Reviewerly/exHarmony huggingface dataset:
Adaptive Smoothing for Visual Improvement of Image Quality via the p(x)-Laplacian Operator Effects of the p(x)-Laplacian Smoothing Operator on Digital Image Restoration: Contribution to an Adaptive Control Criterion

This article concerns the improvement of digital image quality using mathematical tools such as nonlinear partial differential operators. In this paper, to perform smoothing on digital images, we propose to use the p(x)-Laplacian operator. Its smoothing power plays a main role in the restoration process. This enables us to dynamically process certain areas of an image. We used a mathematical model of image regularisation that is described by a nonlinear diffusion Equation (this diffusion is modelled by the p(x)-Laplacian operator). We implemented the continuous model in order to observe the steps of the regularisation process to understand the effects of the different parameters of the model on an image. This will enable parameters to be used and adapted in order to provide a proposed solution.
IRRegularities in the Internet Routing Registry

The Internet Routing Registry (IRR) is a set of distributed databases used by networks to register routing policy information and to validate messages received in the Border Gateway Protocol (BGP). First deployed in the 1990s, the IRR remains the most widely used database for routing security purposes, despite the existence of more recent and more secure alternatives. Yet, the IRR lacks a strict validation standard and the limited coordination across different database providers can lead to inaccuracies. Moreover, it has been reported that attackers have begun to register false records in the IRR to bypass operators' defenses when launching attacks on the Internet routing system, such as BGP hijacks. In this paper, we provide a longitudinal analysis of the IRR over the span of 1.5 years. We develop a workflow to identify irregular IRR records that contain conflicting information compared to different routing data sources. We identify 34,199 irregular route objects out of 1,542,724 route objects from November 2021 to May 2023 in the largest IRR database and find 6,373 to be potentially suspicious.
Action Recognition Based on GCN with Adjacency Matrix Generation Module and Time Domain Attention Mechanism

Different from other computer vision tasks, action recognition needs to process larger-scale video data. How to extract and analyze the effective parts from a huge amount of video information is the main difficulty of action recognition technology. In recent years, due to the outstanding performance of Graph Convolutional Networks (GCN) in many fields, a new solution to the action recognition algorithm has emerged. However, in current GCN models, the constant physical adjacency matrix makes it difficult to mine synergistic relationships between key points that are not directly connected in physical space. Additionally, a simple time connection of skeleton data from different frames makes each frame in the video contribute equally to the recognition results, which increases the difficulty of distinguishing action stages. In this paper, the information extraction ability of the model has been optimized in the space domain and time domain, respectively. In the space domain, an Adjacency Matrix Generation (AMG) module, which can pre-analyze node sets and generate an adaptive adjacency matrix, has been proposed. The adaptive adjacency matrix can help the graph convolution model to extract the synergistic information between the key points that are crucial for recognition. In the time domain, the Time Domain Attention (TDA) mechanism has been designed to calculate the time-domain weight vector through double pooling channels and complete the weights of key point sequences. Furthermore, performance of the improved TDA-AMG-GCN modules has been verified on the NTU-RGB+D dataset. Its detection accuracy at the CS and CV divisions reached 84.5% and 89.8%, respectively, with an average level higher than other commonly used detection methods at present.
Postdigital leadership in education: scoping review of the case of Africa

The subject of postdigital remains an issue being grappled about especially considering the different misconceptions about the era. Leadership in education is considered as a major phenomenon capable of driving success in the sector during the era. Thus, this study was conducted to explore the scholarly perceptions of leadership education in the postdigital era with focus on the African continent. A scoping review method was adopted. Sabinet and Scopus were adopted as the databases for the search using identified key terms. Sequel to the search on the selected databases, five articles were retrieved. The reviewed literature were coded, categorized and thematically analyzed. The finding of the study showed that there is paucity of literature in the subject. A shift is expected with new expectations following the move to a postdigital era; hence, education leaders are expected to prepare, acclimatize themselves and embrace the era for relevance. Also, critical research areas of EdTech were identified and education leaders are to promote and drive studies toward such identified spaces. Meanwhile, glocalization is considered paramount considering the African needs. Thus, education leaders are encouraged to consider the local needs and demands while taking cognizance of global standards and practices in the postdigital era.
A Longitudinal Study of Vulnerable Client-side Resources and Web Developers' Updating Behaviors

