Reviewerly/exHarmony huggingface dataset:
Method of Finding Cover Signal for Audio Steganalysis Calibrated Methods

The paper presents a classification of methods for steganalysis of audio signals. On the basis of this classification, the disadvantages of using various methods are shown. The focus of steganalysis methods is on calibrated methods. In this regard, the work presents the classification of these methods and the basic formulas. Unfortunately, a steganalyst need to know features of cover signal before using some methods of steganalysis. But in practice this is not always possible. The paper proposes a method for finding the cover signal with its previously unknown characteristics. This method include 3 steps: check the data for stationarity; detection of the moments of signal discord; simulate the time series at each piecewise-uniform interval to restore the audio signal. Authors propose, before using steganalysis methods, to find the original audio signal that could be used in steganography by using a method for finding the cover signal.
An Efficient MRI Impulse Noise Multi-stage Hybrid Filter Based on Cartesian Genetic Programming

MRI imaging is one of the most widespread techniques in biomedical applications. Impulse noise is frequently a problem affecting the diagnosis of the disease, and it's a challenging task to denoise. However, the standard linear filter cannot detect the distribution of noise accurately due to the extremely nonlinear characteristic of the impulse noise with the one-dimensional measures to describe the behavior of pixels. The paper proposes to find out the relationship between multiple measures by Cartesian Genetic Programming to comprehensively describe the behavior of pixels from multiple dimensions to increase the robustness of detection results, and more complementary feature detectors are constructed by using the multi-gene output characteristics of the model. In the recovery stage, an adaptive median filter and edge-preserving filter (AMEPF) will be proposed which consists of three-layer filters to enhance the image and reduces the traditional over-dependence on the result of the detection phase, the filter can eliminate noise and protect the integrity of the structure. Different experimental results show that the recovery effect under different impulse noise intensity is better than the previous methods.
Information Literacy Module

The school library is recognized as an information center for learning, staffed by certified and professional school librarians, that collaborates with educators. This International School, known as IS1, offers library lesson periods for their primary and secondary students starting from January 2021 to assist and guide students equipping themselves with information literacy skills in supporting their academic purposes. The five information literacy modules were developed, namely Information Resources, OPAC &amp; WEBPath Express (School Databases), Online Information Resources, Citation &amp; References, and Basic Research Steps. After completing the information literacy modules, researchers interview the subject teachers about the perception of the implementation of the information literacy module with the students. Findings show that the implementation of the IL module was a good initiative to collaborate with school librarians to produce self-guided learning to boost student motivation and achievement.
Improving Taxonomy-based Categorization with Categorical Graph Neural Networks

In search and retrieval, a critical subtask is the classification of user search queries into predefined categories. Traditional supervised multi-class classification algorithms usually treat each category independently. In practical applications however, the categories have implicit relationships. Categories are organized as a tree-based taxonomy, which can be viewed as a graph. In this work, we explore a novel and systematic way of leveraging semantic information for improving taxonomy-based categorization. We propose a class of graph-based network structures, which we call Categorical Graph Neural Networks (CaGNN). CaGNNs leverage relationship information between neighbor categories and overlay the semantic information for each category, thus improving the performance of query categorization. The CaGNN framework can integrate a baseline categorizer with any Graph Neural Network, such as the commonly used Graph Attention Network and Graph Convolutional Network. Over a query categorization dataset of 2k categories and another ad title categorization dataset of 5k categories, CaGNN improves categorizers’ performance significantly compared to a baseline Deep Neural Network model without the CaGNN structure. Notably top 3 prediction recall increases from 90.15% to 91.40% for the ad title categorization task, for which is quite significant at over 90% level for more than 5k categories. By inspecting the learned category embeddings and the flow of message passing, we show that CaGNN effectively encapsulates useful graph structural information. Online A/B testing result shows that an ad ranking model with CaGNN-based features has increased ad click-through rate by 1.81% and reduced defect rate by 2.64%. The model has been deployed to production.
Hyperparameter Choice as Search Bias in AlphaZero

The AlphaZero algorithm has achieved remarkable success in a variety of sequential, perfect information games including Go, Shogi and chess. To better understand how AlphaZero works and leverage that understanding when deploying the system, we study the properties of the α hyperparameter that governs exploration noise in AlphaZero’s search, the only hyperparameter the system’s creators modified when moving among the three aforementioned games. First, we build a formal intuition for its behavior on a simple example meant to isolate the influence of the hyperparameter. Then, by comparing performance of AlphaZero agents with different α values on the game Connect 4, we show that the performance of AlphaZero improves considerably with a good choice of α. This all highlights the importance of α as an interpretable hyperparameter which allows for cross-game tuning that more opaque hyperparameters like model architecture may not.
BIOINSPIRED HYPERHEURISTICS FOR SELECTING SIGNIFICANT ATTRIBUTES IN BIG DATA CLASSIFICATION PROBLEMS

