Reviewerly/exHarmony huggingface dataset:
Information Visualization in the Educational Process: Current Trends

The rapid development of computer visualization techniques as well as virtual and augmented reality has led to the possibility of perfect data visualization and the creation of a special virtual space for educating new generation. Simultaneously, the increase in the amount of data to be processed requires a proper selection and presentation of data for solving specific problems. Education sets such tasks as 1) improving the efficiency of presenting information and its assimilation by stu-dents, and 2) increasing the convenience and quality of the teachers’ work. The purpose of this study is to test an acceleration and improvement of the teacher’s response to students studying with more productive visualized learning material. Meanwhile, the created visualization system was based on minimizing the efforts and costs of its preparation and constant support. Only free cloud-based services and visualization tools were used. Students were given the opportunity to con-stantly, in real time control their learning process and create education markers with the help of perspicuous visual environment. To create a visualization system, already existing works on the implementation and verification of the system was used. The study was based on the results of applying this technology. A survey of 300 students from three universities in China, Russia and Kazakhstan was conducted. The control group consisted of 150 students from the same universi-ties who did not use visualization to master the same educational material. Ac-cording to the results of the study, students who used information visualization showed a sharp increase in the subjective assessment of the speed and quality of their learning (58.58% and 37.73%, respectively, of the total number of partici-pants gave a high rating, while in the control group – only12.25%). Further, the level of anxiety associated with an assimilation of new language material was significantly decreased (13.54% in the study group did not feel anxiety, while on-ly 7% – in the control group).
Nonlocal regularized CNN for image segmentation

Non-local dependency is a very important prior for many image segmentation tasks. Generally, convolutional operations are building blocks that process one local neighborhood at a time which means the convolutional neural networks(CNNs) usually do not explicitly make use of the non-local prior on image segmentation tasks. Though the pooling and dilated convolution techniques can enlarge the receptive field to use some nonlocal information during the feature extracting step, there is no nonlocal priori for feature classification step in the current CNNs' architectures. In this paper, we present a non-local total variation (TV) regularized softmax activation function method for semantic image segmentation tasks. The proposed method can be integrated into the architecture of CNNs. To handle the difficulty of back-propagation for CNNs due to the non-smoothness of nonlocal TV, we develop a primal-dual hybrid gradient method to realize the back-propagation of nonlocal TV in CNNs. Experimental evaluations of the non-local TV regularized softmax layer on a series of image segmentation datasets showcase its good performance. Many CNNs can benefit from our proposed method on image segmentation tasks.
Use of chatbots for user service in higher education institutions

This paper arises from the need to have new tools or communication channels that allow users to answer questions or concerns about different fields at the university level. In particular, the results of the analysis of the use of three chatbots, implemented in a higher education institution, are presented. The results obtained from the surveys, considering the usability of the chatbot and the accuracy of the responses, show that the level of satisfaction of using the chatbots is high, therefore, it is recommended to use this type of systems for the attention of users.
How to Retrain Recommender System?: A Sequential Meta-Learning Method

Practical recommender systems need be periodically retrained to refresh the model with new interaction data. To pursue high model fidelity, it is usually desirable to retrain the model on both historical and new data, since it can account for both long-term and short-term user preference. However, a full model retraining could be very time-consuming and memory-costly, especially when the scale of historical data is large. In this work, we study the model retraining mechanism for recommender systems, a topic of high practical values but has been relatively little explored in the research community. Our first belief is that retraining the model on historical data is unnecessary, since the model has been trained on it before. Nevertheless, normal training on new data only may easily cause overfitting and forgetting issues, since the new data is of a smaller scale and contains fewer information on long-term user preference. To address this dilemma, we propose a new training method, aiming to abandon the historical data during retraining through learning to transfer the past training experience.Specifically, we design a neural network-based transfer component, which transforms the old model to a new model that is tailored for recommendations. To learn the transfer component well, we optimize the future performance'' -- i.e., the recommendation accuracy evaluated in the next time period. Our Sequential Meta-Learning(SML) method offers a general training paradigm that is applicable to any differentiable model. We demonstrate SML on matrix factorization and conduct experiments on two real-world datasets. Empirical results show that SML not only achieves significant speed-up, but also outperforms the full model retraining in recommendation accuracy, validating the effectiveness of our proposals. We release our codes at: https://github.com/zyang1580/SML.
PMForce: Systematically Analyzing postMessage Handlers at Scale

