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7th international conference on Electrical, Control, and Computer Engineering, InECCE 2023

7th International Conference on Electrical, Control, and Com...

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7th international conference on Electrical, Control, and Computer Engineering, InECCE 2023

ISBN: (纸本)9789819738465

the proceedings contain 54 papers. the special focus in this conference is on Electrical, Control, and Computer Engineering. the topics include: the Modified ARIMA Predicting Algorithm Apply on Glucose Values Prediction;Optimal Distributed Generation (DG) Allocation for Transmission Losses Minimization Using Arithmetic Optimization algorithms (AOA);ioT-Enabled Water Quality Sensor: Detecting Concentration of Saccharomyces boulardii Bacteria to Enhance Water Safety;Local Feature Descriptor Based on Directional Structure Map for Improving the Hotspot Detection in the Multispectral Aerial Image of a Large-Scale PV System;deep Learning-Based Yield Prediction for the Die Bonding Semiconductor Manufacturing Process;distribution of Semiconductor Device Losses in Photovoltaic Transformerless Grid Connected Inverter Topologies;state Machine Logic Vibration Control Simulation of Rotary Impact Driver;design and Analysis of Filtration Membrane for Artificial Kidney;Hand Vein Region of Interest (ROI) Extraction Using Faster Region-Based Convolutional Neural Network (R-CNN);comparative Analysis of Superpixel and Gabor Methods for Exudate Feature Extraction in Diabetic Retinopathy Fundus Images;economic and Emission Dispatch Solution Using Evolutionary Mating Algorithm;predictive Model for Electricity Consumption in Malaysia Using Support Vector Regression;Comparison of Stress and Deformation Due to Electromagnetic Torque in SynRM with Flux-Barrier and Segmented Rotor;Fabrication of Dye-Sensitized Solar Cells (DSSC) Using Copper (I) Iodide: A Sustainable Approach to Solar Energy Conversion;detecting Anomalies in Unmanned Aerial Vehicles via the Optimization Method;lego Parts Recognition Based on Its Unique Characteristics;enhancing Power System Resilience through Evolutionary Programming for High Impact Low Probability Events;child Left in the Car Detection: Image Enhancement for Day and Night.

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7th international conference on Electrical, Control, and Computer Engineering, InECCE 2023

7th International Conference on Electrical, Control, and Com...

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7th international conference on Electrical, Control, and Computer Engineering, InECCE 2023

ISBN: (纸本)9789819738502

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complexity of Manipulative Interference in Participatory Budgeting 7th

Complexity of Manipulative Interference in Participatory Bud...

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7th international conference on Algorithmic Decision theory (ADT)

作者： Baumeister, Dorothea Boes, Linus Hillebrand, Johanna Heinrich Heine Univ Dusseldorf Dusseldorf Germany

ISBN: (纸本)9783030877569;9783030877552

A general framework for approval-based participatory budgeting has recently been introduced by Talmon and Faliszewski [17]. they use satisfaction functions to model the voters' agreement with a given outcome based on their approval ballots. We adopt two of their satisfaction functions and focus on two types of rules. that is, rules that maximize the overall voters' satisfaction and greedy rules that iteratively extend a partial budget by an item that maximizes the satisfaction in each incremental step. An important task in participatory budgeting is to study different forms of manipulative interference that may occur in practice. We investigate the computational complexity of different problems related to determining the outcome of a given rule and give a very general formulation of manipulative interference problems. A special focus is on problems dealing with a varying cost of the items and a varying budget limit. the results range from polynomial-time algorithms to completeness in different levels of the polynomial hierarchy.

关键词： Participatory budgeting Control Computational complexity

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Searching for Smallest Universal Graphs and Tournaments with SAT 29

Searching for Smallest Universal Graphs and Tournaments with...

