Reviewerly/exHarmony huggingface dataset:
Adaptive Swarm Intelligent Offloading Based on Digital Twin-assisted Prediction in VEC

Vehicular Edge Computing (VEC) is the transportation version of Mobile Edge Computing (MEC). In VEC, task offloading enables vehicles to offload computing tasks to nearby Roadside Units (RSUs), thereby reducing the computation cost. Recent trends in task offloading cause a proliferation of studies in academia. However, the existing offloading schemes still face many challenges, such as high-dynamic network topology, massive and complex data, dynamic scenes with high-speed vehicles and low-latency requirements. Digital Twin (DT)-based VEC is emerging as a promising solution. It monitors the state of the VEC network in real time through mappings and interactions between the physical and virtual entities. Consequently, the task offloading scheme can make more reasonable offloading decisions at the physical layer and further improve the efficiency of VEC. Above all, we propose a VEC computing offloading scheme, namely, Adaptive <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> warm Intelligent Offloading Scheme Based on Digital- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> win-Assisted P <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> ediction <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</u> n <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VE</u> C (STRIVE). The VEC network architecture is established to combines DT with an improved Generative Adversarial Network (GAN). The powerful prediction ability of GAN is used to assist in constructing DT in the pre-processing phase, reducing the size of the decision space. To adapt to the dynamic nature of VEC, we establish an adaptive model to adjust the real-time parameter under various scenarios. Then, we deploy an improve <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> genet <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</u> c simulat <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> d annealing-ba <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SE</u> d partic <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> e swarm optimization (DIESEL) algorithm to task offloading decision-making, which can provide reliable computing services for vehicles at a lower cost. The simulation results demonstrate that the proposed scheme can effectively reduce computing delay and energy consumption compared with its counterparts.
Real-time Local Obstacle Avoidance and Trajectory Tracking Control of Quadrotor UAVs with Suspended Payload in Complex Environments

Compared with traditional vehicle transportation methods, unmanned aerial vehicles (UAVs) have many advantages in transporting cargo because they are not limited by factors such as road traffic. To address practical applications, this paper presents a local obstacle avoidance control scheme for the quadrotor with suspended payload, which is suitable for complex environments with dense obstacles and external disturbances. Firstly, the overall dynamic model of the quadrotor-payload system is established using Euler-Lagrange equations. Secondly, considering the envelope circle radius switching problem caused by changes in the amplitude of the swing angles of the payload, an obstacle avoidance constraint of the quadrotor-payload system is presented, and an R-function is introduced to improve the traditional artificial potential field method, so as to achieve a less conservative obstacle avoidance. Thirdly, a cascade control scheme based on sliding mode algorithm is designed for the quadrotor-payload system trajectory tracking under external disturbances. Moreover, to avoid payload oscillations during flight, a feed-forward controller is designed to suppress the swing of the payload. Finally, simulation results verify the effectiveness and superiority of the proposed local obstacle avoidance strategy and control scheme.
Process Chaining Without Image and Performance Loss for Pencil Drawing Style Image Conversion in High-Level Synthesis

We are developing a high performance and low power non-photorealistic rendering hardware for an embedded image processing system using High-Level Synthesis (HLS) technology, which automatically converts software to hardware. The hardware of pencil drawing style image conversion which is one of NPRs have been studied as a first step. To make HLS generate a well pipelined hardware module with burst transfer, we have applied the memory access streaming technique for window-level processing. However, this technique has a weak point which lacks the upper and left side edges of the input image while the ideal performance can be achieved. This paper develops a HLS oriented software for the pencil drawing style conversion which can compensate the shaved image without affecting the performance. The experimental result shows that our proposal can obtain the output image without any lack. In addition, the effects lead by our extension to the execution time, hardware resource, and power efficiency are little.
MOCOLNet: A Momentum Contrastive Learning Network for Multimodal Aspect-Level Sentiment Analysis

Multimodal aspect-level sentiment analysis has attracted increasing attention in recent years. However, existing methods have two unaddressed limitations: (1) due to the lack of labelled pre-training data of dedicated sentiment analysis, the methods with a pre-training manner produce suboptimal prediction results; (2) most existing methods employ a self-attention encoder to fuse multimodal tokens, which not only ignores the alignment relationship between different modal tokens but also makes the model unable to capture the semantic links between images and texts. In this paper, we propose a momentum contrastive learning network (MOCOLNet) to overcome above limitations. First, we merge the pre-training stage with the training stage to design an end-to-end training manner which uses less labelled data dedicated to sentiment analysis to obtain better prediction results. Second, we propose a multimodal contrastive learning method to align the different modal representations before data fusing, and design a cross-modal matching strategy to provide semantic interactive information between texts and images. Moreover, we introduce an auxiliary momentum strategy to increase the robustness of model. We also analyse the effectiveness of the proposed multimodal contrastive learning method using a mutual information theory. Experiments verify that the proposed MOCOLNet is superior to other strong baselines.
HCTA:Hierarchical Cooperative Task Allocation in Multi-Agent Reinforcement Learning

