Reviewerly/exHarmony huggingface dataset:
"Whisper and Write Game” as Pre-Writing Activity to Enhance Descriptive Writing Proficiency of Indonesian Seventh Graders

Authors:
raw_author_name: Sri Wahyuni
raw_affiliation_string: Institut Agama Islam Negeri Kediri

Abstract: Pre-writing is a preparation process before writing where students gather ideas, organize information, and plan their writing so that pre-writing has a significant influence on student writing outcomes. This study aims to analyze the effect of using whisper and write games as students' pre-writing activities on the descriptive writing abilities of the seventh-grade students at MTs Mamba'us Sholihin Gresik. This research uses class action research (CAR). The results showed that the improvement was seen from the cycle I and cycle II learning that had been implemented, comparing between cycle I and cycle II. The increase in learning outcomes was 76.19%, the increase obtained from the evaluation results in the first cycle was 14.28% and the second cycle was 90.47%. the learning outcomes of the last cycle show that the mastery of learning exceeds the KKM of 75%. Based on the results of the study it was explained that the use of whispering and writing as pre-writing activity games can help to understand and improve students' skills in writing descriptive text. and this can be seen from the increase in the percentage of student scores in each cycle. and the application of CAR can help students achieve the KKM target. The author admits that the writing of this research has several limitations. The limitation of this study is the inappropriate timing of the research because many agendas clashed with the school, so it is hoped that further research can adjust the time to be appropriate.
Emerging Trends and Innovations in Blockchain-Digital Twin Integration for Green Investments

Authors:
raw_author_name: Muhammad Usman Tariq
raw_affiliation_string: Abu Dhabi University, UAE

Abstract: This chapter explores the dynamic interaction between blockchain technology and digital twins in the context of green investing. It examines the revolutionary influence of the synergy of these cutting-edge technologies on the efficiency, transparency, and dependability of sustainable investing methods. The chapter begins with an informative introduction to the fundamentals of blockchain and digital twins, illuminating their separate functions before revealing their joint potential for strengthening green investment initiatives. A detailed assessment of the most recent improvements in blockchain and digital twin technologies follows, focusing on smart contracts, enhanced data analytics, and real-time monitoring. The actual implementation and effects of this integration are presented via illustrative case studies of renewable energy, sustainable infrastructure, and environmental conservation. This chapter delves deeper into the problems of integrating these technologies, providing practical insights and solutions to technical, regulatory, and operational roadblocks.
Automation of complex text СAPTCHA recognition using conditional generative adversarial networks

Authors:
raw_author_name: A.A. Korepanova
raw_author_name: M.V. Abramov

Abstract: With the rapid development of Internet technologies, the problems of network security continue to worsen. So, one of the most common methods of maintaining security and preventing malicious attacks is CAPTCHA (fully automated public Turing test). CAPTCHA most often consists of some kind of security code, to bypass which it is necessary to perform a simple task, such as entering a word displayed in an image, solving a basic arithmetic equation, etc. However, the most widely used type of CAPTCHA is still the text type. In the recent years, the development of computer vision and, in particular, neural networks has contributed to a decrease in the resistance to hacking of text CAPTCHA. However, the security and resistance to recognition of complex CAPTCHA containing a lot of noise and distortion is still insufficiently studied. This study examines CAPTCHA, the distinctive feature of which is the use of a large number of different distortions, and each individual image uses its own different set of distortions, that is why even the human eye cannot always recognize what is depicted in the photo. The purpose of this work is to assess the security of sites using the CAPTCHA text type by testing their resistance to an automated solution. This testing will be used for the subsequent development of recommendations for improving the effectiveness of protection mechanisms. The result of the work is an implemented synthetic generator and discriminator of the CGAN architecture, as well as a decoder program, which is a trained convolutional neural network that solves this type of CAPTCHA. The recognition accuracy of the model constructed in the article was 63 % on an initially very limited data set, which shows the information security risks that sites using a similar type of CAPTCHA can carry.
Time-Restricted Double-Spending Attack on PoW-based Blockchains

