Reviewerly/exHarmony huggingface dataset:
Small Dataset, Big Gains: Enhancing Reinforcement Learning by Offline Pre-Training with Model-Based Augmentation

Authors:
raw_author_name: Girolamo Macaluso
raw_affiliation_string: Media Integration and Communication Center, University of Florence, 50134 Florence, Italy
raw_author_name: Andrew D. Bagdanov
raw_affiliation_string: Media Integration and Communication Center, University of Florence, 50134 Florence, Italy

Abstract: Offline reinforcement learning leverages pre-collected datasets of transitions to train policies. It can serve as an effective initialization for online algorithms, enhancing sample efficiency and speeding up convergence. However, when such datasets are limited in size and quality, offline pre-training can produce sub-optimal policies and lead to a degraded online reinforcement learning performance. In this paper, we propose a model-based data augmentation strategy to maximize the benefits of offline reinforcement learning pre-training and reduce the scale of data needed to be effective. Our approach leverages a world model of the environment trained on the offline dataset to augment states during offline pre-training. We evaluate our approach on a variety of MuJoCo robotic tasks, and our results show that it can jumpstart online fine-tuning and substantially reduce—in some cases by an order of magnitude—the required number of environment interactions.
An FPGA‐based tool for supporting the design, modeling, and evaluation of hybrid object recognition systems on computer engineering courses

Authors:
raw_author_name: Enrique Guzmán‐Ramírez
raw_affiliation_string: Instituto de Electrónica y Mecatrónica Universidad Tecnológica de la Mixteca Oaxaca Mexico
raw_author_name: Ivan Garcia
raw_affiliation_string: División de Estudios de Posgrado Universidad Tecnológica de la Mixteca Oaxaca Mexico
raw_author_name: Carla Pacheco
raw_affiliation_string: División de Estudios de Posgrado Universidad Tecnológica de la Mixteca Oaxaca Mexico

Abstract: Abstract The field of computer vision is characterized by computationally intensive algorithms and techniques with strict real‐time requirements. Field programmable gate arrays (FPGAs) are based on a concurrent paradigm which allows the design of efficient hardware architectures and has positioned FPGAs as an ideal device for implementing compute‐intensive applications. For this reason, FPGA technology has had a great impact in areas such as computer vision, where one of the main objectives for researchers working in this field is to create efficient automatic object recognition systems. Therefore, the need to provide undergraduates with the necessary skills to design FPGA‐based object recognition systems is evident. With this aim in mind, it is essential that specialization courses related to the design of these systems include the required resources for the student to apply the theoretical knowledge in solving practical problems. In this article, we present a development tool designed to help students, teachers, and researchers during the design‐modeling‐implementation process of object recognition systems based on FPGAs. The proposed tool operates under a modular approach as this facilitates the working on any of the phases of a recognition system and it is considered as a hybrid because the other phases can be developed using a software language. An empirical evaluation involving undergraduates enrolled in a Computer Engineering program was conducted to create a hardware architecture for the DAISY descriptor that uses the homogeneous features of objects immersed in images to produce an efficient representation. By considering similar descriptors such as Scale‐Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG), DAISY is computed by convolving orientation maps instead of using weighted sums of gradient norms, which offers the same kind of invariance at a lower computational cost for the dense case. The results obtained during such an evaluation indicated that students consider this FPGA‐based tool to be an alternative to receiving practical training on designing systems for solving problems related to the area of object recognition.
A real-time wearable AR system for egocentric vision on the edge

Authors:
raw_author_name: Iason Karakostas
raw_author_name: Despoina Gavgiotaki
raw_author_name: Zinovia Stefanidi
raw_author_name: Ioannis Pastaltzidis
raw_author_name: Grigorios Tsipouridis
raw_author_name: Nikolaos Kilis
raw_author_name: Konstantinos C. Apostolakis
raw_author_name: Stavroula Ntoa
raw_author_name: Nikolaos Dimitriou
raw_author_name: George Margetis
raw_author_name: Dimitrios Tzovaras

Abstract: Abstract Real-time performance is critical for Augmented Reality (AR) systems as it directly affects responsiveness and enables the timely rendering of virtual content superimposed on real scenes. In this context, we present the DARLENE wearable AR system, analysing its specifications, overall architecture and core algorithmic components. DARLENE comprises AR glasses and a wearable computing node responsible for several time-critical computation tasks. These include computer vision modules developed for the real-time analysis of dynamic scenes supporting functionalities for instance segmentation, tracking and pose estimation. To meet real-time requirements in limited resources, concrete algorithmic adaptations and design choices are introduced. The proposed system further supports real-time video streaming and interconnection with external IoT nodes. To improve user experience, a novel approach is proposed for the adaptive rendering of AR content by considering the user’s stress level, the context of use and the environmental conditions for adjusting the level of presented information towards enhancing their situational awareness. Through extensive experiments, we evaluate the performance of individual components and end-to-end pipelines. As the proposed system targets time-critical security applications where it can be used to enhance police officers’ situational awareness, further experimental results involving end users are reported with respect to overall user experience, workload and evaluation of situational awareness.
Confidence partitioning sampling filtering

