Reviewerly/exHarmony huggingface dataset:
A Novel Realization of PSK Quantum-noise Randomized Cipher System Based on Series Structure of Multiple Phase Modulators

Quantum-Noise Randomized Cipher (QNRC) is a potentially information-theoretic secure physical-layer encryption method for high-speed and long-distance fiber communications. In order to solve the problem that the modulation and demodulation of M-ary Y-00 ciphertext signal in QNRC system are limited by the ADC chip with high sampling rate and high conversion depth, a novel realization of PSK-QNRC system based on series structure of multiple phase modulators (PM) is proposed, and the performances of the proposed scheme is verified by using commercial simulation software in this paper. The simulation results show that, the 126-PSK QNRC system at 10 Gbit/s over 1000-km optically amplified links is realized based on the series structure of multiple PM. Reducing the mesoscopic power of the ciphertext quantum state can improve the security level of QNRC system, but it will damage the transmission performance of the system at the same time. It is very important to select the appropriate mesoscopic power to optimize the security and reliability of QNRC system.
A Blockchain-Enabled Multiple Object Tracking for Unmanned System With Deep Hash Appearance Feature

The unmanned system based on mobile-edge computing (MEC) and blockchain can conveniently share computing resources and realize multi-devices collaboration. However, frequent data communication in the framework of object detection brings a heavy computational burden. In this paper, a novel blockchain-based multi-view unmanned equipment fusion architecture using multiple object tracking (MOT) technique is designed. Inspired by the idea of person re-identification and hash representation for image retrieval, a novel MOT technique using HashNet to extract deep hash appearance of objects is proposed. In addition, based on the blockchain and MEC technology, we make some improvements in feature fusion and tracking interrupt recovery. We combine deep hash appearance features with motion features and design a tracking interruption recovery mechanism to solve the problem of object occlusion. The experiment results on the MOT challenge dataset demonstrate that the proposed algorithm can handle object occlusion problem effectively and successfully reduce the number of identity switches. In real application scenes our algorithm performs particularly well, showing that our algorithm is more practical.
SAFESTOP: Disturbance Handling in Prioritized Multi-robot Trajectory Planning

Prioritized planning allows the generation of coordinated motion plans for multiple robots in a shared workspace by tracking their spatiotemporal progression along pre-planned paths.With the knowledge where and when collisions would occur, collision-free velocity profiles are calculated according to a prioritization scheme.However, in real-world applications, disturbances in the exact execution of the planned motions are likely.This can affect other planned trajectories and leave the system prone to deadlocks.We present a novel methodology, SAFESTOP, for handling interruptions with minimal impact on other planned tasks and show that it can drastically reduce the number of affected robots as well as the overall delay.The key idea is to maintain the paths and vary the motion of the affected robots, making them yield for others.In contrast to other approaches it supports complex vehicle structures and takes kinematic and dynamic constraints into account.
Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters

PageRank is a widely used graph analytics algorithm to rank vertices using relationship data. Large-scale Page Rank is challenging due to its irregular and communication intensive computational characteristics. We implemented Page Rank on NVIDIA's newly released DGX A100 cluster and compared the performance with two recent notable large-scale Page Rank computations using the Common Crawl dataset. The ShenTu framework computed Page Rank scores using a large number of custom microprocessors connected with an HPC class interconnect. The Hronos framework reported the state-of-the-art performance using 3000 commodity CPU nodes and 10 Gbps Ethernet. The Common Crawl dataset captures link relationships between web pages in a graph with 3.563 billion vertices and 128.736 billion edges. Our implementation demonstrated 13x faster PageRank iteration time than the Hronos framework using a cluster with NVLink connected 32 A100 GPUs.
Novel Multi-Strategy Enhanced Whale Optimization Algorithm

Whale Optimization Algorithm (WOA) is presented recently the state-of-the-art meta-heuristic optimization algorithm which has the critical advantages of fewer hyperparameters and simple framework. Unfortunately, WOA is not suitable to solve multimodal problems because of slow convergence. This paper proposes a novel multi-strategy enhanced whale optimization algorithm (MSEWOA) in order to improve WOA deal with multimodal ability. This paper has been completed testing with 23 benchmark functions. In experiments on multimodal problems with MSEWOA, it performed more effective than WOA and other conventional methods.
Satellite Image Target Super-Resolution With Adversarial Shape Discriminator

