Reviewerly/exHarmony huggingface dataset:
Screw Missing Detection Based on MSER Algorithm

The quality detection of large switchgear in power equipment is of great significance in safety and reliability of power equipment. This paper mainly focuses on the detection of screw missing by using the mechanical arm to collect images in multi-directions of large switchgear. It is difficult to guarantee the recognition rate of all types of screws with traditional detection methods due to the differences in spatial resolution and brightness distribution of each collected image and the large variety of screws on the switchgear, Therefore, this paper proposed a screw missing detection method based on the MSER (Maximum Stable Extreme Value Region) algorithm. The method starts with the detection of screw holes and obtains information of the missing screws by detecting the number of screw holes. First, the MSER algorithm was used to obtain candidate regions of screw holes in the image to be detected, and then a regional feature extraction analysis was added to the MSER algorithm to extract the following five eigenvalues: the roundness of the contours in the regions, the dimensions of the regions, the energy, entropy and homogeneity in the gray level co-generation matrix of the regions. According to the eigenvalues in the region to recognize screw hole, and then judge the missing of the screw. It is demonstrated that the proposed screw missing detection method based on MSER algorithm can solve the problem with difficult target extraction due to the differences in image quality, ambient illumination and geometric distortion, and can be effectively used for target detection which has a complex background and plenty of interference.
A Survey on Machine Learning Techniques for Breast Cancer Diagnosis and Detection

With the advancements in modern engineering techniques there is a considerable improvement in medical field diagnosis today. Utilization of modern machine learning techniques in the image processing domain for Cancer detection helps both doctors and patients for understanding the level of infection properly. This paper presents an overview of the different methodologies for the classification and detection of breast cancer with the help of deep learning and machine learning techniques. We present the review of various algorithms like Artificial Neural Networks (ANN), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbors (KNN) and Naive Bayes (NB). The primary goal of this review is to show comparison, to summarize the challenges, and future trends in the detection and classification of breast cancer. Various publications and databases were accessed as a source of information to conduct this review.
Knowledge Transfer for Deep Reinforcement Agents in General Game Playing

Learning to master new games with nothing but the rules given is a hallmark of human intelligence. This ability has recently been successfully replicated in AI systems through a combination of Knowledge Representation, Monte Carlo Tree Search and Deep Reinforcement Learning: Generalised AlphaZero [7] provides a method for building general game-playing agents that can learn any game describable in a formal specification language. We investigate how to boost the ability of deep reinforcement agents for general game playing by applying transfer learning for new game variants. Experiments show that transfer learning can significantly reduce the training time on variations of games that were previously learned, and our results further suggest that the most successful method is to train a source network that uses the guidance of multiple expert networks.
A Probabilistic Logic Model of Lightning Network

One of the main limitations of blockchain systems based on Proof of Work is scalability, making them unsuitable for e-commerce and small payments. Currently, one of the principal directions to overcome the scalability issue is to use the so-called “layer two” solutions, like Lightning Network, where users can open channels and send payments through them. In this paper, we propose a Probabilistic Logic model of Lightning Network, and we show how it can be adopted to compute several properties of it. We conduct some experiments to prove the applicability of the model, rather than providing a comprehensive analysis of the network.
Augmented reality as a tool to increase the attractiveness of geoscience education

In recent years, more and more students have focused on the study of management, economics and IT subjects. Science subjects go to the background and geosciences are no exception. The study presents the possibilities of using the latest IT technologies, especially augmented reality as a tool to increase the attractiveness of the study of geosciences. With the help of the project, an educational material cooperating with augmented reality was created and subsequently it was tested on a sample of students studying at primary schools. The results are evaluated at the end of the paper, where solutions are also proposed to increase the attractiveness of the study of geosciences. The results showed that using an augmented reality mobile application increased the learning motivation of students.
Exploiting Federated Learning Technique to Recognize Human Activities in Resource-Constrained Environment

