Reviewerly/exHarmony huggingface dataset:
RETRACTED CHAPTER: A Novel Approach for Communication Among Deaf and Dumb People

In our everyday life, the greater part of the undertaking we do includes talking and hearing. Around 9,000,000 people on the planet are a hard of deaf or dumb or both. Correspondences between deaf-dumb imbecilic and a typical individual have consistently been a difficult assignment. In general, people use gesture-based communication by correspondence, but have difficulty communicating with other people who do not understand communication through signs or gestures. This undertaking means to get down this hindrance in correspondence by building up a compact electronic gadget that go about as a keen aide. We planned a basic inserted framework-based gadget for taking care of this issue. We have use flex sensor for getting the information from tragically challenged individuals utilizing gesture-based communication and micro-controller for controlling all tasks. LCD show yield gadget to pass on the message to deaf-dumb and unable to speak individuals. The proposed technique extricates highlight from the sign through Flex Sensor and afterward transmit that sign signals through Bluetooth to Android Mobile. This coordinated element improves the exhibition of the frame work, the framework fills in as a guide to impaired individuals. In this way, our essential objective right now to give a standard way of life to not too sharp individual groups as ordinary ones.
Fuzzy Adaptive Intelligent Controller for AC Servo Motor

Position control applications like robotic arms, CNC machines, and so on, can perform better with the use of 2 phase AC servo motor. The exceptionally nonlinear attributes of this motor have presented issues for its high accuracy in position control. In addition, a periodic motion makes a control system lose its function due to accumulating errors with an increasing number of periods. Although the servo tracking efficiency is good, but it declines periodic time varying signals. So for the better operations of the AC servo motor in the position control, a Fuzzy Adaptive Intelligent Controller (FAIC) is attempted to enhance the periodic reference tracking. The experimental study is conducted with AC servo motor system as shown in Fig. 1 for the identification of the process dynamics. Simulation results obtained to authenticate the proposed controller delivered good tracking performance than the Conventional fuzzy controller and conventional PD controller. For periodic reference tracking, the proposed controller exhibits a minimum Absolute Tracking Error (ATE) profile is the least with FAIC when compared to other controllers. The authors have also tested the robustness of the control strategy.
Towards Knowledge Exchange: State-of-the-Art and Open Problems

We discuss our experience in bringing data exchange to knowledge graphs. This experience includes the development of Kensho, a tool for generating mapping rules and performing knowledge exchange between two Knowledge Bases (KBs). We highlight the challenges addressed in Kensho, including managing the rich structural complexity of KBs and the need to handle incomplete correspondences between property paths. We use Kensho to highlight many open problems related to knowledge exchange including how knowledge translation can inform the task of KB integration and population.
Input-conditioned convolution filters for feature learning

We propose a novel framework which combines the input-conditioned filter generation module and a decoder based network to incorporate contextual information present in images into Convolutional Neural Networks (CNNs). In contrast to traditional CNNs, we do not employ the same set of learned convolution filters for all input image instances. And our proposed decoder network serves the purpose of reducing the transformation present in the input image by learning to construct a representative image of the input image class. Our proposed joint supervision of input-aware framework when combined with techniques inspired by Multi-instance learning and max-pooling, results in a transformation-invariant neural network. We investigated the performance of our proposed framework on three MNIST variations, which covers both rotation and scaling variance, and achieved 0.98% error on MNIST-rot-12k, 1.12% error on Half-rotated MNIST and 0.68% error on Scaling MNIST, which is significantly better than the state-of-the-art results. Our proposed model also showcased consistent improvement on the CIFAR dataset. We make use of visualization to further prove the effectiveness of our input-aware convolution filters. Our proposed convolution filter generation framework can also serve as a plugin for any CNN based architecture and enhance its modeling capacity.
The Very Brief History of Decentralized Blockchain Governance

An emergent form of blockchain governance involves the use of formal games that give participants incentives to identify focal resolutions to normative questions. This symposium contribution provides a brief survey of the literature proposing and critiquing the use of such mechanisms, and it evaluates early laboratory and real-world experiments with this approach to decentralized decisionmaking.
Juxtopia® CAMMRAD PREPARE: Wearable AI-AR Platform for Clinical Training Emergency First Response Teams

