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20th International Conference on data Science, ICdata 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

作者： Smith, Jared Mbonimpa, Eric Wagner, Torrey Langhals, Brent Data Analytics Certificate Program Graduate School of Engineering and Management Air Force Institute of Technology WPAFBOH United States

ISBN: (纸本)9783031858550

Proficiency in foreign languages is crucial for effective global operations, and proficiency can be gained from training courses. In this work, we study the influence of various training courses by analyzing a 9,436-row dataset, including factors such as language, test timing, and their effect on an individual's language test performance. Both logistic regression algorithms and neural networks were employed to analyze the factors influencing language test scores. The key findings reveal that while the neural network model slightly outperformed the logistic regression model in terms of positive class recall (0.69 vs. 0.61), the logistic regression model is preferred due to its greater interpretability and generalizability. Notably, the logistic regression model identified higher initial reading scores and frequent testing as significant predictors of score improvement. Importantly, the logistic regression model achieved an accuracy of 0.78, which significantly surpasses the trivial model's accuracy of 0.51. This study contributes to the field of predictive analytics in language proficiency and provides actionable insights for future research and policy-making in defense language programs. The interpretable logistic regression model can help inform the design of more effective language training courses and testing strategies to enhance global operations. © This is a U.S. government work and not under copyright protection in the U.S.;foreign copyright protection may apply 2025.

关键词： Logistic regression

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Topology-Independent GEVD-Based Distributed Adaptive Node-Specific Signal Estimation in Ad-Hoc Wireless Acoustic Sensor Networks

Topology-Independent GEVD-Based Distributed Adaptive Node-Sp...

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European Signal Processing Conference (EUSIPCO)

作者： Paul Didier Toon Van Waterschoot Marc Moonen Department of Electrical Engineering (ESAT) KU Leuven STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Belgium

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

A low-rank approximation-based version of the topology-independent distributed adaptive node-specific signal estimation (TI-DANSE) algorithm is introduced, using a generalized eigenvalue decomposition (GEVD) for application in ad-hoc wireless acoustic sensor networks. This TI-GEVD-DANSE algorithm as well as the original TI-DANSE algorithm exhibit a non-strict convergence, which can lead to numerical instability over time, particularly in scenarios where the estimation of accurate spatial covariance matrices is challenging. An adaptive filter coefficient normalization strategy is proposed to mitigate this issue and enable the stable performance of TI-(GEVD-)DANSE. The method is validated in numerical simulations including dynamic acoustic scenarios, demonstrating the importance of the additional normalization.

关键词： Wireless communication Wireless sensor networks Adaptive systems Signal processing algorithms Estimation Adaptive filters Acoustic sensors Approximation algorithms Ad hoc networks Acoustics

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Magnificent Frigatebird Optimization: A New Bio-Inspired Metaheuristic Approach for Solving Optimization Problems

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Computers, Materials & Continua 2024年第8期80卷 2721-2741页

作者： Tareq Hamadneh Khalid Kaabneh Ibraheem AbuFalahah Gulnara Bektemyssova Galymzhan Shaikemelev Dauren Umutkulov Sayan Omarov Zeinab Monrazeri Frank Werner Mohammad Dehghani Department of Mathematics Al Zaytoonah University of JordanAmman11733Jordan Faculty of Information Technology Al-Ahliyya Amman UniversityAmman19328Jordan Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Department of Big Data Analytics and Data Science Software Development Company《QazCode》Almaty050063Kazakhstan Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran Faculty of Mathematics Otto-von-Guericke UniversityP.O.Box 4120Magdeburg39016Germany

This paper introduces a groundbreaking metaheuristic algorithm named Magnificent Frigatebird Optimization(MFO),inspired by the unique behaviors observed in magnificent frigatebirds in their natural *** foundation of MFO is based on the kleptoparasitic behavior of these birds,where they steal prey from other *** this process,a magnificent frigatebird targets a food-carrying seabird,aggressively pecking at it until the seabird drops its *** frigatebird then swiftly dives to capture the abandoned prey before it falls into the *** theoretical framework of MFO is thoroughly detailed and mathematically represented,mimicking the frigatebird’s kleptoparasitic behavior in two distinct phases:exploration and *** the exploration phase,the algorithm searches for new potential solutions across a broad area,akin to the frigatebird scouting for vulnerable *** the exploitation phase,the algorithm fine-tunes the solutions,similar to the frigatebird focusing on a single target to secure its *** evaluate MFO’s performance,the algorithm is tested on twenty-three standard benchmark functions,including unimodal,high-dimensional multimodal,and fixed-dimensional multimodal *** results from these evaluations highlight MFO’s proficiency in balancing exploration and exploitation throughout the optimization *** studies with twelve well-known metaheuristic algo-rithms demonstrate that MFO consistently achieves superior optimization results,outperforming its competitors across various *** addition,the implementation of MFO on four engineering design problems shows the effectiveness of the proposed approach in handling real-world applications,thereby validating its practical utility and robustness.

