Reviewerly/exHarmony huggingface dataset:
A Mixed Clustering Approach for Real-Time Anomaly Detection

Anomaly detection in real-time data is accepted as a vital area of research. Clustering techniques have effectively been applied for the detection of anomalies several times. As the datasets are real time, the time of data generation is important. Most of the existing clustering-based methods either follow a partitioning or a hierarchical approach without addressing time attributes of the dataset distinctly. In this article, a mixed clustering approach is introduced for this purpose, which also takes time attributes into consideration. It is a two-phase method that first follows a partitioning approach, then an agglomerative hierarchical approach. The dataset can have mixed attributes. In phase one, a unified metric is used that is defined based on mixed attributes. The same metric is also used for merging similar clusters in phase two. Tracking of the time stamp associated with each data instance is conducted simultaneously, producing clusters with different lifetimes in phase one. Then, in phase two, the similar clusters are merged along with their lifetimes. While merging the similar clusters, the lifetimes of the corresponding clusters with overlapping cores are merged using superimposition operation, producing a fuzzy time interval. This way, each cluster will have an associated fuzzy lifetime. The data instances either belonging to sparse clusters, not belonging to any of the clusters or falling in the fuzzy lifetimes with low membership values can be treated as anomalies. The efficacy of the algorithms can be established using both complexity analysis as well as experimental studies. The experimental results with a real world dataset and a synthetic dataset show that the proposed algorithm can detect the anomalies with 90% and 98% accuracy, respectively.
Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models

Understanding the diagnostic goal of medical reports is valuable information for understanding patient flows. This work focuses on extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring. We investigate the performance of domain-dependent and general state-of-the-art language models and their alignment with domain expertise. To this end, eXplainable Artificial Intelligence (XAI) techniques are used to acquire insight into the inner workings of the model, which are verified on their trustworthiness. The verified XAI explanations are then compared with explanations from a domain expert, to indirectly determine the reliability of the model. BERTje, a Dutch Bidirectional Encoder Representations from Transformers (BERT) model, outperforms RobBERT and MedRoBERTa.nl in both accuracy and reliability. The latter model (MedRoBERTa.nl) is a domain-specific model, while BERTje is a generic model, showing that domain-specific models are not always superior. Our validation of BERTje in a small prospective study shows promising results for the potential uptake of the model in a practical setting.
Developing a Machine Learning Algorithm for Service Robots in Industrial Applications

Robots, which have mostly been effective in areas such as industrial, agricultural, and production facilities, have started to take a place in the service sector, as their technologies have become lower in cost and more easily accessible. This situation has attracted the attention of companies and researchers and has accelerated studies on this subject. In this study, an algorithm was developed for the autonomous mobile robot to serve in industrial areas. In line with this study, it was ensured that the autonomous mobile robot mapped the working environment, determined the working station in this environment, and then carried out transport operations between these working stations in accordance with a given work order. After the mobile robot fulfilled the work order, it went into a waiting state until a new work order was received. For the mobile robot to save energy, it was ensured that it waited close to the point where the work order came in the most, by means of machine learning in the waiting position. The developed algorithms were designed using the NI LabVIEW environment and then simulated in the RobotinoSIM environment and physically tested using the Robotino autonomous mobile robot platform. The experimental results showed that mapping and location reporting using an RGB camera, odometry, and a QR code eliminated permanent location errors, and the robot completed 50 work orders with 100% accuracy.
Secure Access Control to Data in Off-Chain Storage in Blockchain-Based Consent Systems

Data access control is a crucial aspect of data management. Actors who want to share data need systems to manage consent in order to decide who can access their data. This guarantees the privacy of data, which is often sensitive. As a secure distributed ledger, the blockchain is widely used today to manage consent for data access. However, a blockchain is not ideal for storing large volumes of data due to its characteristics. Therefore, it is often coupled with off-chain systems to facilitate the storage of these kinds of data. Therefore, data located outside the blockchain require security procedures. This article proposes a securing mechanism based on data encryption to secure data in off-chain storage in blockchain-based consent systems. The protocol uses a symmetric key system, which prevents the reading of data stored outside the sphere of the blockchain by malicious actors who would have access. The mechanism’s set up allows each set of data to be encrypted with a symmetric key that is anchored in a blockchain. This key is then used by the actors who have obtained the consent of the data owner to access and read the data stored outside the blockchain.
Differentiable samplers for deep latent variable models

