Reviewerly/exHarmony huggingface dataset:
Efficient Multi-Party EdDSA Signature With Identifiable Aborts and its Applications to Blockchain

The security of secret keys for blockchain-based applications is increasingly important, partly because the theft of secret keys will render a significant financial loss. To guarantee the security of secret keys, many multi-party signature protocols have been proposed. However, few of them are designed for EdDSA-based blockchain that is developing in growth. The folklore and the NIST document for standardizing threshold schemes believe that a distributed hash evaluation is required to design multi-party EdDSA protocols, which leads to a relatively large overhead. In this paper, we present two practical multi-party EdDSA protocols for semi-honest and malicious settings. Our protocols eliminate the distributed hashing by securely maintaining a global state, which is feasible for EdDSA-based blockchain. Furthermore, we extend the malicious protocol to resist DoS attacks by identifying corrupted parties in case of execution aborts. We implemented our EdDSA protocols for different parties using Alibaba cloud servers with all instances of type ecs.t5-c1m2.large. Our protocol in the malicious setting takes 1.51-15.3 ms between 2 parties and 5 parties, and are two orders of magnitude faster than the recent threshold EdDSA protocol. These properties (efficient, identifiable abort, high compatibility) make the two protocols ideal for threshold wallets for EdDSA-based cryptocurrency.
A novel hand-eye calibration method using double-layer optimization and outlier sample screening for monocular vision robots

Abstract In monocular vision robot systems, the hand-eye calibration approach is crucial for ensuring operational accuracy. A double-layer (Levenberg–Marquardt ([LM] method) optimization (DLMO) method with outlier points screening is proposed to reduce the influence of random errors in robot vision systems and improve the calibration accuracy of the robot hand-eye matrix. First, the equation of the hand-eye matrix is established, and the initial value of the hand-eye matrix is solved by the linear least square method. Second, the Euler angle transformation is applied to the rotation matrix part to ensure its orthogonality. Next, an optimization model of the hand-eye matrix is constructed, and the traditional LM optimization method is used to optimize the initial hand-eye matrix for the first time. Finally, the optimization model of the hand-eye matrix is modified, and the LM optimization method with the outlier sample points screening is applied to optimize the hand-eye matrix for the second time. Hand-eye calibration tests are conducted on an industrial robot equipped with a monocular vision system using the proposed method. Experimental results demonstrate that the average position error of the calibration results obtained by the proposed DLMO method is 0.22 mm, which is superior to the traditional hand-eye calibration method and meets the working requirements of the vision robot in the industrial field.
Detection algorithm of abnormal flow state fluid on closed vibrating screen based on improved YOLOv5

Due to the difficulty of observing the interior of the closed vibrating screen and the difficulty of realizing automatic unmanned observation, this paper proposes a closed vibrating screen detection algorithm based on improved YOLOv5. A new bounding box loss function α-DEIoUloss was designed according to the motion characteristics of the fluid on the vibrating screen. Secondly, in order to enhance the ability of the network to extract feature information, the convolution in the C3 module of the Backbone part is replaced by Involution. Finally, in order to enable the network to focus on extracting the feature information of the target area, a CBAM module is added to the Neck part. The experimental results show that using the improved bounding box loss function can improve the network’s mAP, Precision and Recall by 5.6%, 1.3% and 2.3%, respectively. In addition, compared with the existing YOLOv5, the method proposed in this paper achieves a significant improvement in detection accuracy at a minimal cost, meeting the requirements of real-time detection. Through experimental comparison and research, the effectiveness and superiority of the proposed method are demonstrated.
Lifelong Machine Learning for Topic Modeling Based on Hellinger Distance

This paper proposes an improved version of the Lifelong Topic Model (LTM) called the HC-LTM. The traditional LTM is known to be biased in the domain selection process and does not fully consider the contextual information of target words when determining similarity. The HC-LTM addresses these issues by combining Word2vec cosine similarity and Hellinger distance between topics to identify similar words and topics, leading to better selection and more effective knowledge acquisition during iterative learning. Additionally, the problem of repetitive calculation of cosine distance is resolved by pre-loading the similarity matrix of word vectors and using Hellinger distance to calculate topic similarity accelerates the convergence of the model. The experimental results on the Amazon product review dataset demonstrate the effectiveness of the HC-LTM model, with a 49% improvement in topic consistency and a 44.57% reduction in time compared to the LTM model.
Climate change in South Kalimantan (Borneo): assessment for rainfall and temperature

