Reviewerly/exHarmony huggingface dataset:
GPT Chat: Opportunities and Challenges in the Learning Process of Arabic Language in Higher Education

Today there are no more people who do not know about technological developments. Everything is very sophisticated and modern. In fact, it is very easy for someone to know something through technology-based media and fast in disseminating various information. All of this is of course thanks to increasingly advanced technology. As recently appeared an application called Chat GPT. GPT Chat is an application that can respond to every question asked by respondents. Chat GPT stands for "Chat-based Generative Pre-trained Transformer". GPT chat is also a language model developed by Open AI that uses the powerful transformers architecture to generate responsive and context-appropriate text conversations. GPT Chat has been widely used by all groups, especially in the world of education. GPT Chat can be used by students in learning Arabic in tertiary institutions such as to improve their skills in kalam and qiraah. The purpose of this study is to find out that the use of gamification in learning Arabic has an influence on students' qiraah skills and also makes it easier to complete assignments. The data collection technique is through the distribution of a questionnaire. In the questionnaire there are statements that will be answered by students in tertiary institutions. The results of this study explain that the use of Char GPT in learning Arabic in tertiary institutions gets a positive response from students such as increasing students' reading skills, increasing motivation in learning, increasing enthusiasm for learning, and making it easier to complete assignments. The conclusion of this research explains that the existence of GPT Chat provides enormous opportunities, especially in the process of learning Arabic because there are many benefits obtained from using GPT Chat. The limitation of this research is that researchers only conduct this research at the tertiary level which incidentally has the ability to think critically.
Quantum correlation of microwave two-mode squeezed state generated by nonlinearity of InP HEMT

This study significantly concentrates on cryogenic InP HEMT high-frequency circuit analysis using quantum theory to find how the transistor nonlinearity can affect the quantum correlation of the modes generated. Firstly, the total Hamiltonian of the circuit is derived, and the dynamic equation of the motion contributed is examined using the Heisenberg-Langevin equation. Using the nonlinear Hamiltonian, some components are attached to the intrinsic internal circuit of InP HEMT to address the circuit characteristics fully. The components attached are arisen due to the nonlinearity effects. As a result, the theoretical calculations show that the states generated in the circuit are mixed, and no pure state is produced. Accordingly, the modified circuit generates the two-mode squeezed thermal state, which means one can focus on calculating the Gaussian quantum discord to evaluate quantum correlation. It is also found that the nonlinearity factors (addressed as the nonlinear components in the circuit) can intensely influence the squeezed thermal state by which the quantum discord is changed. Finally, as the primary point, it is concluded that although it is possible to enhance the quantum correlation between modes by engineering the nonlinear components; however, attaining quantum discord greater than unity, entangled microwave photons, seems a challenging task since InP HEMT operates at 4.2 K.
Life-long phishing attack detection using continual learning

Phishing is an identity theft that employs social engineering methods to get confidential data from unwary users. A phisher frequently attempts to trick the victim into clicking a URL that leads to a malicious website. Many phishing attack victims lose their credentials and digital assets daily. This study demonstrates how the performance of traditional machine learning (ML)-based phishing detection models deteriorates over time. This failure is due to drastic changes in feature distributions caused by new phishing techniques and technological evolution over time. This paper explores continual learning (CL) techniques for sustained phishing detection performance over time. To demonstrate this behavior, we collect phishing and benign samples for three consecutive years from 2018 to 2020 and divide them into six datasets to evaluate traditional ML and proposed CL algorithms. We train a vanilla neural network (VNN) model in the CL fashion using deep feature embedding of HTML contents. We compare the proposed CL algorithms with the VNN model trained from scratch and with transfer learning (TL). We show that CL algorithms maintain accuracy over time with a tolerable deterioration of 2.45%. In contrast, VNN and TL-based models' performance deteriorates by over 20.65% and 8%, respectively.
Human activity recognition in an end-of-life consumer electronics disassembly task

The production of electronic waste, also known as e-waste, has risen with the growing reliance on electronic products. To reduce negative environmental impact and achieve sustainable industrial processes, recovering and reusing products is crucial. Advances in AI and robotics can help in this effort by reducing workload for human workers and allowing them to stay away from hazardous materials. However, autonomous human motion/intention perception is a primary barrier in e-waste remanufacturing. To address the research gap, this study combined experimental data collection with deep learning models for accurate disassembly task recognition. Over 570,000 frames of motion data were collected from inertial measurement units (IMU) worn by 22 participants. A novel sequence-based correction (SBC) algorithm was also proposed to further improve the accuracy of the overall pipeline. Results showed that models (CNN, LSTM, and GoogLeNet) had an overall accuracy of 88–92%. The proposed SBC algorithm improved accuracy to 95%.
Dual Semantic Knowledge Composed Multimodal Dialog Systems

