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Articles 62881 - 62910 of 294419

Full-Text Articles in Physical Sciences and Mathematics

Mask-Guided Deformation Adaptive Network For Human Parsing, Aihua Mao, Yuan Liang, Jianbo Jiao, Yongtuo Liu, Shengfeng He Mar 2022

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. …

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Riconv++: Effective Rotation Invariant Convolutions For 3d Point Clouds Deep Learning, Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung Mar 2022

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 …

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Learning Variable Ordering Heuristics For Solving Constraint Satisfaction Problems, Wen Song, Zhiguang Cao, Jie Zhang, Chi Xu, Andrew Lim Mar 2022

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 …

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Heterogeneous Attentions For Solving Pickup And Delivery Problem Via Deep Reinforcement Learning, Jingwen Li, Liang Xin, Zhiguang Cao, Andrew Lim, Wen Song, Jie Zhang Mar 2022

Heterogeneous Attentions For Solving Pickup And Delivery Problem Via Deep Reinforcement Learning, Jingwen Li, Liang Xin, Zhiguang Cao, Andrew Lim, Wen Song, Jie Zhang

Research Collection School Of Computing and Information Systems

Recently, there is an emerging trend to apply deep reinforcement learning to solve the vehicle routing problem (VRP), where a learnt policy governs the selection of next node for visiting. However, existing methods could not handle well the pairing and precedence relationships in the pickup and delivery problem (PDP), which is a representative variant of VRP. To address this challenging issue, we leverage a novel neural network integrated with a heterogeneous attention mechanism to empower the policy in deep reinforcement learning to automatically select the nodes. In particular, the heterogeneous attention mechanism specifically prescribes attentions for each role of the …

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Strangan: Adversarially-Learnt Spatial Transformer For Scalable Human Activity Recognition, Abu Zaher Md Faridee, Avijoy Chakma, Archan Misra, Nirmalya Roy Mar 2022

Strangan: Adversarially-Learnt Spatial Transformer For Scalable Human Activity Recognition, Abu Zaher Md Faridee, Avijoy Chakma, Archan Misra, Nirmalya Roy

Research Collection School Of Computing and Information Systems

We tackle the problem of domain adaptation for inertial sensing-based human activity recognition (HAR) applications -i.e., in developing mechanisms that allow a classifier trained on sensor samples collected under a certain narrow context to continue to achieve high activity recognition accuracy even when applied to other contexts. This is a problem of high practical importance as the current requirement of labeled training data for adapting such classifiers to every new individual, device, or on-body location is a major roadblock to community-scale adoption of HAR-based applications. We particularly investigate the possibility of ensuring robust classifier operation, without requiring any new labeled …

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The Impact Of Ride-Hail Surge Factors On Taxi Bookings, Sumit Agarwal, Ben Charoenwong, Shih-Fen Cheng, Jussi Keppo Mar 2022

The Impact Of Ride-Hail Surge Factors On Taxi Bookings, Sumit Agarwal, Ben Charoenwong, Shih-Fen Cheng, Jussi Keppo

Research Collection School Of Computing and Information Systems

We study the role of ride-hailing surge factors on the allocative efficiency of taxis by combining a reduced-form estimation with structural analyses using machine-learning-based demand predictions. Where other research study the effect of entry on incumbent taxis, we use higher frequency granular data to study how location-time-specific surge factors affect taxi bookings to bound the effect of customer decisions while accounting for various confounding variables. We find that even in a unique market like Singapore, where incumbent taxi companies have app-based booking systems similar to those from ride-hailing companies like Uber, the estimated upper bound on the cross-platform substitution between …

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Hybrid Tabu Search Algorithm For Unrelated Parallel Machine Scheduling In Semiconductor Fabs With Setup Times, Job Release, And Expired Times, Changyu Chen, Madhi Fathi, Marzieh Khakifirooz, Kan Wu Mar 2022

Hybrid Tabu Search Algorithm For Unrelated Parallel Machine Scheduling In Semiconductor Fabs With Setup Times, Job Release, And Expired Times, Changyu Chen, Madhi Fathi, Marzieh Khakifirooz, Kan Wu

