Reviewerly/exHarmony huggingface dataset:
Anomaly Detection in Biological Early Warning Systems Using Unsupervised Machine Learning

The use of bivalve mollusks as bioindicators in automated monitoring systems can provide real-time detection of emergency situations associated with the pollution of aquatic environments. The behavioral reactions of Unio pictorum (Linnaeus, 1758) were employed in the development of a comprehensive automated monitoring system for aquatic environments by the authors. The study used experimental data obtained by an automated system from the Chernaya River in the Sevastopol region of the Crimean Peninsula. Four traditional unsupervised machine learning techniques were implemented to detect emergency signals in the activity of bivalves: elliptic envelope, isolation forest (iForest), one-class support vector machine (SVM), and local outlier factor (LOF). The results showed that the use of the elliptic envelope, iForest, and LOF methods with proper hyperparameter tuning can detect anomalies in mollusk activity data without false alarms, with an F1 score of 1. A comparison of anomaly detection times revealed that the iForest method is the most efficient. These findings demonstrate the potential of using bivalve mollusks as bioindicators in automated monitoring systems for the early detection of pollution in aquatic environments.
Predictions of Cybersecurity Experts on Future Cyber-Attacks and Related Cybersecurity Measures

The Internet interconnections' exponential growth has resulted in an increase in cyber-attack occurrences with mostly devastating consequences.Malware is a common tool for performing these attacks in cyberspace.The malefactors would either exploit the present weaknesses or employ the distinctive characteristics of the developing technologies.The cybersecurity community should increase their knowledge on the types and arsenals of cyber-attacks, and security measures against cyberattacks should be in place as well.Also, advanced and effective malware defense mechanisms should be established.Hence, this study reviews cyber-attack types, measures and security precautions, and professional extrapolations on cyber-attacks future and the associated security measures.Semi-structured interviews were performed, involving five IT managers and nine Cybersecurity Consultants, to obtain the data.The study findings demonstrate prevention as key for data breach risk prevention.Knowledge of common attack methods and the use of cybersecurity software can facilitate individuals and organizations in thwarting hackers and in preserving their data privacy.Two-factor authorization by consumers and new back-end security protocols and security methods, including Artificial intelligence (AI) application, will encumber hacking attempts.
Challenges and opportunities for modeling coupled human and natural systems

With the growing recognition of coupled human and natural systems (CHANS), modeling CHANS with two-way feedbacks has become a frontier research area and a critical tool to achieve sustainability. The challenges in CHANS modeling and opportunities to advance its science and application to promote the sustainability of CHANS are discussed in this paper.
Anomaly behavior detection analysis in video surveillance: a critical review

Anomaly detection is one of the most researched topics in computer vision and machine learning. Manual detection of an oddity in a video costs significant time and money, so there is a need for an autonomous detection system that can analyze the process and detect the anomaly in the majority of captured video datasets. Through an in-depth study on the recently published works on anomaly detection, a review is prepared to highlight the various tasks performed in abnormal behavior detection. Descriptions along with the pros and cons of various machine-learning and non-machine-learning techniques are discussed in depth. Similarly, more concentration is given to the generation adversarial network (GAN), and a comprehensive description of its design for achieving a better abnormality detection rate is provided. Moreover, a comparison of various state-of-the-art approaches on the basis of their methodologies, advantages, and disadvantages is given. We further quantitatively analyze some of the recent robust approaches at the frame level on the UCSD Ped1 dataset, with the GAN-based model achieving an astonishing performance. We provide various suggestions on how to further increase the performance of GAN for abnormal behavior detection in surveillance videos.
Center-Ness and Repulsion: Constraints to Improve Remote Sensing Object Detection via RepPoints

Remote sensing object detection is a basic yet challenging task in remote sensing image understanding. In contrast to horizontal objects, remote sensing objects are commonly densely packed with arbitrary orientations and highly complex backgrounds. Existing object detection methods lack an effective mechanism to exploit these characteristics and distinguish various targets. Unlike mainstream approaches ignoring spatial interaction among targets, this paper proposes a shape-adaptive repulsion constraint on point representation to capture geometric information of densely distributed remote sensing objects with arbitrary orientations. Specifically, (1) we first introduce a shape-adaptive center-ness quality assessment strategy to penalize the bounding boxes having a large margin shift from the center point. Then, (2) we design a novel oriented repulsion regression loss to distinguish densely packed targets: closer to the target and farther from surrounding objects. Experimental results on four challenging datasets, including DOTA, HRSC2016, UCAS-AOD, and WHU-RSONE-OBB, demonstrate the effectiveness of our proposed approach.
The Sign Language Interpreting Gloves

