Showing papers in "Concurrency and Computation: Practice and Experience in 2019"
TL;DR: In this paper, the concept of one event happening before another in a distributed system is examined, and a distributed algorithm is given for synchronizing a system of logical clocks which can be used to totally order the events.
Abstract: The concept of one event happening before another in a distributed system is examined, and is shown to define a partial ordering of the events. A distributed algorithm is given for synchronizing a system of logical clocks which can be used to totally order the events. The use of the total ordering is illustrated with a method for solving synchronization problems. The algorithm is then specialized for synchronizing physical clocks, and a bound is derived on how far out of synchrony the clocks can become.
8,381 citations
TL;DR: In this article, a group of generals of the Byzantine army camped with their troops around an enemy city are shown to agree upon a common battle plan using only oral messages, if and only if more than two-thirds of the generals are loyal; so a single traitor can confound two loyal generals.
Abstract: Reliable computer systems must handle malfunctioning components that give conflicting information to different parts of the system. This situation can be expressed abstractly in terms of a group of generals of the Byzantine army camped with their troops around an enemy city. Communicating only by messenger, the generals must agree upon a common battle plan. However, one or more of them may be traitors who will try to confuse the others. The problem is to find an algorithm to ensure that the loyal generals will reach agreement. It is shown that, using only oral messages, this problem is solvable if and only if more than two-thirds of the generals are loyal; so a single traitor can confound two loyal generals. With unforgeable written messages, the problem is solvable for any number of generals and possible traitors. Applications of the solutions to reliable computer systems are then discussed.
4,901 citations
TL;DR: The role of Internet of Things in building a smart educational process, and also, in making efficient and effective decisions, which is vital in the authors' daily life are illustrated.
Abstract: The role of education, which propagates knowledge, becomes increasingly significant in the past little years due to the fulminatory expansion in knowledge. Meantime, the model of education process is going via a conversion in which the learning of different students need to be completed in various ways. Therefore, the smart education environment is encouraged. It incorporates different information and communication technologies to activate learning process and adjust to the requirements of different students. The quality of learning process for students can be enhanced through continually monitoring and analyzing the state and activities of different students via information sensing devices and information processing platforms for offering feedback about learning process of different students. The Internet of Things pledges to achieve a great variation in life, goodness of individual's life, and organizations' productivity. Via a vastly dispensed locally smart network of intelligent objects, the IoT has the chance to allow expansions and improvements to essential utilities in various fields, while introducing a novel ecosystem for developing application. Applying the concept of Internet of Things in any education environment will increase the quality of education process because students will learn rapidly, and teachers will fulfill their job efficiently. This paper is designed to illustrate the basic concepts, definitions, characteristics, technology, and challenges of Internet of Things. We also illustrated the role of Internet of Things in building a smart educational process, and also, in making efficient and effective decisions, which is vital in our daily life.
193 citations
TL;DR: A survey of different security risks that pose a threat to the Microservices‐based fog applications is presented and an ideal solution for security issues in services communication of Micro services‐based Fog Services architecture is proposed.
Abstract: Fog computing is used as a popular extension of cloud computing for a variety of emerging applications. To incorporate various design choices and customized policies in fog computing paradigm, Microservices is proposed as a new software architecture, which is easy to modify and quick to deploy fog applications because of its significant features, ie, fine granularity and loose coupling. Unfortunately, the Microservices architecture is vulnerable due to its wildly distributed interfaces that are easily attacked. However, the industry has not been fully aware of its security issues. In this paper, a survey of different security risks that pose a threat to the Microservices‐based fog applications is presented. Because a fog application based on Microservices architecture consists of numerous services and communication among services is frequent, we focus on the security issues that arise in services communication of Microservices in four aspects: containers, data, permission, and network. Containers are often used as the deployment and operational environment for Microservices. Data is communicated among services and is vital for every enterprise. Permission is the guarantee of services security. Network security is the foundation for secure communication. Finally, we propose an ideal solution for security issues in services communication of Microservices‐based fog applications.
99 citations
TL;DR: An efficient and secure access control model has been proposed for the cloud computing environment for resource and knowledge sharing by using attribute‐based encryption, distributed hash table (DHT) network, and identity‐based timed‐release encryption (IDTRE).
