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Toward Drought Modeling in South Asia: Machine Learning Approaches, Challenges, and Opportunities

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IEEE Access 2025年 13卷 87654-87671页

作者： Mana Saleh Al Reshan Muhammad Owais Raza Naeem Ahmed Mahoto Adel Rajab Asadullah Shaikh Mohamed A. Elmagzoub Khairan D. Rajab Department of Information Systems College of Computer Science and Information Systems Najran University Najran Saudi Arabia Emerging Technologies Research Laboratory (ETRL) College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Computer Engineering Istanbul Sabahattin Zaim University İstanbul Türkiye Department of Software Engineering Mehran University of Engineering and Technology Jamshoro Sindh Pakistan Department of Computer Science College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Network and Communication Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia

Drought is an environmental and economic problem. Sustainable ecosystems, water resources, food security, and ecosystem sustainability. Machine all are severely affected by drought. Due to the increasing frequency and severity of droughts caused by climate change. Effective drought modeling is crucial for early warning systems and risk mitigation. Recent advances in machine learning (ML) and deep learning (DL) techniques have been developed as potential drought modeling tools, which offer accurate and reliable drought detection. This review paper summarizes the drought modeling(Drought Prediction, Drought Detection and Drought Forecasting) approaches. This paper focuses on three main aspect. 1) The selection of the region for this study, for this study South Asia(SA) is selected as region of interest (ROI) that offer accurate drought modeling, providing policymakers and decision-makers with insightful information. The geographical scope of this study is the region of South Asia. This region is selected because of its heavy reliance on agriculture. 2) This paper focuses on the current and future trends, challenges, and advances of and vulnerability to droughts. The review offers a thorough grasp of how drought conditions are evaluated by gathering and analyzing the most important drought indicators and metrics specific to South Asia. The paper explores the current state-of-the-art in ML and DL for drought modeling. 3) This review encapsulates the indicator and metrics (Complex Machine learning and deep learning models) for drought modeling which are most relevant to the SA region. This study sum up as most common challenges in drought modeling are, highlighting current challenges such as incomplete and inconsistent datasets, lack of explainable and interpretable models, and unavailability of data for model uncertainty analysis. This study proposes that these problems can be solved with modern machine learning techniques such as explainable machine learning and federa

关键词： Droughts Machine learning Asia Deep learning Indexes Water resources Remote sensing Predictive models Computer science Climate change Environmental monitoring Economics

Counter-Samples: A Stateless Strategy to Neutralize Black Box Adversarial Attacks

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arXiv 2024年

作者： Bokobza, Roey Mirsky, Yisroel Ben-Gurion University Department of Software and Information Systems Engineering Offensive AI Research Lab Israel

Our paper presents a novel defence against black box attacks, where attackers use the victim model as an oracle to craft their adversarial examples. Unlike traditional preprocessing defences that rely on sanitizing input samples, our stateless strategy counters the attack process itself. For every query we evaluate a counter-sample instead, where the counter-sample is the original sample optimized against the attacker’s objective. By countering every black box query with a targeted white box optimization, our strategy effectively introduces an asymmetry to the game to the defender’s advantage. This defence not only effectively misleads the attacker’s search for an adversarial example, it also preserves the model’s accuracy on legitimate inputs and is generic to multiple types of attacks. We demonstrate that our approach is remarkably effective against state-of-the-art black box attacks and outperforms existing defences for both the CIFAR-10 and ImageNet datasets. Additionally, we also show that the proposed defence is robust against strong adversaries as well. © 2024, CC BY-NC-ND.

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Machine Learning-Based Detection of Unmanned Aerial Vehicles Using Radio Frequency Signals

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Machine Learning-Based Detection of Unmanned Aerial Vehicles...

Innovations in Intelligent systems and Applications Conference

作者： Emel Okkalı Şevval Şolpan Kerem Küçük Information Systems Enginneering Kocaeli University Kocaeli Turkiye Software Engineering Kocaeli University Kocaeli Turkiye

ISBN: (数字)9798350379433

ISBN: (纸本)9798350379440

In recent years, Unmanned Aerial Vehicles (UAVs), commonly known as drones, have rapidly gained popularity due to technological advancements and significant cost reductions. Although UAVs have demonstrated their effectiveness in a wide range of applications, from security to agriculture, and from research to rescue operations, they are also being used for malicious activities, leading to significant concerns regarding privacy, security, and safety. This study employs low-complexity machine learning methods to detect UAVs by using Radio Frequency (RF) signals emitted during real-time communication between the UAV and the control device, as opposed to deep learning methods. For this purpose, the performance of K-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (CART) algorithms was evaluated. The results show that these algorithms are effective for UAV detection with an accuracy and F1 score exceeding 99.7%, and for UAV identification with an accuracy and F1 score of 88.4%.

