Reviewerly/exHarmony huggingface dataset:
Cognitive aspects of compound translation: Insights into the relation between implicitation and cognitive effort from a translation process perspective

In this paper we analyse translation of compounds from English to German relating our observations to the phenomenon of implicitation, explicitation and structural equivalence using translation process data from the CRITT translation process research database (TPR-DB). We look into complex nominal phrases, e.g. nominal compounds in English-German translations. We determine a number of product and process features that have an impact on cognitive effort during compound translation. Our results show that in most cases translators opt for the structural equivalent, and that choosing a direct equivalent requires less cognitive effort than choosing an implicitation. This is explained by the fact that direct equivalents can be easily activated whereas changing the structure and using a more implicit, less typical translation requires more effort, which in turn leads to longer production pauses.
Granting Inventor Status to AI in Biotechnology Patents: A Regional Perspective of the Patent Laws in the Gulf Cooperation Council Countries

This work investigated artificial intelligence AIand the development of AI in the Gulf Cooperation Council (GCC) countries, including AI's applications in biotechnology and the current status of AI in some international patent systems. We explored the importance of granting legal protection under patent laws for inventions derived from AI for GCC countries and address the questions: Can AI be an “inventor” under GCC patent laws and are there are any positive impacts to considering AI as an inventor? We concluded that the issue of recognizing AI as an inventor has become necessary, and updating GCC patent laws and policies is required to keep pace with challenges and technical developments.
Multi-Dimension Compression of Feed-Forward Network in Vision Transformers

Vision Transformers (ViTs) have recently made a splash in computer vision domain and achieved state-of-the-art in many vision tasks. Nevertheless, due to their vast model size and high computational costs, rare transformer-based models are adopted in real-world applications. Since the computational costs of attention operation is the square of the input size, some compression methods for the Multi-Head Self-Attention (MHSA) module have been proposed, reducing its FLOPs successfully but almost without parameters reduction. Meanwhile, the number of parameters and computational costs in the Feed-Forward Network (FFN) module exceeds the MHSA larger, while its compression technologies have not been delved deeper. Consequently, we focus our insight on the compression of FFN layer and present a pruning method named Multi-Dimension Compression of Feed-Forward Network in Vision Transformers(MCF), which greatly reduces the model’s parameters and computational costs. Firstly, we identify the critical elements in the output of the FFN module and then employ them to guide the irregular sparsity of this layer, recognizing insignificant elements of FFN layer that have less impact on the output. Successively, to discard the insignificant elements, we transform the irregular sparsity into regular sparsity and prune them, thus reducing the models’ parameters and getting a substantial speed-up during inference. Extensive results on ImageNet-1K validate the effectiveness of our proposed method, which obtains significant parameters and computational costs reduction with almost unimpaired generalization. For example, we compress DeiT-Tiny with 42% reduction in FLOPs and 33% reduction in parameters, almost without losing accuracy on the ImageNet dataset. Further, we verify the effectiveness of our method in the downstream task, using the pruned DeiT-Small as the backbone for the object detection task on the COCO dataset, gaining revenue without compromising its performance.
A Bayes Estimate Density Fuzzy Modular function for improving supply chain sustainability through blockchain entropy prediction

This study triangulates three interrelated and interdependent goals. First, we provide a framework to establish the supply chain (SC) theory within the linchpins of the nine waves of sustainability theory. Second, we provide a real-world example of how blockchain operates to support the premise that hacking a blockchain requires very large capacities of computing power. We also demonstrate a method of detecting the possibility of hacking individual nodes that supply information to the blockchain. Third, we utilize the blockchain and gather thousands of data points and prove statistically that the tokenization of the oil and gas industry will increase market liquidity, market volume, profitability, and sustainability while reducing transaction time. The robotic blockchain is investigated as a mechanism for improving the efficiency of natural gas SC resource use through fuzzy modular function hashing and salting algorithms and entropy density.
A Stochastic Algorithm Based on Cable-stayed Bridges Model for Stochastic Optimization in Machine Learning

