Reviewerly/exHarmony huggingface dataset:
A Fault Diagnosis Scheme for Gearbox Based on Improved Entropy and Optimized Regularized Extreme Learning Machine

The performance of a gearbox is sensitive to failures, especially in the long-term high speed and heavy load field. However, the multi-fault diagnosis in gearboxes is a challenging problem because of the complex and non-stationary measured signal. To obtain fault information more fully and improve the accuracy of gearbox fault diagnosis, this paper proposes a feature extraction method, hierarchical refined composite multiscale fluctuation dispersion entropy (HRCMFDE) to extract the fault features of rolling bearing and the gear vibration signals at different layers and scales. On this basis, a novel fault diagnosis scheme for the gearbox based on HRCMFDE, ReliefF and grey wolf optimizer regularized extreme learning machine is proposed. Firstly, HRCMFDE is employed to extract the original features, the multi-frequency time information can be evaluated simultaneously, and the fault feature information can be extracted more fully. After that, ReliefF is used to screen the sensitive features from the high-dimensional fault features. Finally, the sensitive features are inputted into the optimized regularized extreme learning machine to identify the fault states of the gearbox. Through three different types of gearbox experiments, the experimental results confirm that the proposed method has better diagnostic performance and generalization, which can effectively and accurately identify the different fault categories of the gearbox and outperforms other contrastive methods.
Multi-Hole Drilling Tool Path Planning and Cost Management through Hybrid SFLA-ACO Algorithm for Composites and Hybrid Materials

In the process of drilling multiple holes in composites and hybrid materials, almost 70% of the time is consumed in tool traveling and tool changing. Recently, researchers have focused on this consumption of time for optimization of the tool path. A literature review revealed the following research gap: little work has been performed on the hybridization of metaheuristics. In the present study, the hybridization of SFLA and ACO metaheuristic algorithms is carried out, which is based on this research gap. The hybridization of SFLA and ACO metaheuristic algorithms provides originality and novelty in this study. The main objective of this study is to minimize the tool path in drilling problems. The proposed algorithm was applied to five benchmark multi-hole drilling problems and one industrial problem from the literature. The outcome of this work is evaluated with the results of dynamic programming (DP), ACO, an immune-based evolutionary approach (IA), and a modified SFLA for five benchmark problems. The accuracy of the results was improved by 2.27% using the proposed hybrid algorithm, indicating that the proposed algorithm is superior to DP, ACO, IA, and the modified SFLA. Additionally, the results of the proposed hybrid algorithm for an example industrial problem from the literature were compared with those of the SFLA and modified SFLA. The proposed algorithm reduced the total cost by 6.17% and 3.76% compared with the SFLA and modified SFLA, respectively. Thus, the efficacy of the proposed hybrid algorithm was confirmed, along with its applicability.
Knowledge Graph Completion Algorithm Based on Probabilistic Fuzzy Information Aggregation and Natural Language Processing Technology

The knowledge graph was first used in the information search of the Internet as a way to improve the quality of the search because it contains a huge amount of structured knowledge data. In this paper, the knowledge map algorithm is studied through natural language processing technology and probabilistic fuzzy information aggregation, and the knowledge map completion algorithm is cognitive-fitted. NLP is natural language processing. Based on the experiments in this paper, it can be seen that, after combining the algorithm, the behavior data set of 1000 Amazon users was analyzed, and it can be found that the accuracy of the algorithm improves as the proportion of data in the experiment increases. Among them, the 10% dataset has a correct rate of 0.66; the 30% dataset has a final accuracy rate of 0.68; and the 50% dataset has a final accuracy rate of 0.70. The experimental results of this paper show that using probabilistic fuzzy information aggregation and natural language processing technology as a way to complete the knowledge graph can improve the accuracy of the operation. It plays an important role in the development of intelligent cognition and search engines.
On state transfer in Cayley graphs for abelian groups

In this paper, we characterize perfect state transfer in Cayley graphs for abelian groups that have a cyclic Sylow-2-subgroup. This generalizes a result of Ba\v{s}i\'c from 2013 where he provides a similar characterization for Cayley graphs of cyclic groups.
Early-stage phishing detection on the Ethereum transaction network

