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Articles 15271 - 15300 of 63040
Full-Text Articles in Computer Sciences
Long-Term Traffic Flow Estimation: A Hybrid Approach Using Location-Basedtraffic Characteristic, Tuğberk Ayar, Ferhat Atli̇nar, Mehmet Amaç Güvensan, Hafi̇za İrem Türkmen
Long-Term Traffic Flow Estimation: A Hybrid Approach Using Location-Basedtraffic Characteristic, Tuğberk Ayar, Ferhat Atli̇nar, Mehmet Amaç Güvensan, Hafi̇za İrem Türkmen
Turkish Journal of Electrical Engineering and Computer Sciences
Traffic speed estimation plays a key role in various situations, ranging from individual's trip planning to urban traffic management. Despite many studies on short-term prediction, there is only a limited number of studies focusing on long-term prediction and only a couple of them does go beyond 24 h. On the contrary, this study presents a novel hybrid architecture using location-based traffic characteristic for traffic speed estimation up to 7 days. In this architecture, the introduced mean filtering estimation (MFE) model and long short-term memory (LSTM) neural network are jointly utilized for minimizing the error for traffic flow estimation. Both MFE …
Privacy Preserving Scheme For Document Similarity Detection, Ayad Abdulsada, Salah Al-Darraji, Dhafer Honi
Privacy Preserving Scheme For Document Similarity Detection, Ayad Abdulsada, Salah Al-Darraji, Dhafer Honi
Turkish Journal of Electrical Engineering and Computer Sciences
The problem of detecting similar documents plays an essential role for many real-world applications, such as copyright protection and plagiarism detection. To protect data privacy, the new version of such a problem becomes more challenging, where the matched documents are distributed among two or more parties and their privacy should be preserved. In this paper, we propose new privacy-preserving document similarity detection schemes by utilizing the locality-sensitive hashing technique, which can handle the misspelled mistakes. Furthermore, the keywords' occurrences of a given document are integrated into its underlying representation to support a better ranking for the returned results. We introduced …
Determining Allowable Parametric Uncertainty In An Uncommon Quadrotormodel For Closed Loop Stability, Mehmet Baskin, Mehmet Kemal Leblebi̇ci̇oğlu
Determining Allowable Parametric Uncertainty In An Uncommon Quadrotormodel For Closed Loop Stability, Mehmet Baskin, Mehmet Kemal Leblebi̇ci̇oğlu
Turkish Journal of Electrical Engineering and Computer Sciences
In this article, control oriented uncertainty modeling of an uncommon quadrotor in hover is discussed. This quadrotor consists of two counter-rotating big rotors on longitudinal axis and two counter-rotating small tilt rotors on lateral axis. Firstly, approximate linear model of this vehicle around hover is obtained by using Newton--Euler formulation. Secondly, specific uncertainty is assigned to each parameter. Resulting uncertain model is converted into a linear fractional transformation framework for robustness analysis. Next, the most critical uncertain parameters in terms of robust stability in a proposed quadrotor model are investigated using $ \mu $ sensitivities. Finally, skewed-$ \mu $ analysis …
Scattering Analyses Of Arbitrary Roughness From 2-D Perfectly Conductiveperiodic Surfaces With Moments Method, Yunus Emre Yamaç, Ahmet Kizilay
Scattering Analyses Of Arbitrary Roughness From 2-D Perfectly Conductiveperiodic Surfaces With Moments Method, Yunus Emre Yamaç, Ahmet Kizilay
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a periodic-MoM-based code with high accuracy performance is developed to calculate electromagnetic scattering from a periodic conductive surface in two dimensions with any degree of roughness. Firstly, the existing separate methods in the literature are reviewed step by step to compose a periodic-MoM solution for 2-D periodic surfaces. Then, the dynamic selection of optimal formulation of the periodic-MoM solutions created using these existing methods is evaluated to reduce solution time and obtain high accuracy. In this study, the performance parameters of the existing methods are investigated in solving a real 3-D scattering problem by a periodic-MoM for …
On An Electrostatic Micropump With A Rigorous Mathematical Model, İbrahi̇m Efe, Fati̇h Di̇kmen, Yury Tuchkin
On An Electrostatic Micropump With A Rigorous Mathematical Model, İbrahi̇m Efe, Fati̇h Di̇kmen, Yury Tuchkin