A severe flood hit South Kalimantan in 2021. The flood inundated several cities and regions, including Banjamasin City and its surrounding areas in the Martapura Basin. According to data from the National Disaster Management Agency (BNPB), over 20,000 houses were flooded, and over 100,000 people were forced to relocate. Many flood causes are being debated these days, including climate change issues which are triggered by many factors. However, the significant factor indicating its existence is the increase in temperatures. Rising temperature will result in the intensity of the extreme rainfall and the change of its pattern. Due to this background, this study aims to evaluate rainfall and temperature variability as an indicator of climate change in the study area. The Man-Kendall Test is used in this study to analyze a trend analysis and its relationship to rainfall. The results show a temperature trend of 0.2 to 0.3 degrees Celsius over the last two decades. It indicates that climate change has occurred on a local scale. Further, the trend of rainfall and temperature have increased in all selected locations. The annual rainfall over the last 20 years has also increased by roughly 25 mm per annum.
Two-Factor Authentication Using Leap Motion and Numeric Keypad

Biometric authentication has become popular in modern society. It takes less time and effort for users when compared to conventional password authentication. Furthermore, biometric authentication was considered more secure than password authentication because it was more difficult to steal biometric information when compared to passwords. However, given the development of high-spec cameras and image recognition technology, the risk of the theft of biometric information, such as fingerprints, is increasing. Additionally, biometric authentication exhibits lower and less stable accuracy than that of password authentication. To solve the aforementioned issues, we propose two-factor authentication combining password-input and biometric authentication of the hand. We adopt Leap Motion to measure physical and behavioral features related to hands. Subsequently, a random forest classifier determines whether the hand features belongs to a genuine user. Our authentication system architecture completes the biometric authentication by using a limited amount of data obtained within a few seconds when a user enters a password. The advantage of the proposed method is that it prevents intrusion by biometric authentication even if a password is stolen. Our experimental results for 21 testers exhibit 94.98% authentication accuracy in a limited duration, 2.52 s on an average while inputting a password.
Dynamic Eye-in-Hand Visual Servoing using Adaptive Backstepping with a Supervisory Feature Jacobian

This paper develops two backstepping controllers for the eye-in-hand image-based visual servoing setpoint regulation problem. The first uses knowledge of the feature Jacobian matrix to implement a resolved velocity control in the first step of backstepping. The second provides an adaptive modification to the first, with initial estimates of the feature Jacobian supervising the training of an adaptive feature Jacobian matrix. Experiments demonstrate both methods outperform a classical inner/outer loop control strategy. The backstepping controllers show smoother image trajectories, require less control effort, and have less steady-state error. With the proposed supervisory parameter update, the adaptive method performs well irrespective of the accuracy of the feature Jacobian estimate.
Semi-supervised entity recognition of Chinese government document

There is a large amount of entity information in government documents. Identifying the entity information in government documents is the core foundation of intelligent document processing tasks, such as word segmentation, semantic analysis and knowledge graph construction. To recognize entity, traditional Machine Learning algorithm has the advantage of relatively small tagging corpus requirement. However, this feature also means that this algorithm can hardly capture the implicit semantic information in sentences, which leads to the low accuracy of document entity recognition. Also, this method requires tremendous manual work of feature designing. In contrast, Deep Learning algorithm needs a large tagging corpus. But it gives the algorithm ability to automatically acquire semantic feature information between context. So, the accuracy performance of entity recognition is greatly improved. Combining respective advantages of these above methods, this paper proposes a semi-supervised Deep Learning algorithm framework, which first implement the Conditional Random Field (CRF) and pseudo-labeling to expand the corpus, and then utilize the Dilated Convolution Neural Network (CNN) with Bi-directional Long Short-Term Memory (BiLSTM) plus CRF for extracting entities in official documents. The experimental results show that, compared with other methods, the accuracy, recall rate and F1 value of entity recognition are improved by 5.02%, 5.85% and 5.44% respectively. The proposed method can effectively extract entity information in a document.
DSMRSeg: Dual-Stage Feature Pyramid and Multi-Range Context Aggregation for Real-Time Semantic Segmentation

Real-time semantic segmentation is a challenging task in computer vision. Many researches emphasize real-time inference speed while neglecting segmentation quality. To tackle this problem, we propose a framework called DSMRSeg to achieve high-speed with high-accuracy result after training on only one GPU. Overall, we accomplish this by three core components: (1) Dual-Stage Feature Pyramid Network structure is designed to obtain richer multi-scale information and enhance the entire features hierarchy by bidirectionally propagating features with strong semantics and accurate localization. (2) Multi-Range Context Module is developed to expand receptive fields by aggregating the local dense features and multi-range context information. (3) Light-weight Feature Fusion Module is proposed to merge dual-stage features effectively. We evaluate DSMRSeg on Cityscapes, CamVid and BDD100K datasets and produce competitive results compared with the state-of-the-art methods. Specifically, DSMRSeg achieves 75.5% mIoU on Cityscapes test set, with speed of 40 FPS on one NVIDIA GTX1080 card for 1024 × 512 high-resolution image.
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius

