Reviewerly/exHarmony huggingface dataset:
AIRA-DA: Adversarial Image Reconstruction Alignments for Unsupervised Domain Adaptive Object Detection

Unsupervised domain adaptive object detection is a challenging perception task where object detectors are adapted from a label-rich source domain to an unlabeled target domain, playing a vital role in autonomous driving and robot navigation. Since the camera settings, weather, and light conditions vary depending on the road, it is labor-intensive and impossible to manually annotate all the data to adapt to these scenarios. Recent advances demonstrate the effectiveness of adversarial-based domain alignment, where domain invariance in the feature space is produced by adversarial training between the feature extractor and domain discriminator. However, due to the domain shift, domain discrimination, especially on low-level features, is an easy task. This results in an imbalance of the adversarial training between the domain discriminator and the feature extractor. In this work, we achieve a better domain alignment by introducing an auxiliary regularization task to improve the training balance. Specifically, we propose Adversarial Image Reconstruction (AIR) as the regularizer to facilitate the adversarial training of the feature extractor. We further design a multi-level Feature Alignment(A) module to enhance the adaptation performance. We refer our work as AIRA-DA for ease of presentation. Our evaluations across several datasets of challenging domain shifts demonstrate that the proposed method outperforms all previous methods, of both one- and two-stage, in most settings. Extensive experiments shows that the proposed method is capable of handling weather variation, cross-camera adaptation and synthetic-to-real-world adaptation.
Software Smart Agent for Taif University Services

When it comes to university life, university services, and communication with staff, things can be difficult and complicated, especially for newly joined students. This paper provides TU-Smart Services application which focus on filling the gap between students, university services and staff with in Ta'if University (TU). Our App consists of four main features: Cody, a smart conversational chatbot agent, an interactive campus map to direct students within the campus, digital wallet to show all the university IDs, and Academic advisor communication session. Cody has been developed to ease the communication and transactions between students, university services and staff, while interactive campus map has been developed to guide students in the university campus. This project aims to increase the benefits for Taif University students and visitors to make their university services more efficient and reliable.
Semantic-Aware Video Compression for Automotive Cameras

Assisted and automated driving functions in vehicles exploit sensor data to build situational awareness, however, the data amount required by these functions might exceed the bandwidth of current wired vehicle communication networks. Consequently, sensor data reduction, and automotive camera video compression need investigation. However, conventional video compression schemes, such as H.264 and H.265, have been mainly optimised for human vision. In this paper, we propose a semantic-aware (SA) video compression (SAC) framework that compresses separately and simultaneously region-of-interest and region-out-of-interest of automotive camera video frames, before transmitting them to processing unit(s), where the data are used for perception tasks, such as object detection, semantic segmentation, etc. Using our newly proposed technique, the region-of-interest (ROI), encapsulating most of the road stakeholders, retains higher quality using lower compression ratio. The experimental results show that under the same overall compression ratio, our proposed SAC scheme maintains a similar or better image quality, measured accordingly to traditional metrics and to our newly proposed semantic-aware metrics. The newly proposed metrics, namely SA-PSNR, SA-SSIM, and iIoU, give more emphasis to ROI quality, which has an immediate impact on the planning and decisions of assisted and automated driving functions. Using our SA-X264 compression, SA-PSNR and SA-SSIM have an increase of 2.864 and 0.008 respectively compared to traditional H.264, with higher ROI quality and the same compression ratio. Finally, a segmentation-based perception algorithm has been used to compare reconstructed frames, demonstrating a 2.7% mIOU improvement, when using the proposed SAC method versus traditional compression techniques.
Social Network Analysis and Data Visualization of Football Performance Preceded to the Goal Scored

Social network analysis reveals a significant set of essential information on the behaviour of the players and teams: such as passing sequences between players in the attacking third position of attacking performance. Despite their usefulness, network metrics related to expected goal values for soccer analysis are minimal. Thus, the study compared network approaches that led to Chelsea FC's goal and playing style in the English Premier League in season 21/22. Moreover, ‘expected goal values’ (xG) show the probability of the goal scored, which can be related to the goals scored passing network. The study used centrality in network analysis such as degree prestige, degree centrality, and betweenness centrality to find the significant contributor of the player's position independently during the match that led to the goal scoring and did not consider the playing style of Chelsea FC. Furthermore, the xG values of shots in every game were visualized using Tableau software. A set of adjacency metrics were computed using the highlight videos of goals for every match, and the results of network analysis found that the most received the ball from their teammates were left wing-back and right wing-back, defensive midfielders and right wing-back have the highest degree centrality and attacking midfielder and left wing-back have the highest betweenness centrality. Furthermore, the statistical data from the visualization such as cross percentage, passing percentage, shots and xG percentage can be used to enhance the team performance.
A Hybrid Path planning approach combining Artificial Potential Field and Particle Swarm Optimization for Mobile Robot