关键词： Radio frequency Technological innovation Machine learning algorithms Accuracy Nearest neighbor methods Autonomous aerial vehicles Real-time systems Safety Security Random forests

Switched-Capacitor ADC Error and Performance Analysis 14

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Switched-Capacitor ADC Error and Performance Analysis

14th International Conference on Advanced Computer information Technologies, ACIT 2024

作者： Mychuda, Lesya Mychuda, Zynowij Korobeinikova, Tetiana Zhuravel, Ihor Romanyuk, Olexandr Romanyuk, Oksana Lviv Polytechnic National University Departament of Information Technology Security Lviv Ukraine Lviv Polytechnic National University Departament of Computerized Automatic Systems Lviv Ukraine Vinnytsia National Technical University Department of Software Engineering Vinnytsia Ukraine

ISBN: (纸本)9798350350036

This article presents the findings from a simulation study on an interpolating analog-to-digital converter (ADC), which is classified as a high-precision device. The study reveals that the proposed interpolating ADC significantly enhances both accuracy and speed of conversion, outperforming known analogs by at least twice. For certain sub-ranges, improvements in accuracy and speed are observed to be more than double. The combined output code error of the interpolating ADC is found to be less than the set value at the 2nd stage of conversion by 4-5 times, with errors not exceeding 0.022% for a set 0.1 %, 0.01 % for a set 0.05%, and 0.0022% for a set 0.01%. The conversion duration increases twofold in the millivolt range when the set error values are halved, while in the range above 1V, the duration remains under 300 μ s for both cases. For the highest set error of 0.001%, the duration can reach up to 2 ms. The optimally chosen interpolating converter with an increased accuracy to 0.05% demonstrates typical performance in the 1 mV-1V range and showcases a conversion speed twice as fast in the 1V-10V range with enhanced accuracy. The proposed interpolating converter is thus considered a superior high-precision device, offering significant improvements in both accuracy and speed for various operating sub-ranges. © 2024 IEEE.

关键词： Capacitor bank

What Was Your Prompt? A Remote Keylogging Attack on AI Assistants

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arXiv 2024年

作者： Weiss, Roy Ayzenshteyn, Daniel Amit, Guy Mirsky, Yisroel Ben-Gurion University Israel Department of Software and Information Systems Engineering Offensive AI Research Lab

AI assistants are becoming an integral part of society, used for asking advice or help in personal and confidential issues. In this paper, we unveil a novel side-channel that can be used to read encrypted responses from AI Assistants over the web: the token-length side-channel. We found that many vendors, including OpenAI and Microsoft, have this side-channel. However, inferring the content of a response from a token-length sequence alone proves challenging. This is because tokens are akin to words, and responses can be several sentences long leading to millions of grammatically correct sentences. In this paper, we show how this can be overcome by (1) utilizing the power of a large language model (LLM) to translate these sequences, (2) providing the LLM with inter-sentence context to narrow the search space and (3) performing a known-plaintext attack by fine-tuning the model on the target model’s writing style. Using these methods, we were able to accurately reconstruct 29% of an AI assistant’s responses and successfully infer the topic from 55% of them. To demonstrate the threat, we performed the attack on OpenAI’s ChatGPT-4 and Microsoft’s Copilot on both browser and API traffic. © 2024, CC BY.

关键词： Side channel attack

Addressing Key Challenges of Adversarial Attacks and Defenses in the Tabular Domain: A Methodological Framework for Coherence and Consistency

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arXiv 2024年

作者： Itzhakev, Yael Giloni, Amit Elovici, Yuval Shabtai, Asaf Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva8410501 Israel

Machine learning models trained on tabular data are vulnerable to adversarial attacks, even in realistic scenarios iwhereattackers have accessonly tto he model’soutputs. TResearchers evaluatesuch attacks, by consideringmetrics slikesuccess rate, perturbation magnitude, and query count. However, unlike other data domains, the tabular domain contains complex interdependencies among features, presenting a unique aspect that should be evaluated: the need for the attack to generate coherenctsamples and ensure feature consistency for indistinguishability. Currently, there is no established methodology for evaluating adversarial samples based on these criteria. In this paper, we address this gap by proposing new evaluation criteria tailored for tabular attacks’ quality;we defined anomaly-based framework to assess the distinguishability of adversarial samples and utilize the SHAP explainability technique to identify inconsistencies in the model’s decision-making process caused by adversarial samples. These criteria could form the basis for potential detection methods and be integrated into established evaluation metrics for assessing attack’s quality Additionally, we introduce a novel technique for perturbing dependent features while maintaining coherence and feature consistency within the sample. We compare different attacks’ strategies, examining black-box query-based attacks and transferability-based gradient attacks across four target models. Our experiments, conducted on benchmark tabular datasets, reveal significant differences between the examined attacks’ strategies in terms of the attacker’s risk and effort and the attacks’ quality. The findings provide valuable insights on the strengths, limitations, and trade-offs of various adversarial attacks in the tabular domain, laying a foundation for future research on attacks and defense development. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

