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Join the community, search results for author: ieee, found 66 papers, 11 papers with code, instance paradigm contrastive learning for domain generalization.

no code implementations • IEEE Transactions on Circuits and Systems for Video Technology 2024 • Zining Chen , Weiqiu Wang , Zhicheng Zhao , Fei Su , Member , IEEE , Aidong Men , and Yuan Dong

In this paper, we propose an instance paradigm contrastive learning framework, introducing contrast between original features and novel paradigms to alleviate domain-specific distractions.

An Ultralightweight Hybrid CNN Based on Redundancy Removal for Hyperspectral Image Classification

no code implementations • IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024 • Xiaohu Ma , Wuli Wang , Member , IEEE

Simultaneously, for PW-Conv, we design a spectral convolution with redundancy removal (R2Spectral-Conv).

Meta Reinforcement Learning for Multi-Task Offloading in Vehicular Edge Computing

no code implementations • TMC 2024 • Penglin Dai , Yaorong Huang , Kaiwen Hu , Xiao Wu , Huanlai Xing , and Zhaofei Yu , Member , IEEE

The objective is to design a unified solution to minimize task execution time under different MTO scenarios.

Ultra-Robust Real-Time Estimation of Gait Phase

no code implementations • IEEE Transactions on Neural Systems and Rehabilitation Engineering 2023 • Mohammad Shushtari , Hannah Dinovitzer , Jiacheng Weng , and Arash Arami , Member , IEEE

The estimator is finally tested on a participant walking with an active exoskeleton, demonstrating the robustness of D67 in interaction with an exoskeleton without being trained on any data from the test subject with or without an exoskeleton.

Interaction-Aware Planning With Deep Inverse Reinforcement Learning for Human-Like Autonomous Driving in Merge Scenarios

1 code implementation • journal 2023 • Jiangfeng Nan , Weiwen Deng , Member , IEEE , Ruzheng Zhang , Ying Wang , Rui Zhao , Juan Ding

To consider the interaction factor, the reward function for planning is utilized to evaluate the joint trajectories of the autonomous driving vehicle (ADV) and traffic vehicles.

Spoof Trace Disentanglement for generic face antispoofing

no code implementations • journal 2023 • Yaojie Liu and Xiaoming Liu , Member , IEEE

Yet, it is a challenging task due to the diversity of spoof attacks and the lack of ground truth for spoof traces.

Bio-Inspired Feature Selection in Brain Disease Detection via an Improved Sparrow Search Algorithm

no code implementations • IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2022 • Wenyu Yu , Hui Kang , Geng Sun , Member , Shuang Liang , and Jiahui Li , Student Member , IEEE

Finally, the proposed ISSA is utilized to solve the objective function.

VCI-LSTM: Vector Choquet Integral-based Long Short-Term Memory

no code implementations • IEEE 2022 • Mikel Ferrero-Jaurrieta , Zdenko Taka ́cˇ , Javier Ferna ́ndez , Member , IEEE , Lˇubom ́ıra Horanska ́ , Grac ̧aliz Pereira Dimuro , Susana Montes , Irene D ́ıaz and Humberto Bustince , Fellow , IEEE.

Choquet integral is a widely used aggregation oper- ator on one-dimensional and interval-valued information, since it is able to take into account the possible interaction among data.

Lightweight Deep Neural Network for Joint Learning of Underwater Object Detection and Color Conversion

no code implementations • journal 2022 • Chia-Hung Yeh , Chu-Han Lin , Li-Wei Kang , Member , Chih-Hsiang Huang , Min-Hui Lin , Chuan-Yu Chang , and Chua-Chin Wang , Senior Member , IEEE

Li-Wei Kang is with the Department of Electrical Engineering, National Taiwan Normal University, Taipei 106, Taiwan (e-mail: lwkang@ntnu. edu. tw).

Topology Change Aware Data-Driven Probabilistic Distribution State Estimation Based on Gaussian Process

no code implementations • IEEE Transactions on Smart Grid 2022 • Di Cao , Member , Junbo Zhao , Weihao Hu , Senior Member , Qishu Liao , Qi Huang , Zhe Chen , Fellow , IEEE

Abstract—This paper addresses the distribution system state estimation (DSSE) with unknown topology change.

