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Algorithms and Bounds on the Sums of Powers of Consecutive Primes

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arXiv 2022年

作者： O’Sullivan, Cathal Sorenson, Jonathan P. Stahl, Aryn Mathematics Statistics and Actuarial Science Department Computer Science and Software Engineering Department Butler University IndianapolisIN United States Computer Science and Software Engineering Department Butler University IndianapolisIN United States Physics and Astronomy Department Computer Science and Software Engineering Department Butler University IndianapolisIN United States

We present and analyze algorithms to count and enumerate all integers n ≤ x that can be written as the sum of consecutive kth powers of primes, for fixed k > 1. We show that the number of such integers n is asymptotically bounded by ck x2/(k+1)/(log x)2k/(k+1), where ck is a constant depending solely on k, roughly k2 in magnitude. We also give an asymptotic lower bound of the form (k + 1)2/2 x2/(k+1)/(log x)2k/(k+1) . 2 Our enumeration algorithm is optimal, in that it constructs all such integers n ≤ x using a constant number of arithmetic operations for each n. Our algorithm to count such n takes time proportional to the number of primes up to x1/k, so that finding this list of primes is the bottleneck in its runtime. We also present some computational results, using our new algorithms, that provide empirical data to support our theoretical results. Our work extends the previous work by Tongsomporn, Wananiyakul, and Steuding [6] who examined sums of squares of consecutive *** Codes 11A41, 68Q25 Copyright © 2022, The Authors. All rights reserved.

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HGDRec:Next POI Recommendation Based on Hypergraph Neural Network and Diffusion Model

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IEEE Transactions on Services Computing 2025年第3期18卷 1445-1458页

作者： Yinchen Pan Jun Zeng Ziwei Wang Haoran Tang Junhao Wen Min Gao School of Big Data and software engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong

In recent years, next Point-of-Interest (POI) recommendation is essential for many location-based services, aiming to predict the most likely POI a user will visit next. Current research employs graph-based and sequential methods, which have significantly improved performance. However, there are still limitations: numerous methods overlook the fact that user intent is constantly changing and complex. Furthermore, prior studies have seldom addressed spatiotemporal correlations while considering differences in user behavior patterns. Additionally, implicit feedback contains noise. To address these issues, we propose a recommender model named HGDRec for the next POI recommendation. Specifically, we introduce an approach for extracting trajectory intent by integrating multi-dimensional trajectory representations to achieve a multi-level understanding of user trajectories. Then, by analyzing users’ long trajectories, we construct global hypergraph structures across spatiotemporal regions to comprehensively capture user behavior patterns. Additionally, to further optimize trajectory intent representation, we employ a feature optimization method based on the improved diffusion model. Extensive experiments on three real-world datasets validate the superiority of HGDRec over the state-of-the-art methods.

关键词： Trajectory Diffusion models Spatiotemporal phenomena Feature extraction Optimization Transformers Data mining Noise measurement Data models Correlation

Predicting Crop Prices using Machine Learning Algorithms for Sustainable Agriculture

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Predicting Crop Prices using Machine Learning Algorithms for...

IEEE Region 10 Symposium

作者： Iftekhar Mahmud Puja Rani Das Md. Habibur Rahman Ahmed Rafi Hasan Kamrul Islam Shahin Dewan Md. Farid Department of Computer Science and Engineering United International University United City Madani Avenue Badda Dhaka Bangladesh Software Engineering The Maersk Mc-Kinney Moller Institute University of Southern Denmark Denmark

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364873

The agricultural sector is crucial for South Asia's economy, providing employment and driving GDP growth. Accurate price forecasting is essential for food security, market stability, and facilitating import-export activities. In this study, Decision Tree, AdaBoost, and Random Forest have proven effectiveness in predicting crop prices after analyzing different regression models. The Decision Tree has achieved R-squared $(R^{2})$ values of 0.996, 0.996, and 1 and Mean Squared Error MSE values of 0.242, 4.827, and 0.846 respectively for Rice, Onion, and Oil Seed. Similarly, the AdaBoost has also performed well with R-squared $(R^{2})$ values of 0.737, 0.959 and 1 and MSE values of 175.234, 14.566 and 1.643 respectively for Tomato, Potato and Oil Seed, indicating better prediction accuracy and lower prediction error. Lastly, Wheat has performed well with Linear Regression with R-squared $(R^{2})$ values of 0.980 and MSE values of 0.756. Random Forest Regression has shown consistent high R-squared $(R^{2})$ values and low MSE values. The effectiveness of Decision Tree, AdaBoost, and Random Forest in Bangladesh's agricultural price forecasting highlights their versatility, aiding informed decisions by farmers and policymakers.

关键词： Accuracy Machine learning algorithms Oils Linear regression Predictive models Stability analysis Seeds (agriculture) Forecasting Random forests Regression tree analysis

Evaluating LoRaWAN performance in intentional and unintentional DoS attacks by legacy 900MHz network devices

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Evaluating LoRaWAN performance in intentional and unintentio...

