Reviewerly/exHarmony huggingface dataset:
A Low-Jitter Hand Tracking System for Improving Typing Efficiency in Virtual Reality Workspace

Virtual reality technology has the potential to revolutionize immersive experiences in various applications, including office settings. However, efficient text entry in VR remains a significant challenge. This study addresses this challenge by proposing a machine learning-based solution, the 2S-LSTM typing method, to enhance text entry performance in VR. The 2S-LSTM leverages the back of the hand image. It employs a two-stream Long Short-Term Memory (LSTM) network, combined with a Kalman Filter (KF), to improve hand position tracking accuracy and reduce jitter. The results from questionnaire-based evaluations and typing data analysis demonstrate the superiority of the 2S-LSTM solution over existing solutions like Oculus Quest 2 and Leap Motion in terms of typing efficiency, fatigue reduction, accurate hand position replication, and positive user experience. These findings contribute to the advancement of text entry in VR environments and pave the way for immersive work experiences in the office and beyond.
Fast Data Augmentation for Scene Text Recognition Using CUDA

Scene Text Recognition (STR) is a task in computer vision that is used to read texts in natural scene images. STR currently suffers from data distribution shift due to the lack of large real datasets for training. Data augmentation is a method that has been used in multiple studies to address this issue. However, performing augmentation also introduces computational overhead during training. In this paper, we propose FastSTRAug, a CUDA-based library of 36 augmentation functions specifically designed for STR. When executed through varying image sizes, FastSTRAug is observed to be significantly faster over its serial counterpart in most functions, reaching up to 380x speedup on larger images.
Security Preserving Distributive n-Party Computation<sup>*</sup>

In n-party Computation, data from different sources are integrated to achieve a common goal, which can be beneficial for different applications. However, this goal may be at risk due to the fact that this corresponding data can be the target of attacks from outside attackers as well as inside ones. On the other hand, in Secure n-party Computation, the data can be processed by preserving security and privacy in such a way that no party can know the data of the others. Several schemes have been proposed to address the issues regarding n-party computation. However, some of them are not efficient in terms of computation and communication, or some of them have used a Trusted Initializer (TI), a third party, which is considered as trusted, for communicating between different parties. Therefore, to address these issues, we propose an n-party secure computation protocol without using any Trusted Initializer (TI). We formally simulate our protocol using Automated Verification of Internet Security Protocols and Applications (AVISPA) as well as we elaborate on the formal and informal security analysis of our proposed protocol. Performance analysis of our proposed protocol is also carried out and it is observed our proposed protocol is secure and protects the privacy of the system.
Attention-Fused Shallow Network for Underwater Object Detection

Although generic object-detection methods have achieved a great deal, it needs more exploration for underwater images. Underwater object detection (UOD) is associated with challenges such as degraded image quality, low visibility, low contrast, colour shift, and limited computational capacity availability on the deployment environment. Moreover, most previous studies on deep learning-based underwater object detection have generally concentrated on increasing detection accuracy by utilizing huge networks. This work proposes a two-stage method where in the first stage, underwater images are enhanced based on PCA-fusion method. This step involves mutiple image enhancement steps such as color correction process, followed by the White Patch Retinex Algorithm for white balancing. On the colour-corrected image, we apply global histogram equalization, unsharp masking, and median smoothing separately to improve the contrast, sharpen the image, and reduce the white patch noise. Then, we create a single enhanced image by combining the results of the three methods using the principal component analysis (PCA) based fusion method. Finally, in the second stage, the enhanced image is passed as an input to the attention-fused lightweight single-stage object detection model for localization and classification. Experimental results show that the proposed method outperforms the state-of-the-art algorithms based on various image enhancement and object detection evaluation metrics on the URPC2019 dataset.
Optimizing Arabic Named Entity Recognition through Active Learning and AraBERT

This study explores advancements in Arabic named entity recognition (NER) through the application of active learning techniques and the utilization of large language models, including AraBERT. The research investigates the effectiveness of active learning in selecting informative instances for annotation while leveraging the power of AraBERT to enhance NER performance. The study aims to adapt AraBERT to handle the unique characteristics and requirements of Arabic text. By employing these approaches, the research contributes to the development of more accurate NER systems for Arabic, addressing challenges related to limited labeled data.
Complex Ontology Alignments using OWL Ontologies Embedding

