Reviewerly/exHarmony huggingface dataset:
Action Recognition and Application of Table Tennis Training Based on IOT Perception

As the national sport of our country, table tennis focuses on continuous innovation and development. Table tennis is a high-quality sport characterized by fast movement and great flexibility. Unlike other human behaviors, identifying ping pong shots is idiosyncratic and difficult. Table tennis training is extremely important, and correct training can make athletes progress. In this paper, based on the perception of the Internet of Things, the action recognition and application of table tennis training actions are carried out, and the following conclusions are drawn: (1) the training movements of table tennis are relatively complex, which has a great challenge to the action recognition technology. The action recognition technology based on IoT perception can efficiently identify table tennis training actions. (2) Analyze and study the action recognition algorithm based on IoT perception, and propose a higher recognition accuracy and more stable algorithm DTW. (3) Comparing the accuracy, loss rate, and time impact between the algorithm in this paper and the traditional recognition algorithm, it is concluded that the algorithm proposed in this paper has higher accuracy and lower loss rate than the traditional algorithm. And with good stability, it is not affected by the environment and time. The algorithm in this paper is an algorithm with better performance and more worthy of use.
Multiscale Motion Detection and Recognition Combining Still Images and Video Sequences

In order to improve the sports training and competition effect of sports, this paper combines the accurate recognition method of video image fuzzy features to accurately identify the sports process to explore the movement characteristics of athletes on the sports stage. Moreover, this paper uses the trigger system to select cases and adopts the all-digital trigger method in the digital sports video recognition system. In addition, this paper uses the ADC input overrange as the judgment standard for bad cases and constructs an accurate recognition system for sports training features based on video image recognition. Finally, this paper verifies the effect of the accurate recognition method of local fuzzy features in sports video images. The experimental study verifies the effectiveness of the accurate recognition method of local fuzzy features in sports video images.
Face attribute analysis from structured light: an end-to-end approach

In this work we explore the use of structured-light imaging for face analysis. Towards this and due to lack of a publicly available structured-light face dataset, we (a) firstly generate a synthetic structured-light face dataset constructed based on the RGB-dataset London Face and the RGB-D dataset Bosphorus 3D Face. We then (b) propose a conditional adversarial network for depth map estimation from generated synthetic data. Associated quantitative and qualitative results suggest the efficiency of the proposed depth estimation technique. Further, we (c) study the estimation of gender and age directly from (i) structured-light, (ii) binarized structured-light, as well as (iii) estimated depth maps from structured-light. In this context we (d) study the impact of different subject-to-camera distances, as well as pose-variations. Finally, we (e) validate the proposed gender and age models that we train on synthetic data on a small set of real data, which we acquire. While these are early results, our findings clearly indicate the suitability of structured-light based approaches in facial analysis.
Visualisation of hierarchical multivariate data: Categorisation and case study on hate speech

Multivariate hierarchical data has an important role in many applications. To find the best visualisation that best fits a concrete data is crucial to explore and understand the relationships between the data. This paper proposes a categorisation – Elongated and Compact – of hierarchical data based on the inner shapes of the hierarchies, that is the connectivity degree of the internal nodes, the number of nodes, etc, that can be applied to any hierarchical data. Based on this taxonomy, we explore implicit and explicit layouts – Tree, Circle Packing, Force and Radial – to provide users with a complete view of the data. We hypothesise that Tree and Circle Packing fit with Elongated structures, and Force and Radial fit with Compact ones. In addition, we cluster multivariate features to embed them in the hierarchical layouts. Especially, we propose two different glyphs – one-by-one and all-in-one, and we bet for the one-by-one glyphs as the most suitable for showing the distribution of several features along with the hierarchical structures. To validate our hypotheses, we conducted a user study with 35 participants using a hate speech annotated corpus. This corpus comes from 4359 comments posted in online Spanish newspapers. The results indicated that users preferred the Tree layout over the other three layouts (Circle, Force, Radial) with both types of structures (EC and CC). However, when we focused the analysis only on Radial and Force layouts, both of them scored significantly higher with Compact than with Elongated data. Moreover, participants scored the one-by-one glyph higher than the all-in-one glyph, but the difference was not significant.
Analysis of Various Visual Cryptographic Techniques and Their Issues Based on Optimization Algorithms