Despite significant advances in multi-agent reinforcement learning (MARL) in recent years, learning about cooperative policies for complex tasks remains a challenge. The concept of task allocation provides a valuable tool for designing and comprehending intricate multi-agent systems, as it enables agents with similar roles to collaborate on the same subtasks. However, existing research efforts have not yet provided clear guidelines on how task allocation can better adapt to dynamic environments. This paper introduces a novel framework called Hierarchical Cooperative Task Allocation (HCTA) that is based on long-term behavior analysis. The proposed framework is designed to solve the dynamic task allocation problem in fully cooperative tasks within multi-agent systems. Firstly, we train a task selector based on action chains to enhance the efficiency of task allocation. Secondly, we introduce a hierarchical cooperative mechanism to realize the parallel training of upper-level task selection and lower-level task decision making, so as to accelerate the learning of cooperative strategy. Experimental results show that HTCA significantly improves the learning efficiency of joint policy through dynamic task allocation, and enhances the success rate in complex cooperative tasks in StarCraft II micromanagement benchmark.
DATATALES: Investigating the use of Large Language Models for Authoring Data-Driven Articles

Authoring data-driven articles is a complex process requiring authors to not only analyze data for insights but also craft a cohesive narrative that effectively communicates the insights. Text generation capabilities of contemporary large language models (LLMs) present an opportunity to assist the authoring of data-driven articles and expedite the writing process. In this work, we investigate the feasibility and perceived value of leveraging LLMs to support authors of data-driven articles. We designed a prototype system, DATATALES, that leverages a LLM to generate textual narratives accompanying a given chart. Using DATATALES as a design probe, we conducted a qualitative study with 11 professionals to evaluate the concept, from which we distilled affordances and opportunities to further integrate LLMs as valuable data-driven article authoring assistants.
Steamy Bubbles May Control Old Faithful’s Clock

Scientists built a minigeyser to show why the natural wonder’s water surges so violently between eruptions.
Quantum KNN Classification With K Value Selection and Neighbor Selection

The KNN (K-nearest neighbors) algorithm is one of Top-10 data mining algorithms and is widely used in various fields of artificial intelligence. This leads to that quantum KNN algorithms have developed and achieved certain speed improvements, denoted as Q-KNN. However, these Q-KNN methods must face two key problems as follows. The first one is that they are mainly focused on neighbor selection without paying attention to the influence of K value on the algorithm. The second is that only the neighbor selection process is quantized, and the selection of K value is not quantized. To solve these problems, this paper designs a novel quantum circuit for KNN classification, so as to simultaneously quantumize the neighbor selection and K value selection process. Specifically, the least squares loss and sparse regularization term are first used to construct the objective function of the proposed quantum KNN, so that it can simultaneously obtain the optimal K value and K nearest neighbors of the testing data. And then, a new quantum circuit is proposed to quantumize the process through quantum phase estimation, controlled rotation, and inverse phase estimation techniques. Finally, experiments are conducted with qiskit and matlab to output the quantum and classical results of the algorithm, verifying that the proposed algorithm can output the optimal K value and K nearest neighbors for each testing data.
Mitigating Poor Data Quality Impact with Federated Unlearning for Human-Centric Metaverse

Federated Learning (FL), which has been employed to train machine learning models on the data with a distributed manner, could enhance the immersive user experience for the human-centric metaverse. However, it’s challenging to train machine learning models accurately and promptly with FL for the human-centric metaverse due to massive data communication and user unreliability. User experience could be negatively affected by using low-quality machine learning models for human-centric metaverse, e.g., it cannot scrutinize and arrive at decisions accurately and timely. To resolve this pressing issue, we propose MetaFul a federated unlearning solution which reduces the negative influences of low-quality data with no data transmission by removing low-quality training models at the server side. To be specific, MetaFul includes three main components. (i) Low-throughput federated learning (LT-FL) addresses the issue of large model transmission in FL by decreasing the dimension and the number of transmitted model parameters. (ii) Loss-based model quality assessment (LM-QA) utilizes the model loss generated in LT-FL to estimate user data quality. (iii) Non-communicative federated unlearning (NC-FUL) revokes the low-quality data impact on the FL model with careful designed federated unlearning at the server side. Both LM-QA and NC-FUL have no communications with clients. Finally, extensive evaluations are conducted to show MetaFul could improve the model accuracy by at least 2.5% and decrease the user perception time by at least 19.3% in human-centric metaverse compared to benchmarks.
Handwriting Arabic Words Recognition in KHATT Dataset Based on Faster R-CNN

