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Sludge Age Prediction Using Machine learning (Deep Neural Network vs Deep Bi-LSTM Model)

Sludge Age Prediction Using Machine Learning (Deep Neural Ne...

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international Workshop on Research and Education in Mechatronics (REM)

作者： Djeddou Messaoud Hellal Aouatef Loukam Imed Al Dallal Jehad Bachiri Aida A. Hameed Ibrahim Research Laboratory in Subterranean and Surface Hydraulics (LAHE) Faculty of Sciences and Technology Mohamed Khider University of Biskra Biskra Algeria Hydraulic Department Faculty of sciences and applied science Mohamed Larbi Ben M'Hidi University of Oum el-Bouaghi Algeria Research Laboratory in Subterranean and Surface Hydraulics (LARHYSS) Faculty of Sciences and Technology Mohamed Khider University of Biskra Biskra Algeria Civil Engineering Department Faculty of sciences and Technology Mohamed University of Souk Ahras Algeria Department of Computer Science Gulf University for Science and Technology Kuwait Department of ICT and Natural Sciences Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology (NTNU) Ålesund Norway

ISBN: (数字)9798331505974

ISBN: (纸本)9798331505981

The Sludge Age (SAGE) is a significant operational parameter in activated sludge processes. However, precisely estimating it can be difficult due to non-linear data and changeable operating conditions. The objective of this project is to create a bidirectional long short-term memory (Bi-LSTM) network to forecast sludge age (SAGE) using intricate time-series data obtained from a Wastewater Treatment Plant (WWTP). The Bi-LSTM architecture is recognized for efficiently handling time series and non-uniform data. The datasets used for training and evaluating the Bi-LSTM model was gathered from the Nine Springs Wastewater Treatment Plant located near Madison, Wisconsin, United States. Following the utilization of historical data for training, the suggested Bi-LSTM model outperform the DNN model and successfully forecasted SAGE, offering potential advantages to WWTP managers in enhancing operational performance, system management, and process stability.

关键词： Training Accuracy Computational modeling Time series analysis Computer architecture Predictive models data models Wastewater treatment Long short term memory Testing

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Improving Text Summarization with Transformer Networks and Reinforcement learning for English

Improving Text Summarization with Transformer Networks and R...

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Communication, Control and intelligent Systems (CCIS)

作者： Chinni Bala Vijaya Durga V.Arunadevi Punit Pathak Prema S Hari Nair P.Nagaraj Potti Sreeramulu Chalavadhi Mallikarjuna Rao College of Engineering & Technology Vijayawada A.P. India Department of Science and Humanities Dr.Mahalingam College of Engineering and Technology India Department of School of Liberal Arts and Human Sciences Auro University Surat India Department of English Panimalar Engineering College Chennai India Department of English and Foreign Languages Sagi Rama Krishnam Raju Engineering College (A) Bhimavaram Andhra Pradesh Department of AI & ML K.Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

As the volume of text data continues to grow rapidly, efficient and accurate text summarization has become crucial for managing and processing this information. Traditional summarization methods, including extractive and abstractive approaches, face challenges in maintaining relevance, fluency, and factual consistency, particularly in complex domains such as legal documents. In this research, we propose an advanced summarization framework leveraging transformer networks, specifically designed to enhance the quality of English text summaries. The framework begins with the preprocessing of text data, including lowercasing, elimination of punctuation, HTML tags, stopwords, and stemming/lemmatization to standardize the input. Text feature extraction is then performed using the Term Frequency-Inverse Document Frequency (TF-IDF) method, converting textual data into numerical representations that highlight the significance of terms within documents. The core of the framework employs a transformer model that eschews traditional RNNs in favor of multiple attention layers and positional encoding to capture global dependencies and enable parallel processing. The model's encoder-decoder structure uses self-attention mechanisms and multi-head attention layers to effectively encode the sequence and context of words, leading to high-quality abstractive summaries. The proposed architecture's effectiveness is evaluated using the BillSum dataset, which includes over 22,000 U.S. Congressional bills and human-written summaries. The results highlight the superior performance of the proposed Transformer model, which achieved the highest ROUGE-1, ROUGE-2, and ROUGE-L scores, with values of 37.03, 16.89, and 28.34, respectively and the Proposed method is implemented using python software. Our approach demonstrates significant improvements in summary accuracy and relevance compared to existing methods, making it a promising solution for automated summarization tasks across various domains.

