Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences Commons™

Open Access. Powered by Scholars. Published by Universities.®

Discipline
Institution
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 26311 - 26340 of 63662

Full-Text Articles in Computer Sciences

Cerebro: Context-Aware Adaptive Fuzzing For Effective Vulnerability Detection, Yuekang Li, Yinxing Xue, Hongxu Chen, Xiuheng Wu, Cen Zhang, Xiaofei Xie, Haijun Wang, Yang Liu Aug 2019

Cerebro: Context-Aware Adaptive Fuzzing For Effective Vulnerability Detection, Yuekang Li, Yinxing Xue, Hongxu Chen, Xiuheng Wu, Cen Zhang, Xiaofei Xie, Haijun Wang, Yang Liu

Research Collection School Of Computing and Information Systems

Existing greybox fuzzers mainly utilize program coverage as the goal to guide the fuzzing process. To maximize their outputs, coverage-based greybox fuzzers need to evaluate the quality of seeds properly, which involves making two decisions: 1) which is the most promising seed to fuzz next (seed prioritization), and 2) how many efforts should be made to the current seed (power scheduling). In this paper, we present our fuzzer, Cerebro, to address the above challenges. For the seed prioritization problem, we propose an online multi-objective based algorithm to balance various metrics such as code complexity, coverage, execution time, etc. To address …

Go to article

Diffchaser: Detecting Disagreements For Deep Neural Networks, Xiaofei Xie, Lei Ma, Haijun Wang, Yuekang Li, Yang Liu, Xiaohong Li Aug 2019

Diffchaser: Detecting Disagreements For Deep Neural Networks, Xiaofei Xie, Lei Ma, Haijun Wang, Yuekang Li, Yang Liu, Xiaohong Li

Research Collection School Of Computing and Information Systems

The platform migration and customization have become an indispensable process of deep neural network (DNN) development lifecycle. A highprecision but complex DNN trained in the cloud on massive data and powerful GPUs often goes through an optimization phase (e.g., quantization, compression) before deployment to a target device (e.g., mobile device). A test set that effectively uncovers the disagreements of a DNN and its optimized variant provides certain feedback to debug and further enhance the optimization procedure. However, the minor inconsistency between a DNN and its optimized version is often hard to detect and easily bypasses the original test set. This …

Go to article

Deep Anomaly Detection With Deviation Networks, Guansong Pang, Chunhua Shen, Anton Van Den Hengel Aug 2019

Deep Anomaly Detection With Deviation Networks, Guansong Pang, Chunhua Shen, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Although deep learning has been applied to successfully address many data mining problems, relatively limited work has been done on deep learning for anomaly detection. Existing deep anomaly detection methods, which focus on learning new feature representations to enable downstream anomaly detection methods, perform indirect optimization of anomaly scores, leading to data-inefficient learning and suboptimal anomaly scoring. Also, they are typically designed as unsupervised learning due to the lack of large-scale labeled anomaly data. As a result, they are difficult to leverage prior knowledge (e.g., a few labeled anomalies) when such information is available as in many real-world anomaly detection …

Go to article

Kgat: Knowledge Graph Attention Network For Recommendation, Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua Aug 2019

Kgat: Knowledge Graph Attention Network For Recommendation, Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account. Traditional methods like factorization machine (FM) cast it as a supervised learning problem, which assumes each interaction as an independent instance with side information encoded. Due to the overlook of the relations among instances or items (e.g., the director of a movie is also an actor of another movie), these methods are insufficient to distill the collaborative signal from the collective behaviors of users. In this work, we investigate the utility of knowledge graph (KG), which breaks …

Go to article

Gradient Boosting With Piece-Wise Linear Regression Trees, Yu Shi, Jian Li, Zhize Li Aug 2019

Gradient Boosting With Piece-Wise Linear Regression Trees, Yu Shi, Jian Li, Zhize Li

Research Collection School Of Computing and Information Systems

Gradient Boosted Decision Trees (GBDT) is a very successful ensemble learning algorithm widely used across a variety of applications. Recently, several variants of GBDT training algorithms and implementations have been designed and heavily optimized in some very popular open sourced toolkits including XGBoost, LightGBM and CatBoost. In this paper, we show that both the accuracy and efficiency of GBDT can be further enhanced by using more complex base learners. Specifically, we extend gradient boosting to use piecewise linear regression trees (PL Trees), instead of piecewise constant regression trees, as base learners. We show that PL Trees can accelerate convergence of …

