Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence And Telemedicine Applications In Health Tourism Marketing

Technological developments accelerated with the Industrial Revolution have started to be used effectively in health tourism marketing, and its applicability in health tourism has increased competition between countries and even continents. Technological advances in artificial intelligence and telemedicine have reduced the time and economic costs of travel within the scope of health tourism while increasing the efficiency of health tourism and the number of patients benefiting from health services every day. Artificial intelligence and telemedicine accelerate diagnosis, detection and treatment services, and their use is increasing as an alternative to uninterrupted access to healthcare providers in problems caused by global pandemics such as Covid 19. This study employs qualitative analysis and aims to evaluate artificial intelligence and telemedicine applications in terms of health tourism marketing.
A Silicon Photonic Multi-DNN Accelerator

In shared environments like cloud-based datacenters, hardware accelerators are deployed to meet the scale-out computation demands of deep neural network (DNN) inference tasks. As conventional hardware accelerators optimized for single-DNN execution cannot effectively resolve the dynamic interaction of these inference-as-a-service (INFaaS) tasks, several multi-DNN hardware accelerators have been developed to improve the overall system performance while adhering to the constraints of individual tasks. Some of such multi-DNN hardware accelerators temporally schedule tasks by incorporating the preemption or load-balancing-based algorithm but suffer from resource underutilization because of unmanaged mismatch between resource demand and provision. Other multi-DNN hardware accelerators enable spatial colocation of tasks to improve resource utilization and system flexibility, but the irregular communication patterns between the fragmented resource partitions cannot be adequately supported by the metallic-based interconnects due to their rigidity and other inherent scaling limitations. We introduce a photonic multi-DNN accelerator named Aspire in this paper. The fundamental novelty of Aspire lies in the ability to adaptively create sub-accelerators for different tasks by assembling fine-grained resource partitions in the same architecture. Seamless communications between those fragmented resource partitions from a sub-accelerator are realized by exploiting photonic interconnects. Specifically, Aspire includes three novel designs: (1) a photonic network that can be adaptively partitioned into several sub-networks, each seamlessly connecting the fragmented resource partitions to construct sub-accelerators; (2) a dataflow that simultaneously leverages temporal and spatial data reuse opportunities within each resource partition and across several resource partitions, respectively; (3) an algorithm that allocates resource partitions at task granularity and derives optimal tile size and execution order at DNN layer granularity. Simulation studies show that Aspire outperforms other state-of-the-art multi-DNN accelerators, delivering 64% execution time reduction, 69% energy saving, 51% improvement in service-level agreement satisfaction rate, and 7.9× improvement in fairness.
Detection and Control Framework for Unpiloted Ground Support Equipment within the Aircraft Stand

The rapid advancement in Unpiloted Robotic Vehicle technology has significantly influenced ground support operations at airports, marking a critical shift towards future development. This study presents a novel Unpiloted Ground Support Equipment (GSE) detection and control framework, comprising virtual channel delineation, boundary line detection, object detection, and navigation and docking control, to facilitate automated aircraft docking within the aircraft stand. Firstly, we developed a bespoke virtual channel layout for Unpiloted GSE, aligning with operational regulations and accommodating a wide spectrum of aircraft types. This layout employs turning induction markers to define essential navigation points, thereby streamlining GSE movement. Secondly, we integrated cameras and Lidar sensors to enable rapid and precise pose adjustments during docking. The introduction of a boundary line detection system, along with an optimized, lightweight YOLO algorithm, ensures swift and accurate identification of boundaries, obstacles, and docking sites. Finally, we formulated a unique control algorithm for effective obstacle avoidance and docking in varied apron conditions, guaranteeing meticulous management of vehicle pose and speed. Our experimental findings reveal an 89% detection accuracy for the virtual channel boundary line, a 95% accuracy for guiding markers, and an F1-Score of 0.845 for the YOLO object detection algorithm. The GSE achieved an average docking error of less than 3 cm and an angular deviation under 5 degrees, corroborating the efficacy and advanced nature of our proposed approach in Unpiloted GSE detection and aircraft docking.
An intent recognition pipeline for conversational AI