This report’s primary purpose is to provide a detailed overview of the sign language interpreting gloves project. It starts with an introduction, background and history of the deaf community to better understand the needs and issues of the D/deaf community. In the following literature review, the reader will have the chance to learn about the modern-days challenges faced by hearing-impaired people, broadly explained and elaborated. The psychosocial impact of assistive technology and its effectiveness in improving life quality is discussed next. A lack of new technologies assisting the D/deaf community is not hardly noticed. As elaborated in the previously mentioned chapters, sign language interpreting services are expensive and sign language interpreters cannot be easily booked. These and other findings gathered from the research and also by a survey conducted on a group of deaf individuals indicate that sign language interpreting gloves are the new free interpreters-to-be, and the answer to acquiring the so-wanted independence. Evaluated to have such a significant impact, building this assistive technology that can be easily accessed is the project's focus. The working principles, block diagram and flowchart are also described in the methodology section, where building, not one but a pair of gloves is also documented step by step. Finally, results, discussion and future work will tell us more about the importance of interpreting gloves to close the gap between both hearing and deaf communities.
A Survey on Quantitative Risk Estimation Approaches for Secure and Usable User Authentication on Smartphones

Mobile user authentication acts as the first line of defense, establishing confidence in the claimed identity of a mobile user, which it typically does as a precondition to allowing access to resources in a mobile device. NIST states that password schemes and/or biometrics comprise the most conventional user authentication mechanisms for mobile devices. Nevertheless, recent studies point out that nowadays password-based user authentication is imposing several limitations in terms of security and usability; thus, it is no longer considered secure and convenient for the mobile users. These limitations stress the need for the development and implementation of more secure and usable user authentication methods. Alternatively, biometric-based user authentication has gained attention as a promising solution for enhancing mobile security without sacrificing usability. This category encompasses methods that utilize human physical traits (physiological biometrics) or unconscious behaviors (behavioral biometrics). In particular, risk-based continuous user authentication, relying on behavioral biometrics, appears to have the potential to increase the reliability of authentication without sacrificing usability. In this context, we firstly present fundamentals on risk-based continuous user authentication, relying on behavioral biometrics on mobile devices. Additionally, we present an extensive overview of existing quantitative risk estimation approaches (QREA) found in the literature. We do so not only for risk-based user authentication on mobile devices, but also for other security applications such as user authentication in web/cloud services, intrusion detection systems, etc., that could be possibly adopted in risk-based continuous user authentication solutions for smartphones. The target of this study is to provide a foundation for organizing research efforts toward the design and development of proper quantitative risk estimation approaches for the development of risk-based continuous user authentication solutions for smartphones. The reviewed quantitative risk estimation approaches have been divided into the following five main categories: (i) probabilistic approaches, (ii) machine learning-based approaches, (iii) fuzzy logic models, (iv) non-graph-based models, and (v) Monte Carlo simulation models. Our main findings are summarized in the table in the end of the manuscript.
Restoration and enhancement on low exposure raw images by joint demosaicing and denoising

Restoring high quality images from raw data in low light is challenging due to various noises caused by limited photon count and complicated Image Signal Process (ISP). Although several restoration and enhancement approaches are proposed, they may fail in extreme conditions, such as imaging short exposure raw data. The first path-breaking attempt is to utilize the connection between a pair of short and long exposure raw data and outputs RGB images as the final results. However, the whole pipeline still suffers from some blurs and color distortion. To overcome those difficulties, we propose an end-to-end network that contains two effective subnets to joint demosaic and denoise low exposure raw images. While traditional ISP are difficult to image them in acceptable conditions, the short exposure raw images can be better restored and enhanced by our model. For denoising, the proposed Short2Long raw restoration subnet outputs pseudo long exposure raw data with little noisy points. Then for demosaicing, the proposed Color consistent RGB enhancement subnet generates corresponding RGB images with the desired attributes: sharpness, color vividness, good contrast and little noise. By training the network in an end-to-end manner, our method avoids additional tuning by experts. We conduct experiments to reveal good results on three raw data datasets. We also illustrate the effectiveness of each module and the well generalization ability of this model.
Insider threats among Dutch SMEs: Nature and extent of incidents, and cyber security measures

