Securing Anonymous Authenticated Announcement Protocol For Group Signature In Internet Of Vehicle,
2021
Universiti Malaya
Securing Anonymous Authenticated Announcement Protocol For Group Signature In Internet Of Vehicle, Nur Afiqah Suzelan Amir
Student Works (2020-2029)
The past decade has witnessed a growing interest in vehicular networking and its vast array of potential applications. Improved wireless internet connectivity from vehicles has prompted road safety technologies to emerge. Announcement protocol in Internet of Vehicles (IoV) is an intelligent application to improve road safety, relieve traffic congestion and enhance the comfort of transportation. IoV has drawn extensive attention to provide more enjoyable and safer driving environment. It requires communication between vehicles, roadside units and pedestrian to disseminate safety related messages. Vehicular cloud (VC) computing can guarantee reliable and timely broadcast of safety messages. The integration of IoV and …
Google Books,
2021
James Madison University
Google Books, Jody Condit Fagan
Libraries
Google Books’ (GB) full-text search of more than 40 million books offers significant value for libraries and their patrons. However, Google’s refusal to disclose information about the coverage of GB, as well as observed gaps and inaccuracies in the collection and its metadata, makes it difficult to recommend with confidence for a given research need. While most search and retrieval functions work well, glitches aren’t hard to find, which suggests GB development is focused on user experiences that relate to monetization. Privacy and equity concerns surrounding GB mirror those of other big technology platforms. Still, every librarian should familiarize themselves …
Unsupervised Data Mining Technique For Clustering Library In Indonesia,
2021
Universiti Malaysia Perlis
Unsupervised Data Mining Technique For Clustering Library In Indonesia, Robbi Rahim, Joseph Teguh Santoso, Sri Jumini, Gita Widi Bhawika, Daniel Susilo, Danny Wibowo
Library Philosophy and Practice (e-journal)
Organizing school libraries not only keeps library materials, but helps students and teachers in completing tasks in the teaching process so that national development goals are in order to improve community welfare by producing quality and competitive human resources. The purpose of this study is to analyze the Unsupervised Learning technique in conducting cluster mapping of the number of libraries at education levels in Indonesia. The data source was obtained from the Ministry of Education and Culture which was processed by the Central Statistics Agency (abbreviated as BPS) with url: bps.go.id/. The data consisted of 34 records where the attribute …
Hybrid Cloud Workload Monitoring As A Service,
2021
San Jose State University
Hybrid Cloud Workload Monitoring As A Service, Shreya Kundu
Master's Projects
Cloud computing and cloud-based hosting has become embedded in our daily lives. It is imperative for cloud providers to make sure all services used by both enterprises and consumers have high availability and elasticity to prevent any downtime, which impacts negatively for any business. To ensure cloud infrastructures are working reliably, cloud monitoring becomes an essential need for both businesses, the provider and the consumer. This thesis project reports on the need of efficient scalable monitoring, enumerating the necessary types of metrics of interest to be collected. Current understanding of various architectures designed to collect, store and process monitoring data …
A Microservice-Based Architecture For An Online Product Review Analysis System,
2021
Universiti Malaya
A Microservice-Based Architecture For An Online Product Review Analysis System, Mehdi Mohammadi
Student Works (2020-2029)
Since the emergence of Web 2.0 in late 1999, online product reviews have played a significant role in the e-commerce channels, social media, online forums, and online communities. The majority of studies in the area of product review analysis (PRA) systems have focused on evaluating and improving the techniques for review analysis to increase efficiency, accuracy, effectiveness, usefulness, ranking, spam detection, and feature extraction. The architecture of PRA systems is not the primary concern of research in this field. In this study, some of the implicit and explicit architectures of scientifically reported PRA systems were extracted to examine how the …
Differential Training: A Generic Framework To Reduce Label Noises For Android Malware Detection,
2021
Singapore Management University
Differential Training: A Generic Framework To Reduce Label Noises For Android Malware Detection, Jiayun Xu, Yingjiu Li, Robert H. Deng
Research Collection School Of Computing and Information Systems