Abstract This paper takes the economic cost of road design as the objective function to study the optimization of road construction cost. It is well-know that stochastic optimization is widely regarded as one of the most important and difficult problems in optimization and machine learning. It is noted that various methods for stochastic optimization problems are sensitive to the learning rate (step-size), which is usually underestimated. Therefore, two new stochastic conjugate gradient algorithms are proposed in this paper. We adapt two modified line search techniques to set the stochastic optimization. One uses the modified Armijo line search technology, the other uses the modified weak Wolfe-Powell line search technology. The new search direction has the properties of sufficient descent and trust region, which makes the establishment of global convergence of the two algorithms very understandable and clear. Finally, numerical experiments show that the two new stochastic conjugate gradient methods are competitive compared with other classical methods.
Can There be a Biometric Parameter for Voice?—An Investigation Into the Vocal Cry of Twins

The voice of an individual is the result of interaction between the laryngeal and supra-laryngeal structures of a person. Since the laryngeal and supra-laryngeal structures differ from one person to another, the voice produced by each person becomes unique and recognizably different from another person. If this uniqueness in voice can be found in the acoustic parameters of the voice, it can contribute to the fields of forensic speaker identification and voice biometrics. The best methodology for such a study would be a comparison of the voices of twins who have a number of similar characteristics.To find out whether it is possible to perceptually differentiate the cry of identical twins from each other. To compare the acoustic parameters of cry among the twin pairs to find out the differentiating parameter.This was an observational longitudinal study. Nineteen pairs of twins less than 1 week old who had completed 34 months of gestation period with no major congenital abnormalities were included in the study. The PRAAT software was used for the analysis of the voice samples.The present study on neonates could not identify one single differentiating acoustic parameter among the twins. We infer from the study that rather than an individual parameter, it could be a complex interaction of different parameters that could give individual identity to a person's voice.The high similarity noticed in the acoustic parameters among the twin pairs, suggests that rather than individual parameters, it could be a complex interaction of different parameters that could give individual identity to a person's voice. The study can offer information to automatic speaker recognition, Forensic speaker identification, and voice biometry.
An Enhanced Transformer-Based Approach with Meta-Ensemble Learning for Arabic Sentiment Analysis

Sentiment analysis represents a fundamental task in natural language processing (NLP) critical for extracting and analyzing emotions and opinions represented in textual data. The complexity of the Arabic language presents unique obstacles to accurate sentiment categorization in Arabic sentiment analysis. As cutting-edge NLP models, transformers have demonstrated exceptional performance on various tasks, including sentiment analysis. However, because of the multiplicity of pre-trained transformer models available, fine-tuning for best performance is required, making the procedure time-consuming and demanding significant knowledge. To overcome these challenges, this research paper introduces an ensemble learning approach consolidating multiple transformer models into a unified framework. In the initial stage, a set of baseline transformer models, comprising AraBERT, CAMeIBERT, and Roberta, undergo training and fine-tuning. Subsequently, three distinct ensemble fusion techniques-voting, weighted voting, and meta-learning-are applied to aggregate the predictions generated by these baseline transformer models. Conducting comprehensive experiments on the Egyptian Arabic Corpus, the paper reveals transformer models' superiority over existing methods applied to the corpus. Furthermore, the proposed ensemble approach exhibits enhanced performance for these transformer models, resulting in even more promising outcomes in sentiment classification tasks.
Achieving a Zero False Negative Rate Unsupervised Detection on Controller Area Network (CAN) Bus with LSTM-Based Autoencoder

The increasing complexity and connectivity of modern automotive systems have introduced new challenges in ensuring the security and reliability of Controller Area Network (CAN) bus communication. Cyber-attacks on CAN bus networks can lead to severe consequences, including vehicle malfunctions and compromise of passenger safety. As a result, there is a pressing need for efficient and effective anomaly detection mechanisms to identify and mitigate potential security breaches. TThis paper presents a practical anomaly detection approach that harnesses the capabilities of autoencoders and Long Short-Term Memory (LSTM) neural networks to fortify CAN bus cybersecurity with a focus on real-world implementation. The proposed method leverages the autoencoder's ability to learn the normal behavior of the CAN bus traffic and identify deviations from it, while the LSTM architecture provides temporal sequence modeling to capture dynamic patterns in the data.
A Run-Length and Discrete Cosine Transform Based Technique for Image Splicing Detection