Modern Websites rely on various client-side web resources, such as JavaScript libraries, to provide end-users with rich and interactive web experiences. Unfortunately, anecdotal evidence shows that improperly managed client-side resources could open up attack surfaces that adversaries can exploit. However, there is still a lack of a comprehensive understanding of the updating practices among web developers and the potential impact of inaccuracies in Common Vulnerabilities and Exposures (CVE) information on the security of the web ecosystem. In this paper, we conduct a longitudinal (four-year) measurement study of the security practices and implications on client-side resources (e.g., JavaScript libraries and Adobe Flash) across the Web. Specifically, we first collect a large-scale dataset of 157.2M webpages of Alexa Top 1M websites for four years in the wild. Analyzing the dataset, we find an average of 41.2% of websites (in each year of the four years) carry at least one vulnerable client-side resource (e.g., JavaScript or Adobe Flash). We also reveal that vulnerable JavaScript library versions are frequently observed in the wild, suggesting a concerning level of lagging update practice in the wild. On average, we observe 531.2 days with 25,337 websites of the window of vulnerability due to the unpatched client-side resources from the release of security patches. Furthermore, we manually investigate the fidelity of CVE (Common Vulnerabilities and Exposures) reports on client-side resources, leveraging PoC (Proof of Concept) code. We find that 13 CVE reports (out of 27) have incorrect vulnerable version information, which may impact security-related tasks such as security updates.
PTPerf: On the Performance Evaluation of Tor Pluggable Transports

Tor, one of the most popular censorship circumvention systems, faces regular blocking attempts by censors. Thus, to facilitate access, it relies on "pluggable transports" (PTs) that disguise Tor's traffic and make it hard for the adversary to block Tor. However, these are not yet well studied and compared for the performance they provide to the users. Thus, we conduct a first comparative performance evaluation of a total of 12 PTs-the ones currently supported by the Tor project and those that can be integrated in the future.
Poster: The Impact of the Client Environment on Residential IP Proxies Detection

Residential IP Proxies (RESIPs) enable proxying out requests from a vast network of residential devices without inserting any information revealing it. While RESIPs can be used for legitimate purposes, previous studies also associate them with malicious activities. In our last work, we proposed a server-side detection method for RESIP connections based on the difference in the Round Trip Time at the TCP and TLS layers. In this new work, thanks to real-world connections, we investigate if and how specific factors in the client environment influence the technique. We show that genuine users utilizing web browsers or performing hotspots do not result in false positives for our technique. Moreover, our early results suggest that false positives caused by Mobile TCP Terminating Proxies used by mobile Internet Service Providers have a Round Trip Time difference higher than the detection threshold but much smaller than the average RESIP one. This suggests that we can reduce these false positives by highering the detection threshold for mobile connections.
Evolving Bots: The New Generation of Comment Bots and their Underlying Scam Campaigns in YouTube

This paper presents a pioneering investigation into a novel form of scam advertising method on YouTube, termed "social scam bots'' (SSBs). These bots have evolved to emulate benign user behavior by posting comments and engaging with other users, oftentimes appearing prominently among the top rated comments. We analyzed the YouTube video comments and proposed a method to identify SSBs and extract the underlying scam domains. Our study revealed 1,134 SSBs promoting 72 scam campaigns responsible for infecting 31.73% of crawled videos. Further investigation revealed that SSBs exhibit advances that surpass traditional bots. Notably, they targeted specific audience by aligning scam campaigns with related video content, effectively leveraging the YouTube recommendation algorithm. We monitored these SSBs over a period of six months, enabling us to evaluate the effectiveness of YouTube's mitigation efforts. We also uncovered various strategies they use to evade mitigation attempts, including a novel strategy called "self-engagement," aimed at boosting their comment ranking. By shedding light on the phenomenon of SSBs and their evolving tactics, our study aims to raise awareness and contribute to the prevention of these malicious actors, ultimately fostering a safer online platform.
Poster: Novel Client-Side Watermarking Technique for Tor User De-Anonymization

Most traditional techniques for Tor user de-anonymization rely on the use of server-side originating traffic watermarks. In this poster, we outline the key ideas behind our novel client-side originating watermark scheme and describe some possible ways of how this scheme could be implemented in practice. We also demonstrate the superior real-world performance of this approach vs. those previously discussed in the literature.
Explore Testing Performance and Learning Behaviors

This study explores various approaches to investigate participants' testing performance and learning behaviors in a computer-based spatial rotation learning program. Using multivariate learning and assessment data, including responses, response times, learning times and selected covariates, a comprehensive data analytic framework is developed that not only utilizes the test level information but also the item level information. This top-down and multivariate data analytic framework can shed light on conducting exploratory analysis with high-dimensional and mixed-type multivariate data, especially on how to aggregate information from the test-level and item-level. The findings about participants' testing performance and learning behaviors are valuable in guiding the design of an adaptive learning platform in the future and can also provide some support in developing confirmatory statistical methods to model testing and learning behaviors.
The Impact of Artificial Intelligence on the Employment Structure of the Tourism Industry: An Interview with ChatGPT

