nils-herrmann/clean_scirepeval huggingface dataset:
An FPGA-Based Cloud System for Massive ECG Data Analysis

In this brief, we propose a stand-alone system-on-a-programmable-chip (SOPC)-based cloud system to accelerate massive electrocardiogram (ECG) data analysis. The proposed system tightly couples network I/O handling hardware to data processing pipelines in a single field-programmable gate array (FPGA), offloading both networking operations and ECG data analysis. In this system, we first propose a massive-sessions optimized TCP/IP hardware stack using a macropipeline architecture to accelerate network packet processing. Second, we propose a streaming architecture to accelerate ECG signal processing, including QRS detection, feature extraction, and classification. We verify our design on XC6VLX550T FPGA using real ECG data. Compared to commercial servers, our system shows up to 38× improvement in performance and 142× improvement in energy efficiency.
A Secure File Allocation Algorithm for Heterogeneous Distributed Systems

In this study we develop a secure allocating processing(SAP) algorithm for the S-FAS scheme [13] to improve the security level and consider its performance using the heterogeneous feature of a large distributed system. The SAP allocation algorithm considers load balancing, delayed effects caused by the workload variance of many consecutive requests, and the heterogeneous feature of the storage nodes in the system. We develop a prototype using the multi-threading technique for the S-FAS scheme with the SAP algorithm to guide the file allocation. We did some experiments and the results show that the proposed solution can not only improve the security level, but also improve the throughput of the distributed storage system with heterogeneous vulnerabilities by using the multi-thread technique.
Anti-Counterfeit Scheme Using Monte Carlo Simulation for E-commerce in Cloud Systems

E-commerce using cloud-based trading platforms has become a popular approach with the growth of global development in recent years. However, the existence of counterfeits on the platform has threatened the benefits of all stakeholders. This paper proposes a novel scheme named Anti-Counterfeit Deterministic Prediction Model (ADPM), which is designed for detecting counterfeits by using Monte Carlo Model (MCM) to predict the potential malicious information in e-commerce. We consider the discriminations of the fake merchandises a crucial issue in preventing counterfeits on the online business platforms. The proposed mechanism provides a paradigm of machine-learning with using a novel algorithm that derives from MCM. The main algorithm used in our proposed mechanism is Monte Carlo Model-based Prediction Analysis Algorithm (M-PAA). Our experiment has evaluated that the proposed approach can provision the predictions of the insecure information in e-commerce.
Optimizing Tasks Assignment on Heterogeneous Multi-core Real-Time Systems with Minimum Energy

The main challenge for embedded real-time systems, especially for mobile devices, is the trade-off between system performance and energy efficiency. Through studying the relationship between energy consumption, execution time and completion probability of tasks on heterogeneous multi-core architectures, we propose an Accelerated Search algorithm based on dynamic programming to obtain a combination of various task schemes which can be completed in a given time with a confidence probability by consuming the minimum possible energy. We adopt a DAG (Directed Acyclic Graph) to represent the precedent relation between tasks and develop a Minimum-Energy Model to find the optimal tasks assignment. The heterogeneous multi-core architectures can execute tasks under different voltage level with DVFS which leads to different execution time and different consumption energy. The experimental results demonstrate our approach outperforms state-of-the-art algorithms in this field (maximum improvement of 24.6%).
A Cloud Trust Authority Framework for Mobile Enterprise Information System

With the trend of mobile enterprise information systems, security has become the primary issue as it relates to business secret, decision, and process control. Hence, we carry out a fully customized framework to emphasize on security from trust authority of the cloud certificate authority server, and to guarantee security with the process of the software developments. The core object model, named as secure mobile beans (SMB), can be deployed into the cloud server. Our framework consists of SMB models, object-relation mapping module, SMB translator, and development tools. The use cases show that it can free developers from the complex implementation of security policies during the development stages, shorten the time of mobile application's development effectively.
Resource scheduling for hybrid broadcasting and cellular networks

This paper proposes a novel Resource Scheduling (RS) method by using Hybrid Broadcasting and Cellular Networks (HBCN). In this approach, the hot or common information is assigned with higher priority levels and is preferentially scheduled. The Equivalent Throughput (ET) and fairness are redefined and discussed. Simulations show the ETs of the networks are improved by using the proposed algorithms, the hotter the information, the more efficient the network. Moreover, the simulations also show the more cells in the coverage of the terrestrial broadcasting network, the more efficient of the HBCN.
Energy-Aware Loop Parallelism Maximization for Multi-core DSP Architectures

