Reviewerly/exHarmony huggingface dataset:
Simulator for the Formation Programming Skills Based on Solving Problems of Controlling a Virtual Robot

The article proposes an approach to teaching programming with using the developed simulator, which allows to form basic programming skills and bring their use to automatism. The proposed approach to teaching programming uses the concept of visualizing programming results. It differs from existing block-based visual programming languages in that the program text retains its structure and operator structure, which is extremely important in teaching programming. The simulator is built on the basis of a software package for the visual programming of a virtual executing robot. The warehouse of the simulator includes an interpreter of commands for controlling a robot, a block for visualizing the results of robotic programs, block for the formation of sets of tasks for the passage by the executing robot of different mazes. When developing the interpreter, the syntax and a set of commands of the simulator programming language were determined, taking into account the ease of understanding it (for this, a language in which students communicate freely was used) and the speed of memorization. An example of developing and executing a program for a simulator is described. Possible variants of its use for teaching programming to VET students are discussed.
Affinity Propagation-Based Hybrid Personalized Recommender System

A personalized recommender system is broadly accepted as a helpful tool to handle the information overload issue while recommending a related piece of information. This work proposes a hybrid personalized recommender system based on affinity propagation (AP), namely, APHPRS. Affinity propagation is a semisupervised machine learning algorithm used to cluster items based on similarities among them. In our approach, we first calculate the cluster quality and density and then combine their outputs to generate a new ranking score among clusters for the personalized recommendation. In the first phase, user preferences are collected and normalized as items rating matrix. This generated matrix is then clustered offline using affinity propagation and kept in a database for future recommendations. In the second phase, online recommendations are generated by applying the offline model. Negative Euclidian similarity and the quality of clusters are used together to select the best clusters for recommendations. The proposed APHPRS system alleviates problems such as sparsity and cold-start problems. The use of affinity propagation and the hybrid recommendation technique used in the proposed approach helps in improving results against sparsity. Experiments reveal that the proposed APHPRS performs better than most of the existing recommender systems.
Research on Face Local Attribute Detection Method Based on Improved SSD Network Structure

The existing face detection methods usually had the problem of low accuracy of face recognition in the environment of occlusion interference, which was limited when applied to the face detection task in complex scenes. Therefore, in order to realize high precision and real-time local face recognition in a complex environment, a face local attribute detection method based on improved SSD network structure was proposed. Based on the analysis of the face local attribute detection task, SSD was used as the basic detection network structure, and the VGG16 feature extraction model was used as the framework of face local detection. On this basis, by organically connecting different layers of the SSD network and integrating convolution block attention module, the improved SSD network structure was used to realize face local attribute detection. The proposed model was trained and tested using typical public datasets such as Wider Face, MAFA, and COFW. Experimental results showed that this method had high recognition accuracy, can better detect local features of the human face than other models, and can provide some support for local face attribute detection. This method would provide a theoretical basis and technical support for local face attribute detection in complex scenes.
Quality Aware Features for Performance Prediction and Time Reduction in Video Object Tracking

The existing body of work on video object tracking (VOT) algorithms has studied various image conditions such as occlusion, clutter, and object shape, which influence video quality and affect tracking performance. Nonetheless, there is no clear distinction between the performance reduction caused by scene-dependent challenges such as occlusion and clutter, and the effect of authentic in-capture and post-capture distortions. Despite the plethora of VOT methods in the literature, there is a lack of detailed studies analyzing the performance of videos with authentic in-capture and post-capture distortions. We introduced a new dataset of authentically distorted videos (AD-SVD) to address this issue. This dataset contains 4476 videos with different authentic distortions and surveillance activities. Furthermore, it provides benchmarking results for evaluating ten state-of-the-art visual object trackers (from VOT 2017–2018 challenges) based on the proposed dataset. In addition, this study develops an approach for performance prediction and quality-aware feature selection for single-object tracking in authentically distorted surveillance videos. The method predicts the performance of a VOT algorithm with high accuracy. Then, the probability of obtaining the reference output is maximized without executing the tracking algorithms. We also propose a framework to reduce video tracker computation resources (time and video storage space). We achieve this by balancing processing time and tracking accuracy by predicting the performance in a range of spatial resolutions. This approach can reduce the execution time by up to 34% with a slight decrease in performance of 3%.
Safe, Fast, and Efficient Distributed Receding Horizon Constrained Control of Aerial Robot Swarms