STMGCN: Mobile Edge Computing-Empowered Vessel Trajectory Prediction Using Spatio-Temporal Multigraph Convolutional Network

no code implementations • IEEE Transactions on Industrial Informatics 2022 • Ryan Wen Liu , Maohan Liang , Jiangtian Nie , Yanli Yuan , Zehui Xiong , Member , IEEE , Han Yu

—The revolutionary advances in machine learning and data mining techniques have contributed greatly to the rapid developments of maritime Internet of Things (IoT).

Coverage Control Algorithm for DSNs Based on Improved Gravitational Search

no code implementations • IEEE Sensors Journal 2022 • Yindi Yao , Huanmin Liao , Xiong Li , Student Member , IEEE , Feng Zhao , Xuan Yang , and Shanshan Hu

—In directional sensor networks (DSNs), coverage control is an important way to ensure efficient communication and reliable data transmission.

High-order Correlation Preserved Incomplete Multi-view Subspace Clustering

3 code implementations • IEEE Transactions on Image Processing 2022 • Zhenglai Li , Chang Tang , Xiao Zheng , Xinwang Liu , Senior Member , Wei zhang , Member , IEEE , and En Zhu

Specifically, multiple affinity matrices constructed from the incomplete multi-view data are treated as a thirdorder low rank tensor with a tensor factorization regularization which preserves the high-order view correlation and sample correlation.

A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework

no code implementations • IEEE Transactions on Intelligent Transportation Systems 2022 • Junchen Jin , Member , IEEE , Dingding Rong , Tong Zhang , Qingyuan Ji , Haifeng Guo , Yisheng Lv , Xiaoliang Ma , and Fei-Yue Wang

This paper proposes a short-term traffic speed prediction approach, called PL-WGAN, for urban road networks, which is considered an important part of a novel parallel learning framework for traffic control and operation.

Shallow Network Based on Depthwise Over-Parameterized Convolution for Hyperspectral Image Classification

no code implementations • 1 Dec 2021 • Hongmin Gao , Member , Zhonghao Chen , Student Member , IEEE , Chenming Li

Therefore, this letter proposes a shallow model for HSIC, which is called depthwise over-parameterized convolutional neural network (DOCNN).

Distributed Differential Evolution Based on Adaptive Mergence and Split for Large-Scale Optimization

1 code implementation • IEEE Transactions on Evolutionary Computation 2021 • Yinglan Feng , Liang Feng , Senior Member , Sam Kwong , and Kay Chen Tan , Fellow , IEEE

In this way, the number of subpopulations is adaptively adjusted and better performing subpopulations obtain more individuals.

Double Deep Q-learning Based Real-Time Optimization Strategy for Microgrids

no code implementations • 27 Jul 2021 • Hang Shuai , Xiaomeng Ai , Jiakun Fang , Wei Yao , Senior Member , Jinyu Wen , Member , IEEE

It is challenging to solve this kind of stochastic nonlinear optimization problem.

A Novel Deep Learning Method for Thermal to Annotated Thermal-Optical Fused Images

no code implementations • 13 Jul 2021 • Suranjan Goswami , IEEE Student Member , Satish Kumar Singh , Senior Member , Bidyut B. Chaudhuri , Life Fellow , IEEE

As a part of this work, we also present a new and unique database for obtaining the region of interest in thermal images based on an existing thermal visual paired database, containing the Region of Interest on 5 different classes of data.

Deep Learning Based Autonomous Vehicle Super Resolution DOA Estimation for Safety Driving

no code implementations • IEEE Transactions on Intelligent Transportation Systems 2021 • Liangtian Wan , Yuchen Sun , Lu Sun , Member , Zhaolong Ning , Senior Member , and Joel J. P. C. Rodrigues , Fellow , IEEE

Abstract— In this paper, a novel system architecture including a massive multi-input multi-output (MIMO) or a reconfigurable intelligent surface (RIS) and multiple autonomous vehicles is considered in vehicle location systems.

Content-Preserving Image Stitching with Piecewise Rectangular Boundary Constraints

no code implementations • IEEE Transactions on Visualization and Computer Graphics 2021 • Yun Zhang , Yu-Kun Lai , and Fang-Lue Zhang , Member , IEEE

By analyzing the irregular boundary, we construct a piecewise rectangular boundary.

