Reviewerly/exHarmony huggingface dataset:
Information and Regularization in Restricted Boltzmann Machines

Recent works suggests an interesting interplay between the information flow between inputs features and hidden representations of a learning and the ability of the algorithm to generalize from trained samples to unobserved data. For instance, some of regularization techniques used to control generalization are expected to impact the corresponding information metrics. In this work, we study mutual information in Restricted Boltzmann Machines (RBM) and its relationship with the different regularization techniques. Our results show some evidence on interesting connections between the mutual information (inputs and its representations) with relevant parameters such as: network dimension, matrix norms and dropout probability, which are known to influence the generalization ability of the network. Results are empirically corroborated with a numerical study.
Duplicated Replay Buffer for Asynchronous Deep Deterministic Policy Gradient

Off-Policy Deep Reinforcement Learning (DRL) algorithms such as Deep Deterministic Policy Gradient (DDPG) has been used to teach intelligent agents to solve complicated problems in continuous space-action environments. Several methods have been successfully applied to increase the training performance and achieve better speed and stability for these algorithms. Such as experience replay to selecting a batch of transactions of the replay memory buffer. However, working with environments with sparse reward function is a challenge for these algorithms and causes them to reduce these algorithms' performance. This research intends to make the transaction selection process more efficient by increasing the likelihood of selecting important transactions from the replay memory buffer. Our proposed method works better with a sparse reward function or, in particular, with environments that have termination conditions. We are using a secondary replay memory buffer that stores more critical transactions. In the training process, transactions are select in both the first replay buffer and the secondary replay buffer. We also use a parallel environment to asynchronously execute and fill the primary replay buffer and the secondary replay buffer. This method will help us to get better performance and stability. Finally, we evaluate our proposed approach to the Crawler model, one of the Unity ML-Agent tasks with sparse reward function, against DDPG and AE-DDPG.
No-Reference Stereoscopic Image Quality Assessment Based on the Human Visual System

Stereoscopic image quality assessment (SIQA) is to predict the human perception quality of stereoscopic image pairs, which is more challenging than previous 2D image quality assessment due to the complicated binocular vision mechanism in the human visual system (HVS). Recently witnessed the significant progress of biotechnology and motivated by the deeper research on the HVS, we take a step to bridge the gap between HVS and SIQA by generalizing the optic chiasm algorithm and introducing biological vision fusion mechanism in our work. Firstly, a network structure is proposed in our work, which consists of an optic chiasm module for binocular information exchange and a multi-scale feature extraction module for binocular information fusion. Secondly, a mutual-perception attention fusion module is designed for simulating binocular fusion. In addition, we come up with an innovative data enhancement method. Experimental results show that the image quality assessment score obtained by the network is more consistent with human perception.
Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision

This study is to explore the gesture recognition and behavior tracking in swimming motion images under computer machine vision and to expand the application of moving target detection and tracking algorithms based on computer machine vision in this field. The objectives are realized by moving target detection and tracking, Gaussian mixture model, optimized correlation filtering algorithm, and Camshift tracking algorithm. Firstly, the Gaussian algorithm is introduced into target tracking and detection to reduce the filtering loss and make the acquired motion posture more accurate. Secondly, an improved kernel-related filter tracking algorithm is proposed by training multiple filters, which can clearly and accurately obtain the motion trajectory of the monitored target object. Finally, it is proposed to combine the Kalman algorithm with the Camshift algorithm for optimization, which can complete the tracking and recognition of moving targets. The experimental results show that the target tracking and detection method can obtain the movement form of the template object relatively completely, and the kernel-related filter tracking algorithm can also obtain the movement speed of the target object finely. In addition, the accuracy of Camshift tracking algorithm can reach 86.02%. Results of this study can provide reliable data support and reference for expanding the application of moving target detection and tracking methods.
Application of blockchain in the cluster of unmanned aerial vehicles

