Reviewerly/exHarmony huggingface dataset:
Post-Processing Algorithm for Workplace Safety Risk Monitoring System Based on Deep-Learning Models

Surveillance cameras are ubiquitously used as a part of the safety risk monitoring system. Powered by the artificial intelligence (AI) technologies, the system started to detect the anomalies by implementing deep-learning detection models. However, missed or unexpected warnings are happening frequently. And thus this problem is considered as a major flaw to the AI safety risk monitoring system. This study proposes a post-processing algorithm called PDAR aiming to enhance the accuracy of workplace safety risk monitoring system powered by deep-learning models. The PDAR consists of 3 types of algorithms with 3 typical situations: making warning when an action or an object detected, making warning when an absence or an inaction detected and making warning when joint objects or actions detected. The warning accuracy is significantly increased with PDAR validated with intensive experiments. An improvement of warning accuracy from 55.15% to 98.53% was observed in the test result. Moreover, an evaluation framework is also introduced which evaluate the warning accuracy by considering the missed warning and unexpected warning equally since either a missed warning or an unexpected warning would make serious consequences in safety risk monitoring scenarios. The dataset used in the experiments are now open sourced at: www.github.com/freelectry/PDAR.
ANIMAL RANGER WEBSITE DESIGN: ANIMAL ADOPTION PLATFORM

Currently, both government and non-government organizations such as animal shelters performing rescue and adoption of stray animals. Although the ecosystem is not running well, some animal lovers still adopt stray animals from the shelter. Information that is limited and unclear is also faced by shelters and animal lovers in performing rescue and adoption. We created the Animal Ranger website based on problems. Analysis and system planning are based on interviews with animal shelters and questionnaires for animal lovers, using the UML method. Animal Ranger website was expected to encourage stray animal rescue and adoption of the ecosystem by animal lovers and animal shelters
Semi-supervised Learning for Source Code Function Classification Using Hierarchical Density-Based Clustering

In the field of software development, source code topic classification is critical for program comprehension and software maintenance. Source code topic classification is a challenging task that involves classifying software artifacts into predefined topics. Traditional supervised learning approaches require a large amount of labeled data, which is often expensive and time-consuming to obtain. In this paper, we explore the application of semi-supervised learning techniques for source code and propose a semi-supervised learning approach that leverages both labeled and unlabeled data to improve the accuracy of source code topic classification. We utilize hierarchical density-based clustering to group similar source code artifacts, and then apply semi-supervised learning techniques to assign topics to these code functions. Experimental results on a real-world dataset demonstrate the effectiveness of our proposed approach in achieving high accuracy and less complexity, which is due to using semi-supervised learning.
Runtime Safety Assurance of Autonomous Vehicles

The paper deals with runtime safety assurance of autonomous vehicles (AVs), First, we closely examine the current state of AV technology, highlighting the incredible progress made by industry leaders in ensuring minimal risks in their vehicles. Our analysis of reported disengagements in AVs reveals interesting insights, showing that most disengagements are initiated by human test drivers rather than by the AV systems themselves and that they often occur on streets and freeways. Next, to ensure AVs' safety, we explored various safety architectures, including 1oo2, 1oo2D, and 2oo3, and thoroughly evaluated their reliability. We also introduce the concept of runtime safety assurance (RTA), a system that closely monitors the AV's state and employs emergency measures through a backup controller if the safety conditions are breached. Such a safety architecture - having separate safety and performance loops - has several benefits, which are highlighted in this paper. Furthermore, we emphasize the crucial role of robust sensing and perception systems in AVs, showing that multi-modal sensing and sensor fusion can effectively enhance the system reliability. We validated the reliability of these systems through extensive software testing and comparisons with ground-truth data. Finally, we explore fault-detection techniques for AVs by employing simulations to detect and handle faults in the control system.
Herds of Dumb Models: Deriving Assured Classification From Unbiased Weak Models