The Web has become a platform in which sites rely on intricate interactions that span across the boundaries of origins. While the Same-Origin Policy prevents direct data exchange with documents from other origins, the postMessage API offers one relaxation that allows developers to exchange data across these boundaries. While prior manual analysis could show the presence of issues within postMessage handlers, unfortunately, a steep increase in postMessage usage makes any manual approach intractable. To deal with this increased work load, we set out to automatically find issues in postMessage handlers that allow an attacker to execute code in the vulnerable sites, alter client-side state, or leak sensitive information. To achieve this goal, we present an automated analysis framework running inside the browser, which uses selective forced execution paired with lightweight dynamic taint tracking to find traces in the analyzed handlers that end in sinks allowing for code-execution or state alterations. We use path constraints extracted from the program traces and augment them with Exploit Templates, i.e., additional constraints, ascertaining that a valid assignment that solves all these constraints produces a code-invoking or state-manipulating behavior. Based on these constraints, we use Z3 to generate postMessages aimed at triggering the insecure functionality to prove exploitability, and validate our findings at scale. We use this framework to conduct the most comprehensive experiment studying the security issues of postMessage handlers found throughout the top 100,000 most influential sites yet, which allows us to find potentially exploitable data flows in 252 unique handlers out of which 111 were automatically exploitable.
On the Quality of Real-world Wearable Data in a Longitudinal Study of Information Workers

Over the past few years, an incredible diversity of consumer-grade wearables has emerged with tremendous breadth in capabilities, form factor, and cost. These wearable devices show significant promise for researchers to conduct expansive research studies in terms of scale, scope, and duration. Unfortunately, there is limited public data shared with respect to the data quality, device longevity, and scaling issues that emerge when trying to execute such studies. To that end, we share real-world results with respect to data quality, participant compliance, and device efficacy on a large scale, longitudinal study involving over seven hundred and fifty working professionals over the period of an entire year. In this paper, we present analyses with respect to the different types of data being collected including sleep, heart rate, physical activity, and stress. Furthermore, we explore participants behavior regarding charging frequency, and device robustness to further aid researchers considering large scale wearable studies.
A Real-World Approach on the Problem of Chart Recognition Using Classification, Detection and Perspective Correction

Data charts are widely used in our daily lives, being present in regular media, such as newspapers, magazines, web pages, books, and many others. In general, a well-constructed data chart leads to an intuitive understanding of its underlying data. In the same way, when data charts have wrong design choices, a redesign of these representations might be needed. However, in most cases, these charts are shown as a static image, which means that the original data are not usually available. Therefore, automatic methods could be applied to extract the underlying data from the chart images to allow these changes. The task of recognizing charts and extracting data from them is complex, largely due to the variety of chart types and their visual characteristics. Other features in real-world images that can make this task difficult are photo distortions, noise, alignment, etc. Two computer vision techniques that can assist this task and have been little explored in this context are perspective detection and correction. These methods transform a distorted and noisy chart in a clear chart, with its type ready for data extraction or other uses. This paper proposes a classification, detection, and perspective correction process that is suitable for real-world usage, when considering the data used for training a state-of-the-art model for the extraction of a chart in real-world photography. The results showed that, with slight changes, chart recognition methods are now ready for real-world charts, when taking time and accuracy into consideration.
Online Technologies in STEM Education

STEM education has become the normative base for teaching natural sciences, physical-mathematical disciplines and engineering sciences in a number of coun-tries. This technique has become the basis for a series of reforms for secondary and higher education in the USA, Australia, and some other countries. The meth-od involves the integration of training in the fields of mathematics, technical spe-cialties, scientific research and engineering. The widespread use of this technique and its active research throughout the world over the past ten years is due to the need to improve the quality of technical education and the ever-increasing rate of technological progress. This research is devoted to studying the impact of the STEM education introduction for 3rd year students of technical and pedagogical departments for improving the quality of training. The study involved two groups of students from two universities in Russia and China. The sample consisted of 316 people from each university, and the same amount was for control group to verify the results. The two study groups underwent training using two different STEM methodologies - “amalgam” and “interconnect”, which involve varying degrees of integration of various academic subjects within the coordinated STEM education. Both study groups used online-education integrated with STEM that helped to significantly increase the involvement of students in the learning pro-cess. All three groups passed pre-tests and post-tests on the learning outcomes before and after the introduction of the STEM education. The average grades re-ceived by students on studied disciplines show that the STEM education increas-es the academic performance with the statistical error of the study exceeded. The introduction of the “interconnect” method, which implies a greater integration of subjects during the training, showed provably higher results than the “amalgam” method. However, this study cannot be used to assess the quality and capabilities of each of these methods, since such an assessment requires additional research.
Remote Sensing Image Classification Algorithm Based on Texture Feature and Extreme Learning Machine

