Reviewerly/exHarmony huggingface dataset:
Tourism Bus Rental Application Design in PO. Padi Mas Using the Prototype Method

PO. Padi Mas is a company engaged in a tourism bus rental. Due to the large number of tourism bus procurements that need to be handled, a tool is needed, but in PO. Padi Mas, which is engaged in the transportation of bus rental systems, still uses manual processes in the payment system, data collection, and bus orders made by consumers, so it takes time for employees in this regard. Based on the existing problems, the author intends to create a web-based tourism bus rental application, aiming that the system used can further facilitate employee performance. The method used in software development is using the prototype method. The results of this study are web-based applications that are able to provide solutions to problems that exist in PO. Padi Mas assists customers in ordering buses and assisting PO. Padi Mas in administrative records has become more efficient.
The Optimal Pricing in Blockchain-Enabled Enterprises Operation Considering Privacy Attitude and Privacy Protection

The traceability of Blockchain Technology (BT) has been implemented widely in many industries, but coupled with this fact is that more and more consumers focus on personal privacy information security. This paper mainly studies a BT-enabled operation system with customers’ privacy preferences and enterprises’ privacy protection. From the short-term perspective, we first investigate the impacts of different privacy attitudes from consumers on the optimal pricing and privacy protection decisions of enterprises in two scenarios with and without BT. Then, we give the conditions of enterprises adopting BT. Furthermore, from the long-term perspective, when privacy protection insurance is provided for risk-averse consumers, we also investigate the evolutionary stability strategies between enterprises and consumers using the evolutionary game theory. Finally, numerical analysis is adopted to verify the theoretical results and show that privacy protection and BT are important to the operation of enterprises and the purchasing behavior of consumers.
Topological MIMO Optical Signal Processing

An important advantage of optical signal processing is the possibility of performing various operations in parallel. Among the techniques proposed for parallel optical signal processing, frequency division multiplexing (FDM) is a well-known approach, in which different operations are performed in multiple sub-channels associated with different frequency bands. Here, we demonstrate the possibility of performing parallel optical signal processing by frequency division multiplexing of the edge modes of a topological photonic structure. This leads to the realization of a multiple-input-multiple-output (MIMO) topological optical signal processor that is capable of performing several analog functionalities simultaneously, enhancing the speed of computation significantly. Our theoretical and experimental findings open up the venue towards the realization of a new generation of multi-functional topological optical signal processing systems that are ultra-fast, consume little power, and enjoy strong protection against disorder.
Spectrum-Efficient Analog Pulse Index Modulation for High-Volume Wireless Data Telemetry

Spectrum-efficient, high-density, and high-speed data transmission are essential to store and exchange sensitive information through Internet of Things (IoT) networks. However, the resource-constrained IoT devices and the limited existing frequency spectrum pose a significant challenge to employing the more efficient technique for the next-generation technology. This article presents an innovative pulse index modulation (PIM) architecture with a randomization technique for analog pulse-based packet-less high-volume data transmission. The pulse-based compression simplifies the encoding and decoding schemes for short-range wireless telemetry. The pulse randomization technique creates the pulse blinding by reordering the pulse positions and adds an extra level of physical-layer security. This article describes the design, simulation, and prototype development of PIM to validate the feasibility and compatibility of pulse-based data telemetry using the standard architecture. Simulation results show that the proposed PIM increases the data compression ratio by 2.61 times and improves the processing time by 5.56 time compared to the Huffman coding for a 3-bit system. Test results show that the BER is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 7.5\,\,\times \,\,10^{-4}$ </tex-math></inline-formula> at 10.0 dB SNR, which satisfies the lower bound for data communication and increases the transmission speed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -times by supporting <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${2^{k}*k}$ </tex-math></inline-formula> bits data using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${2^{k}}$ </tex-math></inline-formula> number of pulses, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> is a nonnegative integer number. The proposed PIM has the potential to improve data rate with added security to support the next-generation IoT networks.
Toward the Advantages of Quantum Trajectories on Entanglement Distribution in Quantum Networks

