Reviewerly/exHarmony huggingface dataset:
Adversarial agent-learning for cybersecurity: a comparison of algorithms

Abstract We investigate artificial intelligence and machine learning methods for optimizing the adversarial behavior of agents in cybersecurity simulations. Our cybersecurity simulations integrate the modeling of agents launching Advanced Persistent Threats (APTs) with the modeling of agents using detection and mitigation mechanisms against APTs. This simulates the phenomenon of how attacks and defenses coevolve. The simulations and machine learning are used to search for optimal agent behaviors. The central question is: under what circumstances, is one training method more advantageous than another? We adapt and compare a variety of deep reinforcement learning (DRL), evolutionary strategies (ES) and Monte Carlo Tree Search methods within Connect 4, a baseline game environment, and on both a simulation supporting a simple APT threat model, SNAPT, as well as CyberBattleSim, an open-source cybersecurity simulation. Our results show that when attackers are trained by DRL and ES algorithms, as well as when they are trained with both algorithms being used in alternation, they are able to effectively choose complex exploits that thwart a defense. The algorithm that combines DRL and ES achieves the best comparative performance when attackers and defenders are simultaneously trained, rather than when each is trained against its non-learning counterpart.
Simulation Driven AI: From Artificial to Actual and Vice Versa

In this perspective, we discuss the important role of simulations in building state-of-the-art artificial intelligence (AI) systems. We first explain why simulations become vital in building complex AI systems. Then, we study some challenges and candidate solutions related to simulation-based AI systems. Finally, we discuss future research directions in this field.
A systematic literature review on automatic website generation

Since machine learning became a prominent feature in the modern-day computing landscape, the urge to automate processes has increased. One such process of particular interest has been the automatic generation of websites based on user intention. Though the requirement of such automatic generation is a modern-day need, the quality of the automatic generation still provides a unique set of challenges. As such, to analyze these unique challenges and viable opportunities in automatic website generation, this survey systematically reviews research on the topics of automatic website generation. The analysis initially segments state-of-the-art into three categories based on the dominant strategy used for automatic generation. These strategies are examples-based, mock-up-driven, and artificial intelligence-driven automatic website generation. When considering the example-based strategy, the emphasis is on analyzing how manual design aspects of a professionally developed website are incorporated into generation models and the challenges that arise. Similarly, transformation methods from website visual design into functional GUI code are investigated for the mock-up-driven strategy with a particular reference to the six underlying conversion mechanisms. Finally, artificial intelligence website builders are analyzed based on their ability to build customizable websites to user preferences. Based on this systematic review of 47 research works on the three dominant strategies, this survey outlines unique challenges and future research endeavors that researchers would encounter when developing models that generate websites automatically and provides insights to researchers on selecting a website generation strategy based on user intention appropriately.
Emerging challenges and perspectives in Deep Learning model security: A brief survey

The widespread adoption of Artificial Intelligence and Machine Learning tools opens to security issues that can raise and occur when the underlying ML models integrated into advanced services. The models, in fact, can be compromised in both the learning and the deployment stage. In this work, we provide an overview of some strenuous security risks and concerns that can affect such models. Our focus is on the research challenges and defense opportunities of the underlying ML framework, when it is devised in specific contexts that can compromise its effectiveness. Specifically, the survey provides an overview of the following emerging topics: Model Watermarking, Information Hiding issues and defense opportunities, Adversarial Learning and model robustness, and Fairness-aware models.
Comparison of different predictive models and their effectiveness in sunspot number prediction

Abstract Human activities and health are significantly influenced by solar activity. The sunspot number is one of the most commonly used measures of solar activity. The solar cycle’s quasi-periodic nature makes it an excellent choice for time series forecasting. Four models include three singular models, consisting of Long Short-Term Memory (LSTM), AutoRegressive Integrated Moving Average (ARIMA), and Seasonal AutoRegressive Integrated Moving Average (SARIMA), as well as a hybrid model were implemented to forecast maximum sunspot number of cycles 25 and 26. The hyperparameters of the singular models were optimized using Bayesian optimization. The LSTM-ARIMA hybrid model was able to achieve the best performance. The outstanding results of the LSTM-ARIMA model shows the potential of hybrid methods in improving the overall performance. Moreover, the LSTM model was able to outperform the ARIMA model, which demonstrates the ability of LSTM networks in learning from time-series data. The final model forecasts a peak sunspot number of 137.04 for Solar Cycle 25 in September 2024 and 164.3 for Solar Cycle 26 in December 2034, which is comparable to the National Aeronautics and Space Administration’s (NASA) prediction of 134.4 in October 2024 and 161.2 in December 2034.
Multi-task semi-supervised crowd counting via global to local self-correction

