Reviewerly/exHarmony huggingface dataset:
A Physiotheraphy Coaching System based on Kinect Sensor

This paper addresses the issue of performing physiotherapy treatments remote, at home, by proposing a personal coaching system capable of surveilling the correctness of the exercises and the adherence to the rehabilitation program established by doctors. For identifying the performed exercises, we use a Microsoft Kinect Sensor on top of which we have developed an exercise-detection algorithm part of a recognition module and a user-friendly web interface. The results show that the proposed solution can be successfully used for performing remote physiotherapy exercises, guided by physiotherapists, which brings a major advantage in scenarios as the one brought by the SARS-CoV-2 (COVID19) context.
Design and Implementation of intelligent Learning Companion System Based on WeChat Mini Program

During the period of COVID-19 epidemic situation, in order to ensure that students can study normally, the Ministry of Education has issued measures to “suspend classes without stopping learning”. Therefore, the traditional forms can't meet the needs of learners, more and more people like online education. The existing online learning platforms cannot perfectly meet the learning needs of students, so the students need an online learning system that can meet their individual needs and improve learning efficiency. This article introduces an intelligent learning companion system, the system based on the advantages of WeChat Mini Programs can help learners achieve online learning and meet personalized needs.
Implementation of Data Mining in Grouping Percentage of Blind Letters Age 15+ By Province Using K-Means Algorithm

Abstract Data Mining is a method that is often needed in large-scale data processing, so data mining has important access to the fields of life including industry, finance, weather, science and technology. In data mining techniques there are methods that can be used, namely classification, clustering, regression, variable selection, and market basket analysis. Illiteracy is one of the factors that hinder the quality of human resources. One of the fundamental things that must be fulfilled to improve the quality of human resources is the eradication of illiteracy among the community. The purpose of this research is to determine the clustering of illiterate communities. The results of the study are illiterate data clustering according to the age proportion of 15+, namely 1 node of the high group, the low group has 23 nodes, and the medium group of 10 nodes. The results of this study become input for the government to determine illiteracy eradication policies in Indonesia based on provinces
Lead-Zinc Deposits

The lead-zinc and polymetallic mineralization potential of Mongolia has been studied since the 1900s by joint Russian-Mongolian geological expeditions. Lead-zinc and associated metal deposits are mostly located in Eastern Mongolia, in relation to Mesozoic volcano-plutonic rocks. Geological survey in Mongolia has identified about 15 deposits and 120 occurrences in seven metallogenic belts. According to information by the Mineral Resources and Petroleum Authority of Mongolia, the current known lead resources of Mongolia are about two million tonnes. These include the Khar Tolgoi, Ulaan, Mungun-Undur, Tsav, and Dulaankhar Uul deposits. Since 2006, Mongolia started exporting polymetallic ore concentrate. By 2017, Mongolia exported 70,000 tonnes of zinc concentrate; however, this accounts for less than 1% of the world’s zinc exports. This export is from Tumurtiin Ovoo open pit mine in Eastern Mongolia, which produces annually 350–400 tonnes of zinc ore at 50% concentrate.
A Brief Survey of Steganographic Methods for ECG Signal

The following paper represents a survey of steganography techniques suitable for usage in ECG. A few steganographic methods have been discussed below, it has been believed that these methods can be exploited to bring out more techniques suitable for ECG. The objective of this paper is to provide a comprehensive survey of existing steganographic techniques for ECG.
Image Inpainting by Multiscale Spline Interpolation

Recovering the missing region of images is a task that is called image inpainting. Due to the shape of missing areas, different methods are presented with different views. One of the challenges of this problem is extracting various features which lead to better results. Experimental results show that both global and local features are useful for this task. In this paper, a multi-scale image inpainting method that utilizes both local and global features is proposed. The first step of this method is selecting the scales of the image according to the width of the lines in the mask. Then adaptive image inpainting is applied to the damaged images, and the lost pixels are predicted, and at the end, before voting the results, the repaired image of each scale is up-sampled. The proposed method is tested on damaged images with scratches and creases. The metric that we use to evaluate our approach is PSNR. In this metric, 1.2 dB improvement exists compare with previous works.
Inclination shift signal: thickness or index measurement in transparent media by transmission of generally astigmatic Gaussian beams

The focus error signal is a widespread optical technique that harnesses the shape of a simply astigmatic Gaussian beam to produce an output signal with a well-defined linear range used for many different applications. However, general astigmatic Gaussian beams have not been extensively used for measuring purposes even though they have been known for decades. These beams have interesting properties which were used in this work as a mean to measure a change in the optical path and recover sample information. In this text, the formulae presented by E. Kochkina to represent generally astigmatic Gaussian beams are summarized, the principles of the proposed technique are set forth and two experimental results are presented as a validation of the proposal. A brief introduction to a different approach that enables the setup to perform simultaneous measurements of the thickness and refractive index is also given.
See the Light

