Reviewerly/exHarmony huggingface dataset:
Enhanced wide-activated residual network for efficient and accurate image deblocking

To save bandwidth and storage space as well as speed up data transmission, people usually perform lossy compression on images. Although the JPEG standard is a simple and effective compression method, it usually introduces various visually unpleasing artifacts, especially the notorious blocking artifacts. In recent years, deep convolutional neural networks (CNNs) have seen remarkable development in compression artifacts reduction. Despite the excellent performance, most deep CNNs suffer from heavy computation due to very deep and wide architectures. In this paper, we propose an enhanced wide-activated residual network (EWARN) for efficient and accurate image deblocking. Specifically, we propose an enhanced wide-activated residual block (EWARB) as basic construction module. Our EWARB gives rise to larger activation width, better use of interdependencies among channels, and more informative and discriminative non-linearity activation features without more parameters than residual block (RB) and wide-activated residual block (WARB). Furthermore, we introduce an overlapping patches extraction and combination (OPEC) strategy into our network in a full convolution way, leading to large receptive field, enforced compatibility among adjacent blocks, and efficient deblocking. Extensive experiments demonstrate that our EWARN outperforms several state-of-the-art methods quantitatively and qualitatively with relatively small model size and less running time, achieving a good trade-off between performance and complexity.
Graphics Processing Methods Based on Deep Learning in the Context of Big Data

The purpose of this article is to conduct research on graphics processing (GP) methods based on DL in the context of big data (BD). First, this article studies the characteristics of current BD and investigates related applications. Then based on DL, this article proposes a method of processing graphics content. This method uses three different DL network structures to extract graphic features. In this paper, the OpenMP parallel model is used to optimize the graph search algorithm in parallel on the CPU platform, and the algorithm is optimized in parallel by using the principle of program locality, reducing synchronization overhead and load balancing. Thirdly, in view of the irregularity of the memory access of the graph search algorithm, the FPGA algorithm hardware accelerator is customized using BD technology. Finally, it introduces the development tools of DL algorithm hardware acceleration, and compares them with traditional processing methods. Comparative experiments show that the image processing method based on DL proposed in this paper has an average recognition rate of about 10% higher than traditional processing methods, and has better results, providing an important reference for the development of image processing.
Research on Anti-noise of Image Compression Based on Variational Autoencoder

Abstract We introduced the Variational Compression Autoencoder (VCAE) that introduces noise training, and uses KL divergence to balance compression and anti-noise. Variational autoencoders improve the anti-noise performance of the compression framework through noise training and learning decoupled latent representations. Experiments on the Kodak datasets show that our framework is superior to other deep learning compression frameworks such as compressed autoencoders in the MS-SSIM indicator under the same bit error rate.
A Semantically Sensitive Privacy Protection Method for Trajectory Publishing

Trajectory data set is the indispensable foundation for constructing reliable Internet of Vehicles (IoV) service and location-based service (LBS), while it is likely to be abused by malicious attackers to infer user's privacy.In this paper, we propose a trajectory protection method based on stop points obfuscation, which can confront various privacy attacks and preserve the semantic information to achieve adequate utility.Two strategies for stop point selection are designed, including category-distance priority method and Markov matrix method.Our new method was analyzed and evaluated on a real-world trajectory data set.The experiment result shows that our method can improve the utility of the data set and provide multi-level privacy protection.
The Design of Industrial Security Tasks and Capabilities Required in Industrial Site

As companies' competitiveness has shifted to technology and other intellectual assets and the method of intrusion and extortion on them, the demand for industrial security professionals who can respond professionally them have also become more advanced. The key is to systematically operate the education system and qualification system based on the demands of industrial sites, and the national standardized system created by the government for this purpose is the National Competency Standards. This study was conducted to analyze the current status of industrial security-related tasks and job capabilities within the NCS and indirectly identify the demand of industrial sites for Industrial security. This study derived security-related tasks, tasks requiring security capabilities, and required security capabilities through keyword searches, and as a result, was able to identify Indus-trial security demands that were different from existing security, such as Information security. It is expected that the results of this study, combined with further demand excavation and survey of industrial sites, will be utilized as the basic data for NCS development of industrial security.
Energy Assessment Approach During Context Integration in New-Generation Ubiquitous Networks

Newer technological advancements in ubiquitous networking have boosted digital dependency of users, thereby facilitating human–computer interaction (HCI). Quality of Experience (QoE) is an important aspect to analyze network performance. However, achieving desired level of user satisfaction is quite challenging which has led to the proliferation of context-aware computing (CAC). The paper focused over various context-gathering mechanisms, system architecture and contemplated on how to improve the Quality of Content (QoC).
Bengali Sign Language Characters Recognition by Utilizing Transfer Learned Deep Convolutional Neural Network