Go to article

Let Me In: Guidelines For The Successful Onboarding Of Newcomers To Open Source Projects, Igor Steinmacher, Christoph Treude, Marco Aurélio Gerosa Aug 2019

Let Me In: Guidelines For The Successful Onboarding Of Newcomers To Open Source Projects, Igor Steinmacher, Christoph Treude, Marco Aurélio Gerosa

Research Collection School Of Computing and Information Systems

Many community-based open source software (OSS) projects depend on a continuous influx of newcomers for their survival and continuity, yet newcomers face many barriers to contributing to a project. We provide guidelines based on our previous work for both OSS communities and newcomers to OSS projects.

Go to article

Faster First-Order Methods For Stochastic Non-Convex Optimization On Riemannian Manifolds, Pan Zhou, Xiao-Tong Yuan, Shuicheng Yan, Jiashi Feng Aug 2019

Faster First-Order Methods For Stochastic Non-Convex Optimization On Riemannian Manifolds, Pan Zhou, Xiao-Tong Yuan, Shuicheng Yan, Jiashi Feng

Research Collection School Of Computing and Information Systems

First-order non-convex Riemannian optimization algorithms have gained recent popularity in structured machine learning problems including principal component analysis and low-rank matrix completion. The current paper presents an efficient Riemannian Stochastic Path Integrated Differential EstimatoR (R-SPIDER) algorithm to solve the finite-sum and online Riemannian non-convex minimization problems. At the core of R-SPIDER is a recursive semi-stochastic gradient estimator that can accurately estimate Riemannian gradient under not only exponential mapping and parallel transport, but also general retraction and vector transport operations. Compared with prior Riemannian algorithms, such a recursive gradient estimation mechanism endows R-SPIDER with higher computational efficiency in first-order oracle complexity. …

Go to article

Generalized Majorization-Minimization For Non-Convex Optimization, Hu Zhang, Pan Zhou, Yi Yang, Jiashi Feng Aug 2019

Generalized Majorization-Minimization For Non-Convex Optimization, Hu Zhang, Pan Zhou, Yi Yang, Jiashi Feng

Research Collection School Of Computing and Information Systems

Majorization-Minimization (MM) algorithms optimize an objective function by iteratively minimizing its majorizing surrogate and offer attractively fast convergence rate for convex problems. However, their convergence behaviors for non-convex problems remain unclear. In this paper, we propose a novel MM surrogate function from strictly upper bounding the objective to bounding the objective in expectation. With this generalized surrogate conception, we develop a new optimization algorithm, termed SPI-MM, that leverages the recent proposed SPIDER for more efficient non-convex optimization. We prove that for finite-sum problems, the SPI-MM algorithm converges to an stationary point within deterministic and lower stochastic gradient complexity. To our …

Go to article

Bidding Mechanisms In Graph Games, Guy Avni, Thomas A. Henzinger, Dorde Zikelic Aug 2019

Bidding Mechanisms In Graph Games, Guy Avni, Thomas A. Henzinger, Dorde Zikelic

Research Collection School Of Computing and Information Systems

In two-player games on graphs, the players move a token through a graph to produce a finite or infinite path, which determines the qualitative winner or quantitative payoff of the game. We study bidding games in which the players bid for the right to move the token. Several bidding rules were studied previously. In Richman bidding, in each round, the players simultaneously submit bids, and the higher bidder moves the token and pays the other player. Poorman bidding is similar except that the winner of the bidding pays the “bank” rather than the other player. Taxman bidding spans the spectrum …

Go to article

Who Should Pay The Cost: A Game-Theoretic Model For Government Subsidized Investments To Improve National Cybersecurity, Xinrun Wang, Bo An, Hau Chan Aug 2019

Who Should Pay The Cost: A Game-Theoretic Model For Government Subsidized Investments To Improve National Cybersecurity, Xinrun Wang, Bo An, Hau Chan

Research Collection School Of Computing and Information Systems

Due to the recent cyber attacks, cybersecurity is becoming more critical in modern society. A single attack (e.g., WannaCry ransomware attack) can cause as much as $4 billion in damage. However, the cybersecurity investment by companies is far from satisfactory. Therefore, governments (e.g., in the UK) launch grants and subsidies to help companies to boost their cybersecurity to create a safer national cyber environment. The allocation problem is hard due to limited subsidies and the interdependence between self-interested companies and the presence of a strategic cyber attacker. To tackle the government's allocation problem, we introduce a Stackelberg game-theoretic model where …