In this paper, we propose a novel multi-task semi-supervised method. To sufficiently exploit massive unlabeled data, multi-task pseudo-labels and global to local self-correction strategy are proposed. Specifically, labeled images and massive amounts of unlabeled images with proposed multi-task pseudo-labels are leveraged for model optimization. The density level of the whole image is predicted in classification task. The density is estimated in density regression task. The crowd area is segmented out in segmentation task. To suppress incorrect predictions caused by the inevitable noises from some unlabeled data misleading the model, the counting relationship between classification task and density task is exploited to propose the global self-correction strategy, and the semantic consistency between density task and segmentation task is mined to propose the local self-correction strategy. The classification task and segmentation task contribute in generating the final highly refined density map from the density task. Extensive experiments on six benchmark datasets indicate the superiority of our method over the SOTA methods in semi-supervised paradigm.
Attack Path Detection for IIoT Enabled Cyber Physical Systems: Revisited

In this paper, we propose a generic vulnerability and risk assessment method for IIoT-enabled critical systems. We focus on reducing risk factors and vulnerable structures in order to provide security issues for the IIoT and enabled complex systems. In addition to the existing risk assessment and related methods, we represent the IIoT-enabled network topology as a directed graph, and we develop an attack tree-based approach using graph theory. We assume that each device is a potential critical node due to the existing vulnerabilities, which are defined in the National Vulnerability Database (NVD), and we establish directed relations between nodes, considering cyber and physical interactions. We improve existing attack path-identifying methods using the Depth First Search (DFS) algorithm to find all the paths from the source to the target nodes. In the generated topology, each node has the pre-assigned Common Vulnerability Scoring System (CVSS) scores acting as a weight. We also implement the Floyd-Warshall algorithm to identify path risk levels. Finally, we assess the identified vulnerable paths from varying source and target pairs via path and node-reducing procedures, considering risk thresholds. We perform our simulation on a custom Python simulator, considering the transportation and supply sectors. We compare our results with the previous ones. Simulation results show that our proposed methods and procedures outperform existing risk assessment and filtering methods in terms of running time and attack path identification and filtering.
Textual Feature Extraction Using Ant Colony Optimization for Hate Speech Classification

Feature selection and feature extraction have always been of utmost importance owing to their capability to remove redundant and irrelevant features, reduce the vector space size, control the computational time, and improve performance for more accurate classification tasks, especially in text categorization. These feature engineering techniques can further be optimized using optimization algorithms. This paper proposes a similar framework by implementing one such optimization algorithm, Ant Colony Optimization (ACO), incorporating different feature selection and feature extraction techniques on textual and numerical datasets using four machine learning (ML) models: Logistic Regression (LR), K-Nearest Neighbor (KNN), Stochastic Gradient Descent (SGD), and Random Forest (RF). The aim is to show the difference in the results achieved on both datasets with the help of comparative analysis. The proposed feature selection and feature extraction techniques assist in enhancing the performance of the machine learning model. This research article considers numerical and text-based datasets for stroke prediction and detecting hate speech, respectively. The text dataset is prepared by extracting tweets consisting of positive, negative, and neutral sentiments from Twitter API. A maximum improvement in accuracy of 10.07% is observed for Random Forest with the TF-IDF feature extraction technique on the application of ACO. Besides, this study also highlights the limitations of text data that inhibit the performance of machine learning models, justifying the difference of almost 18.43% in accuracy compared to that of numerical data.
Modelling and Analysis of Hybrid Transformation for Lossless Big Medical Image Compression

Due to rapidly developing technology and new research innovations, privacy and data preservation are paramount, especially in the healthcare industry. At the same time, the storage of large volumes of data in medical records should be minimized. Recently, several types of research on lossless medically significant data compression and various steganography methods have been conducted. This research develops a hybrid approach with advanced steganography, wavelet transform (WT), and lossless compression to ensure privacy and storage. This research focuses on preserving patient data through enhanced security and optimized storage of large data images that allow a pharmacologist to store twice as much information in the same storage space in an extensive data repository. Safe storage, fast image service, and minimum computing power are the main objectives of this research. This work uses a fast and smooth knight tour (KT) algorithm to embed patient data into medical images and a discrete WT (DWT) to protect shield images. In addition, lossless packet compression is used to minimize memory footprints and maximize memory efficiency. JPEG formats' compression ratio percentages are slightly higher than those of PNG formats. When image size increases, that is, for high-resolution images, the compression ratio lies between 7% and 7.5%, and the compression percentage lies between 30% and 37%. The proposed model increases the expected compression ratio and percentage compared to other models. The average compression ratio lies between 7.8% and 8.6%, and the expected compression ratio lies between 35% and 60%. Compared to state-of-the-art methods, this research results in greater data security without compromising image quality. Reducing images makes them easier to process and allows many images to be saved in archives.
GeoGraphVis: A Knowledge Graph and Geovisualization Empowered Cyberinfrastructure to Support Disaster Response and Humanitarian Aid

