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2024 12th International Conference on Systems and Control, ICSC 2024

作者： Louda, Souhaib Karkar, Nora Seghir, Fateh Refoufi, Salim Ferhat Abbas University of Setif 1 LSI Laboratory Department of Electronics Setif Algeria Ferhat Abbas University of Setif 1 LSI Laboratory Department of Computer Science Setif Algeria

ISBN: (纸本)9798331531812

This paper presents a hybrid path-planning algorithm that combines both global and local planning techniques. An offline global path planning approach based on the A -Star graph search algorithm is used to find the optimal path from the start to the goal. However, A -Star has some limitations, such as a lack of smoothness, lower safety margins, and an inability to adapt to dynamic environments. To address these issues, we propose integrating an online local path planning method based on the Artificial Potential Field (APF) for real-time obstacle avoidance. Simulation results demonstrate the effectiveness of the hybrid approach, guiding the mobile robot along the optimal global path while ensuring the locally gener-ated trajectory is smooth enough for execution by the controller. The results also show improved navigation performance, with enhanced efficiency and safety due to the hybrid algorithm. © 2024 IEEE.

关键词： Mobile robots

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CNN-Based Classification and Diagnosis of Alzheimer's from Baseline MRI Scans

CNN-Based Classification and Diagnosis of Alzheimer's from B...

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iNnovative Informatics of Biskra (ISNIB), International Symposium on

作者： Manel Femmam Amel Saraoui Abdeljalil Naceri Maroua Grid Mohamed Elkabir Fareh Sami Slimani Computer Science Department Laboratory of Science for mathematics computer science and engineering applications University Center of Barika Barika Algeria Computer Science Department LINFI Laboratory Biskra University Biskra Algeria

ISBN: (数字)9798331509576

ISBN: (纸本)9798331509583

The diagnosis of neurodegenerative diseases like Alzheimer's remains a medical challenge, relying heavily on physicians' interpretation of symptoms. Early detection and man-agement are critical for improving patient outcomes, but there is a lack of automated tools to assist in decision-making. This project aims to address this gap a framework for detecting Alzheimer's disease from MRI scans. A convolutional neural network (CNN) is used in the proposed method to classify images into four categories: No Dementia (ND), Very Mild Dementia (VMD), Mild Dementia (MD), and Moderate Dementia (MOD). Leveraging the proven capabilities of CNNs in medical image classification, a tailored architecture was designed and implemented. The model delivered strong performance, demonstrating its potential for early detection of Alzheimer's. This system could be used in clinical settings to assist physicians in identifying cases of Alzheimer's disease and improving diagnostic efficiency.

关键词： Deep learning Magnetic resonance imaging Medical services Brain modeling Transformers Convolutional neural networks Alzheimer's disease Informatics Medical diagnostic imaging Overfitting

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LECF: recommendation via learnable edge collaborative filtering

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science China(Information sciences) 2022年第1期65卷 116-130页

作者： Shitao XIAO Yingxia SHAO Yawen LI Hongzhi YIN Yanyan SHEN Bin CUI School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing Key Lab of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications School of Information Technology and Electrical Engineering The University of Queensland Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Computer Science and Technology Peking University National Engineering Laboratory for Big Data Analysis and Applications Peking University

The core of recommendation models is estimating the probability that a user will like an item based on historical interactions. Existing collaborative filtering(CF) algorithms compute the likelihood by utilizing simple relationships between objects, e.g., user-item, item-item, or user-user. They always rely on a single type of object-object relationship, ignoring other useful relationship information in data. In this paper, we model an interaction between user and item as an edge and propose a novel CF framework, called learnable edge collaborative filtering(LECF). LECF predicts the existence probability of an edge based on the connections among edges and is able to capture the complex relationship in data. Specifically, we first adopt the concept of line graph where each node represents an interaction edge; then calculate a weighted sum of similarity between the query edge and the observed edges(i.e., historical interactions) that are selected from the neighborhood of query edge in the line graph for a recommendation. In addition, we design an efficient propagation algorithm to speed up the training and inference of LECF. Extensive experiments on four public datasets demonstrate LECF can achieve better performance than the state-of-the-art methods.

