Reviewerly/exHarmony huggingface dataset:
A Roundness Detection Algorithm of Non-Standard Discontinuous Circle Based on the Curvature Change of Discrete Edge Points

In the practical application scene of image detection, it is of great significance to detect the circle in the image and judge the roundness of the circle.Because the edges of real objects are often discontinuous due to occlusion, roundness detection of discontinuous circles is a common task.Traditional circle detection algorithms mainly include voting/clustering algorithm based on edge points, optimization algorithm and arc segment-based algorithm.For discontinuous circles, because the integrity of the circle edges is different, the voting results of edge points are difficult to be used as the basis for roundness detection.When the algorithms based on optimization and arc segment detect nonstandard circles, a large number of overlapping misjudgments are easy to occur, and the overlapping of objects before and after will lead to maximum suppression, which is difficult to achieve good results.In this paper, the geometric properties of the circle itself are used to classify the edge points, so that the roundness of the discontinuous circle can be calculated and the size of the circle can be judged.In this paper, taking the photos of mutually blocked round fruits as an example, an algorithm is constructed to detect the roundness of non-standard discontinuous round objects.The results show that this algorithm has a good effect on roundness detection of non-standard discontinuous circles.
Use Active Learning to Construct Japanese Emoji Emotion Database

Emojis are now frequently used in online communication, which express rich meaningful information and emotional messages. However, communication will fail if the meaning of different Emojis is not well understood, especially for the speakers of different languages and those from different countries/regions. There are very few researches about the Emoji dataset currently, since the process of building an Emoji database is labor-intensive and time-consuming. To solve this problem, we propose an active learning-based framework for building Japanese text datasets containing Emoji. This approach aims to achieve fast and balanced labeling of data given a small and unevenly distributed source of Emoji data. The active learning algorithm selects unlabeled data with high information content for manual labeling and updates the model parameters with the manually labeled data, in which way a large Emoji database is iteratively constructed. The constructed Japanese Emoji database contains hundred types of Emojis, with at least hundred pieces of Our experiment suggests that the Emoji dataset can be efficiently constructed with balanced data and the result dataset can provide rich information for text emotion classification, by rendering an accuracy of over 82%.
Deep Sentimental Analysis of the Arabic Medieval Documents

This paper discusses a new approach motivated by the contemporary procedures of short texts’ classification by means of deep learning methodology. The method considers the tested collection and the collections of imposters as sequences of short patterns like “tweets”. A deep neural network combined with a CNN layer accomplished by the LSTM ones is trained to distinguish between the imposters' collections at the first stage. The obtained network classifies the bathes of the test collection, transforming its texts into signals, making it possible to label them according to their individual writing style. The suggested procedure intends for new attitudes to study textual material applied to recognize authorship attributes of medieval Arabic documents from a novel computational perspective. Evaluating the outcomes proves the method's high reliability demonstrated in the paper on investigating texts questioningly attributed to Al-Ghazali, comparing them with The Holy Quran and the “One Thousand and One Night”. The proposed methodology suggests a new look at the perusal of medieval documents’ inner structures and possible authorship from the short-patterning and signals processing perspectives.
Weakly-Supervised Generation and Grounding of Visual Descriptions with Conditional Generative Models

Given weak supervision from image- or video-caption pairs, we address the problem of grounding (localizing) each object word of a ground-truth or generated sentence describing a visual input. Recent weakly-supervised approaches leverage region proposals and ground words based on the region attention coefficients of captioning models. To predict each next word in the sentence they attend over regions using a summary of the previous words as a query, and then ground the word by selecting the most attended regions. However, this leads to sub-optimal grounding, since attention coefficients are computed without taking into account the word that needs to be localized. To address this shortcoming, we propose a novel Grounded Visual Description Conditional Variational Autoencoder (GVD-CVAE) and leverage its latent variables for grounding. In particular, we introduce a discrete random variable that models each word-to-region alignment, and learn its approximate posterior distribution given the full sentence. Experiments on challenging image and video datasets (Flickr30k Entities, YouCook2, ActivityNet Entities) validate the effectiveness of our conditional generative model, showing that it can substantially outperform soft-attention-based baselines in grounding.
Application of Practical Teaching in Pedagogy of Higher Vocational Colleges