Deep Reinforcement Learning Based Optimization for IRS Based UAV-NOMA Downlink Networks

no code implementations • 17 Jun 2021 • Shiyu Jiao , Ximing Xie , Zhiguo Ding , Fellow , IEEE

This paper investigates the application of deep deterministic policy gradient (DDPG) to intelligent reflecting surface (IRS) based unmanned aerial vehicles (UAV) assisted non-orthogonal multiple access (NOMA) downlink networks.

Detailed Primary and Secondary Distribution System Model Enhancement Using AMI Data

no code implementations • 29 May 2021 • Karen Montano-Martinez , Sushrut Thakar , Shanshan Ma , Zahra Soltani , Student Member , Vijay Vittal , Life Fellow , Mojdeh Khorsand , Raja Ayyanar , Senior Member , Cynthia Rojas , Member , IEEE

Reliable and accurate distribution system modeling, including the secondary network, is essential in examining distribution system performance with high penetration of distributed energy resources (DERs).

Context-aware taxi dispatching at city-scale using deep reinforcement learning

no code implementations • IEEE Transactions on Intelligent Transportation Systems 2021 • Zhidan Liu , Jiangzhou Li , and Kaishun Wu , Member , IEEE

Abstract— Proactive taxi dispatching is of great importance to balance taxi demand-supply gaps among different locations in a city.

Low-Complexity Symbol Detection and Interference Cancellation for OTFS System

no code implementations • 期刊 2021 • Huiyang Qu , Guanghui Liu , Lei Zhang , Shan Wen , Graduate Student Member , and Muhammad Ali Imran , Senior Member , IEEE

Orthogonal time frequency space (OTFS) is a two-dimensional modulation scheme realized in the delay- Doppler domain, which targets the robust wireless transmissions in high-mobility environments.

Multi-Scale and Multi-Direction GAN for CNN-Based Single Palm-V ein Identification

no code implementations • IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2021 • Huafeng Qin , Mounim A. El-Y acoubi , Y a n t a o L i , Member , IEEE , and Chongwen Liu

Despite recent advances of deep neural networks in hand vein identification, the existing solutions assume the availability of a large and rich set of training image samples.

Joint Trajectory and Power Allocation Design for Secure Artificial Noise Aided UAV Communications

no code implementations • journals 2021 • Milad Tatar Mamaghani , Graduate Student Member , and Yi Hong , Senior Member , IEEE

This paper investigates an average secrecy rate (ASR) maximization problem for an unmanned aerial vehicle (UAV) enabled wireless communication system, wherein a UAV is employed to deliver confidential information to a ground destination in the presence of a terrestrial passive eavesdropper.

Data-Driven Assisted Chance-Constrained Energy and Reserve Scheduling with Wind Curtailment

no code implementations • 2 Nov 2020 • Xingyu Lei , Student Member , Zhifang Yang , Member , Junbo Zhao , Juan Yu , Senior Member , IEEE

Case studies performed on the PJM 5-bus and IEEE 118-bus systems demonstrate that the proposed method is capable of accurately accounting the influence of wind curtailment dispatch in CCO.

Systems and Control Systems and Control

CRPN-SFNet: A High-Performance Object Detector on Large-Scale Remote Sensing Images

no code implementations • 28 Oct 2020 • QiFeng Lin , Jianhui Zhao , Gang Fu , and Zhiyong Yuan , Member , IEEE

Extensive experiments on the public Dataset for Object deTection in Aerial images data set indicate that our CRPN can help our detector deal the larger image faster with the limited GPU memory; meanwhile, the SFNet is beneficial to achieve more accurate detection of geospatial objects with wide-scale range.

Frame-wise Cross-modal Matching for Video Moment Retrieval

1 code implementation • 22 Sep 2020 • Haoyu Tang , Jihua Zhu , Meng Liu , Member , IEEE , Zan Gao , Zhiyong Cheng

Another contribution is that we propose an additional predictor to utilize the internal frames in the model training to improve the localization accuracy.

Attention Transfer Network for Nature Image Matting

1 code implementation • IEEE Transactions on Circuits and Systems for Video Technology 2020 • Fenfen Zhou , Yingjie Tian , Member , IEEE , and Zhiquan Qi

Then, we introduce a scale transfer block to magnify the feature maps without adding extra information.