A common problem in machine learning-based malware detection is that training data may contain noisy labels and it is challenging to make the training data noise-free at a large scale. To address this problem, we propose a generic framework to reduce the noise level of training data for the training of any machine learning-based Android malware detection. Our framework makes use of all intermediate states of two identical deep learning classification models during their training with a given noisy training dataset and generate a noise-detection feature vector for each input sample. Our framework then applies a set of outlier detection …
Accelerating Large-Scale Heterogeneous Interaction Graph Embedding Learning Via Importance Sampling,
2021
Beijing University of Posts and Telecommunications
Accelerating Large-Scale Heterogeneous Interaction Graph Embedding Learning Via Importance Sampling, Yugang Ji, Mingyang Yin, Hongxia Yang, Jingren Zhou, Vincent W. Zheng, Chuan Shi, Yuan Fang
Research Collection School Of Computing and Information Systems
In real-world problems, heterogeneous entities are often related to each other through multiple interactions, forming a Heterogeneous Interaction Graph (HIG in short). While modeling HIGs to deal with fundamental tasks, graph neural networks present an attractive opportunity that can make full use of the heterogeneity and rich semantic information by aggregating and propagating information from different types of neighborhoods. However, learning on such complex graphs, often with millions or billions of nodes, edges, and various attributes, could suffer from expensive time cost and high memory consumption. In this paper, we attempt to accelerate representation learning on large-scale HIGs by adopting …
Revman: Revenue-Aware Multi-Task Online Insurance Recommendation,
2021
Jilin University
Revman: Revenue-Aware Multi-Task Online Insurance Recommendation, Yu Li, Yi Zhang, Lu Gan, Gengwei Hong, Zimu Zhou, Qiang Li
Research Collection School Of Computing and Information Systems
Online insurance is a new type of e-commerce with exponential growth. An effective recommendation model that maximizes the total revenue of insurance products listed in multiple customized sales scenarios is crucial for the success of online insurance business. Prior recommendation models are ineffective because they fail to characterize the complex relatedness of insurance products in multiple sales scenarios and maximize the overall conversion rate rather than the total revenue. Even worse, it is impractical to collect training data online for total revenue maximization due to the business logic of online insurance. We propose RevMan, a Revenue-aware Multi-task Network for online …
Learning To Pre-Train Graph Neural Networks,
2021
Singapore Management University
Learning To Pre-Train Graph Neural Networks, Yuanfu Lu, Xunqiang Jiang, Yuan Fang, Chuan Shi
Research Collection School Of Computing and Information Systems
Graph neural networks (GNNs) have become the de facto standard for representation learning on graphs, which derive effective node representations by recursively aggregating information from graph neighborhoods. While GNNs can be trained from scratch, pre-training GNNs to learn transferable knowledge for downstream tasks has recently been demonstrated to improve the state of the art. However, conventional GNN pre-training methods follow a two-step paradigm: 1) pre-training on abundant unlabeled data and 2) fine-tuning on downstream labeled data, between which there exists a significant gap due to the divergence of optimization objectives in the two steps. In this paper, we conduct an …
Qlens: Visual Analytics Of Multi-Step Problem-Solving Behaviors For Improving Question Design,
2021
Hong Kong University of Science and Technology
Qlens: Visual Analytics Of Multi-Step Problem-Solving Behaviors For Improving Question Design, Meng Xia, Reshika P. Velumani, Yong Wang, Huamin Qu, Xiaojuan Ma
Research Collection School Of Computing and Information Systems
With the rapid development of online education in recent years, there has been an increasing number of learning platforms that provide students with multi-step questions to cultivate their problem-solving skills. To guarantee the high quality of such learning materials, question designers need to inspect how students’ problem-solving processes unfold step by step to infer whether students’ problem-solving logic matches their design intent. They also need to compare the behaviors of different groups (e.g., students from different grades) to distribute questions to students with the right level of knowledge. The availability of fine-grained interaction data, such as mouse movement trajectories from …
A New Feature Selection Method Based On Class Association Rule,
2021
CUNY Graduate Center
A New Feature Selection Method Based On Class Association Rule, Sami A. Al-Dhaheri