The authors propose a hyperheuristic bioinspired algorithm for selecting the most important classification features and assessing the classification accuracy. The algorithm is a modification of two approaches – bats echolocation bioheuristics and differential evolution. The k-nearest neighbors and decision tree classifiers were used in the experiments. The authors also assess the efficiency of the algorithm using reference datasets from the world library UCI (the University of California, Irvine). Based on the results of the experiments, the performance of the algorithm was compared with competing bioheuristics of bats, crows, gray wolves, a hybrid particle swarm algorithm, and gravitational search. We can say that the algorithm leads in the average and best values of the fitness function for most of the tested datasets, and is also statistically significant compared to competing algorithms according to the Wilcoxon T-test and the Friedman test.
A Conceptual Study on Virtual Leadership in Higher Education beyond COVID-19 Pandemic: Overcoming Challenges and Seizing Opportunities

This research is a conceptual study that identifies virtual leadership attributes effective in overcoming challenges and seizing opportunities in higher education beyond pandemic. The outbreak of COVID-19 pandemic affected higher education system globally. Pandemic measures to curb the outbreak such as lockdowns, social distancing, hand washing, wearing masks avoiding mass gathering and assemblies, restrictive movement and staying at home disturbed the traditional educational practice of face-to-face learning worldwide. Current review highlights that COVID-19 brought with it opportunity of introducing virtual learning in order to accommodate students globally. Within a short span of the COVID-19 pandemic, many researchers shared their works on teaching and learning in different ways (Pokhrel & Chhetri, 2021). Researchers identified challenges associated with virtual learning such as, weaknesses of online teaching infrastructure, the limited exposure of teachers to online teaching, the information gap, non-conducive environment for learning at home, equity and academic excellence in terms of higher education (Pokhrel et.al, 2021). Widely identified challenges with e-learning being accessibility, affordability, flexibility, learning pedagogy, life-long learning and educational policy (Murgatrot, 2020). Literature review indicates that Transformational leadership models are perceived to be effective tools of virtual leadership capable of minimizing obstacles in virtual learning environments, increase performance, innovation, creativity, group cohesiveness and learner satisfaction. Obstacles in virtual environment can be overcome through a virtual leader who is capable to communicate effectively with the team using available technology.
2D-3DMatchingNet: Multimodal Point Completion with 2D Geometry Matching

As the performance of RGB-D cameras improves, varieties of real-world partial scans are used in target recognition, face matching, scene segmentation, etc. Meanwhile, sparse and locally missing real-world scans greatly affect the performance of the model. Point cloud completion replaces such sparse and missing scans with dense and uniform point clouds. However, most of the work focuses only on the complete effect of the model in 3D dimension but neglects the completion exterior appearance in 2D space, which we define as 2D3D-mismatch problem. To solve this problem, we not only extract the location features in 3D dimension but also calibrate the point cloud shape features in 2D space. Meanwhile, the proposed method is more robust to real-world point cloud scans and provides multimodal completion results by leveraging an unsupervised strategy: Generative Adversarial Network (GAN). Extensive experiments demonstrate that our method outperforms other unsupervised point cloud completion methods. Notably, our approach generates smoother surface and robustness on out-domain data.
Software Testing Under Agile, Scrum, and DevOps

The adoption of agility at a large scale often requires the integration of agile and non-agile development practices into hybrid software development and delivery environment. This chapter addresses software testing related issues for Agile software application development. Currently, the umbrella of Agile methodologies (e.g. Scrum, Extreme Programming, Development and Operations – i.e., DevOps) have become the preferred tools for modern software development. These methodologies emphasize iterative and incremental development, where both the requirements and solutions evolve through the collaboration between cross-functional teams. The success of such practices relies on the quality result of each stage of development, obtained through rigorous testing. This chapter introduces the principles of software testing within the context of Scrum/DevOps based software development lifecycle.
Deep Reinforcement Learning Based Autonomous Exploration under Uncertainty with Hybrid Network on Graph

This paper mainly focuses on the autonomous exploration of unknown environments for mobile robots with deep reinforcement learning (DRL). To accurately model the environment, an exploration graph is constructed. Then, we propose a novel S-GRU network combing graph convolutional network (GCN) and gated recurrent units (GRU) based on the exploration graph to extract hybrid features. Both the spatial information and the historical information can be extracted by using S-GRU, which could help the optimal action selection by employing DRL. Specifically, In S-GRU, one GRU is performed to extract the inner information related to the historical trajectory, and another is used to combine the current and historical inner information as the current state feature. Simulation experimental results show that our approach is better than GCN-based and information entropy-based approaches on effectiveness, accuracy, and generalization.
Application of Virtual Reality Technology in Distance Rehabilitation Training