A New Multiple Source Domain Adaptation Fault Diagnosis Method between Different Rotating Machines

no code implementations • TRANSACTIONS ON INDUSTRIAL INFORMA TICS 2020 • un Zhu , Nan Chen , Member , IEEE , and Changqing Shen

To solve this issue, transfer learning is proposed by leveraging knowl- edge learned from source domain to target domain.

Learning Person Re-identification Models from Videos with Weak Supervision

no code implementations • 21 Jul 2020 • Xueping Wang , Sujoy Paul , Dripta S. Raychaudhuri , Min Liu , Yaonan Wang , Amit K. Roy-Chowdhury , Fellow , IEEE

In order to cope with this issue, we introduce the problem of learning person re-identification models from videos with weak supervision.

Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach

no code implementations • IEEE Transactions on Industrial Informatics 2020 • Ameer Hamza Khan , Student Member , Shuai Li , and Xin Luo , Senior Member , IEEE

In this article, we present a metaheuristic-based control framework, called beetle antennae olfactory recurrent neural network, for simultaneous tracking control and obstacle avoidance of a redundant manipulator.

Edge server deployment scheme of blockchain in IoVs

no code implementations • 16 Jun 2020 • Liya Xu , Mingzhu Ge , Weili Wu , Member , IEEE

In fact, the application of blockchain in IoVs can be implemented by employing edge computing.

Service Provisioning Framework for RAN Slicing: User Admissibility, Slice Association and Bandwidth Allocation

no code implementations • IEEE Transactions on Mobile Computing 2020 • Yao Sun , Shuang Qin , Member , Gang Feng , Lei Zhang , and Muhammad Ali Imran , SeniorMember , IEEE

Network slicing (NS) has been identified as one of the most promising architectural technologies for future mobile network systems to meet the extremely diversified service requirements of users.

A Simplified 2D-3D CNN Architecture for Hyperspectral Image Classification Based on Spatial–Spectral Fusion

no code implementations • 5 Jun 2020 • Chunyan Yu , Rui Han , Meiping Song , Caiyu Liu , and Chein-I Chang , Life Fellow , IEEE

Abstract—Convolutional neural networks (CNN) have led to a successful breakthrough for hyperspectral image classification (HSIC).

Decision Fusion in Space-Time Spreading aided Distributed MIMO WSNs

no code implementations • 16 May 2020 • I. Dey , H. Joshi , Member , N. Marchetti , Senior Member , IEEE

In this letter, we propose space-time spreading (STS) of local sensor decisions before reporting them over a wireless multiple access channel (MAC), in order to achieve flexible balance between diversity and multiplexing gain as well as eliminate any chance of intrinsic interference inherent in MAC scenarios.

Energy-Efficient Over-the-Air Computation Scheme for Densely Deployed IoT Networks

no code implementations • IEEE 2020 • Semiha Tedik Basaran , Student Member , Gunes Karabulut Kurt , and Periklis Chatzimisios , Senior Member , IEEE

The proposed MMSE estimator provides a signif- icant mean squared error improvement with reducing en- ergy consumption compared to the conventional estimator.

A Lightweight and Privacy-Preserving Authentication Protocol for Mobile Edge Computing

no code implementations • 27 Feb 2020 • Kuljeet Kaur∗ , Sahil Garg∗ , Georges Kaddoum∗ , Member , Mohsen Guizani† , Fellow , IEEE , and Dushantha Nalin K. Jayakody‡ , Senior Member , IEEE.

With the advent of the Internet-of-Things (IoT), vehicular networks and cyber-physical systems, the need for realtime data processing and analysis has emerged as an essential pre-requite for customers’ satisfaction.

Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning

1 code implementation • 24 Feb 2020 • Chongwen Huang , Member , IEEE , Ronghong Mo , Chau Yuen , Senior Member

In this paper, we investigate the joint design of transmit beamforming matrix at the base station and the phase shift matrix at the RIS, by leveraging recent advances in deep reinforcement learning (DRL).

Reinforcement Learning Tracking Control for Robotic Manipulator With Kernel-Based Dynamic Model

no code implementations • TRANSACTION 2020 • Yazhou Hu , Wenxue Wang , Hao liu , and Lianqing Liu , Member , IEEE

In this algorithm, a reward function is defined according to the features of tracking control in order to speed up the learning process, and then an RL tracking controller with a kernel-based transition dynamic model is proposed.