Substantial progress in generative modeling has facilitated the development of perceptual-driven methods for image super-resolution (SR), which shows superior visual quality as rated by human observers. However, we discover that most existing methods are biased to synthesize fake details or enhanced textures and fail to learn representations of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">object shape</i> . They are often misguided to recover the shape of small objects and focus on generating less meaningful high-frequency components. This is problematic when super-resolving satellite imagery since it contains many relatively small and clustered objects in a broad area. In this letter, we propose a new perceptual-driven SR method that has a stronger preference toward shape information. We integrate the scale-space filtering with the discriminator to attenuate high-frequency components in its decision space. It effectively encourages our discriminator to concentrate on structural shape information in differentiating real and fake images. We also devise a cross-scale aggregation network for the generator architecture. Experiments on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WorldView</i> data set demonstrate that our method achieves state-of-the-art performance compared to recent perceptual-driven methods.
Weighted Generalization of Dark Channel Prior with Adaptive Color Correction for Defogging

Images and video captured in water or fog suffer from low contrast and color distortion due to light scattering and absorption. An image formation model for hazy images is commonly used to restore both underwater images and hazy images because of the similarity between the two types of images. However, red light is attenuated faster than blue and green light in underwater, and underwater images are distorted by changes of color tone. Therefore, most current methods are specialized for either hazy images or underwater images. In this paper, we propose a novel defogging method which is efficient for both hazy images and underwater images. Our method is composed of adaptive color correction and weighted generalization of dark channel prior (WGDCP). Experimental results show that our algorithm can recover both underwater images and hazy images.
An Improved Person Re-identification Method by light-weight convolutional neural network

Person Re-identification is defined as a recognizing process where the person is observed by non-overlapping cameras at different places. In the last decade, the rise in the applications and importance of Person Re-identification for surveillance systems popularized this subject in different areas of computer vision. Person Re-identification is faced with challenges such as low resolution, varying poses, illumination, background clutter, and occlusion, which could affect the result of recognizing process. The present paper aims to improve Person Re-identification using transfer learning and application of verification loss function within the framework of Siamese network. The Siamese network receives image pairs as inputs and extract their features via a pre-trained model. EfficientNet was employed to obtain discriminative features and reduce the demands for data. The advantages of verification loss were used in the network learning. Experiments showed that the proposed model performs better than state-of-the-art methods on the CUHK01 dataset. For example, rank5 accuracies are 95.2% (+5.7) for the CUHK01 datasets. It also achieved an acceptable percentage in Rank 1. Because of the small size of the pre-trained model parameters, learning speeds up and there will be a need for less hardware and data.
Improving Traceability Recovery Between Bug Reports and Manual Test Cases

Many software tasks produce text. These artifacts are co-dependent, but maintaining their consistency is challenging; automation is desirable. Research has investigated traceability between bug reports and manual test cases. Since manual system test scripts are a popular way of documenting requirements in agile projects, this kind of traceability allows, for instance, to analyze how bugs are related to requirements. Previous work has assessed three Information Retrieval (IR) techniques (LSI, LDA, and BM25) and a Deep-Learning (DL) algorithm (Word Vector) to recover those links; results indicate the need for improvements in textual processing and representation. In this paper, we applied five improvement techniques to an existing data set of bug reports and manual test cases from Mozilla Firefox. We employ: (i) text and information cleaning, (ii) spell-checking, (iii) terms weighting, (iv) similarity matrices merging, and (v) traceability matrices merging. Merging was applied to matrices produced by IR and DL techniques. We evaluate the techniques by comparing precision, recall, and f-measures, with those reached by previous work as a baseline. We observe a slight increase in precision and recall rates for all traceability recovery techniques (LSI, LDA, BM25, and Word Vector) by combining text and information cleaning, title duplication, and spell-checking. A hybrid strategy, creating a merged traceability matrix containing all traces recovered by at least one of the four recovery techniques, achieved a recall value of 93%.
GLS Integrity Estimation Based on Improved EM Parameter Estimation Method