This letter proposes a reactive, safe, and high-performance distributed constrained control law for ensuring safety in multi-UAV systems. The UAVs possess higher-order nonlinear dynamics and are subject to actuation and collision avoidance constraints. To address the case of real-time multi-UAV collision avoidance, we propose a novel Trajectory-Tube Distributed Explicit Reference Governor (TT-D-ERG) approach based on the idea of embedding future state trajectories into safety tubes. The control law is ideally suited for systems with limited on-board hardware for computation and communication. The safety, efficacy, performance, and scalability of the method are demonstrated and benchmarked against state-of-the-art real-time collision avoidance methods by simulations and field experiments with multiple quadrotor UAVs (videos at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/V4P1O8vhKlo</uri> ).
Visual saliency guided perceptual adaptive quantization based on HEVC intra-coding for planetary images

Due to the limited storage space of spacecraft and downlink bandwidth in the data delivery during planetary exploration, an efficient way for image compression onboard is essential to reduce the volume of acquired data. Applicable for planetary images, this study proposes a perceptual adaptive quantization technique based on Convolutional Neural Network (CNN) and High Efficiency Video Coding (HEVC). This technique is used for bitrate reduction while maintaining the subjective visual quality. The proposed algorithm adaptively determines the Coding Tree Unit (CTU) level Quantization Parameter (QP) values in HEVC intra-coding using the high-level features extracted by CNN. A modified model based on the residual network is exploited to extract the saliency map for a given image automatically. Furthermore, based on the saliency map, a CTU level QP adjustment technique combining global saliency contrast and local saliency perception is exploited to realize a flexible and adaptive bit allocation. Several quantitative performance metrics that efficiently correlate with human perception are used for evaluating image quality. The experimental results reveal that the proposed algorithm achieves better visual quality along with a maximum of 7.17% reduction in the bitrate as compared to the standard HEVC coding.
A Novel LC-DEH Algorithm to Enhance Efficiency and Security for Reliable Data Transmission in Blockchain with IoT-Based Healthcare Systems

The Internet of Things (IoT) is a recent growing technology in modern healthcare applications. Security is the most problematic aspect of IoT-based patient monitoring systems due to IoT's centralized architecture. It also faces issues like collection, a large number of various devices being used, and quality estimation. Blockchain-based IoT healthcare applications enhance security and privacy compared to centralized healthcare applications. Blockchain with IoT is a modern technology due to its special characteristics such as decentralization, transparent communication, maximized storage, and unchangeable information. This chapter presents an overview of blockchain technology and IoT including features, applications, and security issues. It also discusses the benefits and practical security issues in blockchain with IoT-based patient monitoring systems. A cryptography hash algorithm is significant to eliminate many security issues in blockchain-based healthcare. This algorithm uses a secret key and a public key to protect the information from an intruder. Even though various hash algorithms have been developed so far for solving the security issue, it requires an efficient hash algorithm to enhance the security and accuracy for reliable data transmission. For that reason, this chapter introduces lightweight cryptography and double encryption hash algorithm (LC-DEHA) to protect the secret key and public key. The performance of the proposed algorithm is experimentally analysed with various cryptography hash techniques for deployment in IoT in metrics of response time and efficiency. From this analysis, the proposed algorithm is used to enhance security and efficiency with the reduced response time.
Word Sense Disambiguation Using Clustered Sense Labels

Sequence labeling models for word sense disambiguation have proven highly effective when the sense vocabulary is compressed based on the thesaurus hierarchy. In this paper, we propose a method for compressing the sense vocabulary without using a thesaurus. For this, sense definitions in a dictionary are converted into sentence vectors and clustered into the compressed senses. First, the very large set of sense vectors is partitioned for less computational complexity, and then it is clustered hierarchically with awareness of homographs. The experiment was done on the English Senseval and Semeval datasets and the Korean Sejong sense annotated corpus. This process demonstrated that the performance greatly increased compared to that of the uncompressed sense model and is comparable to that of the thesaurus-based model.
Sentiment Analysis of Events on Social Web