Digital images have emerged as the most popular means for sharing information in articles, newspapers, and even courtrooms. However, the widespread use of advanced digital imaging tools has made it easier to forge images. One such technique is image splicing, where multiple source images are merged into a single destination image to conceal or alter its content. Image splicing is an effective forgery technique, as it is difficult to detect by the naked eye. Detection of image splicing is a pattern recognition problem, based on finding image features that are sensitive to splicing. In this paper, we present a new image splicing detection technique based on Run-length features and discrete cosine transform (DCT). Our proposed technique uses support vector machine (SVM) to classify authentic and spliced images. We used SVM instead of generative models to reduce energy consumption and carbon footprint in machine learning applications. Experimental results show that our technique achieves a detection accuracy of 85%, outperforming other steganalysis-based techniques.
A Comprehensive Review on Transformers Models For Text Classification

The rapid progress in deep learning has propelled transformer-based models to the forefront, establishing them as leading solutions for a multiple NLP tasks. These tasks span a wide spectrum, encompassing text classification activities like sentiment analysis, question answering, natural language inference, and news classification. Transformers offer numerous noteworthy benefits compared to alternative language modeling strategies. They possess a remarkable capacity for parallel processing, enabling them to handle multiple segments of a sequence concurrently, thereby significantly expediting both training and inference. Furthermore, transformers excel in capturing extensive contextual relationships within text, thereby enhancing their comprehension of the broader context and resulting in the generation of more logically coherent textual content. Additionally, transformers exhibit heightened adaptability and scalability, simplifying their utilization across various tasks. Hence, this review paper aims to analyze in-depth the most popular transformer techniques specifically applied to text classification tasks: the various BERT models. We also provide an overview of more than 109 research efforts that used various BERT models in text classification, determining the methodology used. In addition, we describe the three variants of the transformer model with an illustration of the most common models for each variant and the appropriate NLP tasks for each variant.
A Ground Truth-Free Metric for Evaluating Depth Estimation Techniques

Although monocular depth estimation is the mainstream technique for depth estimation, existing methods for its evaluation are still limited. This is because it is hard to get ground-truth depth maps except for specific datasets with a small number of samples. This paper proposes a novel deep learning-based technique for evaluating depth estimation models without ground-truth. The basic idea is to use object detection and segmentation model to analyze specific characteristics of the depth maps. In particular, we address edges preservation for the target objects. Hence, we can perform model evaluation for objects across the different depth maps without the existence of ground-truth. We validated the proposed metric by showing the accuracy of four different depth estimation models using ground truth depth maps from KITTI benchmark dataset. Experimental results present the effectiveness of the proposed evaluation metric which highlights the promise of our approach for depth maps evaluation.
Aegyptos: Mobile Application for Hieroglyphs Detection, Translation and Pronunciation

Hieroglyphs have always been fascinating with their stories and the ability to be read in several ways rather than one, which is a challenge in itself to be translated to modern languages. However, the full experience of getting familiar with these captivating historical stories will not be fulfilled if this translation was not on spot, and hence the tough challenge is met. The idea of Aegyptos was brought up to help learn how to read Hieroglyphs with respect to how the same symbol can have two different meanings according to the context, and also help to pronounce them by using a tool at the palm of users' hands with just their phones' live cameras. Performing segmentation on the symbols using different segmentation techniques like Otsu Thresholding, while a lightweight CNN known as SqueezeNet is used for classification with the help of an API to translate the script into a language understood by the user.
Object-Based Video Archive Summarization

Nowadays, footage from surveillance cameras has a huge amount of data and is very long and exhausting to watch. This is where our problem lies, as the need to efficiently explore these videos quickly is rising. So video summarization aids people in exploring videos efficiently by capturing the most important frames in a video. In this paper, we provide a system that summarizes long video footage into short videos displaying the object of interest to the user. The results are very promising, as the system shows the original video where the objects are being tracked, and the customized video shows the summarized movement where the object of interest is the only thing in the video tracked in all the frames of the original video and last is the summarized video which is after extracting the keyframes from the generated customized video, and this video only shows the essential parts of the original video related to this object. There is a percentage of error due to the usage of the Yolo model and the tracking algorithm which are not very accurate as every system has a percentage of error, for static object videos, the duration decreased till it reached 0 seconds, and the compression ratio is 0.005. For nonstatic object videos, the duration on average decreased the half and the size decreased by more than half of the original size. Regarding the summarized video, the duration, size, and compression ratio compared to the customized video did not change much but there was a huge difference if compared to the original video.
Augmented Reality Applications in the Automotive Industry