Abstract: With the rapid development of the Internet, resource and knowledge sharing are two major problems experienced due to the presence of many hackers and malicious users. In this paper, an efficient and secure access control model has been proposed for the cloud computing environment for resource and knowledge sharing by using attribute‐based encryption (ABE), distributed hash table (DHT) network, and identity‐based timed‐release encryption (IDTRE). Here, at first, data or resources are encrypted by using the attributes of users, and encrypted data are divided into the encapsulated ciphertext and extracted ciphertext. Then, IDTRE algorithm has been used to encrypt the decryption key and combined the ciphertext of the key with the extracted ciphertext for creating the ciphertext shares. At last, the ciphertext shares are distributed into the DHT network, and encapsulated ciphertext are stored on the cloud servers. Both the performance and security analysis show the proficiency of the proposed scheme over the existing schemes in a cloud environment.
86 citations
TL;DR: The problem is formulated as an optimization problem and a hybrid metaheuristic algorithm is offered to solve it and the obtained results indicate that the method outperforms all of them in terms of consumed energy and cost.
Abstract: Cloud computing is a type of parallel, configurable, and flexible system, which refers to the provision of applications on virtual data centers. However, reducing the energy consumption and also maintaining high computation capacity have become timely and important challenges. The concept of replication is used to face these challenges. By increasing the number of data replicas, the energy consumption, the performance, and also the cost of creating and maintaining new replicas also are increased. Deciding on the number of required replicas and their location on the cloud system is an NP‐hard problem. In this paper, the problem is formulated as an optimization problem and a hybrid metaheuristic algorithm is offered to solve it. The algorithm uses the global search capability of the Particle Swarm Optimization (PSO) algorithm and the local search capability of the Tabu Search (TS) to get high‐quality solutions. The efficiency of the method is shown by comparing it with simple PSO, TS, and Ant Colony Optimization (ACO) algorithm on different test cases. The obtained results indicate that the method outperforms all of them in terms of consumed energy and cost.
68 citations
TL;DR: The results are promising because the processing overhead of the proposed verification mechanism is negligible, and the prototype implementations for performance and scalability under increasing message rates are promising.
Abstract: Summary
We propose a run-time verification mechanism of things for self-healing capability in the Internet of Things domain. We discuss the software architecture of the proposed verification mechanism and its prototype implementations. To identify faulty running behavior of things, we utilize a complex event processing technique by applying rule-based pattern detection on the events generated real time. For events, we use a descriptor metadata of the measurements (such as CPU usage, memory usage, and bandwidth usage) taken from Internet of Things devices. To understand the usability and effectiveness of the proposed mechanism, we developed prototype applications using different event processing platforms. We test the prototype implementations for performance and scalability under increasing message rates. The results are promising because the processing overhead of the proposed verification mechanism is negligible.
60 citations
TL;DR: This work proposes an Energy and Thermal‐Aware Scheduling (ETAS) algorithm that dynamically consolidates VMs to minimize the overall energy consumption while proactively preventing hotspots and outperforms other state‐of‐the‐art algorithms by reducing overall energy without any hotspot creation.
Abstract: Data centers consume an enormous amount of energy to meet the ever‐increasing demand for cloud resources. Computing and Cooling are the two main subsystems that largely contribute to energy consumption in a data center. Dynamic Virtual Machine (VM) consolidation is a widely adopted technique to reduce the energy consumption of computing systems. However, aggressive consolidation leads to the creation of local hotspots that has adverse effects on energy consumption and reliability of the system. These issues can be addressed through efficient and thermal‐aware consolidation methods. We propose an Energy and Thermal‐Aware Scheduling (ETAS) algorithm that dynamically consolidates VMs to minimize the overall energy consumption while proactively preventing hotspots. ETAS is designed to address the trade‐off between time and the cost savings and it can be tuned based on the requirement. We perform extensive experiments by using the real‐world traces with precise power and thermal models. The experimental results and empirical studies demonstrate that ETAS outperforms other state‐of‐the‐art algorithms by reducing overall energy without any hotspot creation.
54 citations
TL;DR: An adaptive scheme to reduce communication overhead caused by data movement by selectively storing the diagonal blocks of a block‐Jacobi preconditioner in different precision formats (half, single, or double) is proposed.