Being the fifth most-spoken native language of the world, Bengali is spoken by approximately 265 million people worldwide. In Bangladesh alone, among nearly 130 million native speakers, 13 million individuals are undergoing the hearing impairment problems. Hence, developing a recognition system of the Bengali sign alphabets has been an area of interest for decades. Previously, numerous researchers contributed by either developing benchmark datasets or providing ideas regarding classifiers for accurate recognition. In most of the cases, either the dataset in consideration was not well-constructed or the recognition accuracy was not satisfactory. In this research, we began with a benchmark dataset of Bengali sign alphabets with 38 signs. We applied augmentation and a modified InceptionV3 architecture. The experimental analysis showed that our trained model achieved an overall accuracy of 94.41% which outperformed the previous best-known outcomes by a noteworthy margin.
Application of Artificial Intelligence in Computer Network Technology

Under the background of the current era, science and technology are being updated rapidly, and more and more new technologies are widely used in various fields. Artificial intelligence (AI) is one of many new technologies. As a current hot technology, has been continuously developed and applied to all areas of social life, changing people’s lives and bringing great convenience to people’s lives, and also promoting economic and social development. The application of to different fields has become an inevitable development trend.
Support Vector Machine for Digitalization of Lacquerware Process

Based on the theory of participatory design concept, combined with digital technology, this paper puts forward the “genetic algorithm” method and strategy of lacquerware, which not only provides new construction ideas and development ideas for the protection and inheritance of lacquerware , but also highlights the unique cultural heritage function of Yangzhou lacquer ware in digital protection and service. The digital protection of genetic algorithm can stimulate the initiative and enthusiasm of the public to participate in the protection of traditional culture, combine traditional culture with technology and people’s daily life, and truly realize the inheritance of traditional culture under the support of digital technology. At the same time, the method formed by the research is also applicable to the digital protection and inheritance of other traditional handicraft culture, which helps to form a systematic protection mechanism for its intangible culture, provides a new way and method for the protection of more intangible culture, and further expands the service space of art design.
Syntax Error Search Using Parser Combinators

Parser combinators is a popular approach to parsing sequences generated by context-free grammars, which can be specialized data formats (e.g. JSON, YAML), markup languages like XML or HTML. At the same time, this approach is rarely used for parsing programming languages.The purpose of this paper is to study the application of parser combinators for programming languages processing, and more precisely for searching of syntax errors. The method that had been developed during this research was compared with an algorithm of syntax analysis of programming languages using parser-generators. Parser combinator takes less time on average to find a syntax error in the source code. Its average time complexity is linear with respect to the length of the input sequence, while the parser generator has an average quadratic complexity. Moreover, a parser combinator requires less memory than a parser generator.These results can be used for building intelligent code completion tools for fast syntax error detection.
K-Nearest Neighbor method for detecting egg quality conditions using Raspberry Pi

Abstract Food production in Indonesia is very strong and constrained, such as rice, corn, eggs and so on. Eggs that are consumed every day by Indonesian citizens, many consumers of egg food are not alert to the condition or condition of the eggs, whether they are feasible or expired. There are many studies related to the nature of eggs, egg quality and others, in part there is no modeling tool for detecting egg quality conditions based on raspberry pi using the K-Nearest Neighbor method which can be used to see good or bad eggs. In this study, the Raspberry Pi was used as the main component with the aid of a camera as an image capture on eggs using training data of 100 egg samples consisting of 50 good eggs and 50 bad eggs in order to get maximum accuracy. With the help of 2 led as a light source and the software uses the Python language. The purpose of this study was to obtain the expected accuracy by collecting 20 eggs randomly to get a percentage (17/20) * 100 = 85%.
A hybrid feature selection algorithm combining ReliefF and Particle swarm optimization for high-dimensional medical data

Due to high-dimensional feature and strong correlation of features, the classification accuracy of medical data is not as good enough as expected. feature selection is a common algorithm to solve this problem, and selects effective features by reducing the dimensionality of high-dimensional data. However, traditional feature selection algorithms have the blindness of threshold setting and the search algorithms are liable to fall into a local optimal solution. Based on it, this paper proposes a hybrid feature selection algorithm combining ReliefF and Particle swarm optimization. The algorithm is mainly divided into three parts: Firstly, the ReliefF is used to calculate the feature weight, and the features are ranked by the weight. Then ranking feature is grouped according to the density equalization, where the density of features in each group is the same. Finally, the Particle Swarm Optimization algorithm is used to search the ranking feature groups, and the feature selection is performed according to a new fitness function. Experimental results show that the random forest has the highest classification accuracy on the features selected. More importantly, it has the least number of features. In addition, experimental results on 2 medical datasets show that the average accuracy of random forest reaches 90.20%, which proves that the hybrid algorithm has a certain application value.
Design of Mobile Robot Interaction System Based on Slam Particle Filter