Turkish Journal of Electrical Engineering and Computer Sciences
The novel electrostatic micropump model for applications such as in biomedical drug delivery is presented. The geometrical arrangement of the coupling rigid electrodes lets us exploit our mathematically rigorous boundary integral equation formulation and its solution. Thus, the charge densities involving the fringe effects on the plates are obtained by means of analytical regularization method (ARM) constructed for annular strips earlier. The efficiency of the constructed method is demonstrated with respect to the direct integral equation solvers implemented via the entire domain Galerkin method and point matching. The main physical characteristics of the suggested system and their deviation from that …
A New Classification Method Using Soft Decision-Making Based On An Aggregation Operator Of Fuzzy Parameterized Fuzzy Soft Matrices, Samet Memi̇ş, Serdar Engi̇noğlu, Uğur Erkan
A New Classification Method Using Soft Decision-Making Based On An Aggregation Operator Of Fuzzy Parameterized Fuzzy Soft Matrices, Samet Memi̇ş, Serdar Engi̇noğlu, Uğur Erkan
Turkish Journal of Electrical Engineering and Computer Sciences
Recently, a precise and stable machine learning algorithm, i.e. eigenvalue classification method (EigenClass), has been developed by using the concept of generalised eigenvalues in contrast to common approaches, such as k-nearest neighbours, support vector machines, and decision trees. In this paper, we offer a new classification algorithm called fuzzy parameterized fuzzy soft aggregation classifier (FPFS-AC) to combine the modelling ability of soft decision-making (SDM) and classification success of generalised eigenvalues. FPFS-AC constructs a decision matrix by employing the similarity measures of fuzzy parameterized fuzzy soft matrices fpfs -matrices) and a generalised eigenvalue-based similarity measure. Then, it applies an SDM method …
Defect Classification Of Railway Fasteners Using Image Preprocessing And Alightweight Convolutional Neural Network, İlhan Aydin, Mehmet Sevi̇, Mehmet Umut Salur, Erhan Akin
Defect Classification Of Railway Fasteners Using Image Preprocessing And Alightweight Convolutional Neural Network, İlhan Aydin, Mehmet Sevi̇, Mehmet Umut Salur, Erhan Akin
Turkish Journal of Electrical Engineering and Computer Sciences
Railway fasteners are used to securely fix rails to sleeper blocks. Partial wear or complete loss of these components can lead to serious accidents and cause train derailments. To ensure the safety of railway transportation, computer vision and pattern recognition-based methods are increasingly used to inspect railway infrastructure. In particular, it has become an important task to detect defects in railway tracks. This is challenging since rail track images are acquired using a measuring train in varying environmental conditions, at different times of day and in poor lighting conditions, and the resulting images often have low contrast. In this study, …
The Analysis And Optimization Of Cnn Hyperparameters With Fuzzy Tree Modelfor Image Classification, Kübra Uyar, Şaki̇r Taşdemi̇r, İlker Ali̇ Özkan
The Analysis And Optimization Of Cnn Hyperparameters With Fuzzy Tree Modelfor Image Classification, Kübra Uyar, Şaki̇r Taşdemi̇r, İlker Ali̇ Özkan
Turkish Journal of Electrical Engineering and Computer Sciences
The meaningful performance of convolutional neural network (CNN) has enabled the solution of various state-of-the-art problems. Although CNNs achieve satisfactory results in computer-vision problems, they still have some difficulties. As the designed CNN models are deepened to achieve much better accuracy, computational cost and complexity increase. It is significant to train CNNs with suitable topology and training hyperparameters that include initial learning rate, minibatch size, epoch number, filter size, number of filters, etc. because the initialization of hyperparameters affects classification results. On the other hand, it is not possible to make a definite inference for the hyperparameter initialization and there …
Tara: Temperature Aware Online Dynamic Resource Allocation Scheme For Energyoptimization In Cloud Data Centres, Narayanamoorthi Thilagavathi, Arockiasamy John Prakash, Sridhar Sridevi, Vaidyanathan Rhymend Uthariaraj
Tara: Temperature Aware Online Dynamic Resource Allocation Scheme For Energyoptimization In Cloud Data Centres, Narayanamoorthi Thilagavathi, Arockiasamy John Prakash, Sridhar Sridevi, Vaidyanathan Rhymend Uthariaraj
