Reviewerly/exHarmony huggingface dataset:
Driving in the Rain: A Survey toward Visibility Estimation through Windshields

Rain can significantly impair the driver’s sight and affect his performance when driving in wet conditions. Evaluation of driver visibility in harsh weather, such as rain, has garnered considerable research since the advent of autonomous vehicles and the emergence of intelligent transportation systems. In recent years, advances in computer vision and machine learning led to a significant number of new approaches to address this challenge. However, the literature is fragmented and should be reorganised and analysed to progress in this field. There is still no comprehensive survey article that summarises driver visibility methodologies, including classic and recent data-driven/model-driven approaches on the windshield in rainy conditions, and compares their generalisation performance fairly. Most ADAS and AD systems are based on object detection. Thus, rain visibility plays a key role in the efficiency of ADAS/AD functions used in semi- or fully autonomous driving. This study fills this gap by reviewing current state-of-the-art solutions in rain visibility estimation used to reconstruct the driver’s view for object detection-based autonomous driving. These solutions are classified as rain visibility estimation systems that work on (1) the perception components of the ADAS/AD function, (2) the control and other hardware components of the ADAS/AD function, and (3) the visualisation and other software components of the ADAS/AD function. Limitations and unsolved challenges are also highlighted for further research.
Low-illumination underwater image enhancement based on non-uniform illumination correction and adaptive artifact elimination

High-quality underwater images are used to extract information for a variety of purposes, including habitat characterization, species monitoring, and behavioral analysis. However, due to the limitation of non-uniform illumination environment and equipment, these images often have the problem of local over- or underexposure due to non-uniform illumination. Conventional methods cannot fully correct for this, and the dark area artifacts generated in the process of enhancing a low-light image cannot be readily fixed. Therefore, we describe a low-illumination underwater image enhancement method based on non-uniform illumination correction and adaptive artifact elimination. First, to eliminate the influence of non-uniform illumination on underwater images, an illumination equalization algorithm based on non-linear guided filtering corrects the non-uniform bright and dark regions of underwater images, and the dark channel prior algorithm and contrast-limited adaptive histogram equalization algorithm are introduced to prevent excessive enhancement of images and generation of dark regions. Then, in order to adaptively eliminate the dark area artifacts generated during the enhancement process, an adaptive multi-scale Retinex color fidelity algorithm with color restore is proposed to improve the color of the image and adaptively eliminate the dark area artifacts of the image. Then, the gray world white balance algorithm is used to adjust the color distortion caused by the attenuation of light. Finally, a multi-scale Retinex model parameter estimation algorithm is proposed to obtain the illumination component and reflection component of the image, and then, the enhanced image is obtained according to the Retinex model. The results show that the proposed method is superior to other algorithms regarding contrast, color restoration, and comprehensive effect, and improves low-illumination image enhancement technology.
Black-Box Watermarking and Blockchain for IP Protection of Voiceprint Recognition Model

Deep neural networks are widely used for voiceprint recognition, whilst voiceprint recognition models are vulnerable to attacks. Existing protection schemes for voiceprint recognition models are insufficient to withstand various robustness attacks and cannot prevent model theft. This paper proposes a black-box voiceprint recognition model protection framework that combines active and passive protection. It embeds key information into the Mel spectrogram to generate trigger samples that are difficult to detect and remove and injects them into the host model as watermark W, thereby enhancing the copyright protection performance of the voiceprint recognition model. To restrict the use of the model by unauthorized users, the index number corresponding to the model and the encrypted model information are stored on the blockchain, and then, an exclusive smart contract is designed to restrict access to the model. Experimental results show that this framework effectively protects voiceprint recognition model copyrights and restricts unauthorized access.
Digital Literacy of Smallholder Farmers in Tanzania

The objective of this study was to assess the digital literacy of smallholder farmers in their agricultural production activities. Based on six proposed factors, the researchers developed questions to guide studying and measuring digital literacy (DL) of smallholder farmers. On a 5-point Likert scale, 23 questions measured the ability of smallholders to access, manage, interpret, evaluate, create and communicate agriculture information online. Moreover, 257 smallholder farmers were involved in the study. To measure DL level, descriptive statistics and mean scores for the responses were calculated. A comparison of low and high levels of DL with demographic variables was conducted. The item-wise distribution of responses show that smallholders have high DL in accessing and communicating information, while they had low DL in managing, integrating, evaluating and creating information. The item’s mean score reveals that most of the responses were average. The overall mean score for the questionnaire was 75.17 + 5.79, and based on demographic characteristics, 58.0% of the smallholders reported high DL. Overall, smallholder farmers have an average level of DL. The study findings of this study may help governments and responsible institutions to develop strategies to ensure smallholders are digitally literate and use digital technologies in agricultural activities.
Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT

