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arXiv 2025年

作者： Zang, Ying Cao, Runlong Zhang, Jianqi Han, Yidong Cao, Ziyue Hu, Wenjun Zhu, Didi Li, Zejian Zhu, Lanyun Ji, Deyi Chen, Tianrun School of Information Engineering Huzhou University China Institute of Psychology Chinese Academy of Sciences China College of Computer Science and Technology Zhejiang University China School of Software Technology Zhejiang University China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore School of Information Science and Technology University of Science and Technology of China China Tech. Co. LTD. China

Sketches, with their expressive potential, allow humans to convey the essence of an object through even a rough contour. For the first time, we harness this expressive potential to improve segmentation performance in challenging tasks like camouflaged object detection (COD). Our approach introduces an innovative sketch-guided interactive segmentation framework, allowing users to intuitively annotate objects with freehand sketches (drawing a rough contour of the object) instead of the traditional bounding boxes or points used in classic interactive segmentation models like SAM. We demonstrate that sketch input can significantly improve performance in existing iterative segmentation methods, outperforming text or bounding box annotations. Additionally, we introduce key modifications to network architectures and a novel sketch augmentation technique to fully harness the power of sketch input and further boost segmentation accuracy. Remarkably, our model’s output can be directly used to train other neural networks, achieving results comparable to pixel-by-pixel annotations—while reducing annotation time by up to 120 times, which shows great potential in democratizing the annotation process and enabling model training with less reliance on resource-intensive, laborious pixel-level annotations. We also present KOSCamo+, the first freehand sketch dataset for camouflaged object detection. The dataset, code, and the labeling tool will be open sourced. Copyright © 2025, The Authors. All rights reserved.

关键词： Pixels

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Exploiting multivariate LSTM models with multistep price forecasting for agricultural produce in Sri Lankan context 2

Exploiting multivariate LSTM models with multistep price for...

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2nd International Conference on Advancements in Computing, ICAC 2020

作者： Navaratnalingam, Shriram Kodagoda, Nuwan Suriyawansa, Kushnara Srilanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781728184128

In Sri Lanka agricultural produces possess a large supply which involves various stakeholders and thus, fluctuation of the agricultural produce prices has a direct impact on the purchasing decisions of the consumer. So, the main purpose of this study is to address the problem faced by the consumer due to poor awareness of price fluctuation which consequently astonish the consumers and hinder them from making better purchasing decisions. The research study is being specially developed in a way to adapt the Sri Lankan agricultural consumer market that is mainly based on Pettah and Dambulla trade centers. As the study we exploited different types of LSTM model with multivariate inputs along with the different combination of multistep models. The result of the study reveals that better performance was obtained for the multivariate CNN LSTM model with encoder decoder multistep model which provided an average RMSE of 19.46 Sri Lankan rupees per kilogram with an average RMSPE of 14.9%. Also, study reveals a correlation between price fluctuation and standard days of the week, where a better prediction was obtained for Monday and Tuesday with an average RMSE of 17.2 and 17.7 Sri Lankan rupees per kilogram respectively with an average RMSPE of 12.2%. Based on the input timestep considered for model, though 14 days and 21 days provided a similar result with minor variation result reveals that 14 days provided a lesser standard deviation of 0.17 than 21 days standard deviation which is 0.98. © 2020 IEEE.

关键词： Commerce

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Unsupervised Recognition of Unknown Objects for Open-World Object Detection

arXiv

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arXiv 2023年

作者： Fang, Ruohuan Pang, Guansong Zhou, Lei Bai, Xiao Zheng, Jin School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University China School of Computing and Information Systems Singapore Management University Singapore School of Computer Science and Artificial Intelligence Wuhan University of Technology China