This research paper examines the implications and transformative capabilities of Augmented Reality (AR) within the automotive landscape.We examine how AR catalyses radical changes across various automotive functions, from design and manufacturing to customer engagement and vehicle operation.While AR is poised to elevate operational efficiencies significantly, it also presents challenges, such as AR content authoring complexity and hardware constraints that restrict mass consumer adoption.This paper surveys the current AR state-of-the-art technology, systems and pivotal automotive applications.The primary objective is to offer a thorough comprehension of the underlying concepts, methodologies, and applications that underpin the integration of AR within the automotive industry.It concludes by discussing potential barriers to AR implementation and outlines future avenues for research, including software scalability and the integration of cloud and edge computing to alleviate device limitations.
CAB:Cloud Security Assessment and Brokerage

In today's ever-evolving digital landscape, the Cloud Assessment Brokerage (CAB) system emerges as a beacon of innovation, offering enterprises a trustworthy and effective solution for cloud security assessment and brokerage. CAB's foundation rests on the integration of the Cloud Assessment Questionnaire (CAIQ), which facilitates a comprehensive examination of clients' security needs. This information is then subjected to an advanced Multi-Criteria Decision-Making (MCDM) model, bolstered by the formidable Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithms. By seamlessly blending AHP and TOPSIS, CAB empowers businesses with a robust mechanism for meticulously evaluating and selecting the most suitable Cloud Service Provider (CSP) in accordance with their unique security prerequisites. CAB's MCDM model takes into account an array of essential criteria, encompassing data confidentiality, availability, integrity, and various other security measures. The culmination of this process results in a well-informed recommendation for the optimal CSP, providing businesses with a dependable and efficient solution to navigate the intricate terrain of cloud service providers. By aligning their chosen CSP with their specific security requirements, organizations can confidently safeguard their data and effectively mitigate risks.
AraSentiment: Arabic Sentiment Analysis on Data using Machine Learning, and Transformers

A natural language processing method called sentiment analysis automatically detects and measures subjective information in text or audio data. It may be used in many different situations. However, due to the complexity of the Arabic language and the scarcity of labeled audio datasets, sentiment analysis on Arabic audio data poses particular difficulties. This study suggests a fresh method for analyzing Arabic sentiment in audio data utilizing machine learning and transformers. With the Count Vectorizer and n-gram techniques serving as the feature extractors, the objective is to assess the effectiveness of several machine learning algorithms, such as AraBERT, Linear Regression, Decision Tree, Random Forest, and Naive Bayes. The suggested method seeks to achieve high accuracy in sentiment categorization by overcoming the difficulties of Arabic audio sentiment analysis. The study evaluates the proposed approach on a newly created Arabic audio dataset and compares the results with existing approaches. The obtained results in terms of Accuracy (ACC), Precision (PREC), and Recall (REC) show excellent results, with the proposed approach outperforming existing approaches. The discussion section analyzes the impact of different feature extraction methods and machine learning algorithms on the performance of the sentiment analysis model. The results show that the AraBERT model achieves the highest accuracy.
Visual Analysis of Scene-Graph-Based Visual Question Answering

Scene-graph-based Visual Question Answering (VQA) has emerged as a burgeoning field in Deep Learning research, with a growing demand for robust and interpretable VQA systems. In this paper, we present a novel visual analysis approach that addresses two critical objectives in VQA: identifying and correcting prediction issues and providing insights into model decision-making processes through visualizing internal information. Our approach builds on the GraphVQA framework, which uses graph neural networks to process scene graphs representing images and which was trained on the widely-used GQA dataset. Our analysis tool aims at users familiar with the basics of graph-based VQA. By leveraging query-based scene analysis and visualization of crucial internal states, we are able to detect and pinpoint reasons for inaccurate predictions, facilitating model refinement and dataset curation. Identifying expressive internal states is a challenge. Through rigorous computer-based evaluations and presentation of a use case, we demonstrate the effectiveness of our analysis tool and model state visualization.
Visual Analysis of DDPG Models by Exploring the Space of Experience

Deep Reinforcement Learning (DRL) has been remarkably successful, but the lack of RL expertise and the complexity of DRLs hinder model understanding. In this paper, we focus on visual analysis of experience data to improve the interpretability of DRLs, which involves step aggregation, high-dimensional state data analysis, and spatio-temporal modeling of experience data. In addition, we introduce DDPGVis, a visual system which combines multiple views to show statics and allows users to explore the experience space, and its effectiveness is confirmed by case studies.