Reviewerly/exHarmony huggingface dataset:
Deep spatial-constraints networks for unsupervised anomaly detection in multivariate time series data

High-dimensional time series anomaly detection has always been an important challenge in the field of system security. Most existing methods are dedicated to modelling the temporal variation of features to capture anomalous moment points, however as features become more high-dimensional, the associations between features take on a complex spatial structure. This spatial structure information will compensate for the constraints of unsupervised training conditions, and guide the model to be more fully trained. In this study, we propose a detection model that incorporates spatial supervision signals. The model not only simultaneously models the temporal and spatial dependencies, but also simulates the topological structure and physical characteristics of data in the real world through graph structure learning and contrastive learning, providing guidance for anomaly detection. We conducted experiments on two real-world datasets and demonstrated that our model outperforms the baseline. Finally, we conducted detailed data analysis to provide interpretability for the model.
Reasoning simulation of substation power grid fault events based on knowledge map technology

In order to cope with the increasingly complex situation of safe operation of power grid, this paper proposes fault event reasoning of substation power grid based on knowledge map technology. By using the method of knowledge map, the logical relationships such as co-reference relationship, causality relationship and time sequence relationship among substation monitoring events are established, and the rules and patterns among the events are described. Based on the power grid equipment entity and concept map, business logic map and historical case map, and according to the key information flow after fault signal analysis, the auxiliary decision of fault handling operation mode is made by using power grid operation and control logic, rules and experience knowledge. Realize the substation power grid fault analysis and processing function, and further improve the intelligent level of fault management.
Augmented Reality für konstruktivistisches Lernen bei der Arbeit: Aktuelle Perspektiven und zukünftige Anwendungen

Abstract This manuscript for the journal Gruppe. Interaktion. Organisation. (GIO) addresses the use of a new and very promising technology for work-related learning, Augmented Reality. New technological solutions have and continue to impact work in different areas, one of which is work-related learning. While Augmented Reality has been subject to numerous studies in the context of work and work training, there is a need for more detailed discussion of its relation to the theoretical foundations of learning and the therein based implications for designing and testing learning environments in Augmented Reality. For this purpose, our paper first reviews current perspectives on Augmented Reality and its value for work-related learning, before introducing and examining the concept of Instructional Design Methods. These are abstract mechanisms that can be used to support training using Augmented Reality based on expert performance. Through thorough discussion of these methods, we pose new perspectives on their application for future research and practical application.
Machine Learning Technique for Credit Card Fraud Detection

The last couple of years have witnessed a limitation in personal mobility due to restrictions and constrains caused by the COVID-19 pandemic. Leading to an increase in online purchase for many goods and services, and pushing dependency on credit card use to a new record high. Credit card fraud presents a crucial challenge for both consumers and financial institutions. Therefore, there is a real need to develop a strong system to prevent fraudulent credit card transactions. Machine learning can provide solutions to avoid mostly all fraudulent credit card transactions. This paper evaluates 11 machine-learning models and presents a deep learning model for credit card fraud detection using a two-phase evaluation process. Each model uses the same real world credit card dataset. The two phase study first testes the eleven machine-learning algorithms and nominates the four best algorithms to reevaluate in the second phase. Machine learning algorithms and synthetic minority oversampling technique (SMOTE) are used to create the proposed model. The first phase indicates that K-neighbors-classifier (KNN), Support-vector-machine (SVM), and Gaussian-naïve-bayes-classifier (GNB) are the best among the eleven models. The second phase result reveals that the proposed model with SMOTE outperforms the nominated models with an AUC value of 100.00%, an F1-Score value of 99.00%, Precision value of 99.90% and a Recall value of 99.840%
An Intelligent Framework based on CNN and Neutrosophic Technique for Detecting Code Smells and Ranking Code

A code smell is an overt sign that typically points to a more serious issue with the system. The source code has a quality issue, as indicated by the code smell. A software system that has too many smells is challenging to create and maintain. It has taken a lot of work to use metric-based and heuristic-based methods to find smells in Java source code. Recently, machine learning techniques have been used in the context of odor detection; nonetheless, existing techniques are thought to be far from mature. However, researchers and software developers are addressing the aforementioned problems by looking at some of the difficulties associated with recognizing odors using a deep learning-based approach. The big challenge for any company is to choose the best code for their organization in the development process according to their requirements. The main issue in this problem is how to choose the code between the available codes especially when there are many numbers of different types of code smells. This paper presents a novel framework for detecting different types of code smells using CNN. The second main stage in the framework is to rank the available code according to the requirements using multi criteria decision making algorithms. The CNN has achieved accuracy near .955 in the detection process. In the second stage the neutrosophic technique has achieved results better than the other multi criteria decision making algorithms.
Management of forum project activities at the University based on modern approaches to the organization of students' activities