Open-World Object Detection (OWOD) extends object detection problem to a realistic and dynamic scenario, where a detection model is required to be capable of detecting both known and unknown objects and incrementally learning newly introduced knowledge. Current OWOD models, such as ORE and OW-DETR, focus on pseudo-labeling regions with high objectness scores as unknowns, whose performance relies heavily on the supervision of known objects. While they can detect the unknowns that exhibit similar features to the known objects, they suffer from a severe label bias problem that they tend to detect all regions (including unknown object regions) that are dissimilar to the known objects as part of the background. To eliminate the label bias, this paper proposes a novel approach that learns an unsupervised discriminative model to recognize true unknown objects from raw pseudo labels generated by unsupervised region proposal methods. The resulting model can be further refined by a classification-free self-training method which iteratively extends pseudo unknown objects to the unlabeled regions. Experimental results show that our method 1) significantly outperforms the prior SOTA in detecting unknown objects while maintaining competitive performance of detecting known object classes on the MS COCO dataset, and 2) achieves better generalization ability on the LVIS and Objects365 datasets. Code is available at https://***/frh23333/mepu-owod. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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FedABC: Targeting Fair Competition in Personalized Federated Learning

arXiv

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arXiv 2023年

作者： Wang, Dui Shen, Li Luo, Yong Hu, Han Su, Kehua Wen, Yonggang Tao, Dacheng National Engineering Research Center for Multimedia Software School of Computer Science Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Hubei Luojia Laboratory Wuhan China JD Explore Academy China School of Information and Electronics Beijing Institute of Technology China School of Computer Science and Engineering Nanyang Technological University Singapore

Federated learning aims to collaboratively train models without accessing their client’s local private data. The data may be Non-IID for different clients and thus resulting in poor performance. Recently, personalized federated learning (PFL) has achieved great success in handling Non-IID data by enforcing regularization in local optimization or improving the model aggregation scheme on the server. However, most of the PFL approaches do not take into account the unfair competition issue caused by the imbalanced data distribution and lack of positive samples for some classes in each client. To address this issue, we propose a novel and generic PFL framework termed Federated Averaging via Binary Classification, dubbed FedABC. In particular, we adopt the "one-vs-all" training strategy in each client to alleviate the unfair competition between classes by constructing a personalized binary classification problem for each class. This may aggravate the class imbalance challenge and thus a novel personalized binary classification loss that incorporates both the under-sampling and hard sample mining strategies is designed. Extensive experiments are conducted on two popular datasets under different settings, and the results demonstrate that our FedABC can significantly outperform the existing counterparts. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Quantifying information Exposure by Web Browsers

Quantifying Information Exposure by Web Browsers

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Future Technologies Conference, FTC 2020

作者： Mohsen, Fadi Shehab, Mohamed Lange, Maxamilliano Karastoyanova, Dimka Department of Computer Science Bernoulli Institute University of Groningen Groningen Netherlands Department of Software and Information Systems University of North Carloina at Charlotte Charlotte United States Department of Computer Science Engineering and Physics University of Michigan-Flint Flint United States

ISBN: (纸本)9783030630911

Internet surfing entails exchanging numerous HTTP requests between clients and servers. Attached with each request is a string containing plenty of information about the client called User-Agent string. There have been numerous researches that showed the privacy and security risks of User-Agent strings. In this work we aim to minimize those risks by directing users towards using less exposing browsers. In doing so, we propose and test a new method for calculating an exposure score for web browsers based mainly on their User-Agent strings besides other attributes. We then apply this method on a dataset of over a million browsers to infer and compare their exposure scores. Finally, we present a demo web tool that shows how the resulting scores can be leveraged to aid end users. Our results reveal a variation in the exposure scores of these browsers, which creates prospect for safe Internet surfing. © 2021, Springer Nature Switzerland AG.

