nils-herrmann/clean_scirepeval huggingface dataset:
Meta Meta-Analytics for Risk Forecast Using Big Data Meta-Regression in Financial Industry

The growing trend of the e-banking has driven the implementations of big data in financial industry. Data analytic is considered one of the most critical aspects in current economic development, which is broadly accepted in various financial domains, such as risk forecast and risk management. However, gaining an accurate risk prediction is still a challenging issue for current financial service institutions and the hazards can be caused in various perspectives. This paper proposes an approach using meta meta-analytics for risks forecast in big data. The proposed model is Meta Meta-Analytics Risk Forecast Model (MMA-RFM) with a crucial algorithm Regression with Meta Meta-Analytics Algorithm (RMMA). The proposed schema has been examined by the experimental evaluation in which it performs an optimized performance.
A Data-Oriented Method for Scheduling Dependent Tasks on High-Density Multi-GPU Systems

The rapidly-changing computer architectures, though improving the performance of computers, have been challenging the programming environments for efficiently harnessing the potential of novel architectures. In this area, though the high-density multi-GPU architecture enabled unparalleled performance advantage of dense GPUs in a single server, it has increased the difficulty for scheduling diversified and dependent tasks. We therefore propose a data-oriented method for scheduling dependent tasks for this architecture while providing its implementation. In our method, we model a parallel program as a collection of data-dependent tasks for which data dependencies are managed by an expressive matrix. Accordingly, we develop a hierarchical scheduler infrastructure for our model. In this, a top scheduler is built for querying the data-dependency matrix; three downstream schedulers for queuing computation tasks that are exclusively assigned to processor, accelerator or either; and a multitude of bottom schedulers each for providing a processing element with assigned tasks. We experiment our scheduler for examples of Strassen matrix multiplication and Cholesky matrix inversion algorithms on a computer that has 8 Tesla K40 GPUs. The results show that our method is capable of offering the efficient task parallelism while fulfilling the complex task dependencies. When advanced task-oriented schedulers have been widely designed for distributed systems, a lightweight data-driven scheduler could be an alternative and handy approach that can handle the dependent yet diversified tasks of data-intensive applications for the novel high-density multi-accelerator system.
Privacy Protection for Preventing Data Over-Collection in Smart City

In smart city, all kinds of users' data are stored in electronic devices to make everything intelligent. A smartphone is the most widely used electronic device and it is the pivot of all smart systems. However, current smartphones are not competent to manage users' sensitive data, and they are facing the privacy leakage caused by data over-collection. Data over-collection, which means smartphones apps collect users' data more than its original function while within the permission scope, is rapidly becoming one of the most serious potential security hazards in smart city. In this paper, we study the current state of data over-collection and study some most frequent data over-collected cases. We present a mobile-cloud framework, which is an active approach to eradicate the data over-collection. By putting all users' data into a cloud, the security of users' data can be greatly improved. We have done extensive experiments and the experimental results have demonstrated the effectiveness of our approach.
Energy Optimization of Air-Based Information Network with Guaranteed Security Protection

The air-based information network of the Space-Air-Ground Integrated Network has the distinct characteristics of heterogeneous, time-varying, distributed and self-organized, which bring not only new challenges of node mobility model, dynamic network grouping and interconnection and intercommunication of heterogeneous networks, but also higher requirements of network security. Therefore, selecting an efficient and safe communication link is critical when there may not be fixed rules. In this article, we build a model of the air-based information network, and use the method of dynamic programming to select the most efficient communication link by minimizing the energy consumption in the communication model. In the communication model, energy consumption is associated with the security level of the communication link. The experiments show that the presented algorithms can effectively reduce the total cost while satisfying security constraints.
Intelligent Vehicle Detection and Tracking for Highway Driving

Due to the increment of vehicles, the traffic jamming in cities becomes a serious challenge and the safety of people is threatened. Intelligent transportation system (ITS) and intelligent vehicles are critical to the efficiency of city transportation. In the area related with ITS and intelligent vehicles, moving vehicle detection and tracking are the most challenging problems. In this paper, we propose a framework for vehicle detection and tracking and make an in-depth research in key algorithms and techniques. We also conduct a serial of experiments on the basis of the existing results. Experimental results show that our proposed approach is feasible and effective for vehicle detection and tracking.
Energy Efficient Task Assignment with Guaranteed Probability Satisfying Timing Constraints for Embedded Systems