Turkish Journal of Electrical Engineering and Computer Sciences
Cloud data centres, which are characteristic of dynamic workloads, if not optimized for energy consumption, may lead to increased heat dissipation and eventually impact the environment adversely. Consequently, optimizing the usage of energy has become a hard requirement in today's cloud data centres wherein the major part of energy consumption is mostly attributed to computing and cooling systems. Motivated by which this paper proposes an online algorithm for dynamic resource allocation, namely, temperature aware online dynamic resource allocation algorithm (TARA). TARA demonstrates a novel algorithm design to adapt dynamic resource allocation based on the temperature of a data centre using …
Revisiting Neuron Coverage Metrics And Quality Of Deep Neural Networks, Zhou Yang, Jieke Shi, Muhammad Hilmi Asyrofi, David Lo
Revisiting Neuron Coverage Metrics And Quality Of Deep Neural Networks, Zhou Yang, Jieke Shi, Muhammad Hilmi Asyrofi, David Lo
Research Collection School Of Computing and Information Systems
Deep neural networks (DNN) have been widely applied in modern life, including critical domains like autonomous driving, making it essential to ensure the reliability and robustness of DNN-powered systems. As an analogy to code coverage metrics for testing conventional software, researchers have proposed neuron coverage metrics and coverage-driven methods to generate DNN test cases. However, Yan et al. doubt the usefulness of existing coverage criteria in DNN testing. They show that a coverage-driven method is less effective than a gradient-based method in terms of both uncovering defects and improving model robustness. In this paper, we conduct a replication study of …
Androevolve: Automated Android Api Update With Data Flow Analysis And Variable Denormalization, Stefanus A. Haryono, Ferdian Thung, David Lo, Lingxiao Jiang, Julia Lawall, Hong Jin Kang, Lucas Serrano, Gilles Muller
Androevolve: Automated Android Api Update With Data Flow Analysis And Variable Denormalization, Stefanus A. Haryono, Ferdian Thung, David Lo, Lingxiao Jiang, Julia Lawall, Hong Jin Kang, Lucas Serrano, Gilles Muller
Research Collection School Of Computing and Information Systems
The Android operating system is frequently updated, with each version bringing a new set of APIs. New versions may involve API deprecation; Android apps using deprecated APIs need to be updated to ensure the apps’ compatibility with old and new Android versions. Updating deprecated APIs is a time-consuming endeavor. Hence, automating the updates of Android APIs can be beneficial for developers. CocciEvolve is the state-of-the-art approach for this automation. However, it has several limitations, including its inability to resolve out-of-method variables and the low code readability of its updates due to the addition of temporary variables. In an attempt to …
Interpretable Knowledge Tracing: Simple And Efficient Student Modeling With Causal Relations, Sein Minn, Jill-Jênn Vie, Koh Takeuchi, Feida Zhu
Interpretable Knowledge Tracing: Simple And Efficient Student Modeling With Causal Relations, Sein Minn, Jill-Jênn Vie, Koh Takeuchi, Feida Zhu
Research Collection School Of Computing and Information Systems
Intelligent Tutoring Systems have become critically important in future learning environments. Knowledge Tracing (KT) is a crucial part of that system. It is about inferring the skill mastery of students and predicting their performance to adjust the curriculum accordingly. Deep Learning based models like Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) have shown significant predictive performance compared with traditional models like Bayesian Knowledge Tracing (BKT) and Performance Factors Analysis (PFA). However, it is difficult to extract psychologically meaningful explanations from the tens of thousands of parameters in neural networks, that would relate to cognitive theory. There are …
Coordinated Delivery To Shopping Malls With Limited Docking Capacity, Ruidian Song, Hoong Chuin Lau, Xue Luo, Lei Zhao
Coordinated Delivery To Shopping Malls With Limited Docking Capacity, Ruidian Song, Hoong Chuin Lau, Xue Luo, Lei Zhao
Research Collection School Of Computing and Information Systems
