Reviewerly/exHarmony huggingface dataset:
Applying Long-Short Term Memory Recurrent Neural Networks for Real-Time Stroke Recognition

This note discusses how to build a real-time recognizer for stroke gestures based on Long Short Term Memory Recurrent Neural Networks. The recognizer provides both the gesture class prediction and the completion percentage estimation for each point in the stroke while the user is performing it. We considered the stroke vocabulary of the $1 and $N datasets, and we defined four different architectures. We trained them using synthetic data, and we assessed the recognition accuracy on the original $1 and $N datasets. The results show an accuracy comparable with state of the art approaches classifying the stroke when completed, and a good precision in the completion percentage estimation.
Chaotic signal denoising using an improved wavelet thresholding algorithm

In wavelet thresholding denoising algorithms, traditional hard thresholding function is prone to Gibbs phenomenon due to its' discontinuity, while soft thresholding function is inherently biased. Therefore, they are not effective when dealing with chaotic signals. Though some improved thresholding functions are currently available, they are generally asymptotically unbiased and can not completely eliminate the estimation bias. To address this problem, a new thresholding function is designed by introducing transition regions in the hard thresholding function. Polynomial interpolation is used in the transition region to achieve continuity and smoothness. The proposed thresholding function is unbiased outside the transition region. Furthermore, the width of the transition region can be adaptively adjusted according to the noise level. Simulation results show that the wavelet thresholding algorithm based on the proposed thresholding function is more effective than the existing algorithms.
Traffic sign classification via Semi-Supervised model with uncertain labels

Traffic sign classification is the core of intelligent transportation and fundamental for constructing an automatic driving system. While supervised classification tasks demonstrate promising classification performance, a particular challenge is how to ensure the confidence for collecting labelled data. This paper presents a semi-supervised approach via label confidence for traffic sign classification to avoid the interference of uncertain labelled data. The idea of the proposed approach is to compare unsupervised information of the data, the supervised information carried by the learning data, and the supervised information which is given by the classification model in order to detect inconsistencies. The approach is able to build a robust classification model. Experimental results on benchmark and real-world dataset demonstrate that our approach significantly outperforms the existing approaches when uncertain labelled data exists.
Early Prediction of Children’s Disengagement in a Tablet Tutor Using Visual Features

Intelligent tutoring systems could benefit from human teachers’ ability to monitor students’ affective states by watching them and thereby detecting early warning signs of disengagement in time to prevent it. Toward that goal, this paper describes a method that uses input from a tablet tutor’s user-facing camera to predict whether the student will complete the current activity or disengage from it. Training a disengagement predictor is useful not only in itself but also in identifying visual indicators of negative affective states even when they don’t lead to non-completion of the task. Unlike prior work that relied on tutor-specific features, the method relies solely on visual features and so could potentially apply to other tutors. We present a deep learning method to make such predictions based on a Long Short Term Memory (LSTM) model that uses a target replication loss function. We train and test the model on screen capture videos of children in Tanzania using a tablet tutor to learn basic Swahili literacy and numeracy. We achieve balanced-class-size prediction accuracy of 73.3% when 40% of the activity is still left. We also analysed how prediction accuracy varies among tutor activities, revealing two distinct causes of disengagement.
From computational indicators to law into technologies: the Internet of Things, data analytics and encoding in COVID-19 contact-tracing apps

Abstract This paper investigates the data life-cycle of contact-tracing apps (CTAs) in the context of the COVID-19 pandemic. It highlights the socio-legal implications resulting from the design and technology choices that software developers inevitably make. These choices are often neglected by policy-makers due to the inherent technical complexity of algorithmic decision systems and to certain naive belief in technological solutionism. In particular, this paper shows, first, that technology-harvested data do not reflect an objective representation of reality, and therefore require a context within which to be understood and interpreted for policy and legal purposes; and, second, that the use of data analytics to extract insights from these data enables the production of computational indicators. By looking at how CTAs are used to implement pandemic-mitigation restrictions such as lockdowns, quarantines, social distancing and testing, the paper ultimately brings forth the ways in which technologies – and thus their bias and ways of framing social reality – become embedded in the law.
A Hybrid Recommendation Algorithm with Short-term Preference and Knowledge Preference