Developing accurate solar performance models, which infer solar output from widely available external data sources, is increasingly important as the grid's solar capacity rises. These models are important for a wide range of solar analytics, including solar forecasting, resource estimation, and fault detection. The most significant error in existing models is inaccurate estimates of clouds' effect on solar output, as cloud formations and their impact on solar radiation are highly complex. In 2018 and 2019, respectively, the National Oceanic and Atmospheric Administration (NOAA) in the U.S. began releasing multispectral data comprising 16 different light wavelengths (or channels) from the GOES-16 and GOES-17 satellites every 5 minutes. Enough channel data is now available to learn solar performance models using machine learning (ML). In this paper, we show how to develop both local and global solar performance models using ML on multispectral data, and compare their accuracy to existing physical models based on ground-level weather readings and on NOAA's estimates of downward shortwave radiation (DSR), which also derive from multispectral data but using a physical model. We show that ML-based solar performance models based on multispectral data are much more accurate than weather- or DSR-based models, improving the average MAPE across 29 solar sites by over 50% for local models and 25% for global models.
Decision-making bioinspired model for target definition and “satisfactor” selection for physiological needs

Every person, from an early age, has to make decisions to resolve situations that arise in life. In general, different people make different decisions in the same situation, since decision-making takes into account different factors such as age, emotional state, experience, among others. We can make decisions about situations that we classify as: more important than others, routine, unexpected, or trivial. However, making the correct decision(s) in a timely manner for these situations is one of the most complex and delicate challenges that human beings face. This is due to the arduous mental process required to be carried out. Providing such behavior to a virtual entity is possible through the use of Cognitive Architectures (CAs). CAs are an approach for modeling human intelligence and behavior. This paper presents an functional bioinspired computational decision-making model to satisfy the physiological needs of hunger and thirst. Our proposal considers as black boxes other cognitive functions that are part of a general CA (named Cuäyöllötl or brain in Nahuatl). In the proposed case study, it is proved that the decision-making process plays an essential role in determining the objective and selecting the object that satisfies the established need.
Fast and Differentiable Message Passing on Pairwise Markov Random Fields

Despite the availability of many Markov Random Field (MRF) optimization algorithms, their widespread usage is currently limited due to imperfect MRF modelling arising from hand-crafted model parameters and the selection of inferior inference algorithm. In addition to differentiability, the two main aspects that enable learning these model parameters are the forward and backward propagation time of the MRF optimization algorithm and its inference capabilities. In this work, we introduce two fast and differentiable message passing algorithms, namely, Iterative Semi-Global Matching Revised (ISGMR) and Parallel Tree-Reweighted Message Passing (TRWP) which are greatly sped up on a GPU by exploiting massive parallelism. Specifically, ISGMR is an iterative and revised version of the standard SGM for general pairwise MRFs with improved optimization effectiveness, and TRWP is a highly parallel version of Sequential TRW (TRWS) for faster optimization. Our experiments on the standard stereo and denoising benchmarks demonstrated that ISGMR and TRWP achieve much lower energies than SGM and Mean-Field (MF), and TRWP is two orders of magnitude faster than TRWS without losing effectiveness in optimization. We further demonstrated the effectiveness of our algorithms on end-to-end learning for semantic segmentation. Notably, our CUDA implementations are at least 7 and 700 times faster than PyTorch GPU implementations for forward and backward propagation respectively, enabling efficient end-to-end learning with message passing.
Dynamic Gesture Recognition Based on iCPM and RNN

Abstract Aim to solve the problem that continuous complex actions are difficult to be recognized by computer vision technology in actual production, this paper collects gesture video from single-and double-viewpoint, constructs gesture joint point coordinate recognition network model by improved Convolutional Pose Machine(iCPM) model, obtains gesture Gauss heat map and joint point coordinates in each frame of video, and then inputs joint point coordinates with time series into gesture sequence recognition network. Finally, the gesture sequence is obtained. The experimental results show that the recognition accuracy of gesture recognition model based on double-viewpoint is 4.18% ∼ 6.92% higher than that of the single-viewpoint gesture recognition model, which verifies that the gesture recognition model based on dual-viewpoint has better recognition effect. In addition, the video samples of Tin-Lead Soldering including continuous and complex actions verify that the model has ideal recognition ability for dynamic gesture in actual production process.
Referring expression comprehension model with matching detection and linguistic feedback

