Reviewerly/exHarmony huggingface dataset:
Model-Based Adaptation for Sample Efficient Transfer in Reinforcement Learning Control of Parameter-Varying Systems

In this paper, we leverage ideas from model-based control to address the sample efficiency problem of reinforcement learning (RL) algorithms. Accelerating learning is an active field of RL highly relevant in the context of time-varying systems. Traditional transfer learning methods propose to use prior knowledge of the system behavior to devise a gradual or immediate data-driven transformation of the control policy obtained through RL. Such transformation is usually computed by estimating the performance of previous control policies based on measurements recently collected from the system. However, such retrospective measures have debatable utility with no guarantees of positive transfer in most cases. Instead, we propose a model-based transformation, such that when actions from a control policy are applied to the target system, a positive transfer is achieved. The transformation can be used as an initialization for the reinforcement learning process to converge to a new optimum. We validate the performance of our approach through four benchmark examples. We demonstrate that our approach is more sample-efficient than fine-tuning with reinforcement learning alone and achieves comparable performance to linear-quadratic-regulators and model-predictive control when an accurate linear model is known in the three cases. If an accurate model is not known, we empirically show that the proposed approach still guarantees positive transfer with jump-start improvement.
Evolving ensembles of heuristics for the travelling salesman problem

Abstract The Travelling Salesman Problem (TSP) is a well-known optimisation problem that has been widely studied over the last century. As a result, a variety of exact and approximate algorithms have been proposed in the literature. When it comes to solving large instances in real-time, greedy algorithms guided by priority rules represent the most common approach, being the nearest neighbour (NN) heuristic one of the most popular rules. NN is quite general but it is too simple and so it may not be the best choice in some cases. Alternatively, we may design more sophisticated heuristics considering the particular features of families of instances. To do that, we have to consider problem attributes other than the proximity of the next city to build priority rules. However, this process may not be easy for humans and so it is often addressed by some learning procedure. In this regard, hyper-heuristics as Genetic Programming (GP) stands as one of the most popular approaches. Furthermore, a single heuristic, even being good in average, may not be good for a number of instances of a given set. For this reason, the use of ensembles of heuristics is often a good alternative, which raises the problem of building ensembles from a given set of heuristic rules. In this paper, we study the application of two kinds of ensembles to the TSP. Given a set of TSP instances having similar characteristics, we firstly exploit a GP to build a set of heuristics involving a number of problem attributes, and then we build ensembles combining these heuristics by means of a Genetic Algorithm (GA). The experimental study provided valuable insights into the construction and utilisation of single rules and ensembles. It clearly demonstrated that the performance of ensembles justifies the time invested when compared to using individual heuristics.
A Novel GUI Design for Comparison of ROS-Based Mobile Robot Local Planners

Determining the stations of the autonomous mobile robot (AMR) in the factory environment is important in terms of optimizing the system for the shipment of products coming out of the production lines. In this context, studies such as navigating the AMR between stations, docking to the station, and assigning autonomous tasks to other stations are costly in terms of time and energy consumption. In addition, flexible working opportunities are not always available according to environmental conditions. This situation creates the need for an interface where the entire work area can be observed and AMRs can be controlled from a single center in the installation of the system in the field. Although there are interface studies for AMR use, they are basically based on AMR control. In this study, an interface that can be used in AMR control and monitoring was designed. With this interface; It is thought to prevent costly situations such as determining the stations, calculating the time spent in the transportation of products between stations, determining the movement route. The interface developed in this context was used in an application where ROS-based path planning algorithms were compared. A new AMR was designed instead of Turtlebot, and Gazebo and Rviz programs were used. The recording and visualization of the stations in the virtual environment were realized with the interface. A total of six different stations was identified, including the starting point. With three different local planners separately, the AMR was given the task of acting autonomously at each station. Thanks to the developed interface, the distance and time required to reach each station were calculated by performing autonomous movement to the desired points. Thanks to the developed interface, the total distance and time covered were shown in the tables and a comparison of ROS-based path planning algorithms was made.
Review and Alignment of Domain-Level Ontologies for Materials Science