关键词： collaborative filtering recommender system implicit feedback edge-edge similarity line graph

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SEED-VII: A Multimodal Dataset of Six Basic Emotions with Continuous Labels for Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第2期16卷 969-985页

作者： Jiang, Wei-Bang Liu, Xuan-Hao Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Shanghai200240 China

Recognizing emotions from physiological signals is a topic that has garnered widespread interest, and research continues to develop novel techniques for perceiving emotions. However, the emergence of deep learning has highlighted the need for comprehensive and high-quality emotional datasets that enable the accurate decoding of human emotions. To systematically explore human emotions, we develop a multimodal dataset consisting of six basic (happiness, sadness, fear, disgust, surprise, and anger) emotions and the neutral emotion, named SEED-VII. This multimodal dataset includes electroencephalography (EEG) and eye movement signals. The seven emotions in SEED-VII are elicited by 80 different videos and fully investigated with continuous labels that indicate the intensity levels of the corresponding emotions. Additionally, we propose a novel Multimodal Adaptive Emotion Transformer (MAET), that can flexibly process both unimodal and multimodal inputs. Adversarial training is utilized in the MAET to mitigate subject discrepancies, which enhances domain generalization. Our extensive experiments, encompassing both subject-dependent and cross-subject conditions, demonstrate the superior performance of the MAET in terms of handling various inputs. Continuous labels are used to filter the data with high emotional intensity, and this strategy is proven to be effective for attaining improved emotion recognition performance. Furthermore, complementary properties between the EEG signals and eye movements and stable neural patterns of the seven emotions are observed. © 2010-2012 IEEE.

关键词： Eye movements

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Modified honey bees inspired optimization method for transportation problem

Advances in Transportation Studies

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Advances in Transportation Studies 2023年 59卷 71-86页

作者： Nemmich, M.A. Boukhari, N. Boudali, A. Debbat, F. Department of Computer Science University of Sciences and Technology of Oran Mohamed Boudiaf Oran Algeria Department of Mathematics University Djillali liabes of Sidi Bel Abbes Sidi Bel Abbes Algeria Department of Electronics Laboratory of coding and security of information University of Sciences and Technology of Oran Mohamed Boudiaf Oran Algeria Department of Computer Science University Mustapha Stambouli of Mascara Mascara Algeria

The Capacitated vehicle routing problem (CVRP) is a complex transportation issue that involves designing a set of routes for a group of homogenous vehicles to serve a set of customers at shortest distance travelled. It belongs to the class of NP-hard problems with a high computational complexity, and then it is difficult to solve this problem with traditional optimization methods when the problem size is large. Consequently, in this study, an improved swarm optimization metaheuristic approach has been proposed based on intelligent foraging behavior of honey bees with several specifically designed local search operators and features to solve this problem. The effectiveness of the proposed approach was illustrated on the standard benchmark problems. The results revealed that the proposed approach has fast convergence rate and high computational accuracy. Overall, our experiments show that our approach could be a very efficient approach for solving the CVRP and its experimental results are competitive in terms of the quality of the solutions reported in other well-known methods. © 2023, Aracne Editrice. All rights reserved.

关键词： Computational complexity

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Spike Sorting and Event Detection in Neuromorphic Computing 10

Spike Sorting and Event Detection in Neuromorphic Computing

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Vaishnavi, Vakada G. Sai Sree Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Laboratory Department of Computer Science and Engineering Mangalore Surathkal India

ISBN: (纸本)9798350384369

Neuromorphic computing is a cutting-edge field of research that focuses on designing and developing computer systems and hardware architectures inspired by the structure and functioning of the human brain. The main objective of neuromorphic computing is to create computational systems that imitate the parallel processing, learning, and energy efficiency observed in biological neural networks. Two essential tasks in the analysis of brain data from extracellular recordings are spike sorting and event detection. Spike sorting involves identifying and categorizing discrete neural spikes, while event detection focuses on recognizing specific patterns or occurrences within the spiking activity of neuromorphic networks. Typically, each neuron generates spikes with a characteristic shape, and these clusters correspond to the activity of different neurons. Here, the outcome of spike sorting is the identification of which spike corresponds to which neuron. In-depth analysis of the spike sorting and event detection which are two critical processes in interpreting the intricate neuronal activity that underpins brain function are provided. © 2024 IEEE.

关键词： Neurons

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Enhancing Big Data Security Through Anomaly Detection 8

Enhancing Big Data Security Through Anomaly Detection

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Vakkund, Sharan Kumar, Sachin Rao, Shaifali Anusha Hegde, H. Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory Department of Computer Science and Engineering Mangalore575025 India

ISBN: (纸本)9798350350593

Securing the massive and fast-moving data streams typical in Big Data environments presents unique challenges that traditional static security measures simply can't handle. To effectively protect these data flows, we need methods that can analyze traffic in real-time and respond swiftly to potential threats. Anomaly detection is one such method, offering an automated way to identify unusual or suspicious activities within Big Data systems. In this study, we explore several widely-used anomaly detection algorithms, evaluating their effectiveness in identifying anomalies within large datasets. Specifically, we will assess these algorithms using the UNSW-NB15 Dataset, aiming to pinpoint which algorithm, or combination of algorithms, is best suited for the demands of Big Data security. © 2024 IEEE.

