nils-herrmann/clean_scirepeval huggingface dataset:
A Discrete Dynamic Voltage and Frequency Scaling Algorithm Based on Task Graph Unrolling for Multiprocessor System

In this paper, we propose a two-phase discrete DVFS algorithm for a multiprocessor system, dedicated to applications where the period of task graph is smaller than the deadline of task. In those applications, multiple task graphs are unrolled and then concatenated together to form a new task graph. The proposed DVFS algorithm is applied to the new-formed task graph to stretch tasks’ execution time, lower the operating frequencies of processor and achieve the system power efficiency. Experimental results show that the proposed algorithm reduces the energy dissipation by 25% on average, comparing to previous DVFS approaches.
Efficient Implementation of Thermal-Aware Scheduler on a Quad-core Processor

Due to power wall and slow performance improvement in a single core micro-architecture, multiple even many cores based processors rose as the main stream processor. Nevertheless, thermal threats regarding reliability and lifetime of processors are still among the major concerns which received much attention in terms of algorithms and hardware design to reduce processor temperature and keep application performance in recent years. In this paper, we propose and implement a thermal-aware Round-Robin scheduling algorithm for process migration in the Linux environment on a quad-core processor. Bearing designer's goals in mind, such as performance, load-balancing, and reliability, we managed to achieve much bigger temperature fall than previous results of Round-Robin scheduler on a dual-core processor as well as baseline Linux scheduler on a quad-core processor. Moreover, the performance loss due to scheduling overhead is modest in our approach. Our results indicate that thermal-aware scheduling is a valid approach to tackling thermal issues on multi-core processors. There will be increasing demand for thermal-aware scheduling as the number of cores on a single processor increases.
Privacy-Preserving Data Encryption Strategy for Big Data in Mobile Cloud Computing

Privacy has become a considerable issue when the applications of big data are dramatically growing in cloud computing. The benefits of the implementation for these emerging technologies have improved or changed service models and improve application performances in various perspectives. However, the remarkably growing volume of data sizes has also resulted in many challenges in practice. The execution time of the data encryption is one of the serious issues during the data processing and transmissions. Many current applications abandon data encryptions in order to reach an adoptive performance level companioning with privacy concerns. In this paper, we concentrate on privacy and propose a novel data encryption approach, which is called Dynamic Data Encryption Strategy (D2ES). Our proposed approach aims to selectively encrypt data and use privacy classification methods under timing constraints. This approach is designed to maximize the privacy protection scope by using a selective encryption strategy within the required execution time requirements. The performance of D2ES has been evaluated in our experiments, which provides the proof of the privacy enhancement.
Software defined healthcare networks

With the increasingly serious problem of the aging population, creating an efficient and real-time health management and feedback system based on the healthcare Internet of Things (HealthIoT) is an urgent need. Specifically, wearable technology and robotics can enable a user to collect the required human signals in a comfortable way. HealthIoT is the basic infrastructure for realizing health surveillance, and should be flexible to support multiple application demands and facilitate the management of infrastructure. Therefore, enlightened by the software defined network, we put forward a smart healthcare oriented control method to software define health monitoring in order to make the network more elastic. In this article, we design a centralized controller to manage physical devices and provide an interface for data collection, transmission, and processing to develop a more flexible health surveillance application that is full of personalization. With these distinguished characteristics, various applications can coexist in the shared infrastructure, and each application can demand that the controller customize its own data collection, transmission, and processing as required, and pass the specific configuration of the physical device. This article discusses the background, advantages, and design details of the architecture proposed, which is achieved by an open-ended question and a potential solution. It opens a new research direction of HealthIoT and smart homes.
A Multi-order Markov Chain Based Scheme for Anomaly Detection

This paper presents a feasible multi-order Markov chain based scheme for anomaly detection in server systems. In our approach, both the high-order Markov chain and multivariate time series are taken into account, along with the detailed design of training and testing algorithms. To evaluate its effectiveness, the Defense Advanced Research Projects Agency (DARPA) Intrusion Detection Evaluation Data Set is used as stimuli to our model, by which system calls and the corresponding return values form a two-dimensional input set. The calculation result shows that this approach is able to produce several effective indicators of anomalies. In addition to the absolute values given by an individual single-order model, we also notice a novelty unprecedented before, i.e., the changes in ranking positions of outputs from different-order ones also correlate closely with abnormal behaviours. Moreover, the analysis and application proves our approach's efficiency in consuming reasonable cost of time and storage.
Proactive Attribute-based Secure Data Schema for Mobile Cloud in Financial Industry

