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Privacy-Preserving of Power Consumption Big data Based on Improved Group Signature and Homomorphic Encryption

Privacy-Preserving of Power Consumption Big Data Based on Im...

引用

IEEE international conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Rixuan Qiu Ming Ai Fuyong Zheng Liang Liang Yuancheng Li State Grid Jiangxi Information & Telecommunication Company Nanchang China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9781728198279

ISBN: (纸本)9781728181585

Smart grid is a highly integrated power system that combines modern advanced information and communication, sensor measurement and automatic control technology, including a large number of smart devices, with reliability, flexibility, safety and self-healing advantages a great service. However, devices such as smart meter in the network face the issue of disclosing user privacy when collecting, processing, and transmitting large amounts of data. To solve this problem, this paper puts forward a scheme based on improved group signature and homomorphic encryption, using improved group signature to determine the public and private key of smart meter and the signature of electricity data, encrypting and aggregating the electricity data with homomorphic encryption. Finally, in the transmission of data, the electricity data is always in the form of ciphertext, and the group signature scheme with forwarding security can avoid the harm caused by the disclosure of the private key, thus playing a role in privacy-preserving.

关键词： Encryption Power demand Smart grids Smart meters Public key data privacy data models

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Approach for Motion Based Recognition: A Brief Review

Approach for Motion Based Recognition: A Brief Review

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international conference on Communications and Signal processing

作者： Kumari Priyanka Sinha Alok Kumar Rajib Ghosh department of Computer Science and Engineering Nalanda Colloge of Engineering Chandi Nalanda department of Mechanical Engineering Nalanda Colloge of Engineering Chandi Nalanda department of Computer Science and Engineering National Institute of Technology

ISBN: (数字)9781728149882

ISBN: (纸本)9781728149899

GAIT is recognizing of human motion or Motion-based human recognition and records the data as walking, running, jumping, moving body parts. It has a relatively new approach to biometrics identification. GAIT based recognition is used in medical science or healthcare to recognize the physiological behavior changes as a motion study of the patient to study the condition of disease, either it is a cure or not. In defense, through the GAIT recognition technique, enemies activities in remote areas can also be recognized, where the human cannot reaches. Biometrics is now useful in surveillance systems. Vision-based biometrics is used in a robotic automobile where the driverless vehicle is used. Considering the above aspects, this paper describes different types of motion (geometric aspect) involve in the human body such as oscillation motion, linear motion, rectilinear motion while walking, running fast or slow or moderate speed. We record the videos from various angles and studied with sequence of motion by segmenting single video into large multiple images though this, we able to define motion and angles of the human body.

关键词： Legged locomotion Cameras Gait recognition databases conferences Trajectory data models

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Beat and Downbeat Tracking of Symbolic Music data Using Deep Recurrent Neural Networks

Beat and Downbeat Tracking of Symbolic Music Data Using Deep...

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Asia-Pacific Signal and Information processing Association Annual Summit and conference (APSIPA)

作者： Yi-Chin Chuang Li Su National Chung Hsing University Taiwan Academia Sinica Institute of Information Science Taiwan

ISBN: (数字)9789881476883

ISBN: (纸本)9781728181301

Musical beat tracking is one of the most investigated tasks in music information retrieval (MIR). Research endeavors on this task have mostly been focused on the modeling of audio data representations. In contrast, beat tracking of symbolic music contents (e.g., MIDI, score sheets) has been relatively overlooked in the past years. In this paper, we revisit the task of symbolic music beat tracking and present to solve this task with modern deep learning approaches. To the extent of our knowledge, it is the first time that utilizing deep learning approaches to track beats and downbeats of symbolic music data. The proposed symbolic beat tracking framework performs joint beat and downbeat tracking in a multi-task learning (MTL) manner, and we investigate various types of networks which are based on the recurrent neural networks (RNN), such as bidirectional long short-term memory (BLSTM) network, hierarchical multi-scale (HM) LSTM, and BLSTM with the attention mechanism. In the experiments, a systematic comparison of these networks and state-of-art audio beat tracking methods are performed on the MusicNet dataset. Experiment results show that the BLSTM model trained specifically on symbolic data outperforms the state-of-the-art beat tracking methods utilized on synthesized audio. Such a comparison of performance also indicates the technical challenges in symbolic music beat tracking.

关键词： Task analysis Recurrent neural networks data models Deep learning data mining Computer architecture Clustering algorithms

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Encoding Broad Learning System : An Effective Shallow Model For Anti-fraud

Encoding Broad Learning System : An Effective Shallow Model ...