The article actualizes the issues of management of forum project activities at universities. The purpose of the research is to search and test modern forms of interaction between professionals, mentors and students directly during the forum (other similar events), which increase students' motivation, their activity and interest in the implementation of the forum program. Research method: experimental. Based on the implemented grant of Kostroma State University (based on the results of the competition of the Federal Agency for Youth Affairs), the paper shows traditional and innovative forms, the combination of which provided an emergent effect of the success of the forum. The main groups of activity forms of students' work organization that can be used in the implementation of similar projects are identified. The article shows the marketing and digital components that form the elements of innovation, influence the forms of interaction, the development of the competencies of the teaching staff.
A historical perspective on informal institutional and international entrepreneurship

Abstract In this paper, a historical perspective is taken to investigate the variations in institutional and international entrepreneurship that occur throughout Africa. Drawing on the institutional theory of international entrepreneurship, we introduce ethnic fractionalization as an informal institution that influences new venture internationalization in African countries. Further, we identify the historical traumatic shock exerted by the epidemic disease spread by tsetse flies as the driver of the relationship between ethnic fractionalization and new venture internationalization. Based on a sample of 33,621 firm-year observations covering 40 African countries, Our empirical findings provide important insights into international entrepreneurship in Africa and offers a theoretical extension into the historical origins of informal institutions.
Supplementary Figure S2 from Association and Prediction Utilizing Craniocaudal and Mediolateral Oblique View Digital Mammography and Long-Term Breast Cancer Risk

&lt;p&gt;Supplementary Figure S2: Pearson correlation between our algorithm and Volpara shows strong positive linear relationship of 0.81.&lt;/p&gt;
A Logical Analysis of Instrumentality Judgments: Means-End Relations in the Context of Experience and Expectations

Abstract This article proposes the use of temporal logic for an analysis of instrumentality inspired by the work of G.H. von Wright. The first part of the article contains the philosophical foundations. We discuss von Wright’s general theory of agency and his account of instrumentality. Moreover, we propose several refinements to this framework via rigorous definitions of the core notions involved. In the second part, we develop a logical system called Temporal Logic of Action and Expectations ( $$\textsf{TLAE}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>TLAE</mml:mi> </mml:math> ). The logic is inspired by a fragment of propositional dynamic logic based on indeterministic time. The system is proven to be weakly complete relative to its given semantics. We then employ $$\textsf{TLAE}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>TLAE</mml:mi> </mml:math> to formalise and analyse the instrumentality relations defined in the first part of the paper. Last, we point out philosophical implications and possible extensions of our work.
Improving the transferability of adversarial examples via direction tuning

In the transfer-based adversarial attacks, adversarial examples are only generated by the surrogate models and achieve effective perturbation in the victim models. Although considerable efforts have been developed on improving the transferability of adversarial examples generated by transfer-based adversarial attacks, our investigation found that, the big convergence speed deviation along the actual and steepest update directions of the current transfer-based adversarial attacks is caused by the large update step length, resulting in the generated adversarial examples can not converge well. However, directly reducing the update step length will lead to serious update oscillation so that the generated adversarial examples also can not achieve great transferability to the victim models. To address these issues, a novel transfer-based attack, namely direction tuning attack, is proposed to not only decrease the angle between the actual and steepest update directions in the large step length, but also mitigate the update oscillation in the small sampling step length, thereby making the generated adversarial examples converge well to achieve great transferability on victim models. Specifically, in our direction tuning attack, we first update the adversarial examples using the large step length, which is aligned with the previous transfer-based attacks. In addition, in each large step length of the update, multiple examples sampled by using a small step length. The average gradient of these sampled examples is then used to reduce the angle between the actual and steepest update directions, as well as mitigates the update oscillation by eliminating the oscillating component. By doing so, our direction tuning attack can achieve better convergence and enhance the transferability of the generated adversarial examples. In addition, a network pruning method is proposed to smooth the decision boundary, thereby further decreasing the update oscillation and enhancing the transferability of the generated adversarial examples. The experiment results on ImageNet demonstrate that the average attack success rate (ASR) of the adversarial examples generated by our method can be improved from 87.9% to 94.5% on five victim models without defenses, and from 69.1% to 76.2% on eight advanced defense methods, in comparison with that of latest gradient-based attacks. Code is available at https://github.com/HaloMoto/DirectionTuningAttack.
Infrared pedestrian detection using improved UNet and YOLO through sharing visible light domain information