Authors:
raw_author_name: Xingzi Qiang
raw_affiliation_string: School of Internet, Anhui University, Hefei, 230039, People’s Republic of China
raw_author_name: Rui Xue
raw_affiliation_string: School of Electronics and Information Engineering, Beihang University, Beijing, 100191, People’s Republic of China
raw_author_name: Yanbo Zhu
raw_affiliation_string: The Aviation Data Communication Corporation, Floor 16, Bai Yan Building, No. 238, Bei Si Huan Zhong Road, Haidian District, Beijing, 100191, People’s Republic of China

Abstract: Abstract The confidence partitioning sampling filter (CPSF) method proposed in this paper is a novel approach for solving the generic nonlinear filtering problem. First, the confidence probability space (CPS) is defined, which restricts the state transition in a bounded and closed state space in the recursive Bayesian filtering. In the posterior CPS, the weighted grid samples, represented the posterior PDF, are obtained by using the partitioning sampling technique (PST). Each weighted grid sample is treated as an impulse function. The approximate expression of the posterior PDF, as key for the PST implementation, is obtained by using the properties of the impulse function in the integral operation. By executing the selection of the CPS and the PST step repeatedly, the CPSF framework is formed to achieve the approximation of the recursive Bayesian filtering. Second, the difficulty of the CPSF framework implementation lies in obtaining the real posterior CPS. Therefore, the space intersection (SI) method is suggested to obtain the approximate posterior CPS. On this basis, the SI_CPSF algorithm, as an executable algorithm, is formed to solve the generic nonlinear filtering problem. Third, the approximate error between the CPSF framework and the recursive Bayesian filter is analyzed theoretically. The consistency of the CPSF framework to the recursive Bayesian filter is proved. Finally, the performances of the SI_CPSF algorithm, including robustness, accuracy and efficiency, are evaluated using four representative simulation experiments. The simulation results showed that SI_CSPF requires far less samples than particle filter (PF) under the same accuracy. Its computation is on average one order of magnitude less than that of the PF. The robustness of the proposed algorithm is also evaluated in the simulations.
Program Behavior Dynamic Trust Measurement and Evaluation Based on Data Analysis

Authors:
raw_author_name: Shuai Wang
raw_affiliation_string: School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China
raw_author_name: Aiqun Hu
raw_affiliation_string: School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China
raw_author_name: Tao Li
raw_affiliation_string: School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China

Abstract: Industrial control terminals play an important role in industrial control scenarios. Due to the special nature of industrial control networks, industrial control terminal systems are vulnerable to malicious attacks, which can greatly threaten the stability and security of industrial production environments. Traditional security protection methods for industrial control terminals have coarse detection granularity, and are unable to effectively detect and prevent attacks, lacking real-time responsiveness to attack events. Therefore, this paper proposes a real-time dynamic credibility evaluation mechanism based on program behavior, which integrates the matching and symmetry ideas of credibility evaluation. By conducting a real-time dynamic credibility evaluation of function call sequences and system call sequences during program execution, the credibility of industrial control terminal application program behavior can be judged. To solve the problem that the system calls generated during program execution are unstable and difficult to measure, this paper proposes a partition-based dynamic credibility evaluation method, dividing program behavior during runtime into function call behavior and system call behavior within function intervals. For function call behavior, a sliding window-based function call sequence benchmark library construction method is proposed, which matches and evaluates real-time measurement results based on the benchmark library, thereby achieving symmetry between the benchmark library and the measured data. For system call behavior, a maximum entropy system call model is constructed, which is used to evaluate the credibility of system call sequences. Experiment results demonstrate that our method performs better in both detection success rate and detection speed compared to the existing methods.
A diachronic cluster analysis of Danish museum websites

Authors:
raw_author_name: Mette Skov
raw_affiliation_string: Department of Communication and Psychology, Aalborg University, Aalborg, Denmark
raw_author_name: Tanja Svarre
raw_affiliation_string: Department of Communication and Psychology, Aalborg University, Aalborg, Denmark