Adversarial training is one of the most popular ways to learn robust models but is usually attack-dependent and time costly. In this paper, we propose the MACER algorithm, which learns robust models without using adversarial training but performs better than all existing provable l2-defenses. Recent work shows that randomized smoothing can be used to provide a certified l2 radius to smoothed classifiers, and our algorithm trains provably robust smoothed classifiers via MAximizing the CErtified Radius (MACER). The attack-free characteristic makes MACER faster to train and easier to optimize. In our experiments, we show that our method can be applied to modern deep neural networks on a wide range of datasets, including Cifar-10, ImageNet, MNIST, and SVHN. For all tasks, MACER spends less training time than state-of-the-art adversarial training algorithms, and the learned models achieve larger average certified radius.
Teaching basic of programming with the elements of Scratch - evaluation of VBA programming course for logistics students

Digital nations have become dependent on computer applications. One of the most important part of literacy in today's word are the basics of computer applications development and usage. Programming seems to be difficult to learn, therefore, several educational programming languages were developed, so that more people could develop the applications. Modern technologies mean that computer programming is not a skill reserved only for a small group of specialists. Programmers can become people of different ages and interests. The article addresses the problem of basic programming courses for non-computer science students. Its aim is to present the role of visual programming languages in encouraging university students to create algorithms and start programming. The research allows to conclude that Scratch makes introduction to programming more enjoyable, helps to concentrate on the problem and the business process. The comparative analysis showed also differences between groups of students.
RAIN: A Simple Approach for Robust and Accurate Image Classification Networks

It has been shown that the majority of existing adversarial defense methods achieve robustness at the cost of sacrificing prediction accuracy. The undesirable severe drop in accuracy adversely affects the reliability of machine learning algorithms and prohibits their deployment in realistic applications. This paper aims to address this dilemma by proposing a novel preprocessing framework, which we term Robust and Accurate Image classificatioN(RAIN), to improve the robustness of given CNN classifiers and, at the same time, preserve their high prediction accuracies. RAIN introduces a new randomization-enhancement scheme. It applies randomization over inputs to break the ties between the model forward prediction path and the backward gradient path, thus improving the model robustness. However, similar to existing preprocessing-based methods, the randomized process will degrade the prediction accuracy. To understand why this is the case, we compare the difference between original and processed images, and find it is the loss of high-frequency components in the input image that leads to accuracy drop of the classifier. Based on this finding, RAIN enhances the input's high-frequency details to retain the CNN's high prediction accuracy. Concretely, RAIN consists of two novel randomization modules: randomized small circular shift (RdmSCS) and randomized down-upsampling (RdmDU). The RdmDU module randomly downsamples the input image, and then the RdmSCS module circularly shifts the input image along a randomly chosen direction by a small but random number of pixels. Finally, the RdmDU module performs upsampling with a detail-enhancement model, such as deep super-resolution networks. We conduct extensive experiments on the STL10 and ImageNet datasets to verify the effectiveness of RAIN against various types of adversarial attacks.
Pattern-Based Goal-Oriented Development of Fault-Tolerant MAS in Event-B

Goal-oriented development facilitates structuring behaviour of complex multi-agent systems. It allows us to represent the required system behaviour as a set of goals to be accomplished by the agents with corresponding functionality. However, in general, the agents might fail and hence, to ensure goal reachability we should augment the goal-oriented framework with fault tolerance mechanisms. In this paper, we propose a formal pattern-based approach to formal modelling of fault tolerant MAS in Event-B. The framework allows us in a systematic and rigorous way to define complex relationships between the unreliable agents and goals. It is illustrated by a case study – a smart warehouse system.
Object and Obstacle Detection for Self-Driving Cars Using GoogLeNet and Deep Learning

Self-driving cars are the latest innovation in which the car runs by itself. The self-driving cars can be called as autonomous cars. This involves many technologies like artificial intelligence, machine learning and deep learning. When coming to the self-driving cars, the main aspect which it needed to be taken care of is the obstacle detection and the object detection. The object detection by a car in more simpler words object recognition process done by a machine which involves the concepts of machine learning and deep learning. Deep learning helps in achieving the object and the obstacle detection. There are various algorithms which help in the object detection like artificial neural network, convolutional neural network, AlexNet, VGG Net, GoogleNet, etc. GoogleNet is the CNN architecture which makes the image recognition an easier task. For the self-driving cars, obstacle and object detection GoogLeNet are not much addressed in the recent works. So, it can be considered as a latest technology. In this paper, the recent works about the self-driving cars and object detection and obstacle detection and the future scope of it are discussed.
Analysis of Self-presentation and Self-verification of the Users on Twitter

