Reviewerly/exHarmony huggingface dataset:
LGM3A '23: 1st Workshop on Large Generative Models Meet Multimodal Applications

A large language model is a type of artificial intelligence model designed to understand and generate natural language text, such as GPT, T5, RoBERTa, BERT, etc. These models are trained on vast amounts of text data, allowing them to learn the patterns and structures of human language. With the increasing amount of multimodal information such as audio, visual, and text data generated, there is a growing need of leveraging large generative language model for multimodal applications. Recently, a few notable multimodal models (e.g., BLIP, Flamingo, KOSMOS, PaLM-E, LLaVA, Visual ChatGPT, GPT-4, etc.) with a combination of large language models significantly enhanced their understanding and generate more accurate and nuanced responses. The workshop will provide an opportunity for researchers, practitioners, and industry professionals to explore the latest trends and best practices in the field of multimodal applications of large generative models. The workshop will also focus on exploring the challenges and opportunities of integrating large language models with other AI technologies such as computer vision and speech recognition.
Exploring The Internet Of Things: A Comprehensive Guide

In our increasingly interconnected world, the Internet of Things (IoT) is not just a buzzword; it’s a technological revolution that’s transforming industries, businesses, and the way we live. “Exploring the Internet of Things: A Comprehensive Guide” is your essential companion to understanding, navigating, and harnessing the full potential of IoT. This book takes a deep dive into the IoT ecosystem, covering everything from the basics to advanced concepts. Whether you’re a novice or an experienced professional, you’ll find valuable insights to expand your knowledge. The book also provides the practical examples by exploring real-world examples and case studies that demonstrate how IoT is being applied across diverse domains, from healthcare and transportation to smart homes and industrial automation.
POV: Prompt-Oriented View-Agnostic Learning for Egocentric Hand-Object Interaction in the Multi-view World

We humans are good at translating third-person observations of hand-object interactions (HOI) into an egocentric view. However, current methods struggle to replicate this ability of view adaptation from third-person to first-person. Although some approaches attempt to learn view-agnostic representation from large-scale video datasets, they ignore the relationships among multiple third-person views. To this end, we propose a Prompt-Oriented View-agnostic learning (POV) framework in this paper, which enables this view adaptation with few egocentric videos. Specifically, We introduce interactive masking prompts at the frame level to capture fine-grained action information, and view-aware prompts at the token level to learn view-agnostic representation. To verify our method, we establish two benchmarks for transferring from multiple third-person views to the egocentric view. Our extensive experiments on these benchmarks demonstrate the efficiency and effectiveness of our POV framework and prompt tuning techniques in terms of view adaptation and view generalization.
Hashing One With All

The recent trend in unsupervised hashing requires not only a discrete representation space but also the ability to mine the similarities between data points. Determining and maintaining the relations between each datum and all the others maximally utilize the semantic diversity of the training set, but can we explore this on the holistic dataset using a single network end-to-end? In this paper, we take a step towards this vision by proposing Overview Hashing (OH). OH unifies the two ultimate goals of unsupervised hashing, i.e., (1) encoding compact features and (2) learning data similarities on a large scale end-to-end, into one model. In particular, we split the top of an encoder into a binary hash head and a continuous one. For an arbitrary datum, its similarities to all the others in the dataset are reflected in the Hamming distances of their hash heads. The distances then act as the weights to aggregate the continuous heads, shaping the final representation of this datum for loss computation. Hence, training with this representation simultaneously tunes the similarities of this datum to the whole dataset. In the context of a contrastive learning framework, we theoretically endorse our design by linking it to knowledge distillation and the attention mechanisms. Our experiments on the benchmarked datasets show the superiority of OH over the state-of-the-art hashing methods. Code is available at \hrefhttps://github.com/RosieYuu/OH \textcolorred https://github.com/RosieYuu/OH.
The Use of Hallo Application as Supporting Teaching Media in Enhancing Students` Speaking Skill

This research was aimed in increasing students` speaking skill by using Hallo Application based on android smartphone which the students able to use it everywhere. The research used pre-experimental design which used one group pre-test and post-test. The research was planned by using of hallo application as instrument and learning support. In implementation, the researcher made planning, treatment, evaluating and reflecting in every meeting. The result of this research from paired sample test showed the result of the analytical comparisons using t-test. The output shows the average before and after the test (-20,900), standard deviation (4,773), mean standard error (0,871). The bottom difference (-22.682), while the top difference (-19,117). The result of the t test = (-23,983) with a df of 29 and a significance of 0,000. Mean score were higher after teaching using Hallo Application. This means that Ha is accepted and Ho is rejected. In conclusion, the use of Hallo Application is an effective strategy that will use to teach class XI of Senior High School Students.
Persistence in the cryptocurrency market: does size matter?