The semantic heterogeneity issue in the information integration can be solved by using ontology alignment. The goal of the ontology alignment technique is to locate concepts that are semantically identical in two ontologies. But, one of these alignments’ drawbacks is their lack of expressiveness, which can be accounted by using complex alignments. To deal with it, an effective strategy is to model ontologies in vector space and compute their similarity scores to determine the correlation levels. In this paper, a semantic embedding-based ontology matching technique for OWL 2 ontologies is employed to calculate entities’ resemblance in order to increase the alignment accuracy. This technique is reinforced by a stable marriage-based alignment extraction algorithm to establish high-quality matching. The experimental investigation analyses the performance to the benchmark track supplied by the Ontology Alignment Evaluation Initiative (OAEI). Experimental findings demonstrated the effectiveness of our matching method.
Comprehensive Analysis of Image Registration Techniques on Brain MR Images

Medical image registration is an important preprocess of image-guided systems. Since image registration brings the images to the same coordinate system of the specified reference image, image registration should not be neglected to be able to make accurate comparisons between results obtained from medical images. Basically, registration is an optimization problem. The parameters of the specified transformation algorithm are optimized based on specified functions and parameters of registration. In this study, T1-weighted structural 3D brain MR images on IXI dataset have been registered into reference image by the affine transformation in the proposed registration method. During experiments, the effects of several parameters and functions on registration performance have been investigated with different preprocessing techniques applied to brain MR images. After several experiments, the most successful outcome of various experiments was achieved by using Powell optimization function along with Linear Interpolation, when applying Median Filter with CLAHE to images in the suggested registration method. The NCC was used to compare the registration results. The study’s results demonstrate that the proposed registration method outperformed the widely-used registration tool SPM8 with mean NCC of -0.753.
Ontology-based representation for emergency management response

The emergency response process is a complex task that involves the evaluation of various elements to make crucial decisions. However, this complexity poses challenges to communication and information sharing. In light of this, the application of Semantic Web technologies has emerged as a promising solution to overcome these challenges. This paper presents the development of an ontology for emergency response to support knowledge sharing and improve decision-making processes by facilitating communication and information sharing among emergency response actors. The developed ontology, named ERMO (Emergency Response Management Ontology) was implemented using the Web Ontology Language (OWL) in Protege 5.5.0. To ensure the efficacy of our ontology, we adopted a rigorous criteria-based evaluation approach. Finally, in order to validate our approach, we developed a Python framework. This framework comprises a decision-making tool designed to provide stakeholders with the most relevant solution to an emergency stituation.
Multiple Small-Scale Object Detection in Aerial Vehicle Images using Standard or Optimized YOLO Detectors

Widespread employment of low-altitude aerial vehicles that are equipped with cameras and communication devices has increased the range of mobile surveillance applications. Object tracking, anomaly detection, synopsis view construction, or statistical analysis systems using aerial vehicle images require efficient multiple small-scale object detection as an initial stage. In this study, standard YOLO object detectors were trained with the images of VisDrone-2019 using computational resources of a cloud platform, and these detectors were optimized for various hardware systems considering their embedded use in aerial vehicles. Comparisons between six different YOLO versions indicated that YOLOv8-1280 and YOLOv7-1280 were the most precise but the slowest detectors for small-scale objects in aerial images with their 0.489% and 0.396% mean average precision (mAP) values, respectively. However, the computational speeds of YOLOv8-640 and YOLOv7-640 were almost quadrupled without significant precision loss. According to the experiments that compared the standard and optimized versions of YOLOv3, YOLOv4, and YOLOv5, while the optimized detector versions that were generated using TensorRT could process about 2 times more frames per second (fps), up to 6% reduction in their mAP values was observed. Overall, YOLOv5-640 was the best model in trading off the mAP values for fps with its 5.67 and 65.56 fps values in Jetson Nano and Jetson Orin.
Twice-Trained Agglomerative clustering approach using topic modeling over Generic Semantic Core Knowledge Graph

Topic Modeling (TM) can act as a bridge linking unstructured text data to a structured knowledge representation in a Knowledge Graph (KG). The Latent Dirichlet Allocation (LDA) is a commonly used distributional term clustering technique in this regard. However, existing distributional term clustering approaches have not utilized LDA as a bottom-up training strategy with prior knowledge to cluster semantically related terms as concepts across different domains for building and enhancing a GSCKG. We propose to employ a Twice-Trained Agglomerative hierarchical framework using LDA over Generic Semantic Core KG (T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> AggLDA-GSCKG), outlined in five steps. We aim to align term topics with the predefined Core Concepts (CCs) of SCKGs, thereby designating modules of a GSCKG that facilitate both its building and enrichment. Our goal is to adapt the LDA term clustering process by utilizing a topic seed-based LDA model to consider these CCs. During the 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> LDA training, we endeavor to identify hypernym and related relations among noun phrase patterns to construct SCKG, and during the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> training, we aim to enhance it. To achieve this, we will boost the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> training LDA input data by benefiting from CCs’ two prior knowledge techniques for topic-seed terms incorporation namely seed key knowledge injection and entity masking. The evaluation results show that our proposal has an overwhelming term clustering performance over CCs. It outperforms other unsupervised and semi-supervised distributional baselines on two datasets related to fish hunting and ontology domains, with nearly 13 times higher precision compared to that of normal LDA training.
Evaluating the Impact of Perceptually Uniform Encoding on the Performance of Feature Descriptors for HDR Image Retrieval