International Conference on Advanced Communication Technologies and Networking (CommNet)

作者： Seokhyeon Bang Junyoung Jang Minju Ro Yuseon Choi Doyong Kwon Kangyeon Lee Anthony Smith School of Computer Science and Engineering Chung-Ang University Seoul Korea Department of Software Engineering Chonnam National University Gwangju Korea School of Computer Science and Engineering Kyungpook National University Daegu Korea Department of Computer and Information Technology Purdue University Indiana United States

The deployment of LoRaWAN on the Internet of Things (IoT) has increased since its advent and LoRaWAN now predominates the IoT market over other Low Powered Wide Area Networks (LPWAN). However, since LoRaWAN uses Chirp Spread Spectrum (CSS), it is susceptible to wideband jamming attacks. In this paper, we demonstrate with experiments and concrete numerical results that jamming US915 LoRaWAN frequency is possible by the usual data transmission and reception process of 900MHz Canopy, one of the legacy 900MHz network device. Intentional attack is possible in the same manner. The experiments emulate the real-world environment operated in medical and agriculture industries, in outdoor and indoor conditions, respectively. In addition, this paper introduces and utilizes the novel metric, Jamming Effect (JE), that indicates the network performance of wireless networks that spread the data on air.

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A Brief Overview of Deep Learning Approaches for IoT Security

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A Brief Overview of Deep Learning Approaches for IoT Securit...

International Conference on Computing, Mathematics and engineering Technologies (iCoMET)

作者： Muhammad Hassaan Khalid Hanan Sharif Faisal Rehman Muhammad Naeem Ullah Shahbaz Shaukat Hadia Maqsood Chaudhry Nouman Ali Ayaz Hussain Irfana Iftikhar Department of Computer Science & Information Technology Lahore Leads University Lahore Pakistan Department of Software Engineering Mehran university of Engineering and Technology SZAB Khairpur Mir's Pakistan

The continuous increasing usage of internet devices in many areas of human life is continuously growing and demanding a proper method to protect these IoT devices from cyber-attacks and vulnerabilities. In this aspect, deep learning neural networks are a very helpful and beneficial method to provide protection to internet devices in many aspects. This study's objective is to systematically research and examine the background of research about neural networks tactics functional to different internet-based devices' safety use-cases. We put the influences we looked at into many different groups to show where this research is lacking. This particular research or findings mainly captures the articles related to “Deep neural networks”, “Cyber risks” and “IOT” in four databanks, namely Science Direct, ACM Digital Library, IEEEXplore and Springer Link. These studies concern 3 main studies, which involve safety aspects, their own Deep Learning network designs, and the occupied datasets. The final round is to find out the loopholes to be examined in the future and vulnerabilities and drawbacks in the IoT security scenario.

关键词： Deep learning Systematics Neural networks Taxonomy Oral communication Mathematics Libraries

User Centric Semantic Communications

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arXiv 2024年

作者： Liu, Xunze Sun, Yifei Wang, Zhaorui You, Lizhao Pan, Haoyuan Wang, Fangxin Cui, Shuguang FNii and SSE The Chinese University of Hong Kong at Shenzhen Shenzhen China Department of Information and Communication Engineering Xiamen University China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Current studies on semantic communications mainly focus on efficiently extracting semantic information to save bandwidth between a transmitter and a user. Although significant process has been made, a fundamental design problem in the semantic communications is that the semantic information is extracted based on certain criteria at the transmitter side along, without considering the user’s actual requirements. As a result, the current semantic communications may cause the loss of critical information that is of primary interest to the user. In such cases, the semantic transmission makes no sense to the user since all the received information is irrelevant. To solve this problem, this paper presents a user centric semantic communication system, where the user sends its request for the desired semantic information to the transmitter at the start of each transmission. Then, the transmitter extracts the required semantic information accordingly. A key challenge is how the transmitter can understand the user’s requests for semantic information and extract the required semantic information in a reasonable and robust manner. We solve this challenge by designing a well-structured framework and leveraging off-the-shelf products, such as GPT-4, along with several specialized tools for detection and estimation. Evaluation results demonstrate the feasibility and effectiveness of the proposed user centric semantic communications. © 2024, CC BY.