The conventional machine learning (ML) and deep learning (DL) methods use large amount of data to construct desirable prediction models in a central fusion center for recognizing human activities. However, such model training encounters high communication costs and leads to privacy infringement. To address the issues of high communication overhead and privacy leakage, we employed a widely popular distributed ML technique called Federated Learning (FL) that generates a global model for predicting human activities by combining participated agents’ local knowledge. The state-of-the-art FL model fails to maintain acceptable accuracy when there is a large number of unreliable agents who can infuse false model, or, resource-constrained agents that fails to perform an assigned computational task within a given time window. We developed an FL model for predicting human activities by monitoring agent’s contributions towards model convergence and avoiding the unreliable and resource-constrained agents from training. We assign a score to each client when it joins in a network and the score is updated based on the agent’s activities during training. We consider three mobile robots as FL clients that are heterogeneous in terms of their resources such as processing capability, memory, bandwidth, battery-life and data volume. We consider heterogeneous mobile robots for understanding the effects of real-world FL setting in presence of resource-constrained agents. We consider an agent unreliable if it repeatedly gives slow response or infuses incorrect models during training. By disregarding the unreliable and weak agents, we carry-out the local training of the FL process on selected agents. If somehow, a weak agent is selected and started showing straggler issues, we leverage asynchronous FL mechanism that aggregate the local models whenever it receives a model update from the agents. Asynchronous FL eliminates the issue of waiting for a long time to receive model updates from the weak agents. To the end, we simulate how we can track the behavior of the agents through a reward-punishment scheme and present the influence of unreliable and resource-constrained agents in the FL process. We found that FL performs slightly worse than centralized models, if there is no unreliable and resource-constrained agent. However, as the number of malicious and straggler clients increases, our proposed model performs more effectively by identifying and avoiding those agents while recognizing human activities as compared to the state-of-the-art FL and ML approaches.
Cross-Project Change-Proneness Prediction with Selected Source Project

Software change-proneness prediction aims to identity change prone parts of a software where focused attention is required by the managers and other stakeholders. This reduces development and maintenance costs by highlighting the classes which may change and work with the class in such a manner that prevents further changes from occurring often. Prediction requires training data which is generally obtained from historical data of the projects. However, this may not be the case for new projects which have limited or no historical data available. Cross-project change prediction helps solve this issue by using another project as training data to create a prediction model. With the vast number of candidate projects that can be used as a source to train the classifier, the problem of how to select an appropriate source project which can return a decent prediction accuracy with a model trained with it arises in cross-project change prediction.Through this paper, we propose an algorithm to select a source project which can be used to determine change prone classes in a target project with high accuracy. The source project is selected from a pool of 8 open-source projects. Three strategies are used to identity a suitable source project. The results of the three strategies are compared with one another and with a related change-proneness model proposed by Malhotra and Bansal known as the Random Cross-Project Prediction (RCP). Out of the three strategies in the proposed algorithm, the first two strategies performed better in comparison to the prediction performance of the random cross project prediction model with improvements in terms of AUC (1.04% and 1.27%), F-Measure (5.83% and 3.82%), and MCC (14.14% and 7.77%).
High-Accuracy Online Calibration Scheme for Large-Scale Integrated Photonic Interferometric Measurements

High-accuracy interferometric measurement is crucial for integrated photonic interferometric imaging. In this paper, we propose an online calibration approach for large-scale integrated photonic beam combination that can realize high-accuracy measurement. By leveraging a simple four-step switching condition and the optical path differences between interferometric beams, the instrumental characteristics of the beam combiner, including the transmittances, visibilities, and phase relations, could be calibrated accurately. Compared to the conventional approach, the calibration procedure and estimation method are greatly simplified in our proposed scheme. The effectiveness of the proposed approach is validated using a photonic integrated circuit with two interferometric channels. The calibration results show that the differences between the phase relations are less than 1.41% between the proposed and conventional approaches. The proposed scheme shows significant advantages in optical synthetic aperture imaging systems using photonic integrated circuits especially for applications in harsh environments.
Analysis and Discussion on Standard Cost Allocation Model in State Grid

With the rapid development of smart grid, the traditional financial system gradually does not meet the needs of the current power grid financial decision-making. As a new data processing means, data analysis and statistical regression can make up for some shortcomings of the traditional financial system. This paper discusses the application of big data analysis and artificial intelligence theory in the activity-based transformation of power grid standard cost, in order to provide new ideas for power grid cost management. Firstly, the stepwise regression analysis and forward driver selection method are used to select the most significant driver variables from many driver variables, and then on this basis, the selected variables are visually analyzed to further eliminate the high correlation between variables. Finally, the machine learning model in artificial intelligence and lifting algorithm are used to optimize and discuss the cost allocation model. The experimental results show that the average absolute error of the total cost calculated by the model is 6% lower than the actual total cost. Big data analysis and AI can more efficiently process power grid financial data, which is of great help to improve the accuracy of calculation.
Identifying Key Strategies for Reconnaissance in Cybersecurity