Arabic handwriting recognition is challenging because of different writing styles and the shape of a character that changes depending on where it is in a word. Researchers always seek to propose a new state-of-the-art algorithm to address these problems and find better results for detecting and recognizing Arabic text written in handwriting. In this paper, we suggest two models that use deep learning techniques to find and classify the words in a handwritten Arabic text using a faster regional convolutional neural network (Faster-RCNN). This technique embraces three main stages. The initial stage involves preparing and annotating the dataset to identify regions of interest. The subsequent phase focuses on calculating features, while the final stage employs classification and regression layers to classify and localize words into distinct classes. In the feature extraction phase, the first model used VGG16, while the second used ResNet50. To assess the performance of the proposed method, we used the standard KHATT dataset, which has challenges in writing style, font size, and noise effects. To evaluate the accuracy and efficiency of the two models, we used different scales recall, precision, and F1_Score. Experimental evaluations achieved an accuracy rate of 99% and 98% for two networks, VGG16 and ResNet50, respectively.
Influence of Deepfake Terminology on Content-Emerging Threat Reduction

A significant development in the rapidly expanding fields of Deep and machine Learning resulted in architectures that can create convincing synthetic content sometimes known as deepfakes. The danger arises when doctored photographs, films, and audios blur the distinction between fake and real content and are employed as weapons to inflict new levels of damage. This work analyzes the effectiveness of Big Data processing on operational parameters and focuses on data-dimensionality factors. It resolves big-data complications using DL and ML for decision-making mechanisms in the enterprise sectors and corporate sectors using in-depth analysis.
EXPERIENCE OF USING THE «MIRO» PLATFORM FOR DISTANCE EDUCATION

The use of interactive technologies facilitates the pedagogical process for the teacher andimproves the visualization and perception of the educational material by the students.One of such technologies is «Miro», a platform for collaboration of distance work bymeans of online board. The board can be used for working out projects, creatures, designconcepts, brainstorming and educational purposes.The purpose of the work – to analyze the features of using «Miro» virtual board duringdistance learning and to determine its advantages and disadvantages.The results. While getting the feedback from the students, 90% of respondents gavepositive opinion after using this board, 10% had difficulties when using it.Conclusion. Using the «Miro» interactive board helps to stimulate students’ interest andpersonal motivation to investigate the topic and acquire relevant skills.
Case Study: Frac-Hit Occurrence Prediction Using AI &amp; ML

Progress in Petrochemical Science Case Study: Frac-Hit Occurrence Prediction Using AI & ML Shahab D Mohaghegh* West Virginia University, Mehrdad Zamirian, USA *Corresponding author:Shahab D Mohaghegh, West Virginia University, Mehrdad Zamirian, USA Submission: August 10, 2023;Published: August 24, 2023 DOI: 10.31031/PPS.2023.05.000620 ISSN 2637-8035Volume5 Issue4
Histopathological and Ultrasound Correlation in Women Presenting with Breast Lumps in Yaound&amp;#233;, Cameroon

Context: Breast cancer is very deadly among women with higher rates in the developing world. Imaging tools such as ultrasound, can be used to differentiate between the types of breast lumps. This study aimed to determine the value of ultrasound as a first-line examination in the diagnosis of breast masses. Methods: This was a retrospective cross-sectional study of women presenting with breast lumps from November 2022 to June 2023 at the Yaounde General Hospital, which lasted seven months, from November 2022 to June 2023. The sampling was exhaustive and consecutive. Association between variables was studied using the x2 test and concordance between ultrasound and histopathological findings was assessed using the Kappa correlation coefficient. Results: 234 women were included in the study. Their mean age was 46.3 ± 11.4 years. Overall, 15 (6.4%) lumps were benign while 219 (93.6%) were malignant. Triple negative (5.6%) was the most recurrent genomic classification. The correlation between the ultrasound and histopathological findings was significant, with an observed concordance rate at 85.1%, kappa = 0.322 and a p-value < 0.001. Conclusion: The performance of ultrasound in differentiating benign and malignant lesion was high. However, the discordant cases highlight the need for a diagnosis system which blends histopathological and radiological findings for an improved management of patients with breast lumps.
FUTURE NATURAL SCIENCES TEACHERS TRAINING TO USE AUGMENTED REALITY TECHNOLOGIES IN PROFESSIONAL ACTIVITIES