This paper investigates the persistence in the cryptocurrency market, focusing on five distinct groups categorized by their market capitalization during the sample period from 2020 to 2023. The study aims to test two hypotheses: (H1) The degree of persistence in the cryptocurrency market is contingent on market capitalization, and (H2) The efficiency of the cryptocurrency market has increased in recent years. The methodology employed for this examination is R/S analysis. The results indicate that the cryptocurrency market maintains its inefficiency, and no significant variations in persistence are discerned among different cryptocurrency groups, leading to the rejection of H1. Outcomes related to H2 present a nuanced scenario. Specifically, Litecoin and Ripple exhibit supportive evidence for the Adaptive Market Hypothesis, suggesting an improvement in the efficiency of the cryptocurrency market in recent years. A noteworthy revelation pertains to the anomaly observed in Bitcoin. Despite being the most capitalized and liquid cryptocurrency, it demonstrates inefficiency akin to levels observed five years ago. The implications of this study contribute to the comprehension of cryptocurrency market efficiency. The findings challenge the assumptions of the Efficient Market Hypothesis, favoring instead the Adaptive Market Hypothesis. For practitioners, the results hold significance, providing evidence of price predictability, particularly in the case of Bitcoin. This suggests that trend trading strategies remain viable for generating abnormal profits in the cryptocurrency market. Acknowledgments Alex Plastun gratefully acknowledges financial support from the Ministry of Education and Science of Ukraine (0121U100473).
Penguatan Literasi Elektronik Siswa Melalui Pelatihan Kodifikasi Elektronik Tanaman Berbasis QR Code Di SMKN 1 Narmada

memaksimalkan penguasaan teknologi sebagai upaya penguatan literasi digital siswa melalui kodifikasi elektronik tanaman berbasis QR Code. Metode yang digunakan dalam kegiatan pengabdian adalah metode couching/pelatihan dengan bentuk kegiatan ceramah, tanya jawab &amp; praktik. Mitra sasaran dalam kegiatan ini adalah siswa core pertanian SMKN 1 Narmada sebanyak 20 orang dan 2 orang guru sebagai pendamping. Kegiatan pengabdian ini terlaksana dengan baik dan lancar, serta berhasil menunjukkan bahwa para siswa menjadi terampil dalam mendesain template informasi tanaman dan menyandi url google drive melalui platform penyedia QR Code. Disamping itu, hasil evaluasi terhadap pelaksanaan pelatihan ini menunjukkan bahwa terdapat peningkatan pemahaman siswa terhadap kodifikasi tanaman berbasis QR Code dimana perolehan skor pretest memberikan nilai rata-rata sebesar 48,2 kemudian meningkat menjadi 85,4 untuk rata-rata skor posttest. Hasil pengujian dengan menggunakan metode Wilcoxon Signed Rank juga menunjukkan nilai p-value sebesar 0.001. Hasil ini menunjukkan bahwa terdapat peningkatan pemahaman sebelum pelatihan dengan setelah pelatihan. Metode pengabdian yang serupa diharapkan dapat ditindaklanjuti ke skala yang lebih luas yaitu pada dunia usaha dunia industri atau bekerjasama dengan dinas pertanian dan dinas kehutanan sebagai upaya penguatan literasi digital pada masyarakat. Strengthening Students' Elektronik Literacy Through QR Code-Based Plant Electronic Codification Training at SMKN 1 Narmada The aim of this service activity is to produce a QR Code-based electronic codification system and maximize mastery of technology in an effort to strengthen students' elektronik literacy through QR Code-based electronic plant codification. The method used in service activities is the couching/training method in the form of lectures, questions and answers, and practice activities. The target partners in this activity are 20 core agricultural students at SMKN 1 Narmada and 2 teachers as assistants. This service activity was carried out well and smoothly and succeeded in showing that the students became skilled in designing plant seed templates and encoding Google Drive URLs via the QR Code provider platform. Apart from that, the results of the evaluation of the implementation of this training showed that there was an increase in students' understanding of QR Code-based plant codification, where the pretest score gave an average score of 48.2, which then increased to 85.4 for the average posttest score. Test results using the Wilcoxon signed rank method also show a p-value of 0.001. These results show that there is an increase in elektronik literacy before and after the training. It is hoped that similar service methods can be followed up on a wider scale, namely in the business world of industry or in collaboration with agricultural and forestry services, as an effort to strengthen digital literacy in society.
Psychology and Pedagogy of Polyfunctional Professional Activity of Teaching Staff