The paper's goal is to explore what reconnaissance in cybersecurity is, the strategies and methodologies used in reconnaissance, meaning and its applications, description of the tools used for reconnaissance. The Open-Source Intelligence (OSINT) methodology involves specific methods of reconnaissance, gathering and analyzing information. Analysts need to have the tools that allow them to flexibly, tentatively and creatively generate foundations to start a line of inquiry, create and test their ideas, and to fluidly switch between approaches and analytical and reasoning techniques to build important and robust arguments as that particular line of inquiry is finalized. We have performed a survey of the most common open-source recon tools used to conduct reconnaissance activities, identified the missing features of each tool, conducted a study on the design consideration required to develop a reconnaissance tool and proposed a new framework to develop such a tool.
Natural Language Processing for Cardiovascular Applications

Natural language processing (NLP) systems can turn unstructured clinic notes (e.g. progression notes, discharge summary, ECG reports) into query-able structured data to support on-demand knowledge discovery and patient care. However, processing clinical notes and extracting meaningful information is a challenging task due to ambiguity and subtlety of natural language and variations in syntactic styles. In this chapter, we performed a systematic survey starting from 1990 to 2020 and benchmarked 47 NLP publications proposed in cardiovascular domain in term of methodology developed, dataset used, and performance achieved. Finally, we performed a meta-analysis to highlight the popular trend and future direction in this field.
UDapter: Typology-based Language Adapters for Multilingual Dependency Parsing and Sequence Labeling

Abstract Recent advances in multilingual language modeling have brought the idea of a truly universal parser closer to reality. However, such models are still not immune to the “curse of multilinguality”: Cross-language interference and restrained model capacity remain major obstacles. To address this, we propose a novel language adaptation approach by introducing contextual language adapters to a multilingual parser. Contextual language adapters make it possible to learn adapters via language embeddings while sharing model parameters across languages based on contextual parameter generation. Moreover, our method allows for an easy but effective integration of existing linguistic typology features into the parsing model. Because not all typological features are available for every language, we further combine typological feature prediction with parsing in a multi-task model that achieves very competitive parsing performance without the need for an external prediction system for missing features. The resulting parser, UDapter, can be used for dependency parsing as well as sequence labeling tasks such as POS tagging, morphological tagging, and NER. In dependency parsing, it outperforms strong monolingual and multilingual baselines on the majority of both high-resource and low-resource (zero-shot) languages, showing the success of the proposed adaptation approach. In sequence labeling tasks, our parser surpasses the baseline on high resource languages, and performs very competitively in a zero-shot setting. Our in-depth analyses show that adapter generation via typological features of languages is key to this success.1
An Analysis on the Weibo Topic Detection Based on K-means Algorithm

Sina Weibo, as a platform for netizens to express their opinions, generates a large amount of public opinion data and constantly generates new topics. How to detect new and hot topics on Weibo is a meaningful studied issue. Document Clustering is a widely studied problem in Text Categorization. K-means is one of the most famous unsupervised learning algorithms, partitions a given dataset into disjoint clusters following a simple and easy way. But the traditional K-means algorithm assigns initial centroids randomly, which cannot guarantee to choose the maximum dissimilar documents as the centroids for the clusters. A modified K-means algorithm is proposed, which uses Jaccard distance measure for assigning the most dissimilar k documents as centroids, and uses Word2vec as the Chinese text vectorization model. The experimental results demonstrate that the proposed K-means algorithm improves the clustering performance, and is able to detect new and hot topics based on Weibo COVID-19 data.
Basics of Fourier Analysis of Time Series Data : A practical guide to use of the Fourier transform in an industrial setting

There are few mathematical breakthroughs that have had as dramatic impact on the scientific process as the Fourier transform. Defined in 1807 in a paper by Jean Baptiste Joseph Fourier (1) to solve a problem in heat conduction, the integral transform, Equation (i):
Literature Survey for Applications of Artificial Neural Networks