With the considerable exploration of unmanned aerial vehicles in civilian and military fields, the data storage and transmission mechanism of unmanned aerial vehicles exhibit significant security limitations, which cannot meet the strict requirements for data security in civilian and military business scenarios. In recent years, blockchain technology has been tried to be applied to multiple fields. The features of blockchain includes decentralization, immutability, transparency and auditability, make transactions more secure and tamper proof. In view of the many excellent features of blockchain, in this paper, blockchain technology is introduced into the application of cluster of unmanned aerial vehicles, aiming to solve the problems of identity authentication, secure and reliable transmission of data between unmanned aerial vehicles, and access control of the data on the blockchain, thereby to enhance the security of the cluster of unmanned aerial vehicles and to ensure the integrity and security of data.
A Comparison of Handwriting Systems Based on Different Style Characteristics

In the current age of digitalization, recognizing the writer of a handwritten text plays an important role in information proceeding. It has been attractive research problem in document analysis. The handwriting recognition aims to enable the machine to recognize the handwritten characteristics. For that it is used in the forensics, document examination and paleography. This paper present an automatic writer recognition method based on digitalized images of unconstrained writing and describes the different techniques used in text-dependent and text-independent methods to recognize the writer.
Image Based Design Patent Retrieval with Classification and Indexing

As a new image retrieval technology, Content Based Image Retrieval (CBIR) has a broad application prospect. In a normal CBIR system, the mature technology is usually as follows.Firstly, the underlying features of the image in the database are extracted in the preprocessing process. Then, during the retrieval process, the feature is matched with the feature of the Query Image linearly [1], [2], [3]. As the existence of semantic gap, this technique does not perform well enough to satisfy users' need.Compared with the general image on the network, the design patent images have some special properties. For example, the object in one image is singular, the images are fine categorized and little blurred, etc (e.g. Fig. 1). Based on these good properties, this paper presents an image retrieval method of design patent based on classification technology. When the retrieved image is correctly classified with a high probability, it is retrieved in the category; otherwise, it is retrieved in all images by using indexing technology to speed up the retrieval.
Design and implementation of discipline competition management system

Discipline competition management system responds to the call of the Ministry of Education's talent innovation and training, provides a platform for college students to participate in the subject competition, compared with the traditional subject competition, its management technology is more advanced, more perfect management system. Through the use of this system, students can more efficiently understand the competition information, participate in the competition, and realize the efficient communication between students and competition information. Successfully integrating the Internet with discipline competitions will be a big step forward for us. At the beginning of this paper, it introduces the current situation, disadvantages and advantages of the subject competition management system, then describes the design of the system, and finally summarizes the shortcomings of the system.
RETRACTED ARTICLE: Atmospheric environmental changes and a multi-objective optimization model for energy conservation and environmental protection based on improved multi-population genetic algorithm

With the development of computer technology and theory, cloud computing technology suitable for modern computing and large-capacity storage has emerged. Cloud computing technology depends on the Internet. The advancement and development of virtualization technologies such as cloud computing technology is the most cited of current computer technologies. With the development of cloud computing technology, this paper proposes a multi-group genetic algorithm based on interactive multi-functional processing methods, which can achieve the expectation of reducing the energy consumption of the cloud computing center to the greatest extent and at the same time ensure the stability and the running state of the server effectiveness. Through this survey, while reducing the company’s operating costs, it will also contribute to energy conservation and environmental protection. Multi-group genetic algorithms can effectively overcome the shortcomings of standard genetic algorithms, such as optimization range and efficiency, number of iterations, and premature end of iteration. The optimized result can reflect the subjective will of the decision-maker. Experimental examples show the efficiency of the algorithm, and the results show the applicability and feasibility of the algorithm. Based on the changes in the global atmospheric environment in recent years, this article discusses the reasons for the changes and focuses on the impact of human activities. In addition, the environmental issues that have received much attention today are the clues to discuss the climate issues and the harmful effects brought about by changes in the atmospheric environment, which has aroused people’s attention to energy conservation and environmental protection.
Research on the demand-driven O2O &amp; BIA Blended Interactive Teaching Mode

With the rapid development and popularization of network technology, online education has been flourishing. More and more teachers began to explore and try to use online teaching to make up for the lack of classroom teaching. Blended teaching which can effectively integrate online and offline resources, give full play to the respective advantages of online teaching and classroom teaching has attracted more and more attention. This paper aiming at the problems existing in present teaching, takes students as the main body, demand as the driving force, and ability training as the main line, and takes the course “Web Design and Website Construction” as an example, proposes a demand-driven O2O & BIA blended interactive teaching model which can comprehensively improve the quality of talent cultivation.
Composition of Online Teaching and Academic Ability under the Background of Artificial Intelligence and HTML