Latent variable models are a popular class of models in statistics. Combined with neural networks to improve their expressivity, the resulting deep latent variable models have also found numerous applications in machine learning. A drawback of these models is that their likelihood function is intractable so approximations have to be carried out to perform inference. A standard approach consists of maximizing instead an evidence lower bound (ELBO) obtained based on a variational approximation of the posterior distribution of the latent variables. The standard ELBO can, however, be a very loose bound if the variational family is not rich enough. A generic strategy to tighten such bounds is to rely on an unbiased low-variance Monte Carlo estimate of the evidence. We review here some recent importance sampling, Markov chain Monte Carlo and sequential Monte Carlo strategies that have been proposed to achieve this. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.
A Software Defect Prediction Method Based on Program Semantic Feature Mining

As the size and complexity of software systems grow, knowing how to effectively judge whether there are defects in the programs has attracted extensive attention in research. However, current software defect prediction methods only extract semantic information at the syntactic level and lack features to mine defect manifestations at the semantic level of code, because defective software is incomplete or defective in semantic representation. Defective software exhibits incomplete or flawed semantic behavior. This paper proposes a software defect prediction method based on the program semantics feature mining (PSFM) method. Specifically, the semantic information is first extracted from the code grammatical structure information and code text information. Then, the defect feature is mined through the semantic information. Finally, software defects are predicted by using the mined defect features. The experimental results show that, compared with the existing software defect prediction methods, the method in this paper (PSFM method) obtained a higher F-measure value.
Accurate Calibration of a Large Field of View Camera with Coplanar Constraint for Large-Scale Specular Three-Dimensional Profile Measurement

In the vision-based inspection of specular or shiny surfaces, we often compute the camera pose with respect to a reference plane by analyzing images of calibration grids, reflected in such a surface. To obtain high precision in camera calibration, the calibration target should be large enough to cover the whole field of view (FOV). For a camera with a large FOV, using a small target can only obtain a locally optimal solution. However, using a large target causes many difficulties in making, carrying, and employing the large target. To solve this problem, an improved calibration method based on coplanar constraint is proposed for a camera with a large FOV. Firstly, with an auxiliary plane mirror provided, the positions of the calibration grid and the tilt angles of the plane mirror are changed several times to capture several mirrored calibration images. Secondly, the initial parameters of the camera are calculated based on each group of mirrored calibration images. Finally, adding with the coplanar constraint between each group of calibration grid, the external parameters between the camera and the reference plane are optimized via the Levenberg-Marquardt algorithm (LM). The experimental results show that the proposed camera calibration method has good robustness and accuracy.
PLG-ViT: Vision Transformer with Parallel Local and Global Self-Attention

Recently, transformer architectures have shown superior performance compared to their CNN counterparts in many computer vision tasks. The self-attention mechanism enables transformer networks to connect visual dependencies over short as well as long distances, thus generating a large, sometimes even a global receptive field. In this paper, we propose our Parallel Local-Global Vision Transformer (PLG-ViT), a general backbone model that fuses local window self-attention with global self-attention. By merging these local and global features, short- and long-range spatial interactions can be effectively and efficiently represented without the need for costly computational operations such as shifted windows. In a comprehensive evaluation, we demonstrate that our PLG-ViT outperforms CNN-based as well as state-of-the-art transformer-based architectures in image classification and in complex downstream tasks such as object detection, instance segmentation, and semantic segmentation. In particular, our PLG-ViT models outperformed similarly sized networks like ConvNeXt and Swin Transformer, achieving Top-1 accuracy values of 83.4%, 84.0%, and 84.5% on ImageNet-1K with 27M, 52M, and 91M parameters, respectively.
Evolutionary System Design with Answer Set Programming

We address the problem of evolutionary system design (ESD) by means of answer set programming modulo difference constraints (AMT). The goal of this design approach is to synthesize new product variants or generations from existing products. We start by formalizing the underlying system synthesis problem and design space exploration process, which consists of finding the Pareto front with respect to latency, cost, energy, and similarity measures between the two designs. We then present AMT-based encodings to capture all of these aspects. The idea is to use plain ASP for conflict detection and resolution and for routing and to use difference constraints for scheduling. Moreover, we propose a new approach for expressing the similarity that we use at three alternative levels of AMT-based design space exploration, namely, at the strategic, heuristic, and objective levels, which is performed to guide the exploration towards designs of high interest. Last but not least, we systematically evaluate the emerging techniques empirically and identify the most promising AMT techniques.
Correlation between reading time and characteristics of eye fixations and progressive lens design

