases for designing a model for forming future lawyers' civic-mindedness

The relevance of the research is determined by the need to search for new scientific, theoretical, methodological, substantive and technological foundations for the training future lawyers, as the intellectual and political elite of the state, possessing a stable civic position and ready to form a civil society and protect the rights of Russian citizens. The problem of the research is to study the features of training future lawyers and to determine the scientific and theoretical platform for designing a model for the formation of civic position of future lawyers in the legal clinic of a university. The purpose of the research is scientific substantiation and design of a model for the formation of the civic position of future lawyers in the legal clinic of a university. The research methodology and methods: content analysis of scientific research; theoretical analysis of pedagogical dilemmas in the training of future lawyers; forecasts of pedagogical fronts for the development of conditions for the development of the civic position of future lawyers in the process of professional training; designing a model for the development of the civic position of future lawyers in the legal clinic of a university. The research results are: transformational processes in the world change civil society (social stratification, lack of protection of the rights of a separate part of society, etc.) and become challenges to modern education in the training of future lawyers; integration and an interdisciplinary approach in the training of future lawyers determine scientific-theoretical, methodological, substantive and technological approaches to the development of the civil position of a future lawyer; designing a model for the development of the civic position of future lawyers in the legal clinic of a university makes it possible to make forecasts for the required competencies of this category of specialists, optimize the environment of a university - a legal clinic as a place of professional self-realization and manifestation of the civic position of a future lawyer. Key findings are: global trends are challenges for modern legal education; the training of future lawyers should be coupled with the formation of a civic position, understood as a willingness to serve their Fatherland, the state and its citizens; the design of innovative models for the training of future lawyers is focused on the integration of the processes of professional formation and education of a future citizen of Russia with a stable civic position, on the anticipatory nature of the development by future lawyers in the legal clinic of university of professional and personal competencies in demand in the rule of law.
Research and Discovery of Smart Dumb Gloves

Abstract According to the statistics of China’s health organization, there are a large number of deaf-mute people in the whole country. Sign language is used to communicate between deaf and dumb people. When they communicate with people who don’t know sign language, it will bring a lot of inconvenience. Based on the above problems, we propose a sign language gesture recognition system based on data glove. The overall architecture is composed of stnm32 MCU, flex4.5 bending sensor, mpu6050 six axis sensor, Bluetooth transmission module and mobile phone voice app. It collects the user’s hand movement information through flex4.5 bending sensor and mpu6050 six axis sensor, Then the STM32 microcontroller processes the data and compares it with the action data in the database. Then the action voice command is sent to the mobile phone through the Bluetooth transmission module, and finally the voice playback and text display are carried out through the mobile phone app. The software of sign language gesture recognition system is developed by using processing software based on Java language, which is connected with it to complete the recognition of sign language actions, and then output the words to be said through the intelligent voice system.
Adversarial Inverse Reinforcement Learning to Estimate Policies from Multiple Experts

Inverse reinforcement learning is used for complex control tasks by using experts. However, since the learning results depend on the expert, it is impossible to imitate ungiven policies from expert when there are multiple optimal polices for the same goal, or when the environment changes from the training. The problems can be solved by giving multiple experts and representing their features in the latent space. the proposed method extends information maximizing generative adversarial imitation learning with adversarial inverse reinforcement learning to deal with such environment. Experiments show that the proposed method can not only imitate multiple experts, but also estimate ungiven polices.
El juego cooperativo y su influencia en las competencias matemáticas de infantes escolarizados

The review article aims to analyze cooperative play and its influence on the mathematical competencies of school children in Peru. A bibliographic review was carried out with the intention of knowing the state of the question of the study variables, thus, through the analytical-synthetic method, an attempt is made to generate a theoretical synthesis as a contribution to the scientific culture of Peruvian education. Cooperative play contributes to the development of motor skills, being that it could be combined with mathematics to coordinate an interaction between the cognitive and the social, being important to focus the implementation of cooperative play diligently in a position to favorably manage mathematical thinking in the students.
Infinite-dimensional feature aggregation via a factorized bilinear model

Aggregating infinite-dimensional features has demonstrated superiority compared with their finite-dimensional counterparts. However, most existing methods approximate infinite-dimensional features with finite-dimensional representations, which inevitably results in approximation error and inferior performance. In this paper, we propose a non-approximate aggregation method that directly aggregates infinite-dimensional features rather than relying on approximation strategies. Specifically, since infinite-dimensional features are infeasible to store, represent and compute explicitly, we introduce a factorized bilinear model to capture pairwise second-order statistics of infinite-dimensional features as a global descriptor. It enables the resulting aggregation formulation to only involve the inner product in an infinite-dimensional space. The factorized bilinear model is calculated by a Sigmoid kernel to generate informative features containing infinite order statistics. Experiments on four visual tasks including the fine-grained, indoor scene, texture, and material classification, demonstrate that our method consistently achieves the state-of-the-art performance.