Researchers have attracted much attention to recommender system combined with Knowledge Graph (KG) to address the sparsity issue. Nevertheless, the previous methods cannot use auxiliary information from KG well. They also cannot accurately describe user-item interaction behavior so that it is challenging to construct fine-grained user preferences. To address these issues, we propose a Hybrid recommendation algorithm with Short-term preference and Knowledge preference (HSK), where the short-term preference is captured with Recurrent Neural Network (RNN) based on users' interactive items in the short term. The knowledge preference is captured from the KG's contextual knowledge, in which the attention mechanism determines the weight of the contextual knowledge. Then add two preferences to construct the user preference. Also, explicit ratings are introduced to quantify the user-item interaction behavior at a fine-grained level. Finally, to evaluate the proposed method's effectiveness, we use three datasets MovieLens-20M, Book-Crossing, and Last.FM to verify the model and design different sparse datasets for comparative experiments. In particular, we have further visualization of relevance probability besides traditional prediction tasks. The experimental results demonstrate that the proposed method shows significant improvements over widely used approaches.
Research on blockchain in the integrated application of medical services

Changing Money, Business, and the World», ranked among the top 50 management thinkers in the world
Fusion of Virtual Reality Technology and Multi-sensor Mobile Robot

Abstract In recent years, with the rapid development of virtual reality technology, VR, AR and MR can be widely used in the creation of learning scenarios for various majors to realize simulation training, and then solve the requirements of situational and natural interaction of learning media. VR technology is very intuitive and realistic in the research of multi-sensor mobile robot navigation, which can provide an ideal research platform for the research of autonomous navigation technology of mobile robot. Research on a new algorithm combining virtual reality and sensors to improve the performance of the entire system.
An Ensemble Model of Machine Learning for Primary Tumor Prognosis and Prediction

Machine learning is an artificial intelligence division that utilizes various computational, probabilistic, and optimization techniques that enable computers to “read” from previous examples and to identify trends that cannot be discerned from large, noisy, or complex datasets. This is particularly suitable for medical applications, particularly those dependent on complex proteomic and genomic measurements. The primary tumor is the deadliest disease, with a high mortality rate. Machine learning is therefore frequently used in the diagnosis and detection of primary tumors. Machine learning was more recently used to forecast and predict primary tumor diagnosis. The classification of the primary tumor may be defined with different machine learning algorithms, given the extreme impact of the disorder. Logistic regression, SVM, random forest, AdaBoost node, Naive Bayes, K-neighbor grouping, decision tree, and the Gaussian system classifiers are discussed. The dimensional reduction is implemented to simplify the dataset to reduce the measurement time. The objective of this paper is to propose an ensemble model for the prediction of the primary tumor.
Fusion of Blockchain and IoT: The Future of Industry 4.0

Blockchain and the Internet of Things (IoT) are two major disruptive, emerging technologies that are also considered to be the enablers of the Industry 4.0 revolution. Industry 4.0 consists of the integration of manufacturing networks by cyber-physical structures, thereby allowing manufacturing facilities and development processes to transition into automated and complex networks. This autonomous system involves communication between the components of the “smart factory” both internally and with other factories that are built using IoT. This relationship also provides trust between IoT participants, as well as control over resource allocation (e.g., repair time, electricit) and finished products, which can best be accomplished by implementing blockchain technology. This chapter describes one of the possible ways that IoT and Blockchain technologies can be integrated to solve these problems. The chapter also examines the feasibility of integrating blockchain with IoT technology, potential obstacles, and the advantages that such convergence can bring.
Closing the Reality Gap with Unsupervised Sim-to-Real Image Translation

Deep learning approaches have become the standard solution to many problems in computer vision and robotics, but obtaining sufficient training data in high enough quality is challenging, as human labor is error prone, time consuming, and expensive. Solutions based on simulation have become more popular in recent years, but the gap between simulation and reality is still a major issue. In this paper, we introduce a novel method for augmenting synthetic image data through unsupervised image-to-image translation by applying the style of real world images to simulated images with open source frameworks. The generated dataset is combined with conventional augmentation methods and is then applied to a neural network model running in real-time on autonomous soccer robots. Our evaluation shows a significant improvement compared to models trained on images generated entirely in simulation.
Activating Foreign Language Teaching in the Conditions of a Forced Transition to Distance Learning

The paper dwells upon using three types of online tools to activate foreign language teaching: blogs, social networks, and LMS. The task of using these tools has become especially urgent in 2020 due to the forced transition to distance learning because of the COVID-19 pandemic. The authors analyze the usage of the abovementioned tools and carry out a quantitative analysis of their efficiency
입력 중심과 출력 중심 형태초점접근법에 따른 아랍어 문법 학습 비교 연구