The detection of pedestrians using infrared images is essential for security during automatic driving and monitoring under low illumination or occlusion conditions. However, detecting pedestrians in infrared images can be challenging due to the lack of clear visual cues and background noise. To address these challenges, we proposed an efficient approach based on an improved UNet and YOLO network that shares visible light information from multiple related datasets. The proposed approach achieves high detection accuracies, with mean average precision values of 87.7%, 88%, and 97.5% on three different datasets, FLIR, M3FD, and LLVIP, respectively. Moreover, the designed infrared pedestrian detection network is deployed on an edge computing device and tested with a dual-light handheld camera, achieving a real-time detection speed of 25.6 FPS with a superior trade-off between speed and accuracy. These results demonstrate the effectiveness of the proposed approach in detecting pedestrians in challenging infrared image conditions.
Toward credible belief base revision

This paper deals with belief base revision, a form of belief change which consists in restoring consistency with the intention of incorporating a new piece of information from the environment, while minimally modifying the agent's belief state represented by a finite set of propositional formulas. In an effort to guarantee more reliability and rationality for real applications while performing revision, we come up with the idea of credible belief base revision. We define two new formula-based revision operators using tools offered by evidence theory. These operators, uniformly presented in the same spirit as [92], [13], stem from consistent sub-bases maximal with respect to credibility instead of set inclusion or cardinality. Logical properties and productivity of inference from these operators are established, along with complexity results.
A divide and conquer framework for Knowledge Editing

As Pre-trained language models (LMs) play an important role in various Natural Language Processing (NLP) tasks, it is becoming increasingly important to make sure the knowledge learned from LMs is valid and correct. Unlike conventional knowledge bases, LMs implicitly memorize knowledge in their parameters, which makes it harder to correct if some knowledge is incorrectly inferred or obsolete. The task of Knowledge Editing is to correct errors in language models, avoiding the expensive overhead associated with retraining the model from scratch. While existing methods have shown some promising results, they fail on multi-edits as they ignore the conflicts between these edits. In the paper, we propose a novel framework to divide-and-conquer edits with parallel Editors. Specifically, we design explicit and implicit multi-editor models to learn diverse editing strategies in terms of dynamic structure and dynamic parameters respectively, which allows solving the conflict data in an efficient end-to-end manner. Our main findings are: (i) State of the art Knowledge Editing methods with multiple editing capability, such as MEND and ENN, can hardly outperform the fine-tuning method; (ii) Our proposed models outperform the fine-tuning method over the two widely used datasets for Knowledge Editing; (iii) Additional analytical experiments verify that our approach can learn diverse editing strategies, thus better adapting to multiple editing than state-of-the-art methods.
Cluster-based data relabelling for classification

Linear classifiers are generally simpler and more explainable than their nonlinear variants. They can achieve satisfactory classification performance on linearly separable data, but not on nonlinear data. So, linear classifiers need extending, typically by modification of their algorithms, resulting in their nonlinear variants. In this paper we present one general method, cluster-based data relabelling (CBDR), that allows linear classifiers to work effectively on nonlinear data. CBDR partitions the data set into several non-overlapping class-specific clusters and relabels data by the clusters. A linear classifier can then be applied to the relabelled data to seek cluster-based linear decision boundaries instead of class-based decision boundaries. Extensive experimentation has demonstrated that CBDR can significantly enhance the classification performance of linear classifiers, and even outperform their nonlinear variants. Further experimentation has demonstrated that CBDR can also improve the classification performance of nonlinear classifiers. Most significant outperformance was observed on imbalanced data in both cases.
Variable exponent diffusion for image detexturing