关键词： Accuracy Law Text summarization Reinforcement learning Transformer cores Transformers Frequency conversion Software Numerical models Python

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Opcode Sequences-Based Smart Contract Vulnerabilities Detection Using Deep learning

Opcode Sequences-Based Smart Contract Vulnerabilities Detect...

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Jinyao Zhu Xiaofei Xing Guojun Wang Peiqiang Li School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Ethereum is a blockchain platform that allows developers to create smart contracts. Smart contracts are programs that can automatically execute and handle cryptocurrency funds. However, over a hundred thousand new smart contracts are deployed every day and inevitably contain vulnerabilities due to programming errors. Once deployed, smart contracts cannot be fixed or changed, leaving funds at risk. To mitigate it, we use deep learning to detect vulnerabilities in smart contracts. First, we create our own dataset of labeled smart contracts based on opcode sequences, since few smart contract codes and labeled datasets are publicly available. We collect opcode sequences by replaying real-world transactions from the Ethereum Mainnet in our fully synchronized node while we leverage a plugin called "SODA" to label opcode sequences with vulnerability classes. Second, after data collection, we preprocess the data by removing duplicate opcode sequences, normalizing the sequences to the same length, and converting them into vectors. Finally, to detect vulnerabilities in smart contracts, we train a deep classification model using LSTM neural networks. Our model achieved an average accuracy of 82.63% and an F1-score of 79.74% across seven types of vulnerabilities, which is important for securing funds and logic in smart contracts.

关键词： Deep learning Computational modeling Source coding Smart contracts Programming data models Vectors

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An Efficient Spatial-Temporal Representation Method for EEG Emotion Recognition 19

An Efficient Spatial-Temporal Representation Method for EEG ...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and Computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable Computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy Computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Weng, Weining Gu, Yang Chen, Yiqiang Wang, Guoqiang Shi, Nianfeng Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China The Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China School of Computer and Information Engineering Luoyang Institute of Science and Technology Luoyang China

ISBN: (纸本)9798350346558

Affective computing based on electroencephalogram (EEG) has been widely studied due to the high correlation between the brain and emotions. Neurological research shows that emotions are generated by the interaction of neural circuits separated in different brain regions. Recently, many deep learning methods have been demonstrated to recognize different emotions. However, these methods extract features at the data level, ignoring the brain's neural mechanisms of emotion generation, which may result in the features' weak ability to express emotion. To solve this problem and extract more discriminant features according to the neural mechanism of emotion generation, this article proposes an attention based spatial-temporal representation method (ASTR) for emotion recognition. The ASTR method constructs spatial-temporal representations of emotion from neural circuits' activity in different brain regions. In the model, the cascade attention quantifies the multi-granularity correlation and information exchange pattern between brain regions as the spatial features, which measure neural circuits' activation and information interaction in different brain regions. The one-way transformer introduces temporal order in spatial features, calculates temporal variation features from the long-term activity of neural circuits and generates the emotion representation. The representation incorporates spatial-temporal features of neural circuits' activity during emotion generation, which is highly related to emotion. We conducted experiments on SEED and DEAP datasets. The experimental results show that the emotion recognition accuracy of our model is about 3.0% higher than that of the SOTA algorithms, reaching more than 98%. The results also visualize the emotion generation process, revealing how the brain works under different emotions. © 2022 IEEE.

关键词： Emotion Recognition

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DeepBoundary: A Coverage Testing Method of Deep learning Software based on Decision Boundary Representation

DeepBoundary: A Coverage Testing Method of Deep Learning Sof...

