nils-herrmann/clean_scirepeval huggingface dataset:
A Fault-tolerant Architecture with Error Correcting Code for the Instruction-level Temporal Redundancy

Soft error has become an increasingly significant problem in modern computing systems. To overcome soft errors, it has reported that the instruction-level temporal redundancy in out-of-order cores suffers a performance penalty up to 45%. In this work, we propose the fault-tolerant double execution architecture with the fast error correcting code (such as two-dimensional error code) in the instruction reuse buffer. Experimental results show that it gains back IPC loss between 9.14% and 10.15%, with an average around 9.22% compared with the conventional double execution approach.
Modeling and Power Evaluation of On-Chip Router Components in Spintronics

On-chip routers are power hungry components. Besides exploiting current CMOS-based power-saving techniques, it is also desirable to investigate the power saving potential enabled by new technologies and devices. This paper investigates the potential of exploiting the emerging spin-electronics based MTJ (Magnetic Tunnel Junction) devices with application to on-chip router modules, in particular, buffers and crossbars. To this end, we build MTJ models, design circuits based on mixed MTJ-CMOS devices, and evaluate their switching power consumption, using their pure CMOS counterparts as the baseline. Our study shows that the new technology can significantly improve power efficiency for buffers but the gain for crossbars is less clear.
Low-Power Sensor Polling for Context-Aware Services on Smartphones

The growing availability of sensors integrated in smartphones provides much more opportunities for context-aware services, such as location-based and profile-based applications. Power consumption of the sensors contributes a significant part of the overall power consumption on current smartphones. Furthermore, smartphone sensors have to be activated in a stable period to match to the request frequency of those context-aware applications, known as full polling-based detection, which wastes a large amount of energy in unnecessary detecting. In this paper, we propose a low-power sensor polling for context-aware applications, which can dynamically shrink the extra sensor activities so that the unrelated sensors can keep in sleeping status for a longer time. We also provide an algorithm to find the relationship of application invoking and those sensor activities, which is always hidden in the context middleware. With this method, the polling scheduler is able to calculate and match the detecting frequency of various application combinations aroused by user. We evaluate this framework with different kinds of context-aware applications. The results show that our new low-power polling spends a tiny responding delay (97ms) in the middleware to save 70% sensor energy consumption, comparing with the traditional exhausting polling operation.
Minimizing Memory Access Schedule for Memories

According to the characteristics of the "3-D" structure of contemporary DRAM chips, the Row First Column Ordered (RFCO) algorithm is proposed in this paper to minimize memory access schedule length. In memory systems with a single memory controller, assuming that the memory access trace is known before scheduling, the RFCO algorithm can generate schedules which are 7.89% shorter than burst scheduling on average. If memory accesses are coming to the single memory controller in real time, the RFCO algorithm can generate schedules which are 8.03% shorter than burst scheduling on average.
Privacy-Preserving Content-Oriented Wireless Communication in Internet-of-Things

With mobile devices (e.g., Android and iOS devices) and other resource constrained Internet-connected devices (e.g., sensors) becoming the norm in our digitalized society, the capacity to ensure security of data-in-transit and at-rest without incurring unrealistic performance overheads is crucial. Rather than using conventional encryption, in this paper we propose the dynamic privacy protection model. The model is designed for ensuring mobile device user privacy even in large volume of data transmissions, which uses dynamic programming to produce an optimal solution of maximizing privacy protection levels even for resource constrained devices. We then develop an Android app and use it to evaluate the effectiveness of the model. The findings suggest that the proposed model allows us to achieve improved privacy protection.
An Analysis of Server-Side Design for Seed-Based Mobile Authentication

With the growing popularity of mobile apps, the security issues of mobile apps become critical. Similar to traditional cyber security, mobile security includes authentication, data integrity, and data privacy. Seed-based authentication is a novel approach for mobile systems. The key idea is to extract seeds from files such as images and generate random numbers for authentication. However, in seed-based authentication, the server side design is different from traditional username-password paradigm. Few work has been done to address server-side issues on seed-based authentication. In this paper, we focus on analyzing server-side design of seed-based authentication. We develop a prototype system and set up experiments to evaluate our server-side design.
Secure wireless communication system for smart grid with rechargeable electric vehicles

Current transportation systems are emitting a considerable amount of greenhouse gas into the atmosphere. The recent advances in the fuel cell battery is making the zero-carbon electric vehicle (EV) increasingly practical for the commercial market. However, mass use of EVs requires support from a powerful and robust power grid that can efficiently generate, aggregate, and distribute a large amount of energy in real time with low carbon emission. Such a system calls for the application of modern communication technologies that inherently suffer from various cyber attacks. Attacks against the communication system in smart grid can cause catastrophic damage to the highly interdependent smart grid users. In this article, we first propose a smart grid model for supporting mass EV use, then introduce some typical security issues in the communication system, especially for the wireless security in such a smart grid. We mainly introduce two types of security protocols: key agreement from wireless channel estimation and jamming-resilient communication.
A Head Record Cache Structure to Improve the Operations on Big Files in Cloud Storage Servers