Practical teaching is an important part of higher vocational college pedagogy and an important means to improve students' professional skills.Higher vocational colleges should increase their research and investment, actively organize and implement it, and give full play to its teaching role.This paper first outlines the basic concepts and construction principles of practical teaching, discusses the problems existing in the application of practical teaching in higher vocational college pedagogy, and puts forward solutions to the problems.
Virtual Club Shadow: A Real-time Projection of Golf Club Trajectory

In sports like golf, the motion of a club or racket determines the outcome significantly, and it is essential to consistently move such properties with the correct forms to achieve high scores during competitions. This paper proposes a novel projection method that provides real-time visualization of golf club trajectories on the ground. The system can render the trajectory of a golf club along with conventional face-to-path angle information in real-time using an optical motion capture system and a video projector. Furthermore, the system utilizes dynamic changes of color to provide more detailed information about the pose of the golf club. This color-changing method aims to help users understand their club motion trajectories and explicitly adjust their form based on the visualization results.
A Comparative Study of Graph Matching Algorithms in Computer Vision

The graph matching optimization problem is an essential component for many tasks in computer vision, such as bringing two deformable objects in correspondence. Naturally, a wide range of applicable algorithms have been proposed in the last decades. Since a common standard benchmark has not been developed, their performance claims are often hard to verify as evaluation on differing problem instances and criteria make the results incomparable. To address these shortcomings, we present a comparative study of graph matching algorithms. We create a uniform benchmark where we collect and categorize a large set of existing and publicly available computer vision graph matching problems in a common format. At the same time we collect and categorize the most popular open-source implementations of graph matching algorithms. Their performance is evaluated in a way that is in line with the best practices for comparing optimization algorithms. The study is designed to be reproducible and extensible to serve as a valuable resource in the future. Our study provides three notable insights: (i) popular problem instances are exactly solvable in substantially less than 1 s, and, therefore, are insufficient for future empirical evaluations; (ii) the most popular baseline methods are highly inferior to the best available methods; (iii) despite the NP-hardness of the problem, instances coming from vision applications are often solvable in a few seconds even for graphs with more than 500 vertices.
Layer-Wise Optimization of Contextual Neural Networks with Dynamic Field of Aggregation

This paper includes a presentation of experiments performed on Contextual Neural Networks with a dynamic field of view. It is checked how their properties can be affected by the usage of not-uniform numbers of groups in different layers of contextual neurons. Basic classification properties and activity of connections are reported based on simulations with H2O machine learning server and Generalized Backpropagation algorithm. Results are obtained for data sets with a high number of attributes (gene expression of bone marrow cancer and myeloid leukemia) as well as for standard problems from UCI Machine Learning Repository. Results indicate that layer-wise selection of numbers of connection groups can have a positive influence on the behavior of Contextual Neural Networks.
Advanced Descriptive Analytics

This chapter is focused mainly on advanced descriptive analytics techniques. In this chapter, we will first explain the concept of clustering which is a type of unsupervised learning approach. We will then pick one clustering technique which is the k-means clustering. Using the fourth practical business problem, we will explain how we can use the k-means clustering technique to solve a real business problem. Next, we will explain the association rule example and finally network analysis. We will focus the explanation of these techniques on solving business-related problems, particularly for small businesses, and conclude with the fifth business problem which is focused on using network analytics for employee efficiency.
Research and Practice of Online Teaching System for Audio Reading Based on Streaming Media Technology

Streaming media technology transmits an uninterrupted stream of information from the producer to the end user in real time, with the original video or audio stream data, split into many packets for transmission, constantly transcending the technical limitations of the receiving terminal and the transmission pipeline to fully realise the instantaneous transmission of high-definition audio symbols. Streaming media technology has made audio books available to a wider range of audiences, and more and more people are reading and learning anytime and anywhere via smart phones. The shape of education will change in a smarter and more connected direction. This paper investigates the structure of streaming media systems, AAC coding technology and the storage structure of audio reading files, laying the foundation for the development of an online teaching system for audio reading based on streaming media technology, and promoting the development of mobile audio reading platforms in a service-oriented, virtualized and intelligent development trend.
Pengembangan E-Learning untuk Bimbingan Teknis Lembaga Penjaminan Mutu Pendidikan