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IEEE international conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Yue Liu Lichao Feng Xingya Wang Shiyu Zhang College of Computer Science and Technology Nanjing Tech University Nanjing Jiangsu China Institute of Command and Control Engineering Army Engineering University of PLA Nanjing Jiangsu China State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu China

With the increasing application of Deep learning (DL) Software in safety-critical fields such as autonomous driving, we need adequate testing to ensure software quality. Observing the decision-making behavior of a Deep Neural Network (DNN) is an essential step in DL software testing. Taking Guiding Deep learning System Testing Using Surprise Adequacy (SADL) as an example, it uses the independent neuron activation values in the DNN to represent the decision-making behavior. However, the behavior of the DNN needs to be jointly determined by the continuous outputs of all neurons. As a result, the coverage value of SADL constant volatility and lack of stability. To mitigate this problem, we propose a coverage testing method based on the decision boundary representation, DeepBoundary, for the decision-making behavior of DL software. Unlike SADL, DeepBoundary generates decision boundary data to represent the decision behavior of the DNN, which makes the testing results more stable. On this basis, we calculate the kernel density between the testing data and the decision boundary data. It measures the position of the testing data in the decision space and the distance from the decision boundary. Finally, as an adequacy indicator, we calculate the decision boundary density coverage (DBC) of the entire testing set. The experiment on the dataset MNIST and two DL software shows that DeepBoundary can generate actual decision boundary data. The average confidence error in the DNNs output layer is only 4.20E-05. Compared with SADL, DeepBoundary has a stronger correlation with the defect detection ratio, which can more accurately represent testing adequacy.

关键词： Deep learning Software testing System testing Correlation Decision making Neurons Software quality

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A Machine learning Based MPTCP Subflow Control Algorithm for Multi-access Heterogeneous Networks

A Machine Learning Based MPTCP Subflow Control Algorithm for...

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international conference on Communication Technology (ICCT)

作者： Chengbing Chen Lingyun Jiang She Jun Hongbo Zhu Jia Gu Xiangbei Chang School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Electric Power Research Institute Co. Ltd. Nanjing China

Ever growing number of devices and the demand for applications are generating tremendous network traffic today. In the edge network environment, due to the network instability caused by device movement, the single network cannot meet its throughput requirements. Hence, using multi-access network to transmit data packages has become a way to improve throughput. Multi-path TCP utilizes multiple channels to transmit data packages simultaneously. However, MPTCP is unable to control subflows adaptively, and the disorder of data packages caused by difference between paths leads to its poor performance compared with the single path transmission. Therefore, a new MPTCP subflow control algorithm (MSCA) based on Support Vector Machine (SVM) prediction model is proposed. The SDN controller is utilized to continuously monitor the network status and predicts the impact factor of the network based on the path parameters monitored. Then, according to the impact factor, the subflow configurations is dynamically adjusted by the system for each user to improve the average throughput. The system is implemented on the network emulator platform Mininet-WiFi. Emulation results shows the proposed MSCA improves the average throughput compared with traditional scheme. MSCA also responses quickly to the network changes.

关键词： Support vector machines Machine learning algorithms TCPIP Telecommunication traffic Machine learning Predictive models Throughput

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Development of Volumetric Image Descriptor for Urban Object Classification Using 3D LiDAR Based on Convolutional Neural Network

Development of Volumetric Image Descriptor for Urban Object ...

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international conference on Control, Automation and Systems ( ICCAS)

作者： Thu Nguyen Huu Sejin Lee Department of Mechanical Engineering Kongju National University Cheonan Korea

Along with the current rapid development of technology, object classification is being researched, developed, and applied to security systems, autonomous driving, and other applications. A common technique is to use vision cameras to collect data of objects in the surrounding environment. Along with many other methods, LiDAR sensors are being used to collect data in space to detect and classify objects. By using the LiDAR sensors, some disadvantages of image sensors with the negative influence on the image quality by weather, light condition, and period will be covered. There are other researchers who studied the 3D point cloud data for object classification in space. A study of spherical signature descriptor by C.H Bae, et al, based on the spheres though creates the uneven space inside by the shape of the spheres. In this study, a volumetric image descriptor in 3D shape is developed to handle 3D object data in the urban environment obtained from LiDAR sensors and convert it into image data before using deep learning algorithms in the process of object classification. The study showed the potential possibility of the proposal and its further application.

