Reviewerly/exHarmony huggingface dataset:
Recognition System for Emotions from Human Speech

Recognition of emotion from human speech is a technology where the emotion or the sentiment is detected from the person's speech. The voice in the speech not only gives us a useful message but also tells us the emotional state of the person speaking. Predicting these emotions is a tough task because different people have different tone and pitch. In this project we have used Mel frequency Cepstral coefficient (MFCC) for feature extraction and Multi-layer perceptron(MLP) classifier for classification of sentiments. The project is mainly to enhance the communication between human and machine for effectiveness and comprehension.
Re-engineering ERC-20 Smart Contracts with Efficient Snapshots for the Java Virtual Machine

Blockchain replicates the execution of transactions in a decentralized network of machines, supporting trust since each machine, by itself, cannot violate the rules for the transactions. Many applications of blockchains are related to the exchange of tokens, ie. valuable units that can be native, such as bitcoin and ether, or implemented in software, through smart contracts. Standards ensure token interoperability among decentralized applications of a certain platform, but they are general enough to be used in other contexts as well. A trend in blockchain is to implement such standards from a platform to another one, easing the design challenges with trusted and widely-used specifications. Exploiting the target language semantic can result in technological advantages with more efficient contracts. This paper presents a re-engineering of OpenZeppelin&#x2019;s implementation of the ERC-20 standard for fungible tokens on Takamaka, a subset of Java for programming smart contracts in the Hotmoka blockchain. It starts with a literal translation from Solidity to Takamaka, but then describes a novel implementation for making snapshots of tokens, based on tree maps, that is possible in Java, but not in Solidity, and shows that it is much more efficient than the literal translation in Java from Solidity, within the Java Virtual Machine.
ПЕДАГОГІЧНА КАЗКА «МЕТОДИ ВИХОВАННЯ»

The article presents the experience of developing and conducting a pedagogical fairy tale «Methods of education» for students of pedagogical education. The developed scenario of the event contains information about methods of education and suggested situations for testing the acquired knowledge. An example of self-suggestion technique for self-regulation in a stressful situation is also given
The Effect of Risk on Trust Attitude and Trust Behavior in Interaction with Information and Decision Automation

Situational risk has been postulated to be one of the most important contextual factors affecting operator’s trust in automation. However, experimentally it has received only little attention and was directly manipulated even less. To close this gap this study used a virtual reality multi-task environment where the main task entailed making a diagnosis by assessing different parameters. Risk was manipulated via the altitude the task was set in including the possibility of virtually falling in case of a mistake. Participants were aided either by information or decision automation. Results revealed that trust attitude toward the automation was not affected by risk. While trust attitude was initially lower for the decision automation it was equally high in both groups at the end of the experiment after experiencing reliable support. Trust behavior was significantly higher and increased during the experiment for the decision automation supported group in the form of less automation verification behavior. However, this detrimental effect was distinctly attenuated under high risk. This implies that negative consequences of increasing stages of automation in the real world might have been overestimated by studies not incorporating risk.
A Rival Recommendation Approach for Acoustic AR Running Support System Considering the Athletic Ability of Users

In recent years, running has become increasingly popular as an effective exercise activity which could help improve and maintain one’s physical health. However, even if we understand that exercising regularly has many benefits to our body and brain, it is not easy for people to stay motivated. To deal with this issue, in our previous research, we developed a running support system based on the users’ own records. Our system enables users to run and compete against virtual runners in an audio-based augmented reality space. However, in the AR running support system, competing with real users is more immersive than competing against one’s own running records. In this study, we aim to improve the motivation of running users, not only using the users’ own records, we also use the GPS information and time data from other real runners. We believe that by using information from real runners, our virtual running rival generation/recommendation method can improve the enthusiasm of users. Therefore, we propose a rival recommendation approach for our acoustic-based AR running support system considering the athletic ability of users. In addition, as a preliminary analysis, we conduct a questionnaire survey to clarify users’ needs for the future development of our running support system.
Practical Object Detection Using Thermal Infrared Image Sensors