Cyber-security has become one of the most significant issues in the financial industry. New threats continuously emerge when new technologies or approaches are introduced. One security challenge is that financial customers are unaware of the hazards due to the hidden third party. We address this issue and propose a novel approach that is proactive defense means using attributed-based encryption and data self-deterministic schema to protect financial clients' privacy information. The proposed novel approach is named as Proactive Dynamic Secure Data Schema (P2DS), which is designed to ensure that the sensitive data cannot be directly used by the unanticipated party. Three algorithms have been proposed for P2DS, including Static Decryption Attribute Algorithm (SDAA), Corresponded Decryption Attribute Algorithm (CDAA), and Proactive Determinative Encryption Algorithm (PDEA). The main contributions of this paper are providing an effective method of protecting customers' privacy and reducing operating risks of the financial organizations. Our experiment has proved the validity and efficiency of the proposed schema.
Cross-Domain Sentiment Analysis of Product Reviews by Combining Lexicon-Based and Learn-Based Techniques

Product reviews can direct consumers' purchasing behavior and sellers' marketing strategy. Therefor, in this paper, we propose a novel method which combines lexicon-based and learn-based techniques in order to solve cross-domain sentiment analysis of product reviews. We first create three domain lexicons based on the basic lexicon and corpus from three domains containing book, hotel and electronics. Furthermore, we use four kinds of features to build classifiers. Besides, we conduct a series of experiments to evaluate our method by using different lexicons and different classifiers. Experimental results on data of product reviews show that domain lexicons outperform the basic lexicon no matter in which domain. What's more, our method performs much better than the existing state-of-the-art method especially in domains of book and hotel, and is slightly inferior in the domian of electronics.
Fault Tolerant Data Collection in Heterogeneous Intelligent Monitoring Networks

In this work, we focus on the problem of fault tolerant data collection in heterogeneous Intelligent Monitoring Networks(IMNs). IMNs are expected to have a wide range of applications in many fields such as forest monitoring, structural monitoring, and industrial plant monitoring. We present our fault tolerant datacollection scheme in the hierarchical structure of IMNs. We use an interesting technique borrowed from the popular BitTorrent software to maintain a highly efficient and robust data collection in IMNs with heterogeneous and faulty devices. In our proposed scheme, monitoring sensors are instructed to randomly select some overheard transmissions and process them in data fusion. Our preliminary study confirmed the benefits of the fault tolerant data collection strategy.
User-Level Side Channel Attack on Workflow System in Data-Center

Though current workflow systems, known as the major task managing tool in data-center, use UNIX-based user/group authorization mechanisms to defend unauthorized operations, the information in workflow systems, such as DAG (Directed Acyclic Graph), could be easily exposed to any user-level malicious monitor. In this paper, we propose a type of side channel attack to data-center workflow information. By this attack, the DAG of workflow systems can be stolen through monitoring basic system characteristics, such as CPU utilization, memory accessing, disk I/O, etc, which can successfully circumvents UNIX privilege checking. Also, we present a software-diversity based measurecounter to mitigate the attack.
Reliability analysis of an energy-aware RAID system

We develop a mathematical model - MREED - to quantitatively evaluate the failure rate of energy-efficient parallel storage systems. The Power-Aware Redundant Array of Inexpensive Disk (PARAID) aims to reduce energy use of commodity server-class disks without specialized hardware. The goal of PARAID is to skewed striping pattern to adapt to the system load by changing the number of powered disks. By spinning down disks during light workloads, PARAID can reduce power consumption, while still meeting performance demands. We show that MREED can be used to estimate a five-disk PARAID-0 system. We validate the accuracy of MREED using the DiskSim simulator. Our approach shows that MREED can rely on file access pattern to estimate system utilization correctly. Furthermore, even thought PARAID may achieve reasonable reliability, our model shows that PARAID's reliability is affected by data locality.
Towards Power-Efficient Smartphones by Energy-Aware Dynamic Task Scheduling

Smartphones are facing a grand challenge in extending their battery life to sustain an increasing level of processing demand while subject to miniaturized form factor. Dynamic Voltage Scaling (DVS) has emerged as a critical technique to leverage power management by lowering the supply voltage and frequency of processors. In this paper, based on the DVS technique, we propose a novel Energy-aware Dynamic Task Scheduling (EDTS) algorithm to minimize total energy consumption for smartphones while satisfying stringent time constraints for applications. This algorithm utilizes the results from a static scheduling algorithm and aggressively reduces energy consumptions on the fly. Experimental results indicate that the EDTS algorithm can significantly reduce energy consumption for smartphones, compared to the critical path scheduling method and the parallelism-based scheduling algorithm.
Loop scheduling and assignment to minimize energy while hiding latency for heterogeneous multi-bank memory

