Reviewerly/exHarmony huggingface dataset:
KDE-Based Simultaneous Background Model Learning and Entropy-Based Fusion of Cascaded Features for Video Object Segmentation With Shadow Removal

Object detection with shadow removal is one of the challenging issues in computer vision. Dynamic shadow resembles a moving object’s properties, so separating this shadow from the object is a challenging task. This dynamic shadow if not eliminated distorts the shape of the object. In this paper, a novel scheme for moving object detection and shadow removal is proposed based on the background modeling in fused feature space and these models learn to take care of the scene dynamics. Initially, in KDE space, temporal modeling of the spatial KDE (TMS-KDE) is carried out and cascaded features of Gabor and HOG are obtained. Besides, the original video frame is transformed into YCbCr color space and LBP features are extracted. The LBP features and the cascaded features are fused probabilistically to generate fused feature frames which are used in background modeling. The weights for the feature fusion are determined by the proposed entropy based measure. Background modeling and model learning is a pixel based approach and the pixel is classified either background or foreground during the learning process.We have tested our proposed method on a wide range of datasets which includes ATON-CVRR, LASIESTA, CD-net, Kaggle, PETS 2006, SGM-RGBD, SBMI 2015, SBMnet 2016 and VIRAT. The proposed scheme is found to take care of different shadow conditions while detecting the moving object. The performance of the proposed scheme is found to be superior to that of many existing schemes.
Enhancing Spam Comment Detection on Social Media With Emoji Feature and Post-Comment Pairs Approach Using Ensemble Methods of Machine Learning

Every time a well-known public figure posts something on social media, it encourages many users to comment.Unfortunately, not all comments are relevant to the post.Some are spam comments which can disrupt the overall flow of information.This research employed two strategies to address issues in text spam detection on social media.The first strategy was utilizing emojis that had been frequently discarded in many studies.In fact, many social media users use emojis to convey their intentions.The second strategy was utilizing stacked post-comment pairs, which was different from many spam detection systems that solely focused on comment-only data.The post-comment pairs were required to detect whether a comment was relevant (not spam) or spam based on the post context.This research used the SpamID-Pair dataset derived from social media for Indonesian spam comment detection.After a comprehensive investigation, the emoji-text feature, the stacked post-comment pairs, and ensemble voting could boost detection performance (in terms of accuracy and F1).Adding manual features also improved detection performance.Based on the experiment, the best stand-alone methods for spam comment detection are the SVM (RBF kernel) and the soft voting ensemble method for the best average performance.
A Comparative Assessment of Human Factors in Cybersecurity: Implications for Cyber Governance

This paper provides an extensive overview of cybersecurity awareness in the young, educated, and technology-savvy population of the United Arab Emirates (UAE), compared to the United States of America (USA) for advancing the scholarship and practice of global cyber governance. We conducted comparative empirical studies to identify differences in specific human factors that affect cybersecurity behaviour in the UAE and the USA. In addition, we employed several control variables to observe reliable results. We used Hofstede’s theoretical framework on culture to advance our investigation. The results show that the targeted population in the UAE exhibits contrasting interpretations of cybersecurity awareness of critical human factors as compared to their counterparts from the USA. We identify possible explanations for this relatively different behaviour in the UAE population. Our key contributions are to provide valuable information for cybersecurity policymakers in the UAE and Gulf Cooperation Council (GCC) region to further enhance cyber safety, governance, awareness, and trust among citizens.
From Pixel to Peril: Investigating Adversarial Attacks on Aerial Imagery Through Comprehensive Review and Prospective Trajectories

Deep models’ feature learning capabilities have gained traction in recent years, driving significant progress in various Artificial Intelligence (AI) domains. The use of Deep Neural Networks (DNNs) has expanded the scope of Computer Vision (CV) and revealed their vulnerability to deliberate adversarial attacks. These attacks involve the careful introduction of perturbations crafted through complex optimization problems. Exploiting vulnerabilities in advanced deep neural network algorithms present security concerns, particularly in practical applications with high stakes like unmanned aerial vehicles (UAVs) and satellite imagery in computer vision. Adversarial attacks, both in digital and physical dimensions, pose a serious threat in the field. This research provides a comprehensive examination of state-of-the-art adversarial attacks specific to aerial imagery using autonomous platforms such as UAVs and satellites. This review covers fundamental concepts, techniques, and the latest advancements, identifying research gaps and suggesting future directions. It aims to deepen researchers’ understanding of the challenges and threats related to adversarial attacks on aerial imagery, serving as a valuable resource to guide future research and enhance the security of computer vision systems in aerial environments.
An Empirical Study on Authorship Verification for Low Resource Language Using Hyper-Tuned CNN Approach