Research Collection School Of Computing and Information Systems

This research is motivated by a scheduling problem arising in the ion implantation process of wafer fabrication. The ion implementation scheduling problem is modeled as an unrelated parallel machine scheduling (UPMS) problem with sequence-dependent setup times that are subject to job release time and expiration time of allowing a job to be processed on a specific machine, defined as: R|rj,eij,STsd|Cmax. The objective is first to maximize the number of processed jobs, then minimize the maximum completion time (makespan), and finally minimize the maximum completion times of the non-bottleneck machines. A mixed-integer programming (MIP) model is proposed as a solution approach …

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Jscsp: A Novel Policy-Based Xss Defense Mechanism For Browsers, Guangquan Xu, Xiaofei Xie, Shuhan Huang, Jun Zhang, Lei Pan, Wei Lou, Kaitai Liang Mar 2022

Jscsp: A Novel Policy-Based Xss Defense Mechanism For Browsers, Guangquan Xu, Xiaofei Xie, Shuhan Huang, Jun Zhang, Lei Pan, Wei Lou, Kaitai Liang

Research Collection School Of Computing and Information Systems

To mitigate cross-site scripting attacks (XSS), the W3C group recommends web service providers to employ a computer security standard called Content Security Policy (CSP). However, less than 3.7 percent of real-world websites are equipped with CSP according to Google’s survey. The low scalability of CSP is incurred by the difficulty of deployment and non-compatibility for state-of-art browsers. To explore the scalability of CSP, in this article, we propose JavaScript based CSP (JSCSP), which is able to support most of real-world browsers but also to generate security policies automatically. Specifically, JSCSP offers a novel self-defined security policy which enforces essential confinements …

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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 Mar 2022

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 …

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Mrim: Enabling Mixed-Resolution Imaging For Low-Power Pervasive Vision Tasks, Jiyan Wu, Vithurson Subasharan, Tuan Tran, Archan Misra Mar 2022

Mrim: Enabling Mixed-Resolution Imaging For Low-Power Pervasive Vision Tasks, Jiyan Wu, Vithurson Subasharan, Tuan Tran, Archan Misra

Research Collection School Of Computing and Information Systems

While many pervasive computing applications increasingly utilize real-time context extracted from a vision sensing infrastructure, the high energy overhead of DNN-based vision sensing pipelines remains a challenge for sustainable in-the-wild deployment. One common approach to reducing such energy overheads is the capture and transmission of lower-resolution images to an edge node (where the DNN inferencing task is executed), but this results in an accuracy-vs-energy tradeoff, as the DNN inference accuracy typically degrades with a drop in resolution. In this work, we introduce MRIM, a simple but effective framework to tackle this tradeoff. Under MRIM, the vision sensor platform first executes …

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Exploring And Evaluating The Impact Of Covid-19 On Mobility Changes In Singapore, Aldy Gunawan, Linh Chi Tran, Kar Way Tan, I-Lin Wang Mar 2022

Exploring And Evaluating The Impact Of Covid-19 On Mobility Changes In Singapore, Aldy Gunawan, Linh Chi Tran, Kar Way Tan, I-Lin Wang

Research Collection School Of Computing and Information Systems

This paper analyzes the changes in mobility trends due to the impact of the COVID-19 pandemic in Singapore in the six different sectors: Retail and Recreation, Grocery and Pharmacy, Parks, Transit Stations, Workplaces and Residential. The period of observation is from 15 February 2020 to 18 August 2021. The observed patterns obtained from the descriptive data analysis sheds light on the effectiveness of social distancing measures in Singapore as well as the level of compliance among the country’s residents. Correlation analysis is used to explore the relationship between different sectors during the pandemic period. The results reveal a strong sense …

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Analyzing The Impact Of Digital Payment On Efficiency And Productivity Of Commercial Banks: A Case Study In China, Haopeng Wang, Aldy Gunawan Mar 2022

Analyzing The Impact Of Digital Payment On Efficiency And Productivity Of Commercial Banks: A Case Study In China, Haopeng Wang, Aldy Gunawan

Research Collection School Of Computing and Information Systems

Digital payment has become one of the most popular payment methods all around the world, especially in countries that witnessed the rapid development of internet. As a traditional financial institution, commercial banks have been impacted by newly developed payment technology since third payment platforms have attracted customers to use the digital payment for daily consumption, transferring, and even investment. This paper focuses on analyzing whether and how the commercial banks in China have been affected by digital payment by using empirical methods. Systematic Generalized Method of Moments (SYS-GMM) is used to test the relationship between the productivity of commercial banks …