关键词： Adversarial machine learning

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Imitating and exploring the human brain's resting and task-performing states via brain computing: scaling and architecture

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National science Review 2024年第5期11卷 17-28页

作者： Wenlian Lu Longbin Zeng Jiexiang Wang Shitong Xiang Yang Qi Qibao Zheng Ningsheng Xu Jianfeng Feng DTB Consortium Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence(Fudan University) Ministry of Education Fudan University Shanghai Center for Mathematical Sciences Fudan University Department of Computer Science University of Warwick Zhangjiang Fudan International Innovation Center Fudan University

A computational human brain model with the voxel-wise assimilation method was established based on individual structural and functional imaging data. We found that the more similar the brain model is to the biological counterpart in both scale and architecture, the more similarity was found between the assimilated model and the biological brain both in resting states and during tasks by quantitative metrics. The hypothesis that resting state activity reflects internal body states was validated by the interoceptive circuit's capability to enhance the similarity between the simulation model and the biological brain. We identified that the removal of connections from the primary visual cortex(V1) to downstream visual pathways significantly decreased the similarity at the hippocampus between the model and its biological counterpart,despite a slight influence on the whole brain. In conclusion, the model and methodology present a solid quantitative framework for a digital twin brain for discovering the relationship between brain architecture and functions, and for digitally trying and testing diverse cognitive, medical and lesioning approaches that would otherwise be unfeasible in real subjects.

关键词： digital twin brain computational cortico-subcortical model fMRI data assimilation interoceptive circuit

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Tackling the Qubit Mapping Problem with Permutation-Aware Synthesis 4

Tackling the Qubit Mapping Problem with Permutation-Aware Sy...

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4th IEEE International Conference on Quantum Computing and Engineering, QCE 2023

作者： Liu, Ji Younis, Ed Weiden, Mathias Hovland, Paul Kubiatowicz, John Iancu, Costin Argonne National Laboratory Mathematics and Computer Science Division United States Lawrence Berkeley National Laboratory Computational Research Division United States University of California Department of Electrical Engineering and Computer Science Berkeley United States

ISBN: (纸本)9798350343236

We propose a scalable, hierarchical qubit mapping and routing algorithm that harnesses the power of circuit synthesis. First, we decompose large circuits into subcircuits small enough to be directly resynthesized. For each block, we pre-synthesize them for all permutations of its input and output qubits. Following this offline step, we employ a permutation-aware, block-based generalization of the popular SABRE mapping algorithm. This mapping step stitches together blocks by choosing an input-output permutation that minimizes intrablock gate count and required inter-block communication (SWAP and bridge gates). Our approach has a twofold advantage: 1) circuit synthesis may eliminate more two-qubit gates than other optimizing compilers;2) considering all permutations of input and output qubits eliminates communication operations transparently. In contrast, other mapping algorithms can only introduce communication operations. We show that we can produce better-quality circuits than commercial compilers: shorter by up to 68% (18% on average) fewer gates than Qiskit, up to 36% (9% on average) fewer gates than Tket. We outperform BQSkit, a permutation-unaware, synthesis-based compiler, by up to 67% (21% on average) fewer gates. We also exceed experimental optimal mappers such as OLSQ in quality (10.7% shorter circuits) and time to solution. Our scalable, heuristic approach can be seamlessly integrated into any quantum circuit compiler or optimization infrastructure, and it applies well to any qubit technology, such as superconducting and trapped ions. © 2023 IEEE.

关键词： Conformal mapping

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A Geometric Distortion Solution Specifically for Historical Observations and its Implementation

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Research in Astronomy and Astrophysics 2024年第11期24卷 85-94页

作者： F.R.Lin Q.Y.Peng Z.J.Zheng B.F.Guo School of Software Jiangxi Normal UniversityNanchang 330022China Sino-French Joint Laboratory for Astrometry Dynamics and Space ScienceJinan UniversityGuangzhou 510632China Department of Computer Science Jinan UniversityGuangzhou 510632China Guangdong Ocean University Zhanjiang 524000China

Geometric distortion(GD)critically constrains the precision of *** well-established methods to correct GD requires calibration observations,which can only be obtained using a special dithering strategy during the observation ***,this special observation mode is not often used,especially for historical observations before those GD correction methods were *** a result,some telescopes have no GD calibration observations for a long period,making it impossible to accurately determine the GD *** limits the value of the telescope observations in certain astrometric scenarios,such as using historical observations of moving targets in the solar system to improve their *** investigated a method for handling GD that does not rely on the calibration *** this advantage,it can be used to solve the GD models of telescopes which were intractable in the *** method was implemented in Python and released on *** was then applied to solve GD in the observations taken with the 1 m and 2.4 m telescopes at Yunnan *** resulting GD models were compared with those obtained using well-established methods to demonstrate the ***,the method was applied in the reduction of observations for two targets,the moon of Jupiter(Himalia)and binary GSC 2038-0293,to show its *** GD correction,the astrometric results for both targets show ***,the mean residual between the observed and computed position(O-C)for binary GSC 2038-0293 decreased from 36 to 5 mas.

关键词： astrometry-methods data analysis-techniques image processing

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