Recent advances in High Dynamic Range (HDR) imaging have led to various research studies. The features extraction of this kind of images is considered as one of the crucial tasks to several multimedia applications. In this paper, we look forward to testing and studying the impact of the Perceptually Uniform (PU) encoding on HDR feature descriptors. Specifically, we propose to measure and compare the effectiveness of Convolutional Neural Networks (CNN) and Scale Invariant Feature Transform (SIFT) descriptors in the context of HDR image retrieval. Experimental evaluations show that the PU encoding leads to substantial performance improvements over the linear HDR content for the two above-mentioned descriptors. These experiments also demonstrate that the CNN features are more effective than SIFT ones according to the mean Average Precision (mAP) scores. Furthermore, SIFT descriptor is inappropriate for linear HDR content.
PuzzleNN: A Neural Network for Image Segmentation Based on Clustering

In order to create a system for Image Semantic Segmentation that works similarly to the human way of approaching visual problems, we decided to use divide-and-conquer to break an image into smaller pieces, called microimages. We use Spectral Clustering to understand the features and group them to identify the location of different objects in the image. We trained a Convolutional Neural Network to compute the affinity matrix required by Spectral Algorithm. We managed to obtain competitive results on the Berkeley Segmentation Dataset and we hope to continue to improve the method.
Leveraging Convolutional Neural Networks for Malaria Detection from Red Blood Cell Images

In recent years, artificial intelligence (AI) has started to be used more and more in the medical field. This paper presents a study focused on malaria classification based on segmented blood cells from images collected from the National Institutes of Health (NIH) database. The research involved the development of a handcrafted convolutional neural network (CNN), as well as experimentation with various fine-tuning approaches using the VGG16 architecture. The conducted experiments have yielded promising results, providing empirical evidence for the potential effectiveness of these techniques in future applications. The augmented CNN achieved an impressive accuracy of 96.51%, while the VGG16 fully trainable model outperformed it with an accuracy of 96.69%. A problem that needs to be analyzed more carefully in the future concerns the explainability of the results so that they can be used with confidence by healthcare professionals.
A Hybrid Machine Learning Approach for Automatic Experts Recommendation Systems

The Internet’s vast amount of content has led to the creation of recommendation systems that help users find what they need. Among these systems, there is a growing field known as expert recommendation systems. These systems aim to identify highly knowledgeable individuals in specific topics by analyzing their activities and the content associated with them. When a user enters a topic or query, the system generates a ranked list of people who are experts in that area.In this context, we present in this paper a new approach for automatically experts recommendation based on a hybrid machine learning approach.
Survey semantic Arabic language in deep learning based recommendation system

Due to problems, Arabic-speaking internet users have surged, although nothing is done on it. It is challenging to develop a repliable recognition system (RS) for cursive languages such as Arabic. Variations in text size, fonts, word semantics, user Arabic region, etc. complicate these issues. Deep learning models can model big datasets and handle them. Good features can be selected and learned consecutively by both CNNs and RNNs. In numerous studies, both of these neural networks have proven to be superior than their counterparts. This is true for text recognition, voice recognition, and several NLP tasks (NLP), but they are not qualified to deal with the semantics of the text, so we decided to find the best DL technique for semantic Arabic language from a lot of research to be a survey to other searchers. Our paper compared different algorithms and their accuracy.
Novel Approach For Scene Semantic Segmentation Using The Recurrent-Based UNET

In this paper, we propose a novel approach for semantic segmentation based on Deep Learning methods for Autonomous Driving. Our main idea is to develop a fully convolutional neural network (CNN) architecture for semantic segmentation based on the U-Net model. For that, we propose a model with different layers such as a convolutional layer, pooling layer, dropout layer, and fully connected layer. The experiment results obtained, using the CamVid dataset, show that the choice of the number of periods and the depth of the network have a great influence on the best results.
Using Embedding Keyphrases to Evaluate Arabic Text Summaries