In this paper, a hybrid approach for path planning of mobile robot is presented, combining the Particle Swarm Optimization (PSO) technique and the Artificial Potential Field method (APF). The hybrid approach is used to locate a viable route in an environment with many static obstacles. The path planning of mobile robot using only PSO is computationally intensive. And path planning using APF alone has few inherent problems like getting stuck in the local minima and goal non-reachable with obstacles nearby(GNRON). Therefore the proposed approach overcomes the drawbacks of the APF method and gives the waypoints in less time as compared to path planning done using PSO. This hybrid approach is tested for four different scenarios and results show that this approach solves the drawbacks of APF method.
Revisiting the finance-growth nexus in sub-Saharan Africa: Does the level of income inequality matter?

In this paper, the modulating effect of income inequality in the finance–growth nexus using data from 29 sub-Saharan African (SSA) countries is examined. The study examines i) whether financial development still spurs economic growth in the studied countries, and ii) whether income inequality modulates the effect of financial development on economic growth. Three proxies of financial development and four income inequality indicators are used. The financial development indicators used include the financial development index, financial institutions index, and financial markets index, while income inequality is measured by the Palma ratio, the Gini coefficient, the Atkinson index, as well as the composite inequality (Comp) derived from the principal component analysis (PCA). The Generalised Method of Moments (GMM) has been used to examine this linkage. The study found that, on the whole, the beneficial effect of financial sector development on economic growth in the studied countries is weak, at best. This finding has been corroborated by the results of the interaction models, which show that income inequality interacts with financial development to yield a net negative effect on economic growth in four specifications, but a net positive effect in only two specifications. This finding is not surprising given the high level of income inequality that is currently ravaging some of the SSA countries. Policy implications are discussed.
UDF-GAN: Unsupervised dense optical-flow estimation using cycle Generative Adversarial Networks

In estimating optical flow using convolutional neural networks, a gap in accuracy exists between supervised and unsupervised training because supervised methods can obtain more explicit clues from GT data. In this study, we bridge this disparity by introducing a cycle adversarial mechanism to provide additional flow priors. Specifically, this mechanism eliminates the requirement for paired data and enables unsupervised training. Furthermore, it provides more explicit clues for the network and optimizes the flow performance by adversarial training between the generator and the discriminator. Additionally, we propose a redesigned generator named the Recursion-Attention Flow Generator (RAFG) and Homology Isomerism Discriminator (HID) to adapt the UDF-GAN to flow estimation tasks. Finally, we use SpyNet as a teacher network to direct the UDF-GAN to improve the robustness to mode collapse in the early training phase. The experiments were conducted on various public flow datasets, in contrast to several state-of-the-art flow models for flow estimation, that involve CNN- and GAN-based methods trained in unsupervised and supervised manners, respectively.
Incorporating evolutionary adaptions into the cognitive fit model for data visualization

With the proliferation of data has arisen an array of information visualization tools to help humans convert data to information for better decision-making. This ever-expanding visualization toolset has outrun the development of theories of data visualization usage for decision support. In the current research, we contribute to the development of visualization theories in two ways. First, we incorporate our understanding of evolutionary adaptations into the cognitive fit theory to explain why 3-D surface graphs often outperform traditional 2-D graphs in various sensemaking tasks. Drawing on geon theory to interpret the 3-D advantage, we develop the concept of geonic/non-geonic information representations and tasks. We tested and found support for our hypotheses that geonic representations have a speed advantage for the tasks we studied, derived from the human evolved capacity for geographic orienteering, but accuracy depends on the match of representation and task, as geonic or non-geonic. Our experiment compared the geonic representation (3-D surface graph) to non-geonic planar representations (cluster bar, heat map, and line) in geonic (integration, proportion) and non-geonic atomic (acquisition, comparison) tasks. For our second contribution, we build on this result to expand the cognitive fit model to incorporate classic human information processes and evolutionary adaptations. We suggest that the cognitive fit model can expand to include new advances in neuroscience. We discuss the theoretical and practical implications of our experiment and our expanded model.
Low-phase quantization error Mach–Zehnder interferometers for high-precision optical neural network training