This study aims to produce appropriate and effective E-learning to be used in the implementation of technical guidance learning at LPMP East Java. The research carried out is included in the research and development model using the ADDIE method. Data collection techniques were carried out using questionnaires and test instruments. The feasibility of the media is assessed from the data obtained from material experts, media experts, media learning experts, facilitators to targets. The effectiveness of the product is assessed from the test results during field trials to the target. Based on the results of the research conducted, the results showed that: (1) in general the feasibility test from 3 experts showed that the product developed was very feasible to use; (2) The developed e-learning has a good effectiveness in increasing the participants' understanding which is assessed based on the test results. It can be concluded that E-learning is very useful to be developed in learning media, and the effectiveness of the use of E-learning has a positive impact on increasing the understanding of the material for technical guidance participants. The conclusion is that E-learning is very useful to be developed in learning media, and the effectiveness of the use of E-learning has a positive impact on improving the understanding of the material for technical guidance participants.&#x0D; Keywords: Technical Guidance, E-learning, Development
Optimum Design of Flapping Wing Flying Robot by Modified Social Group Optimization

Constrained optimization is very often appealed to handle challenging design problems in engineering area. Herein, heuristic methods are frequently preferred to solve these design problems. For the best design of an engineering problem, the robustness of optimization algorithm occupies an important place.In this paper, a recent engineering problem is handled which evaluates the optimum design of a flapping wing flying robot / ornithopter. Concerning the issue, main function and constrained functions are combined using penalty function to define the problem encountered as a single objective optimization problem. The design problem is evaluated by four recent and promising algorithms that are chaotic dynamic weight particle swarm optimization (CDW-PSO), crystal structure algorithm (CryStAl), adaptive strategy particle swarm optimization (ASPSO), and modified social group optimization (MSGO). The best fitness, processing time and average best fitness evaluations are considered to objectively reveal the most appropriate method for optimum design. Consequently, MSGO and ASPSO achieve the optimum results and outperforms CDW-PSO and CryStAl algorithms for the best fitness-based experiments. Moreover, MSGO yields a remarkable performance than ASPSO by presenting a more robust behavior for average best fitness-based comparisons.
A Double-Weighted Deterministic Extreme Learning Machine Based on Sparse Denoising Autoencoder and Its Applications

Extreme learning machine (ELM) is a feedforward neural network-based machine learning method that has the benefits of short training times, strong generalization capabilities, and will not fall into local minima.However, due to the traditional ELM shallow architecture, it requires a large number of hidden nodes when dealing with high-dimensional data sets to ensure its classification performance.The other aspect, it is easy to degrade the classification performance in the face of noise interference from noisy data.To improve the above problem, this paper proposes a double pseudo-inverse extreme learning machine (DPELM) based on Sparse Denoising AutoEncoder (SDAE) namely, SDAE-DPELM.The algorithm can directly determine the input weight and output weight of the network by using the pseudo-inverse method.As a result, the algorithm only requires a few hidden layer nodes to produce superior classification results when classifying data.And its combination with SDAE can effectively improve the classification performance and noise resistance.Extensive numerical experiments show that the algorithm has high classification accuracy and good robustness when dealing with highdimensional noisy data and high-dimensional noiseless data.Furthermore, applying such an algorithm to Miao character recognition substantiates its excellent performance, which further illustrates the practicability of the algorithm.
Ship detection with lightweight network based on YOLOV3

This paper proposes a lightweight network for ship detection based on YOLOV3. This method reduces the parameters and model size of YOLOV3 while retaining accuracy from three aspects. First, an efficient way is described of replacing the Darknet-53 of YOLOV3 with a reduced network. Second, an optimization form is demonstrated to improve the basic unit. Last, compression and acceleration strategies are implemented in the method. The performance of our method and YOLOV3 is measured on a self-built dataset for ship detection. The method we proposed is 29× less parameters, 85× less size, and 3× less inference time than YOLOV3. In the experiment, the method is deployed on a Qihang Unmanned Surface Vehicle (USV) and realizes real-time detection of ships.
Trusted Blockchain-based Data Fingerprinting Differential-Traceability and SkipList Indexing Methods in Privacy Protection