Stroke is one of the main causes of adult disability. Due to various secondary dysfunction after stroke, many stroke patients can not take care of themselves completely. The disease has a serious impact on the effect of safety of patients, and also brought a heavy burden to their families and society. This paper Introduce the application and new development of virtual reality technology in the field of rehabilitation training for motor dysfunction, and realizes an intelligent video monitoring system based on virtual reality technology on Android platform. In order to realize the system, this paper first discuss the advantages and prospects of the application of virtual reality technology in sports rehabilitation and function of the system.
Less-Individual Motion Features for Near-Future Prediction by using Domain Confusion

This paper proposes a method of extracting features with little individual difference from motion time-series data using domain confusion. Several studies have extracted features for classification tasks using transfer learning. In addition, researchers have applied feature extraction to style conversion, such as generative adversarial networks to transform poses; however, only a few studies have implemented learning to reduce individual differences. If motion features with little individuality are obtained, reproducing motions with individual characteristics becomes possible. When applied to style conversion, it enables person-to-person mapping of movements and detecting anomalies, among others. This study first proposes a method for extracting features with little individual difference using graph structure layers in neural networks; subsequently, we applied it to predict the movement of a person in the near future and compared its accuracy with the conventional method that does not explicitly consider individual differences. The accuracy improvement was confirmed in many cases for prediction up to 0.4 s ahead. For motion prediction up to 0.32 s ahead, accuracy improved in 14 out of 15 motions, and for the average score, accuracy improved by approximately 38%.
Traffic Planning for Unmanned Aerial Vehicle Based on Parallel Multi-swarm PSO Algorithm

With the wide application of UAV technology in life, the traffic/path planning technology of UAV becomes the key technology that restricts the development of UAV. In this paper, parallel particle swarm optimization (PSO) is used to solve the multi-routes planning problem of UAV. Considering the factors of UAV maneuvrability, range constraint, and path threat, the multi-path cost functions are constructed by adjusting the weight of each influencing factor in the cost function, and the multi-path cost functions are optimized by using multi-particle swarm optimization algorithm in parallel to search multiple paths. The simulation results show that the method is effective.
NSF-Based Mixture of Gaussian Processes and Its Variational EM Algorithm

Mixture of Gaussian processes (MGP) is a powerful model for dealing with data with multi-modality. However, input distributions of Gaussian process (GP) components of an MGP are designed to be Gaussians, which cannot model complex distributions that frequently appear in datasets. It has been proven that neural spline flow (NSF) models can transform a simple base distribution into any complex target distribution through the change of variables formula. In this paper, we propose an NSF-based mixture model of Gaussian processes (NMGP), which extends the conventional MGP by using distributions modeled by NSFs for the input variables instead of Gaussians. In addition, a variational EM algorithm is established to estimate the parameters of an NMGP. It is demonstrated by the experimental results that our proposed NMGP models outperform classic MGPs.
Early Diagnosis Prediction with Recurrent Neural Networks

Predicting medical diagnoses early is critical as it can improve treatment outcomes and ultimately save patient lives. Machine learning can help doctors make early predictions by leveraging an abundance of electronic health data. However, medical data is difficult to feed into predictive models because it typically has a large number of missing values. In this work, we propose an early diagnosis prediction model. First, we design an LSTM (Long Short-Term Memory), a type of neural network effective for modeling long time series. Second, to address the issue of missing data values, we implement various data imputation techniques and evaluate their effectiveness when used with the LSTM. Finally, we develop a novel LSTM model named Multi-Label Early Detection (MED), which has the goal of predicting patient diagnoses early in their hospital stay. We compare MED to state-of-the-art baselines using a subset of time series from the MIMIC-III database. We verify that our model obtains comparable AUC scores to that of standard LSTMs while encouraging early predictions.
Linguistic-based Augmentation for Enhancing Vietnamese Sentiment Analysis

Identify customer’s opinions about products, services, and brands bring many benefits to e-commerce development. Capturing customer attitudes helps retailers adjust business decisions. Customers can select the suitable product and the good service by consulting social experiences. However, free-style texts of customer feedback like acronyms, slang words, incorrect grammar, and so on are challenging any machine learning model.
Vietnamese vehicles speed detection with video-based and deep learning for real-time traffic flow analysis system

