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A Novel Knowledge Distillation Framework with Intermediate Loss for Android Malware Detection

A Novel Knowledge Distillation Framework with Intermediate L...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Mengzhen Xia Zhicheng Xu Huijuan Zhu Computer Science and Communication Engineering Jiangsu University ZhenJiang China Mathematical Sciences Jiangsu University ZhenJiang China

With the popularity and self-contained functions of Android operating system, malicious attackers have targeted it primarily. Along with attackers are gradually skilled in avoid deep learning detectors, many in-depth researches on Android malware detection have been done. However, these works have complex models and enormous parameters. To settle this dilemma, we proposed a knowledge distillation architecture with intermediate loss to narrow the capability gap by promoting the student network emulate valuable hint knowledge from the intermediate layers of teacher network, based on Multi-Layer Perceptron (MLP). Besides, our framework utilizes static based features, namely permissions and vulnerabilities to effectively characterize applications and construct dataset. We evaluated our framework from various performance metrics and compared with other state-of-the-art deep neural networks. The experimen-tal results indicates that our framework own better performance and is perspective.

关键词： Knowledge engineering Deep learning Measurement computer science Operating systems Neural networks computer architecture

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Framework for Pre-processing, Feature $\boldsymbol{-}$ extraction, and Specialisation of Nominal data

Framework for Pre-processing, Feature $\boldsymbol{-}$ extra...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Krishna Aryal Meena Jha School of Engineering and Technology Central Queensland University Sydney Australia

There have always been many challenges to understand and use nominal data as effectively as numerical data. The judgment-based scaling of nominal data is not as efficient as a formula-based transformation of numerical data where mean, mode, median, and standard deviation are calculated. In this paper, we propose a novel framework to process nominal data by transforming nominal attributes into equivalent statistically significant values for any given data domain. The transformation process has three stages. The three stages consist of addition of domain knowledge, preservation of collected attributes/features, and evaluation of numerical values by identifying co-relations and similarities of the features. These three stages in the framework will help improve the nominal data processing and will address issues related to knowledge transformation such as adding existing knowledge to the data domain, data loss such as rejecting less relevant attributes, and feature transformation such as transforming the values while processing nominal data.

关键词： computer science Feature extraction data processing data engineering data mining Standards

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Covid-19 Tracker: A data visualization tool for time series data of pandemic in India

Covid-19 Tracker: A data visualization tool for time series ...

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8th ieee asia-pacific conference on computer science and data engineering (ieee csde)

作者： Talavia, Yaksh Singh, Priyanka Manoharan, Sathiamoorthy Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar Gujarat India Univ Auckland Auckland New Zealand

ISBN: (纸本)9781665495523

Covid-19 first originated in Wuhan, China, became a global pandemic and spread to many countries due to its high transmissibility. Digital technology could prove to be of great use in pandemic management with many strategies like screening for infection, tracking, contact tracing, and many more. Covid-19 tracker is one such application of digital technology. This helps in visualizing the pattern of public health through graphs, search results, and tables that can be easily understood by the user. It is built using HTML and JavaScript. Covid-19 Tracker uses data from certain authentic sources and helps to visualize the spread of Covid-19 throughout India. The main features incorporated in our dashboard are: total cases reported, graphs for daily trends, search functionality, state-wise comparative evaluations, and hotspot distribution map of the entire country. This dashboard provides a variety of user involvement possibilities and extracts useful data in a simple and easy-to-understand way.

关键词： Covid-19 Dashboard data visualization

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Combining First-Order and Texture Features with Convolutional Neural Network for Classification of Residential Area Density in Aerial Images

Combining First-Order and Texture Features with Convolutiona...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Adrian S. Remigio Jonel Hong Jeffrey Aborot Computer Software Division DOST-ASTI Quezon city Philippines

With the increasing availability of aerial images, computational methods have been used to efficiently extract information and apply analyses for various applications. The goal of this study is to improve residential area density classification in aerial images by combining first-order and texture features with deep features from convolutional neural network (CNN). Gray level co-occurence matrix (GLCM), gray level run length matrix (GLRLM), gray level size zone matrix (GLSZM), gray level dependence matrix (GLDM), and neighbouring gray tone difference matrix (NGTDM) were calculated from the images as texture features. Classical machine learning (CML) and multilayer perceptron (MLP) models based on handcrafted features alone were also evaluated in terms of residential area density classification. Our results from NPWU-RESISC45 and AID dataset experiments have showed that our proposed combined deep and handcrafted features outperformed popular and state-of-the-art CNN models that are based on deep features alone. Thus, FO and texture features provide additional and non-overlapping information that can support deep features for a more accurate residential area density prediction.