The Juxtopia <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Open-Wear research team collaborated with the Maryland Fire & Rescue Institute (MFRI) to test how the Juxtopia <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> artificial intelligent (AI) wearable augmented reality (AR) intervention may better deliver a hands-free clinical training intervention to firefighter Emergency Medical Technicians (EMT) and prepare them for effective response to hazardous material (HAZMAT) incidences. During a controlled study, human subjects participated in a minimal risk research (i.e., both as victims or caregivers) in which firefighter EMTs participated in a simulated training exercise that mimicked their real-world operations. During the study, there were two testing days. Day one included (10) victims and (20) caregivers who participated in a full day of training and familiarized themselves with wearable AR Head Mounted Display (HMD) and a Juxtopia® Virtual Tutor (JVT) software application. The results demonstrated that an AI instructor enabled AR system can train EMTs in core clinical skills for effective HAZMAT response.
Two-dimensional high-precision non-contact automatic measurement method based on image corner coordinates

Aiming at some shortcomings of the existing non-contact two-dimensional high-precision measurement methods, this paper proposes a two-dimensional high-precision non-contact automatic measurement method based on the corner coordinates of the image. Firstly, this paper designs a set of simple image acquisition device and explains the advantages of the Canny operator used in the image contour detection algorithm. Subsequently, this paper proposes a dimension calibration algorithm based on image corner coordinates, which can convert the pixel size to the actual size, and achieves the function of the algorithm by hierarchical, multi-step processing of the image. Finally, in order to realize the intelligent positioning and selection of the standard size workpiece position, an automatic measurement and positioning system is designed, which can convert the actual size signal into the pulse time control signal. The experimental results show that the measurement method proposed in this paper has the advantages of fast measurement speed, high robustness, low cost and high degree of automation. When using a black-and-white checkerboard paper with an accuracy of 0.1 mm, the measurement accuracy can reach the micron level.
A New Approach for Natural Language Understanding

In this paper, we propose a principle-based approach, combining a corpus-based approach and a knowledge-based approach. There are two parts of a principle: (1) Structure: such as verb frames, address patterns; (2) Statistics: mostly related to collocation and frequency. The method is based on a pipeline of four main stages, allowing to contribute to automatically building ontologies from text corpora, aimed to extract relations between words from the unstructured text.
Technology-Enhanced Learning of Performance

Abstract Technology is used in all aspects of musical practice, whether in developing instruments, recording performances, or linking musicians with audiences. Music education has also benefited from new digital tools, though the integration of technological approaches has not been as pervasive across learning environments and methods. This chapter summarizes recent research examining the attitudes toward and use of music technology in the learning of music performance, with particular focus on the domain of one-to-one teaching. It highlights new and developing hardware and software designed to address specific musical skills, from intonation and rhythmic accuracy to tone quality and expressiveness, and to drive self-regulated learning. The Technology Enhanced Learning of Music Instrument Performance (TELMI) project is presented as a case study demonstrating how state-of-the-art methods of audio and video analysis, motion capture, artificial intelligence, and musician-designed feedback displays are being advanced and integrated to develop the next generation of music learning tools. This chapter sets out an optimistic view of technology’s future as a tool to increase the accessibility, efficiency, and interactivity of music learning, and highlights the many ways in which musicians can take part in the evolution of technology-enhanced learning.
RealSync: A Synchronous Multimodality Media Stream Analytic Framework for Real-Time Communications Applications

While advancements in computing algorithms and hardware have enabled real-time stream analytics in videos, the information-rich audio bonded with video is still usually dropped and wasted. Processing both audio and video stream is not trivial as synchronizing multiple data streams creates much more difficulties than processing only one stream. In this paper, we designed and implemented a lightweight multimodal stream processing system that keeps both streams synchronized in processing and tested in a typical use case profanity filter. While it is inevitable to slow down certain processors to keep the steams synchronized, by careful butter setting, the overall latency is not affected (still 400-500ms). Besides achieving real-time processing, we located a core problem causing bursty audio latency and gave directions for further latency improvements.
Розроблення системи для підбору фільмів з використанням клієнт-серверної архітектури

The system for searching movies based on their parameters has been developed. C # 7 programming language based on WPF framework and Json, LinQ, .NET and XAML technology is used. Shell and quick sorting algorithms and binary search are used to speed up information processing. Using the developed system will save time when selecting films according to certain criteria.
A mobile services recommendation system fuses implicit and explicit user trust relationships