The growing complexity and interdisciplinary nature of Materials Science research demand efficient data management and exchange through structured knowledge representation. Domain-Level Ontologies (DLOs) for Materials Science have emerged as a valuable tool for describing materials properties, processes, and structures, enabling effective data integration, interoperability, and knowledge discovery. However, the harmonization of DLOs, and, more generally, the establishment of fully interoperable multi-level ecosystems, remains a challenge due to various factors, including the diverse landscape of existing ontologies. This work provides a comprehensive overview of the state-of-the-art in DLOs for Materials Science, highlighting their main features and purposes. More than 40 DLOs in Materials Science are considered. Furthermore, an alignment methodology including both manual and automated steps, making use of Top-Level Ontologies’ (TLO) capability of promoting interoperability, and revolving around the engineering of FAIR standalone entities acting as minimal data pipelines (“bridge concepts”), is presented. A proof of concept is also provided. The primary aspiration of this undertaking is to make a meaningful contribution towards the establishment of a unified ontology framework for Materials Science, facilitating more effective data integration and fostering interoperability across Materials Science subdomains.
TopicCAT: Unsupervised Topic-Guided Co-Attention Transformer for Extreme Multimodal Summarisation

The exponential growth of multimedia data has sparked a surge of interest in multimodal summarisation with multimodal output (MSMO). A relatively unexplored but essential task within this field is extreme multimodal summarisation, a process that involves creating extremely concise multimodal summaries to further address the issue of multimedia information overload. In this study, we propose a novel Unsupervised Topic-guided Co-Attention Transformer (TopicCAT) neural network to produce extreme multimodal summaries for video-document pairs. The approach consists of two learning stages for a comprehensive multimodal understanding, guided by topic-based insights: a unimodal learning stage and a cross-modal learning stage, in which a cross-modal topic model is devised to capture the overarching themes present in both documents and videos. To achieve unsupervised learning, eliminating the need for resource-expensive collection of ground-truth multimodal summaries, we propose an optimal transport-based optimisation scheme to evaluate summary coverage from a semantic distribution perspective at the topic-level. Comprehensive experiments demonstrate the effectiveness of our proposed TopicCAT method on a multimodal news dataset, achieving a BERTScore of 84.46 and an accuracy of 0.60.
Enhancing Swarm Intelligence for Obstacle Avoidance with Multi-Strategy and Improved Dung Beetle Optimization Algorithm in Mobile Robot Navigation

The Dung Beetle Optimization (DBO) algorithm is a powerful metaheuristic algorithm that is widely used for optimization problems. However, the DBO algorithm has limitations in balancing global exploration and local exploitation capabilities, often leading to getting stuck in local optima. To overcome these limitations and address global optimization problems, this study introduces the Multi-Strategy and Improved DBO (MSIDBO) Algorithm. The MSIDBO algorithm incorporates several advanced computational techniques to enhance its performance. Firstly, it introduces a random reverse learning strategy to improve population diversity and mitigate early convergence or local stagnation issues present in the DBO algorithm. Additionally, a fitness-distance balancing strategy is employed to better manage the trade-off between diversity and convergence within the population. Furthermore, the algorithm utilizes a spiral foraging strategy to enhance precision, promote strong exploratory capabilities, and prevent being trapped in local optima. To further enhance the global search ability and particle utilization of the MSIDBO algorithm, it combines the Optimal Dimension-Wise Gaussian Mutation strategy. By minimizing premature convergence, population diversity is increased, and the convergence of the algorithm is accelerated. This expansion of the search space reduces the likelihood of being trapped in local optima during the evolutionary process. To demonstrate the effectiveness of the MSIDBO algorithm, extensive experiments are conducted using benchmark test functions, comparing its performance against other well-known metaheuristic algorithms. The results highlight the feasibility and superiority of MSIDBO in solving optimization problems. Moreover, the MSIDBO algorithm is applied to path planning simulation experiments to showcase its practical application potential. A comparison with the DBO algorithm shows that MSIDBO generates shorter and faster paths, effectively addressing real-world application problems.
Real-timing processing of fiber bundle endomicroscopy images in Python using PyFibreBundle

Fiber imaging bundles allow the transfer of optical images from place-to-place along narrow and flexible conduits. Traditionally used extensively in medical endoscopy, bundles are now finding new applications in endoscopic microscopy and other emerging techniques. PyFibreBundle is an open-source Python package for fast processing of images acquired through imaging bundles. This includes detection and removal of the fiber core pattern by filtering or interpolation, and application of background and flat-field corrections. It also allows images to be stitched together to create mosaics and resolution to be improved by combining multiple shifted images. This paper describes the technical implementation of PyFibreBundle and provides example results from three endomicroscopy imaging systems: color transmission, monochrome transmission, and confocal fluorescence. This allows various processing options to be compared quantitatively and qualitatively, and benchmarking demonstrates that PyFibreBundle can achieve state-of-the-art performance in an open-source package. The paper demonstrates core removal by interpolation and mosaicing at over 100 fps, real-time multi-frame resolution enhancement and the first demonstration of real-time endomicroscopy image processing, including core removal, on a Raspberry Pi single board computer. This demonstrates that PyFibreBundle is potentially a valuable tool for the development of low-cost, high-performance fiber bundle imaging systems.
Promoting Explainability in Data-Driven Models for Anomaly Detection: A Step Toward Diagnosis