DISentangled Counterfactual Visual interpretER (DISCOVER) generalizes to natural images

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arXiv 2024年

作者： Rotem, Oded Zaritsky, Assaf Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva84105 Israel

We recently presented DISentangled COunterfactual Visual interpretER (DISCOVER), a method toward systematic visual interpretability of image-based classification models and demonstrated its applicability to two biomedical domains. Here we demonstrate that DISCOVER can be applied to the domain of natural images. First, DISCOVER visually interpreted the nose size, the muzzle area, and the face size as semantic discriminative visual traits discriminating between facial images of dogs versus cats. Second, DISCOVER visually interpreted the cheeks and jawline, eyebrows and hair, and the eyes, as discriminative facial characteristics. These successful visual interpretations across two natural images domains indicate that DISCOVER is a generalized interpretability method. © 2024, CC BY-NC-SA.

关键词： Semantics

Promise and Peril of Collaborative Code Generation Models: Balancing Effectiveness and Memorization

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arXiv 2024年

作者： Chen, Zhi Jiang, Lingxiao Centre for Research on Intelligent Software Engineering School of Computing and Information Systems Singapore Management University Singapore

In the rapidly evolving field of machine learning, training models with datasets from various locations and organizations presents significant challenges due to privacy and legal concerns. The exploration of effective collaborative training settings, which are capable of leveraging valuable knowledge from distributed and isolated datasets, is increasingly *** study investigates key factors that impact the effectiveness of collaborative training methods in code next-token prediction, as well as the correctness and utility of the generated code, showing the promise of such methods. Additionally, we evaluate the memorization of different participant training data across various collaborative training settings, including centralized, federated, and incremental training, showing their potential risks in leaking data. Our findings indicate that the size and diversity of code datasets are pivotal factors influencing the success of collaborative trained code models. We demonstrate that federated learning achieves competitive performance compared to centralized training while offering better data protection, as evidenced by lower memorization ratios in the generated code. However, federated learning can still produce verbatim code snippets from hidden training data, potentially violating data privacy or copyright. Our study further explores the patterns of effectiveness and memorization in incremental learning, emphasizing the importance of the sequence in which individual participant datasets are introduced. Also, we identify the memorization phenomenon of cross-organizational clones as a prevalent challenge in both centralized and federated learning scenarios. Our findings highlight the persistent risk of data leakage during inference, even when training data remains unseen. We conclude with strategic recommendations for practitioners and researchers to optimize the use of multisource datasets, thereby propelling the cross-organizational collaboration forward. Cop

关键词： Collaborative learning

Evaluating software Development Agents: Patch Patterns, Code Quality, and Issue Complexity in Real-World GitHub Scenarios

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arXiv 2024年

作者： Chen, Zhi Jiang, Lingxiao Centre for Research on Intelligent Software Engineering School of Computing and Information Systems Singapore Management University Singapore

In recent years, AI-based software engineering has progressed from pre-trained models to advanced agentic workflows, with software Development Agents representing the next major leap. These agents, capable of reasoning, planning, and interacting with external environments, offer promising solutions to complex software engineering tasks. However, while much research has evaluated code generated by large language models (LLMs), comprehensive studies on agent-generated patches, particularly in real-world settings, are lacking. This study addresses that gap by evaluating 4,892 patches from 10 top-ranked agents on 500 real-world GitHub issues from SWE-Bench Verified, focusing on their impact on code quality. Our analysis shows no single agent dominated, with 170 issues unresolved, indicating room for improvement. Even for patches that passed unit tests and resolved issues, agents made different file and function modifications compared to the gold patches from repository developers, revealing limitations in the benchmark’s test case coverage. Most agents maintained code reliability and security, avoiding new bugs or vulnerabilities;while some agents increased code complexity, many reduced code duplication and minimized code smells. Finally, agents performed better on simpler codebases, suggesting that breaking complex tasks into smaller sub-tasks could improve effectiveness. This study provides the first comprehensive evaluation of agent-generated patches on real-world GitHub issues, offering insights to advance AI-driven software development. © 2024, CC BY.

关键词： software design