The trade-off between system performance and energy efficiency (service time) is critical for battery-based embedded systems. Most of the previous work focuses on saving energy in a deterministic way by taking the average or worst scenario into account. However, such deterministic approaches usually are inappropriate in modeling energy consumption because of uncertainties in conditional instructions on processors and time-varying external environments (e.g., fluctuant network bandwidth and different user inputs). By adopting a probabilistic approach, this paper proposes a model and a set of algorithms to address the Processor and Voltage Assignment with Probability (PVAP) problem of data-dependent aperiodic tasks in real-time embedded systems, ensuring that all the tasks can be done under the time constraint with a guaranteed probability. We adopt a task DAG (Directed Acyclic Graph) to model the PVAP problem. We first use a processor scheduling algorithm to map the task DAG onto a set of voltage-variable processors, and then use our dynamic programming algorithm to assign a proper voltage to each task. Finally, to escape from local optima, a local search with restarts searches the optimal solution from candidate solutions by updating the objective function, until the stop criteria are reached or a time bound is elapsed. The experimental results demonstrate that for probability 1.0, our approach yields slightly better results than the well-known algorithms like ASAP/ALAP (As Soon As Possible/As Late As Possible) and ILP (Integer Linear Programming) with/without DVS (Dynamic Voltage Scaling). However, for probabilities 0.8 and 0.9, our approach significantly outperforms those algorithms (maximum improvement of 50.3 percent).
Exponential ${\rm H}_{\infty}$ Synchronization of General Discrete-Time Chaotic Neural Networks With or Without Time Delays

This brief studies exponential H∞ synchronization of a class of general discrete-time chaotic neural networks with external disturbance. On the basis of the drive-response concept and H∞ control theory, and using Lyapunov-Krasovskii (or Lyapunov) functional, state feedback controllers are established to not only guarantee exponential stable synchronization between two general chaotic neural networks with or without time delays, but also reduce the effect of external disturbance on the synchronization error to a minimal H∞ norm constraint. The proposed controllers can be obtained by solving the convex optimization problems represented by linear matrix inequalities. Most discrete-time chaotic systems with or without time delays, such as Hopfield neural networks, cellular neural networks, bidirectional associative memory networks, recurrent multilayer perceptrons, Cohen-Grossberg neural networks, Chua's circuits, etc., can be transformed into this general chaotic neural network to be H∞ synchronization controller designed in a unified way. Finally, some illustrated examples with their simulations have been utilized to demonstrate the effectiveness of the proposed methods.
An Approach to Minimize Crossings in Phylogenetic Trees

Visualization of phylogenetic trees became popular and has been widely used in biology, taxonomy, cloud computing, and bioinformatics. Different phylogenetic trees can be built based on different measures and methods. As a result, the problem became obvious when deciding which tree is better and most visually appealing. This often involves comparing two phylogenetic trees to each other. The main goal of this paper is to display a pair of alternative phylogenetic trees together while minimizing the number of crossings among the branches of both trees. This paper summarizes different tools and techniques used in visualizing phylogenetic trees in different studies and describes their limitations. After surveying different studies involved in crossings minimization, a naive algorithm to minimize the number of crossings is proposed.
Guest Editor's Introduction to the Special Issue on Security and Privacy on Clouds

The fifteen papers in this special section focus on cloud computer security and privacy. The emerging paradigm of cloud computing provides a new way to address the constraints of limited energy, capabilities, and resources. Researchers and practitioners have embraced cloud computing as a new approach that has the potential for a profound impact in our daily life and world economy. However, security and privacy protection is a critical concern in the development and adoption of cloud computing. To avoid system fragility and defend against vulnerabilities exploration from cyber attacker, various cyber security techniques and tools have been developed for cloud systems.
Global Variable Partition with Virtually Shared Scratch Pad Memory to Minimize Schedule Length

On-chip memory is a critical component of MPSoC architecture. Virtually Shared Scratch Pad Memory (VSSPM) architecture is a novel approach to close the processor-memory speed gap since it can take the advantages of both shared and private SPM. In this paper, we propose an efficient variable partitioning algorithms, Global View Prediction (GVP), to improve the overall throughput. The experimental results demonstrate the effectiveness of our approach.
A Reusable Software Component for Integrated Syntax and Semantic Validation for Services Computing

Extensible Markup Language (XML) syntax and semantic validations are critical to the correct service transaction specification and service integration based on the existing distributed heterogeneous computing services. However, the current Schematron design and implementation based on Extensible Style sheet Language Transformations (XSLT) have limitations in terms of validation correctness and support for system integration. We propose an algorithm that integrates syntactic and semantic validations in order to overcome the aforementioned limitations. The syntactic validation is based on DTD and XSD and the semantic validation is based on the Schematron. The solution is illustrated by several use cases. Our contributions include combining syntax and semantic validations, designing and implementing a reusable software component to implement this integrated validation process, and supporting invoking this integrated validation through the more flexible observer pattern.
Optimization for Communication Energy Efficiency of Air-Based Information Network While Satisfying Timing Constraints