The artificial neural network could probably be the complete solution in recent decades, widely used in many applications. This chapter is devoted to the major applications of artificial neural networks and the importance of the e-learning application. It is necessary to adapt to the new intelligent e-learning system to personalize each learner. The result focused on the importance of using neural networks in possible applications and its influence on the learner's progress with the personalization system. The number of ANN applications has considerably increased in recent years, fueled by theoretical and applied successes in various disciplines. This chapter presents an investigation into the explosive developments of many artificial neural network related applications. The ANN is gaining importance in various applications such as pattern recognition, weather forecasting, handwriting recognition, facial recognition, autopilot, etc. Artificial neural network belongs to the family of artificial intelligence with fuzzy logic, expert systems, vector support machines.
A Systematic Review on Blockchain Technology: Concepts, Applications, and Prospects in Healthcare

Blockchain technology is the “fifth evolution” in the era of computing. The characteristics of blockchain such as immutability, tamper-proof, decentralized, private, and permissioned blockchain have attracted the world's attention. This paper briefs about the core concepts of blockchain, smart contracts, and architecture behind the integration of IoT devices and blockchain. The proposed architecture using the blockchain and Inter Planetary File System (IPFS) improves the way of storing, handling, and sharing medical data and records. A comprehensive study on blockchain-based smart applications across diverse sectors such as financial, manufacturing, supply chain, healthcare sectors is also included in this paper. Finally, the paper also enumerates challenges, limitations, and future trends in the area of blockchain.
How Does Corruption Affect Currency Substitution? Evidence from Nigeria

The article examines the impact of corruption on currency substitution in Nigeria over the 1986–2017 period, using the auto-regressive distributed lag and vector error-correction approaches. The results indicate that corruption and currency substitution are co-integrated. Moreover, corruption promotes currency substitution in the short run, which confirms the safe-haven hypothesis. An increase in income leads to an increase in currency substitution, which further supports the safe-haven hypothesis. Exchange rate depreciation tends to promote currency substitution, especially in the short-run. Finally, inflation and interest rate promote currency substitution in Nigeria. Therefore, it would be necessary for policymakers to explore ways of reducing corruption, ensure macroeconomic stability, foster the equitable distribution of growth and promote productivity to enhance savers’ confidence in the local currency.
LJDA-Net: A Low-Rank Joint Domain Adaptation Network for Industrial Sample Enhancement

The small sample problem is a challenge for data-driven industrial fault diagnosis. The available sample enhancement works have limited application in real industry, due to the difficulty in achieving the expected efficiency and accuracy performance of modeling. To address this issue, this paper proposes a low-rank joint domain adaptation network (LJDA-Net). First, we design the low-rank feature extraction subnet to effectively alleviate the limitation of feature redundancy and improve model training efficiency while ensuring feature accuracy. Second, we design the adaptively joint distribution alignment subnet, in which the internal multi-source domains and each source-target domain are jointly aligned, meanwhile, the correlation of each source-target domain is embedded in the alignment as the adaptive term, thus the performance of sample distribution alignment can be improved. By doing so, fault samples in other working conditions can be employed to enhance the small samples in current working conditions. Finally, benchmark simulated experiments and actual application experiments are conducted to evaluate the proposed method. All the results demonstrate that our LJDA-Net performs favorably against the state-of-the-art methods.
NITRO Educational Mobile Robot Platform for Maze Solving and Obstacle Avoidance

This article presents a Nitro (Network of IcT Robo Clubs project) educational mobile robot platform. The aim is to create a mobile robot platform suitable for beginners’ students in the field of robotics. This article describes the development of a mobile robot platform with an ultrasonic sensor and a servo motor, with the ability to autonomously solve the maze and avoid obstacles. The main components, design, hardware, algorithms, and software for maze solving and obstacle avoidance are described. Finally, the test results of the control algorithms are presented.
Research on Safe Motion Tracking Algorithm of Manipulator Based on Minimum Pseudo-Distance Criterion

To solve the problem of low-accuracy trajectory tracking in a complex environment, a novel collision-free endeffector trajectory tracking algorithm is proposed. In this paper, the minimum pseudo-distance is selected as the objective optimization function, and the nearest point is given the obstacle avoidance escape velocity by the gradient projection method based on sigmoid function. At the same time, an adaptive closed-loop position tracking algorithm is designed to reduce the position error of the robot in real-time. The MATLAB simulation results based on the 6-DOF manipulator (Without considering the end-effector posture) show that the developed algorithm can obtain low-error trajectory tracking outcomes, and while realizing obstacle avoidance subtask of the manipulator, which verifies the reliability and superiority of the proposed approach.