Visual Cryptography (VC) is a process employed for the maintenance of secret information by hiding the secret messages that are embedded within the images. Typically, an image is partitioned into a number of shares that are stacked over one another in order to reconstruct back the original image accurately. The major limitation that existed in the traditional VC techniques is pixel expansion, in which pixel expansion is replaced with a number of sub-pixels in individual share, which causes a considerable impact on the contrast and resolution of the image that further gradually decreases the quality of the image. VC is named for its essential characteristics, such as transmitting the images with two or more shares with an equal number of black pixels and color pixel distribution. The secret message can be decrypted using Human Visual System (HVS). In this paper, 50 research papers are reviewed based on various classification algorithms, which are effectively used for the VC technique. The classification algorithms are categorized into three types, namely, meta-heuristic, heuristic, and evolutionary, and the research issues and challenges confronted by the existing techniques are reported in this survey. Moreover, the analysis is done based on the existing research works by considering the classification algorithms, tools, and evaluation metrics.
A loss matrix-based alternating optimization method for sparse PU learning

Positive and unlabeled (PU) learning is an important research topic in machine learning area, whose aim is to learn a good classifier from PU data. Due to its wide applications, a variety of PU algorithms with promising performance have been proposed. However, few of them consider PU learning from high-dimensional PU data. To fill the gap, in this work, we focus on designing the sparse PU classifier. However, it is difficult to achieve it, since the labels of U samples are uncertain. To this end, a loss matrix-based alternating optimization method, named LMAO-PU is proposed, where a two-stage alternating optimization idea is suggested to solve the difficulty in sparse PU learning. Firstly, a loss matrix is designed to measure the classification performance of the PU classifier on U samples. Then, a two-stage alternating optimization under the guide of loss matrix is developed. Specifically, the first stage is to optimize the PU model, aiming to address the challenge of labels uncertainty of U samples, where two objectives, TPR (True Positive Rate) and a suggested LRU (Loss Rate on Unlabeled), are optimized simultaneously. The second stage is to perform the sparse model optimization, where Sparsity and ErrorRate are adopted as the objectives to obtain the sparse model. The two-stage optimization procedure mentioned above is carried alternately and the sparse PU classifier with high quality is finally achieved. Experiments on 10 high-dimensional datasets demonstrate the superiority of proposed method over six state-of-the-art baselines in terms of sparsity, accuracy and area under ROC curve.
Text Visual Analysis in Auditing: Data Analytics for Journal Entries Testing

Business transaction data includes numeric values of the transactions and the date/time when the transactions are recorded, and textual data such as descriptions. Understanding the textual information of business transactions is also important since this information captures the nature of transactions in a qualitative manner. This study proposes a text visual analysis approach for auditing. We argue that combining text analysis and data visualization can improve the efficiency of audit data analytics for textual data in the organization's accounting information system. We provide a demonstration of the proposed method using a year-around general ledger data set. We use data visualization software Orange and Tableau for the demonstration. The proposed method can be used to understand a client's business and identify abnormal or unusual transactions from not only quantitative information but also qualitative information.
Pattern classification based on regional models

In a supervised setting, the global classification paradigm leverages the whole training data to produce a single class discriminative model. Alternatively, the local classification approach builds multiple base classifiers, each of them using a small subset of the training data. In this paper, we take a path to stand in-between the global and local approaches. We introduce a two-level clustering-based method in which base classifiers operate on a larger portion of the input space than in the traditional local paradigm. In particular, we first obtain a grained input representation by employing a Self-Organizing Map (SOM) to the inputs. We then apply a clustering algorithm (e.g., K-Means) to the SOM units to define input regions — a subset of input samples associated with a specific cluster of SOM units. We refer to this approach as regional classification. We demonstrate the effectiveness of regional classification on several benchmarks. Also, we study the impact of (1) adopting linear and nonlinear base classifiers (e.g., least squares support vector machines) and (2) using cluster validation indexes to determine the optimal number of clusters. Based on the experiments, the regional classification approach achieves competitive performance compared to its global and local counterparts, especially when equipped with linear base classifiers.
A novel multi-objective grammar-based framework for the generation of Convolutional Neural Networks