Go to article

Constructing Strong Designated Verifier Signatures From Key Encapsulation Mechanisms, Borui Gong, Ho Man Au, Haiyang Xue Aug 2019

Constructing Strong Designated Verifier Signatures From Key Encapsulation Mechanisms, Borui Gong, Ho Man Au, Haiyang Xue

Research Collection School Of Computing and Information Systems

A designated verifier signature (DVS) allows a signer to convince a verifier that a message has been endorsed in a way that the conviction cannot be transferred to any third party. This is achieved by the property that the signature can be generated by one of them. Since DVS is publicly verifiable, a valid DVS implies that the signature must be created by either the signer or the verifier. To enhance privacy of signers' identity, a strong DVS (SDVS) disallows public verification. In this paper, we investigate various aspects of SDVS with making two contributions. Firstly, we consider SDVS in …

Go to article

How Can Ai Help To Enhance Diversity And Inclusion?, Keng Siau Aug 2019

How Can Ai Help To Enhance Diversity And Inclusion?, Keng Siau

Research Collection School Of Computing and Information Systems

In many organizations, promoting diversity and enhancing inclusion are still major concerns. Unconscious biases and stereotyping cause us to have preconceived ideas about what an ideal employee or leader should look like. Unconscious biases are also a major roadblock to an inclusive environment and business culture. Organizations have been investing heavily in training programs for their employees attempting to changes these patterns. Human habits, especially unconscious ones, are not easy to overcome. This research looks at the use of AI to enhance diversity and inclusion in organizations. Literature has shown that a more diverse and inclusive workforce has a competitive …

Go to article

Higher Education In The Ai Age, Yizhi Ma, Keng Siau Aug 2019

Higher Education In The Ai Age, Yizhi Ma, Keng Siau

Research Collection School Of Computing and Information Systems

A perfect storm is hitting higher education. Decrease funding from traditional funding sources such as State Governments and transformative changes caused by artificial intelligence (AI) will revolutionize higher education (Siau 2018). Higher education needs to change and evolve quickly and continuously to prepare students for the upheavals in the job market caused by AI, machine learning, and automation. Further, continuous organizational and curriculum changes will be necessary for a higher education institution to stay relevant and to stay afloat. This qualitative research looks at higher education in the AI age. Stakeholders (i.e., administrators, faculty, students, industry recruiters) in higher education …

Go to article

Digital Marketing In The Artificial Intelligence And Machine Learning Age, Z. Ruan, Keng Siau Aug 2019

Digital Marketing In The Artificial Intelligence And Machine Learning Age, Z. Ruan, Keng Siau

Research Collection School Of Computing and Information Systems

We are living in a period of profound change driven by digitization, information and communication technology, artificial intelligence, machine learning, and robotics (Gupta, Keen, Shah, and Verdier, 2017; Wang and Siau, 2019). Traditional marketing is shifting to digital marketing enabled by AI and machine learning. Customer consumption behavior has changed from traditional in-store shopping to online shopping (Thiraviyam, 2018). The large volume of transaction and demographic data enables business analytics, AI, and machine learning to analyze and predict customer behavior to improve customer satisfaction and enhance sales (Siau and Wang, 2018). For example, predictive analytics uses different algorithms to predict …

Go to article

Potential Impact Of Artificial Intelligence On Mental Well-Being, Weiyu Wang, Keng Siau Aug 2019

Potential Impact Of Artificial Intelligence On Mental Well-Being, Weiyu Wang, Keng Siau

Research Collection School Of Computing and Information Systems

Artificial Intelligence (AI) will result in job replacement and job elimination. Some AI technologies, such as self-driving vehicles, have the potential to disrupt existing industries. Self-driving trucks may replace the 3.5 million truck drivers in the US. Scholars at Oxford University estimated that no less than 47% of American jobs and 54% of those in Europe are at a high risk of being taken over by machines. Routine, repetitive, and predictable jobs are expected to be automated (Siau, 2018). Although new jobs will be created, unemployment rate may go up in the short term and the emergence of a “useless …