In this paper, we have developed a system to leverage traffic surveillance cameras to detect vehicle speed. In this system, we use a detection-based tracking paradigm for multiple object tracking then speed is estimated. First, YOLOv4 with transfer learning is applied for vehicle detection, a comparative analysis is carried out to choose trackers that work well with YOLOv4 in this task. Finally tracked vehicles’ traveled distance is back-projected to the 3D world by Haversine method for speed estimation. In order to deploy to edge device, we take the advantage of tensorRT framework and ONXX technology to optimize models and modify model format as well as accelerate inferencing. For the suitability to the Vietnamese traffic scenario, feature extraction models of tracking task and detection task were fine-tuned. The system is then optimized and modified to detect common Vietnamese vehicles’ speed in normal conditions and low light intensity conditions with the video stream fed directly from the preinstalled traffic surveillance camera. The whole system proceeds AI inference and processing with the help of Nvidia Jetson NX Xavier. All modules are packed into a small box which helps simplify the integration to available traffic cameras. This would release the need for radar and sensors which are usually extremely expensive and need a lot of calibration and maintenance.
Research on Dependency Parsing based on Optimized Neural Networks

In view of the complex structure of Chinese sentence patterns, it is difficult to accurately extract the feature information of dependency structure in dependency analysis, which directly affects the accuracy of correlation analysis. In this paper, based on stanfordnlp, the sentence dependency structure cannot be accurately extracted in Chinese dependency analysis. BiLSTM(Bi-directional Long short-term Memory) is used to solve the problem of Long distance dependent feature extraction, and the context feature information is further extracted by combining conditional random field (CRF).Then, a BiLSTM-CRF optimized neural network for Chinese dependency analysis is proposed, and the experimental results are verified. The experimental results show that it is effective to improve the accuracy of Chinese dependency analysis.
Improving Generalization of Reinforcement Learning for Multi-agent Combating Games

Multi-agent combating games have attracted great interest from the research community of reinforcement learning. Most work concentrates on improving the performance of agents, and ignores the generality of the trained model on different tasks. This paper aims to enhance the generality of reinforcement learning, which makes the trained model easily adapt to different combating tasks with a variable number of agents. We divide the observation of an agent to the common part which is related to the number of other agents, and the individual part which includes only its own information. The common observation is designed with a special representation of matrices irrelevant to the number of agents, and we use convolutional networks to extract valuable features from multiple matrices. The extracted features and the individual observation form new input for reinforcement learning algorithms. Meanwhile, the number of agents also changes during the combating process. We introduce a death mask technique to avoid the effects of the dead agents on the loss computation of multi-agent reinforcement learning. Finally, we conducted a lot of experiments in StarCraft-II on unit micromanagement missions. It turned out that the proposed method could significantly improve the generality and transferring ability of the model between different tasks.
System Optimization Design of Legal Attribute of Intelligent Contract Under Block Chain Technology

With the rapid development of cloud computing, the phenomenon of temporary replication is becoming more and more common. The definition of its nature and legal regulation has become the focus of debate at home and abroad. Temporary reproduction belongs to the reproduction in the sense of copyright law, which should be included in the scope of reproduction right regulation and restricted by reasonable use system.
Sentiment Analysis Based on Bi-Dual Inference

In recent years, machine learning has achieved rapid development in solving artificial intelligence tasks in many fields such as translation and image. Dual learning as a new learning paradigm, the core idea is to use primitive tasks (mapping from domain X to domain Y ) and dual tasks (from domain Y to domain X). Mapping to improve the performance of the two tasks. Such as translation from one language to another and reverse translation, emotion recognition and sentence generation, etc. The importance of this duality is shown by these studies that it can promote the learning of two tasks in the dual form. The dual inference is proposed on the basis of dual learning to study how to use structural duality in the reasoning stage of artificial intelligence tasks to improve the performance of the model. In this article, we propose a general Bi-dual inference framework, through the use of different initialization and data access order random seed independent training to obtain different agent models ( f, g ), so that multiple f or g Perform reasoning on a single task. We apply it to five-element sentiment classification, finally, the effectiveness of the model is verified on the data sets NLPCC2014 and NLPCC2018, and good results are obtained.
Construction of Campus Network Security System Based on VPN Technology

According to the current situation of campus network security, this paper analyzes three kinds of more common security systems, including network firewall, network intrusion detection and network isolation technology, expounds their principles and applications, as well as the shortcomings in campus network, and combines VPN technology to find the way to build campus network security. In addition, based on VPN technology, this paper describes the types and key technologies of VPN and studies the construction of campus network security system based on VPN technology.