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IEEE International Symposium on Information Theory

作者： Fangming Zhao Nikolaos Pappas Chuan Ma Xinghua Sun Tony Q. S. Quek Howard H. Yang ZJU-UIUC Institute of Zhejiang University Haining China Department of Computer and Information Science Linköping University Linköping Sweden School of Computer Science Chongqing University Chongqing China School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen China Information System and Technology Design Pillar Singapore University of Technology and Design Singapore

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

We optimize the Age of Information (AoI) in random access networks using the age-threshold slotted ALOHA (TSA) protocol. The network comprises multiple source-destination pairs, where each source sends a sequence of status update packets to its destination over a shared spectrum. The TSA protocol stipulates that a source node must remain silent until its AoI reaches a predefined threshold, after which the node accesses the radio channel with a certain probability. We derive analytical expressions for the transmission success probability and time-average AoI using stochastic geometry tools. Subsequently, we obtain closed-form expressions for the optimal update rate and age threshold that minimize the time-average AoI. In addition, we establish a scaling law for the time-average AoI in random access networks, revealing that the optimal time-average AoI increases linearly with the deployment density. Notably, the growth rate under TSA is half of that under conventional slotted ALOHA.

关键词： Geometry Protocols Closed-form solutions Information age Information theory

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Quantum reversible circuits for multiplicative inverse

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EPJ Quantum Technology 2022年第1期9卷 1-17页

作者： Luo, Qing-bin Yang, Guo-wu Li, Xiao-yu Li, Qiang Department of Computer Science and Technology School of Information Engineering Hubei Minzu University Enshi China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Big data research Center & School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu China

The synthesis of quantum circuits for multiplicative inverse over $\operatorname{GF}(2^{8})$ are discussed in this paper. We first convert the multiplicative inverse operation in $\operatorname{GF}(2^{8})$ to arithmetic operations in the composite field $\operatorname{GF}((2^{4})^{2})$ , and then discuss the expressions of the square calculation, the inversion calculation and the multiplication calculation separately in the finite field $\operatorname{GF}(2^{4})$ , where the expressions of multiplication calculation in $\operatorname{GF}(2^{4})$ are given directly in $\operatorname{GF}(2^{4})$ and given through being transformed into the composite field $\operatorname{GF}((2^{2})^{2})$ . Then the quantum circuits of these calculations are realized one by one. Finally, two quantum circuits for multiplicative inverse over $\operatorname{GF}(2^{8})$ are synthesized. They both use 21 qubits, the first quantum circuit uses 55 Toffoli gates and 107 CNOT gates and the second one uses 37 Toffoli gates and 209 CNOT gates. As an example of the application of multiplication inverse, we apply these quantum circuits to the implementations of the S-box quantum circuit of the AES cryptographic algorithm. Two quantum circuits for implementing the S-box of the AES cryptographic algorithm are presented. The first quantum circuit uses 21 qubits, 55 Toffoli gates, 131 CNOT gates and 4 NOT gates and the second one uses 21 qubits, 37 Toffoli gates, 233 CNOT gates and 4 NOT gates. Through the evaluation of quantum cost, the two quantum circuits of the S-box of AES cryptographic algorithm use less quantum resources than the existing schemes.

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2D Mobile Vocab Library Learning Application 4

2D Mobile Vocab Library Learning Application

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4th International Conference Advancement in Data science, E-Learning and Information systems, ICADEIS 2022

作者： Zhou, Heng Seng Nugraha, Ryan Adhitya Md Fudzee, Mohd Farhan Othman, Muhammad Fakri Syamsuar, Dedy Ismail, Azizan Witarsyah, Deden Suparjoh, Suriawati Nurhayati, Iis Kurnia Sugiat, Maria Apsari Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and IT Parit Raja86400 Malaysia School of Industrial Engineering Telkom University Bandung Indonesia School of Information System Bina Nusantara University Jakarta Indonesia School of Communication and Business Telkom University Indonesia Bandung Indonesia Telkom University Indonesia Faculty of Ecomonics and Business Bandung Indonesia