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29th international conference on Principles and Practice of Constraint Programming, CP 2023

作者： Zhang, Tianwei Szeider, Stefan Algorithms and Complexity Group TU Wien Austria

ISBN: (纸本)9783959773003

A graph is induced k-universal if it contains all graphs of order k as an induced subgraph. For over half a century, the question of determining smallest k-universal graphs has been studied. A related question asks for a smallest k-universal tournament containing all tournaments of order k. this paper proposes and compares SAT-based methods for answering these questions exactly for small values of k. Our methods scale to values for which a generate-and-test approach isn't feasible;for instance, we show that an induced 7-universal graph has more than 16 vertices, whereas the number of all connected graphs on 16 vertices, modulo isomorphism, is a number with 23 decimal digits Our methods include static and dynamic symmetry breaking and lazy encodings, employing external subgraph isomorphism testing. © Tianwei Zhang and Stefan Szeider.

关键词： Directed graphs

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7th international conference on Contemporary Information Technology and Mathematics, ICCITM 2021

7th International Conference on Contemporary Information Tec...

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7th international conference on Contemporary Information Technology and Mathematics, ICCITM 2021

ISBN: (纸本)9781665420327

the proceedings contain 58 papers. the topics discussed include: deep learning model for IDS In the internet of things;real-time big data analytics perspective on applications, frameworks and challenges;real-time routing for Internet of things: a survey on techniques and protocols;contagious patient tracking application spotlight: privacy and security rights;predicting QoS for web service recommendations based on reputation and location clustering with collaborative filtering;multiclass model for quality of web service using machine learning and cloud computing;classification personality traits by using pretrained deep learning models;a comparative study of algorithms of software effort estimation for the robotic and communication systems based on improved accuracy;cohesive summary extraction from multi-document based on artificial neural network;and state of art survey for deep learning effects on semantic web performance.

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Improved YOLOv7-Tiny for Hazardous Vehicle Detection

Improved YOLOv7-Tiny for Hazardous Vehicle Detection

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Advanced algorithms and Control Engineering (ICAACE), international conference on

作者： Yu Tian Dequn Zhao Suming Zhang Hongwei Ma Faculty of Information Technology Beijing University of Technology Beijing China Beijing Aerospace System Engineering Research Institute Beijing China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

In addressing the challenge of distinguishing between regular trucks and hazardous transport vehicles within the park, this paper introduces an enhanced algorithm for hazardous material vehicle detection, leveraging the YOLOv7-tiny model. the algorithm's objective is to swiftly recognize hazardous material markings on vehicles and differentiate hazardous material transport vehicles. Initially, a dataset comprising 8,000 images and 10,000 hazardous material vehicle samples is compiled. Leveraging prior knowledge of hazardous material markings enhances the detection accuracy of hazardous vehicles. Subsequently, the YOLOv7-tiny network undergoes several enhancements: the CoT-ELAN network structure is proposed to improve feature extraction for small targets by integrating attention mechanisms, and a decoupled head structure is introduced to address different aspects of classification and localization, thereby boosting overall performance. Experimental findings showcase the superior performance of the enhanced algorithm on the internally constructed dataset, achieving a 5.6% increase in average precision compared to the traditional YOLOv7-tiny model. With a detection speed of 70.5 FPS on GPU devices, the algorithm effectively meets the hazardous material vehicle detection needs within the park.

关键词： Hazardous materials Performance evaluation Location awareness Head Computational modeling Interference Feature extraction

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Leveraging Application complexity Partition for Android Malware Detection 7

Leveraging Application Complexity Partition for Android Malw...

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7th IEEE international conference on Cloud Computing and Intelligence Systems, CCIS 2021

作者： Li, Zhanbo Jiang, Biao Mao, Baolei Zhuang, Yan Zhang, Hongtao Zhengzhou University Zhengzhou450001 China

ISBN: (纸本)9781665441490

With the widespread use of Android applications, malicious applications seriously threaten information security and personal privacy. Although a lot of researches have been conducted on malware detection by using various detection models, the effect of the complexity characteristics of Android application on the android malware detection is not investigated in depth. In this article, we leverage application complexity partition for Android malware detection to deal with different android application complexity characteristics in fine-grain. We first investigate the impact of application complexity on malware detection, and utilize application complexity to screen out four datasets with different complexity by dividing the original dataset. then, we use frequency difference sorting (FDS) algorithm to extract highly sensitive permissions and API calls that can identify benign and malicious applications. Finally, we evaluate support vector machine (SVM) and four other machine learning methods to perform android malware detection with respect to different application complexity partitions. Experimental results show that ACPDs can achieve 95.18%-99.19% accuracy and 95.45%-99.68% recall in different application complexity datasets, which are better than the 91.02% accuracy of SigPID. the experimental results demonstrate that ACPDs are scalable enough to work well with different machine learning methods and improve machine learning based Android malware detection effectively. © 2021 IEEE.