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The Ai Chip Race, Guansong Pang Mar 2022

The Ai Chip Race, Guansong Pang

Research Collection School Of Computing and Information Systems

The strong demand of computing power for artificial intelligence (AI) and machine learning is accelerating the race to develop cheaper and faster AI chips. The AI chip market was valued 10.6 billion in 2021 and the total revenue is expected to reach 79.8 billion by 2027. To be part of the market, tech giants from different countries have been successively joining the race, while AI chip startups attracting billions of dollars are taking off like a rocket.

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Efficient Certificateless Multi-Copy Integrity Auditing Scheme Supporting Data Dynamics, Lei Zhou, Anmin Fu, Guomin Yang, Huaqun Wang, Yuqing Zhang Mar 2022

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 …

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Towards Efficient Annotations For A Human-Ai Collaborative, Clinical Decision Support System: A Case Study On Physical Stroke Rehabilitation Assessment, Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez I Badia Mar 2022

Towards Efficient Annotations For A Human-Ai Collaborative, Clinical Decision Support System: A Case Study On Physical Stroke Rehabilitation Assessment, Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez I Badia

Research Collection School Of Computing and Information Systems

Artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being explored to support various decision-making tasks in health (e.g. rehabilitation assessment). However, the development of such AI/ML-based decision support systems is challenging due to the expensive process to collect an annotated dataset. In this paper, we describe the development process of a human-AI collaborative, clinical decision support system that augments an ML model with a rule-based (RB) model from domain experts. We conducted its empirical evaluation in the context of assessing physical stroke rehabilitation with the dataset of three exercises from 15 post-stroke survivors and therapists. Our results bring …

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Mwptoolkit: An Open-Source Framework For Deep Learning-Based Math Word Problem Solvers, Yihuai Lan, Lei Wang, Qiyuan Zhang, Yunshi Lan, Bing Tian Dai, Yan Wang, Dongxiang Zhang, Ee-Peng Lim Mar 2022

Mwptoolkit: An Open-Source Framework For Deep Learning-Based Math Word Problem Solvers, Yihuai Lan, Lei Wang, Qiyuan Zhang, Yunshi Lan, Bing Tian Dai, Yan Wang, Dongxiang Zhang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Duplicate record, see https://ink.library.smu.edu.sg/sis_research/7680/. Developing automatic Math Word Problem (MWP) solvers has been an interest of NLP researchers since the 1960s. Over the last few years, there are a growing number of datasets and deep learning-based methods proposed for effectively solving MWPs. However, most existing methods are benchmarked solely on one or two datasets, varying in different configurations, which leads to a lack of unified, standardized, fair, and comprehensive comparison between methods. This paper presents MWPToolkit, the first open-source framework for solving MWPs. In MWPToolkit, we decompose the procedure of existing MWP solvers into multiple core components and decouple …

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Debiasing Nlu Models Via Causal Intervention And Counterfactual Reasoning, Bing Tian, Yixin Cao, Yong Zhang, Chunxiao Xing Mar 2022

Debiasing Nlu Models Via Causal Intervention And Counterfactual Reasoning, Bing Tian, Yixin Cao, Yong Zhang, Chunxiao Xing

Research Collection School Of Computing and Information Systems

Recent studies have shown that strong Natural Language Understanding (NLU) models are prone to relying on annotation biases of the datasets as a shortcut, which goes against the underlying mechanisms of the task of interest. To reduce such biases, several recent works introduce debiasing methods to regularize the training process of targeted NLU models. In this paper, we provide a new perspective with causal inference to fnd out the bias. On the one hand, we show that there is an unobserved confounder for the natural language utterances and their respective classes, leading to spurious correlations from training data. To remove …

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Deconfounded Visual Grounding, Jianqiang Huang, Yu Qin, Jiaxin Qi, Qianru Sun, Hanwang Zhang Mar 2022

Deconfounded Visual Grounding, Jianqiang Huang, Yu Qin, Jiaxin Qi, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