Integrity is not only an important parameter to measure navigation performance, but also the basis of other performance parameters calculation. When the aircraft approaches, the pseudo range error is non Gaussian distribution or bimodal asymmetric distribution. However, the traditional integrity estimation algorithm is based on the assumption that the pseudo distance error follows the zero mean Gaussian distribution, which reduces the system availability. Based on the analysis of traditional integrity estimation algorithms, a pseudo range error distribution model based on Gaussian mixture distribution model is established, and an integrity estimation algorithm based on improved expectation maximization (EM) method is proposed. The algorithm introduces unsupervised learning method, which greatly improves the iterative efficiency and reduces the amount of calculation, and can be used in embedded software. The experimental results of real data show the effectiveness of the algorithm.
A Blockchain-Based Approach to Supporting Reinsurance Contracting

Reinsurance is a strategy used by the insurer to protect themselves from underwriting risk. In this line, an insurer acquires an insurance policy from another called reinsurer. In this way, the contractual responsibility that must be fulfilled between these two entities is specified. Thus, collaborative and transparent efforts are required in this business. In addition, automatic processing and the integrity of the information flow must be guaranteed. Faced with these challenges, blockchain is an innovative technology that allows registering and automating operations using smart contracts and complex cryptographic mechanisms that provide trust and integrity for transactions, without the need for third-party supervision. With these characteristics, this research explores how blockchain technology can become a collaborative and supportive tool in reinsurance contracts. For this, a layered overview and model are designed. Contributions and conclusions are provided at the end of the document.
Integrate multi-agent simulation environment and multi-agent reinforcement learning (MARL) for real-world scenario

Multi-agent deep reinforcement learning has made a great achievement in deep reinforcement learning through modeling a real-world scenario with multiple agents that communicate with a single environment. However, the test and validation of MARL model on the conventional multi-agent simulation are limited. In this study, we analyze an effective method to use a multi-agent simulation to test and validate multi-agent reinforcement learning models and methods as well as propose two requirements, an intuitive interface and the optimization of simulation, to achieve it.
Application of Augmented Reality &amp; Virtual Reality in Architecture and Planning: an Overview

AR and VR – simulation tools created to assist global evolution for saving time. Time as resource is difficult to harness; however, it would make work highly efficient and productive when tackled with automation. All concerned are excited about AR and VR’s involvement in our lifestyle, but not all have comprehended its impact. AR and VR in Architecture &amp; Planning were introduced as assisting tools and has helped generate multiple design options, expanded possibilities of visualization, and provided us with more enhanced, detailed, and specific experience in real-time; enabling us to visualize the result of work at hand well before the commencement of the project. These tools are further being developed for city development decisions, helping citizens interact with local authorities, access public services, and plan their commute. After reviewing multiple research papers on AI, it was observed that all are moving forward with the changes brought by it, without entirely understanding its role. This paper provides an overview of the application of AR &amp; VR in architecture and planning.
An algorithm for determining the parameters of the distribution density function with the application of nonparametric statistics methods

The paper considers an algorithm for determining the unknown function of random variable distribution density presented in the form of a finite trigonometric series. The random variable is given by a sample of values. To calculate the coefficients of the series, we use a set of quantile values of a random variable, obtained on the basis of the function of random value distribution density, recovered from the sample by means of Parzen-Rosenblatt estimates. The problem is reduced to solving a system of equations that are linear towards the desired coefficients. The developed algorithm is exemplified by experimental data processing.
Discriminator Soft Actor Critic without Extrinsic Rewards

In environments where reward design is difficult, imitation learning is often used rather than reinforcement learning. SQIL is an imitation learning algorithm that can learn with little expert data and sample data from the environment. In order to make SQIL more robust to distributional shifts, in this paper, we propose Discriminator Soft Actor Critic (DSAC). The algorithm uses a reward function based on inverse reinforcement learning. We evaluate it in continuous control environments by 4 episodes of expert data.
COVID-19 and E-learning: challenges for Russian professors

In recent years, the term E-learning has become widespread, meaning the process of learning in electronic form via the Internet or Intranet using learning management systems. We analyzed practical instruments that have been used in e-learning in Russia during the pandemic crisis of COVID-19 and students’ reaction. It is emphasized that the e-learning was obligatory for the introduction, which means that the professors and students mostly were not ready for such a decision. Both students and professors had to adapt to the new environment, find new ways of interaction and collaboration. Based on challenges, student’s attitude and perception of the e-learning process, we have shown findings of e-learning barrier factors (including personal perception and language skills). We have provided some practical ways to overcome barrier factors.
Security Education, Training, and Awareness: Incorporating a Social Marketing Approach for Behavioural Change