Artificial Neural Networks (ANNs) are becoming pervasive technology and moving into all realms of socio-technical systems. At the same time, we also have seen an increased interest in ANNs for the functional safety domain. However, their limited assurance hinders their deployment, becoming a, if not the, show-stopper. Resolving the assurance of ANNss, in general, and Convolutional Neural Networks (CNNs)s, in particular, is the target of our research work. First results, while effective still quite inefficient, are reported. The approach presented is based on a novel concept of statistically analyzing the consensus agreement over bias-free model herds. While these non-optimized models are individually weak (or dumb), a herd of these models is employed to extract a common abstract view. In other words, we propose grouping a number of models with the aim of examining their consensus over groups of herds and, hence, being able not only to gauge assurance but also to provide quantitative assurance on new images. The main contribution of our work is to demonstrate how herds of weak models have the ability to identify out-of-scope images (i.e., images of untrained classes) and declare that they do not know what class an image belongs to, stating “I don't know” explicitly.
Importance Analysis Method Application for Feature Selection in a Classification

Classification is often used method in Machine Learning applications. Different factors cause the efficiency of this method. One of the very important factors is the quality of initial data, which among other things, includes a sufficient, but not excessive, number of input attributes. The procedure for defining a sufficient number of input attributes is known as feature selection. There are approaches to feature selection that form three groups: filter, wrapper, and embedded methods. Filters are used for any classifier and have lower computational complexity. The wrapper methods depend on the algorithm of classifier induction. A classification algorithm evaluates a subset of selected attributes according to a classification measure. These methods have high performance, but they require a lot of computation time to execute. Embedded methods perform attribute selection in the process of the classifier induction and depend on the classifier and algorithm of its induction. In this paper, we propose a new wrapper method based on the well-known approach in reliability analysis which is importance analysis. The proposed method's advantage is the independency of the classifier type and acceptable computational complexity. Therefore, it can be used for any type of classifier and the selected attributes are evaluated by the classification measures that increase the classifier's efficiency.
Integrity Assessment of Maritime Object Detection Impacted by Partial Camera Obstruction

The performance and usage of machine learning based object detection in visual data has increased significantly in the past decade. This technology can enable automated decision making in various applications, extracting key information in real time from a camera-based monitoring solution. However, to ensure the resilience and dependability in a safety-critical application, the intelligence provided by the monitoring solution must be reliable. In this context, understanding the impact of potential disturbances in the object detection performance is important. This work conducts an integrity assessment of maritime object detection under partial camera obstruction events. A subset of the ShipSG dataset, which contains thousands of annotated and segmented ship images from a harbor, and the Faster-RCNN object detection algorithm, trained on the seven different ship classes of the dataset, were used. The effect of simulated obstructions, of various intensities and configurations, on the false-positive, misclassification, false negative ratios, and associated detection score distributions were investigated. The outcome suggests that the use of a partial obstruction detection step, and the consideration of that information, can mitigate the consequences of faults in the object detection.
A Military IdAM System Based on SSI and ORCON

Some access control models are used in different fields, including the military sector. An example is the Mandatory Access Control model where resources may be categorized or marked. These models are frequently paired with Identity and Access Management systems, which control the processes of identification, authentication, and authorization. IdAM systems evolved from centralized authority to more user-centric models like Self-Sovereign Identity. The goal of this research is to employ an access control model in conjunction with SSI to manage access to verifiable credentials associated with the identities of individuals or units. To achieve this, we suggest using an ORCON paradigm, in which the creator of verifiable credentials establishes an access policy to improve privacy and protection from unauthorized access attempts.
Anomaly Detection in a Micro-CHP Using Convolutional Autoencoders and Fine Tuning for Domain Adaptation

This paper presents a fine-tuning application for anomaly detection in a YANMAR micro-cogeneration system, employing a convolutional auto encoder with 1- D convolutional layers to capture temporal dynamics. The proposed method aims to address the challenge of detecting anomalies, specifically focusing on gas pressure fluctuations that could potentially lead to CHP breakdown. Initially, the autoencoder was trained on six weeks of normal operational data, sampled at 15-minute intervals, to learn the underlying patterns of system behavior. However, the original model's performance led to undesired false positive detections, mainly when applied to new data collected during a different season, approximately three years after the initial training, which was conducted during a summer month. To tackle the issue of false positives, a fine-tuning process was introduced. The existing autoencoder model was updated using one week of newly sampled data, also collected at 15-minute intervals, to adapt the model to the system's current behavior. This fine-tuning process allowed the autoencoder to better capture the system's dynamics during the different seasons, effectively reducing false alarms. Experimental results demonstrate the effectiveness of the proposed method. The presence of an anomaly in gas pressure, potentially leading to CHP malfunction, was successfully detected almost three months in advance. Additionally, false anomaly detections related to the whole system and to not relevant signals were effectively filtered out by the fine-tuned model. In conclusion, this study highlights the importance of fine-tuning based on recent data to enable the model to adapt to changing conditions and mitigates the impact of seasonality on anomaly detection accuracy. The findings of this research have significant implications for enhancing the reliability and maintenance of micro-CHP systems in real-world applications.
A Formal Account for Reasoning About Uncertain Probabilities in Intelligence Analysis