Textual response generation is an essential task for multimodal task-oriented dialog systems. Although existing studies have achieved fruitful progress, they still suffer from two critical limitations: 1) focusing on the attribute knowledge but ignoring the relation knowledge that can reveal the correlations between different entities and hence promote the response generation, and 2)only conducting the cross-entropy loss based output-level supervision but lacking the representation-level regularization. To address these limitations, we devise a novel multimodal task-oriented dialog system (named MDS-S2). Specifically, MDS-S2 first simultaneously acquires the context related attribute and relation knowledge from the knowledge base, whereby the non-intuitive relation knowledge is extracted by the n-hop graph walk. Thereafter, considering that the attribute knowledge and relation knowledge can benefit the responding to different levels of questions, we design a multi-level knowledge composition module in MDS-S^2 to obtain the latent composed response representation. Moreover, we devise a set of latent query variables to distill the semantic information from the composed response representation and the ground truth response representation, respectively, and thus conduct the representation-level semantic regularization. Extensive experiments on a public dataset have verified the superiority of our proposed MDS-S2. We have released the codes and parameters to facilitate the research community.
Definite Formulae, Negation-as-Failure, and the Base-Extension Semantics of Intuitionistic Propositional Logic

Proof-theoretic semantics (P-tS) is the paradigm of semantics in which meaning in logic is based on proof (as opposed to truth). A particular instance of P-tS for intuitionistic propositional logic (IPL) is its base-extension semantics (B-eS). This semantics is given by a relation called support, explaining the meaning of the logical constants, which is parameterized by systems of rules called bases that provide the semantics of atomic propositions. In this paper, we interpret bases as collections of definite formulae and use the operational view of them as provided by uniform proof-search—the proof-theoretic foundation of logic programming (LP)—to establish the completeness of IPL for the B-eS. This perspective allows negation, a subtle issue in P-tS, to be understood in terms of the negation-as-failure protocol in LP. Specifically, while the denial of a proposition is traditionally understood as the assertion of its negation, in B-eS we may understand the denial of a proposition as the failure to find a proof of it. In this way, assertion and denial are both prime concepts in P-tS.
On Synonymy in Proof-Theoretic Semantics: The Case of \(\mathtt{2Int}\)

We consider an approach to propositional synonymy in proof-theoretic semantics that is defined with respect to a bilateral G3-style sequent calculus \(\mathtt{SC2Int}\) for the bi-intuitionistic logic \(\mathtt{2Int}\). A distinctive feature of \(\mathtt{SC2Int}\) is that it makes use of two kind of sequents, one representing proofs, the other representing refutations. The structural rules of \(\mathtt{SC2Int}\), in particular its cut rules, are shown to be admissible. Next, interaction rules are defined that allow transitions from proofs to refutations, and vice versa, mediated through two different negation connectives, the well-known implies-falsity negation and the less well-known coimplies-truth negation of \(\mathtt{2Int}\). By assuming that the interaction rules have no impact on the identity of derivations, the concept of inherited identity between derivations in \(\mathtt{SC2Int}\) is introduced and the notions of positive and negative synonymy of formulas are defined. Several examples are given of distinct formulas that are either positively or negatively synonymous. It is conjectured that the two conditions cannot be satisfied simultaneously.
Leveraging pre-trained language models for mining microbiome-disease relationships

The growing recognition of the microbiome's impact on human health and well-being has prompted extensive research into discovering the links between microbiome dysbiosis and disease (healthy) states. However, this valuable information is scattered in unstructured form within biomedical literature. The structured extraction and qualification of microbe-disease interactions are important. In parallel, recent advancements in deep-learning-based natural language processing algorithms have revolutionized language-related tasks such as ours. This study aims to leverage state-of-the-art deep-learning language models to extract microbe-disease relationships from biomedical literature.In this study, we first evaluate multiple pre-trained large language models within a zero-shot or few-shot learning context. In this setting, the models performed poorly out of the box, emphasizing the need for domain-specific fine-tuning of these language models. Subsequently, we fine-tune multiple language models (specifically, GPT-3, BioGPT, BioMedLM, BERT, BioMegatron, PubMedBERT, BioClinicalBERT, and BioLinkBERT) using labeled training data and evaluate their performance. Our experimental results demonstrate the state-of-the-art performance of these fine-tuned models ( specifically GPT-3, BioMedLM, and BioLinkBERT), achieving an average F1 score, precision, and recall of over [Formula: see text] compared to the previous best of 0.74.Overall, this study establishes that pre-trained language models excel as transfer learners when fine-tuned with domain and problem-specific data, enabling them to achieve state-of-the-art results even with limited training data for extracting microbiome-disease interactions from scientific publications.
IRNet: An Improved Zero-Shot Retinex Network for Low-Light Image Enhancement