This psycho-pedagogical study featured advanced training of teaching staff as a means of adapting the modern educator to the transforming functions of teaching. The research objective was to describe the ideas that modern teachers have about their diverse professional functionality, as well as to identify related priorities and difficulties. The experimental part of the research aimed at revealing the technological methods that would make teaching productive. It relied on the theoretical and empirical analysis and synthesis, evaluation of students' activities, and a psychological and pedagogical experiment. The functional and role repertoire expanded under the following conditions: subject-subject relationships, active and interactive mode, innovative approach, and cooperation among teachers of all experience levels. The research results can be used to create and improve programs of advanced professional education in the teaching sphere. Teaching personnel should be provided with opportunities for a progressive, consistent, and constant development of their individual, socio-psychological, and professional skills to fulfil their teaching potential and functional repertoire.
Structured Light-Based 3D Reconstruction System with Triangular Mesh Hole Repair

Structured light-based 3D reconstruction technology has emerged as a focal point of research in the field of 3D modeling due to its exceptional speed and accuracy. However, occlusion often occurs due to the limited field of view between the camera and the projector, as well as the complex surface of the object. Consequently, defects and holes are generated in the reconstructed model, impairing the model's reconstruction quality. In this study, we propose a hole repair method based on a triangular mesh that yields superior repaired effects. Our method effectively addresses the deficiencies in point cloud caused by occlusions. The difference in model accuracy between the repaired point cloud and the original point cloud is 3.618%.
An Effective Shadow Extraction Method for SAR Images

Since the target's shadow in a synthetic aperture radar (SAR) image can provide significant features, it is becoming a crucial discriminative feature for interpreting SAR images. In this paper, we propose a new segmentation method based on simple linear iterative clustering (SLIC) superpixel segmentation and merging to extract the targets' shadow regions in SAR images. The process is divided into four stages. Firstly, the original SAR image is preprocessed using logarithmic transform and anisotropic diffusion filtering. Secondly, the preprocessed image is segmented with the SLIC method. Then, we propose a technique based on the shadow superpixel marker to merge superpixels to obtain the shadow region. Finally, the merged edge of the shadow region is smoothed using the morphological closing operation to get the final shadow detection result. The experimental results based on the public Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset demonstrate the feasibility of the proposed method.
Supplemental Material: Expanding the metamorphic devolatilization model: Komatiites as a source for orogenic gold deposits in high-grade metamorphic rocks

&lt;p&gt;File S1: Detailed description of simulation methods. File S2: Gold contents of volcanic rocks in West Australian Craton. File S3: Bulk rock water contents of komatiites in global greenstone belts. Data for Files S2 and S3 are compiled from GEOROC database. &lt;/p&gt;
Source Device Independent Quantum Random Number Generator with Integrated InP Photonics

Quantum physics may be exploited for the generation of true random numbers, important in many applications, including cryptography and scientific simulations. In this paper, the performance of a monolithically integrated quantum random number generator on an indium phosphide platform is simulated using Lumerical INTERCONNECT. The device offers a better level of security than fully trusted systems, making no assumptions about the quantum source. The system is also compact, with a secure generation rate of 13.3984 Gb/s.
Numerical Analysis of a Self-Calibrating Time-Spatial Interleaving Photonic Convolutional Accelerator

Convolutional Neural Networks (CNNs) are fundamental machine learning tools to process image, speech, or audio signal inputs. The convolutional layer is the core building block of a CNN, and it is where most of the computation occurs. Here, we propose an integrated photonic convolutional accelerator based on time-spatial interleaving utilizing standard generic building blocks to reduce hardware complexity. The architecture is suitable for addressing both 2D and 1D convolutional kernels enabling scalability to more complex networks. Furthermore, a numerical simulation demonstrates the viability of a supervised online learning algorithm for loading the kernel weights both in amplitude and in phase taking in consideration fabrication tolerances and thermal cross-talk.
Implementation and Validation of Software Fault Prediction Model using Soft-computing based on Proposed Requirement metric

Abstract The authors have requested that this preprint be removed from Research Square.
RCKD: Response-Based Cross-Task Knowledge Distillation for Pathological Image Analysis

We propose a novel transfer learning framework for pathological image analysis, the Response-based Cross-task Knowledge Distillation (RCKD), which improves the performance of the model by pretraining it on a large unlabeled dataset guided by a high-performance teacher model. RCKD first pretrains a student model to predict the nuclei segmentation results of the teacher model for unlabeled pathological images, and then fine-tunes the pretrained model for the downstream tasks, such as organ cancer sub-type classification and cancer region segmentation, using relatively small target datasets. Unlike conventional knowledge distillation, RCKD does not require that the target tasks of the teacher and student models be the same. Moreover, unlike conventional transfer learning, RCKD can transfer knowledge between models with different architectures. In addition, we propose a lightweight architecture, the Convolutional neural network with Spatial Attention by Transformers (CSAT), for processing high-resolution pathological images with limited memory and computation. CSAT exhibited a top-1 accuracy of 78.6% on ImageNet with only 3M parameters and 1.08 G multiply-accumulate (MAC) operations. When pretrained by RCKD, CSAT exhibited average classification and segmentation accuracies of 94.2% and 0.673 mIoU on six pathological image datasets, which is 4% and 0.043 mIoU higher than EfficientNet-B0, and 7.4% and 0.006 mIoU higher than ConvNextV2-Atto pretrained on ImageNet, respectively.
Degree-Aware Graph Neural Network Quantization