Quantum mechanics allows an information carrier to traverse through multiple trajectories of communication channels simultaneously, this leads us to quantum trajectories where alternative causal orders of communication channels being traversed are in a superposition, so that the relative orders of communication channels become indefinite. It has been shown that entanglement distribution process over quantum trajectory with an EPR pair being prepared by a sender enables quantum teleportation process to be heralded as a noiseless communication process with probability, the heralded result of which is unachievable in classical trajectory with a definite causal order of communication channels. In this work, we investigate the potential advantage of quantum trajectory on the generation of link-level entanglement, the basic element of quantum networks. To this aim, the performance of entanglement distribution process over multiple quantum trajectories where an EPR pair is prepared by a communication provider is analyzed. Besides, the application of entanglement purification protocol on quantum trajectory is first analyzed to reduce the effect of noise on link-level entanglement. The analysis shows that the link-level entanglement generated over quantum trajectory is more robust to long-distance quantum communication, with respect to classical trajectory, which paves the way for designing effective quantum networks.
A Hierarchical Deformable Deep Neural Network and an Aerial Image Benchmark Dataset for Surface Multiview Stereo Reconstruction

Multiview stereo (MVS) aerial image depth estimation is a research frontier in the remote sensing field. Recent deep learning-based advances in close-range object reconstruction have suggested the great potential of this approach. Meanwhile, the deformation problem and the scale variation issue are also worthy of attention. These characteristics of aerial images limit the applicability of the current methods for aerial image depth estimation. Moreover, there are few available benchmark datasets for aerial image depth estimation. In this regard, this article describes a new benchmark dataset called the LuoJia-MVS dataset ( <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://irsip.whu.edu.cn/resources/resources_en_v2.php</uri> ), as well as a new deep neural network known as the hierarchical deformable cascade MVS network (HDC-MVSNet). The LuoJia-MVS dataset contains 7972 five-view images with a spatial resolution of 10 cm, pixel-wise depths, and precise camera parameters, and was generated from an accurate digital surface model (DSM) built from thousands of stereo aerial images. In the HDC-MVSNet network, a new full-scale feature pyramid extraction module, a hierarchical set of 3-D convolutional blocks, and “true 3-D” deformable 3-D convolutional layers are specifically designed by considering the aforementioned characteristics of aerial images. Overall and ablation experiments on the WHU and LuoJia-MVS datasets validated the superiority of HDC-MVSNet over the current state-of-the-art MVS depth estimation methods and confirmed that the newly built dataset can provide an effective benchmark.
Scopus publications database analysis using its API

Every year, international peer-reviewed databases of publications, such as Scopus or Web of Science, are replenished with tens of thousands of papers, which entails the need for a systematic analysis of such databases. An effective solution to this problem is to create a software system that allows to analyze the publication activity of both countries, cities, universities, and individual authors. This article is focused on developing system for Scopus publications analysis. The analysis was conducted for articles published between 1985 and 2022. The volume of processed data is 200 GB. The list of analytics tasks is presented. It includes statistics of changes in the number of articles on the portal, a rating of the most widely published countries, cities, universities and authors, statistics of changes in the size of publications by year were revealed, an analysis of the most popular research areas, the most frequent months of publications. A number of limitations of working with the Scopus API were identified, and the paper presents ways to circumvent them. Proposed software solution is described. The paper contains approach to collect, handle, and visualize dataset. The practical significance of the work is to create a flexible big data analytics tool for Scopus publication activity.
Two-step intelligent approach for photo image fragment forgery detection and identification

The paper presents a two-step approach for detection and identification of fake (forged) fragments in photographic images. At the first step, the image is assigned to an original or a fake class by sequentially analyzing the image metadata (EXIF), then performing the error level analysis (ELA), and finally performing neural network analysis of the image. At the second step, the fake image is divided into fragments, each one is analyzed using a neural network for the fake detection, then the fragments that had recognized as a fake are analyzed by a classifying neural network, which makes it possible to identify specific types of fakes. Software has been developed that implements the proposed approach. The training of the used neural networks was carried out on the CASIA V2 dataset, which contains 4795 images (1701 genuine and 3274 fakes). Studies have been carried out to verify the effectiveness, for which, using the developed software 60 photo images were processed, 30 of which are genuine, and 30 are fakes. The developed software correctly identified both the fake fragments and determined their types.
Data mining for public channels and groups in telegram messenger