Abstract Natural Language Processing (NLP) is one of the Artificial Intelligence applications that is entitled to allow computers to process and understand human language. These models are utilized to analyze large volumes of text and also support aspects like text summarization, language translation, context modeling, and sentiment analysis. Natural language, a subset of Natural Language Understanding (NLU), turns natural language into structured data. NLU accomplishes intent classification and entity extraction. The paper focuses on a pipeline to maximize the coverage of a conversational AI (chatbot) by extracting maximum meaningful intents from a data corpus. A conversational AI can best answer queries with respect to the dataset if it is trained on the maximum number of intents that can be gathered from the dataset which is what we focus on getting in this paper. The higher the intent we gather from the dataset, the more of the dataset we cover in training the conversational AI. The pipeline is modularized into three broad categories - Gathering the intents from the corpus, finding misspellings and synonyms of the intents, and finally deciding the order of intents to be picked up for training any classifier ML model. Several heuristic and machine-learning approaches have been considered for optimum results. For finding misspellings and synonyms, they are extracted through text vector neural network-based algorithms. Then the system concludes with a suggestive priority list of intents that should be fed to a classification model. In the end, an example of three intents from the corpus is picked, and their order is suggested for the optimum functioning of the pipeline. This paper attempts to pick intents in descending order of their coverage in the corpus in the most optimal way possible.
Fraud Detection in Enterprise Resource Planning Systems Using One-Class Support Vector Machine Combined with Convolutional Neural Network: The Case of Spor Istanbul

Fraud Detection in Enterprise Resource Planning Systems Using One-Class Support Vector Machine Combined with Convolutional Neural Network: The Case of Spor Istanbul
Digital Modeling on Large Kernel Metamaterial Neural Network

Deep neural networks (DNNs) utilized recently are physically deployed with computational units (e.g., CPUs and GPUs). Such a design might lead to a heavy computational burden, significant latency, and intensive power consumption, which are critical limitations in applications such as the Internet of Things (IoT), edge computing, and the usage of drones. Recent advances in optical computational units (e.g., metamaterial) have shed light on energy-free and light-speed neural networks. However, the digital design of the metamaterial neural network (MNN) is fundamentally limited by its physical limitations, such as precision, noise, and bandwidth during fabrication. Moreover, the unique advantages of MNN's (e.g., light-speed computation) are not fully explored via standard 3×3 convolution kernels. In this paper, we propose a novel large kernel metamaterial neural network (LMNN) that maximizes the digital capacity of the state-of-the-art (SOTA) MNN with model re-parametrization and network compression, while also considering the optical limitation explicitly. The new digital learning scheme can maximize the learning capacity of MNN while modeling the physical restrictions of meta-optic. With the proposed LMNN, the computation cost of the convolutional front-end can be offloaded into fabricated optical hardware. The experimental results on two publicly available datasets demonstrate that the optimized hybrid design improved classification accuracy while reducing computational latency. The development of the proposed LMNN is a promising step towards the ultimate goal of energy-free and light-speed AI.
HiTeA: Hierarchical Temporal-Aware Video-Language Pre-training

Video-language pre-training has advanced the performance of various downstream video-language tasks. However, most previous methods directly inherit or adapt typical image-language pre-training paradigms to video-language pre-training, thus not fully exploiting the unique characteristic of video, i.e., temporal. In this paper, we propose a Hierarchical Temporal-Aware video-language pre-training framework, HiTeA, with two novel pre-training tasks for yielding temporal-aware multi-modal representation with cross-modal fine-grained temporal moment information and temporal contextual relations between video-text multi-modal pairs. First, we propose a cross-modal moment exploration task to explore moments in videos by mining the paired texts, which results in detailed video moment representation. Then, based on the learned detailed moment representations, the inherent temporal contextual relations are captured by aligning video-text pairs as a whole in different time resolutions with multi-modal temporal relation exploration task. Furthermore, we introduce the shuffling test to evaluate the temporal reliance of datasets and video-language pre-training models. We achieve state-of-the-art results on 15 well-established video-language understanding and generation tasks, especially on temporal-oriented datasets (e.g., SSv2-Template and SSv2-Label) with 8.6% and 11.1% improvement respectively. HiTeA also demonstrates strong generalization ability when directly transferred to downstream tasks in a zero-shot manner.
Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition

Recent video recognition models utilize Transformer models for long-range spatio-temporal context modeling. Video transformer designs are based on self-attention that can model global context at a high computational cost. In comparison, convolutional designs for videos offer an efficient alternative but lack long-range dependency modeling. Towards achieving the best of both designs, this work proposes Video-FocalNet, an effective and efficient architecture for video recognition that models both local and global contexts. Video-FocalNet is based on a spatiotemporal focal modulation architecture that reverses the interaction and aggregation steps of self-attention for better efficiency. Further, the aggregation step and the interaction step are both implemented using efficient convolution and element-wise multiplication operations that are computationally less expensive than their self-attention counterparts on video representations. We extensively explore the design space of focal modulation-based spatiotemporal context modeling and demonstrate our parallel spatial and temporal encoding design to be the optimal choice. Video-FocalNets perform favorably well against the state-of-the-art transformer-based models for video recognition on five large-scale datasets (Kinetics-400, Kinetics-600, SS-v2, Diving-48, and ActivityNet-1.3) at a lower computational cost. Our code/models are released at https://github.com/TalalWasim/Video-FocalNets.
Boosting Adversarial Transferability via Gradient Relevance Attack