Authors:
raw_author_name: Jiang, Yiming
raw_author_name: Zhang, Jiangfan

Abstract: Numerous blockchain applications are designed with tasks that naturally have finite durations, and hence, a double-spending attack (DSA) on such blockchain applications leans towards being conducted within a finite timeframe, specifically before the completion of their tasks. Furthermore, existing research suggests that practical attackers typically favor executing a DSA within a finite timeframe due to their limited computational resources. These observations serve as the impetus for this paper to investigate a time-restricted DSA (TR-DSA) model on Proof-of-Work based blockchains. In this TR-DSA model, an attacker only mines its branch within a finite timeframe, and the TR-DSA is considered unsuccessful if the attacker's branch fails to surpass the honest miners' branch when the honest miners' branch has grown by a specific number of blocks. First, we developed a general closed-form expression for the success probability of a TR-DSA. This developed probability not only can assist in evaluating the risk of a DSA on blockchain applications with timely tasks, but also can enable practical attackers with limited computational resources to assess the feasibility and expected reward of launching a TR-DSA. In addition, we provide rigorous proof that the success probability of a TR-DSA is no greater than that of a time-unrestricted DSA where the attacker indefinitely mines its branch. This result implies that blockchain applications with timely tasks are less vulnerable to DSAs than blockchain applications that provide attackers with an unlimited timeframe for their attacks. Furthermore, we show that the success probability of a TR-DSA is always smaller than one even though the attacker controls more than half of the hash rate in the network. This result alerts attackers that there is still a risk of failure in launching a TR-DSA even if they amass a majority of the hash rate in the network.
OccTransformer: Improving BEVFormer for 3D camera-only occupancy  prediction

Authors:
raw_author_name: Liu, Jian
raw_author_name: Zhang, Sipeng
raw_author_name: Zhang, Wenyuan
raw_author_name: Ding, Yikang
raw_author_name: Xu, Borun
raw_author_name: Wei, Donglai
raw_author_name: Liu, Xianming

Abstract: This technical report presents our solution, "occTransformer" for the 3D occupancy prediction track in the autonomous driving challenge at CVPR 2023. Our method builds upon the strong baseline BEVFormer and improves its performance through several simple yet effective techniques. Firstly, we employed data augmentation to increase the diversity of the training data and improve the model's generalization ability. Secondly, we used a strong image backbone to extract more informative features from the input data. Thirdly, we incorporated a 3D unet head to better capture the spatial information of the scene. Fourthly, we added more loss functions to better optimize the model. Additionally, we used an ensemble approach with the occ model BevDet and SurroundOcc to further improve the performance. Most importantly, we integrated 3D detection model StreamPETR to enhance the model's ability to detect objects in the scene. Using these methods, our solution achieved 49.23 miou on the 3D occupancy prediction track in the autonomous driving challenge.
Evaluating Quantized Large Language Models

Authors:
raw_author_name: Li, Shiyao
raw_author_name: Wang, Luning
raw_author_name: Shi, Xiangsheng
raw_author_name: Dai, Guohao
raw_author_name: Yang, Huazhong

Abstract: Post-training quantization (PTQ) has emerged as a promising technique to reduce the cost of large language models (LLMs). Specifically, PTQ can effectively mitigate memory consumption and reduce computational overhead in LLMs. To meet the requirements of both high efficiency and performance across diverse scenarios, a comprehensive evaluation of quantized LLMs is essential to guide the selection of quantization methods. This paper presents a thorough evaluation of these factors by evaluating the effect of PTQ on Weight, Activation, and KV Cache on 11 model families, including OPT, LLaMA2, Falcon, Bloomz, Mistral, ChatGLM, Vicuna, LongChat, StableLM, Gemma, and Mamba, with parameters ranging from 125M to 180B. The evaluation encompasses five types of tasks: basic NLP, emergent ability, trustworthiness, dialogue, and long-context tasks. Moreover, we also evaluate the state-of-the-art (SOTA) quantization methods to demonstrate their applicability. Based on the extensive experiments, we systematically summarize the effect of quantization, provide recommendations to apply quantization techniques, and point out future directions.
Efficiently Computable Safety Bounds for Gaussian Processes in Active  Learning

Authors:
raw_author_name: Zimmer, Christoph
raw_author_name: Steland, Ansgar
raw_author_name: Lange-Hegermann, Markus

Abstract: Active learning of physical systems must commonly respect practical safety constraints, which restricts the exploration of the design space. Gaussian Processes (GPs) and their calibrated uncertainty estimations are widely used for this purpose. In many technical applications the design space is explored via continuous trajectories, along which the safety needs to be assessed. This is particularly challenging for strict safety requirements in GP methods, as it employs computationally expensive Monte-Carlo sampling of high quantiles. We address these challenges by providing provable safety bounds based on the adaptively sampled median of the supremum of the posterior GP. Our method significantly reduces the number of samples required for estimating high safety probabilities, resulting in faster evaluation without sacrificing accuracy and exploration speed. The effectiveness of our safe active learning approach is demonstrated through extensive simulations and validated using a real-world engine example.
Unsupervised Cross-Domain Image Retrieval via Prototypical Optimal  Transport

Authors:
raw_author_name: Li, Bin
raw_author_name: Shi, Ye
raw_author_name: Yu, Qian
raw_author_name: Wang, Jingya