The paper presents the developed approach and software system for the Data Mining of public channels and groups in the Telegram messenger, for which emotionally colored vocabulary is identified in the texts and the emotional assessment is calculated in relation to objects, processes, events, phenomena, i.e., the opinion of the author is formed. The opinions of the authors are divided into two types: direct opinion and comparison, and the tonality receives three main assessments: positive, negative, neutral. The software system is implemented in the Python using the Dostoevsky library. RuSentiment dataset was used for training. The software system evaluates public opinion based on the analysis of comments in public channels and groups, for which the software system searches for comments and analyzes them, generates results as graphs and statistical values. The developed software system was tested for certain search parameters, which showed the practical applicability of the solution. The software system evaluates the crowd reaction to a post or evaluate how they relate to any fact based on the comments under the post in Telegram.
The virtual laboratory complexes in education system of food technologies

Researcher has presented the modern software systems which are used to train food industry specialists in the bachelor's program of the Moscow State University of Food Production. Author has presented the features of the modern Russian Federation education system. It has been highlighted the most significant elements in the process of training specialists at the bachelor education level. There are the main virtual laboratory installations which are used to train specialists in the areas of 19.00.00 (Industrial ecology and biotechnology), 16.03.03 (Refrigeration, cryogenic equipment and life support systems), 16.04.03 (Refrigeration, cryogenic equipment and life support systems) at the department of Processes, apparatus, refrigeration equipment and technologies. The main elements of three virtual laboratory complexes, which are used for conducting laboratory classes, has described in this paper by author. Researcher has considered the summary plan of the laboratory works, as well as the main positions of the methods for conducting these laboratory classes in the virtual software systems. There is a review of modern virtual laboratory complexes, which are used in the training of specialists in different fields of study in bachelor's and master's programs in this article. Author has described the modern trends in the improvement of the developed educational laboratory work. Also researcher has considered the teaching methods for them in this paper.
Development of a system for building computer networks on a CISCO PACKET TRACER software emulator

Theoretical information about network equipment and protocols for their interaction, information about implemented hardware, such as switches and routers in connection with the active spread of such a profession as a programmer are considered in the article. The introduction of a large number of information equipment in people's lives has led to a global transformation of the digital space. Due to the digital transformation and the need for uninterrupted and high-quality sending of information over long distances with minimal losses, as well as the need to introduce theoretical and practical material on modern technologies for information data transmission networks, principles for constructing telecommunications systems and networks are being developed, specialized hardware for resolving configuration issues by checking the functionality of networks is being designed. In the work, authors have analyzed the demand for the transfer of information data today, have noted the principles of interconnection and building of local networks, have reflected the revised and supplemented material of the manuals. Information about the functioning of devices is also presented on the example of CISCO software and hardware. Based on the studied data, a step-by-step instruction has been developed to teach students to network security technologies, gain theoretical knowledge about the main commands used in the Cisco IOS operating system and about the concept of configuration. It is noted that in order to carry out proper configuration of network equipment, it is necessary to use a software network emulator called Cisco Packet Tracer. On the basis of the data, a laboratory work has been developed. This laboratory work allows to develop the professional competencies of young specialists. Due to the presented work, engineers consider the features of the operation of switches and routers, study the routing of networks and other software and hardware devices.
Standard simplex induced clustering with hierarchical deep dictionary learning

Clustering remains a fundamental but crucial task on unsupervised pattern recognition. A recent novel deep dictionary learning system where hierarchical discriminative dictionary learning layers are embedded within a neural network showed either competitive or state-of-the-art results for image classification. We now propose a new method for image clustering, extending hierarchical deep dictionary learning to the unsupervised learning setting. Clustering is induced in the neural network in a simple way by requesting vector outputs associated to categories to approximately lie in the corners of a standard simplex and by approximating a given expected value on such vectors. This is carried out in a scalable way due to the batch-wise updates as clustering is learned in the deep system. We evaluate our proposal on four benchmark datasets and we either achieve competitive results or outperform state-of-the-art clustering methods. Under the unbalanced scenario clustering is known to be more challenging. For this our method additionally considers Bayesian updates to automatically learn the correct cluster proportions for a small number of clusters.
PROBLEM STANDARTIZATsII PROCESSA DISTANTSIONNOGO PROFESSIONAL QUALIFICATIONS

Standardization of processes and results of intellectual activity causes particular difficulties in educational systems. It is advisable to start standardization in this area with educational and metho-dological materials, and educational and methodological complexes can be considered the most suitable objects of standardization.
Application of Zero-Watermarking for Medical Image in Intelligent Sensor Network Security