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Predicting Phosphoglycerylation with Transformer Features and Deep Learning

Predicting Phosphoglycerylation with Transformer Features an...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Abel Chandra Alok Sharma Iman Dehzangi Tatsuhiko Tsunoda Abdul Sattar Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama Japan Department of Computer Science Rutgers University Camden NJ USA Center for Computational and Integrative Biology Rutgers University Camden USA Department of Biological Sciences Laboratory for Medical Science Mathematics School of Science The University of Tokyo Tokyo Japan Department of Computational Biology and Medical Sciences Laboratory for Medical Science Mathematics Graduate School of Frontier Sciences The University of Tokyo Tokyo Japan

Understanding protein sequences can advance treatments for various diseases. However experimentally obtaining this information is laborious, time-consuming, and expensive. Traditional machine learning techniques, like support vector machine, random forest and logistic regression, offer potential to fast-track this process but are sometimes limited by data complexity. Deep learning algorithms, in contrast, tend to yield higher performance. In this study, we employed a convolutional neural network to predict protein phosphoglycerylation. Features were extracted from pre-trained transformer models and compared with conventional features, such as evolutionary information and physicochemical/biochemical properties. Our results indicate significant performance improvements across all feature types, with the combination of transformerbased features and the convolutional neural network being especially effective. This methodology holds potential for other protein property prediction tasks. Our software and datasets used in this study are publicly available at https://***/abelavit/***.

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Design data Literacy - Impact of data Literacy in Virtual Product Development

Design Data Literacy - Impact of Data Literacy in Virtual Pr...

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8th ieee asia-pacific conference on computer science and data engineering (ieee csde)

作者： Giese, Tim G. Anderl, Reiner Tech Univ Darmstadt Dept Comp Integrated Design DiK Darmstadt Germany

ISBN: (纸本)9781665495523

In today's information age, due to the massive amounts of data generated, an understanding about the interaction with data is becoming increasingly significant. While in virtual product development of technical components, a lot of research regarding the collection and storage of data exists, we identified missing key competencies for the interaction with design data. In the collaborative and multidisciplinary design process of technical products, designers are missing knowledge about: the origin of a data piece, an understanding about the reliability of a source, as well as transparency to whom their data is distributed and how it is further used. Approaching this problem, we identified the necessity of providing designers with relevant skills and information. Therefore, we developed a novel concept for the introduction and application of data Literacy to product development in order to create transparency and awareness for a responsible generation and use of design data. The concept is called Design data Literacy and consists of the 3 major pillars: data Provenance, data Tracking and Tracing, as well as data Sovereignty. To validate the concept, we conducted a prototypical implementation and evaluation. We came to the conclusion that the presented concept can be used effectively to introduce and apply data Literacy to product development.

关键词： data Literacy data Sovereignty data Tracking design product development Design data Literacy

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Comparative Study of Object Detection Models for Safety in Autonomous Vehicles, Homes, and Roads Using IoT Devices

Comparative Study of Object Detection Models for Safety in A...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Mehedi Hasan Kazi Ahnaf Alavee Syed Ziaul Bin Bashar Md. Tahmid Rahman Muhammad Iqbal Hossain Md. Golam Rabiul Alam Department of Computer Science and Engineering BRAC University Dhaka Bangladesh

Object detection plays a pivotal role in enhancing security, surveillance, and automation. It enables timely threat identification, streamlines traffic management, and facilitates efficient resource allocation. By automating object recognition, it creates value through improved safety, productivity, and resource optimization in various domains, from smart cities to industrial settings. This paper explores modern object detection methods to develop a Real-Time Responsive CCTV Camera Model. As we transition to a 5G-connected world, smart devices are poised to manage our daily security. We focus on transforming conventional CCTV cameras into responsive, internet-connected security guards. By evaluating methods like HOG, Viola-Jones, R-CNN, SSD, and YOLO, we aim to select the most efficient algorithm. We found Yolov8 perform best among all the models based on accuracy 99.8% and FPS 40. Our research strives to create cost-effective, intelligent security systems, paving the way for automated alerts and a digitally secured future.

