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Promo-Zen: A Natural Language Processing Based Promotional Text Message Management System

Promo-Zen: A Natural Language Processing Based Promotional T...

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Advancements in Computing (ICAC), international conference on

作者： Chamath Udugampola Nethmi Weerasingha Department of Computer and Data Science NSBM Green University Homagama Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Promotional Short Message Service (SMS) messages, which provide frequent updates about deals and discounts to consumers, are crucial in developing countries like Sri Lanka, as they help alleviate financial pressure during economic crises. However, poor practices by the brands, SMS technology limitations and human memory limitations challenge the ability of consumers to make effective use of this facility. As a solution, this project aimed to develop an Android application to organize and enhance the readability of promotional messages, while also providing mechanisms to prevent users from forgetting promotions. these objectives were achieved by creating a Kotlin app that displays promotional information in a date-sorted weekly dashboard, offers deal-expiration reminders, and optimizes message content for readability. the system utilizes a novel workflow/pipeline combining a Natural Language Processing (NLP) model with Named Entity recognition (NER) capabilities, Regular Expressions and custom logic, to understand the context of and extract the crucial information from the promotional messages. User-acceptance testing carried out demonstrates positive results, reducing traditional access times to promotions of 8–10 seconds to approximately 2 seconds through this application. Additionally, user-testers reported multiple occurrences of the reminders feature ensuring that promotions weren't forgotten before use.

关键词： Named entity recognition Developing countries Message services Feature extraction Logic Data mining pattern matching Testing Context modeling

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6th international conference on Information, Communication and Computing Technology, ICICCT 2021

6th International Conference on Information, Communication a...

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6th international conference on Information, Communication and Computing Technology, ICICCT 2021

ISBN: (纸本)9783030883775

the proceedings contain 20 papers. the special focus in this conference is on Information, Communication and Computing Technology. the topics include: Predicting the Decomposition Level of Forest Trees through Ensembling Methods;can Machine Learning Predict an Employee’s Mental Health?;predicting the Stock Market Trend: An Ensemble Approach Using Impactful Exploratory Data Analysis;detecting Text-Bullying on Twitter Using Machine Learning Algorithms;empirical Laws of Natural Language Processing for Neural Language Generated Text;quantum Based Deep Learning Models for pattern recognition;performance Analysis of Named Entity recognition Approaches on Code-Mixed Data;measurement of Liquid Level Using Prediction Methodology;A MapReduce Approach to Automatic Key File Updates for SPT (Squeeze, Pack and Transfer) Algorithm;web-Based Real-Time Gesture recognition with Voice;performance Enhancement in Big Data by Guided Map Reduce;multi-layer Parallelization in Transportation Management Software;Multipath TCP Security Issues, Challenges and Solutions;story Generation from Images Using Deep Learning;preface;comparative Analysis of Security Protocols in IoT;Minimize Penalty Fees During Reconfiguration of a Set of Light-Tree Pairs in an All-Optical WDM Network;secure Data Transmission Techniques for Privacy Preserving Computation Offloading Between Fog Computing Nodes.

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Credit Card Fraud Detection Using Symbolic Aggregate Approximation

Credit Card Fraud Detection Using Symbolic Aggregate Approxi...

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Blockchain Computing and Applications (BCCA), international conference on

作者： Snit D. Zerea Belsabel T. Woldemichael Feven B. Araya Richard A. Ikuesan College of Technological Innovation Zayed University Abu Dhabi UAE

ISBN: (数字)9798350351538

ISBN: (纸本)9798350351545

In the current digital age, credit card fraud is a cybercrime that costs billions of dollars every year through fraudulent transactions. this study aims to identify fraudulent credit card transactions using Symbolic Aggregate Approximation (SAX) and anomaly discovery through motifs and discords. To do this, credit card data is used as time series data and then divided into equal-length segments. Each segment is then converted into a symbolic representation using a pre-defined threshold. A web-based interface is further developed to load a collection of credit card transaction data, based on the proposed SAX method. the preliminary results show that the proposed approach is capable of identifying fraudulent transactions within a set of transactions. By integrating this approach with other fraud detection mechanisms, a near real-time fraud detection process can be developed. this approach therefore provides a potential for context-independent fraud detection which is a major challenge in the fraud detection domain.

关键词： Machine learning algorithms Costs Aggregates Time series analysis Credit cards Information age Real-time systems Fraud pattern recognition Security

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Attack-Aware Capability Assessment Method Based on Specific Semantic Search

Attack-Aware Capability Assessment Method Based on Specific ...