AbstractFrom the cave paintings of early humans to the popularity of social media these days, it is understandable that for human beings self-expressing is a crucial need for all of us and social media has made it easier by posting content and express our thoughts about the other’s contents. Due to this urge and the competition that has been occurred by the concept of social media, the definition of so many entities has been changed. A good example can be beauty. Due to this issue, the ratio in plastic surgeries and editing the photos has been increased a lot. The interesting challenge is understanding how people present themselves and define their reality. Also, how to make a computer understand these behaviors and then categorize these users. In this paper, we are going to focus on twitter users and will introduce the useful features to feed the algorithm with, to make the analysis of the behavior of the users possible and have a better understanding of the self-presentation of the users on Twitter.KeywordsSocial media analysisUser behavior miningFeature extractionAnalyticsSelf-presentationSelf-verification
Bluff: Interactively Deciphering Adversarial Attacks on Deep Neural Networks

Deep neural networks (DNNs) are now commonly used in many domains. However, they are vulnerable to adversarial attacks: carefully crafted perturbations on data inputs that can fool a model into making incorrect predictions. Despite significant research on developing DNN attack and defense techniques, people still lack an understanding of how such attacks penetrate a model's internals. We present Bluff, an interactive system for visualizing, characterizing, and deciphering adversarial attacks on vision-based neural networks. Bluff allows people to flexibly visualize and compare the activation pathways for benign and attacked images, revealing mechanisms that adversarial attacks employ to inflict harm on a model. Bluff is open-sourced and runs in modern web browsers.
RETRACTED: Coupling of agricultural economy and environment based on 5G network and internet of things system

The used to assess the impact of environmental change in the rural environment; it is keen on a couple of existing biotechnology and financial model. Reasonable criticism of the quality of speech between ordering and implicit environmental and financial cycles. The natural model measures the monetary model and option information used to connect this land. The two gradually developed a level of intelligent coupling of agriculture, natural climate, and the rural economy, increasing the coordination between the coupling sub-frames. In general, the natural agriculture climate also prevents damage to the agricultural economy of the planned progress from essential coordination coupled to a sustained coordination and dominance level. An agricultural economy is based on agriculture and bio-climatic corruption for the rapid development cost. They also are paid to promoting more financial and environmental benefits. The level of coordination in advance, gardening contributes two sub-frames together. These findings are critical connecting lines to improve the viable agricultural economy to improve in the semi-arid region. Rural business analysts to application problems tend to zero is to improve and expand academic discussion lawyers and financial practices given by experts.
Jacobsthal sums and permutations of biquadratic residues

Let p≡1(mod4) be a prime. In this paper, with the help of Jacobsthal sums over finite fields, we study some permutation problems involving biquadratic residues modulo p.
Improved search space shrinking for medical image retrieval using capsule architecture and decision fusion

Medical diagnosis is a challenging procedure that involves issues such as data imbalance, insufficient labels, obscure images, redundancy and lack of effective model training directions to shrink the semantic gap between human knowledge and computer algorithms. Due to privacy norms, sometimes medical images are difficult to access and therefore, retrieval of identical existing images from an existing repository is quite useful. This paper proposes a search space to narrow down the identical images in an archive by using (i) Capsule Networks, followed by a (ii) decision fusion with Wavelet-Discrete Cosine Transform (W-DCT) and Radon Barcodes (RBC). Empirical case study has been applied on IRMA (Image Retrieval in Medical Applications) dataset, ImageCLEFMed-2009, containing 14,410 X-ray images, but the proposed method is generic, reproducible and scalable. Subjective and quantitative performance has been compared with the state-of-art and it has been found superior to yield accuracy of 92.83% and IRMA error of 124.25 for 193 class-code category. Thus, the proof-of-concept helps to improves diagnosis efficiency for automatic image retrieval and annotation by clustering similar images from the underlying repository.
Margin Distribution Analysis

Margin is an important concept in machine learning; theoretical analyses further reveal that the distribution of margin plays a more critical role than the minimum margin in generalization power. Recently, several approaches have achieved performance breakthroughs by optimizing the margin distribution, but their computational cost, which is usually higher than before, still hinders them to be widely applied. In this article, we propose margin distribution analysis (MDA), which optimizes the margin distribution more simply by maximizing the margin mean and minimizing the margin variance simultaneously. MDA is efficient and resistive to class-imbalance naturally, since its objective distinguishes the margin means of different classes and can be broken up into two linear equations. In practice, it can also cooperate with other frameworks such as reweight-minimization when facing complex circumstances with noise and outliers. Empirical studies validate the superiority of MDA in real-world data sets, and demonstrate that simple approaches can also perform competitively by optimizing margin distribution.