Objective The purpose of this study is to evaluate reading time and characteristics of fixations at different distances when looking through different areas of progressive power lenses (PPL) with different power distributions by means of eye-tracking technology. Method A wearable eye tracker system (Tobii-Pro Glasses 3) was used to record the pupil position of 28 PPL subjects when reading at near and distance vision while using 3 different PPL designs: a PPL optimized for distance vision (PPL-Distance), a PPL optimized for near vision (PPL-Near) and one of them balanced for a general use (PPL-Balance). Subjects were asked to read out loud a text displayed on a digital screen located at 5.25m and 0.37m when they were looking through the central and peripheral regions of each PPL. Reading time, total duration of fixations, and the number of fixations were analyzed for each reading condition and PPL. Statistical analysis was carried out using Statgraphics Centurion XVII.II Software. Results The analysis of eye movements at distance-reading vision showed a statistically significant lower reading time (p = 0.004) and lower total duration of fixations (p = 0.01) for PPL-Distance. At near-reading vision, PPL-Near provided statistically significant lower reading time (p&lt;0.001), lower total duration of fixations (p = 0.02), and less fixation count(p&lt;0.001) in comparison with PPL-Balance and PPL-Distance. Conclusions Reading time and fixations characteristics are affected by the power distribution of a PPL. A PPL design with a wider distance region provides better distance-reading performance while a PPL with a wider near area performs better at a near-reading task. The power distribution of PPLs influences the user performance at vision-based tasks. Thus, to provide the user with the best visual experience, PPL selection must consider user needs.
Awareness and Knowledge of Cyber Ethical Behaviour by Students in Higher Education Institutions in South Africa

Students in Higher Education Institutions (HEIs) rely on cyber technologies almost daily to perform a wide variety of tasks related to their studies. However, there are ethical guidelines that govern the use of these technologies that also guard against ethical violations. The study examined the level of awareness and knowledge on the cyber ethical behaviour of students in HEIs in South Africa. The factors influencing unethical cyber behaviour and cyber security measures to prevent cyber unethical behaviour among students in HEIs in South Africa were also investigated in this study, using content analysis method. The study was also guided by ethical theories, namely consequentialism, deontology, virtue ethics, and Kohlberg's theory of moral development. Although most of the students in HEIs are aware of unethical cyber behaviours such as cyber bullying, fraud, hacking, pornography, etc., it appears as if there is no collaborative approach for best practices in computer security and training on cyber ethical behaviour for the students within these institutions.
Cybersecurity and Cyberbiosecurity Insider Threat Risk Management

This study examines the nature of professionals being insider threats to their own organization, as well as the general increase in harder-to-detect threats coming from an ever-widening acceptance of third-party insiders by organizations that rely on them, such as those in the fields of biomedical sciences, synthetic biology, artificial intelligence, and food production engineering. The current and emerging literature on how individuals are motivated to engage in problematic workplace behaviors as a means of gaining their specific goal or need is examined in this chapter. Following that, the chapter articulates malicious cybersecurity and cyberbiosecurity insider threat indicators and then provides best practices for reducing the risk of these threats in organizations involved in food production engineering, synthetic biology, and artificial intelligence.
Event-triggered diffusion nonlinear estimation for sensor networks with unknown cross-correlations

With extensive using of sensor networks, reducing energy consumptions without notable compromise in performance becomes an important topic. However, existing diffusion estimation strategies for sensor networks are not energy-efficient due to frequent communications between nodes. Thus, a novel event-triggered diffusion estimation strategy consisting of incremental update and diffusion update is proposed in this paper. And two ET mechanisms are provided to schedule data transmissions. Based on these, an event-triggered cubature Kalman filtering (ET-CKF) algorithm which can deal with nonlinear estimation is developed to accomplish the incremental update. What is more, existing diffusion estimation strategies cannot guarantee the consistency of fusion estimates without the inaccessible cross-correlations of intermediate estimates in distributed networks. Thus, an event-triggered covariance intersection (ET-CI) fusion algorithm is provided to accomplish the diffusion update. Simulation results verify the effectiveness and advantage of the proposed method.
An Underwater Target Wake Detection in Multi-Source Images Based on Improved YOLOv5

In the marine environment, underwater targets improve "stealth" performance through innovation in technology, so traditional target tracking methods are difficult to use for tracking underwater targets stably and accurately.This paper proposes an improved YOLOv5 (You Look Only Once) based method to detect wake of underwater target accurately in multi-source images.We obtain optical images and thermal images through model simulation and scaled experiment.Improve the generalization ability of the detection network through data augmentation.The proposed method introduces linear features detection to enhance feature propagation, and promote feature reuse.The result shows that the proposed method is better than the original object detection algorithms.The improved algorithm can detect underwater targets status (underwater or surface) and image types (optical wake or thermal wake).
Distributed Multiple Resolvable Group Targets Tracking Based on Hypergraph Matching