Shopping malls are densely located in major cities such as Singapore and Hong Kong. Tenants in these shopping malls generate a large number of freight orders to their contracted logistics service providers, who independently plan their own delivery schedules. These uncoordinated deliveries and limited docking capacity jointly cause congestion at the shopping malls. A delivery coordination platform centrally plans the vehicle routes for the logistics service providers and simultaneously schedules the dock time slots at the shopping malls for the delivery orders. Vehicle routing and dock scheduling decisions need to be made jointly against the backdrop of travel time and …
The Relationship Between Parenting Practices And Cyberbullying Perpetration: The Mediating Role Of Moral Beliefs, Jaeyong Choi, Seungmug (Zech) Lee, Layne Dittmann
The Relationship Between Parenting Practices And Cyberbullying Perpetration: The Mediating Role Of Moral Beliefs, Jaeyong Choi, Seungmug (Zech) Lee, Layne Dittmann
International Journal of Cybersecurity Intelligence & Cybercrime
Criminologists and psychologists have long recognized that parenting practices can affect childhood outcomes and the development of moral beliefs in children. Another body of literature provides evidence that morality is a key cause of antisocial behavior. Yet, a noticeable gap in this line of work has been testing the mediation effects of parenting practices on cyberbullying via moral beliefs. Using a sample of South Korean adolescents, we tested whether moral beliefs mediate the relationships between parenting practices and cyberbullying perpetration. Results show that parental supervision and excessive parenting can influence cyberbullying perpetration and that the impact of parenting practices is …
Social Construction Of Internet Fraud As Innovation Among Youths In Nigeria, Austin Ayodele Mr., Jonathan Kehinde Oyedeji, Huthman Olamide Badmos
Social Construction Of Internet Fraud As Innovation Among Youths In Nigeria, Austin Ayodele Mr., Jonathan Kehinde Oyedeji, Huthman Olamide Badmos
International Journal of Cybersecurity Intelligence & Cybercrime
The proliferation of internet technologies has shaped interactions in contemporary society. Despite the pivotal importance of the internet to the global economy, it has several negative consequences such as internet fraud. This study examined the perception that young adults in Nigeria hold about internet fraud as an innovative means to economic survival rather than as a criminal enterprise. Robert Merton’s Anomie/Strain Theory (AST) was adopted as the theoretical thrust of the study. Adopting a qualitative data collection method, 15 participants were selected using the non-probabilistic purposive and snowballing techniques while opinions were sampled through in-depth interviews in different locations within …
Cybersecurity Risk In U.S. Critical Infrastructure: An Analysis Of Publicly Available U.S. Government Alerts And Advisories, Zachary Lanz
Cybersecurity Risk In U.S. Critical Infrastructure: An Analysis Of Publicly Available U.S. Government Alerts And Advisories, Zachary Lanz
International Journal of Cybersecurity Intelligence & Cybercrime
As threat actor operations become increasingly sophisticated and emphasize the targeting of critical infrastructure and services, the need for cybersecurity information sharing will continue to grow. Escalating demand for cyber threat intelligence and information sharing across the cybersecurity community has resulted in the need to better understand the information produced by reputable sources such as U.S. CISA Alerts and ICS-CERT advisories. The text analysis program, Profiler Plus, is used to extract information from 1,574 U.S. government alerts and advisories to develop visualizations and generate enhanced insights into different cyber threat actor types, the tactics which can be used for cyber …
Using Modified Technology Acceptance Model To Evaluate The Adoption Of A Proposed Iot-Based Indoor Disaster Management Software Tool By Rescue Workers, Preetinder Singh Brar, Babar Shah, Jaiteg Singh, Farman Ali, Daehan Kwak
Using Modified Technology Acceptance Model To Evaluate The Adoption Of A Proposed Iot-Based Indoor Disaster Management Software Tool By Rescue Workers, Preetinder Singh Brar, Babar Shah, Jaiteg Singh, Farman Ali, Daehan Kwak
All Works