The past decade has witnessed an increasing frequency and intensity of disasters, from extreme weather, drought, and wildfires to hurricanes, floods, and wars. Providing timely disaster response and humanitarian aid to these events is a critical topic for decision makers and relief experts in order to mitigate impacts and save lives. When a disaster occurs, it is important to acquire first-hand, real-time information about the potentially affected area, its infrastructure, and its people in order to develop situational awareness and plan a response to address the health needs of the affected population. This requires rapid assembly of multi-source geospatial data that need to be organized and visualized in a way to support disaster-relief efforts. In this paper, we introduce a new cyberinfrastructure solution—GeoGraphVis—that is empowered by knowledge graph technology and advanced visualization to enable intelligent decision making and problem solving. There are three innovative features of this solution. First, a location-aware knowledge graph is created to link and integrate cross-domain data to make the graph analytics-ready. Second, expert-driven disaster response workflows are analyzed and modeled as machine-understandable decision paths to guide knowledge exploration via the graph. Third, a scene-based visualization strategy is developed to enable interactive and heuristic visual analytics to better comprehend disaster impact situations and develop action plans for humanitarian aid.
Learning about Victims of Holocaust in Virtual Reality: The Main, Mediating and Moderating Effects of Technology, Instructional Method, Flow, Presence, and Prior Knowledge

The goal of the current study was to investigate the effects of a virtual reality (VR) simulation of Anne Frank’s hiding place on learning. In a 2 × 2 experiment, 132 middle school students learned about the living conditions of Anne Frank, a girl of Jewish heritage during the Second World War, through desktop VR (DVR) and head-mounted display VR (HMD-VR) (media conditions). Approximately half of each group engaged in an explorative vs. an expository learning approach (method condition). The exposition group received instructions on how to explore the hiding place stepwise, whereas the exploration group experienced it autonomously. Next to the main effects of media and methods, the mediating effects of the learning process variables of presence and flow and the moderating effects of contextual variables (e.g., prior technical knowledge) have been analyzed. The results revealed that the HMD-VR led to significantly improved evaluation, and—even if not statistically significant—perspective-taking in Anne, but less knowledge gain compared to DVR. Further results showed that adding instructions and segmentation within the exposition group led to significantly increased knowledge gain compared to the exploration group. For perspective-taking and evaluation, no differences were detected. A significant interaction between media and methods was not found. No moderating effects by contextual variables but mediating effects were observed: For example, the feeling of presence within VR can fully explain the relationships between media and learning. These results support the view that learning processes are crucial for learning in VR and that studies neglecting these learning processes may be confounded. Hence, the results pointed out that media comparison studies are limited because they do not consider the complex interaction structures of media, instructional methods, learning processes, and contextual variables.
Cognitive Video Surveillance Management in Hierarchical Edge Computing System with Long Short-Term Memory Model

Nowadays, deep learning (DL)-based video surveillance services are widely used in smart cities because of their ability to accurately identify and track objects, such as vehicles and pedestrians, in real time. This allows a more efficient traffic management and improved public safety. However, DL-based video surveillance services that require object movement and motion tracking (e.g., for detecting abnormal object behaviors) can consume a substantial amount of computing and memory capacity, such as (i) GPU computing resources for model inference and (ii) GPU memory resources for model loading. This paper presents a novel cognitive video surveillance management with long short-term memory (LSTM) model, denoted as the CogVSM framework. We consider DL-based video surveillance services in a hierarchical edge computing system. The proposed CogVSM forecasts object appearance patterns and smooths out the forecast results needed for an adaptive model release. Here, we aim to reduce standby GPU memory by model release while avoiding unnecessary model reloads for a sudden object appearance. CogVSM hinges on an LSTM-based deep learning architecture explicitly designed for future object appearance pattern prediction by training previous time-series patterns to achieve these objectives. By referring to the result of the LSTM-based prediction, the proposed framework controls the threshold time value in a dynamic manner by using an exponential weighted moving average (EWMA) technique. Comparative evaluations on both simulated and real-world measurement data on the commercial edge devices prove that the LSTM-based model in the CogVSM can achieve a high predictive accuracy, i.e., a root-mean-square error metric of 0.795. In addition, the suggested framework utilizes up to 32.1% less GPU memory than the baseline and 8.9% less than previous work.
Online attendance system based on facial recognition with face mask detection