Evaluating the quality and effectiveness of text summarization techniques is essential to ensure accurate and informative summaries. In this paper we present a novel method for evaluating Arabic text summarization based on measuring the similarity between embedding keyphrases. We utilize a pretrained BERT model to represent each keyphrase as a dense vector. Then, we quantify the similarity between keyphrases in the system and the human summary. We tested several lengths of keyphrases. After, we built a predictive model by combining different keyphrases based features with diverse linguistic quality features. To evaluate the effectiveness of our method, we compare our embedding keyphrase similarity method to traditional metrics such as ROUGE. Our experiments demonstrate that embedding-based evaluation provides a more nuanced assessment of the summarization quality, considering the semantic similarities between keyphrases. With the incorporation of semantic similarities, our approach captures the contextual relevance of keyphrases and enhances the evaluation process.
Learning Weight of Losses in Multi-Scale Crowd Counting

In this work, we improve the state-of-the-art in crowd counting by further developing a recently proposed multi-scale, and multi-task crowd counting approach. While most of the studies treat density-based architectures, this study proposed a point-based method for crowd analysis. We propose automatic, and optimal weight assignment to constituents of the loss function. This approach, which is applied to each patch, ensures that the weight parameters are updated in each epoch, and added to the optimizer with model parameters rather than remaining constant. For validation of our proposed approach, we use three popular crowd counting datasets, ShanghaiTech A, ShanghaiTech B, and UCF_CC_50. The performance of our approach exceeds the performances of the other studies on the ShanghaiTech dataset, and is highly competitive with the performances of the other studies on the UCF CC 50 dataset.
An Enhanced Feature Selection Approach Using Capuchin Search Algorithm for high-dimensional Biological Data Classification

Meta-heuristic algorithms were effectively utilized in solving a wide array of problems in business, science, data mining, and finance. They are population-based approaches that develop novel optimal solutions based on the natural behaviors of creatures. A meta-heuristic method implied in reducing the number of present features and improving classification accuracy is the wrapper feature selection approach which was specifically proposed in this paper. This approach was based on the Capuchin Search Algorithm (CapSA) [1], which is mainly inspired by the functional behavior of Capuchin monkeys in food-searching actions through leaping, swinging, and climbing, all implemented in solving constrained and global optimization problems. This paper aims to enhance the CapSA algorithm to search in a binary search space rather than a continuous one. The proposed improvements on the algorithm can efficiently outperform other present algorithms with fewer selected features, less computational time, and high solution accuracy. The enhanced algorithm, named (EbCapSA) was tested afterwards on several biological benchmark datasets and then followed by a broad comparative study with four other algorithms, including the Gray Wolf Optimizer (GWO) [2], Particle Swarm Optimization (PSO) [3], the Firefly Algorithm (FA) [4], the Whale Optimization Algorithm (WOA) [5] and the original CapSA Algorithm. Overall results rendered a superiority of the proposed approach and clear outperformance based on the quality of the final solution in searching for the most favorable feature subsets.
AI Based Video and Image Analytics

Physical site security is a strategy for protecting against threats and mitigating the potential negative effects of security incidents. Potential threats must be identified and prioritized according to their criticality and potential impact. The abnormal activity analysis in video scene is very difficult due to several real world constraints. AI-based video surveillance systems are able to analyze video streams in real time and automatically detect suspicious behavior. Using machine learning, these systems can recognize normal behavior patterns and identify abnormal behavior indicating a threat or suspicious activity. This paper develops and discusses a solution for video surveillance based on artificial intelligence (AI), underlying deep learning implementation technology involved. The proposed solution meets the security requirements and allows security operators to react quickly to potential incidents and prevent dangerous situations.
Efficient Three-party Boolean-to-Arithmetic Share Conversion

The advantage of mixed-protocol multi-party secure computation frameworks lies in their ability to utilize different sharing types optimally for diverse tasks. A key module in these frameworks are Boolean-to-arithmetic secret sharing conversion protocols, which transfer a secret value from Boolean secret sharing to arithmetic secret sharing. This conversion process can either take in the Boolean secret sharing of a bit or a secret binary string. In this work, we suggest the application of an innovative correlated random tuple for this task in the semi-honest three-party (3PC) setting. This tuple provides the basis for building Boolean-to-arithmetic share conversion protocols. Specifically, we propose two such protocols in the semi-honest 3PC setting, the first protocol takes as input the Boolean secret sharing of a bit, and the second protocol takes as input the Boolean secret sharing of a secret binary string. When it comes to concrete efficiency, the first protocol shows superior performance compared to the existing state-of-the-art in ABY3 (CCS ’18). It achieves this by reducing the total required communication from 2ℓ bits per party to 4ℓ/3+1 bits (including 4ℓ/3 bits in the setup phase, and 1 bit in the online phase) per party, while maintaining a single round of optimized communication. On the other hand, the second protocol involves two rounds of online communication and its communication cost is comparable to that of ABY2.0 (USENIX’21) that relies on correlated oblivious transfer.