Reliable object detection is critical for autonomous vehicles (AV). An AV must be safely guided towards its destination under different illumination conditions and avoid obstacles. Thermal infrared (TIR) camera sensors can provide robust image quality under any illumination. Past object detection work using TIR sensors focused on detecting only pedestrians by filtering thermal values. Other approaches leveraged the advantages of a pre-trained RGB-based model. However, the thermal threshold-based filtering can increase false positives depending on the TIR camera capability. Moreover, a large and new TIR training dataset is needed to improve the accuracy of the RGB-based object detection networks. The time and effort to annotate new data are significantly high. In this paper, we propose efficient and practical approaches to provide robust object detection from TIR images. We first reduce the cost of training with new data by using an automated process. To increase the final object detection accuracy, we next propose fusion methods that combine results from dual TIR camera sensors. Finally, we substantiate the practical feasibility of our approach and evaluate the substantial improvement in object detection accuracy. We use various detection networks and datasets on discrete Nvidia GPUs and an Nvidia Xavier embedded platform, commonly used by automotive OEMs.
Blockchain based OAuth for IoT

We propose the integration of blockchain with the OAuth standard. Two blockchains are considered for the implementation: Ethereum where ACL and token processing is performed on-chain and MultiChain where the processing is performed off-chain. The experiments show that the on-chain token generation provides an additional overhead compared to the off-chain one. However, it provides a secure token management and access traceability. The main advantage of our contribution is to propose a decentralized authorization system while relying on a standard specification.
Continual Learning with Differential Privacy

In this paper, we focus on preserving differential privacy (DP) in continual learning (CL), in which we train ML models to learn a sequence of new tasks while memorizing previous tasks. We first introduce a notion of continual adjacent databases to bound the sensitivity of any data record participating in the training process of CL. Based upon that, we develop a new DP-preserving algorithm for CL with a data sampling strategy to quantify the privacy risk of training data in the well-known Averaged Gradient Episodic Memory (A-GEM) approach by applying a moments accountant. Our algorithm provides formal guarantees of privacy for data records across tasks in CL. Preliminary theoretical analysis and evaluations show that our mechanism tightens the privacy loss while maintaining a promising model utility.
Evaluation Index of School Sports Resources Based on Artificial Intelligence and Edge Computing

As the demand for education continues to increase, the relative lack of physical resources has become a bottleneck hindering the development of school physical education to a certain extent. This research mainly discusses the evaluation index system of school sports resources based on artificial intelligence and edge computing. Human resources, financial resources, and material resources in school sports resources are the three major resources in resource science. University sports stadium information publicity uses Internet technology to establish a sports information management platform and mobile Internet terminals to optimize university sports resources and stadium information management services. It uses artificial intelligence technology to improve venue information management. It establishes a comprehensive platform for venue management information, collects multidimensional information, provides information resources and accurate information push, and links venue information with public fitness needs. Using edge computing to realize nearby cloud processing of video data, reduce the phenomenon of black screen jams during live broadcast, improve data computing capabilities, and reduce users’ dependence on the performance of terminal devices, build a smart sports resource platform, combine artificial intelligence (AI) to create smart communities, smart venues, and realize intelligent operations such as event service operations and safety prevention and control in important event venues. During the live broadcast of the student sports league, the nearby cloud processing of video data is realized in the form of edge computing, which improves the data computing ability and reduces the performance dependence on the user terminal equipment itself. In the academic survey of college physical education teachers, undergraduates accounted for 26.99%, masters accounted for 60.3%, and doctoral degrees accounted for 12.8%. This research will help the reasonable allocation of school sports resources.
Face Recognition Through Multi-Resolution Images

In this paper, we introduce a new method for face recognition in multi-resolution images. The proposed method is composed of two phases: an off-line phase and an inference phase. In the off-line phase, we built the Kernel Partial Least Squares (KPLS) regression model to map the LR facial features to HR ones. The KPLS predictor was then used in the inference phase to map HR features from LR features. We applied in both phases the Block-Based Discrete Cosine Transform (BBDCT) descriptor to enhance the facial feature description. Finally, the identity matching was carried out with the K-Nearest Neighbor (KNN) classifier. Experimental study was conducted on the AR and ORL databases and the obtained results proved the efficiency of the proposed method to deal with LR and VLR face recognition problem.
A Fuzzy Testing Method for Gas-Related Vulnerability Detection in Smart Contracts