The purpose of this study is to compare the effects of focus on form approach: input-based and output-based activity, input-output combined activity on intermediate level learners’ Arabic grammar proficiency.Focus on form approach, which has been long studied as an alternative instruction method that can enable balanced learning of accuracy and fluency of the respective language, is a learner-focused proactive instruction method that overtly draws learners’ attention to linguistic elements as they arise incidentally in lessons in which the overriding focus is on meaning or communication(Long, 1991).The results of the study are as follows. First, it was found that input-based activity, output-based activity, input-output combined activity focus on form approach and the traditional rule-explaining activity all improved the achievement from a short-term point of view for Arabic grammar teaching methods. Also, among the four activities, it was found that focus on form approach, which implements both input and output based activity, had the biggest effect. Such result shows that input and output-based activity are both needed for drawing attention to and to learn target grammar elements, and that learners can better learn the target grammar when both activities are combined.Second, input-based activity, output-based activity and input-output combined activity focus on form approach showed significant results in the long-term efficacy of Arabic grammar learning. Among them, the experiment group that performed both input and output activities had the biggest significant effect from a long-term learning perspective.
A Distributed Energy Transaction Method Based on Blockchain

Aiming at the multi-agent, multi-mode and multi-rule characteristics of the distributed energy trading market,a distributed energy trading method based on blockchain is proposed. In view of the randomness and volatility of distributed energy, the use of the decentralized characteristics of blockchain technology meets the needs of distributed energy market transactions; in order to improve the reliability of the distributed energy transaction process, it is proposed to use smart contracts to separate the transaction and the communication process between the blockchain and the energy fund account; with the help of Ethereum’s “Raiden Network”, this “off-chain transaction” method, frequent and efficient transactions are carried out, realizing the “tripartite” of distributed energy producers, grid institutions and users “win-win” to promote the opening and healthy development of the distributed energy market.
Risk Based Improvement Performance Strategy for Operation and Maintenance Activities of River and River Infrastructure (BBWS Ciliwung-Cisadane Authority)

The Ministry of Public Works and Public Housing (PUPR) has the duty and responsibility to carry out effective and efficient river operation activities and river infrastructure including overcoming floods during the rainy season, but these activities are deemed necessary for improvement in planning procedures. To realize this goal, it is necessary to manage risks that can reduce river performance and river infrastructure as flood control. With risk management in this activity, it is possible to apply the sequence of risks that occur which are then used as recommendations for action (risk response) and risk-based development strategies to improve river performance and river infrastructure to control floods through operation and maintenance activities to build a functional and sustainable system. Suitable for long-term planning and short-term maintenance needs.
Siamese CNN-based rank learning for quality assessment of inpainted images

Existing NR-IIQA (no reference-based inpainted image quality assessment) algorithms assess the quality of an inpainted image via artificially designed unnaturalness expression, which often fail to capture inpainted artifacts. This paper presents a new deep rank learning-based method for NR-IIQA. The model adopts a siamese deep architecture, which takes a pair of inpainted images as input and outputs their rank order. Each branch utilizes a CNN structure to capture the global structure coherence and a patch-wise coherence assessment module (PCAM) to depict the local color and texture consistency in an inpainted image. To train the deep model, we construct a new dataset, which contains thousands of pairs of inpainted images with ground-truth quality ranking labels. Rich ablation studies are conducted to verify the key modules of the proposed architecture. Comparative experimental results demonstrate that our method outperforms existing NR-IIQA metrics in evaluating both inpainted images and inpainting algorithms.
PlasticNet: A Memory Template Extractor with Plastic Neural Networks for Object Tracking

Visual object tracking plays an important role in military guidance, human-computer interaction, and robot visual navigation. With the high performance and real-time speed, the Siamese network has become popular in recent years, which localizes the target by comparing the similarity of the appearance template and the candidate boxes in the search region. However, the appearance template is only extracted from the current frame, which results in the missing of the target for constantly changing appearance. In this paper, we propose a template extractor that can capture the latest appearance features from the previously predicted templates, named PlasticNet. We take inspiration from the memory mechanism of neuroscience (synaptic plasticity): the connections between neurons will be enhanced when they are stimulated at the same time. We combined it with recurrent networks to realize the PlasticNet. Our method can easily be integrated into existing siamese trackers. Our proposed model is applied in SiamRPN and improved performance. Extensive experiments on OTB2015, VOT2018, VOT2016 datasets demonstrate that our PlasticNet can effectively adapt to appearance changes.
On the Level of Precision of a Heterogeneous Transfer Function in a Statistical Neural Network Model