A Mach–Zehnder interferometer is a basic building block for linear transformations that has been widely applied in optical neural networks. However, its sinusoidal transfer function leads to the inevitable dynamic phase quantization error, which is hard to eliminate through pre-calibration. Here, a strongly overcoupled ring is introduced to compensate for the phase change without adding perceptible loss. Two full-scale linearized Mach–Zehnder interferometers are proposed and experimentally validated to improve the bit precision from 4-bit to 6- and 7-bit, providing ∼3.5× to 6.1× lower phase quantization errors while maintaining the same scalability. The corresponding optical neural networks demonstrate higher training accuracy.
CBFISKD: A Combinatorial-Based Fuzzy Inference System for Keylogger Detection

A keylogger is a type of spyware that records keystrokes from the user’s keyboard to steal confidential information. The problems with most keylogger methods are the lack of simulated keylogger patterns, the failure to maintain a database of current keylogger attack signatures, and the selection of an appropriate threshold value for keylogger detection. In this study, a combinatorial-based fuzzy inference system for keylogger detection (CaFISKLD) was developed. CaFISKLD adopted back-to-back combinatorial algorithms to identify anomaly-based systems (ABS) and signature-based systems (SBS). The first combinatorial algorithm used a keylogger signature database to match incoming applications for keylogger detection. In contrast, the second combinatorial algorithm used a normal database to detect keyloggers that were not detected by the first combinatorial algorithm. As simulated patterns, randomly generated ASCII codes were utilized for training and testing the newly designed CaFISKLD. The results showed that the developed CaFISKLD improved the F1 score and accuracy of keylogger detection by 95.5% and 96.543%, respectively. The results also showed a decrease in the false alarm rate based on a threshold value of 12. The novelty of the developed CaFISKLD is based on using a two-level combinatorial algorithm for keylogger detection, using fuzzy logic for keylogger classification, and providing color codes for keylogger detection.
On the Influence of the University Tenure on the Digital Pandemic Stress in Higher Education Faculty

This paper conducts quantitative research on the levels of stress generated in Latin American professors due to the abrupt digitalization of higher education resulting from the COVID-19 pandemic. Specifically, the differences in this digital stress between professors from private and public universities are analyzed. For this purpose, a validated questionnaire has been used and passed to a sample of 750 professors from 20 different Latin American countries, whose responses have been statistically analyzed. As results, it has been obtained that there are no significant differences between the average levels of digital stress of professors of private and public universities due to the pandemic. However, the way in which this digital stress has affected Latin American professors according to gender and age is different depending on the university tenure. As a consequence, some implications and recommendations derived from the results are presented.
Content Disarm and Reconstruction of PDF Files

Content Disarm and Reconstruction (CDR) is a zero-trust file methodology that proactively extracts threat attack vectors from documents and media files.While extensive literature on CDR emphasizes its importance, a detailed discussion of how the CDR process works, its effectiveness, and its drawbacks is not presented.Therefore, this paper presents PdfCDR, the first PDF CDR system in which the validation, the prevention rate, and the received visual similarity effect of disarming and reconstruction are presented and measured.Furthermore, PdfCDR suggests for the first time a novel method dealing with new emerging exploits by automatically converting detection rules to disarm and reconstruction rules.As a result, PdfCDR can prevent evasive attacks without any software upgrades and utilize the cyber security community knowledge to prevent cyber attacks as soon as they are advertised.The effectiveness of the novel PdfCDR against well-known PDF datasets shows that it disarmed not only the malicious components, but the reconstructed file is also usable and functional.However, since CDR relies on understanding the file format, any CDR solution should handle each supported file type separately due to the vast difference in each file format.Hence, this paper focuses on the Portable Document Format (PDF) file type that attackers commonly exploit.The results indicate that PdfCDR successfully CDR 90% of the malicious files while the remaining 10% were encrypted or had abnormal structures compared to the standard and were quarantined.
Improved Light-Weight Target Detection Method Based on YOLOv5

Aiming at the problems of the YOLOv5, such as large model size, enormous amount of computation, and low accuracy of target box regression, a new model with fewer parameters, less computation, faster convergence speed, and higher accuracy was proposed.Firstly, the improved SKConv was used to greatly reduce the number of model parameters and increase the receptive field range of the network.Secondly, C-ECA, an optimized version of the channel attention module ECA, was added to the model to obtain attention information in a cross-channel way, so that the model could more accurately focus on important features among complex features.Then, the MSM structure is designed to effectively improve the feature extraction ability of the network.Finally, the backbone network is deepened to extract more intermediate features, and the depth of the feature pyramid and detection layer are increased accordingly so that the network can make full use of intermediate features and accurately detect more targets.The experimental results show that compared with YOLOv5s, the number of parameters of the final models is reduced by 18.6%, and the calculation amount is reduced by 8.1%, and when tested on the VOC2007 test dataset, mAP@0.5 and mAP@0.5:0.95improved by 3.3 percentage points and 5.6 percentage points, respectively.The improved model has significantly improved the ability of target detection in various complex environments and realizes the target detection model with low parameters and high accuracy, which provides important ideas for the design of lightweight target detection networks.
B-NER: A Novel Bangla Named Entity Recognition Dataset With Largest Entities and Its Baseline Evaluation