Based on the market demand for service-oriented robot, the automatic delivery system of robot is designed in this paper. The system is controlled by ArduinoMega2560 control board, ROS (Robot Operating System) as the robot operating system, combined with Slam particle filter algorithm to complete the robot positioning and navigation functions. At the same time, based on the Qt software interactive platform to control the robot walking, using the steering gear to open and close the storage box, 4G module SMS notification. In summary, this paper designs an interactive platform for indoor file transfer service robot based on ROS and Slam particle filter algorithm. Through this platform, we can control file access, notify the operation of pickup, and improve the comfort of human-computer interaction.
Design and Implementation of Arbitrary Point FFT Based on RISC-V SoC

This paper designs and implements a dedicated microprocessor architecture based on the RISC-V (the fifth-generation Reduced Instruction Set Computing) with only 20 instructions for arbitrary-point FFT (Fast Fourier Transform) algorithm. Moreover, the corresponding SoC (System-on-Chip) is also built for the expansibility and reconfigurability. The methodology of software and hardware co-verification is applied to validate the correctness of the systematic functions by comparing the simulation results between MATLAB, Visual Studio 2019 and VIVADO 2019.1. Finally, the Xilinx Artix-7 (XC7A100TFGG484-2) FPGA (Field Programmable Gate Array) platform is used for implementation and prototyping of the proposed hardware system, which totally uses 1897 LUTs (Look-Up Tables), 361 FFs (Flip-Flops) and 25 BRAMs (Block Random Access Memory) and consumes 2.016W at 100MHz. The experimental results show that the presented system can realize the FFT algorithm at any point by reconfiguring the parameters of software and expanding the capacity of the memory, and it is suitable for the embedded applications due to the small area and low power consumption.
Do You Feel a Connection? How the Human-Like Design of Conversational Agents Influences Donation Behaviour

Conversational agents (CAs) are rapidly changing the way humans and computers interact. Through developments in natural language processing, CAs are increasingly capable of conducting human-like conversations with users. Furthermore, human-like features (e.g., having a name or an avatar) lead to positive user reactions as if they were interacting with a real human conversational partner.CAs promise to replace or supplement traditional interactions between humans (e.g., counseling, interviews). One field of CA-human interaction that is not yet fully understood in developing human-like CAs is donating to a good cause. Notably, many charities rely on approaching people on the streets to raise funds.Against this background, the questions arise: How should a CA for raising funds for non-profit organizations be designed and how does human-like design of a CA influence the user’s donation behavior. To explore these two questions, we conducted a 2 × 2 experiment with 134 participants.
Possibilities of Adaptive Testing Usage in Students' e-Learning

The paper considers the possibilities of adaptive testing in the organization of e-learning in the educational environment of the university with the help of electronic educational resources. The author analyzes the possibilities of adaptive testing and the practice of its application in the works of many authors. A variant of using the adaptive testing apparatus for managing the student's educational vector is proposed.
Monitoring floor vibrations to evaluate objectively physical activity during housework activities

The self-monitoring of physical activity (PA) is an effective supporting strategy for the adoption of active lifestyles. However, accurate predictions of the PA remain challenging in certain situations. In the present study, we developed a smart-home floor-vibration monitoring system able to quantify PA during housework activities. Ten subjects performed four different activities (sitting, ironing, cooking, and cleaning) in the Ocha-House, an independent experimental house with accelerometers installed on the floor. The floor vibrations were monitored and the PA was evaluated objectively using two Actigraph GT9X activity trackers (worn on the waist and the wrist, respectively). A data processing algorithm was designed to process the floor-vibration data, estimate the number of steps, and predict the volume of physical activity performed in the house. A good correlation was found between the quantity of floor vibrations measured by the Ocha-House and the objective evaluations of PA made by the waist-worn monitor (r=0.9, p<; 0.00l). Compared to the two GT9X monitors, the floor vibration system was also able to produce more accurate predictions of the number of steps. The Ocha-House floor-vibration monitoring system was able to perform quantitative assessments of PA for four different housework activities. We believe that floor vibration-based smart-home systems can provide relevant pieces of information for the objective evaluation of PA when people are staying at home.
Blockchain Technology in Automobile Insurance Claim Systems Research