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international conference on Electronic Engineering and Informatics (EEI)

作者： Daojuan Zhang Qi Zhao Yuman Wang Shu Li Zheng Tian Yizhen Sun State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute co. Ltd Beijing China Hunan Provincial Key Laboratory of Internet of Things in Electricity State Grid information & Communication Company of Hunan Electric Power Corporation Changsha China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

In this paper, we propose an attack perception capability assessment method based on the specific semantic search for the problem of attack perception capability assessment of security protection equipment for power monitoring systems. the method aims to improve the recognition and localization accuracy of common attacks by accurately correlating the output products of security equipment with the execution of attack test cases. the research employs an attack-specific semantic generation technique that combines attack attribute values with timestamps to form specific semantics for subsequent matching. pattern matching algorithm, specifically the BM (Boyer-Moore) algorithm, is further utilized to match the specific semantics with the output products of the security device Snort to achieve attack localization. Experimental results demonstrate that Snort exhibits high accuracy in perceiving the majority of attack types and exhibits an enhanced perception rate compared to certain baseline methods. In summary, the method proposed in this paper effectively improves the sensing ability of security equipment to attacks on the power monitoring system and provides a new idea for the performance evaluation of security protection equipment.

关键词： Location awareness Performance evaluation Accuracy Semantic search Sensors Security Protection Informatics Monitoring pattern matching

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Image Segmentation of Endoscopic Cataract Based on Semantic Segmentation 6

Image Segmentation of Endoscopic Cataract Based on Semantic ...

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2022 6th international conference on Machine Vision and Information Technology, CMVIT 2022

作者： Chen, Jingshi Ye, Haili Yin, Huayi Wang, Da-Han Li, Jianmin School of Computer and Information Engineering Xiamen University of Technology Xiamen361000 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361000 China

Medical imaging technology has become one of the indispensable computer-assisted intervention methods in clinical disease diagnosis and treatment, including identifying and locating lesion areas, detecting and segmenting tissue and organ lesions in different modalities. the wide application of medical image analysis in clinical examination and medical auxiliary diagnosis has effectively improved the diagnostic efficiency of doctors. In this paper, we propose a novel and effective model to achieve semantic segmentation of cataract datasets. this model uses the self-supervised method BYOL for parameter pre-training, which improves the model's ability to extract image consistency features. In addition, we have added a lightweight Coordinate attention mechanism to the backbone network to enable the model to independently learn the correlation between the channel and the space and enhance the ability of network feature expression. Experiments are conducted on the cataract endoscopy fine-grained segmentation data set, showing the effectiveness of the proposed method for segment the organs and surgical instruments in the cataract surgical microscope image, which demonstrates the accuracy and robustness of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Endoscopy

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6th international conference on Interactive Collaborative Robotics, ICR 2021

6th International Conference on Interactive Collaborative Ro...

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6th international conference on Interactive Collaborative Robotics, ICR 2021

ISBN: (纸本)9783030877248

the proceedings contain 19 papers. the special focus in this conference is on Interactive Collaborative Robotics. the topics include: the Efficiency Improvement of Robots Group Operation by Means of Workload Relocation;visual Data Processing Framework for a Skin-Based Human Detection;classification of Aerial Manipulation Systems and Algorithms for Controlling the Movement of Onboard Manipulator;‘MEOW’ – A Social Robot Platform for School;a Framework to Process Natural Speech in Service Robots Using a Combination of a Speech recognition System, Universal Dependencies Extracted by Means of the Stanford Parser and an Expert System;prioritizing Tasks Within a Robotic Transportation System for a Smart Hospital Environment;dedicated Payload Stabilization System in a Service Robot for Catering;collision Avoidance for Mobile Robots Using Proximity Sensors;analysis of Kinematic Diagrams and Design Solutions of Mobile Agricultural Robots;dronePort: Smart Drone Battery Management System;semantic Segmentation in the Task of Long-Term Visual Localization;algorithm for Radio Survey of the Cyber-Physical Systems Operating Areas Using Unmanned Aerial Vehicles;step Path Simulation for Quadruped Walking Robot;the Problem Statement of Cognitive Modeling in Social Robotic Systems;adaptive Event Triggered Control of Nonholonomic Mobile Robots;development of a Multi-sensor Emotional Response System for Social Robots;a Technique to Provide an Efficient System Recovery in the Fog- and Edge-Environments of Robotic Systems.

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CogIE: An Information Extraction Toolkit for Bridging Texts and CogNet 59

CogIE: An Information Extraction Toolkit for Bridging Texts ...

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Joint conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL) / 11th international Joint conference on Natural Language Processing (IJCNLP) / 6th Workshop on Representation Learning for NLP (RepL4NLP)

作者： Jin, Zhuoran Chen, Yubo Sui, Dianbo Wang, Chenhao Xue, Zhipeng Zhao, Jun Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

ISBN: (纸本)9781954085565

CogNet is a knowledge base that integrates three types of knowledge: linguistic knowledge, world knowledge and commonsense knowledge. In this paper, we propose an information extraction toolkit, called CogIE, which is a bridge connecting raw texts and CogNet. CogIE has three features: versatile, knowledge-grounded and extensible. First, CogIE is a versatile toolkit with a rich set of functional modules, including named entity recognition, entity typing, entity linking, relation extraction, event extraction and framesemantic parsing. Second, as a knowledgegrounded toolkit, CogIE can ground the extracted facts to CogNet and leverage different types of knowledge to enrich extracted results. third, for extensibility, owing to the design of three-tier architecture, CogIE is not only a plug-and-play toolkit for developers but also an extensible programming framework for researchers. We release an open-access online system (1) to visually extract information from texts. Source code, datasets and pre-trained models are publicly available at Github (2), with a short instruction video (3).