Go to article

Ai-Fashion: Collaborative Ai In The Fashion Industry, Y. Luo, Keng Siau Aug 2019

Ai-Fashion: Collaborative Ai In The Fashion Industry, Y. Luo, Keng Siau

Research Collection School Of Computing and Information Systems

Abstract The word vintage is generally accepted to mean clothing produced in the period between 1920s and 1980s (Cervellon et al., 2012). According to Fischer (2015), fashion usually means rapid changes and up-to-date trendiness. Vintage dressing, however, has been a fashionable trend for over 40 years. Can AI be used to predict the next fashion trend? Fashion industry is currently exploring the use of AI to analyze customer behavior and predict next year’s fashion trends. Predicting the correct next trend is vital to the competitiveness and survivability of fashion brands. Research in this area is not new. For example, research …

Go to article

Improving Urban Crowd Flow Prediction On Flexible Region Partition, Xu Wang, Zimu Zhou, Yi Zhao, Xinglin Zhang, Kai Xing, Fu Xiao, Zheng Yang, Yunhao Liu Aug 2019

Improving Urban Crowd Flow Prediction On Flexible Region Partition, Xu Wang, Zimu Zhou, Yi Zhao, Xinglin Zhang, Kai Xing, Fu Xiao, Zheng Yang, Yunhao Liu

Research Collection School Of Computing and Information Systems

Accurate forecast of citywide crowd flows on flexible region partition benefits urban planning, traffic management, and public safety. Previous research either fails to capture the complex spatiotemporal dependencies of crowd flows or is restricted on grid region partition that loses semantic context. In this paper, we propose DeepFlowFlex, a graph-based model to jointly predict inflows and outflows for each region of arbitrary shape and size in a city. Analysis on cellular datasets covering 2.4 million users in China reveals dependencies and distinctive patterns of crowd flows in not only the conventional space and time domains, but also the speed domain, …

Go to article

Simulated Annealing For The Multi-Vehicle Cyclic Inventory Routing Problem, Aldy Gunawan, Vincent F. Yu, Audrey Tedja Widjaja, Pieter Vansteenwegen Aug 2019

Simulated Annealing For The Multi-Vehicle Cyclic Inventory Routing Problem, Aldy Gunawan, Vincent F. Yu, Audrey Tedja Widjaja, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

This paper studies the Multi-Vehicle Cyclic Inventory Routing Problem (MV-CIRP) as the extension of the Single-Vehicle CIRP (SV-CIRP). The objective is to minimize both distribution and inventory costs at the customers and to maximize the collected rewards simultaneously. The problem is treated as a single objective optimization problem. A subset of customers is selected for each vehicle including the quantity to be delivered to each customer. For each vehicle, a cyclic distribution plan is developed. We construct a mathematical programming model and propose a simulated annealing (SA) metaheuristic for solving both SV-CIRP and MV-CIRP. For SV-CIRP, experimental results on benchmark …

Go to article

Fintech Empowerment: Data Science, Ai, And Machine Learning, Keng Siau, Michael Hilgers, Langtao Chen, Steve Liu, Fiona Fui-Hoon Nah, Richard Hall, Barry Flachsbart Aug 2019

Fintech Empowerment: Data Science, Ai, And Machine Learning, Keng Siau, Michael Hilgers, Langtao Chen, Steve Liu, Fiona Fui-Hoon Nah, Richard Hall, Barry Flachsbart

Research Collection School Of Computing and Information Systems

The article discusses how data science, artificial intelligence and machine learning are affecting the evolution of “fintech,” the technologies used to deliver financial services. After presenting fintech’s competitive advantages in combination with these other advanced technologies, the article posits that financial institutions that don’t move forward with the innovations will be eliminated from the marketplace.