As cryptocurrency is widely accepted and used, attendant illegal activities have attracted extensive attention, especially phishing scams, which bring great losses to both customers and countries. From the perspective of crime prevention, early warning of such illegal behaviors is of great significance. However, most existing studies focus on detecting phishing scams that have already occurred and been reported. In addition, previous studies ignore the temporal order of users' appearance and thus cannot accurately extract features reflecting users' transaction patterns. In this paper, we propose a framework called early-stage phishing detection to address the problem of early phishing detection. According to the phishing amount, we first divide the process of phishing scams into three stages: early stage, middle stage, and late stage. Then, we develop a feature extraction method to capture features from both the local network structures and the time series of transactions. In experiments, the dataset is strictly partitioned by time series, and experimental results show that our proposed method outperforms existing graph embedding methods on a real-world Ethereum transaction dataset. Finally, we select the ten most important features and analyze the differences between phishing users and normal users on these features, which provide useful insights for regulators and platforms to detect phishing scams in advance.
Learning the progression patterns of treatments using a probabilistic generative model

Modeling a disease or the treatment of a patient has drawn much attention in recent years due to the vast amount of information that Electronic Health Records contain. This paper presents a probabilistic generative model of treatments that are described in terms of sequences of medical activities of variable length. The main objective is to identify distinct subtypes of treatments for a given disease, and discover their development and progression. To this end, the model considers that a sequence of actions has an associated hierarchical structure of latent variables that both classifies the sequences based on their evolution over time, and segments the sequences into different progression stages. The learning procedure of the model is performed with the Expectation-Maximization algorithm which considers the exponential number of configurations of the latent variables and is efficiently solved with a method based on dynamic programming. The evaluation of the model is twofold: first, we use synthetic data to demonstrate that the learning procedure allows the generative model underlying the data to be recovered; we then further assess the potential of our model to provide treatment classification and staging information in real-world data. Our model can be seen as a tool for classification, simulation, data augmentation and missing data imputation.
Important Arguments Nomination Based on Fuzzy Labeling for Recognizing Plagiarized Semantic Text

Plagiarism is an act of intellectual high treason that damages the whole scholarly endeavor. Many attempts have been undertaken in recent years to identify text document plagiarism. The effectiveness of researchers’ suggested strategies to identify plagiarized sections needs to be enhanced, particularly when semantic analysis is involved. The Internet’s easy access to and copying of text content is one factor contributing to the growth of plagiarism. The present paper relates generally to text plagiarism detection. It relates more particularly to a method and system for semantic text plagiarism detection based on conceptual matching using semantic role labeling and a fuzzy inference system. We provide an important arguments nomination technique based on the fuzzy labeling method for identifying plagiarized semantic text. The suggested method matches text by assigning a value to each phrase within a sentence semantically. Semantic role labeling has several benefits for constructing semantic arguments for each phrase. The approach proposes nominating for each argument produced by the fuzzy logic to choose key arguments. It has been determined that not all textual arguments affect text plagiarism. The proposed fuzzy labeling method can only choose the most significant arguments, and the results were utilized to calculate similarity. According to the results, the suggested technique outperforms other current plagiarism detection algorithms in terms of recall, precision, and F-measure with the PAN-PC and CS11 human datasets.
Accurate and lightweight MRI super-resolution via multi-scale bidirectional fusion attention network

High-resolution magnetic resonance (MR) imaging has attracted much attention due to its contribution to clinical diagnoses and treatment. However, because of the interference of noise and the limitation of imaging equipment, it is expensive to generate a satisfactory image. Super-resolution (SR) is a technique that enhances an imaging system's resolution, which is effective and cost-efficient for MR imaging. In recent years, deep learning-based SR methods have made remarkable progress on natural images but not on medical images. Most existing medical images SR algorithms focus on the spatial information of a single image but ignore the temporal correlation between medical images sequence. We proposed two novel architectures for single medical image and sequential medical images, respectively. The multi-scale back-projection network (MSBPN) is constructed of several different scale back-projection units which consist of iterative up- and down-sampling layers. The multi-scale machine extracts different scale spatial information and strengthens the information fusion for a single image. Based on MSBPN, we proposed an accurate and lightweight Multi-Scale Bidirectional Fusion Attention Network(MSBFAN) that combines temporal information iteratively. That supplementary temporal information is extracted from the adjacent image sequence of the target image. The MSBFAN can effectively learn both the spatio-temporal dependencies and the iterative refinement process with only a lightweight number of parameters. Experimental results demonstrate that our MSBPN and MSBFAN are outperforming current SR methods in terms of reconstruction accuracy and parameter quantity of the model.
Prediction of Equipment Effectiveness using Hybrid Moving Average-Adaptive Neuro Fuzzy Inference System (MA-ANFIS) for decision support in Bus Body Building Industry