With the development of satellite technology, the satellite imagery of the earth’s surface and the whole surface makes it possible to survey surface resources and master the dynamic changes of the earth with high effici... | Find, read and cite all the research you need on Tech Science Press
3D reconstruction of line-structured light based on binocular vision calibration rotary axis

High-precision 3D topography measurement is essential to ensure quality and performance of shaft parts. Generally, the main difficulty of measuring rotary objects on a turntable is the low accuracy of axis calibration. To solve this problem, this study introduces two methods for calibrating the rotation axis of the turntable and proposes a method for reconstructing the 3D shape of the object by converting the rotation axis parameters of the binocular vision. A calibration camera is used to shoot a 2D checkerboard calibration board against the rotating table, and the calibration process of a high-precision space rotation axis is developed and implemented. This laser vision measurement system consists of a calibration camera, a measurement camera, and a rotation system. This system can realize reconstruction of the 3D shape of the object in accordance with the rotation axis parameters in any position. The experiment on measuring standard parts verifies the effectiveness of the algorithm. It provides a new idea for the measurement of rotary object shapes.
The Rise of Service Robots in Retailing: Literature Review on Success Factors and Pitfalls

Learning Outcomes Service robots (SRs) in retail is an emerging topic for both scholars and practitioners. Most of the studies are performed in the field using observational methods. To date, there is a lack of using uniform frameworks in order to fully understand customer–robot interactions. The dark side of robots in retail is still an underexplored area.
AIDEx - An Open-source Platform for Real-Time Forecasting Sepsis and A Case Study on Taking ML Algorithms to Production

Sepsis, a dysregulated immune response to infection, has been the leading cause of morbidity and mortality in critically ill patients. Multiple studies have demonstrated improved survival outcomes when early treatment is initiated for septic patients. In our previous work, we developed a real-time machine learning algorithm capable of predicting onset of sepsis four to six hours prior to clinical recognition. In this work, we develop AIDEx, an open-source platform that consumes data as FHIR resources. It is capable of consuming live patient data, securely transporting it into a cloud environment, and monitoring patients in real-time. We build AIDEx as an EHR vendor-agnostic open-source platform that can be easily deployed in clinical environments. Finally, the computation of the sepsis risk scores uses a common design pattern that is seen in streaming clinical informatics and predictive analytics applications. AIDEx provides a comprehensive case study in the design and development of a production-ready ML platform that integrates with Healthcare IT systems.
Blockchain and Distributed System

In recent years, blockchain technology has received more and more attention. Blockchain is a storage technology for public decentralized databases. The emergence of blockchain technology makes it possible to solve the trust problem of distributed system nodes within the wide area network. This article elaborated on the current advantages and disadvantages of blockchain technology and the main problems facing the development of wide-area distributed systems. This paper attempts to combine the ideas of blockchain and distributed system, and gives a complete design scheme of blockchain for building a wide area network distributed system. The designed blockchain not only retains the tamper-proof and traceable characteristics of the existing blockchain technology, but also can overcome the node trust problem of the wide-area distributed system.
Artificial intelligence based tracking model for functional sports training goals in competitive sports

As an advanced training concept, functional physical training is gradually recognized by top athletes for its high training effect and low sports injury. Functional physical training should gradually develop from elite athletes to grassroots athletes, so as to lay a solid foundation for the development of competitive sports. Because particle filtering is susceptible to external factors in moving target tracking, this paper designs a method for sparse coding using local image blocks of the target, establishes a static “impression” and dynamic model for the appearance of the target. The tracking problem is regarded as a binary classification problem between the foreground target and the background image. During the tracking process, the dual particle filter is implemented to alleviate the tracking drift, so that the algorithm can adaptively capture the changes in the target appearance At the same time, it can reduce the update caused by wrong positioning. The subjects’ FMS test and Y balance test have improved in varying degrees; the pressure distribution of the forefoot, arch, and heel tends to be rationalized, and the ratio of internal and external splayed feet has decreased. Experiments show that this particle filter moving target tracking scheme can adapt to changes in the environment and overcome the inflexibility of the global template when dealing with local changes in the target.
Dependency-Based Compositional Synthesis