In recent years, with the development of advanced mobile applications, people’s various daily behavior data, such as geographic location, social information, hobbies, are more easily collected. To process these data, data cross-boundary fusion has become a key technology, and there are some challenges, such as solving the problems of the cross-boundary business integrity, cross-boundary value complementarity and so on. Mobile Services Recommendation requires improved recommendation accuracy. User trust is an effective measure of information similarity between users. Using trust can effectively improve the accuracy of recommendations. The existing methods have low utilization of general trust data, sparseness of trust data, and lack of user trust characteristics. Therefore, a method needs to be proposed to make up for the shortcomings of explicit trust relationships and improve the accuracy of user interest feature completion. In this paper, a recommendation model is proposed to mine the implicit trust relationships from user data and integrate the explicit social information of users. First, the rating prediction model was improved using the traditional Singular Value Decomposition (SVD) model, and the implicit trust relationships were mined from the user’s historical data. Then, they were fused with the explicit social trust relationships to obtain a crossover data fusion model. We tested the model using three different orders of magnitude. We compared the user preference prediction accuracies of two models: one that does not integrate social information and one that integrates social information. The results show that our model improves the user preference prediction accuracy and has higher accuracy for cold start users. On the three data sets, the average error is reduced by 2.29%, 5.44% and 4.42%, suggesting that it is an effective data crossover fusion technology.
SELECTING EDUCATIONAL INFORMATION AND REFERENCE RESOURCES FOR DISTANCE LEARNING

The article is focused on the importance of selecting information resources for distant learning. In the era of globalization and information progress the modernization of the higher education system has a great importance for introducing the new approaches to organization of educational activities. The emergence of accessible information technologies allows the students to receive education outside the educational institutions, which caused the formation of distance learning. The demand for distance learning is increasing every year. The technologies for developing distance learning will be very important in the future. Virtual laboratories are especially popular in distance learning in chemical and technological departments of the universities. This paper presents a comparative analysis of virtual laboratories and reference electronic resources for use in distance learning for technical universities. Safety of experiments, clarity, lack of significant equipment costs, possibility of repeated experiments and quick analysis of results, lack of reference to the schedule of classes and the ability to conduct experiments at convenient time, as well as, if necessary, a wide coverage of students with different levels of training are the undoubted advantages of the distant resource. There has been given a comparative analysis of Russian and foreign virtual laboratories and the free reference electronic resources that serve as a tool in distance learning at technical universities. There has been stated the need to improve the implementation of functional elements for increasing adoption the interactivity of a virtual laboratory or information resource
Image classification optimization and results analysis based on VGG network

Classification accuracy and network performance are affected by many factors while classifying Image by convolution neural network. The paper takes garbage image classification as an example, the common performance optimization methods of garbage image classification by using VGGNet model were summarized. And VGG-16 was used to test the target data set of garbage image. The classification accuracy under different conditions was measured and compared. Finally, the performance of the parameters under different optimization schemes is analyzed.
Three-Stream Graph Convolutional Networks for Zero-Shot Action Recognition

Recent developments in action recognition have resulted in an increase in the number of action categories. To accommodate the increase, an action dataset requires a large number of expensive and laboriously annotated videos. Thus, zero-shot action recognition (ZSAR) has become increasingly important. At present, there are two main ZSAR methods: one uses the video RGB image data, and the other uses the skeleton data of the human body. Conventional approaches use only one of these types of data and ignore the other data, thereby reducing the model accuracy. In this paper, we propose a three-stream graph convolutional network that processes both types of data. We use a two-stream graph convolutional network for RGB data and a motion branch for skeleton data. Combining these two outputs with a weighted sum, our model predicts final results for ZSAR. With experiments on the dataset UCF101, we show that our model provides better accuracy than a baseline model.
A Generalized Bayesian Approach for Localizing Static Natural Obstacles on Unpaved Roads

This paper presents an approach that implements sensor fusion and recursive Bayesian estimation (RBE) to improve a vehicle's ability to perform obstacle detection and localization in unpaved road environments. The proposed approach utilizes RADAR, LiDAR and stereovision fully for sensor fusion to detect and localize static natural obstacles. Each sensor is characterized by a probabilistic sensor model which quantifies level of confidence (LOC) and probability of detection (POD) associatively. Deploying these sensor models enables the fusion of heterogeneous sensors without extensive formulations and with the incorporation of each sensor's strengths. An Extended Kalman filter (EKF) is formulated and implemented for robust and computationally efficient RBE of obstacles' locations while a sensor-equipped vehicle moves and observes them. Results with a test vehicle show the successful detection and localization of a static natural object on an unpaved road has demonstrated the effectiveness of the proposed approach.
Addressing Cyber-Risk in Financial Institutions and in the Financial System