Many high-performance DSP processors employ multi-bank on-chip memory to improve performance and energy consumption. This architectural feature supports higher memory bandwidth by allowing multiple data memory accesses to be executed in parallel. This paper studies the scheduling and assignment problem on minimizing the total energy consumption while satisfying timing constraint with heterogeneous multi-bank memory for applications with loop. An algorithm, TASL (Type Assignment and Scheduling for Loops), is proposed. The algorithm uses loop scheduling and assignment with the consideration of variable partition to find the best configuration for both memory and ALU.
Energy-Aware Optimal Task Assignment for Mobile Heterogeneous Embedded Systems in Cloud Computing

Recent quick expansions of mobile heterogeneous embedded systems have led to a remarkable hardware upgrade that support multiple core processors. The energy consumption is becoming greater along with the computation capacity grows. Cloud computing is considered one of the solutions to mitigating energy costs. However, the simply offloading the computations to the remote side cannot efficiently reduce the energy consumptions when the energy costs caused by wireless communications are greater than it is on mobile devices. In this paper, we focus on the problem of energy wastes when tasks are assigned to remote cloud servers or heterogeneous core processors. Our solution aims to minimize the total energy cost of the mobile heterogeneous embedded systems by using an optimal task assignment to heterogeneous cores and mobile clouds. The propose model is named as Energy-Aware Heterogeneous Resource Management Model (EA-HRM2), which is supported by a main algorithm Optimal Heterogeneous Task Assignment (OHTA) algorithm. Our experimental evaluations have proved our approach is effective to save energy when deploying heterogenous embedded systems in mobile cloud systems.
Design and Optimization of Traffic Balance Broker for Cloud-Based Telehealth Platform

The use of cloud computing for the better health care is more and more important. Patient's real-time physiological signals, such as electrocardiogram (ECG) and blood pressure, should be transmitted to hospital servers for remote monitoring, and stored in Data Centers (DCs) so that the authorized doctors are able to access the patient's disease history. This implies challenges in brokering between the cloud consumers and providers when a huge number of people gets the real-time services from the distributed medical organizations. This paper proposes a probability-based bandwidth model in a telehealth cloud system, which helps cloud broker to allocate the most efficient computing nodes and links. This brokering mechanism considers the location of Personal Health Record (PHR) in cloud and schedules the real- time signal with a low information transfer between different hosts. Furthermore, our broker uses a bandwidth evaluation for the model, and we also compare various predicting methods to obtain the best bandwidth allocating algorithm. We simulate an inter-host environment for measuring the performance of our bandwidth allocating method with various data coherence protocols, which controls the domain of PHR in cloud, and the results show that our model is effective at determining the best performing service, and the inserted service validates the utility of our approach.
Dynamic and Leakage Energy Minimization With Soft Real-Time Loop Scheduling and Voltage Assignment

With the shrinking of technology feature sizes, the share of leakage in total power consumption of digital systems continues to grow. Traditional dynamic voltage scaling (DVS) fails to accurately address the impact of scaling on system power consumption as the leakage power increases exponentially. The combination of DVS and adaptive body biasing (ABB) is an effective technique to jointly optimize dynamic and leakage energy dissipation. In this paper, we propose an optimal soft real-time loop scheduling and voltage assignment algorithm, loop scheduling and voltage assignment to minimize energy, to minimize both dynamic and leakage energy via DVS and ABB. Voltage transition overhead has been considered in our approach. We conduct simulations on a set of digital signal processor benchmarks based on the power model of 70 nm technology. The simulation results show that our approach achieves significant energy saving compared to that of the integer linear programming approach.
A capacity improving and energy saving scheduling scheme in push-based converged wireless broadcasting and cellular networks

This paper proposes a capacity improving and energy saving scheduling scheme in push-based Converged Wireless Broadcasting and Cellular Network (CWBCN). We maximize the network capacity and alleviate request congestion through broadcasting/multi-casting the most popular services, and locally caching for further request on the user side. In the proposed scheme, we firstly introduce a UE (User Equipment)-based caching mechanism by considering both the frequency and recency of the popular services. Simulations show that the proposed mechanism brings a significant improvement on the capacity of the CWBCN. Based on the mechanism, a sleep-awake algorithm is proposed to further reduce the energy consumption of the UEs. Simulations show that with the proposed algorithm for the converged network can reduce 20%-30% energy consumption by comparing to the traditional one.
A Novel Secure Big Data Cyber Incident Analytics Framework for Cloud-Based Cybersecurity Insurance

