Reviewerly/exHarmony huggingface dataset:
Global Balanced Experts for Federated Long-Tailed Learning

Federated learning (FL) is a prevalent distributed machine learning approach that enables collaborative training of a global model across multiple devices without sharing local data. However, the presence of long-tailed data can negatively deteriorate the model’s performance in real-world FL applications. Moreover, existing re-balance strategies are less effective for the federated long-tailed issue when directly utilizing local label distribution as the class prior at the clients’ side. To this end, we propose a novel Global Balanced Multi-Expert (GBME) framework to optimize a balanced global objective, which does not require additional information beyond the standard FL pipeline. In particular, a proxy is derived from the accumulated gradients uploaded by the clients after local training, and is shared by all clients as the class prior for re-balance training. Such a proxy can also guide the client grouping to train a multi-expert model, where the knowledge from different clients can be aggregated via the ensemble of different experts corresponding to different client groups. To further strengthen the privacy-preserving ability, we present a GBME-p algorithm with a theoretical guarantee to prevent privacy leakage from the proxy. Extensive experiments on long-tailed decentralized datasets demonstrate the effectiveness of GBME and GBME-p, both of which show superior performance to state-of-the-art methods. The code is available at here.
PreSTU: Pre-Training for Scene-Text Understanding

The ability to recognize and reason about text embedded in visual inputs is often lacking in vision-and-language (V&L) models, perhaps because V&L pre-training methods have often failed to include such an ability in their training objective. In this paper, we propose PreSTU, a novel pre-training recipe dedicated to scene-text understanding (STU). PreSTU introduces OCR-aware pre-training objectives that encourage the model to recognize text from an image and connect it to the rest of the image content. We implement PreSTU using a simple transformer-based encoder-decoder architecture, combined with large-scale image-text datasets with scene text obtained from an off-the-shelf OCR system. We empirically demonstrate the effectiveness of this pre-training approach on eight visual question answering and four image captioning benchmarks.
Augmenting and Aligning Snippets for Few-Shot Video Domain Adaptation

For video models to be transferred and applied seamlessly across video tasks in varied environments, Video Unsupervised Domain Adaptation (VUDA) has been introduced to improve the robustness and transferability of video models. However, current VUDA methods rely on a vast amount of high-quality unlabeled target data, which may not be available in real-world cases. We thus consider a more realistic Few-Shot Video-based Domain Adaptation (FSVDA) scenario where we adapt video models with only a few target video samples. While a few methods have touched upon Few-Shot Domain Adaptation (FSDA) in images and in FSVDA, they rely primarily on spatial augmentation for target domain expansion with alignment performed statistically at the instance level. However, videos contain more knowledge in terms of rich temporal and semantic information, which should be fully considered while augmenting target domains and performing alignment in FSVDA. We propose a novel SSA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> lign to address FSVDA at the snippet level, where the target domain is expanded through a simple snippet-level augmentation followed by the attentive alignment of snippets both semantically and statistically, where semantic alignment of snippets is conducted through multiple perspectives. Empirical results demonstrate state-of-the-art performance of SSA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> lign across multiple cross-domain action recognition benchmarks. Code will be provided at: https://github.com/xuyu0010/SSA2lign.
CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields

Neural Radiance Fields (NeRF) have the potential to be a major representation of media. Since training a NeRF has never been an easy task, the protection of its model copyright should be a priority. In this paper, by analyzing the pros and cons of possible copyright protection solutions, we propose to protect the copyright of NeRF models by replacing the original color representation in NeRF with a watermarked color representation. Then, a distortion-resistant rendering scheme is designed to guarantee robust message extraction in 2D renderings of NeRF. Our proposed method can directly protect the copyright of NeRF models while maintaining high rendering quality and bit accuracy when compared among optional solutions. Project page: https://luo-ziyuan.github.io/copyrnerf.
Research on Key Technologies of End to side Computing Network based on Field Level AI Reasoning for Terminal Equipment

This study analyzes the field-level AI reasoning technology of its terminal equipment and applies it to optimizing computing resources under the cooperation of multiple UAVs. The experimental results indicates that the performance of the ICEM method is superior to the other three methods, and the maximum value is 15 × 10SUP8/SUP bits/Joule. An increase of 10 thresholds can reduce the number of iterations 50-fold when the amount of CDF iterations is 0.9. This can reach more than 20 times if the number of UAVs and mobile terminal devices doubles. The UAV will run at the maximum speed in most time slots, and the maximum computation of the terminal equipment can reach 15 Mbits when the entire working cycle is 30 seconds. Therefore, the algorithm proposed in this study achieves higher computing energy efficiency based on less convergence time and is effective in end-to-end computing power network computing resource scheduling.
Systematic intelligent analysis tools and practices for the management and control of power grid transmission channels under the background of big data