In the sixth assessment of the international panel on climate change (IPCC) one can read sentences such as “cumulative net CO2 emissions over the last decade (2010-2019) are about the same size as the 11 remaining carbon budget likely to limit warming to 1.5C (medium confidence).” Such reports are very common in the intelligence community, but a formal account for dealing with the degree of belief and of confidence is still missing. In this paper, we propose a formal account for allowing such degrees of belief and the associated confidence to be used to label arguments in abstract argumentation settings. We focus on the task of probabilistic inference over a chosen query building upon Sato’s distribution semantics which has been already shown to encompass a variety of cases including the semantics of Bayesian networks. Borrowing from the vast literature on such semantics, we examine how such tasks can be dealt with in practice when considering uncertain probabilities.
Target Detection of Dark web Hidden Services Network through MoniTOR and Social Network Analysis

The use of TOR tends to offer more anonymity than the surface web, especially between specific users and individual site hosts. However, the connecting practices used by individuals who build TOR sites establish a structure that not only enables users to navigate between various material sources but also permits examination of the Dark Net’s core organizational principles. The study employs a new Dark web navigation tool (MoniTOR) in conjunction with a tried-and-true approach used in criminological research. The purpose of the research is to use MoniTOR’s data collection technique to reconstruct a network of Dark web actors or sites using Social Network Analysis. SNA is an effective methodology that can be used to examine the connections and interconnections inside the Dark web. A network of more than 682,000 TOR hidden services (including domains and subdomains) was found by looking at connections between TOR sites that MoniTOR had logged. More than 500,000 of these domains joined to form a single enormous component that accounted for 73 percent of observed nodes and 79 percent of reported connections between sites. This information can be used to have a better understanding of how hidden services of interest are positioned in the Dark web, even spotting new potential threats or illegal activities. Through this evidence-based approach, it will be possible to apply a targeted network disruption strategy.
ChatGPT Act as an Intelligence Officer

This paper explores the potential of Large Language Models (LLMs), specifically ChatGPT, to facilitate the work of intelligence officers. LLMs have demonstrated remarkable capabilities in natural language processing and generation, making them valuable tools for information retrieval, analysis, and decision-making. This paper presents an initial overview of the capabilities of LLMs in intelligence operations. Through a case study on ChatGPT, we illustrate how intelligence officers can leverage LLMs to enhance their work, including data collection, fusion, analysis, report generation, and information sharing. The results showcase the vast potential and the limits of LLMs in supporting intelligence officers in their complex and demanding tasks.
Coordinated Trajectory Planning for Ground-Based Multi-Robot Systems in Exploration and Surveillance Missions

This paper presents a solution to the problem of coordinated trajectory planning for a multi-robot system used in reconnaissance and surveillance missions. The algorithm ensures collision avoidance between robotic units deployed in an obstacle environment by using a set-theoretic approach that takes into account the closed-loop dynamics of trajectory tracking errors. The proposal explicitly considers the performance of the control algorithm, possible state and actuation constraints, and external disturbances that may affect the robots’ motion capabilities. The proposed method for coordinated motion planning was tested in an numerical simulation campaign involving two remotely controlled tracked mobile robots operating in an indoor environment with obstacles.
On the aggregation functions used in fuzzy signatures based medical image analysis

The paper proposes the use of fuzzy signatures for modeling and analysis of pre-processed medical images, as an example, CT images of the liver are analyzed. Fuzzy signatures are used for the case of distinguishing larger and smaller malignant lesions from each other and from other (benign) nodular diseases in liver computed tomography images. As computed tomography phases are sometimes missing, the treatment of missing data is also briefly addressed. As the size of the malignant lesion influences its manifestation on the images, separate sub-signatures are developed for large and small lesions with the size being a separate layer of the signature. From the medical experts’ point of view besides the tree structure of the signature it is crutial to determine the aggregations themselves, which model the ways experts fuse and combine the available information. For the subtrees for small and large lesions in the sub-roots algebraic multiplication seems to be the best fitting t-norm, while in the subtree weighted means.
Boosting the automated Information Processing for Reconnaissance