The future development of Industry 4.0 places paramount importance on human-centered/-centric factors in the production, design, and management of logistic systems, which has led to the emergence of Industry 5.0. However, effectively integrating human-centered/-centric factors in logistics scenarios has become a challenge. A pivotal technological solution for dealing with such a challenge is to distinguish and track moving objects such as humans and goods. Therefore, an algorithm model combining YOLOv5 and DeepSORT for logistics warehouse object tracking is designed, where YOLOv5 is selected as the object-detection algorithm and DeepSORT distinguishes humans from goods and environments. The evaluation metrics from the MOT Challenge affirm the algorithm’s robustness and efficacy. Through rigorous experimental tests, the combined algorithm demonstrates rapid convergence (within 30 ms), which holds promising potential for applications in real-world logistics warehouses.
Poster: Continual Network Learning

We make a case for in-network Continual Learning as a solution for seamless adaptation to evolving network conditions without forgetting past experiences. We propose implementing Active Learning-based selective data filtering in the data plane, allowing for data-efficient continual updates. We explore relevant challenges and propose future research directions.
Automatic detection of sunspots from solar images using fractional-order derivatives and extraction of their attributes

A huge collection of solar images to visualize sunspot are acquired by various solar observatories spread across the globe. This necessitates efficient tools for detecting and analyzing the sunspots encompassing diverse solar features. Particularly sunspot characterization requires precise intensity refinement to distinguish the intricate sunspot structures the umbra and penumbra. Accordingly, this work delivers a sunspot detection module by forging Fractional-Order Derivative Mask (FODM) in segmentation for embracing the diverse solar regions present in solar images. The mechanism initially localizes the statistical intensity variations and bounds them followed by FODM generalization for effectively outlining the heterogenous regions concerned with diverse solar regions. Rigorous analysis of the developed mechanism in terms of the diverse Receiver Operating Characteristics (ROC) parameters was performed on images obtained from diverse benchmarked solar repositories. The proposed model’s nature of acutely packing high-frequency image features using the localized statistical intensity characterization supersedes the conventional Integer Order Differential Mask (IODM) investigated using the ROC parameters. Furthermore, relative ROC analysis with trending models reveals the consistent superiority of this scheme. Additionally, comparative analysis in terms of the diverse extracted solar features with relevant solar bulletins affirms the model’s efficacy.
Cyberattack Detection in Social Network Messages Based on Convolutional Neural Networks and NLP Techniques

Social networks have captured the attention of many people worldwide. However, these services have also attracted a considerable number of malicious users whose aim is to compromise the digital assets of other users by using messages as an attack vector to execute different types of cyberattacks against them. This work presents an approach based on natural language processing tools and a convolutional neural network architecture to detect and classify four types of cyberattacks in social network messages, including malware, phishing, spam, and even one whose aim is to deceive a user into spreading malicious messages to other users, which, in this work, is identified as a bot attack. One notable feature of this work is that it analyzes textual content without depending on any characteristics from a specific social network, making its analysis independent of particular data sources. Finally, this work was tested on real data, demonstrating its results in two stages. The first stage detected the existence of any of the four types of cyberattacks within the message, achieving an accuracy value of 0.91. After detecting a message as a cyberattack, the next stage was to classify it as one of the four types of cyberattack, achieving an accuracy value of 0.82.
Enhancing Image Clarity: A Non-Local Self-Similarity Prior Approach for a Robust Dehazing Algorithm

When light propagates in foggy weather, it is affected and scattered by suspended particles in the air. As a result, images taken in this environment often suffer from blurring, reduced contrast, loss of details, and other issues. The primary challenge in dehazing images is to estimate the transmission coefficient map in the atmospheric degradation model. In this paper, we propose a dehazing algorithm based on the optimization of the “haze-line” prior and non-local self-similarity prior. First, we divided the input haze image into small blocks and used the nearest neighbor classification algorithm to cluster the small patches, which were referred to as “patch-lines”. Based on the characteristics of these “patch-lines”, we could estimate the transmission coefficient map for the image. We then applied the transmission map to a weighted least squares filter to smooth it. Finally, we calculated the clear image using the haze degradation model. The experimental results demonstrate that our algorithm enhanced the image contrast and preserved the fine details, both qualitatively and quantitatively.
Uni2Mul: A Conformer-Based Multimodal Emotion Classification Model by Considering Unimodal Expression Differences with Multi-Task Learning