Abstract: We propose an adaptive scheme to reduce communication overhead caused by data movement by selectively storing the diagonal blocks of a block Jacobi preconditioner in different precision formats (half, single, or double) This specialized preconditioner can then be combined with any Krylov subspace method for the solution of sparse linear systems to perform all arithmetic in double precision We assess the effects of the adaptive-precision preconditioner on the iteration count and data transfer cost of a preconditioned conjugate gradient solver A preconditioned conjugate gradient method is, in general, a memory-bound algorithm, and therefore its execution time and energy consumption are largely dominated by the costs of accessing the problem's data in memory Given this observation, we propose a model that quantifies the time and energy savings of our approach based on the assumption that these two costs depend linearly on the bit length of a floating point number Furthermore, we use a number of test problems from the SuiteSparse matrix collection to estimate the potential benefits of the adaptive block-Jacobi preconditioning scheme
54 citations
TL;DR: This work proposes a framework to improve the intelligent false alarm reduction for DIDS based on edge computing devices that can provide energy efficiency and reduce the workload for the central server and the delay as compared to the similar studies.
Abstract: To protect assets and resources from being hacked, intrusion detection systems are widely implemented in organizations around the world. However, false alarms are one challenging issue for such systems, which would significantly degrade the effectiveness of detection and greatly increase the burden of analysis. To solve this problem, building an intelligent false alarm filter using machine learning classifiers is considered as one promising solution, where an appropriate algorithm can be selected in an adaptive way in order to maintain the filtration accuracy. By means of cloud computing, the task of adaptive algorithm selection can be offloaded to the cloud, whereas it could cause communication delay and increase additional burden. In this work, motivated by the advent of edge computing, we propose a framework to improve the intelligent false alarm reduction for DIDS based on edge computing devices. Our framework can provide energy efficiency as the data can be processed at the edge for shorter response time. The evaluation results demonstrate that our framework can help reduce the workload for the central server and the delay as compared to the similar studies.
54 citations
TL;DR: Performance results from Isambard, the first production supercomputer to be based on Arm CPUs that have been optimized specifically for HPC, are presented and node‐level benchmark results comparing ThunderX2 with mainstream CPUs, including Intel Skylake and Broadwell, as well as Xeon Phi are presented.
Abstract: In this paper, we present performance results from Isambard, the first production supercomputer to be based on Arm CPUs that have been optimized specifically for HPC. Isambard is the first Cray XC50 “Scout” system, combining Cavium ThunderX2 Arm‐based CPUs with Cray's Aries interconnect. The full Isambard system will be delivered in the summer of 2018, when it will contain over 10 000 Arm cores. In this work, we present node‐level performance results from eight early‐access nodes that were upgraded to B0 beta silicon in March 2018. We present node‐level benchmark results comparing ThunderX2 with mainstream CPUs, including Intel Skylake and Broadwell, as well as Xeon Phi. We focus on a range of applications and mini‐apps important to the UK national HPC service, ARCHER, as well as to the Isambard project partners and the wider HPC community. We also compare performance across three major software toolchains available for Arm: Cray's CCE, Arm's version of Clang/Flang/LLVM, and GNU.
TL;DR: The Paxon parliament's protocol provides a new way of implementing the state machine approach to the design of distributed systems as mentioned in this paper, despite their frequent forays from the chamber and the forgetfulness of their messengers.
Abstract: Recent archaeological discoveries on the island of Paxos reveal that the parliament functioned despite the peripatetic propensity of its part-time legislators. The legislators maintained consistent copies of the parliamentary record, despite their frequent forays from the chamber and the forgetfulness of their messengers. The Paxon parliament's protocol provides a new way of implementing the state machine approach to the design of distributed systems.
TL;DR: In this model, Rough Set, Particle Swarm Optimization, and Least Square Support Vector Machine are applied to solve the problem of product quality prediction and a RS‐PSO‐LSSVM synthesis algorithm is established, which is used to improve the learning and generalization ability of LSSVM.