Cache and prefetching is now widely used in storage systems for disks to speed up accessing data. Although cache and prefetching are well accepted technologies in storage field, they are unsuitable for cloud storage systems because most of requests in cloud is for large files, which leads to undesirable hit rate and speed performance. To solve this issue, an improved head record cache (HRC) structure model is proposed in this paper based on reshuffling disk cache structure and prefetching technologies, aiming at improving reading performance in a cloud environment. Compared to previous researches, this model has better read performance in a cloud environment, since HRC increases hit rate. The experimental results demonstrate that the system has 18% better reading performance than traditional cloud storage system.
Similarity-Based Node Distance Exploring and Locality-Aware Shuffle Optimization for Hadoop MapReduce

To shorten the networking delay from MapTracker to ReduceTracker has attractive potential to gain high performance shuffle for MapReduce. As the original MapReduce shuffle has no locality-aware feature when assigning reduce-tasks over computing nodes, we plan to present a similarity-based distance in the proposed Cloud Node Space to evaluate distance between two computing nodes in data center. Then, we implement a centralized and statistic-based locating service prior networking shuffle to place reduce-tasks near their corresponding data. Experimental results show that, comparing with Hadoop version, this service can achieve 2.3X speedup on shuffle time and bandwidth budget decreases by 60%.
Advanced Fully Homomorphic Encryption Scheme Over Real Numbers

The broad implementation of cloud computing have led to a dramatically growing in exchanging and using data throughout multiple parties. The main problem restricting the implementation of cloud computing is that users lack controls in cloud systems, from which security and privacy concerns become a major issue for cloud users. Logically, an applicable Fully Homomorphic Encryption (FHE) scheme is an effective solution to protecting data throughout the data usage lifecycle in the cloud system, due to the full control on users' own. However, there is no efficacious FHE scheme developed yet for meeting practical demands by the reason of either unqualified accuracy rate or intolerable latency time. Focus on this issue, we propose an advanced FHE scheme designed for operating real numbers, which is named as Full Homomorphic Encryption over Real Numbers (FHE-RN). Our approach has superb performances in both accuracy and efficiency, which has been proved by our experimental evaluations.
Energy consumption analysis of parallel sorting algorithms running on multicore systems

Sorting algorithms have been ubiquitously used in numerous applications nowadays. As the data size scales up exponentially, energy-efficiency is gradually becoming equally important as performance for sorting algorithms that process large scale data. Unfortunately, the energy consumption behaviour of various sorting algorithms is not fully explored, although the performance of sorting algorithms have been well studied. Will the sorting algorithms consume more energy when they are parallelized and executed on multicore computers? How to improve the energy-efficient performance of sorting algorithms? In this paper, we comprehensively analyze the performance and energy consumption of three traditional sorting algorithms, Odd-Even Sort, ShellSort and QuickSort, on a multicore system. Our experimental results show that algorithms with better performance tend to conserve energy as well, when evaluated on a computer with eight AMD cores. In addition, for the same algorithm, we observe that more energy savings may be achieved when task granularity is properly selected.
Modeling for CPU-Intensive Applications in Cloud Computing

CPU-intensive application is one of the most commonly used application types in cloud computing. In order to effectively deal with CPU-intensive applications and improve the application efficiency of cloud computing, we researched on CPU-intensive applications. Through this study, we find some common features and characteristics of this kind of applications. Based on these characteristics, we establish a mathematical model for the CPU-intensive applications, which can be used to predict and analyze whether an unknown application is CPU intensive. To verify the correctness of the model, we have done extensive experiments. The experimental results show that the model is correct and reasonable. It can effectively distinguish CPU intensive application from other kinds of applications. This is very helpful as it can serve as the basis of the study in special process strategies for CPU intensive applications, which may much benefit the efficiency improvement of cloud resources.
Model Risk Management Systems-Back-End, Middleware, Front-End and Analytics

Building an effective Model Risk Management (MRM) system requires advanced skills in mutliple fields, such as technology, econometrics, and business. It is a dilemma that MRM practitioners are struggling with. Very often the enterprises are seeking someone who can understand their business ideas and transform them to technology solutions or come up with technology solutions to support their MRM ideas. However, many quantitative analysts fail in the required software development skills, while they are critical in this type of the project. We are witnesses these struggles where some model risk leaders fail in finding competent 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. In this paper, we propose a MRM framework based on a review of MRM systems. Also we present the methodology of producing an effective MRM system.
DASS: A Web-Based Fine-Grained Data Access System for Smartphones