The emerging complexities due to an increasing availability of data sources and advanced technologies for data and information processing in the defense sector require careful considerations. In this study, we looked at the different aspects related to the exploitation of the advancing technologies for the reconnaissance operations. Specifically, system architecture, data fusion, human in the loop and knowledge modeling are explored, with an exemplary case of Large Language Models (LLM) on reconnaissance data is shown.
Detecting Targeted Phishing Websites for Brand Protection and Cyber Defence Using Computer Vision

Cybercrime continues to pose a significant threat to modern society, requiring a solid emphasis on cyber-attack prevention, detection and response by civilian and military organisations aimed at brand protection. This study applies a novel framework to identify, detect and mitigate phishing attacks, leveraging the power of computer vision technology and artificial intelligence. The primary objective is to automate the classification process, reducing the dwell time between detection and executing courses of action to respond to phishing attacks. When applied to a real-world curated dataset, the proposed classifier achieved relevant results with an F1-Score of 95.76% and an MCC value of 91.57%. These metrics highlight the classifier’s effectiveness in identifying phishing domains with minimal false classifications, affirming its suitability for the intended purpose. Future enhancements include considering a fuzzy logic model that accounts for the classification probability in conjunction with the domain creation date and the uniqueness of downloaded resources when accessing the website or domain.
FLWB: a Workbench Platform for Performance Evaluation of Federated Learning Algorithms

Federated learning is a technique that allows to collaboratively train a shared machine learning model across distributed devices, where the data are stored locally on devices. Most innovations the research community proposes in federated learning are tested through custom simulators. An analysis of the literature shows the lack of workbench platforms for the performance evaluation of FL projects. This paper aims to fill the gap by presenting FLWB, a general-purpose, configurable, and scalable workbench platform for easy deployment and performance evaluation of Federated Learning projects. Through experiments, we demonstrated the ease with which a FL system can be implemented and deployed with FLWB.
Evaluating defense services performance on military cloud continuum systems

A Battlefield Management System (BMS) is a tactical Command and Control system which supports military operations on the battlefield to provide a continuous and coherent representation of the state of a tactical scenario. The implementation of a BMS may benefit from the possibilities offered by Cloud Continuum technologies, while considering the security requirements when exploiting their infrastructure. Cloud Continuum may be used in place of the existing solutions, provided that proper performance evaluation techniques are available to assess its effectiveness and account for the impact of security constraints.In this paper we propose a modeling approach for a Battlefield Management System based on Cloud Continuum technologies and a performance evaluation framework to support its design and management before and during a mission.
Integrating Blockchain for Enhanced Coordination and Security in Semi-Centralized Robotic Swarms

Swarm robotics is a field that studies the design and coordination of large groups of robots that can perform complex tasks through collective behaviors. One of the challenges in swarm robotics is to find the optimal balance between decentralization and centralization, as each approach has its advantages and disadvantages. In this paper, we propose a semi-centralized framework that integrates blockchain technology for enhanced coordination and security in swarm robotic systems. Our framework uses a centralized control unit that serves as a coordination hub for the robotic swarm while also leveraging blockchain technology to provide a secure and distributed ledger for data storage and communication. Thus, blockchain technology mitigates the reduced resilience, which is the price to pay for introducing semi-centralized control in robotic swarms, by ensuring the distributed management of data and programs. In the paper, we exemplify the application of our semi-centralized framework to optimize a path-finding problem for a swarm of robots. We also characterize a series of levels in allocating the blockchain infrastructure to cope with the corresponding levels of security threats.
Innovations in Explainable AI : Bridging the Gap Between Complexity and Understanding

The integration of Artificial Intelligence (AI) into various domains has witnessed remarkable advancements, yet the opacity of complex AI models poses challenges for widespread acceptance and application. This research paper delves into the field of Explainable AI (XAI) and explores innovative strategies aimed at bridging the gap between the intricacies of advanced AI algorithms and the imperative for human comprehension. We investigate key developments, including interpretable model architectures, local and visual explanation techniques, natural language explanations, and model-agnostic approaches. Emphasis is placed on ethical considerations to ensure transparency and fairness in algorithmic decision-making. By surveying and analyzing these innovations, this research contributes to the ongoing discourse on making AI systems more accessible, accountable, and trustworthy, ultimately fostering a harmonious collaboration between humans and intelligent machines in an increasingly AI-driven world.