In recent years, the adoption of deep Convolutional Neural Networks (CNNs) has stood out in solving computer vision tasks, such as image classification. Researchers have proposed several architectures with varying sizes, complexities, and an increasing number of trainable parameters. For this reason, finding an optimized configuration and architecture with reduced complexity and high performance has become a very difficult task, since these configurations are totally dependent on the target classification problem and mostly depend on the optimization of a specialist in the area. To assist in the search for these optimal configurations, this work proposes the use of a multi-objective grammatical evolution framework, composed of a multi-objective search engine, a new context-free grammar responsible for creating the problem search space and a process mapping of individuals. Such a framework automatically generates and optimizes CNNs for a given image classification problem, without the need for human intervention from an expert. The framework navigates the search space using two objective functions seeking to maximize two metrics: accuracy and F 1 -score. The proposal was validated in the CIFAR-10, CIFAR-100, MNIST, KMNIST and EuroSAT datasets and the results show that the proposed method is able to generate simpler networks, but that statistically outperform (more complex) state-of-the-art CNNs in both metrics considered in the study. • An evolutional framework that optimizes CNNs with no need of an expert is introduced. • A context-free grammar responsible for modelling the architecture of CNNs is proposed. • The framework is tested on 5 datasets, guided by two metrics (accuracy and F 1 -score). • The results obtained are analyzed and compared to other known architectures. • The framework is capable of generating models that surpass other known architectures.
An improved anti‐phishing model utilizing TF‐IDF and AdaBoost

Summary A behavior over the internet, which results into false utilization of the resources is termed as phishing and the techniques to identify them are termed as anti‐phishing frames and algorithms. This research work introduces a new relative similarity approach utilizing similarity index co‐relation by applying Cosine similarity. In order to incorporate the co‐relation, term frequency and inverse document frequency (TF‐IDF) is also calculated. AdaBoost is employed to improve the classification accuracy of the proposed work by means of enhancing the performance of weak learner to classify website into legitimate and phishing one. The simulation analysis has shown that the proposed work outperformed the existing works in terms of accuracy, TPR, and precision of detecting phishing sites to offer secure browsing. Overall model evaluation against 50,000 URL instances have demonstrated an average precision, TPR, F‐measure, accuracy and execution time of 98.64%, 96.52%, 97.56%, 98.014%, and 3.485 s, respectively for phishing detection. This proves the success of the proposed model in shielding phishing attack.
Use of social media as a marketing and information provision tool by the City of Cape Town Libraries

Background: Social media use by libraries has facilitated communication and marketing of services to user communities. The City of Cape Town Libraries adopted social media usage in the 2015–2016 financial year. This study is set out to assess librarians’ perception and response to the implementation and adoption of social media for library services.Objectives: The research sought to appraise the City of Cape Town public librarians’ experiences of social media use in the provision of information services, evaluate librarians’ perceptions of social media use for information provision services and recommend ways in which social media information services provision can be improved upon.Method: A quantitative research method and a descriptive survey research design approach was adopted for the study, and a web-based questionnaire was used for data collection. The study was anchored on the technological acceptance model, and employed total enumeration sampling to collect data from the 102 City of Cape Town librarians-in-charge.Results: City of Cape Town librarians have positive perception and acceptance towards the use of social media to perform library-related duties. Librarians utilised social media in engaging with library users, including marketing of library services, and promotion of library events. Experiences including lack of guidance in dealing with copyright issues and organisational policy favouring a single social media platform were reported.Conclusion: Library services through is vastly positive but expansion across multiple social media platforms is necessary to encourage further engagement with users. A third construct: preference of choice is proposed for the TAM model.
Deep Supervised Cross-modal Hashing for Ship Image Retrieval

Abstract The retrieval of multimodal ship images obtained by remote sensing satellites is an important content of remote sensing data analysis, which is of great significance to improve the ability of marine monitoring. In this paper, We propose a novel cross-modal ship image retrieval method, called Deep Supervised Cross-modal Hashing(DSCMH). It consists of a feature learning part and a hash learning part used for feature extraction and hash code generation separately, both two parts have modality-invariant constraints to keep the cross-modal invariability, and the label information is also brought to supervise the above process. Furthermore, we design a class attention module based on the cross-modal class center to strengthen class discrimination. The experiment results show that the proposed method can effectively improve the cross-modal retrieval accuracy of ship images and is better than several state-of-the-art methods.
Detection of Self-explosive Insulators in Aerial Images Based on Improved YOLO v4

Abstract In view of the low detection efficiency of traditional aerial image-based self-explosive insulators and the need to manually extract the features of self-explosive insulators, a self-explosive insulator detection algorithm based on the improved YOLO v4 is proposed: We use the hybrid data augmentation method to increase the number of defective samples of self-explosive insulators and increase the diversity of self-explosive insulators to improve the detection accuracy of the algorithm. In addition, the channel attention mechanism is combined with the YOLO v4 algorithm to improve the feature extraction capability of algorithms to self-explosion insulators. The proposed algorithm is verified using the expanded self-explosive insulator dataset and the traffic signs dataset TT100K. The experimental results show that the proposed algorithm has a mAP of 92.0 % on the self-explosive insulator dataset and has an F1-score of 78.70% on TT100K dataset, which has positive significance for the research on self-explosive insulator detection.
Image Based Classification of Methods-Time Measurement Operations in Assembly Using Recurrent Neuronal Networks