Liuzhou Bureau, an ultra-high-voltage transmission company of the South Network, is responsible for the construction, operation and maintenance management of the main network frame of West Power East in the central and northern Guangxi. The line channels are mostly mountain forests and forest farms, and tree barrier management is an important part of safe production. Traditional artificial tree barrier data analysis has problems such as massive data processing difficulties, long data analysis time, and prone to statistical errors and omissions. Relying on the intelligent analysis and control platform, this project realizes the intelligent and visual management of tree barrier data based on artificial intelligence technology, bids farewell to the traditional mode of manually checking data one by one, gets rid of manual intervention, realizes automatic comparison analysis and update of data, achieves the purpose of intuitive visualization of tree barrier, and solves the tree barrier The problem of data processing is of great significance for tree barrier data analysis and processing.
QASiNa: Religious Domain Question Answering Using Sirah Nabawiyah

Nowadays, Question Answering (QA) tasks receive significant research focus, particularly with the development of Large Language Model (LLM) such as Chat GPT [1]. LLM can be applied to various domains, but it contradicts the principles of information transmission when applied to the Islamic domain. In Islam we strictly regulates the sources of information and who can give interpretations or tafseer for that sources [2]. The approach used by LLM to generate answers based on its own interpretation is similar to the concept of tafseer, LLM is neither an Islamic expert nor a human which is not permitted in Islam. Indonesia is the country with the largest Islamic believer population in the world [3]. With the high influence of LLM, we need to make evaluation of LLM in religious domain. Currently, there is only few religious QA dataset available and none of them using Sirah Nabawiyah especially in Indonesian Language. In this paper, we propose the Question Answering Sirah Nabawiyah (QASiNa) dataset, a novel dataset compiled from Sirah Nabawiyah literatures in Indonesian language. We demonstrate our dataset by using mBERT [4], XLM-R [5], and IndoBERT [6] which fine-tuned with Indonesian translation of SQuAD v2.0 [7]. XLM-R model returned the best performance on QASiNa with EM of 61.20, F1-Score of 75.94, and Substring Match of 70.00. We compare XLM-R performance with Chat GPT-3.5 and GPT-4 [1]. Both Chat GPT version returned lower EM and F1-Score with higher Substring Match, the gap of EM and Substring Match get wider in GPT-4. The experiment indicate that Chat GPT tends to give excessive interpretations as evidenced by its higher Substring Match scores compared to EM and F1-Score, even after providing instruction and context. This concludes Chat GPT is unsuitable for question answering task in religious domain especially for Islamic religion.
SCALE: Semantic Code Analysis via Learned Embeddings

Existing pre-trained models in NLP have demonstrated impressive success, surpassing previous benchmarks in various language-related tasks. However, when it comes to the field of code understanding, these models still face notable limitations. Code isomorphism, which deals with determining functional similarity between pieces of code, presents a challenging problem for NLP models. In this paper, we explore two approaches to code isomorphism. Our first approach, dubbed SCALE-FT, formulates the problem as a binary classification task, where we feed pairs of code snippets to a Large Language Model (LLM), using the embeddings to predict whether the given code segments are equivalent. The second approach, SCALECLR, adopts the SimCLR framework to generate embeddings for individual code snippets. By processing code samples with an LLM and observing the corresponding embeddings, we assess the similarity of two code snippets. These approaches enable us to leverage function-based code embeddings for various downstream tasks, such as code-optimization, code-comment alignment, and code classification. Our experiments on the CodeNet Python800 benchmark demonstrate promising results for both approaches. Notably, our SCALE-FT using Babbage001 (GPT-3) achieves state-of-the-art performance, surpassing various benchmark models such as GPT-3.5 Turbo and GPT-4. Additionally, Salesforce’s 350-million parameter CodeGen, when trained with the SCALE-FT framework, surpasses GPT-3.5 and GPT-4.
Low-Resource Clickbait Spoiling for Indonesian via Question Answering