Inadequate illumination often causes severe image degradation, such as noise and artifacts. These types of images do not meet the requirements of advanced visual tasks, so low-light image enhancement is currently a flourishing and challenging research topic. To alleviate the problem of low brightness and low contrast, this paper proposes an improved zero-shot Retinex network, named IRNet, which is composed of two parts: a Decom-Net and an Enhance-Net. The Decom-Net is designed to decompose the raw input into two maps, i.e., illuminance and reflection. Afterwards, the subsequent Enhance-Net takes the decomposed illuminance component as its input, enhances the image brightness and features through gamma transformation and a convolutional network, and fuses the enhanced illumination and reflection maps together to obtain the final enhanced result. Due to the use of zero-shot learning, no previous training is required. IRNet depends on the internal optimization of each individual input image, and the network weights are updated by iteratively minimizing a series of designed loss functions, in which noise reduction loss and color constancy loss are introduced to reduce noise and relieve color distortion during the image enhancement process. Experiments conducted on public datasets and the presented practical applications demonstrate that our method outperforms other counterparts in terms of both visual perception and objective metrics.
The vertical position of visual information conditions spatial memory performance in healthy aging

Abstract Memory for objects and their location is a cornerstone of adequate cognitive functioning across the lifespan. Considering that human visual perception depends on the position of stimuli within the visual field, we posit that the position of objects in the environment may be a determinant aspect of mnemonic performance. In this study, a population of 25 young and 20 older adults completed a source-monitoring task with objects presented in the upper or lower visual field. Using standard Pr and multinomial processing tree analyses, we revealed that although familiarity-based item memory remained intact in older age, spatial memory was impaired for objects presented in the upper visual field. Spatial memory in aging is conditioned by the vertical position of information. These findings raise questions about the view that age-related spatial mnemonic deficits are attributable to associative dysfunctions and suggest that they could also originate from the altered encoding of object attributes.
Development, Service-Oriented Architecture, and Security of Blockchain Technology for Industry 4.0 IoT Application

The Internet of Things (IoT) paradigm is laying the groundwork for a world in which many of our everyday devices will be connected and will interact with their surroundings to gather data and automate some operations. Among other things, such a concept necessitates seamless authentication, data privacy, security, attack resilience, simplicity of deployment, and self-maintenance. Blockchain, a technology created with the Bitcoin cryptocurrency, can provide such advantages. To create blockchain-based IoT (BIoT) applications, a full discussion of how to modify blockchain to meet the unique requirements of IoT is offered in this paper. The most important BIoT applications are detailed after a brief introduction to blockchain, with the goal of highlighting how blockchain can affect conventional cloud-based IoT applications. Then, several factors that have an impact on the design, development, and deployment of a BIoT application are covered, along with present obstacles and potential improvements. Lastly, a list of recommendations is provided to help future BIoT researchers and developers understand some of the problems that need to be solved before deploying the upcoming generation of BIoT applications. Doi: 10.28991/HIJ-2023-04-01-010 Full Text: PDF
Real-Time Spatial Trajectory Planning for Urban Environments Using Dynamic Optimization

Planning trajectories for automated vehicles in urban environments requires methods with high generality, long planning horizons, and fast update rates. Using a path-velocity decomposition, we contribute a novel planning framework, which generates foresighted trajectories and can handle a wide variety of state and control constraints effectively. In contrast to related work, the proposed optimal control problems are formulated over space rather than time. This spatial formulation decouples environmental constraints from the optimization variables, which allows the application of simple, yet efficient shooting methods. To this end, we present a tailored solution strategy based on ILQR, in the Augmented Lagrangian framework, to rapidly minimize the trajectory objective costs, even under infeasible initial solutions. Evaluations in simulation and on a full-sized automated vehicle in real-world urban traffic show the real-time capability and versatility of the proposed approach.
IBGJO: Improved Binary Golden Jackal Optimization with Chaotic Tent Map and Cosine Similarity for Feature Selection