In air-based information network of the Space-Air-Ground Integrated Network, it is critical to select an efficient communication link for each communication. However, some communication may not have fixed rules. In this article, we build a model of the air-based information network, and use the dynamic programming to select the most efficient communication link by minimizing the energy consumption in the communication model. In the communication model, energy consumption is associated with the distance between aircrafts and the relative velocity of the aircrafts. The experiments show that the presented algorithms can effectively reduce the total cost while satisfying timing constraints with guaranteed confidence probabilities. For example, there has been an achievement that an average reduction of 9.9% per 20 time units in the 100-subnetwork model satisfying timing constraints based on the presented algorithm.
Efficiency-Aware Workload Optimizations of Heterogeneous Cloud Computing for Capacity Planning in Financial Industry

The broad implementation of cloud computing has brought a dramatic change to multiple industries, which derives from the development of the Internet-related technologies. This trend has enabled global enterprises to apply distributed computing techniques to reach many benefits. An effective risk management approach is required for service deliveries and a capacity planning is considered one of the convincing methods for financial industry. However, executing a capacity planning is still encountering a great challenge from bottlenecks of the Web server capacities. The unstable service demands often result in service delays, which embarrasses the competitivenesses of the enterprises. This paper addresses this issue and proposes an approach, named Efficiency-aware Cloud-based Workload Optimization (ECWO) Model, using greedy programming to predict server workloads of heterogeneous cloud computing in financial industry. The main algorithms used in the proposed model are Task Mapping Algorithm (TMA) and Efficiency-Aware Task Assignment (EATA) Algorithm. Our experimental evaluations have examined the performance of the proposed scheme.
Resource Management in Sustainable Cyber-Physical Systems Using Heterogeneous Cloud Computing

The substantial growth of the distributed computing using heterogeneous computing has enabled great expansions in Cyber Physical Systems (CPS). Combining CPS with heterogeneous cloud computing is an alternative approach for increasing sustainability of the system. However, execution of resource management in cloud systems is still encountering a few challenges, including the bottlenecks of the Web server capacities and task assignments in the heterogeneous cloud. The unstable service demands often result in service delays, which embarrasses the competitiveness of the enterprises. This paper addresses the problem of the task assignment in heterogeneous clouds, which is proved as a NP-hard problem. The proposed approach is called Smart Cloud-based Optimizing Workload (SCOW) Model that uses predictive cloud capacities and considers sustainable factors to assign tasks to heterogeneous clouds. To reach the optimization objective, we propose a few algorithms, which include Workload Resource Minimization Algorithm (WRM), Smart Task Assignment (STA) Algorithm, and  Task Mapping Algorithm (TMA). Our experimental evaluations have examined the performance of the proposed scheme.
Security-Aware Information Classifications Using Supervised Learning for Cloud-Based Cyber Risk Management in Financial Big Data

With the fast development of Web-based solutions, a variety of paradigms and platforms are emerging as value creators or improvers in multiple industries. This trend has also enable financial firms to improve their business processes and create new services. Sharing data between financial service institutions has become an option of achieving value enhancements. However, the concern of the privacy information leakage has also arisen, which impacts on both financial organizations and customers. It is important for stakeholders in financial services to be aware of the proper information classifications, by which determining which information can be shared between the financial service institutions. This paper focuses on this issue and proposes a new approach that use combined supervised learning techniques to classify the information in order to avoid releasing those information that can be harmful for either financial service providers or customers. The proposed model is entitled as Supervised learning-Based Secure Information Classification (SEB-SIC) model, which is mainly supported by the proposed Decision Tree-based Risk Prediction (DTRP) algorithm. The proposed scheme is a predictive mechanism that uses the historical data as the training dataset. The performance of our proposed mechanism has been assessed by the experimental evaluations.
Impacts of Inaccurate Information on Resource Allocation for Multi-Core Embedded Systems

Multi-core technologies are widely used in embedded systems. Stochastic resource allocation can guarantee certain quality of the services (QoS). In heterogeneous embedded system resource allocation, execution time distributions of different tasks on cores are predicted before scheduling. The difference between actual execution time and estimated execution time may lead to allocations which are not robust. In this paper, we present an evaluation of impacts of inaccurate information on resource allocation. We propose a systematic measure of the robustness degradation and evaluate how inaccurate probability parameters affect the robustness of resource allocations. Furthermore, we compare the performance of three widely used greedy heuristics when using the inaccurate information.
Cyber Incident Classifications Using Ontology-Based Knowledge Representation for Cybersecurity Insurance in Financial Industry