With the advance of semiconductor, multi-core architecture is inevitable in today's embedded system design. Nested loops are usually the most critical part in multimedia and high performance DSP (Digital Signal Processing) systems. Hence, maximizing loop parallelism is an important issue to improve the performance of a modern compiler. This paper studies how to maximize the system performance with the consideration of energy reduction for applications with multidimensional nested loops on multi-core DSP architectures. An algorithm, EALPM (Energy-Aware Loop Parallelism Maximization), is proposed in this paper. We implemented a two phase strategy. First, the strategy uses retiming and loop transformation to parallelize nested loops. Then the strategy employs a novel voltage assignment algorithm to reduce total energy consumption. The experimental results show that on average, both performance and energy-saving can be significantly improved using the EALPM algorithm.
A Privacy-Protection Data Separation Approach for Fine-Grained Data Access Management

Mobile devices like smartphones and tablets hold private and critical data of users. Mobile apps often require access to data on mobile devices, thus data privacy becomes an issue. Most time, mobile systems grant apps with excessive permission to data such as images and location. It allows apps to collect data more than they need without notifying the user again. This study focuses on a new approach to provide fine-grained data access permission authorization for mobile systems. We study image and location, as two typical kinds of persistent and temporary data, to separating mobile data with different privacy level and applying a finer-grained data access management in local mobile system. We design benchmarks and implement several mobile apps based on our approach, and the evaluation shows the feasibility of our approach.
Public Vehicles for Future Urban Transportation

This paper advocates a new paradigm of transportation systems for future smart cities, namely, public vehicles (PVs), that provides dynamic ridesharing trips at requests. Passengers will enjoy more convenient and flexible transportation services with much less expense. In the PV system, both the number of vehicles and required parking spaces will be significantly reduced. There will be less traffic congestion, less energy consumption, and less pollution. In this paper, the concept, method, and algorithm for the PV system are described. The key issue of effectively implementing the PV system is to design efficient planning and scheduling algorithms. The PV-path problem is formulated, which is NP-complete. Then, a practical approach is proposed, which can serve people anywhere and anytime. The simulation results show that, to achieve the same performance (e.g., total time, waiting time, and travel time), the number of vehicles in the PV system can be reduced by around 90% and 57% compared with the conventional vehicle system and Uber Pool, respectively, and the total traveling distance can be reduced by 34% and 14%.
Grounding high efficiency cloud computing architecture: HW-SW co-design and implementation of a stand-alone web server on FPGA

With the advent of the cloud computing, web servers, as the major channel in cloud computing, need to be redesigned to meet performance and power constraints. Considerable efforts have been invested in distributed web servers and web caching with different optimizing strategies, but few existing studies have been directly focused on improving the web server itself, not to mention complete hardware-favored web services. In this paper, we propose a novel architecture of web server and implement it on FPGA. After taking challeng with significant difficulties in design and implementation, we manage to complete an evaluation system which confirms that hardware-favored architecture brings higher throughput, lower power consumption as well as stand-alone web service functionalities due to direct pipelining execution of web service protocols in hardware without operating system.
Failure Rate Minimization with Multiple Function Unit Scheduling for Heterogeneous WSNs

Failure-Rate Minimization is becoming one of the major design issues in wireless sensor network (WSN) architecture due to multiple available Functional-units (FUs). There is a tradeoff between reliability and performance, such as timing constraint. This paper studies how to minimize the total failure rate while satisfying performance requirement for WSN applications. Two novel algorithms are proposed to solve the FRMFS (Failure Rate Minimization with FU Scheduling) problem. We use these FU scheduling algorithms to minimize system failure rate without sacrificing performance. Our results show that the average improvement on failure-rate reduction is significant with the use of our algorithms.
Brain-Based Computer Interfaces in Virtual Reality

Virtual Reality (VR) research is accelerating the development of inexpensive real-time Brain Computer Interface (BCI). Hardware improvements that increase the capability of Virtual Reality displays and Brain Computer wearable sensors have made possible several new software frameworks for developers to use and create applications combining BCI and VR. It also enables multiple sensory pathways for communications with a larger sized data to users' brains. The intersections of these two research paths are accelerating both fields and will drive the needs for an energy-aware infrastructure to support the wider local bandwidth demands in the mobile cloud. In this paper, we complete a survey on BCI in VR from various perspectives, including Electroencephalogram (EEG)-based BCI models, machine learning, and current active platforms. Based on our investigations, the main findings of this survey highlights three major development trends of BCI, which are entertainment, VR, and cloud computing.
Maximum loop distribution and fusion for two-level loops considering code size