Dissertations, Theses, and Capstone Projects
Feature selection is a key process for supervised learning algorithms. It involves discarding irrelevant attributes from the training dataset from which the models are derived. One of the vital feature selection approaches is Filtering, which often uses mathematical models to compute the relevance for each feature in the training dataset and then sorts the features into descending order based on their computed scores. However, most Filtering methods face several challenges including, but not limited to, merely considering feature-class correlation when defining a feature’s relevance; additionally, not recommending which subset of features to retain. Leaving this decision to the end-user may …
Evoking Empathy: A Framework For Describing Empathy Tools,
2021
Singapore Management University
Evoking Empathy: A Framework For Describing Empathy Tools, Sydney Pratte, Anthony Tang, Lora Oehlberg
Research Collection School Of Computing and Information Systems
Empathy tools are experiences designed to evoke empathetic responses by placing the user in another’s lived and felt experience. The problem is that designers do not have a common vocabulary to describe empathy tool experiences; consequently, it is difficult to compare/contrast empathy tool designs or to think about their efficacy. To address this problem, we analyzed 26 publications on empathy tools to develop a descriptive framework for designers of empathy tools. Based on our analysis, we found that empathy tools can be described along three dimensions: (i) the amount of agency the tool allows, (ii) the user’s perspective while using …
Multi-Decoder Attention Model With Embedding Glimpse For Solving Vehicle Routing Problems,
2021
Singapore Management University
Multi-Decoder Attention Model With Embedding Glimpse For Solving Vehicle Routing Problems, Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang
Research Collection School Of Computing and Information Systems
We present a novel deep reinforcement learning method to learn construction heuristics for vehicle routing problems. In specific, we propose a Multi-Decoder Attention Model (MDAM) to train multiple diverse policies, which effectively increases the chance of finding good solutions compared with existing methods that train only one policy. A customized beam search strategy is designed to fully exploit the diversity of MDAM. In addition, we propose an Embedding Glimpse layer in MDAM based on the recursive nature of construction, which can improve the quality of each policy by providing more informative embeddings. Extensive experiments on six different routing problems show …
Relative And Absolute Location Embedding For Few-Shot Node Classification On Graph,
2021
Singapore Management University
Relative And Absolute Location Embedding For Few-Shot Node Classification On Graph, Zemin Liu, Yuan Fang, Chenghao Liu, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Node classification is an important problem on graphs. While recent advances in graph neural networks achieve promising performance, they require abundant labeled nodes for training. However, in many practical scenarios there often exist novel classes in which only one or a few labeled nodes are available as supervision, known as few-shot node classification. Although meta-learning has been widely used in vision and language domains to address few-shot learning, its adoption on graphs has been limited. In particular, graph nodes in a few-shot task are not independent and relate to each other. To deal with this, we propose a novel model …
Evidence Aware Neural Pornographic Text Identification For Child Protection,
2021
Singapore Management University
Evidence Aware Neural Pornographic Text Identification For Child Protection, Kaisong Song, Yangyang Kang, Wei Gao, Zhe Gao, Changlong Sun, Xiaozhong Liu
Research Collection School Of Computing and Information Systems
Identifying pornographic text online is practically useful to protect children from access to such adult content. However, some authors may intentionally avoid using sensitive words in their pornographic texts to take advantage of the lack of human audits. Without prior knowledge guidance, real semantics of such pornographic text is difficult to understand by existing methods due to its high context-sensitivity and heavy usage of figurative language, which brings huge challenges to the porn detection systems used in social media platforms. In this paper, we approach to the problem as a document-level porn identification task by locating and integrating sentence-level evidence …
An Exploratory Study On The Introduction And Removal Of Different Types Of Technical Debt In Deep Learning Frameworks,
2021
Singapore Management University