Abstract: Quality control in the production process is the core of the enterprise to ensure product quality, and quality prediction is the key link of quality control and quality management. Aiming at the quality prediction of parts in the production process, a product quality prediction model is established. In this model, Rough Set (RS), Particle Swarm Optimization (PSO), and Least Square Support Vector Machine (LSSVM) are applied to solve the problem of product quality prediction and a RS‐PSO‐LSSVM synthesis algorithm is established. First, the 5M1E analysis of production process for parts is carried out, and the index system of influencing factors is established. Based on this index system, the condition attributes and decision attributes of RS are determined, in which RS is used to the reduction to extract rules and the optimal condition attribute value is obtained, which is used as the pre‐processing of LSSVM input data. Second, in order to improve the learning and generalization ability of LSSVM, PSO is used to optimize the relevant parameters and find the optimal solution. Finally, an example is given to verify the feasibility and effectiveness of the product quality prediction model and the RS‐PSO‐LSSVM comprehensive algorithm established above, and the prediction accuracy is higher than that of the RS‐LSSVM algorithm.
TL;DR: The results show that the proposed algorithm MOGA has significantly improved not only in terms of budget, deadline, and energy but also improved the utilization of cloud's resources as compared to the competitive algorithms of this work.
Abstract: Scheduling the tasks of a workflow to the cloud resources is a well‐known N‐P hard problem. The stakeholders involved in a cloud environment have different interests in scheduling problem. In addition to the traditional objectives like makespan, budget, and deadline, optimized in workflow scheduling, considering the green aspect of cloud, (ie, energy consumption) increase the problem complexity. Moreover, the interests of a cloud's stakeholders are conflicting, and satisfying all these interests simultaneously is a big problem. In this paper, we proposed a new Multi‐Objective Genetic Algorithm(MOGA) for workflow scheduling in a cloud environment. MOGA considered the conflicting interest of the cloud stakeholders for optimization and provided a solution, which not only minimizes the makespan under the budget and deadline constraints but also provided an energy efficient solution using the dynamic voltage frequency scaling. We provided a gap search algorithm in this paper, which is used to optimize the resource utilization of the cloud's resources. We compared our results with genetic algorithms considering the budget, deadline, and energy efficiency individually. We also compared the performance of MOGA with Multi‐objective Particle Swarm Optimization (MOPSO) with the same objectives as those of MOGA. To the best of our knowledge, there is no solution presented in the literature that considers the diverse objectives considered in this work. The results show that our proposed algorithm MOGA has significantly improved not only in terms of budget, deadline, and energy but also improved the utilization of cloud's resources as compared to the competitive algorithms of this work.
TL;DR: The experimental results show that RADAR delivers better outcomes in terms of execution cost, resource contention, execution time, and SLA violation while it delivers reliable services.
Abstract: Cloud computing utilizes heterogeneous resources that are located in various datacenters to provide an efficient performance on a pay‐per‐use basis. However, existing mechanisms, frameworks, and techniques for management of resources are inadequate to manage these applications, environments, and the behavior of resources. There is a requirement of a Quality of Service (QoS) based autonomic resource management technique to execute workloads and deliver cost‐efficient and reliable cloud services automatically. In this paper, we present an intelligent and autonomic resource management technique named RADAR. RADAR focuses on two properties of self‐management: firstly, self‐healing that handles unexpected failures and, secondly, self‐configuration of resources and applications. The performance of RADAR is evaluated in the cloud simulation environment and the experimental results show that RADAR delivers better outcomes in terms of execution cost, resource contention, execution time, and SLA violation while it delivers reliable services.
TL;DR: This paper proposes an approach to automatically generating a related work section by comparing the main text of the paper being written with the citations of other papers that cite the same references and verifies the general summarization method based on connotation and extension through citation.
Abstract: Summary
Related work is a component of a scientific paper, which introduces other researchers' relevant works and makes comparisons with the current author's work. Automatically generating the related work section of a writing paper provides a tool for researchers to accomplish the related work section efficiently without missing related works. This paper proposes an approach to automatically generating a related work section by comparing the main text of the paper being written with the citations of other papers that cite the same references. Our approach first collects the papers that cite the reference papers of the paper being written and extracts the corresponding citation sentences to form a citation document. It then extracts keywords from the citation document and the paper being written and constructs a graph of the keywords. Once the keywords that discriminate the two documents are determined, the minimum Steiner tree that covers the discriminative keywords and the topic keywords is generated. The summary is generated by extracting the sentences covering the Steiner tree. According to ROUGE evaluations, the experiments show that the citations are suitable for related work generation and our approach outperforms the three baseline methods of MEAD, LexRank, and ReWoS. This work verifies the general summarization method based on connotation and extension through citation.
TL;DR: This paper proposes an algorithm that assesses information credibility on social networks in order to detect fake or malicious information using a fuzzy analytic hierarchy process to assign proper weights to the proposed metrics.