Abstract: Unsupervised cross-domain image retrieval (UCIR) aims to retrieve images sharing the same category across diverse domains without relying on labeled data. Prior approaches have typically decomposed the UCIR problem into two distinct tasks: intra-domain representation learning and cross-domain feature alignment. However, these segregated strategies overlook the potential synergies between these tasks. This paper introduces ProtoOT, a novel Optimal Transport formulation explicitly tailored for UCIR, which integrates intra-domain feature representation learning and cross-domain alignment into a unified framework. ProtoOT leverages the strengths of the K-means clustering method to effectively manage distribution imbalances inherent in UCIR. By utilizing K-means for generating initial prototypes and approximating class marginal distributions, we modify the constraints in Optimal Transport accordingly, significantly enhancing its performance in UCIR scenarios. Furthermore, we incorporate contrastive learning into the ProtoOT framework to further improve representation learning. This encourages local semantic consistency among features with similar semantics, while also explicitly enforcing separation between features and unmatched prototypes, thereby enhancing global discriminativeness. ProtoOT surpasses existing state-of-the-art methods by a notable margin across benchmark datasets. Notably, on DomainNet, ProtoOT achieves an average P@200 enhancement of 24.44%, and on Office-Home, it demonstrates a P@15 improvement of 12.12%. Code is available at https://github.com/HCVLAB/ProtoOT.
Sunshine to Rainstorm: Cross-Weather Knowledge Distillation for Robust  3D Object Detection

Authors:
raw_author_name: Huang, Xun
raw_author_name: Wu, Hai
raw_author_name: Li, Xin
raw_author_name: Fan, Xiaoliang
raw_author_name: Wen, Chenglu
raw_author_name: Wang, Cheng

Abstract: LiDAR-based 3D object detection models have traditionally struggled under rainy conditions due to the degraded and noisy scanning signals. Previous research has attempted to address this by simulating the noise from rain to improve the robustness of detection models. However, significant disparities exist between simulated and actual rain-impacted data points. In this work, we propose a novel rain simulation method, termed DRET, that unifies Dynamics and Rainy Environment Theory to provide a cost-effective means of expanding the available realistic rain data for 3D detection training. Furthermore, we present a Sunny-to-Rainy Knowledge Distillation (SRKD) approach to enhance 3D detection under rainy conditions. Extensive experiments on the WaymoOpenDataset large-scale dataset show that, when combined with the state-of-the-art DSVT model and other classical 3D detectors, our proposed framework demonstrates significant detection accuracy improvements, without losing efficiency. Remarkably, our framework also improves detection capabilities under sunny conditions, therefore offering a robust solution for 3D detection regardless of whether the weather is rainy or sunny
TAMM: TriAdapter Multi-Modal Learning for 3D Shape Understanding

Authors:
raw_author_name: Zhang, Zhihao
raw_author_name: Wang, Yu-Xiong

Abstract: The limited scale of current 3D shape datasets hinders the advancements in 3D shape understanding, and motivates multi-modal learning approaches which transfer learned knowledge from data-abundant 2D image and language modalities to 3D shapes. However, even though the image and language representations have been aligned by cross-modal models like CLIP, we find that the image modality fails to contribute as much as the language in existing multi-modal 3D representation learning methods. This is attributed to the domain shift in the 2D images and the distinct focus of each modality. To more effectively leverage both modalities in the pre-training, we introduce TriAdapter Multi-Modal Learning (TAMM) -- a novel two-stage learning approach based on three synergetic adapters. First, our CLIP Image Adapter mitigates the domain gap between 3D-rendered images and natural images, by adapting the visual representations of CLIP for synthetic image-text pairs. Subsequently, our Dual Adapters decouple the 3D shape representation space into two complementary sub-spaces: one focusing on visual attributes and the other for semantic understanding, which ensure a more comprehensive and effective multi-modal pre-training. Extensive experiments demonstrate that TAMM consistently enhances 3D representations for a wide range of 3D encoder architectures, pre-training datasets, and downstream tasks. Notably, we boost the zero-shot classification accuracy on Objaverse-LVIS from 46.8 to 50.7, and improve the 5-way 10-shot linear probing classification accuracy on ModelNet40 from 96.1 to 99.0. Project page: \url{https://alanzhangcs.github.io/tamm-page}.
Enhancing 2D Path Search and PID Control for Autonomous Mobile Robots: A* Algorithm Optimization and Hardware

Authors:
raw_author_name: Chengzhi Zhou

Abstract: Intelligent autonomous mobile robots find diverse applications across various fields. This article delves into the optimization of path search planning and implementation for these robots in specific scenarios. The study enhances the traditional right-hand principle's search strategy, integrating a forward-looking approach grounded in the goal-oriented law with A* algorithm path planning. Emphasizing the algorithm's lightweight and high efficiency, the study also considers its interaction with hardware systems. The real-world application scenario is abstracted into a two-dimensional planar environment. Collaborative efforts between hardware and software systems result in a comprehensive robot system, validated through tests ensuring swift navigation from the starting point to the endpoint along the planned path.
Analisis Deiksis dalam Channel YouTube Satu Persen pada Daftar Putar “Manajemen Waktu”