The field of healthcare is considered to be the most promising application of intelligent sensor networks.However, the security and privacy protection of medical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention.Fortunately, digital watermarking provides an effective method to solve this problem.In order to improve the robustness of the medical image watermarking scheme, in this paper, we propose a novel zero-watermarking algorithm with the integer wavelet transform (IWT), Schur decomposition and image block energy.Specifically, we first use IWT to extract low-frequency information and divide them into non-overlapping blocks, then we decompose the sub-blocks by Schur decomposition.After that, the feature matrix is constructed according to the relationship between the image block energy and the whole image energy.At the same time, we encrypt watermarking with the logistic chaotic position scrambling.Finally, the zero-watermarking is obtained by XOR operation with the encrypted watermarking.Three indexes of peak signal-to-noise ratio, normalization coefficient (NC) and the bit error rate (BER) are used to evaluate the robustness of the algorithm.According to the experimental results, most of the NC values are around 0.9 under various attacks, while the BER values are very close to 0. These experimental results show that the proposed algorithm is more robust than the existing zero-watermarking methods, which indicates it is more suitable for medical image privacy and security protection.
Implementation of Rapid Code Transformation Process Using Deep Learning Approaches

Our previous work has introduced the newly generated program using the code transformation model GPT-2, verifying the generated programming codes through simhash (SH) and longest common subsequence (LCS) algorithms.However, the entire code transformation process has encountered a time-consuming problem.Therefore, the objective of this study is to speed up the code transformation process signi cantly.This paper has proposed deep learning approaches for modifying SH using a variational simhash (VSH) algorithm and replacing LCS with a piecewise longest common subsequence (PLCS) algorithm to faster the veri cation process in the test phase.Besides the code transformation model GPT-2, this study has also introduced Microso MASS and Facebook BART for a comparative analysis of their performance.Meanwhile, the explainable AI technique using local interpretable model-agnostic explanations (LIME) can also interpret the decision-making of AI models.The experimental results show that VSH can reduce the number of quali ed programs by 22.11%, and PLCS can reduce the execution time of selected pocket programs by 32.39%.As a result, the proposed approaches can signi cantly speed up the entire code transformation process by 1.38 times on average compared with our previous work.
Multi-Source Data Privacy Protection Method Based on Homomorphic Encryption and Blockchain

Multi-Source data plays an important role in the evolution of media convergence.Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media data.However, it also faces serious problems in terms of protecting user and data privacy.Many privacy protection methods have been proposed to solve the problem of privacy leakage during the process of data sharing, but they suffer from two flaws: 1) the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain; 2) the inability to solve the problem of the high computational complexity of ciphertext in multi-source data privacy protection, resulting in long encryption and decryption times.In this paper, we propose a multi-source data privacy protection method based on homomorphic encryption and blockchain technology, which solves the privacy protection problem of multi-source heterogeneous data in the dissemination of media and reduces ciphertext processing time.We deployed the proposed method on the Hyperledger platform for testing and compared it with the privacy protection schemes based on k-anonymity and differential privacy.The experimental results show that the key generation, encryption, and decryption times of the proposed method are lower than those in data privacy protection methods based on k-anonymity technology and differential privacy technology.This significantly reduces the processing time of multi-source data, which gives it potential for use in many applications.
Optimization of Linear Quantization for General and Effective Low Bit-Width Network Compression

Current edge devices for neural networks such as FPGA, CPLD, and ASIC can support low bit-width computing to improve the execution latency and energy efficiency, but traditional linear quantization can only maintain the inference accuracy of neural networks at a bit-width above 6 bits. Different from previous studies that address this problem by clipping the outliers, this paper proposes a two-stage quantization method. Before converting the weights into fixed-point numbers, this paper first prunes the network by unstructured pruning and then uses the K-means algorithm to cluster the weights in advance to protect the distribution of the weights. To solve the instability problem of the K-means results, the PSO (particle swarm optimization) algorithm is exploited to obtain the initial cluster centroids. The experimental results on baseline deep networks such as ResNet-50, Inception-v3, and DenseNet-121 show the proposed optimized quantization method can generate a 5-bit network with an accuracy loss of less than 5% and a 4-bit network with only 10% accuracy loss as compared to 8-bit quantization. By quantization and pruning, this method reduces the model bit-width from 32 to 4 and the number of neurons by 80%. Additionally, it can be easily integrated into frameworks such as TensorRt and TensorFlow-Lite for low bit-width network quantization.