These days, Data volume has experienced enormous increase in volume, giving new challenges in technology and application. Data production has been expected at the rate of 2.5 Exabyte (1Ex-abyte=1.000.000Terabytes) of data per day. The main sources of data are: sensors collect climate information, traffic and flight information, social media sites (Twitter and Facebook are popular examples), digital pictures and videos (YouTube users upload 72 hours of new video content per minute), etc. Out of them social media becomes the prominent representative for the data source of big data. Social big data comes from the combination of social media and big data. Here, the data is mostly unstructured or semi-structured. The classical approaches, techniques, tools and frameworks for management of data have become insufficient for processing this huge volume of data and not capable for providing efficient solution to handle the increased production of data. The major challenge in data mining of big data is, its inadequate approaches to analyze massive amount of online data (or data streams). Specially, the field of sentiment analysis and predictive analysis has become so much promising area to place an organization at doom or at boom by provide accurate decision at accurate time. The current paper provides an insight of machine learning algorithm both supervised and unsupervised method; and the traditional knowledge extraction process. The application field of sentiment analysis, the issues those are faced during data collection and cleaning. This study flourishes a complete picture of recommendation system based on the sentiment analysis of events. The key motivation of the paper is to incorporate the event sentiment analysis and give the feedback and recommendation and illustrate the ongoing researches in the field of sentiment analysis and its application.
A new energy trading model for virtual power plants based on blockchain

As a distributed system with decentralization, trust maintenance, and security assurance, blockchain has successfully served a variety of scenarios involving information exchange and ledger recording. In the blockchain system, the characteristics of peer and autonomy of each node, coupled with the traceability of the blockchain system, the nature of information disclosure and the nature of distributed information storage, make it ubiquitous in the modern Internet of Power Energy It has unique advantages such as safety and efficiency in application scenarios such as the Internet of Power and Energy. First introduced the core application principles of blockchain technology and analyzed the application feasibility of blockchain technology based on the Ethereum network in the virtual power plant (Virtual Power Plant VPP) energy transaction; then on the basis of the Ethereum blockchain platform In the above, a power energy transaction scheme under the virtual power plant architecture is proposed; then the test is carried out in the standard Ethereum test network and virtual environment to verify the feasibility, safety and scalability of the method; finally, the block Analyze and prospect the opportunities and challenges of chain technology in the field of combining virtual power plants and &#x201C;Internet +&#x201D;.
Blockchain-Based Distributed Reputation for a Cap-and-Trade Carbon Emission System

Reputation has been widely used in the energy field in recent years. However, their reputation mechanisms are usually centralized even if they are designed for distributed energy systems, which could cause vulnerability to single point failures. This paper explores the design of blockchain-based distributed reputation for a cap-and-trade carbon emission system. The blockchain technology is adopted to achieve distributed management of reputation scores and realize a peer-to-peer carbon trading market. Simulation experiments are carried out to demonstrate the influence of the proposed reputation rules on reputation scores. In addition, a case study shows how reputation affects the results of carbon trading. As far as we know, this paper is one of the few works that incorporate distributed reputation in a carbon emission system.
Topological invariants of nonunitary quantum walk with chiral symmetry

We propose an one-dimensional model of nonunitary quantum walk with chiral symmetry. The nonunitarity is caused by gain and loss that they stagger in time and act on different walking directions. The degenerate point of the quasi-energy on the dispersion-relation diagram becomes a line segment and the corresponding topological defect is characterized by the vorticity. The winding number is still well defined due to chiral symmetry. It is found that the vorticity, as a topological invariant, equals the difference of winding numbers within both sides of topological defect. Meanwhile, a pair of vorticities can be derived in two time symmetric frames and they count the number of topological defects with zero- and π- quasi-energy. At boundary in inhomogeneous system, appearing or disappearing of edge state is not affected by nonunitary action. The existing of the edge states can be measured by the average displacement calculated by the escape probability, and the bulk-boundary corresponding is shown to be invalid.
TahcoRoll: fast genomic signature profiling via thinned automaton and rolling hash

Abstract Objectives Genomic signatures like k -mers have become one of the most prominent approaches to describe genomic data. As a result, myriad real-world applications, such as the construction of de Bruijn graphs in genome assembly, have been benefited by recognizing genomic signatures. In other words, an efficient approach of genomic signature profiling is an essential need for tackling high-throughput sequencing reads. However, most of the existing approaches only recognize fixed-size k -mers while many research studies have shown the importance of considering variable-length k -mers. Methods In this paper, we present a novel genomic signature profiling approach, TahcoRoll, by extending the Aho–Corasick algorithm (AC) for the task of profiling variable-length k -mers. We first group nucleotides into two clusters and represent each cluster with a bit. The rolling hash technique is further utilized to encode signatures and read patterns for efficient matching. Results In extensive experiments, TahcoRoll significantly outperforms the most state-of-the-art k -mer counters and has the capability of processing reads across different sequencing platforms on a budget desktop computer. Conclusions The single-thread version of TahcoRoll is as efficient as the eight-thread version of the state-of-the-art, JellyFish, while the eight-thread TahcoRoll outperforms the eight-thread JellyFish by at least four times.
Replay Attack Detection for Speaker Verification Using Different Features Level Fusion System