In this paper, we propose a technique combining loop distribution with loop fusion to improve the timing performance without increasing the code size of the transformed loops. We first develop the loop distribution theorems that state the conditions distributing any two-level nested loop in the maximum way. Based on the loop distribution theorems, we design an algorithm to conduct maximum loop distribution. Then we propose a technique of maximum loop distribution with direct loop fusion, which performs maximum loop distribution followed by direct loop fusion. The experimental results show that the execution time of the transformed loops by our technique is reduced 41.9% on average compared to the original loops without the increase of the code size.
Throughput Maximization for Intel Desktop Platform under the Maximum Temperature Constraint

As processor power consumption continues to grow, thermal issue is becoming critical in the design of computing systems. While there have been extensive theoretical researches conducted on thermal aware computing, most of these researches are based on idealized theoretical models and, sometimes, unrealistic assumptions. In this paper, we develop a practical thermal aware scheduling algorithm to optimize its throughput under a given temperature constraint. We identify limitations of related theoretical work and implement a practical algorithm on an Intel i5 desktop computing running Linux 2.6.34. We also use SPEC2000 benchmark to validate the effectiveness of the proposed algorithm.
Robust H∞ fusion filtering for discrete-time nonlinear delayed systems with missing measurement

This paper explores the problem of multi-sensor robust H∞ fusion filtering for a class of discrete-time stochastic nonlinear systems with missing measurement and time delays. This discrete-time nonlinear system model is composed of a linear dynamic system and a bounded static nonlinear operator. The missing measurements from multi-sensors are described by a binary switching sequence that obeys a conditional probability distribution. By employing the Lyapunov-Krasovskii functional method with the stochastic analysis approach, a centralized fusion filter is designed such that, for all possible missing observations, the fusion error systems is globally asymptotically stable in the mean square, and the prescribed H∞ performance constraint is met. A simulation example is provided to illustrate the design procedure and expected performance.
Authentication and Identification Methods Used in Keystroke Biometric Systems

Keystroke dynamics authentication is not as widely used compared to other biometric systems. In recent years, keystroke dynamic authentication systems have gained interest because of low cost and integration with existing security systems. Many different methods have been proposed for data collection, feature representation, classification, and performance evaluation. The work presents a detailed survey of the most recent research in keystroke dynamic authentication. Research is evaluated by the conditions under which data was collected, classification algorithms used, and system performance. This work also identifies some shortcomings of the current research issues that need to be addressed for keystroke dynamics to mature. Some recommendations for future research are made, with the goal of improving keystroke dynamics system performance and robustness.
Context Awareness through Reasoning on Private Analysis for Android Application

Smartphone contains diverse sensitive private information, such as phone number, contact and credit. The Android platform employs permission mechanism to protect intensive information from illegal leakage. There are several tools to assist users to manage apps' permissions, such as App Ops, Privacy Guard, and XPrivacy. However, the permission mechanism of Android is complicated, and only a few of smartphone users are familiar with Android security mechanism. In addition, it is difficult for smartphone users to know whether the privacy information exposure by Android applications is legal or not. Reverse engineering and program data flow analysis are basic approaches to analysis applications. Although it needs analysts to manual analysis results, its efficiency is largely depends on analysts' skills and experience. To improve analysis efficiency, we take a case-based reasoning method to simulation the security expert analysis applications. We translate the result of Android static analysis tools into cases and build a case reasoning library. Experiments show that it can help analysts to aid analysis Android applications.
A Multiple Priority Queueing Genetic Algorithm for Task Scheduling on Heterogeneous Computing Systems

On the distributed or parallel heterogeneous computing systems, an application is usually decomposed into several independent and/or interdependent sets of cooperating subtasks and assigned to a set of available processors for execution. Heuristic-based task scheduling algorithms consist of the two typical phases of task prioritization and processor selection. However, heuristic-based task scheduling algorithms produce a different makespan (completion time /schedule length) using the different task prioritization on a distributed or parallel heterogeneous computing systems. Therefore, the role of a good scheduling algorithm is to efficiently assign each subtask to a priority depending on the resources needed to minimize makespan. In this paper, a multiple priority queueing genetic algorithm (MPQGA) for task scheduling on heterogeneous computing systems is proposed. The basic idea of our approach is to exploit the advantages of both evolutionary and heuristic based algorithms while avoiding their drawbacks. Our algorithm incorporates a genetic algorithm (GA) approach to assign priority for each subtask while using a heuristic based heterogeneous earliest finish time (HEFT) approach to search for a solution for mapping subtasks to processors. The software simulation results, over a large set of randomly generated graphs as well as graphs for real-world problems with various characteristics, show that the makespan is increased when the number of nodes or communication to computation ratios (CCR) increased and decreased with the increasing parallelism or number of available processors. The proposed MPQGA algorithm significantly outperforms several related algorithms in terms of the schedule quality. The average makespan reduction is about 5.3%.
Energy-Aware Data Allocation With Hybrid Memory for Mobile Cloud Systems