Abstract: The credibility of information in social networks has attracted a lot of interest due to its important role in spreading information. We argue that the quality of information or objects created in social networks can be analyzed by using their provenance data. In this paper, we propose an algorithm that assesses the credibility of information on social networks to detect the propagation of fake or malicious information. To test the usability of the proposed algorithm, we introduce a prototype implementation and discuss it in detail. We test the prototype software on a large-scale synthetic social provenance dataset. The initial results are promising.
TL;DR: This paper explores how different power caps affect the performance of numerical algorithms with different computational intensities, and determines the impact, in terms of performance and energy usage, that these caps have on a system running scientific applications.
Abstract: The emergence of power efficiency as a primary constraint in processor and system design poses new challenges concerning power and energy awareness for numerical libraries and scientific applications. Power consumption also plays a major role in the design of data centers, which may house petascale or exascale‐level computing systems. At these extreme scales, understanding and improving the energy efficiency of numerical libraries and their related applications becomes a crucial part of the successful implementation and operation of the computing system. In this paper, we study and investigate the practice of controlling a compute system's power usage, and we explore how different power caps affect the performance of numerical algorithms with different computational intensities. Further, we determine the impact, in terms of performance and energy usage, that these caps have on a system running scientific applications. This analysis will enable us to characterize the types of algorithms that benefit most from these power management schemes. Our experiments are performed using a set of representative kernels and several popular scientific benchmarks. We quantify a number of power and performance measurements and draw observations and conclusions that can be viewed as a roadmap to achieving energy efficiency in the design and execution of scientific algorithms.
TL;DR: The authors' analysis for on‐demand instances resulted in multiple linear regression equations that represent the influence of characteristics of the processor and RAM memory in the composition of the price of different types of instances available on the Amazon EC2 provider.
Abstract: In this paper, we conduct statistical analyses for two Amazon cloud pricing models: on demand and spot. On demand cloud instances are charged a fixed price and can only be terminated by the user, with very high availability. On the other hand, spot instances are charged a dynamic price determined by a market‐driven model and can be revoked by the provider when the spot price becomes higher than the user‐defined price, having possibly low availability. Our analysis for on‐demand instances resulted in multiple linear regression equations that represent the influence of characteristics of the processor and RAM memory in the composition of the price of different types of instances available on the Amazon EC2 provider. In order to analyze the Amazon spot pricing, we used time‐smoothed moving averages by 12‐hour periods, aiming to provide a price‐availability trade‐off to the user. Our experiments with spot price histories from September to November 2016 show that the user's bid can be set at 30% of the on‐demand price, with an availability above of 90%, depending on instance type.
TL;DR: An Artificial Bee Colony Algorithm based on Genetic Operators (ABC‐GO) is proposed to find a solution to join the query optimization problems in the distributed database systems and has shown that the cost of the query evaluation is minimized and the quality of Top‐K query plans is improved for a given distributed query.
Abstract: As the main factor in the distributed database systems, query optimization is aimed at finding an optimal execution plan to reduce the runtime. In such systems, because of the repeated relations on various sites, the query optimization is very challenging. Moreover, the query optimization issue with large‐scale distributed databases is an NP‐hard problem. Therefore, in this paper, an Artificial Bee Colony Algorithm based on Genetic Operators (ABC‐GO) is proposed to find a solution to join the query optimization problems in the distributed database systems. The ABC algorithm has the global–local search capabilities and genetic operators to create new candidate solutions for improving the performance of the ABC algorithm. The obtained results have shown that the cost of the query evaluation is minimized and the quality of Top‐K query plans is improved for a given distributed query. Moreover, this method decreases the overhead. However, it needs a longer execution time.
TL;DR: A survey of dynamic branch prediction techniques is presented in this paper, where the authors classify the works based on key features to underscore their differences and similarities, and recommend further research in this area and will be useful for computer architects, processor designers, and researchers.
Abstract: Branch predictor (BP) is an essential component in modern processors since high BP accuracy can improve performance and reduce energy by decreasing the number of instructions executed on wrong-path. However, reducing the latency and storage overhead of BP while maintaining high accuracy presents significant challenges. In this paper, we present a survey of dynamic branch prediction techniques. We classify the works based on key features to underscore their differences and similarities. We believe this paper will spark further research in this area and will be useful for computer architects, processor designers, and researchers.