Managers are driven to accomplish significantly higher levels of operational performance due to the difficulty of today's dynamic production environment. Typically, the precision of production facilities and the efficiency of manufacturing systems are significant variables in productivity. Thus, predicting machine performance has become an inevitable challenge for production managers. However, the question of how managers can reliably assess the effectiveness of equipments for resource allocation remains unaddressed properly. This issue has received little attention in previous research, but it is important in today's manufacturing environment. This study introduces a hybrid moving average - adaptive neuro-fuzzy inference system (MA-ANFIS) to predict the possible effectiveness of equipment. Three real-world problems are considered when developing and evaluating three distinct equipment effectiveness prediction models. The evaluation confirms that the hybrid MA-ANFIS model based on Gaussian membership function outperforms other developed models. This comprehensive solution is packaged as a decision support system. This aids production managers in evaluating the equipment effectiveness, and effectively improving equipment's performance to reduce time and cost of bus body building.
Blockchain in Banking

Blockchain technology is one of the innovations in financial technology that serves as the backbone of Bitcoin and Cryptocurrency. A blockchain is a secure ledger of all data transfer activities that eliminate the need for third-party involvement. It is a technology that records transactions using distributed databases and cryptography. There are three types of blockchain which are public, private, and Consortium or Federated blockchain. Many industries have used blockchain technology. Blockchain in banking is one of the industries that use blockchain the most and need by the country and organization. Many use cases on blockchain in banking, such as Know Your Customer (KYC), clearing and settlement, trade finance, payments, smart contract, and syndicated loans. These use cases could see the impact of implementing blockchain in banking.
Tamil Offensive Language Detection: Supervised versus Unsupervised Learning Approaches

Studies on natural language processing are mainly conducted in English, with very few exploring languages that are under-resourced, including the Dravidian languages. We present a novel work in detecting offensive language using a corpus collected from YouTube containing comments in Tamil. The study specifically aims to compare two machine learning approaches—namely, supervised and unsupervised—to detect offensive patterns in textual communications. In the first setup, offensive language detection models were developed using traditional machine learning algorithms such as Random Forest, Logistic Regression, Support Vector Machine, and AdaBoost, and assessed based on human labeling. Conversely, we used K -means ( K = 2) to cluster the unlabeled data before training the same set of machine learning algorithms to detect offensive communications. Performance scores indicate unsupervised clustering to be more effective than human labeling with ensemble classifiers achieving an impressive accuracy of 99.70% and 99.87% respectively for balanced and imbalanced datasets, hence showing that the unsupervised approach can be used effectively to detect offensive language in low-resourced languages.
<i>LIPAuth</i> : Hand-dependent Light Intensity Patterns for Resilient User Authentication

Authentication mechanisms deployed on access control systems undertake the responsibility of judging user identity to prevent unauthorized individuals from illegally approaching. In this article, we propose LIPAuth leveraging hand-dependent L ight I ntensity P attern to Auth enticate users. To be specific, lights released by a screen, are blocked and reflected by one hand above it; in this propagation process, hands exhibit user-specific ability in driving light absorption and attenuation due to owning unique structures, thereby outputting discriminative intensity patterns representing user identity. To implement LIPAuth , we first study the impact of screen contents on light intensity patterns, also explore the possibility of embedding hand structure biometrics into these patterns. We then design a customized dynamic stimulus-response mechanism for LIPAuth and make it resilient to the risks of potential registration profile leakage. Subsequently, we construct a joint pipeline consisting of signal processing and a learning-based generative adversarial network to overcome interference from variable user behaviors. More importantly, LIPAuth just utilizes common sensors to capture light signals, hence achieving low cost. We finally conduct extensive experiments in three scenarios to evaluate the authentication performance of LIPAuth prototype.
Cascades of Concepts of Virtual Time Travel Games for the Training of Industrial Accident Prevention