Effective information security education, training, and awareness (SETA) is essential for protecting organisational information resources. Although many organisations invest significantly in SETA, incidents resulting from employee noncompliance are still increasing. We argue that this may indicate that current SETA programs are sub-optimal in improving security compliance behaviour among employees, as they lack sufficient grounding in theory. This study proposes a new process for SETA development based on the social marketing approach. The proposed process involves selecting specific behaviour, developing a deeper understanding of the target audience, and using theory-informed intervention strategies for changing behaviour. The SETA development process provides a sound basis for future empirical work that will include focus groups and action research.
Development of The Smart Chicken Eggs Incubator Based on Internet of Things Using The Object Oriented Analysis and Design Method

Smart Chicken Eggs Incubator is one of the applications of the Internet of Things in the field of animal husbandry specifically on the hatching of chicken eggs. Breeders carry out the process of hatching chicken eggs using artificial incubators and in the process of hatching chicken eggs, these problems arise in the process of monitoring the environment inside the incubator such as temperature, humidity, and egg changes which are still done manually. This research explains the development of a prototype Smart Chicken Eggs Incubator System by implementing the Internet of Things which has three subsystems namely embedded systems, web-based applications, and Telegram bot. Web application software on the Smart Chicken Eggs Incubator System was developed using the Object-Oriented Analysis and Design (OOAD) method. The web apps built can be used to monitor the conditions of the incubator based on sensor data that has been sent during the egg hatching process. While in the Telegram bot, real-time conditions of temperature, humidity, egg transfer, and photos of the situation inside the incubator can be monitored, as well as message notifications if there are conditions on the incubator that change beyond the specified limits. Prototype testing was carried out for 21 days, with the amount of data entered as many as 3,402 records with time intervals every 5 minutes and get the optimal time for each entry is 9 minutes to 10 minutes.
Nested Pipeline Hardware Self-Organizing Map for High Dimensional Vectors

This paper proposes a hardware Self-Organizing Map (SOM) for high dimensional vectors. The proposed SOM is based on nested architecture with pipeline processing. Due to homogeneous modular structure, the nested architecture provides high expandability. The original nested SOM was designed to handle low-dimensional vectors with fully parallel computation, and it yielded very high performance. In this paper, the architecture is extended to handle much higher dimensional vectors by using sequential computation, which requires multiple clocks to process a single vector. To increase the performance, the proposed architecture employs pipeline computation, in which search of winner neuron and weight vector update are carried out simultaneously. Operable clock frequency for the system was 60 MHz, and its throughput reached 15012 million connection updates per second (MCUPS).
The development of ontological model for increasing the competitiveness of university graduates in information technologies

Abstract The relevance of this work is supported by the need to ensure the professional training compliance with both formal and informal requirements of the education system and the labour market. A lack of methods and technologies being able to harmonize the outcome of the information technologies curriculum and the labour market demands is the reason why the university graduates find it difficult to compete on the labour market. The paper describes the method for an ontological model development that would be able to form the content of the IT-competencies and to assess them in the university students. It is based on two thesis: the first, the boundaries of the subject area of competencies should be defined and, the second, a method of taking into account individual characteristics and needs for creation of an individual professional training and self-study plan should be preliminary considered and developed. The article presents the ontological model developed by the stated method to form the content of the IT-competencies and to assess them for the case of the subject area of 1C: Enterprise platform business solutions. The knowledge base created by the ontological model can be integrated into the decision-making intelligence information systems intended for the sphere of education, corporate training and HR management. This article is aimed at the professionals developing new tools and methods for management of distance education and distance learning.
Entropy-Based Feature Selection for Data Clustering Using k-Means and k-Medoids Algorithms

Clustering method splits a large dataset into smaller subsets, where each subset is called a cluster. Every cluster has the same characteristics and each cluster is different from all other clusters. The most common clustering algorithms are the k-Means clustering algorithm and the k-Medoids clustering algorithm. Clustering of high-dimensional dataset may become difficult. To overcome the problem, dimension of the dataset is reduced. In the present work, we reduce dimension of a dataset by selecting suitable subset of features using entropy-based method. We calculate entropy using both Euclidean and Manhattan distances. We experiment with three widely used datasets from the Machine Learning Repository of the University of California, Irvine (UCI). From the results of experimentation, we can conclude that our approach produces higher clustering accuracies than those of previous works.