This study aims to investigate the impact of artificial intelligence (AI) on the workforce in the tourism sector, suggest measures that managers and employees can utilize to mitigate the adverse effects of AI, and predict the future of employment in tourism. In the study, the interview technique was used in qualitative research approaches. ChatGPT was used as an interviewer. Eight questions were posed during the interviews using the ChatGPT question screen (https://chat.openai.com/chat). The study showed that artificial intelligence (AI) will emerge in fifteen new job positions within tourism. The study revealed that integrating artificial intelligence (AI) in the tourism industry has several advantages for employees, such as enhancing job satisfaction and commitment, making more informed decisions, reducing their workload and burnout, formulating more effective business strategies and service offerings, continuing professional development, discover new career paths, and acquiring practical problem-solving abilities. The study highlighted that AI has potentially adverse effects on employees on subjects, including job displacement, job mismatches, skills gap, reskilling challenges, adopting, creativity, decision-making, workload, burnout, ethical, fairness, job performance, job satisfaction, sense of monotony, career advancement, and identity lost.
Inherent-Attribute-Aware Dual-Graph AutoEncoder for Rating Prediction

AutoEncoder-based rating prediction methods with external attributes have received wide attention due to their ability to accurately capture users’ preferences. However, existing methods still have two significant limitations: i) external attributes are often unavailable in the real world due to privacy issues, leading to low quality of representations; and ii) existing methods lack considering complex associations in users’ rating behaviors during the encoding process. To meet these challenges, this paper innovatively proposes an Inherent-Attribute-aware Dual-Graph AutoEncoder, named IADGAE, for rating prediction. To address the low quality of representations due to the unavailability of external attributes, we propose an inherent attribute perception module that mines inductive user active patterns and item popularity patterns from users’ rating behaviors to strengthen user and item representations. To exploit the complex associations hidden in users’ rating behaviors, we design an encoder on the item-item co-occurrence graph to capture the co-occurrence frequency features among items. Moreover, we propose a dual-graph feature encoder framework to simultaneously encode and fuse the high-order representations learned from the user-item rating graph and item-item co-occurrence graph. Extensive experiments on three real datasets demonstrate that IADGAE is effective and outperforms existing rating prediction methods, which achieves a significant improvement of 4.51%-41.63% in the RMSE metric.
Can Strategically Prioritizing Ransomware Protection and Implementing Specific Recommended Actions Potentially Reduce the Effects of a Ransomware Attack on an Organization?

The purpose of this research study was to explore what can be learned by prioritizing for ransomware and data loss protection during organizational strategic planning to mitigate the negative effects of these events while identifying specific actions and recommendations organizations can take to mitigate the effects of ransomware and associated data loss. The problem addressed was that many organizations have become or will become victims of massive data losses caused by ransomware attacks because ransomware security controls and data loss prevention are not prioritized during strategic planning (Breckenridge, 2020). The methodology used was a qualitative metasynthesis which examined six case studies that were chosen as a representative set of organizations that have experienced common effects that are often felt in the aftermath of either a ransomware attack, poor strategic planning for cybersecurity controls, or data loss. The findings show the emergence of several themes including a common lack of strategic planning, the inability to recognize network anomalies leading to malware installations that resulted in data breaches, the lack of or misuse of security controls, and the heavy consequences suffered as a result of not putting effort into preventing the data breaches. Additional examination focused on the need for a proactive approach to data loss prevention which demands the attention of organizational leadership at the highest levels.
The impact of using Artificial intelligence in projects management Decision-Making within private sector in the kingdom of Saudi Arabia

Artificial intelligence (AI) has already transformed the world and has made an effective impact in a range of fields. It has gained a wide interest to ground itself into business and commercial use and has transformative potential for private sector organizations through enabling increased productivity and novel ways to deliver different services. We have found out that in the private sector AI appears to be lagging, and the phenomena has really limited attention from academic research community. This paper outlines how AI technologies could help project managers to provide faster decision making in Project Management within the private sector. We were able to understand that most Project Managers had difficulties in applying AI in decision-making processes. Although, the majority perceive this technology as being useful and would use it for their daily tasks. Also, date was collected from different regions in KSA and primary research in which two of the biggest companies’ COs were interviewed and a survey was done by more than 100 persons including project managers and team leaders in which to give the conclusion that AI application improves data quality and integrity leading to improved speed and effectiveness of decision making within the private sector in the Kingdom of Saudi Arabia. Additionally, this research seeks to contribute to the growing body of knowledge on AI- driven project management decision-making within the private sector in Saudi Arabia. By examining the benefits, challenges, and potential risks associated with AI implementation.
Geodesic-Based Relaxation For Deep Canonical Correlation Analysis