TL;DR: This research focuses on securing the entire process of data migration to cloud from devices while the in‐cloud data flow is monitored by the Information Flow Control policies specified by the users.
Abstract: Recent developments in the cloud technologies have motivated the migration of distributed large systems, specifically the Internet of Things to the cloud architecture. Since Internet of Things consist of a vast network and variety of objects, the cloud platform proves to be an ideal option. It is essential for the proper functioning of the Internet of Things to be able to share data among the system processes. The biggest problem faced during the transition of the IoTs to the cloud is the security of data especially while data sharing within the cloud and among its tenants. Information Flow Control mechanisms are one of the many solutions to enable a controlled sharing of data. Integration of Information Flow Control Systems to the existing architecture requires various levels of re‐engineering efforts. Moreover, most of the Information Flow Control systems focus on data flow within the cloud and neglect the security and integrity of data while it is being transferred to the cloud from various devices. This research focuses on securing the entire process of data migration to cloud from devices while the in‐cloud data flow is monitored by the Information Flow Control policies specified by the users. We have developed a prototype for the proposed model, and results are evaluated on the basis of energy consumption and execution time. As proposed model provides security services such as privacy, integrity, and authentication, hence it takes more execution time and consumes more energy as compared with the existing model.
TL;DR: The proposed system reduces load balancing in Cloud Quantum Computation (CQC) and overcomes issues in load balancing and load scheduling; this can be proved by its precision and privacy calculation.
Abstract: Cloud computing is a growing environment. Many of the users are interested to outsource their data in cloud; however, load balancing in cloud is still at risk. Resource allocation plays a major role in load balancing. In this scheduling problem, independent task in cloud computing can allocate resource by the summary of modified canopy fuzzy c‐means algorithm (MCFCMA). To allocate task to their corresponding resource, particle swarm‐based optimization algorithm (PSO) is used. In proposed scheme, first independent task selected based on load feed‐back, cluster the requested task using MCFCMA and schedule task to each virtual machine. VM selects parallel execution in virtual machine manager. Calculate feature value using PSO algorithm. Allocate resource to the task. Since our proposed system selects resource based on parallel execution, it reduces load balancing in Cloud Quantum Computation (CQC). The proposed system overcomes issues in load balancing and load scheduling; this can be proved by its precision and privacy calculation.
TL;DR: This paper proposes a lightweight anonymous mutual authentication and key agreement scheme for WBAN that uses only hash function operations and XOR operations, and uses the automatic security verification tool ProVerif to verify the security properties of the scheme.
Abstract: Wireless body area network (WBAN) is a special wireless mobile sensor network, which is mainly applied to the medical field. It can monitor the physical condition of patients and send this vital and sensitive information to a server that provides medical and health services. Because of the openness and mobility of WBAN, it is easier for the adversary to obtain, corrupt, or replace the data transmitted in the channel, or launch various attacks. Therefore, data security and privacy issues are the most challenging problems in WBANs. Moreover, most wearable sensors in WBAN are resource‐constrained devices, traditional security schemes may not be suitable for WBAN. Therefore, in this paper, we propose a lightweight anonymous mutual authentication and key agreement scheme for WBAN. This scheme uses only hash function operations and XOR operations. We use the automatic security verification tool ProVerif to verify the security properties of our scheme and informal security analysis is discussed. We also compared the proposed scheme with many related works and the results indicate that our scheme has either more advantages in terms of computation cost, energy consumption, and communication cost or lower security risk.
TL;DR: Experimental results show that it is possible to employ softwareulnerability detection based on ML techniques, however, ML‐based techniques suffer poor performance on both cross‐project and class imbalance problem in software vulnerability detection.
Abstract: Software vulnerability is a critical issue in the realm of cyber security. In terms of techniques, machine learning (ML) has been successfully used in many real‐world problems such as software vulnerability detection, malware detection and function recognition, for high‐quality feature representation learning. In this paper, we propose a performance evaluation study on ML based solutions for software vulnerability detection, conducting three experiments: machine learning‐based techniques for software vulnerability detection based on the scenario of single type of vulnerability and multiple types of vulnerabilities per dataset; machine learning‐based techniques for cross‐project software vulnerability detection; and software vulnerability detection when facing the class imbalance problem with varying imbalance ratios. Experimental results show that it is possible to employ software vulnerability detection based on ML techniques. However, ML‐based techniques suffer poor performance on both cross‐project and class imbalance problem in software vulnerability detection.