Despite many recent advances, reactive synthesis is still not really a practical technique. The grand challenge is to scale from small transition systems, where synthesis performs well, to complex multi-component designs. Compositional methods, such as the construction of dominant strategies for individual components, reduce the complexity significantly, but are usually not applicable without extensively rewriting the specification. In this paper, we present a refinement of compositional synthesis that does not require such an intervention. Our algorithm decomposes the system into a sequence of components, such that every component has a strategy that is dominant, i.e., performs at least as good as any possible alternative, provided that the preceding components follow their (already synthesized) strategies. The decomposition of the system is based on a dependency analysis, for which we provide semantic and syntactic techniques. We establish the soundness and completeness of the approach and report on encouraging experimental results.
Observing Geometry of Quantum States in a Three-Level System

In quantum mechanics, geometry has been demonstrated as a useful tool for inferring nonclassical behaviors and exotic properties of quantum systems. One standard approach to illustrate the geometry of quantum systems is to project the quantum state space onto the Euclidean space via measurements of observables on the system. Despite the great success of this method in studying two-level quantum systems (qubits) with the celebrated Bloch sphere representation, it is still difficult to reveal the geometry of multidimensional quantum systems. Here we report the first experiment measuring the geometry of such projections beyond the qubit. Specifically, we observe the joint numerical ranges of a triple of observables in a three-level photonic system, providing a complete classification of these ranges. We further show that the geometry of different classes reveals ground-state degeneracies of a Hamiltonian as a linear combination of the observables, which is related to quantum phases in the thermodynamic limit. Our results offer a versatile geometric approach for exploring the properties of higher-dimensional quantum systems.
Prediction technique of aberration coefficients of interference fringes and phase diagrams based on convolutional neural network

In this study, we present a new way to predict the Zernike coefficients of optical system. We predict the Zernike coefficients through the function of image recognition in the neural network. It can reduce the mathematical operations commonly used in the interferometers and improve the measurement accuracy. We use the phase difference and the interference fringe as the input of the neural network to predict the coefficients respectively and compare the effects of the two models. In this study, python and optical simulation software are used to confirm the overall effect. As a result, all the Root-Mean-Square-Error (RMSE) are less than 0.09, which means that the interference fringes or the phase difference can be directly converted into coefficients. Not only can the calculation steps be reduced, but the overall efficiency can be improved and the calculation time reduced. For example, we could use it to check the performance of camera lenses.
Start Code-Based Encryption and Decryption Framework for HEVC

In this article, we propose a new selective encryption and decryption framework based on the start code for high efficiency video coding. There is a growing need to encrypt video information to protect video content from privacy invasion and intellectual property infringement caused by information leakage. Although encrypting an entire video is a straightforward approach, the cost to encrypting a large amount of video data is substantial, considering the resulting computational complexity. Consequently, selective encryption algorithms have been actively researched in recent years and have contributed to reducing the computational complexity. However, existing selective encryption algorithms have certain drawbacks. For instance, it is difficult to separate the video encryption algorithm from the video compression algorithm because the encryption framework is based on the syntax elements. Further, a partial reconstruction of the encrypted video bitstream is often unavoidable. To solve these problems, the proposed method encrypts the bitstream based on the start code rather than on the syntax elements. Encrypting the bitstream partially, based on the start code, makes it easy to separate the video encryption algorithm from the video compression algorithm. Furthermore, encrypting the part adjacent to the start code protects the video content, as video reconstruction using a video decoder is impossible, unless the correct start code is returned to the bitstream. The experimental results show that the proposed method reduced the encryption and decryption times by approximately 97% and 98%, respectively, compared to the encryption and decryption of the entire video bitstream.
Learning to Detect Bipolar Disorder and Borderline Personality Disorder with Language and Speech in Non-Clinical Interviews

Bipolar disorder (BD) and borderline personality disorder (BPD) are both chronic psychiatric disorders. However, their overlapping symptoms and common comorbidity make it challenging for the clinicians to distinguish the two conditions on the basis of a clinical interview. In this work, we first present a new multi-modal dataset containing interviews involving individuals with BD or BPD being interviewed about a non-clinical topic . We investigate the automatic detection of the two conditions, and demonstrate a good linear classifier that can be learnt using a down-selected set of features from the different aspects of the interviews and a novel approach of summarising these features. Finally, we find that different sets of features characterise BD and BPD, thus providing insights into the difference between the automatic screening of the two conditions.