The process of data interaction is often criticized by the public for data privacy leakage, attribution disputes, as well as being maliciously tampered with and difficult to trace the source of data. This paper proposes a data privacy protection and fingerprint tracing method based on the features of decentralization, traceability and non-tampering in blockchain technology. And we combined with the protection of data by secure network platform in trusted computing and differential-traceability algorithm (DTA). We propose a data privacy protection and fingerprint traceability method, and use the SkipList table index structure to improve interaction efficiency. We discuss the proposed data fingerprint-based traceability framework model in a trusted environment using the features of blockchain and trusted computing platforms. In this paper, the data can be processed safely and efficiently to solve the privacy problem, the transaction data is encrypted and recorded on the chain by DTA, and the interaction process uses the SkipList table index to enhance retrieval efficiency. Finally, through multiple experiments and comparison of the results obtained from security testing, it is verified that the blockchain-trusted computing data privacy protection and data fingerprint traceability SkipList table indexing method can achieve traceability for data privacy protection, transaction security management and peer-to-peer verification of encryption and decryption algorithms. We not only provide a secure, trustworthy and efficient data privacy protection model, but also bring a time efficiency optimization of 6ms per 10,000 queries.
Large Scale Real-World Multi-person Tracking

This paper presents a new large scale multi-person tracking dataset. Our dataset is over an order of magnitude larger than currently available high quality multi-object tracking datasets such as MOT17, HiEve, and MOT20 datasets. The lack of large scale training and test data for this task has limited the community’s ability to understand the performance of their tracking systems on a wide range of scenarios and conditions such as variations in person density, actions being performed, weather, and time of day. Our dataset was specifically sourced to provide a wide variety of these conditions and our annotations include rich meta-data such that the performance of a tracker can be evaluated along these different dimensions. The lack of training data has also limited the ability to perform end-to-end training of tracking systems. As such, the highest performing tracking systems all rely on strong detectors trained on external image datasets. We hope that the release of this dataset will enable new lines of research that take advantage of large scale video based training data.
Application of Linear Congruential Generator (LCG) Algorithm in Android Based Mathematics Education Game

Mathematics is a lesson learned from elementary, junior high, high school to tertiary levels. Many students have a negative attitude towards mathematics because mathematics is considered very boring and is considered the most difficult subject among other subjects, this can cause students to find it difficult to learn mathematics. Of course, the very rapid development of technology cannot be separated from the role of mathematics. Mathematics is a science that is the basis for the development of modern technology. Apart from that, mathematics also has an important role in various aspects of life. Solutions to help students understand mathematics, including through educational games. Educational games are techniques that can motivate and provide a better learning experience. The Linear Congruential Generator (LCG) algorithm is a random number generation method that is widely used in computer programs that utilize linear models. The results showed that the Linear Congruential Generator (LCG) method in the mathematics education game was successfully applied. Based on the results of game testing using questionnaires to students, teachers, and the general public (student guardians), it was found that the application made was included in the "Very Good" category with an average result of 80.55%.
Attacks on a Certificate based Generic Access Scheme for Internet of Drones

In recent years, IoDs have begun to develop in the field of specialization, and their application scenarios are becoming more and more extensive, from street scene monitoring and patrolling in daily life to unmanned reconnaissance aircraft in the military field. However, the IoD will inevitably encounter data security problems in the process of working. For example, the IoD is easy be stolen by attackers in the process of transmitting the data monitoring the street scene to the control center. Therefore, it is very necessary to establish a secure communication protocol in the process of IoD and ground communication. In 2021, Shehzad Ashraf Chaudhry et al. provided an Internet access scheme for drones based on a certificate. They claim that their scheme can provide anonymity and can resist IoD simulation attacks, MITM attacks, and replay attacks. Although the protocol can resist the above attacks, once the protocol encounters the IoD physical capture attacks, the information communicated between the IoD and the ground control center will also be obtained by the attacker. In this article, we utilize D-Y attacker model to cryptanalyze their scheme. During the analysis of the protocol, and it is found that their protocol can not resist IoD physical capture attacks, so their protocol is not secure.
Towards a New Quantitative Availability Model for Computer Systems Based on Classifications of Security Requirements

Cloud computing is an emerging computing paradigm that replaces computing as a personal asset with computing as a public service. As such, it offers all the advantages of a public utility system, in terms of economy of scale, flexibility, and convenience, but it poses major problems including the loss of availability. In this article, the authors define and refine a taxonomy of basic security requirements suitable for all contexts and systems; then the resulted hierarchical model is used to create a new approach to quantifying the availability of it systems. This new measure is inspired from the mean failure cost (MFC). Measure and called availability mean failure cost (MFCa) is the average monetary value of loss per unit of time of use of each participant. This metric gives us a more accurate estimate, clear refinement, and useful interpretation for availability-related decision making using MFCa. How this metric can be used to analyze cloud computing as a business model is something to be explored.