Broad Learning System Based on Maximum Correntropy Criterion

no code implementations • 24 Dec 2019 • Yunfei Zheng , Badong Chen , Shiyuan Wang , Senior Member , Weiqun Wang , Member , IEEE

As an effective and efficient discriminative learning method, Broad Learning System (BLS) has received increasing attention due to its outstanding performance in various regression and classification problems.

Localization and Clustering Based on Swarm Intelligence in UAV Networks for Emergency Communications

no code implementations • IEEE Internet of Things Journal 2019 • Muhammad Yeasir Arafat , Sangman Moh , Member , IEEE

Second, we propose an energy-efficient swarm-intelligence-based clustering (SIC) algorithm based on PSO, in which the particle fitness function is exploited for inter-cluster distance, intra-cluster distance, residual energy, and geographic location.

VSSA-NET: Vertical Spatial Sequence Attention Network for Traffic Sign Detection

no code implementations • 5 May 2019 • Yuan Yuan , Zhitong Xiong , Student Member , Qi. Wang , Senior Member , IEEE

Our contributions are as follows: 1) We propose a multi-resolution feature fusion network architecture which exploits densely connected deconvolution layers with skip connections, and can learn more effective features for the small size object; 2) We frame the traffic sign detection as a spatial sequence classification and regression task, and propose a vertical spatial sequence attention (VSSA) module to gain more context information for better detection performance.

GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection

1 code implementation • 5 May 2019 • Qi. Wang , Senior Member , Zhenghang Yuan , Qian Du , Xuelong. Li , Fellow , IEEE

In order to better handle high dimension problem and explore abundance information, this paper presents a General End-to-end Two-dimensional CNN (GETNET) framework for hyperspectral image change detection (HSI-CD).

Discrete-Time Impulsive Adaptive Dynamic Programming

no code implementations • IEEE Transactions on Cybernetics 2019 • Qinglai Wei , Ruizhuo Song , Member , IEEE , Zehua Liao , Benkai Li , and Frank L. Lewis

Abstract—In this paper, a new iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal impulsive control problems for infinite horizon discrete-time nonlinear systems.

Generalization of the Dark Channel Prior for Single Image Restoration

no code implementations • IEEE Transactions on Image Processing 2019 • Yan-Tsung Peng , Keming Cao , and Pamela C. Cosman , Fellow , IEEE

Abstract— Images degraded by light scattering and absorption, such as hazy, sandstorm, and underwater images, often suffer color distortion and low contrast because of light traveling through turbid media.

A MIP Model for Risk Constrained Switch Placement in Distribution Networks

no code implementations • IEEE 2019 • Milad Izadi , Student Member , IEEE and Amir Safdarian , Member , IEEE

The model is applied to the RBTS-Bus4 and a real distribution network.

PEA265: Perceptual Assessment of Video Compression Artifacts

no code implementations • 1 Mar 2019 • Liqun Lin , Shiqi Yu , Tiesong Zhao , Member , Zhou Wang , Fellow , IEEE

To monitor and improve visual QoE, it is crucial to develop subjective and objective measures that can identify and quantify various types of PEAs.

A Provably Secure and Efficient Identity-Based Anonymous Authentication Scheme for Mobile Edge Computing

no code implementations • 22 Feb 2019 • Xiaoying Jia，Debiao He , Neeraj Kumar , and Kim-Kwang Raymond Choo , Senior Member , IEEE

Mobile edge computing (MEC) allows one to overcome a number of limitations inherent in cloud computing, although achieving the broad range of security requirements in MEC settings remains challenging.

Location-Centered House Price Prediction: A Multi-Task Learning Approach

no code implementations • 7 Jan 2019 • Guangliang Gao , Zhifeng Bao , Jie Cao , A. K. Qin , Timos Sellis , Fellow , IEEE , Zhiang Wu

Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire house data for modeling, or split the entire data and model each partition independently.

DATS: Dispersive Stable Task Scheduling in Heterogeneous Fog Networks

no code implementations • Conference 2018 • Zening Liu , Xiumei Yang , Yang Yang , Kunlun Wang , and Guoqiang Mao , Fellow , IEEE

Abstract—Fog computing has risen as a promising architecture for future Internet of Things (IoT), 5G and embedded artificial intelligence (AI) applications with stringent service delay requirements along the cloud to things continuum.