With the continuous improvement of the performance of intelligent devices, the continuous improvement of HTML5 and related technologies, and the rapid de velopment of mobile social applications such as wechat and Sina. A variety of innovative, well-made mobile HTML5 advertising has entered the public eye. HTML5 advertising on the mobile terminal combines the characteristics of mobile intelligent devices, and brings the audience audio-visual enjoyment, emotional and spiritual interactive experience. Autonomous learning has been attached great importance to college teaching. With the continuous improvement of the level of science and technology, network teaching has gradually penetrated into the actual teaching environment of colleges and universities. The teaching contents of network teaching courses and open courses in Colleges and universities fully reflect the important value of network teaching. Artificial intelligence technology will have a broad and far-reaching impact on the education system, and promote the overall reform of the teaching system. This paper analyzes the influence of artificial intelligence technology on education, and then clarifies the elements of teaching system and the relationship between learning methods and learning resources in the era of artificial intelligence.
Cryptanalysis of a Dynamic Universal Accumulator over Bilinear Groups

In this paper we cryptanalyse the two accumulator variants proposed by Au et al. [], which we call the \(\alpha \)-based construction and the common reference string-based (\(\mathcal {CRS}\) -based) construction. We show that if non-membership witnesses are issued according to the \(\alpha \)-based construction, an attacker that has access to multiple witnesses is able to efficiently recover the secret accumulator parameter \(\alpha \) and completely break its security. More precisely, if p is the order of the underlying bilinear group, the knowledge of \(O(\log p\log \log p)\) non-membership witnesses permits to successfully recover \(\alpha \). Further optimizations and different attack scenarios allow to reduce the number of required witnesses to \(O(\log p)\), together with practical attack complexity. Moreover, we show that accumulator's collision resistance can be broken if just one of these non-membership witnesses is known to the attacker. We then show how all these attacks for the \(\alpha \)-based construction can be easily prevented by using instead a corrected expression for witnesses.
Semantic Web for Sharing Medical Resources

The classical web is very helpful for using in different domain, especially in medicine. But still trying to improve results for this, we propose to use semantic web for sharing medical resources. In this article, we will discover the principle of the semantic web, which consists understanding the true meaning of the concepts in order to simplify the presentation of the data, which then serves to give the precise result during the research and the advantage of using the semantic web for sharing of medical resources. We use logical «Protege» to present the concepts and relation between them. And the limits that require innovation.
Rotation Invariant Aerial Image Retrieval with Group Convolutional Metric Learning

Remote sensing image retrieval (RSIR) is the process of ranking database images depending on the degree of similarity compared to the query image. As the complexity of RSIR increases due to the diversity in shooting range, angle, and location of remote sensors, there is an increasing demand for methods to address these issues and improve retrieval performance. In this work, we introduce a novel method for retrieving aerial images by merging group convolution with attention mechanism and metric learning, resulting in robustness to rotational variations. For refinement and emphasis on important features, we applied channel attention in each group convolution stage. By utilizing the characteristics of group convolution and channel-wise attention, it is possible to acknowledge the equality among rotated but identically located images. The training procedure has two main steps: (i) training the network with Aerial Image Dataset (AID) for classification, (ii) fine-tuning the network with triplet-loss for retrieval with Google Earth South Korea and NWPU-RESISC45 datasets. Results show that the proposed method performance exceeds other state-of-the-art retrieval methods in both rotated and original environments. Furthermore, we utilize class activation maps (CAM) to visualize the distinct difference of main features between our method and baseline, resulting in better adaptability in rotated environments.
Research on the optimal search problem of UAV based on improved genetic algorithm

Traditional genetic algorithm is prone to precocious problem. An improved genetic algorithm based on improved ring is proposed. First, the parent generation is improved and optimized by the improved circle method, then the offspring are obtained by crossing, and the inferior individuals produced after crossing are reduced by keeping the excellent genes to the maximum extent in the process of crossing, and the convergence rate is improved. The population diversity of the algorithm is guaranteed by improved ring and by choosing crossover probability and the mutation probability. Finally, the optimal search path is obtained by selecting the parent and offspring. The simulation results show that compared with the traditional Hamilton trilateral exchange algorithm, the improved genetic algorithm is simple, fast, and it can jump out of local convergence and obtain the optimal solution. It is a more reasonable UAV route method to solve the optimal search problem.
Bit Plane Slicing and Quantization-Based Color Image Watermarking in Spatial Domain