Abstract: Earlier studies describing and categorising the development of museum websites either build on theoretical knowledge or the analysis of a qualitative sample of relatively few, but prominent museum websites. The present study takes a broader approach and aims to characterise the development across Danish museum websites through the years 2005–2020. The methodological approach is a diachronic (temporal) analysis of Danish museum websites using data extracted from the Danish Web. A cluster analysis was used to identify similarities between museum websites. The cluster analysis shows that the museum website representations possess distinct attributes pivotal to their degree of resemblance with others, such as collection type, geographical location, organisational structure and whether the communication and dissemination strategy focus on disseminating collection content or extends to a broader communication scope. However, the quantitative text analysis approach to studying archived museum websites did not identified a new typology. Instead, the study suggests how a cluster analysis of the Danish museum web sphere can constitute an initial step in comprehending domain development, and ultimately discerning the attributes that characterise the different museum website clusters.
Deepfakes: current and future trends

Authors:
raw_author_name: Ángel Fernández Gambín
raw_affiliation_string: Department of Computer Science, Oslo Metropolitan University, Oslo, Norway
raw_author_name: Anis Yazidi
raw_affiliation_string: Department of Computer Science, Oslo Metropolitan University, Oslo, Norway
raw_author_name: Athanasios Vasilakos
raw_affiliation_string: Department of Computer Science, University of Agder, Grimstad, Norway
raw_author_name: Hårek Haugerud
raw_affiliation_string: Department of Computer Science, Oslo Metropolitan University, Oslo, Norway
raw_author_name: Youcef Djenouri
raw_affiliation_string: Department of Microsystems, University of South-Eastern Norway, Kongsberg, Norway

Abstract: Abstract Advances in Deep Learning (DL), Big Data and image processing have facilitated online disinformation spreading through Deepfakes. This entails severe threats including public opinion manipulation, geopolitical tensions, chaos in financial markets, scams, defamation and identity theft among others. Therefore, it is imperative to develop techniques to prevent, detect, and stop the spreading of deepfake content. Along these lines, the goal of this paper is to present a big picture perspective of the deepfake paradigm, by reviewing current and future trends. First, a compact summary of DL techniques used for deepfakes is presented. Then, a review of the fight between generation and detection techniques is elaborated. Moreover, we delve into the potential that new technologies, such as distributed ledgers and blockchain, can offer with regard to cybersecurity and the fight against digital deception. Two scenarios of application, including online social networks engineering attacks and Internet of Things, are reviewed where main insights and open challenges are tackled. Finally, future trends and research lines are discussed, pointing out potential key agents and technologies.
SCGFormer: Semantic Chebyshev Graph Convolution Transformer for 3D Human Pose Estimation

Authors:
raw_author_name: Mengxiao Yin
raw_affiliation_string: Guangxi Key Laboratory of Multimedia Communications and Network Technology, Nanning 530004, China; School of Computer and Electronic Information, Guangxi University, Nanning 530004, China

Abstract: With the rapid advancement of deep learning, 3D human pose estimation has largely freed itself from reliance on manually annotated methods. The effective utilization of joint features has become significant. Utilizing 2D human joint information to predict 3D human skeletons is of paramount importance. Effectively leveraging 2D joint data can improve the accuracy of 3D human skeleton prediction. In this paper, we propose the SCGFormer model to reduce the error in predicting human skeletal poses in three-dimensional space. The network architecture of SCGFormer encompasses Transformer and two distinct types of graph convolution, organized into two interconnected modules: SGraAttention and AcChebGconv. SGraAttention extracts global feature information from each 2D human joint, thereby augmenting local feature learning by integrating prior knowledge of human joint relationships. Simultaneously, AcChebGconv broadens the receptive field for graph structure information and constructs implicit joint relationships to aggregate more valuable adjacent features. SCGraFormer is tested on widely recognized benchmark datasets such as Human3.6M and MPI-INF-3DHP and achieves excellent results. In particular, on Human3.6M, our method achieves the best results in 9 actions (out of a total of 15 actions), with an overall average error reduction of about 1.5 points compared to state-of-the-art methods, demonstrating the excellent performance of SCGFormer.
USING MACHINE LEARNING ALGORITHMS FOR NATURAL HABITATS ASSESSMENT

Authors:
raw_author_name: Cosmin SABO
raw_author_name: Rosca Oana MARE
raw_author_name: Bogdan VASILESCU