关键词： Semantics

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Brain Tumor Image Segmentation Based on Grouped Convolution 6

Brain Tumor Image Segmentation Based on Grouped Convolution

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2022 6th international conference on Machine Vision and Information Technology, CMVIT 2022

作者： Wu, Mingliang Ye, Hai-Li Wu, Yun Li, Jianmin School of Computer and Information Engineering Xiamen University of Technology Xiamen361000 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361000 China

the automatic segmentation of MRI multi-modal images of brain tumors is one of the important research contents of disease detection and analysis. Due to the heterogeneity of tumors, it is difficult to achieve efficient and accurate automatic segmentation of brain tumors. Traditional segmentation methods based on machine learning cannot handle complex scenes such as complex edges and overlapping categories. In clinical assisted diagnosis, it is of great significance to apply deep learning to two-dimensional natural image segmentation and three-dimensional medical image segmentation. In this paper, we propose a three-dimensional network model to achieve precise segmentation of brain tumors. the model adopts an encoder-decoder structure and replaces ordinary convolution with grouped convolution to reduce network parameters and improve network performance. the model improves the problem of information exchange between different groups through channel mixing. Experiments conducted on the BraTS (Brain Tumor Segmentation) 2018 challenge dataset prove that our network greatly reduces the computational cost while ensuring segmentation accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Convolution

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Fault Diagnosis in Power Transmission Line using Decision Tree and Random Forest Classifier

Fault Diagnosis in Power Transmission Line using Decision Tr...

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international conference on Condition Assessment Techniques in Electrical Systems (CATCON)

作者： Somesh Lahiri Chakravarty Abhijnan Maiti Abhinandan De Department of Electrical Engineering Indian Institute of Technology (BHU) Varanasi Uttar Pradesh India Department of Electrical Engineering Indian Institute of Engineering Science and Technology Shibpur West Bengal India

Fault diagnosis includes both detection of the type of fault that has occurred as well as locating the fault on a vast stretch of transmission network. there exists wide scope of application of Artificial Intelligence (AI) and Machine Learning (ML) based pattern recognition techniques in fault detection. the method proposed in this paper attempts to correctly identify the location of fault, type of fault and the faulty phase of transmission line in the form of an encoded 13-bit binary number. this number is then decoded and appropriate remedial measures are taken to isolate the fault. In this paper, Decision Tree (DT) and Random Forest based classification techniques have been proposed for fault diagnosis in a radial transmission line. Python platform has been used to simulate the proposed fault classifiers and implemented on a developed SIMULINK model of a 100 km long radial transmission line. the proposed method delivered fault recognition accuracy in the range of 95 – 100% under different fault scenarios.

关键词： Fault diagnosis Power transmission lines Software packages Fault detection Transmission line measurements pattern recognition Decision trees

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Research on Vulnerability Detection Techniques Based on Static Analysis and Program Slice

Research on Vulnerability Detection Techniques Based on Stat...

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international conference on Electronic Engineering and Informatics (EEI)

作者： Shubin Yuan Chenyu Liu Jianheng Shi Kai Zhang Wei Pu Xinyu Liu Liqun Yang CNOOC Information Technology Co. Ltd. Beijing Branch Beijing China CNOOC Information Technology Co. Ltd. Shenzhen China School of Cyber Science and Technology Beihang University Beijing China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Binary program vulnerabilities are particularly difficult to detect and understand using traditional methods due to their concealment and complexity. To address this challenge, we propose an innovative binary code vulnerability detection frame- work that combines program slicing techniques with a hybrid neural network model to achieve high-precision identification and classification of vulnerabilities in binary code. Specifically, we first generate semantically relevant binary slices using Program Dependence Graphs (PDG). these slices are then processed by a Bidirectional Long Short-Term Memory Network (BLSTM) and an attention mechanism to extract deep semantic features, while a Convolutional Neural Network (CNN) is employed to extract sequential features of the slices. Additionally, we introduce a Siamese network to evaluate the similarity between different code fragments, enhancing the model's ability to recognize unknown vulnerability patterns. Experimental results demonstrate that our approach performs exceptionally well in binary program vulnerability detection, significantly improving both the accuracy and efficiency of the detection process.

关键词： Attention mechanisms Accuracy Neural networks Semantics Binary codes Static analysis Feature extraction pattern recognition Convolutional neural networks Long short term memory

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