We focus on the confounding bias between language and location in the visual grounding pipeline, where we find that the bias is the major visual reasoning bottleneck. For example, the grounding process is usually a trivial languagelocation association without visual reasoning, e.g., grounding any language query containing sheep to the nearly central regions, due to that most queries about sheep have groundtruth locations at the image center. First, we frame the visual grounding pipeline into a causal graph, which shows the causalities among image, query, target location and underlying confounder. Through the causal graph, we know how to break the …

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Neuron Coverage-Guided Domain Generalization, Chris Xing Tian, Haoliang Li, Xiaofei Xie, Yang Liu, Shiqi Wang Mar 2022

Neuron Coverage-Guided Domain Generalization, Chris Xing Tian, Haoliang Li, Xiaofei Xie, Yang Liu, Shiqi Wang

Research Collection School Of Computing and Information Systems

This paper focuses on the domain generalization task where domain knowledge is unavailable, and even worse, only samples from a single domain can be utilized during training. Our motivation originates from the recent progresses in deep neural network (DNN) testing, which has shown that maximizing neuron coverage of DNN can help to explore possible defects of DNN (i.e.,misclassification). More specifically, by treating the DNN as a program and each neuron as a functional point of the code, during the network training we aim to improve the generalization capability by maximizing the neuron coverage of DNN with the gradient similarity regularization …

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Learning Program Semantics With Code Representations: An Empirical Study, Jing Kai Siow, Shangqing Liu, Xiaofei Xie, Guozhu Meng, Yang Liu Mar 2022

Learning Program Semantics With Code Representations: An Empirical Study, Jing Kai Siow, Shangqing Liu, Xiaofei Xie, Guozhu Meng, Yang Liu

Research Collection School Of Computing and Information Systems

Program semantics learning is the core and fundamental for various code intelligent tasks e.g., vulnerability detection, clone detection. A considerable amount of existing works propose diverse approaches to learn the program semantics for different tasks and these works have achieved state-of-the-art performance. However, currently, a comprehensive and systematic study on evaluating different program representation techniques across diverse tasks is still missed. From this starting point, in this paper, we conduct an empirical study to evaluate different program representation techniques. Specifically, we categorize current mainstream code representation techniques into four categories i.e., Feature-based, Sequence-based, Tree-based, and Graph-based program representation technique and …

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Generating Music With Emotions, Chunhui Bao, Qianru Sun Mar 2022

Generating Music With Emotions, Chunhui Bao, Qianru Sun

Research Collection School Of Computing and Information Systems

We focus on the music generation conditional on human emotions, specifically the positive and negative emotions. There is no existing large-scale music datasets with the annotation of human emotion labels. It is thus not intuitive how to generate music conditioned on emotion labels. In this paper, we propose an annotation-free method to build a new dataset where each sample is a triplet of lyric, melody and emotion label (without requiring any labours). Specifically, we first train the automated emotion recognition model using the BERT (pre-trained on GoEmotions dataset) on Edmonds Dance dataset. We use it to automatically ‘`label’' the music …

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State Graph Reasoning For Multimodal Conversational Recommendation, Yuxia Wu, Lizi Liao, Gangyi Zhang, Wenqiang Lei, Guoshuai Zhao, Xueming Qian, Tat-Seng Chua Mar 2022

State Graph Reasoning For Multimodal Conversational Recommendation, Yuxia Wu, Lizi Liao, Gangyi Zhang, Wenqiang Lei, Guoshuai Zhao, Xueming Qian, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Conversational recommendation system (CRS) attracts increasing attention in various application domains such as retail and travel. It offers an effective way to capture users’ dynamic preferences with multi-turn conversations. However, most current studies center on the recommendation aspect while over-simplifying the conversation process. The negligence of complexity in data structure and conversation flow hinders their practicality and utility. In reality, there exist various relationships among slots and values, while users’ requirements may dynamically adjust or change. Moreover, the conversation often involves visual modality to facilitate the conversation. These actually call for a more advanced internal state representation of the dialogue …

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Revisiting Neuron Coverage Metrics And Quality Of Deep Neural Networks, Zhou Yang, Jieke Shi, Muhammad Hilmi Asyrofi, David Lo Mar 2022