Within the Natural Language Processing (NLP) framework, Named Entity Recognition (NER) is regarded as the basis for extracting key information to understand texts in any language. As Bangla is a highly inflectional, morphologically rich, and resource-scarce language, building a balanced NER corpus with large and diverse entities is a demanding task. However, previously developed Bangla NER systems are limited to recognizing only three familiar entities: person, location, and organization. To address this significant limitation, we introduce a novel Bangla NER dataset B-NER, which was created using 22,144 manually annotated Bangla sentences collected from Bangla newspapers and Bangla Wikipedia. This dataset includes a total of 9,895 unique words which were manually categorized into eight different entity types, such as a person, organization, event, artifact, time indicator, natural phenomenon, geopolitical entity, and geographical location. Inter-annotator agreement experiments were conducted to validate the quality of annotations performed by three annotators, resulting in a Kappa score of 0.82. In this paper, we provide an outline of the annotation guideline illustrated with examples, discuss the B-NER dataset properties, and present benchmark evaluations of the dataset. To establish that B-NER is more comprehensive and balanced in comparison to other publicly accessible datasets, we conducted cross-dataset modeling and validation, i.e. trained NER model on one dataset while tested on another, and found that the model trained on B-NER performed the best in that settings. Furthermore, we performed exhaustive benchmark evaluations based on Bidirectional LSTM with fastText embeddings and sentence transformer models. Among these models, fine-tuned IndicBERT achieved noticeable results with a Macro-F1 of 86%. This dataset and baseline results will be publicly available under a CC-BY 4.0 license in the CoNLL-2002 format to facilitate further research on Bangla NER.
A New Dataset for Forged Smartphone Videos Detection: Description and Analysis

The advancement of Internet technology has significantly impacted daily life, which is influenced by digital videos taken with smartphones as the most popular type of multimedia. These digital videos are extensively sent through various social media websites such as WhatsApp, Instagram, Facebook, Twitter, and YouTube. The development of intelligent and simple editing tools has favoured the transformation of multimedia content on the Internet. As a result, these digital videos’ credibility, reliability, and integrity have become critical concerns. This paper presents a video forgery (Copy-move forgery) dataset in which 250 original videos are manipulated mainly by two forgery techniques: Insertion and Deletion. Inserting transparent objects into the original video without raising suspicion is one type of manipulation performed. Another type of forgery presented on the dataset is the removal of objects from the original video without notifying the viewers. The videos were collected from five different mobile devices, namely, IPhone 8 Plus, Nokia 5.4, Samsung A50, Xiomi Redmi Note 9 Pro and Huawei Y9-1. The forged videos were created using a popular video editing software called Adobe After Effects as well as usage of other software such as Adobe Photoshop and AfterCodecs. Since the source of the videos is known, PRNU-based methods can be suitable for applying to the dataset. Experiments were performed using classical and deep learning methods. The results are recorded and discussed in detail, showing that improvements are essential for the dataset. Furthermore, the forged videos of this dataset are comparatively large when compared to other datasets that performed copy-move forgery.
Access control scheme based on blockchain and attribute-based searchable encryption in cloud environment

Abstract With the rapid development of cloud computing technology, how to achieve secure access to cloud data has become a current research hotspot. Attribute-based encryption technology provides the feasibility to achieve the above goal. However, most of the existing solutions have high computational and trust costs. Furthermore, the fairness of access authorization and the security of data search can be difficult to guarantee. To address these issues, we propose a novel access control scheme based on blockchain and attribute-based searchable encryption in cloud environment. The proposed scheme achieves fine-grained access control with low computation consumption by implementing proxy encryption and decryption, while supporting policy hiding and attribute revocation. The encrypted file is stored in the IPFS and the metadata ciphertext is stored on the blockchain, which ensures data integrity and confidentiality. Simultaneously, the scheme enables the secure search of ciphertext keyword in an open and transparent blockchain environment. Additionally, an audit contract is designed to constrain user access behavior to dynamically manage access authorization. Security analysis proves that our scheme is resistant to chosen-plaintext attacks and keyword-guessing attacks. Theoretical analysis and experimental results show that our scheme has high computational and storage efficiency, which is more advantageous than other schemes.