Clickbait spoiling aims to generate a short text to satisfy the curiosity induced by a clickbait post. As it is a newly introduced task, the dataset is only available in English so far. Our contributions include the construction of manually labeled clickbait spoiling corpus in Indonesian and an evaluation on using cross-lingual zero-shot question answering-based models to tackle clikcbait spoiling for low-resource language like In-donesian. We utilize selection of multilingual language models. The experimental results suggest that XLM-RoBERTa (large) model outperforms other models for phrase and passage spoilers, meanwhile, mDeBERTa (base) model outperforms other models for multipart spoilers.
Enhanced Convolutional Neural Networks Based Learner Authentication for Personalized E-Learning System

In the contemporary landscape, the fusion of information technology and the rapid advancement of artificial intelligence have ushered school education into a transformative phase characterized by digitization and heightened intelligence. Concurrently, the global paradigm shift caused by the Covid-19 pandemic has catalyzed the evolution of e-learning, accentuating its significance. Amidst these developments, one pivotal facet of the online education paradigm that warrants attention is the authentication of identities within the digital learning sphere. Within this context, our study delves into a solution for online learning authentication, utilizing an enhanced convolutional neural network architecture, specifically the residual network model. By harnessing the power of deep learning, this technological approach aims to galvanize the ongoing progress of online education, while concurrently bolstering its security and stability. Such fortification is imperative in enabling online education to seamlessly align with the swift evolution of the educational landscape. This paper’s focal proposition involves the deployment of the YOLOv5 network, meticulously trained on our proprietary dataset. This network is tasked with identifying individuals’ faces culled from images captured by students’ open online cameras. The resultant facial information is then channeled into the residual network to extract intricate features at a deeper level. Subsequently, a comparative analysis of Euclidean distances against students’ face databases is performed, effectively ascertaining the identity of each student. It’s noteworthy that the innovative approach surpasses conventional YOLOv5 performance by offering specialized face detection, a critical function particularly relevant in the current pandemic milieu where face masks are commonplace. Furthermore, our methodology seamlessly extends the traditional YOLOv5 network’s capabilities.
Review of research on synonym equivalence relation mining

Synonym discovery, also known as synonym relation mining or synonym extraction, aims to identify and establish synonymous relationships between words, phrases, or sentences. The primary objective of this relationship mining is to enhance the performance of natural language processing (NLP) tasks, such as information retrieval, question-answering systems, text summarization, and machine translation. Considering that there are still numerous areas and issues awaiting further research in synonym relation mining, this paper provides a comprehensive review of the research methods employed in this field over the past two decades. The review is organized into four main categories. The first category explores the u e of language models for synonym extraction, including techniques such as word embeddings and the recent BERT model. The second category focuses on computing semantic similarity in synonym semantic spaces. The third category examines neural network-based approaches for synonym relation mining. Lastly, the fourth category delves into constructing synonym relationships based on knowledge graphs. Additionally, this paper provides an outlook and summary of potential future developments in this direction, with the aim of offering valuable guidance for future research in the field.
Is Metaverse Better than Video Conferencing in Promoting Social Presence and Learning Engagement?

While previous studies have examined the use of the metaverse to support the development of students’ learning outcomes, there has been little research to date on the use of the educational metaverse in learning experiences to provide possibilities for online learning. This study adopted a quasi-experimental design and contrasted the differences between metaverse and video conferencing in terms of social presence and learning engagement. One group of 28 students enrolled in the seminar class used both metaverse and video conferencing for online learning. The findings indicated that compared with video conferencing, the metaverse had a significant impact on social presence. Regarding learning engagement, the influence of the educational metaverse on behavioral and cognitive engagement was not significant, but students who used the metaverse showed a significant increase in emotional engagement. Based on the research results, several practical implications were provided for applying metaverse to synchronous online learning.
Empowering Dialogue Systems with Affective and Adaptive Interaction: Integrating Social Intelligence

As spoken language technologies continue to rapidly advance, the need for more natural human-computer interaction in our daily lives becomes increasingly apparent to leverage the full potential of Spoken Dialogue Systems (SDSs). In this paper, we identify key advances needed to empower machines with social intelligence in spoken language interaction from both perception and synthesis aspects. To illustrate the limitations of current SDSs, we outline two social intelligence capabilities that are present in human conversations but not yet fully realized in machines, namely affect anticipation and voice adaptation. We provide experimental analysis of affect anticipation and theoretical analysis of voice adaptation to demonstrate the feasibility and necessity of these capabilities for empowering dialogue systems with affective and adaptive interaction to achieve human-level social intelligence.
Design and Implementation Plagiarism Checker Application with DetectGPT using Scheduler Algorithm