关键词： Point cloud compression Image sensors Training Visualization Three-dimensional displays Laser radar Shape

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Research on the Training Mode of Computer Professionals Driven by the Professional Certification of engineering Education and the Integration of Industry and Education 2

Research on the Training Mode of Computer Professionals Driv...

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2nd international conference on Big data engineering and Education, BDEE 2022

作者： Yang, Yujun Yang, Yimei Li, Wei Li, Liyun School of Computer and Artificial Intelligence School of Software Huaihua University Huaihua Hunan418000 China

ISBN: (数字)9781665481847

ISBN: (纸本)9781665481847

Combining with enterprises and implementing the integration of industry and education has become a common practice in professional construction and talent training in many local undergraduate colleges. However, under the background of engineering education professional certification, how to effectively implement professional construction and high-quality talent training is a problem worthy of exploration. Starting from the systematicness of talent training, combined with the elements of engineering education certification and industry education integration, this paper puts forward the computer professional talent training mode driven by engineering education certification and industry education integration, and analyzes the specific implementation strategy and feasibility of the two driving together, so as to provide reference for computer professional talent training and professional construction in local colleges and universities. © 2022 IEEE.

关键词： Integration

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Graph Ordering: Towards the Optimal by learning 22nd

Graph Ordering: Towards the Optimal by Learning

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22nd international conference on Web Information Systems engineering (WISE)

作者： Zhao, Kangfei Rong, Yu Yu, Jeffrey Xu Huang, Wenbing Huang, Junzhou Zhang, Hao Chinese Univ Hong Kong Hong Kong Peoples R China Tencent AI Lab Shenzhen Peoples R China Tsinghua Univ Shaoyang Peoples R China Univ Texas Arlington Arlington TX USA

ISBN: (纸本)9783030908881;9783030908874

Graph ordering concentrates on optimizing graph layouts, which has a wide range of real applications. As an NP-hard problem, traditional approaches solve it via greedy algorithms. To overcome the short-sightedness and inflexibility of the hand-crafted heuristics, we propose a learning-based framework: Deep Ordering Network with Reinforcement learning (DON-RL) to capture the hidden structure from partial vertex order sets over a specific large graph. In DON-RL, we propose a permutation invariant neural network DON to encode the information from partial vertex order. Furthermore, to alleviate the combinatorial explosion for partial vertex order sets and make the efficient training data sampling, we propose RL-Sampler, a reinforcement learning-based sampler to tune the vertex sampling probabilities adaptively during the training phase of DON. Comprehensive experiments on both synthetic and real graphs validate that our approach outperforms the state-of-the-art heuristic algorithm consistently. The case study on graph compression demonstrates the potentials of DON-RL in real applications.

关键词： Graph ordering Deep learning Reinforcement learning

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Expert Recommendations with Temporal Dynamics of User Interest in CQA 22nd

Expert Recommendations with Temporal Dynamics of User Intere...

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22nd international conference on Web Information Systems engineering (WISE)

作者： Lv, Xiaoqi Ji, Ke Chen, Zhenxiang Ma, Kun Wu, Jun Li, Yidong Xu, Guandong Univ Jinan Sch Informat Sci & Engn Jinan 250022 Peoples R China Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Univ Technol Sydney Data Sci & Machine Intelligence Lab Adv Analyt Inst Ultimo Australia

ISBN: (纸本)9783030908881;9783030908874

Community question answering (CQA) has become an essential service of promoting knowledge sharing in social platforms. To make question answering more efficient, several expert recommendation methods for CQA have been proposed, but most of them focus on the similarity matching between user interest and question content while ignoring the temporal dynamics of user interest, whose changes may decrease the quality of recommendation results. In this paper, a long and short term-based expert recommendation model (LSTERM) via attention mechanism-based CNN and Bi-GRU, which considers not only user interest but also user expertise, is proposed. The model can learn the embedded user/question feature representation from various content information by using attention mechanism-based CNN and then track the change of user interest and expertise over time by using Bi-GRU. Experiment results on real data demonstrate that with temporal dynamics, the recommendation accuracy is substantially improved compared with other state-of-the-art methods.

关键词： Expert recommendation Community question answering Deep learning Attention mechanism Recurrent neural network

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