As a recent emerging industry, cybersecurity insurance has been growing ambitiously fast, which mainly serves the financial industry and assists financial firms to reduce cybersecurity risks. Understanding the risk classification is an important hemisphere for operating cybersecurity insurance. However, the classification representation will be complicated when the service system becomes large. Improper presentation of the risks can result in financial loss or operational mistakes. This paper addresses this concern and proposes an approach using ontology-based knowledge representation for cybersecurity insurance. The approach is named as Semantic Cyber Incident Classification (SCIC) model, which uses knowledge representation deriving from semantic techniques. Our approach is specifically designed for targeting at cybersecurity insurance domain, which has been assessed by our experiments.
Spatial graph grammars for Web information transformation

This paper presents an approach to spatial specifications for Web information transformation. Extended from the reserved graph grammar (RGG), a spatial graph grammar (SGG) is proposed. The paper illustrates a detailed example that applies the SGG to transform a XML Web document to a WML structure for the display on mobile devices. The SGG formalism is general enough for a wide range of applications such as multimedia interfaces, electronic publishing and XML document conversion.
Voltage Assignment and Loop Scheduling for Energy Minimization while Satisfying Timing Constraint with Guaranteed Probability

Low energy consumption is an important problem in real-time embedded systems and loop is the most energy consuming part in most cases. Due to the uncertainties in execution time of some tasks, this paper models each varied execution time as a probabilistic random variable. We use rotation scheduling and DVS (Dynamic Voltage Scaling) to minimize the expected total energy consumption while satisfying the timing constraint with a guaranteed confidence probability. Our approach can handle loops efficiently. In addition, it is suitable to both soft and hard real-time systems. And even for hard real-time, we have good results.
CLMRS: Designing Cross-LAN Media Resources Sharing Based on DLNA

Digital Living Network Alliance (DLNA) puts forward an interoperable architecture of home network equipment to implement the simple and seamless interoperability between household appliances, mobile devices and computers, so as to enhance and enrich users' experience. Because DLNA is designed to implement the sharing of multimedia resources between devices in a family environment, it does not support cross-LAN accessing. Currently, the cross-network cooperative work between smart devices has become a hot issue. To overcome this problem, this paper presents CLMRS - a cross-LAN accessing solution of C/S architecture based on DLNA technology. CLMRS can be used to implement cross-network media resources sharing by designing DLNA gateway/router of applicaion-level. We use DLNA gateway as a agent of LAN which is designed to transpond communication messages and to redirect address. To achieve cross-LAN media resources sharing, CLMRS adapts DLNA router as a transfer server to transpond communication data and to manage users access right. We also optimize the transmission path of media Steam to reduce the pressure of DLNA router. In order to protect user privacy, CLMRS sets a family account to limit the access right and proposes an efficient and secure authentication mechanism with anonymity. Our design and theoretical model are validated via implementing an instance of DLNA cross-network communication. Experimental results show that the approach is practical and can protect user privacy effectively.
Loop scheduling with complete memory latency hiding on multi-core architecture

The widening gap between processor and memory performance is the main bottleneck for modern computer systems to achieve high processor utilization. In this paper, we propose a new loop scheduling with memory management technique, iterational retiming with partitioning (IRP), that can completely hide memory latencies for applications with multi-dimensional loops on architectures like CELL processor (J.A. Kahle et al., 2005). In IRP, the iteration space is first partitioned carefully. Then a two-part schedule, consisting of processor and memory parts, is produced such that the execution time of the memory part never exceeds the execution time of the processor part. These two parts are executed simultaneously and complete memory latency hiding is reached. Experiments on DSP benchmarks show that IRP consistently produces optimal solutions as well as significant improvement over previous techniques
City of White Plains Parking App: Case Study of a Smart City Web Application

The City of White Plains has a residential population of 57,000 people, which increases every day by 225,000 people who come in from the outlying suburbs to work and shop in the city. Due to the traffic created and the large number of spread-out parking locations, there is an urgent demand of creating an mobile app allowing drivers to find available parking spaces easily. However, the solution specifically focusing on this issue has been rarely investigated by the prior research. In this paper, we represent our case study of a smart city web application using semantic design for a governmental organization's parking app. The app navigates customers to parking structures in conjunction with their destination. The app's user interface was constructed using HTML, CSS, and JavaScript, and a tag-based network structure to facilitate exploratory behavior. The work culminated in a completed user interface, a report detailing its perceived benefits, and an evaluation of its application. Use cases are described for three applications-the Passport app currently employed by the City of White Plains, the app developed in this study, and a future app using Internet of Things (IoT) technology.