Insider threats represent a latent risk to all organisations, whether they are large companies or Small or Medium-sized Enterprises (SMEs). Insiders, the individuals with privileged access to the assets of organisations, can compromise their proper functioning and cause serious consequences that can be direct—such as financial—or indirect—such as reputational. Insider incidents can have a negative impact on SMEs, as their resources are often limited, making it paramount to implement adequate cyber security measures. Despite its indisputable relevance, the empirical study of insider incidents from a criminological point of view has received little attention. This paper presents the results of an exploratory study that aims to understand the nature and extent of three types of insider incidents—malicious, negligent, and well-meaning—and how they are related to the adoption of cyber security measures. To that end, we administered a questionnaire among a panel of 496 Dutch SME entrepreneurs and managers and analysed the results quantitatively and qualitatively. The results show that although the prevalence of insider incidents is relatively low among Dutch SMEs, few organisations report a disproportionate number of incidents that often entail serious consequences. A regression model shows that there are cyber security measures related to both higher and lower incident likelihood. The implications of these findings for the cyber security policies of SMEs are discussed.
The Role of Leadership and Digital Transformation in Higher Education Students’ Work Engagement

Teaching and learning processes should be subject to continuous change due to the constant evolution of social, educational and technological environments, which ultimately results in higher levels of student engagement. The current paper describes the technological changes faced by higher education institutions as a result of digital transformation challenges. Further, transformational and transactional leadership styles’ effectiveness is regarded within the context of higher education institutions’ digital enhancements. Over time, these factors have led to contextual shifts that have disengaged students from learning and thus self-development. The current research aimed to examine how higher education institutions should apply different leadership styles within digitally transformed contexts so as to increase students’ learning engagement and reduce the risk of failure in their future developments within (inter)national labor markets. Data gathering and analysis involved a qualitative approach: an online survey was distributed, resulting in 856 responses. Through structural equation modeling, the data revealed a valid higher education digital transformation assessment tool; the results also emphasize the increased role of transactional leadership, as opposed to the traditional transformational style, within a highly digitized higher education institutional framework. Consequently, the linear relationship of students’ work engagement with leadership proved to also be enhanced by quadratic effects. The current study stresses the importance of internal and external peers in higher education performance through high levels of student learning (work) engagement through leadership and a uniformly developed digitally transformed higher education environment.
Nonstationary Time Series Prediction Based on Deep Echo State Network Tuned by Bayesian Optimization

The predictions from time series data can help us sense development trends and make scientific decisions in advance. The commonly used forecasting methods with backpropagation consume a lot of computational resources. The deep echo state network (DeepESN) is an advanced prediction method with a deep neural network structure and training algorithm without backpropagation. In this paper, a Bayesian optimization algorithm (BOA) is proposed to optimize DeepESN to address the problem of increasing parameter scale. Firstly, the DeepESN was studied and constructed as the basic prediction model for the time series data. Secondly, the BOA was reconstructed, based on the DeepESN, for optimal parameter searching. The algorithm is proposed within the framework of the DeepESN. Thirdly, an experiment was conducted to verify the DeepESN with a BOA within three datasets: simulation data generated from computer programs, a real humidity dataset collected from Beijing, and a power load dataset obtained from America. Compared with the models of BP (backpropagation), LSTM (long short-term memory), GRU (gated recurrent unit), and ESN (echo state network), DeepESN obtained optimal results, which were 0.0719, 18.6707, and 764.5281 using RMSE evaluation. While getting better accuracy, the BOA optimization time was only 323.4 s, 563.2 s, and 9854 s for the three datasets. It is more efficient than grid search and grey wolf optimizer.
PTA-Det: Point Transformer Associating Point Cloud and Image for 3D Object Detection

In autonomous driving, 3D object detection based on multi-modal data has become an indispensable perceptual approach when facing complex environments around the vehicle. During multi-modal detection, LiDAR and a camera are simultaneously applied for capturing and modeling. However, due to the intrinsic discrepancies between the LiDAR point and camera image, the fusion of the data for object detection encounters a series of problems, which results in most multi-modal detection methods performing worse than LiDAR-only methods. In this investigation, we propose a method named PTA-Det to improve the performance of multi-modal detection. Accompanied by PTA-Det, a Pseudo Point Cloud Generation Network is proposed, which can represent the textural and semantic features of keypoints in the image by pseudo points. Thereafter, through a transformer-based Point Fusion Transition (PFT) module, the features of LiDAR points and pseudo points from an image can be deeply fused under a unified point-based form. The combination of these modules can overcome the main obstacle of cross-modal feature fusion and achieves a complementary and discriminative representation for proposal generation. Extensive experiments on KITTI dataset support the effectiveness of PTA-Det, achieving a mAP (mean average precision) of 77.88% on the car category with relatively few LiDAR input points.
Study of the Various Selection Techniques in Genetic Algorithms

This study examines a variety of selection strategies typically employed in contemporary genetic algorithms. This paper examines the classification of various selection procedures and their merits and downsides. Key Words: Genetic Algorithms, Genetic operators, diversity
Multi-Scale Feature Learning for Language Identification of Overlapped Speech