In the present scenario, the popularity of Automatic speaker verification (ASV) is on an increasing note. The authentication of individuals by doing analysis on speech utterances represents an ASV. However, the speech signals are spoofed under real time scenario and it becomes much more difficult to differentiate between genuine and spoof speech samples. Hence, it becomes vulnerable to replay attacks which need to be deal with. Recently, it has been widely used in many smart devices that require user verification, such as smart speakers and smartphones. In order to overcome this problem, we propose new approach using fusion of different feature extraction techniques together to improve the system performance against the replay attack for speaker verification system by reducing equal error rate (EER). In this work, we fused constant-Q cepstral coefficients (CQCC), linear prediction cepstral coefficients (LPCC) and linear frequency cepstral coefficients (LFCC) methods and yields better results compare to the other tradition stand-alone feature extraction methods. ASV spoof-2017 database is used to evaluate the proposed system, and simulation results were conducted using MATLAB 2019.
Improved SiamFC Target Tracking Algorithm Based on Anti-Interference Module

The SiamFC target tracking algorithm has attracted extensive attention because of its good balance between speed and performance, but the tracking effect of the SiamFC algorithm is not satisfactory in complex background scenes. When SiamFC algorithm uses deep semantic features for tracking, it has good recognition ability for different types of objects, but it has insufficient discrimination for the same types of objects. Therefore, we propose an effective anti-interference module to improve the discrimination ability of the algorithm. The anti-interference module uses another feature extraction network to extract the features of the candidate target images generated by the SiamFC main network. In addition, we set up the feature vector set to save the feature vectors of the tracking target and the template image. Finally, the tracking target is selected by calculating the minimum cosine distance between the feature vector of the candidate target and the vector in the feature vector set. A large number of experiments show that our anti-interference module can effectively improve the performance of SiamFC algorithm, and the performance of this algorithm can be comparable to the popular algorithms.
On Attacks To Federated Learning and a Blockchain-empowered Protection

Federated learning has been increasingly studied to cope with the scalability and privacy issues characterizing current and upcoming large-scale infrastructures, such as the Internet of Things, 5G networks, vehicular applications, and so on. This approach partitions the data storage and AI model training in a series of local learners, whose results are aggregated by a central server and then redistributed back to the learners to have a trained global model. However, despite avoiding outsourcing sensitive data to cloud-hosted services and fragmenting the workload for data processing, such a decentralized learning approach lays the overall solution open to various kinds of attacks, able to fully compromise the accuracy of the obtained global model. This study aims to quantitatively assess the impact of two widely-recognized attacks against federated learning and propose a tentative protection means by using blockchain.
Real-Time 3D Pedestrian Tracking with Monocular Camera

Target tracking has always been a popular research area in computer vision, and many important methods have been proposed. However, most methods can only solve partial and slight occlusion. If the target is lost, a common solution is to keep detecting, reidentify the target when it reappears, and then link the broken tracks together, but this makes tracking discontinuous. There are two key points in this problem: continuous tracking and occlusion judgment. In this paper, we propose a target tracking method with a short-time prediction function to solve this problem. For continuous tracking, we establish a 3D dynamic model to estimate the motion state of the target in each frame. For occlusion judgment, we use a depth prediction network to estimate the depth of the target and then determine whether the target is occluded by the depth. Without relying on depth sensors or multiple cameras, we achieve depth estimation using only a single monocular image, which greatly expands the application of our method. Benefit from the introduction of motion estimation and depth prediction, the tracking accuracy of our method has been significantly improved, especially for better robustness to occlusion. Even when the target is completely occluded, it can be tracked for a short time without reidentification. In addition, we improve the speed of depth prediction through knowledge distillation by 2.08 times, and the final tracking speed reaches 52.6 Hz on GPU, which meets the real-time tracking requirements.