ISBN: (纸本)9781665463874

Technology is continuously evolving nowadays, and learners are exposed to digital devices at an early stage. It will lead learners to use digital devices to learn or play with instead of using books or traditional puzzle games. Thus, this project will develop an English Language vocabulary learning application using 2D learning content applying an information- based learning and quiz approach, and 2D gamification approach in the game module and executed on the Android platform. Furthermore, the Multimedia Mobile Content Development (MMCD) methodology is selected and used to develop the V ocab Library application is to ensure that the development process can run smoothly and efficiently as planned. This paper is based on the research from online journals and the questionnaire that had distributed. The Vocab Library application is appropriate for 7-year-old target users to learn English vocabulary on a mobile platform, based on the good feedback received from the target users. Lastly, this learning application is designed to help today's children understand and learn English vocabulary, which will benefit children's future. © 2022 IEEE.

关键词： Digital devices

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Fairness-aware Multiobjective Evolutionary Learning

arXiv

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arXiv 2024年

作者： Zhang, Qingquan Liu, Jialin Yao, Xin The Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Shenzhen518055 China The Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The School of Data Science Lingnan University Hong Kong

Multiobjective evolutionary learning (MOEL) has demonstrated its advantages of training fairer machine learning models considering a predefined set of conflicting objectives, including accuracy and different fairness measures. Recent works propose to construct a representative subset of fairness measures as optimisation objectives of MOEL throughout model training. However, the determination of a representative measure set relies on dataset, prior knowledge and requires substantial computational costs. What's more, those representative measures may differ across different model training processes. Instead of using a static predefined set determined before model training, this paper proposes to dynamically and adaptively determine a representative measure set online during model training. The dynamically determined representative set is then used as optimising objectives of the MOEL framework and can vary with time. Extensive experimental results on 12 well-known benchmark datasets demonstrate that our proposed framework achieves outstanding performance compared to state-of-the-art approaches for mitigating unfairness in terms of accuracy as well as 25 fairness measures although only a few of them were dynamically selected and used as optimisation objectives. The results indicate the importance of setting optimisation objectives dynamically during training. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Leveraging Adaptive Algorithms for Real-Time Data Analysis

Leveraging Adaptive Algorithms for Real-Time Data Analysis

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International Conference on Computing and Networking Technology (ICCNT)

作者： Girija Shankar Sahoo R Hannah Jessie Rani Manish Kumar Goyal Vazeer Ali Mohammed D Little Femilin Jana Namrata N. Wasatkar Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electrical and Electronics Engineering Faculty of Engineering and Technology Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer & Information System Lewis University Illinois USA Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper proposes the use of adaptive algorithms for real-time data analysis. Adaptive algorithms are a set of practical methods used for acting information-driven optimization and studying. They have a wide variety of applications and may be implemented to a selection of facts analysis obligations. This paper offers an overview of existing adaptive algorithms and descriptions of several new programs in the area of actual-time facts analysis. In particular, the paper discusses the notion of adaptive thresholding, which allows for practical real-time evaluation of large datasets. The paper also provides a novel technique for making use of these algorithms in an online learning environment. Results from simulations illustrate the ability of the proposed method in real-international packages. Sooner or later, the paper presents numerous key lessons discovered and affords recommendations for future research and packages in this area.

关键词： Adaptation models Data analysis Real-time systems Optimization

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Coordinate Attention Guided Dual-Teacher Adaptive Knowledge Distillation for Image Classification

SSRN

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SSRN 2023年

作者： Ma, Dongtong Zhang, Kaibing Cao, Qizhi Li, Jie Gao, Xinbo School of Electronics and Information Xi’an Polytechnic University Xi'An710048 China School of Computer Science Xi’an Polytechnic University Xi'An710048 China Video and Image Processing System Laboratory School of Electronic Engineering Xidian University Xi’an710071 China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China