This article proposes a solution of distributed multiple resolvable group targets tracking (DMRGTT) by exploring the labeled multi-Bernoulli (LMB) filter and distributed fusion rules including generalized covariance intersection (GCI) and weighted arithmetic average (WAA). Due to the proximity of targets’ positions within the same group and the label mismatching problem for the fusion between LMB densities, only utilizing the similarity or distance between targets from different sensors cannot return a satisfactory solution of label matching (LM) results. To cope with this problem, the hypergraph matching (HM) theory is adopted to assist in finding the matched labels. Specifically, the hyperedge-to-hyperedge (H2H) matching matrix is first constructed and a vertex-to-vertex (V2V) matrix is returned. Then, the V2V matrix is further combined with the traditional distance matrix to construct a joint cost matrix. Next, substitute the joint cost matrix into the Hungarian algorithm and the LM results are obtained. Lastly, pairwise GCI-LMB and WAA-LMB fusion processes are performed based on the obtained LM results. Experiment results reveal the effectiveness and robustness of the proposed fusion approaches.
A Machine Learning-Based Framework for Detecting Credit Card Anomalies and Fraud

Expeditious growth in e-commerce has emerged in an increasing number of online shopping societies. These shoppers rely on credit cards as a payment method or use a mobile wallet for their purchases. Thus, credit cards have become the main payment method in the e-world. Given the many transactions that occur daily, criminals propose tremendous opportunities to find different ways of attacking and stealing credit card information. Fraudulent credit card transactions are a serious business issue. These types of scams can result in significant economic and personal losses. As a result, businesses are increasingly devoted to developing new ideas and methods for detecting and preventing fraud, as well as acquiring their customer’s trust and protecting their privacy. In recent years, learning algorithms have emerged as important in research fields aimed at developing solutions to this issue. In this paper, the proposed framework will develop a two-stage anomaly and fraud detection model that uses a strengthened anomaly clustering algorithm in the first stage to find fraud patterns in transactions. The second stage uses a learning classifier to detect fraud in transactions based on fraud patterns and clients’ spending behavior. The transactions will be well balanced using hybrid sampling techniques to enhance the performance of the model. This framework also aims to optimize the learning classifier model, which will improve other existing models in terms of performance, computation time, and accuracy.
Online continual learning with declarative memory

Deep neural networks are enjoying unprecedented attention and success in recent years. However, catastrophic forgetting undermines the performance of deep models when the training data are arrived sequentially in an online multi-task learning fashion. To address this issue, we propose a novel method named continual learning with declarative memory (CLDM) in this paper. Specifically, our idea is inspired by the structure of human memory. Declarative memory is a major component of long-term memory which helps human beings memorize past experiences and facts. In this paper, we propose to formulate declarative memory as task memory and instance memory in neural networks to overcome catastrophic forgetting. Intuitively, the instance memory recalls the input-output relations (fact) in previous tasks, which is implemented by jointly rehearsing previous samples and learning current tasks as replaying-based methods act. In addition, the task memory aims to capture long-term task correlation information across task sequences to regularize the learning of the current task, thus preserving task-specific weight realizations (experience) in high task-specific layers. In this work, we implement a concrete instantiation of the proposed task memory by leveraging a recurrent unit. Extensive experiments on seven continual learning benchmarks verify that our proposed method is able to outperform previous approaches with tremendous improvements by retaining the information of both samples and tasks.
Cost-effective framework for gradual domain adaptation with multifidelity

In domain adaptation, when there is a large distance between the source and target domains, the prediction performance will degrade. Gradual domain adaptation is one of the solutions to such an issue, assuming that we have access to intermediate domains, which shift gradually from the source to the target domain. In previous works, it was assumed that the number of samples in the intermediate domains was sufficiently large; hence, self-training was possible without the need for labeled data. If the number of accessible intermediate domains is restricted, the distances between domains become large, and self-training will fail. Practically, the cost of samples in intermediate domains will vary, and it is natural to consider that the closer an intermediate domain is to the target domain, the higher the cost of obtaining samples from the intermediate domain is. To solve the trade-off between cost and accuracy, we propose a framework that combines multifidelity and active domain adaptation. The effectiveness of the proposed method is evaluated by experiments with real-world datasets.