关键词： Web browsers

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FineDiffusion: Scaling up Diffusion Models for Fine-grained Image Generation with 10,000 Classes

arXiv

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arXiv 2024年

作者： Pan, Ziying Wang, Kun Li, Gang He, Feihong Lai, Yongxuan School of Informatics Xiamen University China Institute of Software Chinese Academy of Sciences China School of Mathematics and Information Engineering Longyan University China Shenyang Institute of Computing Technology of Chinese Academy of Sciences China University of Chinese Academy of Sciences China School of Computer Science and Technology Soochow University China

The class-conditional image generation based on diffusion models is renowned for generating high-quality and diverse images. However, most prior efforts focus on generating images for general categories, e.g., 1000 classes in ImageNet-1k. A more challenging task, large-scale fine-grained image generation, remains the boundary to explore. In this work, we present a parameter-efficient strategy, called FineDiffusion, to fine-tune large pre-trained diffusion models scaling to large-scale fine-grained image generation with 10,000 categories. FineDiffusion significantly accelerates training and reduces storage overhead by only fine-tuning tiered class embedder, bias terms, and normalization layers' parameters. To further improve the image generation quality of fine-grained categories, we propose a novel sampling method for fine-grained image generation, which utilizes superclass-conditioned guidance, specifically tailored for fine-grained categories, to replace the conventional classifier-free guidance sampling. Compared to full fine-tuning, FineDiffusion achieves a remarkable 1.56× training speed-up and requires storing merely 1.77% of the total model parameters, while achieving state-of-the-art FID of 9.776 on image generation of 10,000 classes. Extensive qualitative and quantitative experiments demonstrate the superiority of our method compared to other parameter-efficient fine-tuning methods. The code and more generated results are available at our project website: https://***/. © 2024, CC BY.

关键词： Image enhancement

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A Consumer Electronics-Enhanced UAV System for Agricultural Farm Tracking With Fuzzy SMO and Actuator Fault Detection Control Algorithms

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IEEE Transactions on Consumer Electronics 2025年

作者： Bilal, Hazrat Aslam, Muhammad Shamrooz Tian, Yibin Ullah, Inam Ayouni, Sarra Vasilakos, Athanasios V. College of Mechatronics and Control Engineering College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou2211106 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan Princess Nourah bint Abdulrahman University Department of Information Systems College of Computer and Information Sciences Saudi Arabia Grimstad Norway

The adoption of agricultural robots, or agrobots, has revolutionized modern farming operations, ranging from crop monitoring to automated harvesting, significantly boosting productivity. Motivated by the rapid advancements in agrobots and their integration into smart agricultural practices, this study proposes an autonomous trajectory tracking system for wheat farms using quadcopter UAVs. To address actuator fault detection, including stuck faults and partial loss of efficiency, a TSF-H∞-SMO (Takagi-Sugeno Fuzzy-based H∞ Sliding Mode Observer) fault detection framework is introduced. The approach initiates with the derivation of a TSF (Takagi-Sugeno Fuzzy) attitude control model that integrates an uncertainty term, constructed from the original nonlinear dynamics of the UAV and approximated through local linear models at four equilibrium positions. An actuator fault model is subsequently integrated to develop a comprehensive TSF-UAV model, accounting for actuator faults. The TSF-H∞-SMO is then designed using matrix coordinate transformation to enable precise fault detection. The fault detection capabilities of the TSF-H∞-SMO are evaluated through simulations on the TSF-UAV model under SISO (single-input single-output) actuator fault scenarios. The experimental results validate the proposed system, demonstrating its ability to detect a range of actuator faults accurately and promptly. The analysis reveals a proportional relationship between the amplitude of the state change and the severity of the fault, attributed to the interaction between system states and actuator flaps. This approach underscores the potential for deploying autonomous UAV-based fault detection and trajectory tracking systems in agricultural applications. Furthermore, integrating such advanced fault-tolerant control algorithms holds promise for consumer technology applications, where precision, reliability, and robustness are critical to enhancing system performance and operational efficiency. © 1

关键词： Agricultural robots

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Cyberbullying Classification Using a Natural Language Processing and Machine Learning Techniques

Cyberbullying Classification Using a Natural Language Proces...