In this mobile period, users enjoy various functions and convenience from mobile devices, but the coarse-grained data access permission of current mobile operating system brings about serious risk of privacy leakage. In other words, users mobile data are over-collected by apps. This study focuses on a new framework to provide fine-grained data access mechanism for mobile systems. With the help of cloud storage, we separate mobile data by types, contents, and privacy levels. We design and implement the DASS system to provide fine-grained data access management, while keep it transparent to users. Experimental results show that our system can protect users&#x2019; privacy greatly while keep the extra cost within a reasonable range.
Phase-Change Memory Optimization for Green Cloud with Genetic Algorithm

Green cloud is an emerging new technology in the computing world in which memory is a critical component. Phase-change memory (PCM) is one of the most promising alternative techniques to the dynamic random access memory (DRAM) that faces the scalability wall. Recent research has been focusing on the multi-level cell (MLC) of PCM. By precisely arranging multiple levels of resistance inside a PCM cell, more than one bit of data can be stored in one single PCM cell. However, the MLC PCM suffers from the degradation of performance compared to the single-level cell(SLC) PCM, due to the longer memory access time. In this paper, we present a genetic-based optimization algorithm for chip multiprocessor (CMP) equipped with PCM memory in green clouds. The proposed genetic-based algorithm not only schedules and assigns tasks to cores in the CMP system, but also provides a PCM MLC configuration that balances the PCM memory performance as well as the efficiency. The experimental results show that our genetic-based algorithm can significantly reduce the maximum memory usage by 76.8 percent comparing with the uniform SLC configuration, and improve the efficiency of memory usage by 127 percent comparing with the uniform 4 bits/cell MLC configuration. Moreover, the performance of the system is also improved by 24.5 percent comparing with the uniform 4 bits/cell MLC configuration in terms of total execution time.
TERN: A Self-Adjusting Thermal Model for Dynamic Resource Provisioning in Data Centers

Dynamic resource provisioning becomes a practical approach to achieving high thermal and energy efficiency, improving scalability, and optimizing reliability for e-commercial applications running in modern data centers. In this paper, we propose a self-adjusting model called TERN to predict thermal behaviors of hardware resources for client sessions. Our TERN contains two major components: (1) a resource utilization model being responsible for estimating hardware usage based on the number of running client transactions, and (2) a thermal model that discovers correlation between resource utilization and their temperatures. TERN is conducive to predicting thermal trends of diverse workload conditions with a changing transaction mix. We apply the TPC-W benchmark to characterize the resource demands of each type of transactions. Then, we conduct a thermal profiling study of the benchmark with various transaction mixes. TERN judiciously adjusts the models to maintain prediction accuracy for dynamically changing request patterns. Experimental results show that TERN provides a simple yet powerful solution for resource provisioning in thermal-aware data centers where exist rapidly changing workload conditions.
Data Placement and Duplication for Embedded Multicore Systems With Scratch Pad Memory

Scratch pad memories (SPM) are attractive alternatives for caches on multicore systems since caches are relatively expensive in terms of area and energy consumption. The key to effectively utilizing SPMs on multicore systems is the data placement algorithm. In this paper, two polynomial time algorithms, regional data placement for multicore (RDPM) and regional data placement for multicore with duplication (RDPM-DUP), have been proposed to generate near-optimal data placement with minimum total cost. There is only one copy for each data in RDPM, while RDPM-DUP allows data duplication. Experimental results show that the proposed RDPM algorithm alone can reduce the time cost of memory accesses by 32.68% on average compared with existing algorithms. With data duplication, the RDPM-DUP algorithm further reduces the time cost by 40.87%. In terms of energy consumption, the proposed RDPM algorithm with exclusive copy can reduce the total cost by 33.47% on average. When RDPM-DUP is applied, the improvement increases up to 38.15% on average.
Online Data Allocation for Hybrid Memories on Embedded Tele-health Systems

The developments of wearable devices such as Body Sensor Networks (BSNs) have greatly improved the capability of tele-health industry. Large amount of data will be collected from every local BSN in real-time. These data is processed by embedded systems including smart phone and tablet before has been transferred to distributed storage systems. Traditional on-chip SRAMs cause critical power leakage issues and occupy relatively large chip areas. Therefore, hybrid memories, which combine volatile memories with non-volatile memories, are widely adopted in reducing the latency and energy cost on multi-core systems. However, most of the current works are about static data allocation for hybrid memories. Those mechanisms cannot achieve better data placement in real-time. Hence, we propose an online hybrid memories placement for embedded tele-health system. Experimental results demonstrate the effectiveness of our approach.
Low-Power, Intelligent Sensor Hardware Interface for Medical Data Preprocessing