Abstract: The potential of AI to process and interpret large volumes of data can provide researchers with a powerful tool to understand and monitor biodiversity on a global scale. In this paper we aimed to identify dominant individual plant species in natural protected habitats. Mapping the dominant species from the targeted natural habitats was followed by testing machine learning algorithm for differentiating similar species using satellite images. In the end we validated the data generated by machine learning algorithms through extensive field observations. Using the Sentinel-2 mission 10m resolution data and comprehensive field mapping we managed to see different phenology variations between diverse types of plant communities. Using the NDVI and NDII vegetation indexes and Random Forest algorithm during the dominant species phenology stages for each consecutive 10-day periods between May 1st and September 10th, revealed distinct responses to climate fluctuations and environmental factors. The natural habitats different signatures are strongly influenced by their ecological and conservation status and are not yet suitable for identification, but could help improve AI’s automatic detection for multiannual analysis if a favorable conservation trend is reached. The main achievement of the proposed methodology is the ability to differentiate between different species of deciduous trees, with machine learning training accuracy generally exceeding 95% and classification accuracy surpassing 90%.
Cross‐modal knowledge learning with scene text for fine‐grained image classification

Authors:
raw_author_name: Yingchi Mao
raw_affiliation_string: Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University  Nanjing China; School of Computer and Information Hohai University  Nanjing China
raw_author_name: Zicheng Wang
raw_affiliation_string: Power China Kunming Engineering Corporation Limited  Kunming Yunnan China; School of Computer and Information Hohai University  Nanjing China
raw_author_name: Bingbing Nie
raw_affiliation_string: Huaneng Lancang River Hydropower Corporation Limited  Kunming Yunnan China
raw_author_name: Chang Li
raw_affiliation_string: Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University  Nanjing China; School of Computer and Information Hohai University  Nanjing China

Abstract: Abstract Scene text in natural images carries additional semantic information to aid in image classification. Existing methods lack full consideration of the deep understanding of the text and the visual text relationship, which results in the difficult to judge the semantic accuracy and the relevance of the visual text. This paper proposes image classification based on Cross modal Knowledge Learning of Scene Text (CKLST) method. CKLST consists of three stages: cross‐modal scene text recognition, text semantic enhancement, and visual‐text feature alignment. In the first stage, multi‐attention is used to extract features layer by layer, and a self‐mask‐based iterative correction strategy is utilized to improve the scene text recognition accuracy. In the second stage, knowledge features are extracted using external knowledge and are fused with text features to enhance text semantic information. In the third stage, CKLST realizes visual‐text feature alignment across attention mechanisms with a similarity matrix, thus the correlation between images and text can be captured to improve the accuracy of the image classification tasks. On Con‐Text dataset, Crowd Activity dataset, Drink Bottle dataset, and Synth Text dataset, CKLST can perform significantly better than other baselines on fine‐grained image classification, with improvements of 3.54%, 5.37%, 3.28%, and 2.81% over the best baseline in mAP, respectively.
Strategic Approaches to Cybersecurity Learning: A Study of Educational Models and Outcomes

Authors:
raw_author_name: Madhav Mukherjee
raw_affiliation_string: Institute of Cybersecurity and Cryptology, School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia
raw_author_name: Ngoc Thuy Le
raw_affiliation_string: Institute of Cybersecurity and Cryptology, School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia
raw_author_name: Yang-Wai Chow
raw_affiliation_string: Institute of Cybersecurity and Cryptology, School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia
raw_author_name: Willy Susilo
raw_affiliation_string: Institute of Cybersecurity and Cryptology, School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia

Abstract: As the demand for cybersecurity experts in the industry grows, we face a widening shortage of skilled professionals. This pressing concern has spurred extensive research within academia and national bodies, who are striving to bridge this skills gap through refined educational frameworks, including the integration of innovative information applications like remote laboratories and virtual classrooms. Despite these initiatives, current higher education models for cybersecurity, while effective in some areas, fail to provide a holistic solution to the root causes of the skills gap. Our study conducts a thorough examination of established cybersecurity educational frameworks, with the goal of identifying crucial learning outcomes that can mitigate the factors contributing to this skills gap. Furthermore, by analyzing six different educational models, for each one that can uniquely leverage technology like virtual classrooms and online platforms and is suited to various learning contexts, we categorize these contexts into four distinct categories. This categorization introduces a holistic dimension of context awareness enriched by digital learning tools into the process, enhancing the alignment with desired learning outcomes, a consideration sparsely addressed in the existing literature. This thorough analysis further strengthens the framework for guiding education providers in selecting models that most effectively align with their targeted learning outcomes and implies practical uses for technologically enhanced environments. This review presents a roadmap for educators and institutions, offering insights into relevant teaching models, including the opportunities for the utilization of remote laboratories and virtual classrooms, and their contextual applications, thereby aiding curriculum designers in making strategic decisions.