nils-herrmann/clean_scirepeval huggingface dataset:
Fast Numerical Evaluation for Symbolic Expressions in Java

The symbolic-numeric computation has been extensively developed in scientific computing for experimenting mathematics in numerical programs, like in optimization problems and finite element methods. Many software and libraries have been developed to support symbolic-numeric computation especially in the recent years. However, most of the implementations are cumbersome and inefficient for numerically evaluating symbolic expressions. The popular implementation chooses the way that generates C/C++/FORTRAN source codes for symbolic expressions and compiles the source files using the external compilers. The compiled machine codes are then linked back to the symbolic manipulation language environment. Thi sprocess suffers from slow compilation and significant overhead of external function calls. To address this problem, this paper presents a handy approach that provides fast numerical evaluation for symbolic expressions in Java. In our approach, Java bytecode is generated in memory for symbolic expressions and further Just-In-Time (JIT) compiled to machine codes onJava Virtual Machine (JVM) at runtime. We have developedSymJava (https://github.com/yuemingl/SymJava) to implement our approach and tested a range of benchmark problems. The results show that SymJava is 1~3 orders of magnitude faster than the existing implementations including Matlab, Mathematica, Sage, Theano and SymPy. Additionally, SymJava offers a human friendly programming style for symbolic expressions by overloading operators in Java. Our approach opens up a new avenue for the development of next generation symbolic-numeric software.
Static Security Optimization for Real-Time Systems

An increasing number of real-time applications like railway signaling control systems and medical electronics systems require high quality of security to assure confidentiality and integrity of information. Therefore, it is desirable and essential to fulfill security requirements in security-critical real-time systems. This paper addresses the issue of optimizing quality of security in real-time systems. To meet the needs of a wide variety of security requirements imposed by real-time systems, a group-based security service model is used in which the security services are partitioned into several groups depending on security types. While services within the same security group provide the identical type of security service, the services in the group can achieve different quality of security. Security services from a number of groups can be combined to deliver better quality of security. In this study, we seamlessly integrate the group-based security model with a traditional real-time scheduling algorithm, namely earliest deadline first (EDF). Moreover, we design and develop a security-aware EDF schedulability test. Given a set of real-time tasks with chosen security services, our scheduling scheme aims at optimizing the combined security value of the selected services while guaranteeing the schedulability of the real-time tasks. We study two approaches to solve the security-aware optimization problem. Experimental results show that the combined security values are substantially higher than those achieved by alternatives for real-time tasks without violating real-time constraints.
QoS-Aware Fault-Tolerant Scheduling for Real-Time Tasks on Heterogeneous Clusters

Fault-tolerant scheduling plays a significant role in improving system reliability of clusters. Although extensive fault-tolerant scheduling algorithms have been proposed for real-time tasks in parallel and distributed systems, quality of service (QoS) requirements of tasks have not been taken into account. This paper presents a fault-tolerant scheduling algorithm called QAFT that can tolerate one node's permanent failures at one time instant for real-time tasks with QoS needs on heterogeneous clusters. In order to improve system flexibility, reliability, schedulability, and resource utilization, QAFT strives to either advance the start time of primary copies and delay the start time of backup copies in order to help backup copies adopt the passive execution scheme, or to decrease the simultaneous execution time of the primary and backup copies of a task as much as possible to improve resource utilization. QAFT is capable of adaptively adjusting the QoS levels of tasks and the execution schemes of backup copies to attain high system flexibility. Furthermore, we employ the overlapping technology of backup copies. The latest start time of backup copies and their constraints are analyzed and discussed. We conduct extensive experiments to compare our QAFT with two existing schemes-NOQAFT and DYFARS. Experimental results show that QAFT significantly improves the scheduling quality of NOQAFT and DYFARS.
An FPGA Based PCI-E Root Complex Architecture for Standalone SOPCs

We present an FPGA (field programmable gate array) based PCI-E (PCI-Express) root complex architecture for SOPCs (System-on-a-Programmable-Chip) in this paper. In our work, the system on the FPGA serves as a PCIE master device rather than a PCIE endpoint, which is usually a common practice as a co-processing device driven by a desktop computer or a server. We use this system to control a PCIE endpoint, which is also an FPGA based endpoint implemented on another FPGA board. This architecture requires only IP cores free of charge. We also provide basic software driver so that specific device driver can be developed on it to control popular PCIE device in the future, i.e. ethernet card or graphic card. The whole architecture has been implemented on Xilinx Virtex-6 FPGAs to indicate that this architecture is a feasible approach to standalone SOPCs, which has better efficiencies than those with additional generic controlling processors.
SNC: A Cloud Service Platform for Symbolic-Numeric Computation Using Just-In-Time Compilation