Authorship verification is a crucial process employed to determine the authorship of a given text by analyzing distinct aspects of the writer's style, such as vocabulary, syntax, and punctuation.This process has gained significant research attention in various domains, including intellectual property rights, plagiarism detection, cybercrime investigations, copyright infringement, and forensics.While extensive studies have been conducted on multiple languages worldwide, encompassing Western European languages like Italian and Spanish, as well as Asian languages such as Bengali and Chinese, the investigation of authorship verification in Urdu has been comparatively limited, despite its status as a prominent South Asian language.This limitation can be attributed to the intricate and distinctive morphology of Urdu, which necessitates specific methodologies that cannot be directly applied in the same manner as other languages.To bridge this gap, we propose an innovative approach for authorship verification in Urdu, leveraging Convolutional Neural Networks (CNNs) with three distinct hyper-tuned parameters: ADAM, SGD, and RMSProp.To facilitate the development of this approach, we have curated a new corpus called UAVC-22, specifically tailored for Urdu authorship verification.This corpus offers enhanced robustness in terms of authors' classes and unique words.We have developed 9 authorship verification models, utilizing three different text embedding techniques, namely Word2Vec, GloVe, and FastText, we have performed a comparative analysis with traditional machine learning models such as Support Vector Machines (SVM) and Random Forest to assess the superiority and efficacy of the CNN-based approach.The optimized CNN-ADAM model with FastText achieved the highest accuracy of 98% for the Urdu dataset UAVC-22.
Traffic Conflicts Analysis in Penang Based on Improved Object Detection With Transformer Model

Current road safety detection and analysis tends to focus on hit-and-run accidents that have already occurred, while ignoring near-misses that may pose a potential safety risk. A monitoring method for near-misses based on the improved YOLOv7 of transformers is proposed in this work. First, the backbone network of YOLOv7 is improved using a G3HN structure (gn Conv) with recursive gate convolution. Second, the global attention mechanism (GAM) is added to the probe to improve recognition accuracy. Finally, the object detection results are inverted by inverse perspective mapping (IPM) to obtain the centroid of the object, and then the probability of near miss is calculated and analyzed using the DN-based, PICUD-based, and PSD-based methods. This experimentwas based on the POL37 Closed-Circuit Television (CCTV) dataset from Penang, Malaysia. The experimental results show that the improved algorithm proposed in this paper can effectively identify small targets in the object detection phase with a detection accuracy of 94.8%, smaller models, and faster training speed, and can fulfill the surveillance task of near-miss events in CCTV surveillance scenes.
Image Inpainting of Thangka Murals Using Edge-Assisted Feature Fusion and Self Attention Based Local Refine Network

Thangka murals hold significant cultural value in Tibet, but unfortunately, many of these precious murals have sustained damage over time. Therefore, the restoration of Thangka murals is crucial in preserving Tibet’s cultural heritage. The potential of Gated Convolution for Thangka mural restoration is significant due to its impressive performance. However, existing Gated Convolution-based approaches for Thangka restoration face two challenges: (1) restoration results using Gated Convolution still show blurring at the pixel level, and (2) Gated Convolution remains dependent on the perceptual field of the convolution during image inpainting. To address two challenges, we propose a novel Thangka mural inpainting method that incorporates two key components: (1) an Edge-assisted Feature Fusion (EFF) method that leverages edge information to assist in inpainting the texture details of the image and enhance the consistency of textures and edges, and (2) a Self Attention based Local Refine (SALR) network that effectively enhances the perceptual field of the convolution and captures long-distance dependencies. The experimental results demonstrate that our proposed method is effective in repairing broken areas of Thangka murals, even on a small dataset (N=5605). To validate the effectiveness of our proposed architecture and its components, we conduct a series of ablation experiments. The results demonstrate the necessity of our approach and highlight the contributions of each component. Moreover, the proposed end-to-end approach exhibits broad applicability and can be extended to other profiling tasks based on small datasets.
MLGN:A Multi-Label Guided Network for Improving Text Classification

Within natural language processing, multi-label classification is an important but challenging task. It is more complex than single-label classification since the document representations need to cover fine-grained label information, while the labels predicted by the model are often related. Recently, large pre-trained language models have achieved great performance on multi-label classification tasks, typically using embedding of [CLS] vector as the semantic representation of entire document and matching it with candidate labels. However, existing methods tend to ignore label semantics, and the relationships between labels and documents are not effectively mined. In addition, the linear layers used for fine-tuning do not take the correlations between labels into account. In this work, we propose a Multi-Label Guided Network (MLGN) capable to guide document representation with multi-label semantic information. Furthermore, we utilize correlation knowledge to enhance the original label prediction in downstream tasks. The extensive experimental trials show that MLGN transcends previous works on several publicly available datasets. Our source code is available at https://github.com/L199Q/MLGN.
Corrections to “The Structural Information Potential and Its Application to Document Triage”