With the application and promotion of blockchain technology, Ethereum is a blockchain platform in which developers can develop and run programs called smart contracts. When the input provided by the smart contract program will lead to excessive gas consumption, the gas shortage error will appear. In order to avoid this kind of error, many methods are proposed to estimate the gas limit of functions in smart contracts. However, in the case of complex contracts, underestimation often occurs. Therefore, on the basis of analyzing the operation mechanism and common vulnerability principle of Ethereum smart contract, this paper uses fuzzy testing technology to detect the vulnerability of smart contract. Firstly, the control flow diagram of smart contract execution is constructed based on Ethereum bytecode, and then the corresponding constraints are designed according to the characteristics of smart contract vulnerabilities. The constraint solver is used to generate software test cases to detect three gas related vulnerabilities: (1) Nested Calls (2) Greedy Contracts (3) Unchecked External Calls. The experimental results show that only this experimental tool can detect three gas-related vulnerabilities. Securify, Contractfuzzer, zeus and oyente can detect only one gas-related vulnerability, while mythril can identify two gas related vulnerabilities. The F value of securify (62.5%), oyente (68.1%), and mythril (6%, 40.8%) are not as high as the F value (72.2%, 87%) of this experimental tool, so the experimental method has higher accuracy.
Artificial Neural Networks in Medicine

Artificial neural networks (ANNs) have proven to be efficacious for modeling decision problems in medicine, including diagnosis, prognosis, resource allocation, and cost reduction problems. Research using ANNs to solve medical domain problems has been increasing regularly and is continuing to grow dramatically. This chapter examines recent trends and advances in ANNs and provides references to a large portion of recent research, as well as looking at the future direction of research for ANN in medicine.
A Survey on AAC Fringe Vocabulary to Support Personal Assistance Service

Objectives: There are increasing demands for personal assistance services which help the disabled to carry out their daily life. To use augmentative and alternative communication (AAC) approaches more effectively in daily communication, it is essential to include the appropriate core and fringe vocabulary in the AAC system. Considering the fact that there are few studies in Korea which have reported fringe vocabulary compared to core vocabulary, this study is aimed to develop a noun list that can be used to promote interactive communication between the disabled and their assistants in each communication situation.Methods: A literature review was conducted to elicit frequently used nouns in each communication situation. Then, a survey was carried out to identify nouns with high word commonality across 105 personal assistants.Results: Sixty-three nouns with more than 50% of word commonality across personal assistants were identified from the 244 fringe vocabulary; consisting of 112 nouns in the area of going out, 29 in cleaning, 27 in personal hygiene, 26 in cooking, 20 in meal supporting, 20 in other service areas, and 10 in commuting.Conclusion: The identified nouns are expected to help people with complex communication needs to express their needs and wants more clearly and effectively using those nouns embedded in their AAC systems. The list of nouns can be used not only for people who receive personal assistance services, but also for people who rely on AAC systems to express their daily needs.
Fundamentals of Federated Learning

In this chapter, we provide an overview of the fundamentals of FL. First, we discuss a brief history of machine learning. Second, we present the key design challenges for FL over wireless networks. Next, we critically discuss the key design aspects, such as resource allocation, incentive mechanism design, security, and privacy of FL over wireless networks. Finally, we critically discuss existing FL algorithms.
Feature back-projection guided residual refinement for real-time stereo matching network

In recent stereo matching research, deep convolutional neural networks (CNNs) have shown excellent performance to estimate depth from stereo image pairs. Previous works mainly focus on improving the robust performance of the stereo matching network to obtain higher matching accuracy. In this paper, we propose an end-to-end real-time stereo matching network (FBPGNet). FBPGNet manifests its characteristics in three parts: feature extraction module (FEM), initial disparity estimation module (IDEM), feature back-projection guided residual refinement module (FBPG) The FEM is designed to capture semantic and contextual information, which is composed of residual block, dilation convolution and spatial attention mechanism. The IDEM is proposed to produce an initial low-resolution (LR) disparity map, which utilizes an hourglass 3D convolution architecture. In addition, the FBPG is employed to refine the up-sampled low-resolution disparity map, which takes the features from the FEM and the low-resolution disparity map as guide information. Experiments show that the proposed stereo matching network has comparable prediction accuracy and inference speed compared with recent real-time stereo matching networks, and can achieve 25 fps on high-end GPU. • We design a lightweight but efficient module to extract features. The module is composed of linear residual network, dilation convolution and spatial attention mechanism. • We propose a lightweight 3D convolutional neural network with an hourglass structure to generate the initial disparity map. • We propose a feature back-projection guided residual refinement module. This module uses a back-projection generator to generate high-frequency features to guide the disparity refinement. • Experiments show that our proposed stereo matching network can achieve 25 fps on high-end GPU.
THANOS: The Aspect Classification Model for Imbalanced Vietnamese E-commerce Review Data