TL;DR: The framework adapts the Notebook with built‐in cyberGIS capabilities to accelerate gateway application development and sharing while associated data, analytics, and workflow runtime environments are encapsulated into application packages that can be elastically reproduced through cloud‐computing approaches.
Abstract: The interdisciplinary field of cyberGIS (geographic information science and systems (GIS) based on advanced cyberinfrastructure) has a major focus on data‐ and computation‐intensive geospatial analytics. The rapidly growing needs across many application and science domains for such analytics based on disparate geospatial big data poses significant challenges to conventional GIS approaches. This paper describes CyberGIS‐Jupyter, an innovative cyberGIS framework for achieving data‐intensive, reproducible, and scalable geospatial analytics using Jupyter Notebook based on ROGER, the first cyberGIS supercomputer. The framework adapts the Notebook with built‐in cyberGIS capabilities to accelerate gateway application development and sharing while associated data, analytics, and workflow runtime environments are encapsulated into application packages that can be elastically reproduced through cloud‐computing approaches. As a desirable outcome, data‐intensive and scalable geospatial analytics can be efficiently developed and improved and seamlessly reproduced among multidisciplinary users in a novel cyberGIS science gateway environment.
TL;DR: This work investigates the benefits provenance can offer to social computing and trade‐off implications, and shows experimental results of how provenance data can help better visualize interaction and performance data during a collective's run‐time.
Abstract: Complex systems such as Collective Adaptive Systems that include a variety of resources, are increasingly being designed to include people in task‐execution. Collectives, encapsulating human resources/services, represent one type of an application within which people with different type of skills can be engaged to solve one common problem or work on the same project. Mechanisms of managing social collectives are dependent on functional and non‐functional parameters of members of social collectives. In this work, we investigate the benefits provenance can offer to social computing and trade‐off implications. We show experimental results of how provenance data can help better visualize interaction and performance data during a collective's run‐time. We present novel metrics that can be derived from provenance, and lastly, we discuss privacy implications. If utilized ethically, provenance can help in developing more efficient provisioning and management mechanisms in social computing.
TL;DR: A mating selection mechanism combined with the achievement scale function and angle information index is used to generate elite offspring of the internal population and the balanceable fitness estimation method is employed to select and update the external archive.
Abstract: It is difficult to protect users' privacy and to process private information due to the complexity and uncertainty of such information. To protect private information quickly and accurately, a many‐objective optimization algorithm framework based on the hybrid elite selection strategy is proposed in this paper. First, a mating selection mechanism combined with the achievement scale function and angle information index is used to generate elite offspring of the internal population. Then, the balanceable fitness estimation method is employed to select and update the external archive. To test performance, the proposed algorithm is tested on many‐objective optimization problems (MaOPs) and compared with five state‐of‐the‐art algorithms. Experimental simulation results show that the proposed algorithm is more effective in solving MaOPs and can inspire development of a better privacy protection strategy.
TL;DR: This work utilizes the causality and the uncertainty of the Bayesian Network in order to propose a new Deep Bayesian network architecture and provides a new learning algorithm for this multi‐layeredBayesian Network with latent variables.
Abstract: Classical Datamining methods are facing various challenges in the era of Big Data. Between the need of fast knowledge extraction and the high flows of data acquired in small slots of time, these methods became shifted. The variability and the veracity of the Big Data perplex the Machine Learning process. The high volume of Big Data yields to a congested learning because the classic methods are designed for small sets of features. Deep Learning has recently emerged in the aim of handling voluminous data. The concept of the Deep induces the conversion of the features into a new abstracted representation in order to optimize an objective. Although the Deep Learning methods are experimentally promising, their parameterization is exhaustive and empirical. To tackle these problems, we utilize the causality and the uncertainty of the Bayesian Network in order to propose a new Deep Bayesian Network architecture. We provide a new learning algorithm for this multi‐layered Bayesian Network with latent variables. We evaluate the proposed architecture and learning algorithms over benchmark datasets. We used high‐dimensional data in order to simulate the Big Data challenges, which are imposed by the volume and veracity aspects. We demonstrate the effectiveness of our contribution under these constraints.