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IEEE international conference on Big data

作者： Rui Zhong Zhen Zhang Rongheng Lin Hua Zou State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Science and Technology on Communication Networks Laboratory National Computer Network Emergency Response Technical Team/Coordination Center of China

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

The criminal behavior of telecom fraud is increasing rapidly with the development of the communication industry, causing huge losses every year. The commonly used traditional fraud detection methods are less flexible. Currently, a more accurate and timely method is needed to deal with the evolving fraud. However, with the development of deep learning, more and more complex structures and a large number of parameters lead to the more time-consuming training process and poor interpretability which are unacceptable in the field of anti-fraud. Therefore, this paper proposes a shallow model called Encoding Broad Learning by incorporating the denoising autoencoder into the Broad Learning System (BLS). We use the first 15 seconds of the call content to process the text data of the fraudulent call by constructing TF-IDF, adding Gaussian noise to it, and combining with the denoising autoencoder to learn more general and robust features in the data. Then it is transformed into a neural network based on BLS, and fraudulent calls are identified on this model. This method is extremely suitable for fraud identification systems with few data features and high real-time training requirements. At the same time, in order to further improve the training efficiency and solve the potential memory explosion problem, we propose an integrated learning algorithm for parallel training of EBLS. Experiments and detailed analysis of the above methods are carried out. The results show that compared with the existing classic network algorithms, this method has a faster training speed, can ensure the accuracy and timeliness of online fraud identification, and help quickly identify fraudulent calls. The paper visualized the training results of these algorithms after processing the relevant parameters of EBLS training. These algorithms have strong interpretability and better meet the high security requirements in the field of anti-fraud.

关键词： Training Learning systems Noise reduction Big data Encoding Telecommunications Security

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SOSNet: A Graph Convolutional Network Approach to Fine-Grained Cyberbullying Detection

SOSNet: A Graph Convolutional Network Approach to Fine-Grain...

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IEEE international conference on Big data

作者： Jason Wang Kaiqun Fu Chang-Tien Lu Thomas Jefferson High School for Science and Technology Alexandria VA USA Virginia Tech Falls Church VA USA

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Amidst the COVID-19 pandemic, cyberbullying has become an even more serious threat. Our work aims to investigate the viability of an automatic multiclass cyberbullying detection model that is able to classify whether a cyberbully is targeting a victim's age, ethnicity, gender, religion, or other quality. Previous literature has not yet explored making fine-grained cyberbullying classifications o f s uch m agnitude, a nd existing cyberbullying datasets suffer from quite severe class imbalances. To combat these challenges, we establish a framework for the automatic generation of balanced data by using a semi-supervised online Dynamic Query Expansion (DQE) process to extract more natural data points of a specific class from Twitter. W e also propose a Graph Convolutional Network (GCN) classifier, using a graph constructed from the thresholded cosine similarities between tweet embeddings. With our DQE-augmented dataset, which we have made publicly available, we compare our GCN model using eight different tweet embedding methods and six other classification models over two sizes of datasets. Our results show that our proposed GCN model matches or exceeds the performance of the baseline models, as indicated by McNemar statistical tests.

关键词： Social networking (online) Pandemics Semantics Big data Natural language processing data mining Read only memory

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Broad Learning with Attribute Selection for Rheumatoid Arthritis

Broad Learning with Attribute Selection for Rheumatoid Arthr...

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IEEE international conference on Systems, Man and Cybernetics

作者： Jie Yang Shigao Huang Rui Tang Quanyi Hu Kun Lan Han Wang Qi Zhao Simon Fong University of Macau China Chongqing Industry&Trade Polytechnic Chongqing China Cancer Center Institute of Translational Medicine Faculty of Health Sciences University of Macau China Kunming University of Science and Technology Kunming China Institute of Data Science City Univerity of Macau Macao China Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai China

The following topics are dealt with: learning (artificial intelligence); feature extraction; medical signal processing; convolutional neural nets; deep learning (artificial intelligence); neural nets; neurophysiology;... 详细信息

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

关键词： Deep learning Rough sets Computer architecture Predictive models Arthritis Entropy Medical diagnostic imaging

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Detect Turn-takings in Subtitle Streams with Semantic Recall Transformer Encoder

Detect Turn-takings in Subtitle Streams with Semantic Recall...

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international conference on Asian Language processing (IALP)

作者： Yuhai Liang Qiang Zhou School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Institute of Artificial Intelligence Beijing China Tsinghua University Beijing China

ISBN: (数字)9781728176895

ISBN: (纸本)9781728176901

Subtitles are precious dialogue text data because of similarity to human conversation, but the lack of turn structures limits their applications in many NLP tasks. The previous work takes turn-taking detection (TTD) in subtitles as a sentence-pair classification problem to predict if there is a turn-taking happened between the two adjacent utterances. The results are not good enough. For considering the dialogue context information, we innovatively take TTD as a sentence-level sequence labelling problem, predicting the turn-takings through the whole subtitle stream. First, we present a novel fine-tuning method that enables BERT to encode utterances into embedding set with effective turntaking features. Then, we propose the Semantic Recall Transformer (SRT) model to detect the turn-takings among the utterance embedding set, by taking the turn-taking features and context information into account at the same time. Compared with the baselines, it achieves state-of-the-art on both English and Chinese subtitle corpus. Moreover, we explored the impacts of the length of the subtitle stream and the number of conversation participants on our model performance, which show the performance can be further improved in the future.