Go to article

Shared Dynamic Data Audit Supporting Anonymous User Revocation In Cloud Storage, Yinghui Zhang, Chen Chen, Dong Zheng, Rui Guo, Shengmin Xu Aug 2019

Shared Dynamic Data Audit Supporting Anonymous User Revocation In Cloud Storage, Yinghui Zhang, Chen Chen, Dong Zheng, Rui Guo, Shengmin Xu

Research Collection School Of Computing and Information Systems

Collusion between revoked users and cloud service providers can pose a threat to the security of cloud storage data. If the original legitimate users cannot be revoked securely, it will lead to the leakage of shared data, thus affecting the security of cloud storage. In this paper, we combine vector commitment and anonymous revocation of group signature to propose an integrity audit scheme for cloud storage data that can support data modification. The anonymity of the group signature ensures that users’ privacy information will not be snooped by the server. The proposed scheme supports the dynamic operation of stored data …

Go to article

Definitions And Mathematical Models Of Single Vehicle Routing Problems With Profits, Pieter Vansteenwegen, Aldy Gunawan Aug 2019

Definitions And Mathematical Models Of Single Vehicle Routing Problems With Profits, Pieter Vansteenwegen, Aldy Gunawan

Research Collection School Of Computing and Information Systems

In this chapter, single vehicle routing problems with profits are introduced anddefined. Three variants are considered: the profitable tour problem, the prizecollecting traveling salesperson problem, and the orienteering problem. The difference between these variants is the way in which the profit and the travel cost, mostlydistance or time, are modeled. Profit and travel cost can be modeled as (part of) theobjective or as a constraint. All three problems differ from the well-known travelingsalesperson problem, for which the only objective is to find the shortest route to visitall customers in a given set. In vehicle routing problems with profits, some customerswill …

Go to article

Topic Enhanced Word Embedding For Toxic Content Detection In Q&A Sites, Do Yeon Kim, Xiaohang Li, Sheng Wang, Yunying Zhuo, Ka Wei, Roy Lee Aug 2019

Topic Enhanced Word Embedding For Toxic Content Detection In Q&A Sites, Do Yeon Kim, Xiaohang Li, Sheng Wang, Yunying Zhuo, Ka Wei, Roy Lee

Research Collection School Of Computing and Information Systems

Increasingly, users are adopting community question-and-answer (Q&A) sites to exchange information. Detecting and eliminating toxic and divisive content in these Q&A sites are paramount tasks to ensure a safe and constructive environment for the users. Insincere question, which is founded upon false premises, is one type of toxic content in Q&A sites. In this paper, we proposed a novel deep learning framework enhanced pre-trained word embeddings with topical information for insincere question classification. We evaluated our proposed framework on a large real-world dataset from Quora Q&A site and showed that the topically enhanced word embedding is able to achieve better …

Go to article

State-Of-The-Art Solution Techniques For Optw And Toptw, Pieter Vansteenwegen, Aldy Gunawan Aug 2019

State-Of-The-Art Solution Techniques For Optw And Toptw, Pieter Vansteenwegen, Aldy Gunawan

Research Collection School Of Computing and Information Systems

In Chaps. 2 and 3, different orienteering problems (or routing problems with profits) were introduced. The single vehicle problems were discussed in Chap. 2: the profitable tour problem (PTP), the prize-collecting traveling salesperson problem (PCTSP), and the orienteering problem (OP). The multi vehicle problems were discussed in Chap. 3: the team orienteering problem (TOP) and the team orienteering problem with time windows (TOPTW). For discussing the state-of-the-art solution techniques for these different orienteering problems in Chaps. 4, 5, and 6, the problems will be classified differently, based on the similarities between the solution techniques. Therefore, the PTP and PCTSP are …

Go to article

State-Of-The-Art Solution Techniques For Op And Top, Pieter Vansteenwegen, Aldy Gunawan Aug 2019

State-Of-The-Art Solution Techniques For Op And Top, Pieter Vansteenwegen, Aldy Gunawan

Research Collection School Of Computing and Information Systems

Definitions and mathematical models of the OP and the TOP were introduced in Chaps. 2 and 3. In this chapter, we will discuss the benchmark instances and state-of-the-art solution techniques for both OP and TOP. Some illustrations of benchmark instances and solutions are included in order to increase the understanding in the difficulty of solving this problem and to provide additional insights. The solution techniques are classified into two different categories: exact approaches and (meta)heuristic techniques.