Advancements in IoT technology have been instrumental in the design and implementation of various ubiquitous services. One such design activity was carried out by the authors of this paper, who proposed a novel cloud-centric IoT-based disaster management framework and developed a multimedia-based prototype that employed real-time geographical maps. The multimediabased system can provide vital information on maps that can improve the planning and execution of evacuation tasks. This study was intended to explore the acceptance of the proposed technology by the specific set of users that could potentially lead to its adoption by rescue agencies for carrying out indoor rescue …
The Role Of 3d Ct Imaging In The Accurate Diagnosis Of Lung Function In Coronavirus Patients, Ibrahim Shawky Farahat, Ahmed Sharafeldeen, Mohamed Elsharkawy, Ahmed Soliman, Ali Mahmoud, Mohammed Ghazal, Fatma Taher, Maha Bilal, Ahmed Abdel Khalek Abdel Razek, Waleed Aladrousy, Samir Elmougy, Ahmed Elsaid Tolba, Moumen El-Melegy, Ayman El-Baz
The Role Of 3d Ct Imaging In The Accurate Diagnosis Of Lung Function In Coronavirus Patients, Ibrahim Shawky Farahat, Ahmed Sharafeldeen, Mohamed Elsharkawy, Ahmed Soliman, Ali Mahmoud, Mohammed Ghazal, Fatma Taher, Maha Bilal, Ahmed Abdel Khalek Abdel Razek, Waleed Aladrousy, Samir Elmougy, Ahmed Elsaid Tolba, Moumen El-Melegy, Ayman El-Baz
All Works
Early grading of coronavirus disease 2019 (COVID-19), as well as ventilator support machines, are prime ways to help the world fight this virus and reduce the mortality rate. To reduce the burden on physicians, we developed an automatic Computer-Aided Diagnostic (CAD) system to grade COVID-19 from Computed Tomography (CT) images. This system segments the lung region from chest CT scans using an unsupervised approach based on an appearance model, followed by 3D rotation invariant Markov–Gibbs Random Field (MGRF)-based morphological constraints. This system analyzes the segmented lung and generates precise, analytical imaging markers by estimating the MGRF-based analytical potentials. Three Gibbs …
Improving User Experience And Communication Of Digitally Enhanced Advanced Services (Deas) Offers In Manufacturing Sector, Mohammed Soheeb Khan, Vassilis Charissis, Phil Godsiff, Zena Wood, Jannat F. Falah, Salsabeel F.M. Alfalah, David K. Harrison
Improving User Experience And Communication Of Digitally Enhanced Advanced Services (Deas) Offers In Manufacturing Sector, Mohammed Soheeb Khan, Vassilis Charissis, Phil Godsiff, Zena Wood, Jannat F. Falah, Salsabeel F.M. Alfalah, David K. Harrison
All Works
Digitally enhanced advanced services (DEAS), offered currently by various industries, could be a challenging concept to comprehend for potential clients. This could result in limited interest in adopting (DEAS) or even understanding its true value with significant financial implications for the providers. Innovative ways to present and simplify complex information are provided by serious games and gamification, which simplify and engage users with intricate information in an enjoyable manner. Despite the use of serious games and gamification in other areas, only a few examples have been documented to convey servitization offers. This research explores the design and development of a …
Performance Evaluation Of Different Rasberry Pi Models As Mqtt Servers And Clients, Trent N. Ford, Eric Gamess, Christopher Ogden
Performance Evaluation Of Different Rasberry Pi Models As Mqtt Servers And Clients, Trent N. Ford, Eric Gamess, Christopher Ogden
Research, Publications & Creative Work
Performance analysis for devices in Internet of Things (IoT) environments is an important consideration, especially with their increasing integration in technological solutions, worldwide. The Single Board Computers (SBCs) of the Raspberry Pi Foundation have been widely accepted by the community, and hence, they have been incorporated in numerous IoT projects. To ease their integration, it is essential to assess their network performance. In this paper, we made an empirical performance evaluation of one of the most popular network protocols for IoT environments, named the Message Queuing Telemetry Transport (MQTT) protocol, on Raspberry Pi. To do so, we set up two …
Human-Algorithm Collaboration Works Best If Humans Lead (Because It Is Fair!), David De Cremer, Jack Mcguire
Human-Algorithm Collaboration Works Best If Humans Lead (Because It Is Fair!), David De Cremer, Jack Mcguire
Research Collection Lee Kong Chian School Of Business