Multimodal emotion classification (MEC) has been extensively studied in human–computer interaction, healthcare, and other domains. Previous MEC research has utilized identical multimodal annotations (IMAs) to train unimodal models, hindering the learning of effective unimodal representations due to differences between unimodal expressions and multimodal perceptions. Additionally, most MEC fusion techniques fail to consider the unimodal–multimodal inconsistencies. This study addresses two important issues in MEC: learning satisfactory unimodal representations of emotion and accounting for unimodal–multimodal inconsistencies during the fusion process. To tackle these challenges, the authors propose the Two-Stage Conformer-based MEC model (Uni2Mul) with two key innovations: (1) in stage one, unimodal models are trained using independent unimodal annotations (IUAs) to optimize unimodal emotion representations; (2) in stage two, a Conformer-based architecture is employed to fuse the unimodal representations learned in stage one and predict IMAs, accounting for unimodal–multimodal differences. The proposed model is evaluated on the CH-SIMS dataset. The experimental results demonstrate that Uni2Mul outperforms baseline models. This study makes two key contributions: (1) the use of IUAs improves unimodal learning; (2) the two-stage approach addresses unimodal–multimodal inconsistencies during Conformer-based fusion. Uni2Mul advances MEC by enhancing unimodal representation learning and Conformer-based fusion.
Remote Sensing, Petrological and Geochemical Data for Lithological Mapping in Wadi Kid, Southeast Sinai, Egypt

The Wadi Samra–Wadi Kid district in southeastern Sinai, Egypt, has undergone extensive investigation involving remote sensing analysis, field geology studies, petrography, and geochemistry. The main aim of this study is the integration between remote sensing applications, fieldwork, and laboratory studies for accurate lithological mapping for future mineral exploration in the study region. The field relationships between these coincident rocks were studied in the study area. Landsat-8 (OLI) data that cover the investigated area were used in this paper. The different rock units in the study area were studied petrographically using a polarizing microscope, in addition to major and trace analysis using ICP-OES tools. The Operational Land Imager (OLI) images were used with several processing methods, such as false color composite (FCC), band ratio (BR), principal component analysis (PCA), and minimum noise fraction (MNF) techniques for detecting the different types of rock units in the Wadi Kid district. This district mainly consists of a volcano-sedimentary sequence as well as diorite, gabbro, granite, and albitite. Geochemically, the metasediments are classified as pelitic graywackes derived from sedimentary origin (i.e., shales). The Al2O3 and CaO contents are medium–high, while the Fe2O3 and TiO2 contents are very low. Alkaline minerals are relatively low–medium in content. All of the metasediment samples are characterized by high MgO contents and low SiO2, Fe2O3, and CaO contents. The granitic rocks appear to have alkaline and subalkaline affinity, while the subalkaline granites are high-K calc-alkaline to shoshonite series. The alkaline rocks are classified as albitite, while the calc-alkaline series samples vary from monzodiorites to granites. The outcomes of this study can be used for prospecting metallic and industrial mineral exploration in the Wadi Kid district.
Comparison of Texture, Shape, and Combined Feature Vectors for Classification of Breast Ultrasound Images

The most prevalent malignancy nowadays in women is breast cancer. Breast cancer risk factors include changes in lifestyle, hormonal changes, genetic mutations, radiation therapy, and alcohol consumption. Ultrasound is the predominantly used mode of examination due to its portability, low cost, and lack of ionising radiation. Despite these benefits, the quality of ultrasound images can be lowered by the presence of speckle noise and low contrast. With the advancement of computer technology, medical image processing techniques, and artificial intelligence-based algorithms, numerous opportunities for the research community to investigate the potential of computer-aided diagnosis (CAD) for the classification of breast abnormalities using ultrasound images have arisen. Tissue-specific features must be computed during medical image characterisation; e.g., when analysing breast abnormalities, the texture, as well as the shape of the tumour region, are considered important for diagnostic purposes. As a result, the current study compared the performance of texture, shape, and combined feature vectors on a total of 110 breast ultrasound images collected from a scan centre in Patiala. The results of the experiments show that the PCA-SVM classifier achieves an overall accuracy of 93.9% using feature vectors containing texture features (computed using 2D Gabor wavelet transform) combined with shape features. Radiologists can use the proposed CAD system to verify their diagnosis in the course of their regular medical procedures.
You Can (Not) Say What You Want: Using Algospeak to Contest and Evade Algorithmic Content Moderation on TikTok

Social media users have long been aware of opaque content moderation systems and how they shape platform environments. On TikTok, creators increasingly utilize algospeak to circumvent unjust content restriction, meaning, they change or invent words to prevent TikTok’s content moderation algorithm from banning their video (e.g., “le$bean” for “lesbian”). We interviewed 19 TikTok creators about their motivations and practices of using algospeak in relation to their experience with TikTok’s content moderation. Participants largely anticipated how TikTok’s algorithm would read their videos, and used algospeak to evade unjustified content moderation while simultaneously ensuring target audiences can still find their videos. We identify non-contextuality, randomness, inaccuracy, and bias against marginalized communities as major issues regarding freedom of expression, equality of subjects, and support for communities of interest. Using algospeak, we argue for a need to improve contextually informed content moderation to valorize marginalized and tabooed audiovisual content on social media.
Farklı Dizi Aralığında Eğim Açısının Fotovoltaik Sistemlerin Performansina Etkisi