Anomaly detection has become a critical task in industry. Data-driven models are often used for anomaly detection due to their ability to learn patterns from data and identify behaviors that deviate from the learned patterns. Furthermore, they are simple to implement as they do not rely on complex physical models to make predictions. However, one major limitation of these models is their lack of explainability, which hinders the diagnosis of detected anomalies.&#x0D; Explainability provides transparency and interpretability, allowing stakeholders to understand the reasons for the detected deviation. In the absence of explainability, it is challenging to determine why a particular instance was classified as abnormal. Without an understanding of the underlying reason for the anomaly, it becomes difficult to prescribe a reliable diagnostic. This can result in missed opportunities for preventing or mitigating damage caused by the anomaly. Explainability can also help in detecting false positives and false negatives, especially, to distinguish between abnormal behaviors and sensor failures.&#x0D; Hydro-Quebec is the principal actor in electricity management in Quebec, Canada. The overwhelming majority of the production comes from hydroelectric generating units. Power grid sustainability then strongly depends on the efficient health supervision of these assets. In this study, we introduce a data-driven semi-supervised algorithm for anomaly detection, with emphasis on statistical explainability. This feature needs to be distinguished from the traditional explainable models, that build upon physics to interpret observations. Here, the purpose is to track the sources of deviations through statistics. This model does not belong to diagnosis tools, because its sole output is not sufficient to find the root causes of a problem. However, it makes a bridge toward such tools by providing clues about origin of failures.&#x0D; The algorithm performs in two-stages. First a model is trained to learn the normal behavior of the generating unit for a given set of operating conditions. This part involves clustering for data reduction and kriging for regression. Second, it compares the multidimensional prediction with the actual realization. It quantifies the deviation of the asset to its expected behavior and provides an explainable indicator for anomaly detection.&#x0D; After introducing the background foundations of the method, some examples are given that demonstrate the advantage of interpretability for support to operation and diagnosis. It will be shown how such an algorithm can be deployed in an operational environment and how it should be combined with other tools to improve assets health management.
Health Disparities Through Generative AI Models: A Comparison Study Using a Domain Specific Large Language Model

Health disparities are differences in health outcomes and access to healthcare between different groups, including racial and ethnic minorities, low-income people, and rural residents. An artificial intelligence (AI) program called large language models (LLMs) can understand and generate human language, improving health communication and reducing health disparities. There are many challenges in using LLMs in human-doctor interaction, including the need for diverse and representative data, privacy concerns, and collaboration between healthcare providers and technology experts. We introduce the comparative investigation of domain-specific large language models such as SciBERT with a multi-purpose LLMs BERT. We used cosine similarity to analyze text queries about health disparities in exam rooms when factors such as race are used alone. Using text queries, SciBERT fails when it doesn’t differentiate between queries text: “race” alone and “perpetuates health disparities.” We believe clinicians can use generative AI to create a draft response when communicating asynchronously with patients. However, careful attention must be paid to ensure they are developed and implemented ethically and equitably.
GAN-RXA: A Practical Scalable Solution to Receiver-Agnostic Transmitter Fingerprinting

Radio frequency fingerprinting has been proposed for device identification. However, experimental studies also demonstrated its sensitivity to deployment changes. Recent works have addressed channel impacts by developing robust algorithms accounting for time and location variability, but the impacts of receiver impairments on transmitter fingerprints are yet to be solved. In this work, we investigate the receiver-agnostic transmitter fingerprinting problem, and propose a novel two-stage supervised learning framework (RXA) to address it. In the first stage, our approach calibrates a receiver-agnostic transmitter feature-extractor. We also propose two deep-learning approaches (SD-RXA and GAN-RXA) in this first stage to improve the receiver-agnostic property of the RXA framework. In the second stage, the calibrated feature-extractor is utilized to train a transmitter classifier with only one receiver. We evaluate the proposed approaches on the transmitter identification problem using a large-scale WiFi dataset. We show that when a trained transmitter-classifier is deployed on new receivers, the RXA framework can improve the classification accuracy by 19.5%, and the outlier detection ROC AUC by 12.0% compared to a naive approach without calibration. Moreover, GAN-RXA can further increase the closed-set classification accuracy by 5.0%, and the outlier detection ROC AUC by 12.5% compared to the RXA approach.
Redefining School Libraries for the Digital Age: Developing Comprehensive Digital Collection Strategies