Recently, academics and industries are increasingly paying attention to blockchain technology because it is used to improve traditional companies. Insurance company is one type of the traditional and inflexible companies. Its operation is not transparent, based on paper contracts, relying on human intervention and other characteristics resulting in low efficiency. The transparency, tamper-proof, decentralization and other advantages of blockchain-based Internet of Things and smart contract technology enable people to improve the traditional processes and structures of the insurance industry. This paper summarizes the application of block chain technology in the auto insurance claim systems. By comparing the implementations of various systems, the existing achievements and their advantages and disadvantages are described. In addition, this paper prospects the key directions of future research on the implementation of blockchain.
Privacy Preserving Data Deduplication in cloud using Advanced Encryption Standard

Massive quantities of data are actually delivered daily, and most data is intemperate. Data deduplication is one of the technique mostly used to precise the data that is redundant storage in privacy preservation. deduplication this became a challenging procedure. Many data deduplication methods are been suggested, but many of them have struggled to protect privacy and ownership. We suggested a portion where data, privacy and security are duplicated. The Advanced Encryption Standard (AES) is a system wherein deduplication is worked out in a safe manner.
Developing a Framework for Generating Hotel Recommendation

Recommendation Systems (RS) are utilized in a variety of areas. RS provide suggestions to items that a particular user is most likely interested in. There has been a huge amount of research in this field. An intelligent approach to generate recommendations by using heterogeneous data is proposed in this paper. For increasing the performance of the recommendation system, we consider the surrounding environments of the considered hotels. Feedback system is considered as a computer program in information technology (IT). The analysis of the feedbacks provided by the users is essential for the improvement of accuracy RS. By analyzing both textual and numeric feedbacks, a better suggestion will be provided to the users.
Finding Fuzziness in Neural Network Models of Language Processing

Humans often communicate by using imprecise language, suggesting that fuzzy concepts with unclear boundaries are prevalent in language use. In this paper, we test the extent to which models trained to capture the distributional statistics of language show correspondence to fuzzy-membership patterns. Using the task of natural language inference, we test a recent state of the art model on the classical case of temperature, by examining its mapping of temperature data to fuzzy-perceptions such as cool, hot, etc. We find the model to show patterns that are similar to classical fuzzy-set theoretic formulations of linguistic hedges, albeit with a substantial amount of noise, suggesting that models trained solely on language show promise in encoding fuzziness.
Construction of Curriculum System of Preschool Education Specialty Based on Virtual Reality Technology

With the rapid development of China’s modernization, higher requirements are put forward for the cultivation of the existing professionals in preschool education. The construction and reform of professional curriculum system has become the top priority of the current preschool education reform. Virtual reality technology is a kind of information technology rising recently, which has been widely used in education. According to the needs of the construction of the existing preschool education curriculum system, this paper puts forward the research on the construction of University preschool education curriculum system based on virtual reality technology. This paper makes an in-depth research and Analysis on the preschool education major and virtual reality technology. According to the survey of the current situation of preschool education curriculum, only 34.5% of the students are satisfied with the curriculum. The main problems are that the teaching content is divorced from reality and the experience is not strong. Therefore, according to the existing main problems, this paper makes targeted optimization and improvement. At the same time, combining with the virtual reality technology, the reform of information practice teaching is carried out. This paper makes an assumption on the construction of the curriculum system of preschool education major in universities. The system is mainly divided into professional basic courses, professional core courses, and professional elective courses. Different professional courses effectively correspond to the cultivation of relevant abilities, which plays a positive role in strengthening the comprehensive ability of students majoring in preschool education.
Encrypted Image Retrieval Scheme Based on Lightweight Neural Network

With the advent of the era of big data and the rapid development of cloud computing, in order to save local storage costs and facilitate operation, choosing cloud server as the storage platform for enterprises or individuals is a good solution. However, considering the privacy leakage of the cloud server platform, image files are usually stored in the cloud server in an encrypted form, which brings major challenges to traditional plaintext retrieval solutions. How to ensure the retrieval accuracy and efficiency of the massive ciphertext image data in the cloud server is a major problem currently facing. In response to the above problems, this paper proposes an encrypted image retrieval scheme based on a lightweight neural network, which uses the deep neural network model MobileNetV2 to accurately extract the keyword set of the image, and uses the keyword set to roughly classify the image set to efficiently retrieve the match, then, the image feature vector is accurately matched to realize the fine classification of the image to retrieve the final image.