In the above article [1], a fewof the sections require corrections and some additional experiments to strengthen thework.
Artificial Intelligence (AI) Based Predictive Maintenance of Waste Heat Recovery System

Decarbonization and sustainability urge the deployment and utilization of distributed energy systems for high-efficiency gains.The dispatchable devices (gas turbines or diesel engines) are integrated with a wasteto-energy system to harness the energy lost or waste heat and support heat and cold loads.This paper investigates the characteristics of a waste heat recovery system and its performance degradation mechanism to assess its maintenance necessity and optimize maintenance frequency and the associated maintenance and downtime costs.The effectiveness of the waste heat recovery system (WHRS) is regularly estimated using the measured inlet and outlet parameters (flow and temperature) to identify the need for maintenance.The effectiveness changes not only with degradation but also with inlet conditions that deviate from the design conditions.Therefore, the operators are instructed to operate this system at the rated inputs and gauge its actual effectiveness.However, this approach did not provide much information on the rootcause parameters, i.e., the fouling formation and thickness in the shell and tube sides, which are quite important to decide the type of maintenance and the associated cost and duration.This paper studies the performance characteristics of the waste heat recovery system with reference to all critical and influential parameters (i.e., fouling thickness, heat transfer coefficients, and off-design inlet conditions) using a rigorous physics-based model.An AI model was developed using the derived performance characteristics to predict the fouling thickness estimation.The developed prediction model is able to accurately estimate the fouling thickness on the gas and water sides, and the error or deviation is within ±0.3 mm.By deploying this prediction model, the critical parameters can be monitored in real-time, and the performance degradation trajectory paves the way to understand degradation status and estimate the right maintenance time frame to schedule maintenance proactively, considering the maintenance cost and downtime effects.
DepthFake: A Depth-Based Strategy for Detecting Deepfake Videos

Fake content has grown at an incredible rate over the past few years. The spread of social media and online platforms makes their dissemination on a large scale increasingly accessible by malicious actors. In parallel, due to the growing diffusion of fake image generation methods, many Deep Learning-based detection techniques have been proposed. Most of those methods rely on extracting salient features from RGB images to detect through a binary classifier if the image is fake or real. In this paper, we proposed DepthFake, a study on how to improve classical RGB-based approaches with depth-maps. The depth information is extracted from RGB images with recent monocular depth estimation techniques. Here, we demonstrate the effective contribution of depth-maps to the deepfake detection task on robust pre-trained architectures. The proposed RGBD approach is in fact able to achieve an average improvement of $$3.20\%$$ and up to $$11.7\%$$ for some deepfake attacks with respect to standard RGB architectures over the FaceForensic++ dataset.
NSFASonto: An Ontology for South African National Student Financial Aid Scheme Operations

The South African government established the National Student Financial Aid Scheme (NSFAS) to help underprivileged families with finances to access higher education. NSFAS and partner institutions run heterogeneous systems and use different processes and data representations for students and funding operations; which create duplications and inconsistencies in the data, resulting in errors and delays in the approval of students' applications and disbursement of funds. To address these challenges, this study developed an ontology of NSFAS operations, namely, NSFASonto. The resulting NSFASonto ontology was evaluated for reasoning and consistency with the HermiT reasoner and SPARQL queries within Protégé and displayed promising results. The NSFASonto ontology can be leveraged in web-based applications to integrate the NSFAS operations with partner institutions for efficient approval and delivery of financial aid to students across South Africa. Furthermore, the NSFASonto ontology could be reused in other countries to build automated systems for student financial aid operations/processes.
Image Watermarking Backdoor Attacks in CNN-Based Classification Tasks

In these last years, neural networks are becoming the basis for different kinds of applications and this is mainly due to the stunning performances they offer. Nevertheless, all that glitters is not gold: such tools have demonstrated to be highly sensitive to malicious approaches such as gradient manipulation or the injection of adversarial samples. In particular, another kind of attack that can be performed is to poison a neural network during the training time by injecting a perceptually barely visible trigger signal in a small portion of the dataset (target class), to actually create a backdoor into the trained model. Such a backdoor can be then exploited to redirect all the predictions to the chosen target class at test time. In this work, a novel backdoor attack which resorts to image watermarking algorithms to generate a trigger signal is presented. The watermark signal is almost unperceivable and is embedded in a portion of images of the target class; two different watermarking algorithms have been tested. Experimental results carried out on datasets like MNIST and GTSRB provide satisfactory performances in terms of attack success rate and introduced distortion.
Public Health Data and International Privacy Rules and Practices: A Case Study of Singapore