Language identification is the front end of multilingual speech-processing tasks. The study aims to enhance the accuracy of language identification in complex acoustic environments by proposing a multi-scale feature extraction method. This method replaces the baseline feature extraction network with a multi-scale feature extraction network (SE-Res2Net-CBAM-BILSTM) to extract multi-scale features. A multilingual cocktail party dataset was simulated, and comparative experiments were conducted with various models. The experimental results show that the proposed model achieved language identification accuracies of 97.6% for an Oriental language dataset and 75% for a multilingual cocktail party dataset Furthermore, comparative experiments show that our model outperformed three other models in the accuracy, recall, and F1 values. Finally, a comparison of different loss functions shows that the model performance was better when using focal loss.
Reflections on the Data Governance Act

Abstract The European Union (EU) has been pursuing a new strategy under the umbrella label of digital sovereignty. Data is an important element in this strategy. To this end, a specific Data Governance Act was enacted in 2022. This new regulation builds upon two ideas: reuse of data held by public sector bodies and voluntary sharing of data under the label of data altruism. This short commentary reviews the main content of the new regulation. Based on the review, a few points are also raised about potential challenges.
Synthetized Multilanguage OCR Using CRNN and SVTR Models for Realtime Collaborative Tools

Background: Remote diagnosis using collaborative tools have led to multilingual joint working sessions in various domains, including comprehensive health care, and resulting in more inclusive health care services. One of the main challenges is providing a real-time solution for shared documents and presentations on display to improve the efficacy of noninvasive, safe, and far-reaching collaborative models. Classic optical character recognition (OCR) solutions fail when there is a mixture of languages or dialects or in case of the participation of different technical levels and skills. Due to the risk of misunderstandings caused by mistranslations or lack of domain knowledge of the interpreters involved, the technological pipeline also needs artificial intelligence (AI)-supported improvements on the OCR side. This study examines the feasibility of machine learning-supported OCR in a multilingual environment. The novelty of our method is that it provides a solution not only for different speaking languages but also for a mixture of technological languages, using artificially created vocabulary and a custom training data generation approach. Methods: A novel hybrid language vocabulary creation method is utilized in the OCR training process in combination with convolutional recurrent neural networks (CRNNs) and a single visual model for scene text recognition within the patch-wise image tokenization framework (SVTR). Data: In the research, we used a dedicated Python-based data generator built on dedicated collaborative tool-based templates to cover and simulated the real-life variances of remote diagnosis and co-working collaborative sessions with high accuracy. The generated training datasets ranged from 66 k to 8.5 M in size. Twenty-one research results were analyzed. Instruments: Training was conducted by using tuned PaddleOCR with CRNN and SVTR modeling and a domain-specific, customized vocabulary. The Weight &amp; Biases (WANDB) machine learning (ML) platform is used for experiment tracking, dataset versioning, and model evaluation. Based on the evaluations, the training dataset was adjusted by using a different language corpus or/and modifications applied to templates. Results: The machine learning models recognized the multilanguage/hybrid texts with high accuracy. The highest precision scores achieved are 90.25%, 91.35%, and 93.89%. Conclusions: machine learning models for special multilanguages, including languages with artificially made vocabulary, perform consistently with high accuracy.
Learned Image Compression With Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modules

Recently deep learning-based image compression methods have achieved significant achievements and gradually outperformed traditional approaches including the latest standard Versatile Video Coding (VVC) in both PSNR and MS-SSIM metrics. Two key components of learned image compression are the entropy model of the latent representations and the encoding/decoding network architectures. Various models have been proposed, such as autoregressive, softmax, logistic mixture, Gaussian mixture, and Laplacian. Existing schemes only use one of these models. However, due to the vast diversity of images, it is not optimal to use one model for all images, even different regions within one image. In this paper, we propose a more flexible discretized Gaussian-Laplacian-Logistic mixture model (GLLMM) for the latent representations, which can adapt to different contents in different images and different regions of one image more accurately and efficiently, given the same complexity. Besides, in the encoding/decoding network design part, we propose a concatenated residual blocks (CRB), where multiple residual blocks are serially connected with additional shortcut connections. The CRB can improve the learning ability of the network, which can further improve the compression performance. Experimental results using the Kodak, Tecnick-100 and Tecnick-40 datasets show that the proposed scheme outperforms all the leading learning-based methods and existing compression standards including VVC intra coding (4:4:4 and 4:2:0) in terms of the PSNR and MS-SSIM. The source code is available at https://github.com/fengyurenpingsheng.