关键词： Task analysis Bit error rate Semantics Labeling Feature extraction data models Training

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A Machine Learning-Based Error Model of Voltage-Scaled Circuits

A Machine Learning-Based Error Model of Voltage-Scaled Circu...

引用

IEEE/IFIP international conference on Dependable Systems and Networks â€“ Supplemental Volume (DSN-S)

作者： Dongning Ma Xun Jiao ECE Department Villanova University

ISBN: (数字)9781728172606

ISBN: (纸本)9781728172613

Various approximation methods demonstrate the effectiveness of voltage scaling in digital circuits in order to explore the energy-error trade-off. An accurate error model is of critical importance for assessing the error behavior of voltage-scaled circuits and its effects on the application quality. However, existing error models of voltage-scaled circuits overlook the effects of input data and error rate disparity among different bits. To tackle this challenge, we propose a machine learning-based error model of voltage-scaled circuits that can predict the timing error rate for each output bit. We train this model using random forest methods with input features and output labels extracted from gate-level simulation. We evaluate the model accuracy on different circuits. Across all bit positions, voltage levels, and circuits, the model achieves on average a relative error of 1.06%. The model also achieves an average per-voltage Root Mean Square Error (RMSE) of 0.92% and per-bit RMSE of 1.02%. Exposing this error rate even up to the application level, the model can estimate the quality of an image processing application under voltage scaling with an average accuracy of 97.5%.

关键词： Timing Integrated circuit modeling Predictive models data models Computational modeling Error analysis Forestry

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Anytime Algorithm for Cell-based DBSCAN by Connecting Randomly Selected Cells

Anytime Algorithm for Cell-based DBSCAN by Connecting Random...

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IIAI international conference on Advanced Applied Informatics (IIAIAAI)

作者： Tatsuhiro Sakai Keiichi Tamura Hajime Kitakami Toshiyuki Takezawa Institute of Science and Engineering Academic Assembly Shimane University Shimane Japan Graduate School of Information Sciences Hiroshima City University Hiroshima Japan

ISBN: (纸本)9781728173986

With the growing interest in big data, speed-up techniques for clustering are required. The density-based spatial clustering of applications with noise (DBSCAN) has been well known in database domains. Since the DBSCAN algorithm was first proposed, several speed-up methods have been introduced. In the previous work, cell-based DBSCAN as a fast DBSCAN algorithm that divides the whole dataset into smaller cells and connects them to form clusters was proposed. In this study we propose a novel clustering algorithm called anytime algorithm for cell-based DBSCAN. The proposed algorithm connects some randomly selected cells and calculates the clustering result at high speed. Next, it repeats this process, which improves the accuracy of clustering, thereby yielding the precise results. Experimental results demonstrated that the proposed algorithm can calculate the clustering results with high accuracy at high speed.

关键词： Clustering algorithms Parallel processing Big data Spatial databases Informatics

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Analyzing Information Diffusion in Ego-centric Twitter Social Network

Analyzing Information Diffusion in Ego-centric Twitter Socia...

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international conference on Signal processing and Communication (ICSC)

作者： Ankit Kumar Dipansha Chhabra Bhavya Mendiratta Adwitiya Sinha Department of Computer Science and Engineering Jaypee Institute of Science and Technology Noida India

ISBN: (数字)9781728154930

ISBN: (纸本)9781728154947

The ever dynamic world of social network is progressively about innovation and this headway in advancements has been an amalgamation of several advantages and disadvantages. The most quickly developing segment of the web is the social media life. The simplicity of correspondence with the individuals living far with the rise of social media life is deserving of acclaim. These applications are presently even broadly utilized for advancing organizations or occasions. Speaking of social media, Twitter has emerged out to be the most influential social media platform. Therefore, it has common possibility to be the premise of future ego-centric networks and information exchange frameworks, notwithstanding novel types of internet administrations misusing the properties of human social connections. In this respect, our research focuses on study of information flow within ego-centric network with real data set extracted from Twitter user handle. Our proposed model performs high-level soft analysis via community detection techniques based on network homophily. This is followed by applying of various diffusion models to study information diffusion rate in ego network.

关键词： Twitter data mining Mathematical model Image edge detection Time complexity Integrated circuit modeling

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