States pursued many operative measures to fight Covid-19 disease like "Contact Tracing" Apps. With the recent amendments to the law, the Singapore police could obtain any data, including information gathered by the contact tracing app and wearable token to facilitate criminal probes. This study aims to analyze rules and standards concerning personal data processing and the privacy policy of the TraceTogether App from both domestic and international perspectives. Doctrinal legal study using explanatory and comparative approaches was applied. This study examined and evaluated important international and regional regulations and guidelines concerning the privacy of contact tracing apps in fighting pandemic. The police can use personal data collected by the 'TraceTogether' App to investigate serious crimes. The results reveal that police access to contact tracing data for irrelevant purposes is a broken promise and eradicates individuals' trust in the future. The findings show that Singapore police access to personal data is against international standards and regulations on data protection, although the Singapore Personal Data Protection Act does not apply to public sectors like Police. The results highlighted that while public health is important, privacy, ethics and human rights must be observed at the same time. Singapore's government is the "custodian" of the contact tracing data, and "stringent measures" should be established to safeguard the personal data. The results are noteworthy for lawmakers since the urgent codification done by the Singapore government to legalize such access is against principles of the personal data protection law, ethics, international rules, norms, and practices.
System of Preparation for Interdisciplinary Final Project in Technological University

Educational system of the country is a social system which develops a person, forms not only skills and competences but ideals, points of view, values which make a person installed in professional and social community. The purpose of the paper is to present a system of preparation for successful presentation of final project with the focus on training productive language skills online in the framework of differentiated approach used in technological university. As an example, the online part of the course “Professional English” was used, developed for the Masters of the major “Chemical Technology”. The program has been operated with online support for 5 years (140 students). The final assessment in the form of the final project helps advanced students become a part of working society as they have an opportunity to present their research in English in front of their professors and potential employers, that enlarge their job perspectives. A differentiated approach is used as well to give a chance for the students with initial low level of the language to train additionally and have equal chances to become a part of social system.
Ontology-Driven Parliamentary Analytics: Analysing Political Debates on COVID-19 Impact in Canada

Parliamentary debates are usually published in Parliament's websites to allows citizens to be informed on the latest national debates, proposals and decisions. To enhance citizen experience and engagement, functionalities such as debates annotation and question answering are necessary. Annotating text requires semantic content and ontologies are known for their ability to describe a common vocabulary for a domain and can be a solid base for annotation and question answering. We report on an ongoing study to enhance parliamentary analytics using an ontology and knowledge graph to sharpen annotations and facilitate their query by end-users. As a salient case, a sample of debates are collected on the COVID-19 impact in Canada, as its complexity shows the relevance of using advanced knowledge representation techniques. We focused on the development of a new "Impact of COVID-19 in Canada Ontology" (ICCO) that provides contextualized semantic information on impact in numerous policy areas, as this ontology is entirely built from Canadian parliamentary debates. It has been evaluated and validated by experts. Our conclusion underscores the importance of integrating ontology-driven parliamentary analytics within the broader context of digital transformation in legislative institutions, and the need for new platforms supporting free and open Digital Humanities.
The Effect of Generative Artificial Intelligence on Q&amp;amp;A Platforms – Consequences for Questions, Answers, Question Readability, and Novelty

This empirical study examines the effects of generative artificial intelligence (GAIs), such as ChatGPT, on Question & Answer (Q&A) platforms. By analyzing data from StackExchange (SE), a leading Q&A website, we explore how the demand and supply of answers change with the availability of GAIs. Our findings indicate a significant decrease in both on SE following the launch of ChatGPT. The decrease in questions and users is particularly driven by casual users abandoning the platform. At the same time, the questions that remain on the platform become more challenging to read and exhibit more novelty relative to previous years. These findings contribute to the understanding of GAI’s impact on UGC platforms and bear practical implications for platforms, users, and developers of GAI.
What Do We Know About Algorithmic Collusion Now? New Insights from the Latest Academic Research

Algorithmic collusion has captured the attention of the global antitrust community for the past several years. Deng (2020) provided a comprehensive survey of the pertinent literature in economics and computer science and a critical discussion. Over the past three years, new insights have emerged from academic research. These new insights have not only deepened our understanding of the intricate relationship between algorithms and competition but also begun challenging some previous findings once considered compelling evidence supporting the plausibility of autonomous algorithmic tacit collusion. In this article, I discuss these new insights, with a focus on four topics: (1) the nuanced ways algorithms affect prices, (2) the crucial role of algorithmic design, (3) the consequences of third-party pricing algorithms, and (4) some considerations when assessing algorithmic impact in litigation.