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Advances in Computing Research on science engineering and Technology (ACROSET), International Conference on

作者： Nikhilesh Pramod Mankar Tushar Mahure Anushka A. Joshi Sagar Dhanraj Pande Aditya Khamparia Ankita Shrikrushna Nathe Fadi Al Turjman School of Engineering and Technology Pimpri Chinchwad University (PCU) Pune India Independent Researcher Amravati India Department of Computer Science Babasaheb Bhimrao Ambedkar University Amethi India Department of Information Technology Kavikulguru Institute of Technology and Science Nagpur India Artificial Intelligence Software Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Kyrenia Turkey

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

The usage of online entertainment has increased dramatically after some time with the enhancement of the Internet and has turned into the most compelling systems administration stage in this century. Notwithstanding, the upgrade of a social network regularly makes adverse consequences on society that add to two or three terrible peculiarities, for example, online maltreatment, badgering cyberbullying, cybercrime, and internet savaging. Cyberbullying habitually prompts genuine mental and actual misery, especially for ladies and kids, and even occasionally make them to use self-destruction methods. Multiple number of episodes have as of late happened worldwide because of online badgering, like sharing private talks, bits of hearsay, and sexual comments. In this way, the distinguishing proof of harassing messages or messages via online entertainment has gained a significant measure for development for consideration with in scientists. The ground or motive for this exploration is to create a full-fledged way or a method to differentiate online harmful and tormenting drafts by combining various methods and techniques in the domain of natural language processing and AI

关键词： Cyberbullying Entertainment industry Machine learning Gain measurement Natural language processing Internet Bayes methods computer crime

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Generalized planar Turán numbers related to short cycles

arXiv

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arXiv 2024年

作者： Gyo, Ervin Karim, Hilal Hama Alfréd Rényi Institute of Mathematics HUN-REN Budapest Hungary Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary

Given two graphs H and F, the generalized planar Turán number exP(n, H, F) is the maximum number of copies of H that an n-vertex F-free planar graph can have. We investigate this function when H and F are short cycles. Namely, for large n, we find the exact value of exP(n, Cl, C3), where Cl is a cycle of length l, for 4 ≤ l ≤ 6, and determine the extremal graphs in each case. Also, considering the converse of these problems, we determine sharp upper bounds for exP(n, C3, Cl), for 4 ≤ l ≤ 6. © 2024, CC BY-NC-SA.

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Multi-Objective Fine-Tuning for Enhanced Program Repair with LLMs

arXiv

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arXiv 2024年

作者： Yang, Boyang Tian, Haoye Ren, Jiadong Zhang, Hongyu Klein, Jacques Bissyandé, Tegawendé F. Le Goues, Claire Jin, Shunfu School of Information Science and Engineering Yanshan University China Jisuan Institute of Technology Beijing JudaoYouda Network Technology Co. Ltd. China School of Computer Science Carnegie Mellon University United States SnT University of Luxembourg Luxembourg School of Big Data and Software Engineering Chongqing University China

Large language models (LLMs) have demonstrated remarkable capabilities on a broad spectrum of downstream tasks. Within the realm of software engineering, specialized tasks on code, such as program repair, present unique challenges, necessitating fine-tuning to unlock state-of-the-art performance. Fine-tuning approaches proposed in the literature for LLMs on program repair tasks are however generally overlooking the need to reason about the logic behind code changes, beyond syntactic patterns in the data. High-performing fine-tuning experiments also usually come at very high computational costs. With MOREPAIR, we propose a novel perspective on the learning focus of LLM fine-tuning for program repair: we not only adapt the LLM parameters to the syntactic nuances of the task of code transformation (objective 1), but we also specifically fine-tune the LLM with respect to the logical reason behind the code change in the training data (objective 2). Such a multi-objective fine-tuning will instruct LLMs to generate high-quality patches. We apply MOREPAIR to fine-tune four open-source LLMs with different sizes and architectures. Experimental results on C++ and Java repair benchmarks show that the implemented fine-tuning effectively boosts LLM repair performance by 7.6% to 10% in Top-10 repair suggestions. We further show that our fine-tuning strategy yields superior performance compared to the incumbent state-of-the-art in fine-tuned models for program repair, Fine-tune-CoT and RepairLLaMA. Copyright © 2024, The Authors. All rights reserved.

关键词： Repair

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