The remarkable increasing demands of mitigating losses from cyber incidents for financial firms has been driving the rapid development of the Cybersecurity Insurance (CI). The implementations of CI have covered a variety of aspects in cyber incidents, from hacking to frauds. However, CI is still at its exploring stage so that there are a number of dimensions that are uncovered by the current applications. The cyber attack on critical infrastructure is one of the serious issues that prevents the expansions of CI. This paper addresses CI implementations focusing on cloud-based service offerings and proposes a secure cyber incident analytics framework using big data. Our approach is designed for matching different cyber risk scenarios, which uses repository data. Our simulation has provided the theoretical proof of the adoptability and feasibility.
Battery-aware task scheduling in distributed mobile systems with lifetime constraint

A distributed mobile system consists of a group of heterogeneous mobile devices connected by wireless network. Due to the fact that most of the mobile devices are battery based, the lifetime of a mobile system depends on both the battery behavior and the energy consumption characteristics of tasks. In this paper, we present a set of models for task scheduling in mobile systems equipped with Dynamic Voltage Scaling (DVS) processors. We propose battery-aware algorithms to obtain task schedules satisfying the battery lifetime constraints. The simulations with randomly generated Directed Acyclic Graphs (DAG) show that our proposed algorithms generate better schedules that can satisfy the battery lifetime constraints.
Inferring Information Propagation over Online Social Networks: Edge Asymmetry and Flow Tendency

Inferring the underlying information propagation over online social networks is important because it leads to new insights and enables forecasting, as well as influencing information propagation. In this paper, we analyze propagation processes in online social networks, when only limited details of propagation are available. We use data crawled from Chinese largest recommendation social network -- Douban Network, and study how the information propagates through the user relationship network of Douban. By using the users' follow relationship information, and time sequence of registering for participation in events, we build the potential propagation paths of events. After analyzing the propagation processes of 30,778 events in which about 1.47 million users are involved, we observe the statistical characteristics of propagation paths of those events, including the different types of participants and size distribution of connected participants. Further, we find that information propagation between node pairs are asymmetric. Moreover, based on the asymmetric property between node pairs, we propose a concept -- Information Potential Energy, that describes the capability that nodes disseminate information over a network. Finally, we propose a Flow Shell (FS) model that can efficiently and correctly calculate the nodes' Information Potential Energy, and validate it.
Information Security Policy for E-Commerce in Saudi Arabia

Information is a key element in the modern society. It is therefore important that information is well secured and structures put in place to steer effectiveness. This study will concentrate on information security structure within Saudi government. In order to establish the information value within the government, the E-government structure set by Saudi will form the basis of this study. The E-government structure within Saudi Arabia will be used to open up vulnerability and barriers to effective information technology. The study recognizes the fact that effective information relay in an E-commerce environment must face barriers, hence there is a need to have clear information security structure.
Key Establishment in Multi-core Parallel Systems

The trend toward high processing power at a reasonable cost continues with the emergence of multi-core architectures with large number of cores. In such computing systems, a major technological challenge is to design the internal, on-chip communication network.This not only depends on high performance in latency, bandwidth, and fairness in contention under heavy loads, but also depends on an efficient authentication mechanism and protection of integrity and privacy of applications from hardware and software attacks. In this paper, we present a technique to establish secret keys for the cores employed by the same application to communicate securely. Our approach is based on key pre-distribution, in which each of the cores chooses a number of keys from a large key pool. When a group of cores are employed by an application, they discover their shared keys and establish a group key for secured communication. The key discovery and the process to connect partitioned groups use space-efficient Bloom filter to ensure the security and efficiency of the key establishment process. Our performance evaluation demonstrates the efficiency of the proposed framework.
Understanding Model Risk Management -- Model Rationalization in Financial Industry

Building a good financial Model Risk Management (MRM) system requires advanced skills in technology, econometrics and financial businesses. Very often we have businesses that have different models with similar functionality. The question that we are trying to answer in the paper is how to rationalize different models within an Enterprise Risk Management (ERM) of in organization. It is a dilemma that Risk Management Practitioners are struggling with. Very often they are looking for someone who can understand their business ideas and transform them to technology solutions or come up with technology solutions to support their MRM ideas. While quantitative Analysts are very critical in this type of project, very often they fail in the software development skills that are required. We are witnesses these struggles where some model risk leaders fail to find some good technologists or econometricians to implement their risk management philosophy. Also we have cases where technologists or econometricians cannot receive proper requirements to implement the functionality that the business needs. Here we are trying to understand how we can rationalize on different models that have some common functionality. For example how to look at or consolidate the functionality of the following models, such as Probability of Default (PD), Loss Given Default (LGD), Allowance for Loan and Lease Losses (ALLL), Stress Testing (ST) and others Financial Models. In this paper, we will present a framework for Model Rationalization or how to consolidate models. Also we will present a methodology that can be used to test a model that has been consolidated through our framework, Model Rationalization.