This paper presents an online system for recording attendance based on facial recognition incorporating facial mask detection. The main objective of this project is to develop an effective attendance system based on face recognition and face mask detection, and to provide this service online through a browser interface. This would allow any user to use this system without the need to install special software. They simply need to open the interface of this system in a browser through any terminal. Recording attendance information online allows data to be easily recorded in a centralized online database. Since faces are used as biometric signatures in this project, all users registered in the system will have their profiles loaded with their face-images samples. Initially, before face recognition can be done, the model training phase based on SVM will be carried out, mainly to develop a trained model that can perform face recognition. A set of synthetic data will also be used to train the same model so that it can perform identification for users wearing face masks. The server application is coded in Python and uses the Open-Source Computer Vision (OpenCV) library for image processing. For web interfaces and the database, PHP and MySQL are used. With the integration of Python and PHP scripting programs, the developed system will be able to perform processing on online servers, while being accessible to users through a browser from any terminal. According to the results and analysis, an accuracy of about 81.8% can be achieved based on a pre-trained model for face recognition and 80% for face mask detection.
Data-driven clustering and Bernoulli merging for the Poisson multi-Bernoulli mixture filter

This paper proposes a clustering and merging approach for the Poisson multi-Bernoulli mixture (PMBM) filter to lower its computational complexity and make it suitable for multiple target tracking with a high number of targets. We define a measurement-driven clustering algorithm to reduce the data association problem into several subproblems, and we provide the derivation of the resulting clustered PMBM posterior density via Kullback-Leibler divergence minimisation. Furthermore, we investigate different strategies to reduce the number of single target hypotheses by approximating the posterior via merging and inter-track swapping of Bernoulli components. We evaluate the performance of the proposed algorithm on simulated tracking scenarios with more than one thousand targets.
Spatial–Temporal Dynamic Graph Attention Network for Skeleton-Based Action Recognition

Human body skeleton, acting as a spatiotemporal graph, is increasing attentions of researchers to adopt graph convolutional networks (GCN) to mine the discriminative features from skeleton joints. However, one of GCN’s flaws is its inability to handle long-distance reliance between joints. In this regard, graph attention network (GAT) was recently suggested, which combines graph convolutions with a self-attention mechanism to extract the most informative joint of a human skeleton and increase the model accuracy. However, GAT can compute only static attention: for each query node, the attention rank is same which severely hurts and limits the expressivity of an attention mechanism. In this work, we present a spatial-temporal dynamic graph attention network (ST-DGAT) to learn the spatial-temporal patterns of skeleton sequences. For dynamic graph attention, we tweak the order of weighted vector operations in GAT, our approach achieves a global approximate attention function, making it strictly superior to GAT. Experiments show that by fixing the order of internal operation of GAT the proposed model achieved better action classification results while maintaining the same computing cost as GAT. The proposed framework has been evaluated on well-known publicly available large-scale datasets NTU60, NTU120, and Kinetics-400, which notably outperforms state-of-the-art (SOTA) results with an accuracy of 96.4%, 88.2%, and 61.0%, respectively.
Adoption of Blockchain-Fog-IoMT Framework in Healthcare 4.0 Digital Revolution

Healthcare is the fastest growing sector that implements the latest technologies for providing healthcare services. Most of the IoMT healthcare systems are using cloud-based systems for providing smart healthcare services. Fog Computing is introduced in loT-based healthcare environments to reduce the additional computing and storage required by cloud servers. Healthcare 4.0 focuses on a smart environment that guarantees security, privacy, reliability, integrity, easy access, trust, transparency, faster processing, etc. Blockchain-integrated IoT-fog systems are used for providing security, and privacy and avoiding single-point failure in healthcare systems. In this paper, we have proposed a Blockchain-Fog-IoMT framework for faster processing of healthcare data and reducing the storage and processing overhead on the cloud server. We have also minimized the latency, resource utilization and response time with the implementation of fog computing at the nearest network of the user devices. Ethereum Blockchain and smart contract has been integrated with our system to address the security, privacy, scalability, transparency, reliability, and interoperability challenges. Block validation and transaction validation are done with the use of proof-of-work. Moreover, this model helps to create a decentralized platform for providing healthcare services, facilitates remote-patient tracking and monitoring, and is cost-effective.