Blockchain for Secure and Efficient Data Sharing in Vehicular Edge Computing and Networks

no code implementations • IEEE INTERNET OF THINGS JOURNAL, VOL. 6, NO. 3 2018 • Jiawen Kang , Rong Y u , Xumin Huang , Maoqiang Wu , Sabita Maharjan , Member , Shengli Xie , and Y an Zhang , Senior Member , IEEE

Due to limited resources with vehicles, vehicular edge computing and networks (VECONs) i. e., the integration of mobile edge computing and vehicular networks, can provide powerful computing and massive storage resources.

Optimal Training for Residual Self-Interference for Full-Duplex One-Way Relays

no code implementations • 13 Aug 2018 • Xiaofeng Li , Cihan Tepedelenlio˘glu , and Habib ¸Senol , Member , IEEE

For the former, we propose a training scheme to estimate the overall channel, and for the latter the CRB and the optimal number of relays are derived when the distance between the source and the destination is fixed.

Medical Image Synthesis with Deep Convolutional Adversarial Networks

1 code implementation • IEEE Transactions on Biomedical Engineering 2018 • Dong Nie , Roger Trullo , Jun Lian , Li Wang , Caroline Petitjean , Su Ruan , Qian Wang , and Dinggang Shen , Fellow , IEEE

To better model a nonlinear mapping from source to target and to produce more realistic target images, we propose to use the adversarial learning strategy to better model the FCN.

Single Image Dehazing Using Color Ellipsoid Prior

1 code implementation • IEEE Transactions on Image Processing 2018 • Trung Minh Bui , Student Member , and Wonha Kim , Senior Member , IEEE

The proposed method constructs color ellipsoids that are statistically fitted to haze pixel clusters in RGB space and then calculates the transmission values through color ellipsoid geometry.

Significantly Fast and Robust Fuzzy C-MeansClustering Algorithm Based on MorphologicalReconstruction and Membership Filtering

no code implementations • IEEE 2018 • Tao Lei , Xiaohong Jia , Yanning Zhang , Lifeng He , Hongy-ing Meng , Senior Member , and Asoke K. Nandi , Fellow , IEEE

However, the introduction oflocal spatial information often leads to a high computationalcomplexity, arising out of an iterative calculation of the distancebetween pixels within local spatial neighbors and clusteringcenters.

An Integrated Platform for Live 3D Human Reconstruction and Motion Capturing

no code implementations • 8 Dec 2017 • Dimitrios S. Alexiadis , Anargyros Chatzitofis , Nikolaos Zioulis , Olga Zoidi , Georgios Louizis , Dimitrios Zarpalas , Petros Daras , Senior Member , IEEE

The latest developments in 3D capturing, processing, and rendering provide means to unlock novel 3D application pathways.

Robust Single Image Super-Resolution via Deep Networks With Sparse Prior

1 code implementation • journals 2016 • Ding Liu , Zhaowen Wang , Bihan Wen , Student Member , Jianchao Yang , Member , Wei Han , and Thomas S. Huang , Fellow , IEEE

We demonstrate that a sparse coding model particularly designed for SR can be incarnated as a neural network with the merit of end-to-end optimization over training data.

A Decentralized Cooperative Control Scheme With Obstacle Avoidance for a Team of Mobile Robots

no code implementations • journal 2013 • Hamed Rezaee , Student Member , and Farzaneh Abdollahi , Member , IEEE

The problem of formation control of a team of mobile robots based on the virtual and behavioral structures is considered in this paper.

A Grid-Based Evolutionary Algorithm for Many-Objective Optimization

1 code implementation • IEEE Transactions on Evolutionary Computation 2013 • Shengxiang Yang , Member , IEEE , Miqing Li , Xiaohui Liu , and Jinhua Zheng

Balancing convergence and diversity plays a key role in evolutionary multiobjective optimization (EMO).

Physiological Parameter Monitoring from Optical Recordings with a Mobile Phone

no code implementations • 29 Jul 2011 • Christopher G. Scully , Student Member , Jinseok Lee , Joseph Meyer , Alexander M. Gorbach , Domhnull Granquist-Fraser , Yitzhak Mendelson , Member , and Ki H. Chon , Senior Member , IEEE

We show that a mobile phone can serve as an accurate monitor for several physiological variables, based on its ability to record and analyze the varying color signals of a fingertip placed in contact with its optical sensor.