This study aims to analyze the strategy for developing digital collections in school libraries. The method used is qualitative research with data collection techniques including observation, interviews, and documentation. The research was conducted at the Widura Library of Vocational High School 3 Yogyakarta. The study involved three informants, consisting of the head librarian and school library staff. The results show that the school library already has a digital collection as part of its development. The process of developing digital collections in school libraries includes analyzing user needs, mapping digital collection sources, and evaluating development. The development of digital collections is still limited to obtaining electronic school book collections from the Ministry of Education. Some strategies that can be implemented by school libraries include collaborating with other libraries and downloading digital collections that are available for free from digital collection providers.
EXTRACURRICULAR ACTIVITIES IN THE FUNCTION OF IMPROVING PRACTICAL SKILLS OF FUTURE STUDENTS IN THE FIELD OF ЕLECTRICAL ENGINEERING AND COMPUTING

This paper is based on the authors’ many years of experience in teaching, organizing special courses and one-day workshops for different groups of students, as well as IEEESTEC students’ project conferences. The manuscript contains a brief description and results of the implementation and evaluation of the special course “Let’s put knowledge into practical work” for grammar school students. In order to continue their study and provide students with additional knowledge, the Arduino course was designed. Since 2018, by attending the course as an extracurricular activity, students gain new knowledge in the field of electronic components and microcontroller programming. Based on the conducted analyzes and evaluations, as well as current pedagogical trends, the authors give recommendations on how to overcome problems that may arise due to a lack of practical knowledge and increase students’ self-confidence and indicate the importance of a course designed in this way for those freshmen who want to acquire some new skills and knowledge.
Table detection for visually rich document images

Table Detection (TD) is a fundamental task to enable visually rich document understanding, which requires the model to extract information without information loss. However, popular Intersection over Union (IoU) based evaluation metrics and IoU-based loss functions for the detection models cannot directly represent the degree of information loss for the prediction results. Therefore, we propose to decouple IoU into a ground truth coverage term and a prediction coverage term, in which the former can be used to measure the information loss of the prediction results. Besides, considering the sparse distribution of tables in document images, we use SparseR-CNN as the base model and further improve the model by using Gaussian Noise Augmented Image Size region proposals and many-to-one label assignments. Results under comprehensive experiments show that the proposed method can consistently outperform state-of-the-art methods with different IoU-based metrics under various datasets and demonstrate that the proposed decoupled IoU loss can enable the model to alleviate information loss.
Understanding the Connection Between Hackers and Their Hacks: Analyzing USDOJ Reports for Hacker Profiles

Recently, it seems as if hacking-related stories can be found in the news every day.To study, and hopefully prevent, this new type of crime, the field of cyber criminology has emerged.This study adds to the existing cybercrime literature by examining hacking behavior specifically.It determines if there is a relationship between the age, gender, and nationality of hackers and characteristics of the cyberattacks that they perpetrate.To do this, this study analyzes 122 United States Department of Justice (USDOJ) press reports from January 2019 to December 2021.Some key results include the finding that older hackers and international hackers are more likely to build/maintain software in their cyberattacks.Also, older hackers are less likely to use follow-up access in their hacks compared to younger hackers.Finally, international hackers are more likely to have more sophisticated attacks than nationals, and individual hackers are less likely to have sophisticated hacks than those working in groups.Implications of this study are that law enforcement can create a more accurate profile of hackers based on their hacks to guide them in investigations.
Cybercrime and Digital Forensics: Bridging the gap in Legislation, Investigation and Prosecution of Cybercrime in Nigeria

The advancement of Information and Communication Technologies (ICT) opens new avenues and ways for cybercriminals to commit crime.The primary goal of this paper is to raise awareness regarding gaps that exist with regards to Nigeria's capabilities to adequately legislate, investigate and prosecute cases of cybercrimes.The major source of cybercrime legislation in Nigeria is an act of the National Assembly which is majorly a symbolic legislation rather than a full and active legislation.In perusing these avenues of inquiry, the authors seek to identify systemic impediments which hinder law enforcement agencies, prosecutors, and investigators from properly carrying out their duties as expected.