Plentiful adversarial attack researches have revealed the fragility of deep neural networks (DNNs), where the imperceptible perturbations can cause drastic changes in the output. Among the diverse types of attack methods, gradient-based attacks are powerful and easy to implement, arousing wide concern for the security problem of DNNs. However, under the black-box setting, the existing gradient-based attacks have much trouble in breaking through DNN models with defense technologies, especially those adversarially trained models. To make adversarial examples more transferable, in this paper, we explore the fluctuation phenomenon on the plus-minus sign of the adversarial perturbations’ pixels during the generation of adversarial examples, and propose an ingenious Gradient Relevance Attack (GRA). Specifically, two gradient relevance frameworks are presented to better utilize the information in the neighbor-hood of the input, which can correct the update direction adaptively. Then we adjust the update step at each iteration with a decay indicator to counter the fluctuation. Experiment results on a subset of the ILSVRC 2012 validation set forcefully verify the effectiveness of GRA. Furthermore, the attack success rates of 68.7% and 64.8% on Tencent Cloud and Baidu AI Cloud further indicate that GRA can craft adversarial examples with the ability to transfer across both datasets and model architectures. Code is released at https://github.com/RYC-98/GRA.
Contrastive Feature Masking Open-Vocabulary Vision Transformer

We present Contrastive Feature Masking Vision Trans-former (CFM-ViT) - an image-text pretraining methodology that achieves simultaneous learning of image- and region-level representation for open-vocabulary object detection (OVD). Our approach combines the masked autoencoder (MAE) objective into the contrastive learning objective to improve the representation for localization tasks. Un-like standard MAE, we perform reconstruction in the joint image-text embedding space, rather than the pixel space as is customary with the classical MAE method, which causes the model to better learn region-level semantics. Moreover, we introduce Positional Embedding Dropout (PED) to address scale variation between image-text pretraining and detection finetuning by randomly dropping out the positional embeddings during pretraining. PED improves detection performance and enables the use of a frozen ViT backbone as a region classifier, preventing the forgetting of open-vocabulary knowledge during detection finetuning. On LVIS open-vocabulary detection benchmark, CFM-ViT achieves a state-of-the-art 33.9 APr, surpassing the best approach by 7.6 points and achieves better zero-shot detection transfer. Finally, CFM-ViT acquires strong image-level representation, outperforming the state of the art on 8 out of 12 metrics on zero-shot image-text retrieval benchmarks.
Spectrum-guided Multi-granularity Referring Video Object Segmentation

Current referring video object segmentation (R-VOS) techniques extract conditional kernels from encoded (low-resolution) vision-language features to segment the decoded high-resolution features. We discovered that this causes significant feature drift, which the segmentation kernels struggle to perceive during the forward computation. This negatively affects the ability of segmentation kernels. To address the drift problem, we propose a Spectrum-guided Multi-granularity (SgMg) approach, which performs direct segmentation on the encoded features and employs visual details to further optimize the masks. In addition, we propose Spectrum-guided Cross-modal Fusion (SCF) to perform intra-frame global interactions in the spectral domain for effective multimodal representation. Finally, we extend SgMg to perform multi-object R-VOS, a new paradigm that enables simultaneous segmentation of multiple referred objects in a video. This not only makes R-VOS faster, but also more practical. Extensive experiments show that SgMg achieves state-of-the-art performance on four video benchmark datasets, outperforming the nearest competitor by 2.8% points on Ref-YouTube-VOS. Our extended SgMg enables multi-object R-VOS, runs about 3 faster while maintaining satisfactory performance. Code×is available at https://github.com/bo-miao/SgMg.
Exploring the metafunctions to improve EFL learners’ writing ability in the perspective of systemic functional linguistics

Writing in academic English is challenging and complex to learn, the root reasons for the issue, may include differences in the cultural backgrounds of the students, a deficiency in critical thinking skills, and a deficiency in linguistic proficiency. This study set out to investigate how metafunctions may be used to enhance students' writing skills. A research approach incorporating a qualitative case study will be employed to assess ninth-grade students' text composition. The data were gathered through collecting student writing over the school year 2021/2022. The students’ writings were the texts that were learnt in grade nine based on the curriculum. This study used Systemic Functional Linguistic theory and English texts to provide the framework for the text analysis. The texts were analyzed using the metafunction analysis in Hallidayan SFL to uncover the students’ ability to write English text. The teacher applied the Genre-based Approach when instructing his or her students on how to write. The results presented that metafunctions analysis was very important because there were ideational, interpersonal, and textual metafunctions. The ideational metafunction revealed the use of some processes in the students’ writing (material, mental, relational, and verbal), interpersonal revealed the importance of mood and residue elements in students’ writing and textual metafunction showed the importance of thematic progression pattern. This study indicated that to alleviate students’ challenges and strengthen their academic writing and critical thinking capacity, teachers should have a better understanding of the theory of Systemic Functional Linguistics.
Multiclass Image Classification Based on Quantum-Inspired Convolutional Neural Network