The increasing use of Generative AI in the field of writing in the academic world creates problems in the field of plagiarism. this problem has prompted an urgent need for plagiarism detection tools. However, not all universities are able to implement a system that is able to detect sentences or paragraphs resulting from generative AI. Recent research seeks to overcome this obstacle using AI itself, specifically through the DetectGPT model. However, the implementation of this technology has limitations, such as requiring large computing resources and quite a long examination time. Using a GPU can speed up the process, but not all users can implement it. Solutions to minimize processing time include using more than one worker, however, scheduling is essential to maximize plagiarism detection efficiency. This research proposes the implementation of the above system with the help of the scheduler. The results obtained in this system prototype are an average waiting time for checking of 457,778 seconds to complete 10 tasks with the help of 3 workers running at the same time.
ANALISIS DATA KUNJUNGAN WISATAWAN MANCANEGARA KE BALI DENGAN DATA MINING

The tourism industry is the main industry in the Balinese economy. Therefore, tourist visits data is interesting to analyze. The data to be analyzed are data on the level of foreign tourist arrivals, GDP per capita, exchange rates against the Rupiah, and the total population of the Australia, the United States, Japan, Malaysia and France. The time range used is 2014 to 2020. The data mining method can be used to objectively find patterns from the data. Orange Data Mining software is used in this study because it is based on a GUI that makes it easy for users. The methods used are decision trees, linear regression, and K-Means. The results show that a country with a larger GDP per capita has a higher influence on increasing tourism visits to Bali.
Modern Approaches to Evaluating the Effectiveness of Higher Education Programs

Object: The application of modern approaches to assessing the effectiveness of academic programs in the university is a keytask of the higher education management, ensuring the quality of the educational process and the formation of professional competencies of graduates.Methods: The study included methods of systematizing the information of the analyzed literature sources, among them analysis, synthesis, structuring. Methods of expert evaluations were also applied, the peculiarity of which lies in the scientific understanding of the organization of all stages of the examination, as well as in the application of quantitative methods at each stage. The analytical part of this study was based on the use of statistical methods of data processing.Results: The proposed methodology for assessing the effectiveness of educational programs has been tested in the bachelor'sacademic programs of D. Serikbayev East Kazakhstan Technical University. This methodology makes it possible to assess the demand for educational programs on qualitative and quantitative parameters and to develop management decisions to develop their potential.Conclusions: The study of domestic and foreign experience allowed recognizing many problems of methodological nature inthe field of modern approaches to assess the effectiveness of academic programs of higher education institutions and propose modern methods of their evaluation.
Enhancing Indonesian customer complaint analysis: LDA topic modelling with BERT embeddings

Social media data can be mining for recommended systems to know the best trends or patterns. The customers have the freedom to ask questions about the product, tell their demands, and convey their complaints through social media. By mining social media data, companies can gain valuable insights into customer preferences, opinions, and sentiments. This information can be utilized to improve products and services, tailor marketing strategies, and enhance overall customer satisfaction. Topic modelling is a text mining technique that extracts the content from the raw and unlabelled data. Latent Dirichlet Allocation is popular for topic modelling research cause flexible and adaptive. But that method has issues with sparsity, performs poorly when documented in the short text and there is no correlation between topics that are actually important in text data. BERT is Bidirectional Encoder Representations from Transformer is designed to pre-train deep bidirectional representations from unlabelled text. The result of this research proves that Latent Dirichlet Allocation and BERT can be arranged on the topic of Indonesian customer complaints. BERT-Base Multilingual Cased and LDA have the highest coherence score. The combination of BERT-Base Multilingual Uncased and LDA has the highest silhouette score. BERT Multilingual are potential for improving the LDA method for Indonesian customer complaints topic modelling.
Secret image sharing scheme based on compressed sensing

In traditional secret image-sharing schemes, all the data of a secret image has to be processed, which prolongs the algorithm execution. Meanwhile, the inflated data become a burden for network transmission and disk storage when many secret images need to be routinely shared. Compressed sensing technology measures the original image perceptually through a proper measurement matrix, and the measured data cover the vast majority of the useful information of the original image. While ensuring precise reconstruction, the original image is compressed from high dimensional to low dimensional, and the amount of image data decreases dramatically. Thus, a number of problems caused by the large amount of data in traditional secret image-sharing schemes could be solved by compressed sensing. In this paper, we combine traditional secret image-sharing with compressed sensing technology and show, through experiments, that the proposed method can clearly reduce the amount of data that needs to be processed and effectively shorten the algorithm execution time. The experimental results reveal that our method can shorten the image-sharing time by 2.7% to 57.3% and the image restoration time by 3.3% to 57.7% under different compression ratios.