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Using Novel Hybrid Convolutional Neural Network for Dysarthria Diagnosis

Using Novel Hybrid Convolutional Neural Network for Dysarthr...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Navod Neranjan Thilakarathne Kasorn Galajit Jessada Karnjana Win Pa Pa Hayati Yassin Faculty of Integrated Technologies Universiti Brunei Darussalam Brunei Darusalam NECTEC National Science and Technology Development Agency Pathum Thani Thailand University of Computer Studies Yangon Myanmar

Dysarthria is a motor speech disorder characterized by articulation and phonation difficulties resulting from speech muscle weakness, paralysis, or incoordination. A precise and timely diagnosis of dysarthria is essential for effective treatment and management of the condition, as it may deteriorate over time or be a precursor to a much more serious disease. On the other hand, this is becoming a severe problem in recent times owing to the rising ageing population and the prevalence of neurological disorders among such people. Hence early detection of dysarthria is deemed essential for the timely management of the disease. In recent years, Artificial Intelligence (AI) applications have shown promising results in various audio processing tasks and incorporated into pathological voice analysis for disease diagnosis. The majority of previous studies on dysarthria detection employed Machine Learning (ML) and Deep Learning (DL) models as the disease classification models. In light of this, this study presents a novel hybrid approach for classifying dysarthria based on audio data using Convolutional Neural Network (CNN) and Support Vector Machine (SVM). According to the experimental results, the proposed classification schema achieves an accuracy of 98.25 % compared to previous research work. Overall, our proposed method aims to automate the classification process, enabling faster and more reliable diagnoses.

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Devised Deephashing Models Through Self-Supervised Learning for Image Retrieval

Devised Deephashing Models Through Self-Supervised Learning ...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Sooin Kim Youngjin Jeon Won-gyum Kim Doosung Hwang Donghoon Kim Dept. of Software Science Dankook University Yongin South Korea Research Center AiDeep Seoul South Korea Dept. of Computer Science Arkansas State University Jonesboro AR USA

Many image database systems utilize content-based retrieval technology using perceptual hashing to facilitate image comparison and retrieval. However, conventional hashing functions generate different hash codes for the same image domain even with minor changes, making accurate searches in the image database impossible. To overcome these limitations, a method has been devised to enable the search for images similar to queries synthesizing visual feature information, including texture, shape, and color of images. This paper proposes deephashing models that incorporate both variational autoencoders and vision trans-formers, capable of capturing semantic patterns and preserving the semantic similarity between data points. To validate this proposal, we conduct a comparative evaluation by comparing its performance with widely-used supervised learning-based models. The experimental analysis shows that the proposed approach generates compressed and discriminative hash codes with fixed-length binary representations that maintain semantic similarity and enable the search even for similar images. When evaluating the performance of our proposed model in comparison to the other studies, noticeable improvements are observed across the selected metrics in deephashing based on self-supervised learning.

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Research on satellite Modeling and Real-time Simulation Monitoring Based on MBSE 2

Research on satellite Modeling and Real-time Simulation Moni...

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2nd asia-pacific conference on Communications Technology and computer science, ACCTCS 2022

作者： Huang, Yuqian Hu, Baokun Diao, Wanwan Xia, Zhenghua School of Information Science and Technology Hangzhou Normal University Hangzhou China

ISBN: (纸本)9781665400343

The problems existing in the text-based systems engineering method include heavy workload for change management, poor readability of design documents, low reuse rate, inability to simulate and so on. This paper firstly draws on the Magic Grid methodology, which is suitable for complex product development, and comprehensively describes complex systems from multiple views, forming a set of model-based systems engineering modeling methods(MBSE)with strong traceability and logicality;Secondly, the method is applied to the power subsystem, and the models under different views are created using the system modeling language SysML (systems modeling language);Finally, based on the model, real-time simulation monitoring is carried out for the behavior activity graph, and the model simulation process can be traced back. It is verified that the modeling method can better design the system and meet the requirements of complex system design.e system and meet the requirements of complex system design. © 2022 ieee.

关键词： Modeling languages

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