The task of referring expression comprehension (REC) is to localise an image region of a specific object described by a natural language expression, and all existing REC methods assume that the object described by the referring expression must be located in the given image. However, this assumption is not correct in some real applications. For example, a visually impaired user might tell his robot ‘please take the laptop on the table to me’. In fact, the laptop is not on the table anymore. To address this problem, the authors propose a novel REC model to deal with the situation where expression‐image mismatching occurs and explain the mismatching by linguistic feedback. The authors' REC model consists of four modules: the expression parsing module, the entity detection module, the relationship detection module, and the matching detection module. They built a data set called NP‐RefCOCO+ from RefCOCO+ including both positive samples and negative samples. The positive samples are original expression‐image pairs in RefCOCO+. The negative samples are the expression‐image pairs in RefCOCO+, whose expressions are replaced. They evaluate the model on NP‐RefCOCO+ and the experimental results show the advantages of their method for dealing with the problem of expression‐image mismatching.
Situational Awareness Model of IoV Based on Fuzzy Evaluation and Markov Chain

With the rapid development of the automobile industry, and the continuous advancement of the Internet of Things technology, the emergence of the Internet of Vehicles (IoV) has also led to the security of the IoV. However, scholars have focused on the security research of the components of the IoV, or Research on the safety analysis of the vehicle interior network, or research on vehicle behavior safety. There is almost no research on the overall situation of the entire vehicle at home and abroad. This thesis first analyzes the classic situational awareness model, puts forward the ideal function that the IoV situational awareness should have, and proposes an IoV situational awareness model based on the fuzzy evaluation, AHP hierarchical analysis and Markov chain according to the current situation of the Internet of Vehicles. And design experiments to verify its feasibility.
An Improved On-Policy Reinforcement Learning Algorithm

The paper aims to find the paths for a mobile agent over a stochastic environment. The stochastic environment is chosen to mimic the real world as the agent’s actions do not uniquely determine the outcome. We placed the agent in the initial state and allowed to traverse each state, which has different rewards function with the goal to maximize its reward. Various algorithms, viz. SARSA and Q-Learning, etc., are used by many scholars to evaluate the path for the agent. Here, a reinforced learning algorithm is proposed in collaborating with the idea of information theory. The motive was to make the algorithm explore and learn. After that, it decides the best possible action to take based on policy that it learned. The learning rate of the algorithm is kept varying to check the performance of the proposed algorithm.
Error Decoding of Locally Repairable and Partial MDS Codes

In this work it is shown that locally repairable codes (LRCs) can be list-decoded efficiently beyond the Johnson radius for a large range of parameters by utilizing the local error-correction capabilities. The corresponding decoding radius is derived and the asymptotic behavior is analyzed. A general list-decoding algorithm for LRCs that achieves this radius is proposed along with an explicit realization for LRCs that are subcodes of Reed-Solomon codes (such as, e.g., Tamo-Barg LRCs). Further, a probabilistic algorithm of low complexity for unique decoding of LRCs is given and its success probability is analyzed. The second part of this work considers error decoding of LRCs and partial maximum distance separable (PMDS) codes through interleaved decoding. For a specific class of LRCs the success probability of interleaved decoding is investigated. For PMDS codes, it is shown that there is a wide range of parameters for which interleaved decoding can increase their decoding radius beyond the minimum distance such that the probability of successful decoding approaches 1 when the code length goes to infinity.
Construction of a neural network model for performance prediction in shot put athletes

Abstract This paper is to investigate the role of artificial neural network models in the prediction of sports performance and to establish artificial neural network models to evaluate the correlation between athletes’ special performance and special physical quality. The results show that the artificial neural network model overcomes the shortcomings of multiple regression models and gray models that require a pre-determined mathematical model, and more accurately reflects the functional relationship between the training indexes of the special quality of shot put athletes and the special athletic performance, and more accurately predicts the special performance of shot put athletes. It further showed that the special physical quality of shot put athletes is an important basis for their special athletic ability. With the physical quality index as the independent variable and the special performance as the dependent variable, the prediction model of the athletes’ special performance can be established, which can accurately diagnose and evaluate the development level of the athletes’ physical quality, and clarify the key points and objectives of the training content, so as to improve the scientific level of the training of shot put.
The “Ultimate Empathy Machine” Revisited

Virtual realities can be understood as computer-mediated simulations that create the feeling that users are present in a real-physical environment. Enthusiasts postulate that virtual experiences of reality have a more intense effect on the users than traditional media. Social and ethical implications of this technology arise not only from its potential for the extension of individual experiences of different realities, but also from its increasing pervasion of each area of everyday life. On the basis of a literature review, the article shows to what extent virtual realities can be understood as a drastic push in the mediatization of society. Building on this theoretical framework, different personal as well as social implications of VR and AR applications are specified and discussed from the perspective of media ethics.
Voice Controlled Wheel Chair with Intelligent Stability