In this paper, a robust and fast watermarking scheme in spatial domain is introduced to embed a color watermark image over a color host image. The host image was converted from RGB to YCbCr. The watermark image was compressed using bit plane slicing followed by embedding into the luminance channel of the host image. Quantization process with pseudo-random steps was incorporated to embed the watermark into the host image and minimize the security vulnerabilities in the watermarked image. The experimental results indicate that the proposed watermarking scheme has better performance compared to existing related works in terms of robustness, time complexity and invisibility.
What is Digital History after all?

The article summarizes the author&amp;rsquo;s report at a discussion organized by the editorial board of the Istoricheskaya Informatika journal that addressed the book &amp;ldquo;What is Digital History?&amp;rdquo; by Salmi H. published in 2020. The book is named a manual for students thus justifying the opinion to consider the material presented to readers as representing a set of the most established positions and opinions in the field of digital history. The book discusses such issues as the time of digital history birth, its definition, subject field, functions as well as the importance for the widespread dissemination of historical knowledge in society. The author criticizes the concept of prerequisites for digital history birth outlined by H. Salmi which completely ignores the History and Computing movement in Western Europe and North America which played an important role in the formation of Russian historical information science and which was the discussion panel where many digital history issues were raised and resolved. The structure of the book is discussed as well. The article emphasizes the author's viewpoint on the importance of geographic information systems and technologies for historical research which he clearly underestimates. It is concluded that historical information science and digital history are different spheres of interdisciplinarity.
Theoretical and applied aspects of library automation: Analyzing Russian Science Citation Index

The 20-year document flow in library automation is analyzed based on the Russian Science Citation Index. The study goal was to evaluate the state of library automation as reflected in the document flow in library and information studies. The stages of library automation are specified; selection principles for related publications retrieval and analysis are characterized. The dynamics and statistical data of the dataflow are presented; sustainable interest toward the problem is revealed. Based on the 2- and 5-year impact factor, 10 most productive journals on the subject are identified, among them “Scientific and technical libraries” and “Bibliotekovedenie” [Russian journal of Library Science] journals. Organizations leading in library automation are introduced. The sectoral and content structures of the array of interest are characterized; the current trends in library automation related to expanding ALIS functionality and implementation of information and communication technologies are identified. The ranked list of publications on individual automated library and information systems is included.
Adversarial Attacks on Object Detectors with Limited Perturbations

Deep convolutional neural networks are widely witnessed vulnerable to adversarial attacks. Recently, great progress has been achieved in attacking object detectors. However, current attacks neglect the practical utility and rely on global perturbations on the target image with a large number of patches or pixels. In this paper, we present a novel attack framework named DTTACK to fool both one-stage and two-stage object detectors with limited perturbations. A novel divergent patch shape consisting of four intersecting lines is proposed to effectively affect deep convolutional feature extraction with limited pixels. In particular, we introduce an instance-aware heat map as a self-attention module to help DTTACK focus on salient object areas, which further improves the attacking performance. Extensive experiments on PASCAL-VOC, MS-COCO, as well as an online detection system demonstrate that DTTACK surpasses the state-of-the-art methods by large margins.
Development and Implementation of Project-Driven Productive Training Platform Based on Enterprise Framework

This article mainly introduces the use of the Java Web Enterprise Framework Struts2+Hibernate+Spring to develop productive management platform. It focuses on the design, implementation and years of application to demenstrate the productive training management platform based on the MVC model. In short, the productive training in vocational colleges is not a new concept, but a challenge to meet the real process control standards of enterprises. The enterprise-level work processes and the training practice in teaching are combined to be integrated into the platform workflow, so as to construct the multifunctional integration mode of building, teaching, learning, practising and testing, which is designed to make the trainees in productive training practice improve their professional skills and professional performance. Besides designing the framework, this paper also proposes the data based evaluation of the framework. The machine learning model is integrated to conduct the numerical overview.