This article introduces several methods for aspect classification using machine learning models for data collected from customers' reviews on two e-commerce sites and mainly focuses on handling data imbalance to improve classifier performance. To this end, we describe the problem as a binary classifier at the sentence level. Sentences will be expressed as feature vectors using One-hot combined with Chi-square statistics, and we will use basic machine learning models such as Naive Bayes, SVM, Random Forest, Linear Regression for training before classifying the aspects included in. We tested the strategy on a dataset generated in Vietnamese based on the comments from users who purchased products on e-commerce sites. The experimental results suggest that the method produces better outcomes than before the imbalance handling.
CamerAI: Chase Camera in a Dense Environment using a Proximal Policy Optimization-trained Neural Network

CamerAI is an autonomous chase camera that uses a PPO-trained neural network. Our results suggest that a simple, fully connected network with only two hidden layers of 128 neurons can perform basic chase camera functionality. We contribute a set of inputs and outputs with their respective coordinate spaces, as well as a custom reward function that can be used to train the network with reinforcement learning. Our findings highlight the importance of correct coordinate spaces, the need for a continuous reward function, normalization as well as timely resets during training to allow the network to explore its environment. We additionally present an evaluation of the output of CamerAI during 10 hours of chasing a bot that is randomly exploring a commercial video game map in which CamerAI was able to keep the player visible 96 % of the time.
Automated assessment of breast density on mammogram images based on convolutional neural networks

Inception_ResNet_V2 model is one of the most effective deep learning methods and one of the outputs of artificial intelligence in image classification as accurately as possible, as it can be relied upon to create highly accurate classification models in the context of medical images analysis. In this work, we will test the effectiveness of this model by applying it to the assessment of breast density in radiographs, based on the Breast imaging-reporting and data system (BI-RADS) standard. This method solved the problem of lack of data and training and gave us impressive results, with accuracy reaching 99% in the two-class classification and 97% in the fourth classification. This can be considered remarkable progress in the process of early detection of breast cancer.
Analysis of the Application of Computer Virtual Reality Technology in College Physical Learning

In the process of physical education in colleges and universities, teachers should actively introduce new teaching methods to improve the quality of curriculum education on the basis of students' interest in learning. The emergence of computer virtual reality technology has brought another innovation opportunity in the teaching mode of physical education in colleges and universities. This paper points out that virtual reality technology can provide virtual teaching demonstration, situation simulation, virtual interaction, and other fresh teaching contents for college physical education, and effectively improve students' initiative and learning efficiency in the process of physical education course learning.
Joint Utility and Frequency for Pattern Classification

High-frequency itemset mining (HFIM) and high-utility itemset mining (HUIM) aim to discover itemsets with high occurrence and high utility, respectively, in a transaction database. A number of efficient algorithms have been developed to identify these high-utility itemsets (HUIs) or high-frequency itemsets (HFIs). Such algorithms play an increasingly important role in many occasions especially for analysis in commercial enterprises. In this paper, we propose a new model called joint utility and frequency for pattern classification, and two new algorithms, namely UFC <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gen</inf> and UFC <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fast</inf> . Both algorithms are designed to categorize each itemset into different type of patterns by setting the minimum thresholds of utility and frequency. We compare these algorithms on two datasets. The experimental results show that both algorithms can successfully collect three different types of itemsets from all candidate itemsets based on frequency and utility, and the list-based UFC <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fast</inf> algorithm outperforms the level-wise-based UFC <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gen</inf> algorithm in terms of execution time.
A Dual Reinforcement Network for Classical and Modern Chinese Text Style Transfer

Text style transfer aims to change the stylistic features of a sentence while preserving its content. Although remarkable progress have been achieved in English style transfer, Chinese style transfer, such as classical and modern Chinese style transfer, still relies heavily on manual process. In this paper, we first construct an unsupervised dual reinforcement model to transfer text between classical and modern Chinese styles using a non-disentangled approach, in which the style-transfer-accuracy reward and content-preservation reward are specially designed for model optimization. Meanwhile, we leverage a priori knowledge-based synonym dictionary to build a pseudo-parallel corpus for pre-training to provide a warm start. Experimental evaluations show that our model outperforms state-of-art networks by a large margin.
Visual tracking

Visual tracking is one of the most classical problems in computer vision, and its history goes back to the beginning of the 80s when classical concepts such as the Lucas-Kanade tracker and matched filters were developed. The purpose of this chapter is to give an overview of the development of the field, starting from those two approaches and concluding with deep learning-based approaches as well as the transition to video segmentation. The overview is limited to holistic models for generic tracking in the image plane, and a particular focus is given to discriminative models, the MOSSE (minimum output sum of squared errors) tracker, and DCFs (discriminative correlation filters).