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 …

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Coordinated Delivery To Shopping Malls With Limited Docking Capacity, Ruidian Song, Hoong Chuin Lau, Xue Luo, Lei Zhao Mar 2022

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 …

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Wifitrace: Network-Based Contact Tracing For Infectious Diseases Using Passive Wifi Sensing, Amee Trivedi, Camellia Zakaria, Rajesh Krishna Balan, Ann Becker, George Corey, Prashant Shenoy Mar 2022

Wifitrace: Network-Based Contact Tracing For Infectious Diseases Using Passive Wifi Sensing, Amee Trivedi, Camellia Zakaria, Rajesh Krishna Balan, Ann Becker, George Corey, Prashant Shenoy

Research Collection School Of Computing and Information Systems

Contact tracing is a well-established and effective approach for the containment of the spread of infectious diseases. While Bluetooth-based contact tracing method using phones has become popular recently, these approaches suffer from the need for a critical mass adoption to be effective. In this paper, we present WiFiTrace, a network-centric approach for contact tracing that relies on passive WiFi sensing with no client-side involvement. Our approach exploits WiFi network logs gathered by enterprise networks for performance and security monitoring, and utilizes them for reconstructing device trajectories for contact tracing. Our approach is specifically designed to enhance the efficacy of traditional …

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Sdac: A Slow-Aging Solution For Android Malware Detection Using Semantic Distance Based Api Clustering, Jiayun Xu, Yingjiu Li, Robert H. Deng, Xu Ke Mar 2022

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 …

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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 Mar 2022

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 …

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Oria Stage Ii Expansion: Keep River Post-Development Aquatic Fauna And Targeted Sawfish Survey - 2021, Indopacific Environmental, Department Of Primary Industries And Regional Development, Western Australia Mar 2022

Oria Stage Ii Expansion: Keep River Post-Development Aquatic Fauna And Targeted Sawfish Survey - 2021, Indopacific Environmental, Department Of Primary Industries And Regional Development, Western Australia

Irrigated Agricultural Development in the North

This report presents the results of the 2021 Keep River Aquatic Fauna and Targeted Sawfish Survey which represents the second of three post-development surveys required to be completed as per the project conditions of the Goomig Irrigation Development. Whilst a limited number of potentially impacted sites were not able to be surveyed in 2021 as a result of i) COVID-19 travel restrictions implemented by the Western Australian Government during the survey and ii) access limitations as a result of rain associated with the wet season due to the late completion of the survey, the data collected and current analyses were …

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Bug Triage Automation Approaches: A Comparative Study, Dr Khaled Nagaty, Madonna Mayez, Khaled Ahmed Nagay Dr. Mar 2022

Bug Triage Automation Approaches: A Comparative Study, Dr Khaled Nagaty, Madonna Mayez, Khaled Ahmed Nagay Dr.

Computer Science

Bug triage is an essential task in the software maintenance phase. It is the process of assigning a developer (fixer) to bug report. Triaging process is performed by the triager, who has to analyze developers’ profiles and bug reports for the purpose of making a suitable assignment. Manual assignment consumes time, financial resources and human resources; to get a high-quality software with minimum cost, automating this process is necessary. Previous researchers tackled this problem as a classification problem from different perspectives, either information retrieval approach or machine learning algorithms, some researchers handled it as an optimization problem using optimization and …

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Estimation Of The Optimal Nitrogen Dose In A Brachiaria Humidicola-Corn Rotation System In The Colombian Eastern Plains, K. Enciso, S. Burkart Feb 2022

Estimation Of The Optimal Nitrogen Dose In A Brachiaria Humidicola-Corn Rotation System In The Colombian Eastern Plains, K. Enciso, S. Burkart

IGC Proceedings (1977-2023)

Improving nitrogen (N) use efficiency by optimizing the N fertilizer application dose is one way to reduce greenhouse gas (GHG) emissions in agriculture and livestock production, especially in higher demanding crops such as corn. Taking a Brachiaria humidicola (Bh)-corn rotation system in the Colombian Eastern Plains, we seek to determine both the optimal economic dose (OED) and the optimal technical dose (OTD) of N, which allow to maximize income at producer level and minimize environmental impacts. This particular rotation system was chosen as research subject given the presence of the residual effect of Biological Nitrogen Inhibition (BNI) in …

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