Abstract We consider a variational approach to the problem of structure + texture decomposition (also known as cartoon + texture decomposition). As usual for many variational problems in image analysis and processing, the energy we minimize consists of two terms: a data-fitting term and a regularization term. The main feature of our approach consists of choosing parameters in the regularization term adaptively. Namely, the regularization term is given by a weighted $$p(\cdot )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>(</mml:mo> <mml:mo>·</mml:mo> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> -Dirichlet-based energy $$\int \!a({\varvec{x}}){\,\!|\,\!}\nabla u {\,\!|\,\!}^{p({\varvec{x}})}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>∫</mml:mo> <mml:mspace /> <mml:mi>a</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> <mml:mrow> <mml:mspace /> <mml:mspace /> <mml:mo>|</mml:mo> <mml:mspace /> <mml:mspace /> </mml:mrow> <mml:mi>∇</mml:mi> <mml:mi>u</mml:mi> <mml:msup> <mml:mrow> <mml:mspace /> <mml:mspace /> <mml:mo>|</mml:mo> <mml:mspace /> <mml:mspace /> </mml:mrow> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>(</mml:mo> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> , where the weight and exponent functions are determined from an analysis of the spectral content of the image curvature. Our numerical experiments, both qualitative and quantitative, suggest that the proposed approach delivers better results than state-of-the-art methods for extracting the structure from textured and mosaic images, as well as competitive results on image enhancement problems.
Design and Evaluation of a Wearable Glove for Sign Language Translation Using Cross-Correlation and Flex Sensors

In this paper, the novel method for interacting with computers and other digital devices using hand gestures is presented. The method is based on the use of a specially made glove equipped with flex sensors that detect finger movements and wirelessly transmit them to the object being controlled. Coating with Polydimethylsiloxane (PDMS) is very important for the flex sensors on the glove; which help to dampen and cancel out any vibration brought on by the user’s movements, to ensure accurate and reliable detection of hand gestures. Multiple users’ varied hand gestures were used in a series of experiments to gauge the effectiveness of the method. The findings shows that the cross-correlation techniques is capable of effectively differentiating between various hand gestures, even when some of them is purposefully made to resemble one another. Also, for making this approach applicable for use in real-world situations; that method was resilient to noise and other types of interference. The approach will be discussed in the end section of in this paper, demonstrating how it can be used to interact with digital environments and control various computer functions. This study’s findings suggest that this approach could have significant implications for the design and implementation of user interfaces for a range of digital devices, such as smartphones and virtual reality systems. By giving users a more intuitive and natural way to interact with technology, the strategy has the potential to enhance user experience and increase productivity.
A Survey of the Most Common Task Scheduling Algorithms

Cloud computing (CC) has changed the way organizations manage and access computing resources. CC allows businesses to scale up or down their computing needs on demand, without the need to invest in expensive hardware and infrastructure. Task scheduling in the Cloud environment is considered one of the challenges that could affect the performance because the cloud provider provides services to many users with different demands. Most of the existing task scheduling algorithms are concerned with enhancing the performance of the task execution by using appropriate algorithms such as heuristic algorithms. These heuristic algorithms can be categorized into individual and hybrid algorithms examples of individual algorithms are: Particle Swarm optimization algorithm (PSO), Genetic Algorithm (GA), Ant Colony optimization (ACO), Cuckoo search (CS) algorithms, etc., and the hybrid algorithms are: BF-PSO-Tabu is a combination of a Particle Swarm optimization (PSO) algorithm, best-fit (BF), and Tabu algorithms, where the initial population of the PSO is defined using the best-fit algorithm instead of being defined randomly, and the Tabu algorithm is applied for tuning the final result. According to the work in this paper, a modification to the BF-PSO-Tabu (BSPSOTS) algorithm has been introduced, where the best-fit (BF) algorithm is replaced by the worst-fit (WF) algorithm to define the initial population (WFPSOTS). A comparative study has been done among some individual and hybrid algorithms. The comparative study results prove that the proposed hybrid algorithm (WFPSOTS) achieves better performance than the others with respect to Makespan, Resource utilization, and Load balancing.
An Image Restoration Method for the Sparse Aperture Optical System Based on Deep Learning

In the sparse aperture optical system, the decrease of modulation transfer function (MTF) in the mid-to-high space frequency region inevitably leads to the image blur. To obtain high-resolution images, the Wiener filtering and blind deconvolution algorithms are used to restore the imaging results. This paper proposes a deep learning image restoration method based on a multi-layer progressive image restoration network (MPR-Net) for the sparse aperture system. A dataset was constructed by simulating imaging to train the network, and the image restoration effect of MPR-Net was compared with that of Wiener filtering. Numerical simulation results show that MPRNet demonstrates strong image restoration ability and good generalization ability with less dependence on the dataset.