In this paper, we investigate the problem of graph neural network quantization. Despite the great success on convolutional neural networks, directly applying current network quantization approaches to graph neural networks faces two challenges. First, the fixed-scale parameter in the current methods cannot flexibly fit diverse tasks and network architectures. Second, the variations of node degree in a graph leads to uneven responses, limiting the accuracy of the quantizer. To address these two challenges, we introduce learnable scale parameters that can be optimized jointly with the graph networks. In addition, we propose degree-aware normalization to process nodes with different degrees. Experiments on different tasks, baselines, and datasets demonstrate the superiority of our method against previous state-of-the-art ones.
GESTURE RECOGNITION OF MACHINE LEARNING AND CONVOLUTIONAL NEURAL NETWORK METHODS FOR KAZAKH SIGN LANGUAGE

Recently, there has been a growing interest in machine learning and neural networks among the public, largely due to advancements in technology which have led to improved methods of computer recognition of objects, sounds, texts, and other data types. As a result, human-computer interactions are becoming more natural and comprehensible to the average person. The progress in computer vision has enabled the use of increasingly sophisticated models for object recognition in images and videos, which can also be applied to recognize hand gestures. In this research, popular hand gesture recognition models, such as the Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Support Vector Machine (SVM) were examined. These models vary in their approaches, processing time, and training data size. The important feature of this research work is the use of various machine learning algorithms and methods such as CNN, LSTM, and SVM. Experiments showed different results when training neural networks for sign language recognition in the Kazakh sign language based on the dactyl alphabet. This article provides a detailed description of each method, their respective purposes, and effectiveness in terms of performance and training. Numerous experimental results were recorded in a table, demonstrating the accuracy of recognizing each gesture. Additionally, specific hand gestures were isolated for testing in front of the camera to recognize the gesture and display the result on the screen. An important feature was the use of mathematical formulas and functions to explain the working principle of the machine learning algorithm, as well as the logical scheme and structure of the LSTM algorithm.
Peranan Dan Fungsi Sistem Informasi Manajemen Rumah Sakit (SIMRS): Tinjauan Teoritis

The hospital is a health service institution that provides comprehensive individual and group health services that provide inpatient, outpatient and emergency services. Hospital services cannot be separated from the hospital management information system (HMIS). HMIS is part of a health information system in the form of a network of coordination, reporting and administrative procedures to obtain precise and accurate information. The purpose of this theoretical review is to find out how the role and function of the hospital information management system are. The method used is a theoretical review by analyzing and comparing existing theories. The results of this theoretical review are to support community management in order to improve health services for the whole community whose role is to improve service efficiency, finance, health facilities, inventory, staff, information and data integration. It can be concluded that HIMS is an important instrument in the operation of a hospital in modern times. So that the process of collecting, processing, and presenting data about hospitals can be done much easier. In its role and function, HIMS is integrated and systematic and improves service quality, maintains good and correct standards of medical practice and becomes a very effective coordinating tool.
Blockchain Technology for Military Application

This study explores the potential of using blockchain technology in military operations and supply chain management. Blockchain's transparency and security benefits are highlighted, aiding data exchange, authentication, and logistics. The article emphasizes improved transparency, efficiency, and interoperability among military units. It also examines blockchain's impact on supply chain visibility, traceability, and asset authentication. Challenges like scalability and data confidentiality are addressed, with proposed solutions for successful integration into the military sector.
Blockchain-Assisted Cross-Domain Data Sharing in Industrial IoT

In the context of the burgeoning Industrial Internet of Things (IIoT), the proliferation of interconnected devices has created a reservoir of data resources distributed across diverse domains. However, due to the conflict between proprietary data and the use of data, it is a challenge to fully obtain data value in an efficient and legal way. To release the data value in an efficient and legal way, blockchain is considered a promising technology for data security and privacy, which has been widely introduced to cross-domain data governance. In this paper, we propose a blockchain-assisted cross-domain data sharing (BCDS) in IIoT. Specifically, by deploying the permissioned blockchain, we design a zero-knowledge proof scheme to verify data ownership under the criterion of confidence and anonymity. Besides, to prevent the thrid-party from decrypting data, we design a key agreement protocol to ensure that only recipient is authorized to decrypt data based on private key. Furthermore, we theoretically analyze the security performance of schemes. Extensive experiments in simulation computer systems and testbed deployment are conducted to demonstrate the effectiveness and efficiency of the proposed scheme.