Resource scheduling is one of the most important issues in mobile cloud computing due to the constraints in memory, CPU, and bandwidth. High energy consumption and low performance of memory accesses have become overwhelming obstacles for <bold> <italic>chip multiprocessor</italic></bold> (CMP) systems used in cloud systems. In order to address the daunting “memory wall” problem, hybrid on-chip memory architecture has been widely investigated recently. Due to its advantages in size, real-time predictability, power, and software controllability, scratchpad memory (SPM) is a promising technique to replace the hardware cache and bridge the processor–memory gap for CMP systems. In this paper, we present a novel hybrid on-chip SPM that consists of a static random access memory (RAM), a <bold> <italic>magnetic RAM</italic></bold> (MRAM), and a <bold> <italic>zero-capacitor RAM</italic></bold> for CMP systems by fully taking advantages of the benefits of each type of memory. To reduce memory access latency, energy consumption, and the number of write operations to MRAM, we also propose a novel <bold> <italic>multidimensional dynamic programming data allocation</italic></bold> (MDPDA) algorithm to strategically allocate data blocks to each memory. Experimental results show that the proposed MDPDA algorithm can efficiently reduce the memory access cost and extend the lifetime of MRAM.
Guest Editor's Introduction to the Special Section on Social Network Security

The nine papers included in this special section focus on cyber security methods to protect social network services. The emerging paradigm of social network provides an enormous number of novel approaches to implementing advanced networking communications and data analysis schemes efficiently using existing datasets, networks, and infrastructure. Social networks have had a great impact on people’s daily life and global businesses, as has been addressed by recent research. However, the security issue is also a critical concern when adopting social network technologies in practice. Considering the uniqueness of social networks, the mechanism is now facing a variety of security challenges from multiple dimensions, such as mobile apps, wireless communication, cloud systems, big data, and security operations. Compared with traditional security issues, the applications of social networks are operated in a dynamic circumstance involving different internal and external inputs and factors, which requires new security mechanisms in distinct operational environments. The complexity of the technical implementations may result in unexpected consequences when adopting social network technologies. It is therefore important for current researchers and practitioners to address the security issues and seek out efficient ways to handle different hazards. For the purpose of preventing social network-based solutions from the threats of social networks, a variety of cyber security approaches or mechanism have been proposed.
Keystroke Biometric User Verification Using Hidden Markov Model

Biometric systems such as fingerprint, iris, DNA became popular methods in user authentication. Compared to these biometric systems, keystroke biometric authentication systems have not gained so much attention because of lower accuracy compared to other biometric systems. A number of researches have been conducted on keystroke biometric using different generative and discriminative classifiers. As Hidden Markov Models have proven a great success in voice recognition, this study investigates Hidden Markov Models in keystroke dynamic. This paper proposes a novel user verification technique using 1-substate Hidden Markov Model through keystroke dynamic. To verify the effectiveness of the proposed system, extensive experiments have been conducted and 80% accuracy was achieved by the proposed system.
Spoofing-Jamming Attack Strategy Using Optimal Power Distributions in Wireless Smart Grid Networks

As an emerging fast-growing technology, smart grid networks (SGNs) have been dramatically accepted by the current power supply industry for achieving high performance power governance system. The wireless SGN (WSGNs) have enabled numerous flexible power management solutions without the restrictions of the wired infrastructure. The cognitive radio network (CRN) is one of the widely deployed wireless networking approaches. The communication security is a major concern while CRN is used in WSGNs. Currently, jamming and spoofing are two common attack approaches that are active in the deployment of WSGNs when using CRNs. This paper proposes an attack strategy, maximum attacking strategy using spoofing and jamming (MASS-SJ), which utilizes an optimal power distribution to maximize the adversarial effects. Spoofing and jamming attacks are launched in a dynamic manner in order to interfere with the maximum number of signal channels. Our proposed approach has been evaluated by our experiments and the results have shown the positive performance of using MAS-SJ.
Energy Aware Loop Scheduling for High Performance Multi-Module Memory

The speed gap between processor and memory is the major bottleneck for modern computing systems. Many modern processors, such as the CELL processor, employ multi-core, multimodule architecture to hide memory access latency. However, making effective use of multiple memory modules remains difficult, considering the combined effect of performance and energy requirements. This paper studies the scheduling and assignment problem that optimize both energy and performance. An efficient algorithm, EALSPP (Energy Aware Loop Scheduling with Prefetching and Partition), is proposed. The algorithm attempts to maximize energy saving while hiding memory latency with the combination of loop scheduling, data prefetching, memory partition, and heterogeneous memory module type assignment. Experimental results demonstrate the effectiveness of our approach.