Performance Analysis of Two Hop Amplify-and-Forward Systems with Interference at the Relay

no code implementations • journal 2010 • Himal A. Suraweera , Member , HariK.Garg , and A. Nallanathan , Senior Member , IEEE

Abstract—We analyze the performance of a two hop channel state information (CSI)-assisted amplify-and-forward system, with co-channel interference at the relay.

Efficiently Indexing Large Sparse Graphs for Similarity Search

no code implementations • 18 Feb 2010 • Guoren Wang , Bin Wang , Xiaochun Yang , IEEE Computer Society , and Ge Yu , Member , IEEE

Abstract—The graph structure is a very important means to model schemaless data with complicated structures, such as protein- protein interaction networks, chemical compounds, knowledge query inferring systems, and road networks.

ANALYSIS OF CALIBRATED SEA CLUTTER AND BOAT REFLECTIVITY DATA AT C- AND X-BAND IN SOUTH AFRICAN COASTAL WATERS

no code implementations • IEEE 2007 • Ron Rubinstein , Member , Tomer Peleg , Student Member , and Michael Elad , Fellow , IEEE

Abstract—The synthesis-based sparse representation model for signals has drawn considerable interest in the past decade.

Parameter-free Geometric Document Layout Analysis

no code implementations • IEEE Transactions on Pattern Analysis and Machine Intelligence 2001 • Seong-Whan Lee , Senior Member , IEEE , and Dae-Seok Ryu

Based on the proposed periodicity measure, multiscale analysis, and confirmation procedure, we could develop a robust method for geometric document layout analysis independent of character font sizes, text line spacing, and document layout structures.

IEEE-Research-Paper-Finder 0.0.2

pip install IEEE-Research-Paper-Finder Copy PIP instructions

Released: Jul 18, 2021

This is a command line tool to extract research paper details along with their download links with the help of keywords .

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License: MIT License (MIT)

Author: Subhomoy Roy Choudhury

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The main project is in this github repository https://github.com/subhomoy-roy-choudhury/IEEE-Research-Paper-Finder-pip

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The May 2024 issue of IEEE Spectrum is here!

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Ieee spectrum, follow ieee spectrum, support ieee spectrum, enjoy more free content and benefits by creating an account, saving articles to read later requires an ieee spectrum account, the institute content is only available for members, downloading full pdf issues is exclusive for ieee members, downloading this e-book is exclusive for ieee members, access to spectrum 's digital edition is exclusive for ieee members, following topics is a feature exclusive for ieee members, adding your response to an article requires an ieee spectrum account, create an account to access more content and features on ieee spectrum , including the ability to save articles to read later, download spectrum collections, and participate in conversations with readers and editors. for more exclusive content and features, consider joining ieee ., join the world’s largest professional organization devoted to engineering and applied sciences and get access to all of spectrum’s articles, archives, pdf downloads, and other benefits. learn more →, join the world’s largest professional organization devoted to engineering and applied sciences and get access to this e-book plus all of ieee spectrum’s articles, archives, pdf downloads, and other benefits. learn more →, access thousands of articles — completely free, create an account and get exclusive content and features: save articles, download collections, and talk to tech insiders — all free for full access and benefits, join ieee as a paying member., ai copilots are changing how coding is taught, professors are shifting away from syntax and emphasizing higher-level skills.

Generative AI is transforming the software development industry. AI-powered coding tools are assisting programmers in their workflows, while jobs in AI continue to increase. But the shift is also evident in academia—one of the major avenues through which the next generation of software engineers learn how to code.

Computer science students are embracing the technology, using generative AI to help them understand complex concepts, summarize complicated research papers, brainstorm ways to solve a problem, come up with new research directions, and, of course, learn how to code.

“Students are early adopters and have been actively testing these tools,” says Johnny Chang , a teaching assistant at Stanford University pursuing a master’s degree in computer science. He also founded the AI x Education conference in 2023, a virtual gathering of students and educators to discuss the impact of AI on education.

So as not to be left behind, educators are also experimenting with generative AI. But they’re grappling with techniques to adopt the technology while still ensuring students learn the foundations of computer science.

“It’s a difficult balancing act,” says Ooi Wei Tsang , an associate professor in the School of Computing at the National University of Singapore . “Given that large language models are evolving rapidly, we are still learning how to do this.”