关键词： Optimization metaheuristic magnificent frigatebirds exploration exploitation

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PID-YOLOX: An X-Ray Prohibited Items Detector Based on YOLOX 13

PID-YOLOX: An X-Ray Prohibited Items Detector Based on YOLOX

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Li, Mingyuan Ma, Bowen Wang, Hao Li, Yingbo Chen, Dongyue Jia, Tong College of Information Science and Engineering Northeastern-University Shenyang China Foshan Graduate School of Innovation Northeastern University China Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education China

ISBN: (纸本)9798350315196

X-ray prohibited items detection is an effective and crucial measure in various security inspection scenarios. However, the overlapping phenomenon in X-ray images exacerbates the foreground-background class imbalance, and the imaging principle of X-ray images results in missing texture features. To address these challenges, we propose an end-to-end X-ray Prohibited Items Detector (PID-YOLOX) based on YOLOX-Tiny, which offers fast detection speed and high accuracy. Specifically, we introduce the Generalized Label Assignment (GLA) scheme to tackle the foreground-background class imbalance problem, and the Multi-Cardinality Attention (MCA) mechanism to alleviate the issue of missing texture features. Our experimental results show that PID-YOLOX achieves 54.9% average precision (AP) on the PIXray dataset, surpassing YOLOX-Tiny by 2.2% AP. Furthermore, extensive experiments demonstrate that PID-YOLOX is superior to the state-of-the-art methods, indicating its potential applications in the prohibited items detection field. © 2023 IEEE.

关键词： Textures

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Binocular active stereo matching based on multi-scale random forest 13

Binocular active stereo matching based on multi-scale random...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Liu, Jiatong Pu, Mingqi Wang, Ghutian Chen, Dongyue Jia, Tang College of Information Science and Engineering Northeastern University Shenyang China Foshan Graduate School of Innovation Northeastern University China Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education China

ISBN: (纸本)9798350315196

With the rapid development of 3D vision technology, the existing passive binocular cameras can no longer meet the practical needs of depth perception. Therefore, this paper proposes a binocular active stereo matching method based on multi-scale random forest. Firstly, a binocular active vision system consists of a near-infrared random speckle projector and a binocular camera is constructed, and the system was calibrated using Zhang's calibration method. Secondly, gamma image enhancement and image difference method are used for processing to reduce the impact of ambient light on the measurement results. Next, extract the points of interest and use the multi-scale random forest algorithm to match the window where the points of interest are located to generate a sparse structured light anchor disparity map globally. Finally, using the Census transforms as the matching cost, iterate continuously, refine the disparity and get a dense disparity map. The experimental results show that this system can achieve good depth perception accuracy and robustness under complex indoor lighting conditions. © 2023 IEEE.

关键词： Binoculars

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Differentially private data publication with multi‐level data utility

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High-Confidence Computing 2022年第2期2卷 61-68页

作者： Honglu Jiang S.M.Sarwar Haotian Yu Sheikh Ariful Islam Department of Informatics and Engineering Systems The University of Texas Rio Grande ValleyBrownsvilleUSA Department of Computer science The University of Texas Rio Grande ValleyEdinburgUSA Department of Data Analytics The George Washington UniversityWashingtonUSA

Conventional private data publication mechanisms aim to retain as much data utility as possible while ensuring sufficient privacy protection on sensitive *** data publication schemes implicitly assume that all data analysts and users have the same data access privilege ***,it is not applicable for the scenario that data users often have different levels of access to the same data,or different requirements of data *** multi-level privacy requirements for different authorization levels pose new challenges for private data *** PPDP mechanisms only publish one perturbed and private data copy satisfying some privacy guarantee to provide relatively accurate analysis *** find a good tradeoffbetween privacy preservation level and data utility itself is a hard problem,let alone achieving multi-level data utility on this *** this paper,we address this challenge in proposing a novel framework of data publication with compressive sensing supporting multi-level utility-privacy tradeoffs,which provides differential ***,we resort to compressive sensing(CS)method to project a n-dimensional vector representation of users’data to a lower m-dimensional space,and then add deliberately designed noise to satisfy differential ***,we selectively obfuscate the measurement vector under compressive sensing by adding linearly encoded noise,and provide different data reconstruction algorithms for users with different authorization *** experimental results demonstrate that ML-DPCS yields multi-level of data utility for specific users at different authorization levels.

关键词： Differential privacy Compressive sensing Multi-level utility Privacy preservation

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MatchPrompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering

MatchPrompt: Prompt-based Open Relation Extraction with Sema...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Wang, Jiaxin Zhang, Lingling Liu, Jun Liang, Xi Zhong, Yujie Wu, Yaqiang Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University China Lenovo Research Beijing China

Relation clustering is a general approach for open relation extraction (OpenRE). Current methods have two major problems. One is that their good performance relies on large amounts of labeled and pre-defined relational instances for pre-training, which are costly to acquire in reality. The other is that they only focus on learning a high-dimensional metric space to measure the similarity of novel relations and ignore the specific relational representations of clusters. In this work, we propose a new prompt-based framework named MatchPrompt, which can realize OpenRE with efficient knowledge transfer from only a few predefined relational instances as well as mine the specific meanings for cluster interpretability. To our best knowledge, we are the first to introduce a prompt-based framework for unlabeled clustering. Experimental results on different datasets show that MatchPrompt achieves the new SOTA results for OpenRE. © 2022 Association for Computational Linguistics.