Autonomous algorithms are increasingly being used by organizations to reach ever increasing heights of organizational efficiency. The emerging business model of today therefore appears to be one where autonomous algorithms are gradually expanding their occupation into becoming a leading decision-maker, and humans by default become increasingly more subordinate to such decisions. We address the question of whether this business perspective is consistent with the sort of collaboration employees want to have with algorithms at work. We explored this question by investigating in what way humans preferred to collaborate with algorithms when making decisions. Using two experimental studies (Study 1, n …
Deep Learning For Anomaly Detection: A Review, Guansong Pang, Chunhua Shen, Longbing Cao, Anton Van Den Hengel
Deep Learning For Anomaly Detection: A Review, Guansong Pang, Chunhua Shen, Longbing Cao, Anton Van Den Hengel
Research Collection School Of Computing and Information Systems
Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection, has emerged as a critical direction. This article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages, and disadvantages and discuss how they address the aforementioned challenges. …
Efficient Certificateless Multi-Copy Integrity Auditing Scheme Supporting Data Dynamics, Lei Zhou, Anmin Fu, Guomin Yang, Huaqun Wang, Yuqing Zhang
Efficient Certificateless Multi-Copy Integrity Auditing Scheme Supporting Data Dynamics, Lei Zhou, Anmin Fu, Guomin Yang, Huaqun Wang, Yuqing Zhang
Research Collection School Of Computing and Information Systems
To improve data availability and durability, cloud users would like to store multiple copies of their original files at servers. The multi-copy auditing technique is proposed to provide users with the assurance that multiple copies are actually stored in the cloud. However, most multi-replica solutions rely on Public Key Infrastructure (PKI), which entails massive overhead of certificate computation and management. In this article, we propose an efficient multi-copy dynamic integrity auditing scheme by employing certificateless signatures (named MDSS), which gets rid of expensive certificate management overhead and avoids the key escrow problem in identity-based signatures. Specifically, we improve the classic …
Mg2vec: Learning Relationship-Preserving Heterogeneous Graph Representations Via Metagraph Embedding, Wentao Zhang, Yuan Fang, Zemin Liu, Min Wu, Xinming Zhang
Mg2vec: Learning Relationship-Preserving Heterogeneous Graph Representations Via Metagraph Embedding, Wentao Zhang, Yuan Fang, Zemin Liu, Min Wu, Xinming Zhang
Research Collection School Of Computing and Information Systems
Given that heterogeneous information networks (HIN) encompass nodes and edges belonging to different semantic types, they can model complex data in real-world scenarios. Thus, HIN embedding has received increasing attention, which aims to learn node representations in a low-dimensional space, in order to preserve the structural and semantic information on the HIN. In this regard, metagraphs, which model common and recurring patterns on HINs, emerge as a powerful tool to capture semantic-rich and often latent relationships on HINs. Although metagraphs have been employed to address several specific data mining tasks, they have not been thoroughly explored for the more general …
Meta-Transfer Learning Through Hard Tasks, Qianru Sun, Yaoyao Liu, Zhaozheng Chen, Chua Tat-Seng, Schiele Bernt
Meta-Transfer Learning Through Hard Tasks, Qianru Sun, Yaoyao Liu, Zhaozheng Chen, Chua Tat-Seng, Schiele Bernt
Research Collection School Of Computing and Information Systems
Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural networks (DNNs) tend to overfit using a few samples only, typical meta-learning models use shallow neural networks, thus limiting its effectiveness. In order to achieve top performance, some recent works tried to use the DNNs pre-trained on large-scale datasets but mostly in straight-forward manners, e.g., (1) taking their …
Sdac: A Slow-Aging Solution For Android Malware Detection Using Semantic Distance Based Api Clustering, Jiayun Xu, Yingjiu Li, Robert H. Deng, Xu Ke
Sdac: A Slow-Aging Solution For Android Malware Detection Using Semantic Distance Based Api Clustering, Jiayun Xu, Yingjiu Li, Robert H. Deng, Xu Ke
Research Collection School Of Computing and Information Systems