Knowledge distillation (KD) refers to transferring the knowledge learned from a teacher network with complex architecture and strong learning ability to another student network with light-weight and weak learning ability through a specific distillation strategy. However, most existing KD approaches to image classification often employ a single teacher network to guide the training of the student network. When the teacher network makes an erroneous prediction, the transferred knowledge will deteriorate the performance of the student network. To address or mitigate the above issue, we develop a novel KD approach called Coordinate Attention Guided Dual-Teacher Adaptive Knowledge Distillation (CAG-DAKD), to deliver more discriminative and comprehensive knowledge obtained from two teacher networks to a compact student network. Specifically, we integrate the positive prediction distribution of two teacher networks according to whether the two teacher networks predict correctly and the magnitude of the cross-entropy to deliver better output distribution to guide the student network. Furthermore, to distill the most valuable knowledge from the first teacher network that has a similar architecture to the student network, a coordinate attention mechanism is introduced into different layers of the first teacher network so that the student network can effectively learn more discriminative feature representations. We conduct extensive experiments on three standard image classification datasets: CIFAR10, CIFAR100, and ImageNet to verify the superiority of the proposed method over other state-of-the-art competitors. © 2023, The Authors. All rights reserved.

关键词： Distillation

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Reinforced Heterogeneous Graphlet Design for Knowledge Graph Representation Learning

SSRN

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SSRN 2023年

作者： Gong, Jibing Lin, Yuting Zhao, Yi Lin, Tianyu Fang, Xiaohan Feng, Xinchao Peng, Jiquan School of Information Science and Engineering Yanshan University Qinhuangdao066004 China The Key Laboratory for Computer Virtual Technology System Integration of Hebei Province Yanshan University Qinhuangdao066004 China

Knowledge Graphs have been practical tools to represent and integrate plentiful structural and semantic information in mainstream industrial scenarios. While promising, the heterogeneity and complexity of KGs pose a formidable obstacle, especially for graph representation learning. Most existing KG embedding models omit the dynamic high-order connectivity patterns, and heavily rely on the handcrafted patterns to handle the complex semantic relationships, thereby limiting their ability to adaptively capture nuanced and intricate relationships of knowledge graphs in different tasks. To fill this gap, we present ReHGD — a model designed for KGs that focuses on adaptively designing typed graphlets through a cooperative Multi-agent Reinforcement Learning algorithm. This approach can learn more discriminative graph representations tailored to specific downstream tasks. Specifically, ReHGD engages in the creation of typed graphlets through a two-stage process: it (1) establishes a reinforced chain design module to generate chains without predefined rules, and (2) employs a buffer-aware sampling technique to derive episodic chains from prior experiences. Subsequently, motifs are deduced by applying commute count and Hadamard product operations to the episodic chain-based subgraphs. Finally, ReHGD utilize chain and motif aggregations to learn graph representations. Experimental results on three real-world KGs show that ReHGD outperforms baselines on entity classification. © 2023, The Authors. All rights reserved.

关键词： Knowledge graph

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Prototype Implementation and Experimental Evaluation for LoRa-Backscatter Communication systems with RF Energy Harvesting and Low Power Management

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IEEE Transactions on Communications 2024年

作者： Tang, Xiaoqing Liu, Xin Xie, Guihui Cui, Yongqiang Li, Dong Hubei University School of Artificial Intelligence Wuhan China Ministry of Education Key Laboratory of Intelligent Sensing System and Security Hubei University Wuhan China Wuhan Qingchuan University Institute of Electronic Information Wuhan China School of Automation Wuhan China South-Central Minzu University College of Electronics and Information Engineering Wuhan China Macau University of Science and Technology School of Computer Science and Engineering Avenida Wai Long Taipa Macau China