This work proposes an interface design of a low-power programmable system on chip for intelligent wireless sensor nodes to reduce the overall power consumption of the heart disease monitoring system, by lending them the capability of processing complex functions and performing rapid computations on a large amount of data at the node. This facilitates the node to intelligently monitor a medical signal for impending events instead of transmitting the signal to the base station constantly. Lowering the transmission data rate decreases the transmission power consumption in a node, thereby lengthening the node life and in turn increasing the reliability of the network. This work also implements a thresholding technique, which controls the data transmission rate depending on the value of the monitored signal, and a cardiac monitoring system that performs computations at the node for the detection of either a skipped heart beat or a reduced heart rate variability, in which event the signal is transmitted to the base station for monitoring/recording or alerting the crew. The performance analysis of the system shows that there are reductions in the system power consumption and data transmission rate, which in turn reduces the network traffic and averts congestion.
TIGER: Thermal-Aware File Assignment in Storage Clusters

In this paper, we present a thermal-aware file assignment technique called TIGER for reducing the cooling cost of storage clusters in data centers. We show that peak inlet temperatures of storage nodes depend on not only CPU utilization but also I/O activities, which rely on file assignments in a cluster. The TIGER scheme aims to lower peak inlet temperatures of storage clusters by dynamic thermal management through file placements. TIGER makes use of cross-interference coefficients to estimate the re-circulation of hot air from the outlets to the inlets of data nodes. TIGER first calculates the thresholds of disks in each data node based on its contribution to heat re-circulation in a data center. TIGER undertakes two steps to achieve high I/O performance while reducing cooling cost. First, TIGER assigns groups of files with similar service times to shorten I/O response times. Second, TIGER ensures that load imbalance does not exceed a specified threshold. We evaluate performance of TIGER in terms of both cooling energy conservation and response time of a storage cluster. Our results confirm that TIGER reduces cooling-power requirements for clusters by offering about 10 to 15 percent cooling-energy savings without significantly degrading I/O performance.
Cost Minimization for Heterogeneous Systems with Gaussian Distribution Execution Time

Time and cost are the most critical performance metrics for computer systems including embedded system, desktop, laptop, mainframe computer, and smart phone. In real world, the execution time may not be fixed and it usually follows Gaussian distribution. In this paper, we analyze how to minimize total cost while satisfying time constraints for heterogeneous systems with Gaussian execution time. This is called HAP-G (Heterogeneous Assignment with Probability - Gaussian) problem, which is a NP-complete problem. However, for simple path special cases, we find a polynomial-time optimal solution and propose the HAP-G-SP algorithm, and the experimental results show the effectiveness of our approach.
Security-Aware Efficient Mass Distributed Storage Approach for Cloud Systems in Big Data

Implementing cloud computing empowers numerous paths to Web-based computing service offerings for meeting diverse needs. However, cloud data security and privacy information protection have also become a critical issue restraining the cloud applications. One of the major concerns in security is that cloud operators will have a chance to reach sensitive data, which dramatically increases users' anxiety and reduces the adoptability of cloud computing in many fields, such as the financial industry and governmental agencies. This paper focuses on this issues and proposes a novel approach that can efficiently split the file and separately store the data in the distributed cloud servers, in which the data cannot be directly reached by cloud service operators. The proposed scheme is entitled as Security-Aware Efficient Distributed Storage (SAEDS) model, which is mainly supported by the proposed algorithms, named Secure Efficient Data Distributions (SED2) Algorithm and Efficient Data Conflation (EDCon) Algorithm. Our experimental evaluations have assessed both security and efficiency performances.
An Efficient Data Selection Policy for Search Engine Cache Management

Caching is an effective optimization in search engine. The data selection policy plays a key role in caching, which places the data to be cached in memory. However, the current data selection policies are not suitable to the hybrid storage architecture with solid state disks (SSDs), which have gradually replaced hard disk drives (HDDs) in search engines. In this paper, we present an Efficient Data Selection policy (EDS) for search engine cache management, which views cache media as a knapsack, and views results and posting lists as items. The best benefit can be computed by greedy algorithms. In order to verify the effectiveness, we carry out a series of experiments to study essential factors of data selection in different architectures, including HDD, SSD, and SSD-based hybrid storage architecture, which uses SSD as a secondary cache for memory. The experimental results demonstrate that the proposed policy improves the hit ratio by 20.04% and the retrieval performance on HDD, SSD, and hybrid architecture by 31.98%, 28.72% and 23.24%, respectively.