Regulators and financial institutions must focus the financial industry on significant systems investments to prevent current and future cyber infrastructure threats. This paper organizes the latest research and views on cyber-risk management to enable insights into frequency and severity of cyber-risk events, the magnitude of cyber-risk losses, and the best practices methods and frameworks for organizing and implementing a cyber-risk management program. This paper also describes cryptographic best practices and the evolving research on protection of cyber-attacks in the coming Quantum computing era. The paper concludes that governments must work with their financial market constituents to use one or more of the cyber-risk management frameworks to standardize collection and reporting of cyber-risk events. Finally, government and industry members must work together to finance and rebuild financial institutions’ legacy systems and financial industry infrastructure to enable cyber-risk management solutions.
Research on Cloud Classroom Construction of Higher Vocational Colleges Based on Campus Micro-Cloud Platform

Abstract With the rapid development and popularization of the Internet, the Internet era has arrived, and the Internet has entered people’s lives and has become an inseparable part. The system adopts RESTful architecture design and implements server-side interface to realize the unification of server interface; adopts JS (JavaScript) inter-modulation technology to realize it. The system adopts WeChat authorized login method to realize user login function. The system software is an online classroom software. Users need to be able to watch the course videos, so a player is required to play the course videos. After the experimental comparison and test, the improved server reduces the corresponding time and error rate of the system, and the instantaneous high concurrency performance of the network has been significantly improved, which can provide more stable technical support for the campus cloud classroom.
Promoting Collaborative Learning through VR Technologies in the Era of COVID-19

Most governments have temporarily implemented school closures and urge individuals to remain in their homes as parts of practicing social distancing to contain the spread of coronavirus disease 2019 (COVID-19) infections. However, collaborative learning experiences are crucial to help students develop knowledge and social skills. Being informed by existing research and theories, this paper aims to explore the applications of virtual reality (VR) to promote collaborative learning and to share perspectives on the affordances and challenges of applying VR technologies in this pandemic.
Circle Loss Based Metric Learning For Few-Shot Classification

Few-shot classification aims to identify new categories from only a handful of labeled examples and has drawn considerable attention in machine learning. An efficient and effective approach for solving few-shot classification has featured episodic training based metric learning which attempts to learn a task-generic embedding space on extremely large episodes, each with a small labeled support set and its corresponding query set. Distances between embeddings of examples from the query set and support set are then used as a notion of similarity to do classification. This paper first adds a margin between different classes to increase the inter-class distance in the embedding space(we call this method ML-FSC). Furthermore, considering that a similarity score that deviates far from the optimum should be emphasized, this paper introduces the circle loss into the episodic training based metric learning, which simply re-weights each similarity to highlight the less-optimized similarity scores, contributing to a more discriminative embedding learning(we call this method CL-FSC). We conduct comprehensive experiments to validate our method and we set new state-of-the-art performance on three popular few-shot classification benchmarks, namely omniglot, miniImageNet and tieredImageNet.
Modeling and Verification of Temporal Constraints for Web Service Composition

This paper aims to verify temporal constraints for Web service composition. The expected deployment of such verification when composing services strongly depends on the development of an adequate solution that guarantees a high level of service quality to the system users. Given the importance of e-commerce solutions for Algerian citizens that are favorable to it due to the current confinement situation during the Covid-19 pandemic, we develop a Web service composition that studies the speed distribution of Every Consumer Goods in Algeria by using the Timed Colored Petri Nets formalism. Once the temporal constraints are identified and the formal model is developed, we analyze the performance by creating a monitor on which multiple simulations are performed by using the software CPN Tools allowing the collection of several time data, which are evaluated thereafter using the Java Framework.
Indian Sign Language Recognition Using Convolutional Neural Networks

The Communication plays an essential role in our daily life. People who are hearing and/or speech impaired find it difficult to communicate with others. To aid the deaf and dumb in communicating with others sign languages are used. There are many sign languages. In this paper we worked with Indian sign language. A convolutional neural network is used to identify the images and classify them. Convolutional neural network comes under deep learning algorithms. Various actions are performed in each layer of the neural network to classify the image correctly. Employing convolutional neural networks increases the accuracy of the system. The data set consists of hand gestures which are already processed and the model is trained with the dataset. The model takes hand gestures as input and converts those signs into text. Real time video feed is used as input. The model can be further trained to detect more signs depending on the efficiency of the device.