This paper is about a wheel chair controlled by voice command developed to help the physically challenged people. The physically challenged person as user can control the wheel chair by voice commands such as forward, reverse etc. ‘Julian’ a grammar based recognizer is used in this system. The basic commands, the short motion command and the command of verification are the three commands given to the system. Julian has successful recognition rate of 98% of commands given to it based on the studies and experiments conducted. Patients and physically challenged people are depending on others while moving on wheel chair hence they do not have freedom in motion. This voice command controlled wheel chair provides freedom of motion to patients and physically challenged to be independent on movement to the respective places. Arduino control the motor by motor driver based on the commands processed by the voice recognition module. This voice command controlled wheel chair provides motion on the patient or physically challenged people voice. Gyroscopic sensor is used to maintain the level straight of the wheel chair when the person travelling in the slope it prevents the person to feel off from the chair which is operated by servo motor.
Using Data Augmentation and Neural Networks to Improve the Emotion Analysis of Brazilian Portuguese Texts

Information and Communication Technologies present as an interesting alternative for the mitigation of barriers that arise in the context of communication of information, mainly as technologies aimed at the machine translation of content in oral language into sign language. After years, despite the improvement of these technologies, the use of them still divides the opinions of the Deaf Community, due to the low emotional expressiveness of 3D avatars. Therefore, as a way to assist the machine translation of texts in oral language to sign language, this study aims to evaluate the influence of the parameters of a data augmentation method in a textual dataset and the use of neural networks for emotion analysis of Bazilian Portuguese texts. The analysis of emotions in texts presents a relevant challenge in diversity due to the nuances and different forms of expression that the human language uses. In this context, the use of deep neural networks has gained enough space as a way to deal with these challenges, mainly with the use of algorithms that deal with emotion analysis as a textual classification task, such as the MultiFiT approach. To circumvent the scarcity of data in Brazilian Portuguese aimed at this task, some strategies for increasing data were evaluated and applied to improve the database used in training. The results of the emotion analysis experiments with Transfer Learning pointed to accuracy above 94% in the best case.
Intelligent OAM of New Generation Access Network Based on SDN

With the rapid development of the optical access network and the trend of full service access bearer, the operation, administration, and maintenance (OAM) of the optical access network also face more and more challenges. This paper analyzes the limitations of the traditional access network OAM interface, introduces the key enable technologies in access network to implement agile, automated and intelligent OAM. It provides application scenario for intelligent OAM and supports passive OAM. The evolution from OAM to active OAM effectively improves the intelligent OAM capabilities of the optical access network, thereby helping network operators to cope with various challenges in OAM of new services, enhancing service operation capabilities, and improving the end user's business experience.
Information Security Implications of Machine-Learning-Based Automation in ITO Service Delivery – An Agency Theory Perspective

The trend of information technology outsourcing (ITO) to service providers (SPs) is growing. SPs bring improvements through transformation projects and migrate outsourced scopes to their service delivery platforms (SDPs). For realizing economies of scales for themselves, and improving the information security and bringing efficiencies for their clients, the SPs implement machine-learning-based automation (MLA) for ITO service delivery on SDPs. However, MLA is not a silver bullet and exposes the outsourced scopes to new types of information security risks (ISRs). This paper aims at exploring those ISRs and understanding their implications. It applies agency theory to examine differing viewpoints of multiple organizations engaged in an ITO relationship. The study investigates an ITO setup of three organizations in the telecom industry. To gain insights into ISR implications, a qualitative approach was followed using a case study method and data was collected through interviews. Adversarial attack scenarios, ISRs and ISR implications on ITO service delivery are presented. To the best of our knowledge, it is the first study investigating the ISRs of MLA in ITO service delivery.
Deep Learning-Based Fully Automated Detection and Segmentation of Breast Mass

In the field of breast mass detection, there are many of small-scale masses in the image. However, most of the existing target detection models have low accuracy in detecting small-scale masses, which is prone to error detection and missing detection. In order to improve the detection accuracy of small-scale masses, this paper proposed a small scale target detection model Dense-Mask R-CNN based on Mask R-CNN, which is suitable for breast masses detection. Firstly, this paper improves the internal structure of FPN, and modifies the lateral connection mode in the original FPN structure to dense connection. Secondly, modify the size of the anchor of RPN to improve the location accuracy of small-scale masses. This paper uses the CBIS-DDSM dataset for all experiments. The results show that the AP value of the improved model for detecting breast masses reached 0.65 in the test set, which was 0.04 higher than that of the original Mask R-CNN.