Deep canonical correlation analysis (DCCA) is often applied to paired data samples from diverse sources to extract meaningful common information. However, when the data sources are heterogeneous, some of the useful information may be complementary but not exactly common. In spite of this fact, existing techniques learn maximally correlated representations from multiple views and are formulated so that they aim to yield identical latent subspaces for each view. This approach is sub-optimal in estimating the true signal subspaces for heterogeneous data sources. We propose a residual relaxation for deep canonical correlation analysis (RDCCA) based on a subspace distance metric, which generalizes the existing problem formulation and extracts representations that are better estimates of the actual, non-identical subspaces. We demonstrate that when using such a relaxation, the learned representations are closer to the true ones and that RDCCA outperforms CCA and DCCA in scenarios with heterogeneous data.
Holistic FOD Detection Via Surface Map and Yolo Networks

The detection of foreign object debris (FOD) in real-time is crucial for airport safety and security. In this paper, we propose a holistic FOD detection framework that utilizes both present and past data. A baseline map is constructed first based on frames extracted from clean runway/taxiway videos. During periodic inspections, detection-time frames and aligned baseline images are combined and fed into a modified YOLOv7 network to detect FOD. We explore and compare different configurations of the detection networks to determine the tradeoffs in performance.
Belief Propagation of Pareto Front in Multi-Objective MDP Graphs

In the context of Markov Decision Processes (MDPs), the framework of forward-backward probability propagation on factor graphs has proven to be useful for finding optimal policies. However, in cases involving vector rewards, there is a need to evaluate a trade-off among constituent objectives. In this work, assuming multiple rewards, we show how to use the framework of belief propagation for dynamically generating the Pareto front and propagating it as a forward flow distribution. The idea is applied to path planning on discrete 1D and 2D grids where different sets of states have vector rewards in the form of priors.
Stream-Based Active Learning with Adaptive Uncertainty and Diversity Thresholds

This paper proposes Stream-based Active Learning method using Adaptive uncertainty and Diversity Thresholds (SALAT), which aims to reduce the amount of data required for labeling in stream-based data settings while accounting for concept drift. The proposed method uses adaptive uncertainty and diversity thresholds, which are monitored by a sliding window of data selection history to ensure that the data selection is neither too conservative nor too aggressive. The effectiveness of SALAT is validated on the MNIST and FashionMNIST datasets, where it achieves similar performance to the baseline with fewer training samples. The experiments also confirm the hypothesis that a fixed threshold approach is deficient, and an adaptive threshold significantly reduces the data requirement to reach an adequate level of performance. The paper shows that SRLRT can handle concept drift effectively, making it applicable to various real-world applications such as text classification, image classification, and speech recognition.
A Discriminative Approach To Unsupervised Domain Adaptation in Coarse-To-Fine Classifiers

Conventional machine learning models often exhibit poor performance when tested on data that comes from a different probability distribution than that of the training data. This phenomenon is known as ‘domain shift’. To overcome this problem, domain adaptation (DA) methods have been proposed to reduce the discrepancy between the two domains and improve the performance on the target domain. However, most of the existing DA techniques for classification have focused on One Level Classifiers (OLCs), which are not suitable for data that follows a hierarchical classification structure. In this paper, we propose a discriminative DA approach for coarse-to-fine (C2F) classifiers, which provide both coarse and fine labels. Our approach learns an invariant feature representation of the data across domains for both the coarse and finer levels. Our experimental results on well-known digit datasets demonstrate that the proposed algorithm outperforms the base C2F model in terms of the classification accuracy of the target domain data.
Deep Reinforcement Learning With Action Masking for Differential-Drive Robot Navigation Using Low-Cost Sensors

Driving a wheeled differential-drive robot to a target can be a complicated matter when trying to also avoid obstacles. Usually, such robots employ a variety of sensors, such as LiDAR, depth cameras, and others, that can be quite expensive. To this end, in this paper, we focus on a simple differential-drive wheeled robot that uses only inexpensive ultrasonic distance sensors and touch sensors. We propose a method for training a Reinforcement Learning (RL) agent to perform robot navigation to a target while avoiding obstacles. In order to increase the efficiency of the proposed approach we design appropriate action masks that can significantly increase the learning speed and effectiveness of the learned policy. As we experimentally demonstrated, the proposed agent can robustly navigate to a given target even in unknown procedurally generated environments, or even when denying part of its sensor input. Finally, we show a practical use-case using object detection to dynamically search for, and move to objects within unknown environments. The code used for conducted experiments is available online on Github.