关键词： Android malware

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Proceedings of 2021 IEEE 7th international conference on Bio Signals, Images and Instrumentation, ICBSII 2021

Proceedings of 2021 IEEE 7th International Conference on Bio...

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7th IEEE international conference on Bio Signals, Images and Instrumentation, ICBSII 2021

ISBN: (纸本)9781665441261

the proceedings contain 62 papers. the topics discussed include: detection of autism spectrum disorder from EEG signals using pre trained deep convolution neural networks;cloud based analysis and classification of EEG signals to detect epileptic seizures;a review on the medical applications of electroencephalography (EEG);frame work for EEG based emotion recognition based on hybrid neural network;computational approach to guide mind controlled robotic arm using BCI – a review;a new lead system for improved recording of p-wave amplitude and its significance with existing optimal leads;automated EEG analysis for early diagnosis of epilepsy: a comparative study to determine relative accuracy of arithmetic and Huffman coding algorithms;and automated detection of brain abnormality using deep-learning-scheme – a study.

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Face Mamba: A Facial Emotion Analysis Network Based on VMamba*

Face Mamba: A Facial Emotion Analysis Network Based on VMamb...

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Machine Learning and Natural Language Processing (MLNLP), international conference on

作者： Longfei Li Qihang Sun Lingyun Zhao Huifang Sun Fangzheng Zhao Bing Gu Shandong University of Science and Technology TaiAn China

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

In recent years, convolutional neural networks and Transformer and its variants, such as Vision Transformer and Swin Transformer, have been the main models for facial expression recognition tasks. However, both have corresponding problems. In convolutional neural networks (such as CNN), CNN mainly focuses on local features in image processing, has poor grasp of global information, and is difficult to handle large data sets. In Transformer, its performance is limited by factors such as the large number of parameters and the high complexity of attention calculation. Inspired by the Mamba model's ability to model long sequences, we propose a facial emotion analysis network based on VMamba (visual state space model). this network not only reduces the computational complexity to linear by using the selective SSM module and Cross-Scan Module (CSM) feature scanning mechanism in the VMamba model, but also shows good performance in feature extraction. In order to enhance the performance in facial emotion analysis, we also propose the FaceR module to extract features from the input face image. A large number of experiments have proved that the model has good performance in facial expression recognition tasks, with accuracies of 66.57%, 62.65% and 90.58% on the AffectNet-7, AffectNet-8 and RAF-DB datasets respectively.

关键词： Analytical models Visualization Computational modeling Face recognition Feature extraction Transformers Real-time systems Natural language processing Convolutional neural networks Computational complexity

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Aloha-NOMA for Ambient Backscatter M2M Communication in IoT Networks with Random Power Levels and Frequency Channel Assignment 7

Aloha-NOMA for Ambient Backscatter M2M Communication in IoT ...

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7th IEEE international conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2021

作者： Halbouni, Asmaa Hani Ghani, Muhammad Afiq Bin Abdul Habaebi, Mohamed Hadi International Islamic University Malaysia Department of Electrical and Computer Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9781728175225

Internet of things as known as IoT is a network pointing to a series of interconnected devices that are communicating with each other. M2M communication protocol is integral part of IoT to allowing multiple devices to communicate smoothly. Aloha-NOMA for ambient backscatter M2M communication can provide high throughput matched with low complexity of Aloha-NOMA while leveraging on the low power and cost of ambient backscatter communication links. this paper critically studies the application of Aloha-NOMA for backscatter M2M communication utilizing multichannel and random power levels. In this paper, multichannel Aloha-NOMA throughput analysis had been done in order to observe the impact of adding sub channels. throughput increased directly proportional as number of sub channels. then, Aloha-NOMA performances are compared against Ambient Backscatter Aloha-NOMA in which more channels on the line with backscatter channels. Results indicate the combination of random power levels selection with random frequency channel selection yields significant throughput improvement. © 2021 IEEE.

关键词： Machine-to-machine communication

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