Time travel games are a recent form of edutainment media having high potential in areas such as environmental education and prevention training. At TLIC 2021, the present authors delivered a contribution advocating planning of human training experiences as dynamically as driving a disturbed technical system back into a normal mode of operation. A trainee’s inappropriate actions may be possibly ruining a—fortunately only virtual—technical installation. Time travel backwards offers opportunities to influence the fate to make good of the damage. This requires concepts of Artificial Intelligence to plan and execute time travel adaptively according to a trainee’s training history, to a trainee’s strengths and weaknesses, and to possibly changing environmental conditions. There is introduced a cascade of gradually more intricate categories of time travel games. With every step from one category to the next, the deployed AI gets more powerful and effective in providing adaptive guidance of trainees. The most advanced time travel games are those that allow for the dynamic modification of events experienced in the virtual past. In this way, the game system evolves over time and adapts to the needs of human trainees with emphasis on guidance for trainees who fail repeatedly. Consequently, every trainee gets an individual training experience. The AI takes care that all trainees experience their individual success as a result of their own efforts—i.e., as a gratification for mastery. All concepts and their respective representations are illustrated through examples from a novel application. This includes the way in which concepts are used and the impact they have on affective and effective training.
Fusing surveillance videos and three‐dimensional scene: A mixed reality system

Abstract Augmented Virtual Environments (AVE) or Virtual‐Reality Fusion systems fuse dynamic videos with static three‐dimensional (3D) models of a virtual environment to provide an optimal solution for visualizing and understanding multichannel surveillance systems. However, texture distortion caused by viewpoint changes in such systems is a critical issue that needs to be addressed. To minimize texture fusion distortion, this paper presents a novel virtual environment system in two phases, offline and online phases, to dynamically fuse multiple surveillance videos with a virtual 3D scene. In the offline phase, a static virtual environment is obtained by performing a 3D photogrammetric reconstruction from the input images of the scene. In the online phase, the virtual environment is augmented by fusing multiple videos through two optional strategies. One strategy is to dynamically map images of different videos onto a 3D model of the virtual environment, and the other is to extract moving objects and represent them as billboards. The system can be used to visualize a 3D environment from any viewpoint augmented by real‐time videos. Experiments and user studies in different scenarios demonstrate the superiority of our system.
Self and desired partner descriptions in the online romance scam: a linguistic analysis of scammer and general user profiles on online dating portals

The online romance scam is a cybercrime that has resulted in substantial impact on victims, both psychologically and financially. The display of a personal advertisement by scammers on online dating websites in the form of the user profile is often the first step in the scam strategy to attract potential victims. This study examined differences in the use of language in self- and desired partner-description found in both the scammer profiles and a set of general user profiles on online dating websites. Using a corpus-based methodology, 500 user profiles identified as having fraudulent characteristics were compared with the same number of user profiles downloaded from five popular dating portals. Words that are statistically different in their frequencies of occurrence in the two sets of data were generated using a corpus analysis software and then categorized based on semantic and contextual criteria. The analysis uncovered linguistic features that are distinctive of the scammer profiles, revealing strategies used by online scammers in constructing and engaging a certain type of victim, by intensively invoking love, romance and commitment, and conjuring hope of a future as a couple through pertinent use of lexical and function words. Implications of the study include its contribution to the understanding and identification of the linguistic indicators of romance fraud user profiles which are important in efforts to raise public awareness and for future development of computerized scam detection tools.
A New Parallel Intelligence Based Light Field Dataset for Depth Refinement and Scene Flow Estimation

Computer vision tasks, such as motion estimation, depth estimation, object detection, etc., are better suited to light field images with more structural information than traditional 2D monocular images. However, since costly data acquisition instruments are difficult to calibrate, it is always hard to obtain real-world scene light field images. The majority of the datasets for static light field images now available are modest in size and cannot be used in methods such as transformer to fully leverage local and global correlations. Additionally, studies on dynamic situations, such as object tracking and motion estimates based on 4D light field images, have been rare, and we anticipate a superior performance. In this paper, we firstly propose a new static light field dataset that contains up to 50 scenes and takes 8 to 10 perspectives for each scene, with the ground truth including disparities, depths, surface normals, segmentations, and object poses. This dataset is larger scaled compared to current mainstream datasets for depth estimation refinement, and we focus on indoor and some outdoor scenarios. Second, to generate additional optical flow ground truth that indicates 3D motion of objects in addition to the ground truth obtained in static scenes in order to calculate more precise pixel level motion estimation, we released a light field scene flow dataset with dense 3D motion ground truth of pixels, and each scene has 150 frames. Thirdly, by utilizing the DistgDisp and DistgASR, which decouple the angular and spatial domain of the light field, we perform disparity estimation and angular super-resolution to evaluate the performance of our light field dataset. The performance and potential of our dataset in disparity estimation and angular super-resolution have been demonstrated by experimental results.