Go to article

Applications Of The Op, Pieter Vansteenwegen, Aldy Gunawan Aug 2019

Applications Of The Op, Pieter Vansteenwegen, Aldy Gunawan

Research Collection School Of Computing and Information Systems

In recent years, we observe from literature that the VRP and OP, including their variants, have been used to model many different planning and scheduling problems from practice, such as the routing of technicians, athlete recruitment, or military applications. Recently, other practical applications, such as the tourist trip design problem, the mobile crowdsourcing problem, the smuggler search problem, the wildfire routing problem, and the integration of vehicle routing, inventory management, and customer selection problems, have been studied and use the OP as a basic model. In this chapter, various practical applications will be discussed in more detail. We will describe …

Go to article

Improving Optimization Of Convolutional Neural Networks Through Parameter Fine-Tuning, Nicholas C. Becherer, John M. Pecarina, Scott L. Nykl, Kenneth M. Hopkinson Aug 2019

Improving Optimization Of Convolutional Neural Networks Through Parameter Fine-Tuning, Nicholas C. Becherer, John M. Pecarina, Scott L. Nykl, Kenneth M. Hopkinson

Faculty Publications

In recent years, convolutional neural networks have achieved state-of-the-art performance in a number of computer vision problems such as image classification. Prior research has shown that a transfer learning technique known as parameter fine-tuning wherein a network is pre-trained on a different dataset can boost the performance of these networks. However, the topic of identifying the best source dataset and learning strategy for a given target domain is largely unexplored. Thus, this research presents and evaluates various transfer learning methods for fine-grained image classification as well as the effect on ensemble networks. The results clearly demonstrate the effectiveness of parameter …

Go to article

Social Engineering: How U.S. Businesses Strengthen The Weakest Link Against Cybersecurity Threats, Lily J. Pharris Aug 2019

Social Engineering: How U.S. Businesses Strengthen The Weakest Link Against Cybersecurity Threats, Lily J. Pharris

Doctoral Dissertations and Projects

The purpose of this transcendental phenomenological qualitative study was to investigate how IS professionals working in U.S. businesses make sense of their lives and experiences as they address and prevent vulnerabilities to social engineering attacks. This larger problem was explored through an in-depth study of social engineering and its effect on IS professionals working in U.S. businesses operating within healthcare, financial services, and educational industries across the central and northwest regions of Louisiana. Through its use of a phenomenological research design, the study bridged a gap in the social engineering literature, which was primarily comprised of studies that utilized a …

Go to article

Comparing Security Self-Efficacy Amongst College Freshmen And Senior, Female And Male Cybersecurity Students, Lane H. Melton Aug 2019

Comparing Security Self-Efficacy Amongst College Freshmen And Senior, Female And Male Cybersecurity Students, Lane H. Melton

Doctoral Dissertations and Projects

This study sought to determine if there was a difference in the self-efficacy of freshman and senior, female and male Cybersecurity students relating to threats associated with various information systems. The design for this quantitative study was non-experimental, causal-comparative and known as group comparison used to determine if there was a causal relationship between variables. The method used to make that determination utilized a self-efficacy survey developed by Phelps (2005), to identify the independent variables specific level of self-efficacy. Research was conducted at a small, southern university with total of 33 participants. Each student was enrolled in the Computer Science …

Go to article

The Impact Of Leadership Style On The Adoption Of Agile Software Development: A Correlational Study, Mike Kipreos Aug 2019

The Impact Of Leadership Style On The Adoption Of Agile Software Development: A Correlational Study, Mike Kipreos

Doctoral Dissertations and Projects

Public and private organizations continue to rely prohibitively on classic software development methodologies such as the waterfall. Private industry more so than the public sector has shown evidence for a higher rate of success when using agile software development methods. Consequently, the purpose of this quantitative correlation study was to examine variables that best predict the adoption of agile methodologies in software development. The study made use of the UTAUT and MLQ–5X surveys. The primary variable under study was leadership style. Secondary variables included: performance expectancy, effort expectancy, social influence, and facilitating factors. A pilot study (N = 30) was …

Go to article

Blocks' Network: Redesign Architecture Based On Blockchain Technology, Moataz Hanif Aug 2019

Blocks' Network: Redesign Architecture Based On Blockchain Technology, Moataz Hanif

Doctoral Dissertations and Master's Theses

The Internet is a global network that uses communication protocols. It is considered the most important system reached by humanity, which no one can abandon. However, this technology has become a weapon that threatens the privacy of users, especially in the client-server model, where data is stored and managed privately. Additionally, users have no power over their data that store in a private server, which means users’ data may interrupt by government or might be sold via service provider for-profit purposes. Furthermore, blockchain is a technology that we can rely on to solve issues related to client-server model if appropriately …

Go to article

First Prev Next Last