A novel slow-aging solution named SDAC is proposed to address the model aging problem in Android malware detection, which is due to the lack of adapting to the changes in Android specifications during malware detection. Different from periodic retraining of detection models in existing solutions, SDAC evolves effectively by evaluating new APIs' contributions to malware detection according to existing API's contributions. In SDAC, the contributions of APIs are evaluated by their contexts in the API call sequences extracted from Android apps. A neural network is applied on the sequences to assign APIs to vectors, among which the differences of API …
Update Recovery Attacks On Encrypted Database Within Two Updates Using Range Queries Leakage, Jianting Ning, Geong Sen Poh, Xinyi Huang, Robert H. Deng, Shuwei Cao, Ee-Chien Chang
Update Recovery Attacks On Encrypted Database Within Two Updates Using Range Queries Leakage, Jianting Ning, Geong Sen Poh, Xinyi Huang, Robert H. Deng, Shuwei Cao, Ee-Chien Chang
Research Collection School Of Computing and Information Systems
Recently, reconstruction attacks on static encrypted database supporting range queries have been proposed. However, attacks on encrypted database within two updates in the similar setting have not been studied extensively. As far as we know, the only work is the update recovery attack presented by Grubbs et al. (CCS 2018). Following their seminal work, we present new update recovery attacks for dense dataset (i.e. at least one record corresponding to each value in the range), which enable a deeper understanding of the impact caused by leakages due to updates on dynamic encrypted database. Our first attack aims at recovering the …
Mask-Guided Deformation Adaptive Network For Human Parsing, Aihua Mao, Yuan Liang, Jianbo Jiao, Yongtuo Liu, Shengfeng He
Mask-Guided Deformation Adaptive Network For Human Parsing, Aihua Mao, Yuan Liang, Jianbo Jiao, Yongtuo Liu, Shengfeng He
Research Collection School Of Computing and Information Systems
Due to the challenges of densely compacted body parts, nonrigid clothing items, and severe overlap in crowd scenes, human parsing needs to focus more on multilevel feature representations compared to general scene parsing tasks. Based on this observation, we propose to introduce the auxiliary task of human mask and edge detection to facilitate human parsing. Different from human parsing, which exploits the discriminative features of each category, human mask and edge detection emphasizes the boundaries of semantic parsing regions and the difference between foreground humans and background clutter, which benefits the parsing predictions of crowd scenes and small human parts. …
Riconv++: Effective Rotation Invariant Convolutions For 3d Point Clouds Deep Learning, Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung
Riconv++: Effective Rotation Invariant Convolutions For 3d Point Clouds Deep Learning, Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung
Research Collection School Of Computing and Information Systems
3D point clouds deep learning is a promising field of research that allows a neural network to learn features of point clouds directly, making it a robust tool for solving 3D scene understanding tasks. While recent works show that point cloud convolutions can be invariant to translation and point permutation, investigations of the rotation invariance property for point cloud convolution has been so far scarce. Some existing methods perform point cloud convolutions with rotation-invariant features, existing methods generally do not perform as well as translation-invariant only counterpart. In this work, we argue that a key reason is that compared to …
Learning Variable Ordering Heuristics For Solving Constraint Satisfaction Problems, Wen Song, Zhiguang Cao, Jie Zhang, Chi Xu, Andrew Lim
Learning Variable Ordering Heuristics For Solving Constraint Satisfaction Problems, Wen Song, Zhiguang Cao, Jie Zhang, Chi Xu, Andrew Lim
Research Collection School Of Computing and Information Systems
Backtracking search algorithms are often used to solve the Constraint Satisfaction Problem (CSP), which is widely applied in various domains such as automated planning and scheduling. The efficiency of backtracking search depends greatly on the variable ordering heuristics. Currently, the most commonly used heuristics are hand-crafted based on expert knowledge. In this paper, we propose a deep reinforcement learning based approach to automatically discover new variable ordering heuristics that are better adapted for a given class of CSP instances, without the need of relying on hand-crafted features and heuristics. We show that directly optimizing the search tree size is not …