Image based classification enables the acquisition and transfer of data from manual assembly workstations into a digital environment. Based on the Methods-Time Measurement method, assembly processes are transformed into short, discrete basic operations that are recognised by means of image processing and used as input data for a multilayer neural network. A recurrent neural network algorithm is investigated for its applicability in combination with the sensor data. The five basic MTM operations reaching, grasping, bringing, releasing, and positioning are classified and additional influencing factors, as well as the implementation of an object recognition, are investigated. The following paper addresses the question of the extent to which manual assembly processes can be reliably derived from visual sensor data and classified by machine learning algorithms.
Autonomous Driving Based on Imitation and Active Inference

We advance a novel computational model of acquiring a hierarchical action sequence and its use for minimizing the divergence between observation and prediction. The model is grounded in a principled framework to learn and cognize the dynamic surrounding of a learning agent, which exemplifies the model’s functioning by presenting a simulation of overtaking scenarios in autonomous driving. The learning agent integrates imitation learning and active inference to form hierarchical representations from expert demonstrations. During the online learning phase, the learning agent improves the action selection procedure based on the prior knowledge (exploiting) and the novel interactions with a dynamic environment (exploring). The proposed method applies an active knowledge sampling during the learning agent’s movements to make a dynamic inference in the Bayesian structure by message passing through the multi-levels. A line-changing driving scenario with different levels of complexity is organized to verify the proposed framework’s efficiency by dealing with single and multiple dynamic objects interacting in the environment.
Adsorbate-adsorbent potential energy function from second virial coefficient data: a non-linear Hopfield Neural Network approach

The Hopfield Neural Network has been successfully applied to solve ill-posed inverse problems in different fields of chemistry and physics. In this work, the non-linear approach for this method will be applied to retrieve the empirical parameters of potential energy function, $$E_{p}(r)$$ , between adsorbate and adsorbent from experimental data. Since the adsorption data is related to the second virial coefficient and therefore to $$E_{p}(r)$$ through an integral equation, the Hopfield Neural Network will be used to find the best parameters which fits the experimental data. Initially simulated results will be analyzed to verify the method performance for data sets with and without noise addition. Then, experimental data for adsorption of propionitrile on activated carbon will be treated. Results presented here corroborate to the robustness of this method.
Influence-aware memory architectures for deep reinforcement learning in POMDPs

Abstract Due to its perceptual limitations, an agent may have too little information about the environment to act optimally. In such cases, it is important to keep track of the action-observation history to uncover hidden state information. Recent deep reinforcement learning methods use recurrent neural networks (RNN) to memorize past observations. However, these models are expensive to train and have convergence difficulties, especially when dealing with high dimensional data. In this paper, we propose influence-aware memory , a theoretically inspired memory architecture that alleviates the training difficulties by restricting the input of the recurrent layers to those variables that influence the hidden state information. Moreover, as opposed to standard RNNs, in which every piece of information used for estimating Q values is inevitably fed back into the network for the next prediction, our model allows information to flow without being necessarily stored in the RNN’s internal memory. Results indicate that, by letting the recurrent layers focus on a small fraction of the observation variables while processing the rest of the information with a feedforward neural network, we can outperform standard recurrent architectures both in training speed and policy performance. This approach also reduces runtime and obtains better scores than methods that stack multiple observations to remove partial observability.
Immersive VR Network Management Analysis considering Automatic Topology Discovery Algorithms

In order to make up for the deficiency of a two-dimensional user interface, enhance the visualization of network information, and comprehensively and effectively improve the efficiency of network management, this paper attempts to use virtual reality technology to design the network management system and uses the research results of related fields for reference to carry out the specific practice, providing a new idea for the design and development of the network management system. The automatic topology discovery algorithm is introduced into the network management analysis, and the immersive network management is modularized by using the method of dividing subnets. Finally, the test results show that through the functional design of the system and the hierarchical description of the structural model, the practical method is given. Finally, the operation process of the simulation system is analyzed in detail. The proposed method can effectively reduce the network load and reduce the network bandwidth and greatly improve the slow convergence of the penetration VR network topology.