Feature selection is a crucial process in machine learning and data mining that identifies the most pertinent and valuable features in a dataset. It enhances the efficacy and precision of predictive models by efficiently reducing the number of features. This reduction improves classification accuracy, lessens the computational burden, and enhances overall performance. This study proposes the improved binary golden jackal optimization (IBGJO) algorithm, an extension of the conventional golden jackal optimization (GJO) algorithm. IBGJO serves as a search strategy for wrapper-based feature selection. It comprises three key factors: a population initialization process with a chaotic tent map (CTM) mechanism that enhances exploitation abilities and guarantees population diversity, an adaptive position update mechanism using cosine similarity to prevent premature convergence, and a binary mechanism well-suited for binary feature selection problems. We evaluated IBGJO on 28 classical datasets from the UC Irvine Machine Learning Repository. The results show that the CTM mechanism and the position update strategy based on cosine similarity proposed in IBGJO can significantly improve the Rate of convergence of the conventional GJO algorithm, and the accuracy is also significantly better than other algorithms. Additionally, we evaluate the effectiveness and performance of the enhanced factors. Our empirical results show that the proposed CTM mechanism and the position update strategy based on cosine similarity can help the conventional GJO algorithm converge faster.
ERASOR2: Instance-Aware Robust 3D Mapping of the Static World in Dynamic Scenes

A map of the environment is an essential component for robotic navigation.In the majority of cases, a map of the static part of the world is the basis for localization, planning, and navigation.However, dynamic objects that are presented in the scenes during mapping leave undesirable traces in the map, which can impede mobile robots from achieving successful robotic navigation.To remove the artifacts caused by dynamic objects in the map, we propose a novel instance-aware map building method.Our approach rejects dynamic points at an instance-level while preserving most static points by exploiting instance segmentation estimates.Furthermore, we propose effective ways to consider the erroneous estimates of instance segmentation, enabling our proposed method to be robust even under imprecise instance segmentation.As demonstrated in our experimental evaluation, our approach shows substantial performance increases in terms of both, the preservation of static points and rejection of dynamic points.
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In the realm of multi-class classification, the twin K-class support vector classification (Twin-KSVC) generates ternary outputs {−1,0,+1} by evaluating all training data in a “1-versus-1-versus-rest” structure. Recently, inspired by the least-squares version of Twin-KSVC and Twin-KSVC, a new multi-class classifier called improvements on least-squares twin multi-class classification support vector machine (ILSTKSVC) has been proposed. In this method, the concept of structural risk minimization is achieved by incorporating a regularization term in addition to the minimization of empirical risk. Twin-KSVC and its improvements have an influence on classification accuracy. Another aspect influencing classification accuracy is feature selection, which is a critical stage in machine learning, especially when working with high-dimensional datasets. However, most prior studies have not addressed this crucial aspect. In this study, motivated by ILSTKSVC and the cardinality-constrained optimization problem, we propose ℓp-norm least-squares twin multi-class support vector machine (PLSTKSVC) with 0<p<1 to perform classification and feature selection at the same time. The technique employed to solve the optimization problems associated with PLSTKSVC is user-friendly, as it involves solving systems of linear equations to obtain an approximate solution for the proposed model. Under certain assumptions, we investigate the properties of the optimum solutions to the related optimization problems. Several real-world datasets were tested using the suggested method. According to the results of our experiments, the proposed method outperforms all current strategies in most datasets in terms of classification accuracy while also reducing the number of features.
Transfer Learning-Based YOLOv3 Model for Road Dense Object Detection

Stemming from the object overlap and undertraining from the few samples, the road dense object detection is confronted with the poor object identification performance and the inability to recognize edge objects. Based on this, one transfer learning-based YOLOv3 approach for identifying dense objects in the road has been proposed. Firstly, Darknet-53 network structure is adopted to obtain pre-trained YOLOv3 model, then the transfer training is introduced as the output layer for the special dataset of 2000 images containing vehicles; in the proposed model, one random function is adapted to intialize and optimize the weights of the transfer training model, which is seperately designed from the pre-trained YOLOv3; and the object detection classifier replaces the fully connected layer, which further improves the detection effect, the reduced size of the network model can further reduce the training and detection time, and can be better applied to actual scenarios. The experimental results demonstrate that the object detection accuracy of the presented approach is 87.75% for the Pascal VOC 2007 dataset, which is superior to the traditional YOLOv3 and the traditional YOLOv2 by 3.05% and 11.15%, respectively. Besides, the test was carried out using UA-DETRAC, a public road vehicle detection dataset, the object detection accuracy of the presented approach reaches 79.23% in detecting images, which is 4.13% better than the traditional YOLOv3, and compared with the existing relatively new object detection algorithm YOLOv5, the detection accuracy is 1.36% better. Moreover, the detection speed of the proposed YOLOv3 method reaches 31.2 Fps/s in detecting images, which is 7.6 Fps/s faster than the traditional YOLOv3, and compared with the existing relatively new object detection algorithm YOLOv5, the speed is 4.3 Fps/s faster; the proposed YOLOv3 performs 79.38Bn of floating point operations per second in detecting video, which obviously surpasses the traditional YOLOv3 and the newer object detection algorithm YOLOv5.