Multiclass image classification is considered a challenging task in computer vision that requires correctly classifying an image into one of the multiple distinct groups.In recent years, quantum machine learning has emerged as a topic of significant interest among researchers.Using quantum concepts such as superposition and entanglement, quantum machine learning algorithms provide a more efficient method of processing and classifying high-dimensional image data.This paper proposes a new image classification model using quantum-inspired convolutional neural network architecture or, shortly, QCNN.The proposed model consists of two main phases; pre-processing and classification based on the QCNN phase.Seven benchmark datasets with different characteristics are adopted to evaluate the performance of the proposed model.The experimental results revealed that the proposed QCNN outperformed its classical version.Additionally, the results demonstrated the effectiveness of the proposed model compared with the state-of-the-art models.
A chat client-server application for E-learning

This paper proposes a chat software application implemented in C programming language to support the learners attending an e-learning program. The Open System Interoperability (OSI) model and the UDP and TCP communication protocols are described to help learners understand and communicate through basic computer technology networks. The purpose of the described application is to support e-learning as a distance learning methodology. Specifically, learners can post their messages and provide important feedback for future improvement of the e-learning program.
HTML5 package media: How does it affect elementary school students’ concept understanding and self-efficacy?

Background: HTML5 Package media, equipped with features and displays, provides a clearer visual display in learning geometry compared to traditional lecture-based teaching. This approach facilitates students in observing and understanding geometry concepts, positively impacting their self-efficacy.Aim: The study aims to assess the impact of HTML5 packages on students' understanding of geometry concepts and their self-efficacy.Method: The research, a quasi-experimental study, used simple random sampling to select 63 students. Data were collected through tests and questionnaires, and the analysis was conducted using MANOVA.Result: The findings reveal a significant effect of the HTML5 Package on both concept understanding and self-efficacy. The experimental group scored higher than the conventional group, with significant values in Pilae Trace, Wilk Lambda, Hotelling Trace, and Roy's Largest Root analyses.Conclusion: The HTML5 Package media effectively enhances the understanding of geometry concepts and self-efficacy among students. While the effect size is modest, it is notably better than conventional learning methods. The study suggests broader application and research involving more students and diverse mathematical topics could yield more influential results.
THE EFFECT OF USING BIG BOOK MEDIA ON LEARNING INTEREST AND LEARNING RESULTS OF SOCIAL SCIENCE SUBJECT AT UPT SDN 027 LIMPOMAJANG, KECAMATAN BAEBUNTA KABUPATEN LUWU UTARA

This study aimed to determine the use of Big Book media toward students' interest and learning outcomes in Bahasa Indonesia subject at UPT SDN 027 Limpomajang Baebunta, Luwu Utara. This research used a quantitative approach and quasi-experimental research type. The population was all students from class I to class VI w hile the sample s of this study were students in class IV-A (experimental class) and class IV-B (control class). The data collection techniques used were: questionnaires, tests, and documentation. T he Kolmogorov-Smirnov test and the Homogeneity of Variances test were operated t o answer the formulation of the problem, and finally T-test was operated to test the hypothesis that has been proposed. The results of this study were: (1) there was a significant influence between Big Book media on social studies interest s as evidenced by the tcount&gt; t-table (7,656&gt; 2,0003) and the t significance value for the questionnaire variable wa s 0,000 ; this value was smaller than the probability 0.05 (0.000 &lt;0.05) ; (2) there was a significant influence between Big Book media on social studies learning outcomes as evidenced by the value of tcount&gt; t-table (3.982&gt; 2.0003) for the T- test variable. The significance value of t for the test variable was equal to 0.000 and this value was smaller than the probability of 0.05 (0.000 &lt;0.05) ; and (3) there was a significant influence between Big Book media on the social studies learning interest and learning outcomes as evidenced by the value of tcount&gt; t-table (7,656&gt; 2,0003) for the questionnaire variable, while for the test variable (3,982&gt; 2,0003). The significance value of t for the questionnaire and test variables was both 0,000 and this value was is smaller than the probability of 0.05 (0,000 &lt;0.05).