关键词： Latent semantic analysis

Fast Parallel Bayesian Network Structure Learning

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arXiv 2022年

作者： Jiang, Jiantong Wen, Zeyi Mian, Ajmal Department of Computer Science and Software Engineering The University of Western Australia Australia

Bayesian networks (BNs) are a widely used graphical model in machine learning for representing knowledge with uncertainty. The mainstream BN structure learning methods require performing a large number of conditional independence (CI) tests. The learning process is very time-consuming, especially for high-dimensional problems, which hinders the adoption of BNs to more applications. Existing works attempt to accelerate the learning process with parallelism, but face issues including load unbalancing, costly atomic operations and dominant parallel overhead. In this paper, we propose a fast solution named Fast-BNS on multi-core CPUs to enhance the efficiency of the BN structure learning. Fast-BNS is powered by a series of efficiency optimizations including (i) designing a dynamic work pool to monitor the processing of edges and to better schedule the workloads among threads, (ii) grouping the CI tests of the edges with the same endpoints to reduce the number of unnecessary CI tests, (iii) using a cache-friendly data storage to improve the memory efficiency, and (iv) generating the conditioning sets on-the-fly to avoid extra memory consumption. A comprehensive experimental study shows that the sequential version of Fast-BNS is up to 50 times faster than its counterpart, and the parallel version of Fast-BNS achieves 4.8 to 24.5 times speedup over the state-of-the-art multi-threaded solution. Moreover, Fast-BNS has a good scalability to the network size as well as sample size. Fast-BNS source code is freely available at https: //***/jjiantong/FastBN. Copyright © 2022, The Authors. All rights reserved.

关键词： Bayesian networks

A GUI Based Approach to Detect Energy Bugs in Android Applications 16

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A GUI Based Approach to Detect Energy Bugs in Android Applic...

16th International Conference on Emerging Technologies, ICET 2021

作者： Naseer, Ayesha Nadeem, Aamer Zaman, Qamar Uz Capital Universoty of Science Technology Department of Computer Science Islamabad Pakistan Capital Universoty of Science Technology Software Engineering Islamabad Pakistan Capital Universoty of Science Technology Department of Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665494373

Smartphones have improved in terms of their processing speed and memory capacity. The increased usage of smartphones has led to the widespread availability of smartphone applications. However, many smartphone applications suffer from energy inefficiency problems due to improper usage of resources. When resources are left unreleased after use, they keep consuming smartphone's battery power even if the application is not using these resources. This behavior is called resource-leak which causes energy-bugs. Different solutions based on static, dynamic and hybrid analysis exist in the literature to detect energy-bugs due to resource-leaks. However, most of the existing solutions suffer from the problem of false positives due to energy-bugs detection at the method level. They consider each method in isolation and check that if the particular method has acquired a resource but has not released it within that method. In practice, it is common that resources are acquired in one method and released in another method. In this paper, we propose a dynamic analysis technique to perform energy-bug detection at the application level to overcome the problem of false-positives. Our aim is to detect energy bugs, because serious energy issues can arise in smartphones due to energy bugs. Besides, we have considered methods in which resource is acquired or released, and if both methods execute in a single path, then the resource will be released and there will be no energy bug at application level. To evaluate our proposed approach, we have performed experiments on 11 real-life Android applications. Experimental results show that our approach is able to identify real energy bugs at the application level as well as identify false positives. © 2021 IEEE.

关键词： Smartphones

Iterative Optimal Attention and Local Model for Single Image Rain Streak Removal

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arXiv 2025年

作者： Li, Xiangyu Fan, Wanshu Shen, Yue Wang, Cong Wang, Wei Yang, Xin Zhang, Qiang Zhou, Dongsheng National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University Dalian China Department of Computing The Hong Kong Polytechnic University Hong Kong School of Cyber Science and Technology Sun Yat-Sen University Shenzhen China School of Computer Science and Technology Dalian University of Technology Dalian China National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University School of Computer Science and Technology Dalian University of Technology Dalian China

High-fidelity imaging is crucial for the successful safety supervision and intelligent deployment of vision-based measurement systems (VBMS). It ensures high-quality imaging in VBMS, which is fundamental for reliable visual measurement and analysis. However, imaging quality can be significantly impaired by adverse weather conditions, particularly rain, leading to blurred images and reduced contrast. Such impairments increase the risk of inaccurate evaluations and misinterpretations in VBMS. To address these limitations, we propose an Expectation Maximization Reconstruction Transformer (EMResformer) for single image rain streak removal. The EMResformer retains the key self-attention values for feature aggregation, enhancing local features to produce superior image reconstruction. Specifically, we propose an Expectation Maximization Block seamlessly integrated into the single image rain streak removal network, enhancing its ability to eliminate superfluous information and restore a cleaner background image. Additionally, to further enhance local information for improved detail rendition, we introduce a Local Model Residual Block, which integrates two local model blocks along with a sequence of convolutions and activation functions. This integration synergistically facilitates the extraction of more pertinent features for enhanced single image rain streak removal. Extensive experiments validate that our proposed EMResformer surpasses current state-of-the-art single image rain streak removal methods on both synthetic and real-world datasets, achieving an improved balance between model complexity and single image deraining performance. Furthermore, we evaluate the effectiveness of our method in VBMS scenarios, demonstrating that high-quality imaging significantly improves the accuracy and reliability of VBMS tasks. Codes are at https://***/ghfkahfk/EMResformer. Copyright © 2025, The Authors. All rights reserved.

关键词： Rain