Less Emphasis on Syntax, More on Problem Solving

The fundamentals and skills themselves are evolving. Most introductory computer science courses focus on code syntax and getting programs to run, and while knowing how to read and write code is still essential, testing and debugging—which aren’t commonly part of the syllabus—now need to be taught more explicitly.

“We’re seeing a little upping of that skill, where students are getting code snippets from generative AI that they need to test for correctness,” says Jeanna Matthews , a professor of computer science at Clarkson University in Potsdam, N.Y.

Another vital expertise is problem decomposition. “This is a skill to know early on because you need to break a large problem into smaller pieces that an LLM can solve,” says Leo Porter , an associate teaching professor of computer science at the University of California, San Diego . “It’s hard to find where in the curriculum that’s taught—maybe in an algorithms or software engineering class, but those are advanced classes. Now, it becomes a priority in introductory classes.”

“Given that large language models are evolving rapidly, we are still learning how to do this.” —Ooi Wei Tsang, National University of Singapore

As a result, educators are modifying their teaching strategies. “I used to have this singular focus on students writing code that they submit, and then I run test cases on the code to determine what their grade is,” says Daniel Zingaro , an associate professor of computer science at the University of Toronto Mississauga . “This is such a narrow view of what it means to be a software engineer, and I just felt that with generative AI, I’ve managed to overcome that restrictive view.”

Zingaro, who coauthored a book on AI-assisted Python programming with Porter, now has his students work in groups and submit a video explaining how their code works. Through these walk-throughs, he gets a sense of how students use AI to generate code, what they struggle with, and how they approach design, testing, and teamwork.

“It’s an opportunity for me to assess their learning process of the whole software development [life cycle]—not just code,” Zingaro says. “And I feel like my courses have opened up more and they’re much broader than they used to be. I can make students work on larger and more advanced projects.”

Ooi echoes that sentiment, noting that generative AI tools “will free up time for us to teach higher-level thinking—for example, how to design software, what is the right problem to solve, and what are the solutions. Students can spend more time on optimization, ethical issues, and the user-friendliness of a system rather than focusing on the syntax of the code.”

Avoiding AI’s Coding Pitfalls

But educators are cautious given an LLM’s tendency to hallucinate . “We need to be teaching students to be skeptical of the results and take ownership of verifying and validating them,” says Matthews.

Matthews adds that generative AI “can short-circuit the learning process of students relying on it too much.” Chang agrees that this overreliance can be a pitfall and advises his fellow students to explore possible solutions to problems by themselves so they don’t lose out on that critical thinking or effective learning process. “We should be making AI a copilot—not the autopilot—for learning,” he says.

“We should be making AI a copilot—not the autopilot—for learning.” —Johnny Chang, Stanford University

Other drawbacks include copyright and bias. “I teach my students about the ethical constraints—that this is a model built off other people’s code and we’d recognize the ownership of that,” Porter says. “We also have to recognize that models are going to represent the bias that’s already in society.”

Adapting to the rise of generative AI involves students and educators working together and learning from each other. For her colleagues, Matthews’s advice is to “try to foster an environment where you encourage students to tell you when and how they’re using these tools. Ultimately, we are preparing our students for the real world, and the real world is shifting, so sticking with what you’ve always done may not be the recipe that best serves students in this transition.”

Porter is optimistic that the changes they’re applying now will serve students well in the future. “There’s this long history of a gap between what we teach in academia and what’s actually needed as skills when students arrive in the industry,” he says. “There’s hope on my part that we might help close the gap if we embrace LLMs.”

• How Coders Can Survive—and Thrive—in a ChatGPT World ›
• AI Coding Is Going From Copilot to Autopilot ›
• OpenAI Codex ›

Rina Diane Caballar is a writer covering tech and its intersections with science, society, and the environment. An IEEE Spectrum Contributing Editor, she's a former software engineer based in Wellington, New Zealand.

Yes! Great summary of how things are evolving with AI. I’m a retired coder (BS comp sci) and understand the fundamentals of developing systems. Learning the lastest systems is now the greatest challenge. I was intrigued by Ansible to help me manage my homelab cluster, but who wants to learn one more scripting language? Turns out ChatGPT4 knows the syntax, semantics, and work flow of Ansible and all I do is tell is to “install log2ram on all my proxmox servers” and I get a playbook that does just that. The same with Docker Compose scripts. Wow.

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Python Project Topics With Abstracts and Base Papers 2024

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