The essential characteristics of the most used technologies of augmented reality in the modern school of Ukraine have been outlined. Difficulties faced by practicing teachers and students in the context of the problem implementation have been determined by means of a questionnaire. The criteria, based on which it is expedient to outline a list of educational applications of augmented reality, with which it is advisable to familiarize future teachers (clarity of the interface, degree of popularization in Ukraine, technical accessibility, quality of the program content, compliance with the age characteristics of schoolchildren) have been singled out. A methodological system of the formation of future teachers’ abilities to use augmented reality technologies in the process of physics, chemistry, biology, and natural sciences studying has been proposed. It consists of two subsystems in which direct and indirect ways of skills formation are combined: subsystem I – to provide first level (bachelor’s) students with special goal-oriented relevant skills (direct way); subsystem II – practical application of acquired skills by the students at the second (master’s) level of higher education, who act as subjects of both educational and pedagogical activities (indirect way). A list of courses at which it is advisable to form relevant skills in various ways has been determined, namely (“Digital technologies in education and science”, “Digital technologies in professional activity”, “Innovative learning technologies”, “Theory and methodology of physics, chemistry, biology, natural sciences teaching”, etc.). Educational applications of augmented reality, which are expedient to use by natural sciences teachers in their professional activities have been characterized. The effectiveness of the proposed methodological system has been experimentally verified. Research has indicated that its implementation contributes to the improvement of the quality of future teachers’ readiness for educational activities in the conditions of the information society.
Lightweight Semantic Segmentation Network Leveraging Class-Aware Contextual Information

Balancing model size, segmentation accuracy, and inference speed is a key challenge in image semantic segmentation. This paper introduces a novel lightweight semantic segmentation network, CACNet (Class-Aware Context Network), featuring the innovative Class-Aware Context Enhancement Module (CACEM). CACEM is designed to explicitly intertwine category and context information, addressing the shortcomings of traditional convolutional networks in capturing and encoding inter-category relationships. It operates by normalizing pixel probability distributions via softmax, mapping pixels to categories, and generating new feature maps that accurately encapsulate these relationships. Additionally, the network utilizes multi-scale context information and employs dilated convolutions, followed by upsampling to blend this context with single-channel category information. This process, enhanced by Fourier adaptive attention mechanisms, allows CACNet to capture intricate feature structures and manipulate features in the frequency domain for improved segmentation accuracy. On the Cityscapes and CamVid datasets, CACNet demonstrates competitive accuracies of 70.8 and 74.6 respectively, with a compact model size of 0.52M and an inference speed over 58FPS on GTX 2080Ti GPU platform. This blend of compactness, speed, and accuracy positions CACNet as an efficient choice in resource-constrained environments.
The Purpose, Objectives and Main Directions of the Development of Inclusive Higher Education in the Russian Federation

&lt;p&gt;The article provides an analysis of the current state of inclusive higher education in Russia based on the results of the implementation of the Interdepartmental Comprehensive Action Plan to ensure accessibility of vocational education for people with disabilities for 2016-2018 (approved by the Government of the Russian Federation on May 23, 2016 N 3467p-P8).According to this Plan a set of measures was implemented to help ensure that persons with disabilities receive high-quality higher education. The development and adoption of this document marked the beginning of a new stage in the formation and development of inclusive higher education in the Russian Federation, which determined the goals, objectives and ways to achieve the main indicators of creating an accessible educational environment in Russian universities. Today in the Russian Federation a new Interdepartmental Comprehensive Action Plan has been developed and approved to increase the accessibility of secondary vocational and higher education for people with disabilities and limited health capabilities, including career guidance and employment of these persons. It was approved by the Deputy Prime Minister of the Russian Federation Tatyana Golikova No. 14000p-P8 (hereinafter referred to as the Interdepartmental Comprehensive Plan, ICP, VO, SPO). The 2021 ICP was formed by the Russian Ministry of Education and Science together with the Russian Ministry of Education, the Russian Ministry of Labor, other federal executive bodies and public organizations. The activities of the Interdepartmental Comprehensive Plan bring together more than 25 performers. The development of this Plan, as a strategic planning document based on the principles of succession and continuity, taking into account the stages and results of the implementation of previously adopted documents and largely determines the main directions for the development of inclusive higher education.&lt;/p&gt;