Battery free Internet of Things (BF IoT), which is realized by harvesting ambient energy to power the IoT devices, has many advantages such as low power, low cost, free-maintenance, easy deployment, and environmental protection. As the two key enabling technologies of BF IoT, the integration of energy harvesting, management and backscatter communication (BackCom) is expected to meet practical applications. However, several fundamental issues need to be addressed to fully develop the potential of such integration. In this paper, we propose an ultra-low power energy harvesting and management scheme that effectively reduces the startup and quiescent power consumption. In the case of extremely limited energy harvested, the Long Range (LoRa) BackCom digital waveform generation algorithm with ultra-low power consumption and low complexity is studied, which can support low-power and low-cost micro-controllers (MCUs). Then, a radio frequency (RF) front-end scheme that is compatible with RF energy harvesting (RFEH), BackCom, and On-Off-Keying (OOK) receiving is proposed. On this basis, we design and implement a fully functional BF LoRa Tag, which integrates tag antennas, LoRa BackCom, low-power OOK receiving, multi-parameter real-time sensing, RFEH and management, at a cost of only $6. Our test results show that the BF LoRa Tag can be self-powered by harvesting the RF energy as low as -19 dBm, with the cold-start power as low as 280 nW, the quiescent power as low as 49 nW, the OOK receiving power consumption as low as 1.74 μW, the LoRa BackCom power consumption of only 251 μW, and the communication distance up to 445 meters. Compared with the prototypes in existing literature, the BF LoRa Tag not only has lower power consumption, wider coverage, and the lowest cost, but also has complete system functions. Finally, we discuss the reciprocity between stations and receivers, as well as the BF LoRa cellular communication network, which provides useful references for the practica

关键词： Mobile telecommunication systems

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Approximate state estimation with large-scale sensor networks under event-based sensor scheduling strategy

Approximate state estimation with large-scale sensor network...

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IEEE Conference on Decision and Control

作者： Xinhui Liu Meiqi Cheng Dawei Shi Ling Shi MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing Institute of Technology Beijing China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we investigate the issues of real-time sensor scheduling and state estimator design within large-scale sensor network systems. Specifically, data redundancy sometimes occurs in large-scale sensor arrays due to the excessive proximity of sensing units in the spatial domain, leading to high similarity in their measurement data. Currently, it is difficult to account for such redundancy in sensor scheduling algorithms found in existing literature, where the optimal subset of sensors is generally selected by optimizing objective functions formulated from certain performance criteria. To tackle this problem, we introduce an event-based sensor scheduling strategy, the triggering condition of which is designed founded on the similarity of sensor data, so as to identify the most informative subset of sensors for state estimation. To evaluate the impact of the sensor scheduling protocol on system observability, we propose a new notion of $\mathscr{E}(\varepsilon)$-observability, based on which an observability criterion is derived. In addition, we have designed a set-valued state estimation algorithm, which takes into account the intricate measurement information structure inherent within the sensor selection mechanism. The performance enhancement of the proposed estimator is also investigated. Finally, numerical experiments are conducted to validate the effectiveness of the proposed estimation algorithm and to verify the performance improvement.

关键词： Protocols Event detection Scheduling algorithms Redundancy Approximation algorithms Real-time systems Sensors State estimation Observability Sensor arrays

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Generative Positive-Unlabeled Classification for Hunting Small Open Reading Frames

Generative Positive-Unlabeled Classification for Hunting Sma...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jie Chen Wenbin Liao Du Wen Jianqiang Li Fangzhong Wang College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Laboratory of Synthetic Microbiology School of Chemical Engineering & Technology Tianjin University China

The annotation of Open Reading Frames (ORFs) is a crucial step in gene annotation, as it precisely delineates the specific regions of expressed genes. However, small Open Reading Frames (smORFs), in comparison to ORFs, are shorter in length, exhibit lower expression abundance, and are more challenging to predict. Particularly in the presence of noise in prokaryotic data and limited availability of positive sample data, the difficulty of prediction is amplified. Therefore, it is necessary to study smORF prediction methods. However, current machine learning models use limited data for modeling and overlook the existence of undiscovered positive samples within the negative samples. Additionally, they do not incorporate prior knowledge that can be calibrated to enhance the 3-nt periodicity. This work utilizes a multimodal VAE for data dimensionality reduction and employs a GAN to generate latent vectors for data augmentation. It incorporates PU learning to leverage unknown samples and combines Riboseq data from experiments with and without antibiotic treatment. Additionally, an adversarial training mechanism is employed to enhance the model’s robustness.

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