A heterogeneous function of the statistical neural network is presented from two transfer functions: symmetric saturated linear and hyperbolic tangent sigmoid. The precision of the derived heterogeneous model over their respective homogeneous forms are established, both at increased sample sizes hidden neurons. Results further show the sensitivity of the heterogeneous model to increase in hidden neurons.
Improving Human Happiness Analysis Based on Transfer Learning: Algorithm Development and Validation

Background Happiness refers to the joyful and pleasant emotions that humans produce subjectively. It is the positive part of emotions, and it affects the quality of human life. Therefore, understanding human happiness is a meaningful task in sentiment analysis. Objective We mainly discuss 2 facets (Agency/Sociality) of happiness in this paper. Through analysis and research on happiness, we can expand on new concepts that define happiness and enrich our understanding of emotions. Methods This paper treated each happy moment as a sequence of short sentences, then proposed a short happiness detection model based on transfer learning to analyze the Agency and Sociality aspects of happiness. First, we utilized the unlabeled training set to retrain the pretraining language model Bidirectional Encoder Representations from Transformers (BERT) and got a semantically enhanced language model happyBERT in the target domain. Then, we got several single text classification models by fine-tuning BERT and happyBERT. Finally, an improved voting strategy was proposed to integrate multiple single models, and “pseudo data” were introduced to retrain the combined models. Results The proposed approach was evaluated on the public dataset happyDB. Experimental results showed that our approach significantly outperforms the baselines. When predicting the Agency aspect of happiness, our approach achieved an accuracy of 0.8653 and an F1 score of 0.9126. When predicting Sociality, our approach achieved an accuracy of 0.9367 and an F1 score of 0.9491. Conclusions By evaluating the dataset, the comparison results demonstrated the effectiveness of our approach for happiness analysis. Experimental results confirmed that our method achieved state-of-the-art performance and transfer learning effectively improved happiness analysis.
Threat Artificial Intelligence and Cyber Security in Health Care Institutions

In this work we go beyond what is called unsupervised learning, a decision-making method that results in large numbers of false positives and negatives. The study was carried out in cryopreservation laboratories and aims to gain access to the General Data Protection Regulation (GDPR) implementation. Indeed, on the one hand, using Threat Artificial Intelligence, Chaos, Entropy and Security (TAICE&S) based methodology for problem solving one may mimic behaviors that are similar to the best human analysts. With the entry into force of the GDPR in the health institutions of the European Union (EU), stronger rules (TAICE based) on data protection (Security) mean people have more control over their personal data and businesses benefit from a level playing field. To respond to this challenge, a workable tool had to be built exploring the dynamics between TAICE&S and Logic Programming for Knowledge Representation and Reasoning, leading to the implementation of an agency based on TAICE/Cyber Security based techniques for problem solving, which is consistent with an Artificial Neural Network approach to problem definition. It is therefore possible to provide a full-bodied TAICE method to assist in threat identification and evaluation, activity prediction, mitigation, and response strategies. Using TAI procedures, one may identify patterns and matches in the activity of threat players, that combined with the issues of Chaos and Entropy gives us an opportunity to mimic how qualified specialists react in scenarios where models break off.
Embedding non-linear filtering in autonomous underwater vehicle dynamics via the Kolmogorov backward equation

This paper revisits the state vector of an autonomous underwater vehicle (AUV) dynamics coupled with the underwater Markovian stochasticity in the ‘non-linear filtering’ context. The underwater stochasticity is attributed to atmospheric turbulence, planetary interactions, sea surface conditions and astronomical phenomena. In this paper, we adopt the Itô process, a homogeneous Markov process, to describe the AUV state vector evolution equation. This paper accounts for the process noise as well as observation noise correction terms by considering the underwater filtering model. The non-linear filtering of the paper is achieved using the Kolmogorov backward equation and the evolution of the conditional characteristic function. The non-linear filtering equation is the cornerstone formalism of stochastic optimal control systems. Most notably, this paper introduces the non-linear filtering theory into an underwater vehicle stochastic system by constructing a lemma and a theorem for the underwater vehicle stochastic differential equation that were not available in the literature.
A Case Study on Combining Agile Requirements Development and SOFL

This paper presents a requirement development process combined with agile software development and the Structured Object-Oriented Formal Language (SOFL) to produce reliable software. Agile methods have made outstanding contributions to handle changes by short iterative development, though the requirements are less rigorous for verification of the corresponding programs. SOFL supports defining, and modifying requirements, and verifying programs based on the specifications, as a result of unifying the power of structured methods and object-oriented methods. We conducted a case study to assess the combined requirement development process and observe reworks caused by requirement changes. The result shows that our proposed process is effective to define the data and constraints, though changes in domains’ properties need continuous studies.