An Exploratory Study On The Introduction And Removal Of Different Types Of Technical Debt In Deep Learning Frameworks, Jiakun Liu, Qiao Huang, Xin Xia, Emad Shihab, David Lo, Shanping Li
Research Collection School Of Computing and Information Systems
To complete tasks faster, developers often have to sacrifice the quality of the software. Such compromised practice results in the increasing burden to developers in future development. The metaphor, technical debt, describes such practice. Prior research has illustrated the negative impact of technical debt, and many researchers investigated how developers deal with a certain type of technical debt. However, few studies focused on the removal of different types of technical debt in practice. To fill this gap, we use the introduction and removal of different types of self-admitted technical debt (i.e., SATD) in 7 deep learning frameworks as an example. …
Visual Analysis Of Discrimination In Machine Learning,
2021
Hong Kong University of Science and Technology
Visual Analysis Of Discrimination In Machine Learning, Qianwen Wang, Zhenghua Xu, Zhutian Chen, Yong Wang, Shixia Liu, Huamin Qu
Research Collection School Of Computing and Information Systems
The growing use of automated decision-making in critical applications, such as crime prediction and college admission, has raised questions about fairness in machine learning. How can we decide whether different treatments are reasonable or discriminatory? In this paper, we investigate discrimination in machine learning from a visual analytics perspective and propose an interactive visualization tool, DiscriLens, to support a more comprehensive analysis. To reveal detailed information on algorithmic discrimination, DiscriLens identifies a collection of potentially discriminatory itemsets based on causal modeling and classification rules mining. By combining an extended Euler diagram with a matrix-based visualization, we develop a novel set …
Delineating Knowledge Domains In Scientific Domains In Scientific Literature Using Machine Learning (Ml),
2021
Mizoram University
Delineating Knowledge Domains In Scientific Domains In Scientific Literature Using Machine Learning (Ml), Abhay Maurya, Smarajit Paul Choudhury Mr., Kshitij Jaiswal Mr.
Library Philosophy and Practice (e-journal)
The recent years have witnessed an upsurge in the number of published documents. Organizations are showing an increased interest in text classification for effective use of the information. Manual procedures for text classification can be fruitful for a handful of documents, but the same lack in credibility when the number of documents increases besides being laborious and time-consuming. Text mining techniques facilitate assigning text strings to categories rendering the process of classification fast, accurate, and hence reliable. This paper classifies chemistry documents using machine learning and statistical methods. The procedure of text classification has been described in chronological order like …
Automation Of Crawling Blogosphere Based On Pattern Recognition,
2021
University of Arkansas Little Rock
Automation Of Crawling Blogosphere Based On Pattern Recognition, Anal Kanti Roy
Theses and Dissertations
Social media plays an important role in the propagation and dissemination of ideas and thoughts. Compared to other social media platforms, blogs provide a convenient platform for users to post detailed information, engage in active discussions and share the content on other social media sites, such as Facebook and Twitter. Thus, the blogosphere has been an enormous and ever-growing part of the open-source intelligence. In order to track and monitor online social behavior particularly from blogs, the first challenging part is to mine the vast pool of unstructured data. To scale up this process and cope with the continuously changing …
Strategies Project Managers Use That Reduce Information Technology Project Failures In The Insurance Industry,
2021
Walden University
Strategies Project Managers Use That Reduce Information Technology Project Failures In The Insurance Industry, Jamarcus Snipes
Walden Dissertations and Doctoral Studies
The worldwide failure rate of information technology (IT) projects is approximately 65%. Insurance project managers who fail to successfully implement IT projects negatively impact organizational performance and profitability. Grounded in the management by objectives theory, the purpose of this qualitative multiple case study was to explore strategies project managers use to reduce IT project failures in the insurance industry. The participants comprised four IT project managers in Alabama who successfully used strategies to reduce IT project failures in the insurance industry. Data were collected from virtual semistructured interviews, company documents, existing literature, and journal notes. Thematic analysis was used to …