The optimum tilt angle for a photovoltaic (PV) system depends on the row spacing because it affects the amount of shading on the panels. This study modeled PV systems for four different panel row spacings of 2 m, 2.5 m, 3 m, and 4 m in a fixed 3000 m² area in Konya province, Turkey. For different panel row spacings, the system performances were compared using a constant tilt angle of 35°, expressed as a proper angle for PV installations at the considered location. In addition, the optimum tilt angle is found for four different cases in terms of electricity generation. In systems with 35° tilt angles at electricity were produced annually as 622.77 MWh, 566.49 MWh, 495.36 MWh, and 385.72 MWh, respectively, for panel row spacings of 2 m, 2.5 m, 3 m, and 4 m. In addition, these electricity productions are 6.19%, 4.41%, 2.56%, and 0.92% higher with optimum tilt angles as 1°, 15°, 21° and 27°. Similarly, the Performance Ratio (PR) values obtained with the optimum angles are 20.61%, 8.39%, 4.12%, and 1.44%, higher than the fixed tilt angle cases. According to the economic analysis, systems with a fixed tilt angle for these panel row spacings pay back themselves in 5.13, 4.67, 4.44, and 4.28 years, respectively, while systems at optimum angles pay back themselves in a shorter time by 5.83%, 4.26%, 2.49%, and 0.91%. Furthermore, the highest NPV/INV, IRR, and ROI values were obtained from the system with 3 m panel row spacing with the optimum tilt angle of 21° as 0.915, 20.42%, and 91.57%, respectively, which is techno economically found to be the most feasible case.
Analyzing the Trends of Responses to COVID-19 Related Tweets from News Stations: An Analysis of Three Countries

During the COVID-19 pandemic, news stations have used social media platforms such as Twitter to deliver information to the general public. To understand the trends as well as impact of these posts, we analyze 500 k tweets and responses across 15 news outlets from USA, Canada, and UK, through three research questions. The first question is related to topic popularity where a zero-shot classification algorithm is used to determine what type of COVID-19 related tweets users are mostly interacting with. Then, the second question looks to determine how the audiences differ in their responses between news stations within each country by using a sentiment, emotion, and stance analysis algorithm as well as statistical hypothesis test. Lastly, the third question uses the previous analyzes’ results along with the political leanings of each news station to see if there is a correlation in differences. As a result, we discover that the topic of vaccine is the most popular, audiences in the USA and UK have a considerable amount of differences in their responses, and that the differences in political leanings strongly match with differences in audience response.
Investigating the impact of scenario and interaction fidelity on training experience when designing immersive virtual reality-based construction safety training

Safety training is an effective method to reduce safety issues. Immersive virtual reality (IVR) for safety training has gained attention in high-risk industries, but the trade-off between realism and abstraction in IVR-based training is not fully understood. This study investigated the impact of scenario and interaction fidelity on IVR-based forklift safety training, focusing on presence and usability. Scenario fidelity varied the Level of Development (LOD) in IVR model (high LOD vs. low LOD), while interaction fidelity varied the control interface (steering wheel and pedals vs. keyboard). A 2-by-2 experiment with 120 participants examined high/low scenario and interaction fidelity. Results showed scenario fidelity enhanced presence and usability significantly, while interaction fidelity had no effect. The realism of training scenarios is more influential for presence and immersion than control interface realism. These findings aim to balance IVR simulation fidelity and learning outcomes and help guide the development of cost-efficient IVR-based safety training.
Enhancing stability control of inverted pendulum using Takagi–Sugeno fuzzy model with disturbance rejection and input–output constraints

Abstract The Takagi–Sugeno (T–S) fuzzy model is a versatile approach widely used in system control, often in combination with other strategies. This paper addresses key control challenges linked to the T–S system and presents important considerations to ensure its successful application using the Lyapunov theorem. One crucial aspect is determining the optimal number of premise variables and selecting accurate fuzzy rules for the T–S model. Additionally, the theorem based on Linear Matrix Inequality (LMI) is developed to enable effective disturbance rejection. To enhance stability control, constraints are imposed on the output angle and control input of a rotary inverted pendulum (RIP). By integrating T–S fuzzy control, disturbance rejection, and input/output constraints, robust stability in controlling the RIP is achieved. Extensive simulations are performed to showcase the efficiency of the suggested method, and the simulation results are thoroughly discussed and analyzed to verify the efficacy of the control method.