关键词： Extraction

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Single-Stage Grasping Algorithm for the Manipulator Based on Rotational Gaussian Encoding 13

Single-Stage Grasping Algorithm for the Manipulator Based on...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Liu, Zhenghao Liu, Yizhe Yang, Guowei Chen, Dongyue Jia, Tong College of Information Science and Engineering Northeastern University Shenyang China Foshan Graduate School of Innovation Northeastern University China Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education China

ISBN: (纸本)9798350315196

As we all know, there is an unsolved problem for robots to accurately and quickly grasp unknown objects in an unstructured environment. In order to describe the pose information of objects more accurately, a rotational Gaussian encoding method is proposed in this paper. Different from the traditional 2-D Gaussian encoding to represent ground-truth, this method introduces rotation information based on 2-D Gaussian. Grasping is made more accurate and robust by treating the grasp pose as a rotating bounding box in the image plane. In addition, a balanced loss function is introduced in this paper. This loss function not only solves the problem of positive and negative sample imbalance in the traditional cross-entropy loss but also reduces the penalty of points around the Gaussian center location. In this paper, a pixel-level single-stage grasping algorithm is designed in an end-to-end manner without the intermediate process of using anchor points, which saves time and improves the accuracy of grasping simultaneously. Then, the proposed model in this paper is evaluated on two standard datasets, Cornell dataset and Jacquard dataset, achieving 96.8% and 94.7% accuracy, respectively. Finally, we use a 6 DoF UR5e robotic arm for real-world grasping experiments. The success rate of single-object grasping is 94.7% and the time to generate the grasp frame is 8 ms. Experiments demonstrat that the algorithm is equally effective in real-world environments and has a faster detection time. © 2023 IEEE.

关键词： Signal encoding

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Software-Defined Number Formats for High-Speed Belief Propagation

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IEEE Transactions on Emerging Topics in Computing 2025年

作者： Molahosseini, Amir Sabbagh Lee, Junkyu Vandierendonck, Hans School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom Institute for Analytics and Data Science University of Essex United Kingdom

This paper presents the design and implementation of Software-Defined Floating-Point (SDF) number formats for high-speed implementation of the Belief Propagation (BP) algorithm. SDF formats are designed specifically to meet the numeric needs of the computation and are more compact representations of the data. They reduce memory footprint and memory bandwidth requirements without sacrificing accuracy, given that BP for loopy graphs inherently involves algorithmic errors. This paper designs several SDF formats for sum-product BP applications by careful analysis of the computation. Our theoretical analysis leads to the design of 16-bit (half-precision) and 8-bit (mini-precision) widths. We moreover present highly efficient software implementation of the proposed SDF formats which is centered around conversion to hardware-supported single-precision arithmetic hardware. Our solution demonstrates negligible conversion overhead on commercially available CPUs. For Ising grids with sizes from 100×100 to 500×500, the 16- and 8-bit SDF formats along with our conversion module produce equivalent accuracy to double-precision floating-point format but with 2.86× speedups on average on an Intel Xeon processor. Particularly, increasing the grid size results in higher speed-up. For example, the proposed half-precision format with 3-bit exponent and 13-bit mantissa achieved the minimum and maximum speedups of 1.30× and 1.39× over single-precision, and 2.55× and 3.40× over double-precision, by increasing grid size from 100×100 to 500×500. © 2013 IEEE.

关键词： Digital arithmetic

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A Deep Reinforcement Learning-based Resource Management Scheme for SDN-MEC-supported XR Applications 19

A Deep Reinforcement Learning-based Resource Management Sche...

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19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022

作者： Trinh, Bao Muntean, Gabriel-Miro Dublin City University Insight Sfi Research Centre for Data Analytics Dublin Ireland Dublin City University School of Electronic Engineering Dublin Ireland

ISBN: (纸本)9781665431620

The Multi-Access Edge Computing (MEC) paradigm provides a promising solution for efficient computing services at edge nodes, such as base stations (BS), access points (AP), etc. By offloading highly intensive computational tasks to MEC servers, critical benefits in terms of reducing energy consumption at mobile devices and lowering processing latency can be achieved to support high Quality of Service (QoS) to many applications. Among the services which would benefit from MEC deployments are eXtended Reality (XR) applications which are receiving increasing attention from both academia and industry. XR applications have high resource requirements, mostly in terms of network bandwidth, computation and storage. Often these resources are not available in classic network architectures and especially not when XR applications are run by mobile devices. This paper leverages the concepts of Software Defined Networking (SDN) and Network Function Virtualization (NFV) to propose an innovative resource management scheme considering heterogeneous QoS requirements at the MEC server level. The resource assignment is formulated by employing a Deep Reinforcement Learning (DRL) technique to support high quality of XR services. The simulation results show how our proposed solution outperforms other state-of-the-art resource management-based schemes. © 2022 IEEE.

关键词： Quality of service

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