Authors:
raw_author_name: None Asep Purwo Yudi Utomo

Abstract: The studies we took in this research are from the field of pragmatics. The pragmatics study in this research focuses on deictic analysis. Deixis is a part of pragmatics that discusses the expressions or context in a sentence. This research aims to analyze various types of deixis contained in the 'Time Management' video on the Satu Persen YouTube channel. This research was carried out using a qualitative approach, namely a method as a research procedure that produces descriptive data. The research techniques used to produce data are observation techniques, note-taking techniques, writing techniques, and listening techniques. In the analysis of deixis in a series of videos contained in the 'Time Management' playlist via the One Percent YouTube channel that we studied, five types of deixis can be found, namely persona deixis (first person singular, first person plural, second person singular, second person plural, third person singular), place deixis, time deixis (past, present, future), discourse deixis (anaphora, cataphora), and social deixis. Each type of deixis has its own function depending on its context. This research will be interesting for readers who have difficulty managing time.
DAWN: Dashboard for Agricultural Water Use and Nutrient Management—A Predictive Decision Support System to Improve Crop Production in a Changing Climate

Authors:
raw_author_name: Xin-Zhong Liang
raw_affiliation_string: Department of Atmospheric and Oceanic Science, and Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland;
raw_author_name: Melissa Kenney
raw_affiliation_string: Institute on the Environment, University of Minnesota, Saint Paul, Minnesota;
raw_author_name: Michael Gerst
raw_affiliation_string: Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland;
raw_author_name: Ximing Cai
raw_affiliation_string: Department of Civil and Environmental Engineering, University of Illinois Urbana–Champaign, Urbana, Illinois;
raw_author_name: Yufeng He
raw_affiliation_string: Department of Civil and Environmental Engineering, University of Illinois Urbana–Champaign, Urbana, Illinois;
raw_author_name: Christopher M. U. Neale
raw_affiliation_string: Daugherty Water for Food Global Institute, University of Nebraska–Lincoln, Lincoln, Nebraska;
raw_author_name: Greg Newman
raw_affiliation_string: USDA UV-B Monitoring and Research Program, Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, Colorado;
raw_author_name: Sanghoon Shin
raw_affiliation_string: Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland;
raw_author_name: Xuesong Zhang
raw_affiliation_string: Hydrology and Remote Sensing Laboratory, ARS, USDA, Beltsville, Maryland

Abstract: Abstract Climate change presents huge challenges to the already-complex decisions faced by U.S. agricultural producers, as seasonal weather patterns increasingly deviate from historical tendencies. Under USDA funding, a transdisciplinary team of researchers, extension experts, educators, and stakeholders is developing a climate decision support Dashboard for Agricultural Water use and Nutrient management (DAWN) to provide Corn Belt farmers with better predictive information. DAWN’s goal is to provide credible, usable information to support decisions by creating infrastructure to make subseasonal-to-seasonal forecasts accessible. DAWN uses an integrated approach to 1) engage stakeholders to coproduce a decision support and information delivery system; 2) build a coupled modeling system to represent and transfer holistic systems knowledge into effective tools; 3) produce reliable forecasts to help stakeholders optimize crop productivity and environmental quality; and 4) integrate research and extension into experiential, transdisciplinary education. This article presents DAWN’s framework for integrating climate–agriculture research, extension, and education to bridge science and service. We also present key challenges to the creation and delivery of decision support, specifically in infrastructure development, coproduction and trust building with stakeholders, product design, effective communication, and moving tools toward use.
Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and  Understanding -- A Survey

Authors:
raw_author_name: Fang, Xi
raw_author_name: Xu, Weijie
raw_author_name: Tan, Fiona Anting
raw_author_name: Zhang, Jiani
raw_author_name: Hu, Ziqing
raw_author_name: Qi, Yanjun
raw_author_name: Sengamedu, Srinivasan
raw_author_name: Faloutsos, Christos

Abstract: Recent breakthroughs in large language modeling have facilitated rigorous exploration of their application in diverse tasks related to tabular data modeling, such as prediction, tabular data synthesis, question answering, and table understanding. Each task presents unique challenges and opportunities. However, there is currently a lack of comprehensive review that summarizes and compares the key techniques, metrics, datasets, models, and optimization approaches in this research domain. This survey aims to address this gap by consolidating recent progress in these areas, offering a thorough survey and taxonomy of the datasets, metrics, and methodologies utilized. It identifies strengths, limitations, unexplored territories, and gaps in the existing literature, while providing some insights for future research directions in this vital and rapidly evolving field. It also provides relevant code and datasets references. Through this comprehensive review, we hope to provide interested readers with pertinent references and insightful perspectives, empowering them with the necessary tools and knowledge to effectively navigate and address the prevailing challenges in the field.