Cloud services have been widely employed in IT industry and scientific research. By using Cloud services users can move computing tasks and data away from local computers to remote datacenters. By accessing Internet-based services over lightweight and mobile devices, users deploy diversified Cloud applications on powerful machines. The key drivers towards this paradigm for the scientific computing field include the substantial computing capacity, on-demand provisioning and cross-platform interoperability. To fully harness the Cloud services for scientific computing, however, we need to design an application-specific platform to help the users efficiently migrate their applications. In this, we propose a Cloud service platform for symbolic-numeric computation– SNC. SNC allows the Cloud users to describe tasks as symbolic expressions through C/C++, Python, Java APIs and SNC script. Just-In-Time (JIT) compilation through using LLVM/JVM is used to compile the user code to the machine code. We implemented the SNC design and tested a wide range of symbolic-numeric computation applications (including nonlinear minimization, Monte Carlo integration, finite element assembly and multibody dynamics) on several popular cloud platforms (including the Google Compute Engine, Amazon EC2, Microsoft Azure, Rackspace, HP Helion and VMWare vCloud). These results demonstrate that our approach can work across multiple cloud platforms, support different languages and significantly improve the performance of symbolic-numeric computation using cloud platforms. This offered a way to stimulate the need for using the cloud computing for the symbolic-numeric computation in the field of scientific research.
On Resource Scheduling of Wireless Converged Broadcasting and Cellular Networks with Popular Services Being Preferentially Delivered

This paper studies on Resource Scheduling (RS) of Wireless Converged Broadcasting and Cellular Networks (CBCN) with popular services being preferentially delivered. In the proposed model of CBCN, the popular services are preferentially delivered by Wireless Broadcasting and Buffering Network (WBBN), and the other less popular services are delivered by Cellular Network (CN) with point-to-point (p2p) mode. With the help of return channels in CBCN, WBBN, which is the broadcasting part of the CBCN, can preferentially broadcast the most popular services to users. As a result, the efficiency of the CBCN is improved. The capacity of the WBBN is studied. We prove that the capacity of the WBBN subjects to Power Law distribution. The minimal time to schedule all the services in the CBCN is researched. Simulations show that we can properly choose the broadcasting bandwidth in the CBCN and the numbers of the most popular services to be delivered by the BN to minimize the scheduling time. Simulations also show the more popular of the services the larger broadcasting bandwidth is preferred to and the smaller of the minimal scheduling time of the CBCN.
Guest Editorial Smart Grid Cyber-Physical Security

This special section is devoted to the latest research advances in cyber-physical security and privacy of smart grids. A smart grid integrates advanced communication technologies and efficient bi-directional energy dispatch into the physical grid with the goal of achieving effective and efficient power management. However, the integration would make the mission-critical energy infrastructure more vulnerable to a wide variety of malicious cyber-attacks. Therefore, it is imperative to investigate the critical threats to the smart grid cyber-physical security and privacy, as well as to propose effective countermeasures to improve security and resilience of the smart grid.
Peak Temperature Minimization for Embedded Systems with DVS Transition Overhead Consideration

Peak temperature is a critical issue in chip multiprocessor (CMP) architecture design. Dynamic Voltage Scaling (DVS) is used nowadays to reduce power consumption and peak temperature. However, traditional DVS in literature usually fails to take into consideration of task dependencies and voltage transition overhead such as energy consumption. In this paper, we propose the ROtation Scheduling and voltage Assignment (ROSA) algorithm, to minimize peak temperature under energy consumption constraints in voltage transition. We conduct experiments on Kernel Linux equipped with 4 Intel Core Quad CPUs. The experimental results demonstrate that our algorithm can reduce the peak temperature on chip with 4.9°C more comparing with the List Scheduling Solution (LSS).
Enabling Cloud Computing in Emergency Management Systems

Emergencies, including both natural and man-made disasters, increasingly pose an immediate threat to life, health, property, and environment. For example, Hurricane Katrina, the deadliest and most destructive Atlantic tropical cyclone of the 2005 Atlantic hurricane season, led to at least 1,883 people's death and an estimated loss of -108 billion property. To reduce the damage by emergencies, a wide range of cutting-edge technologies on medicine and information are used in all phases of emergency management. This article proposes a cloud-based emergency management system for environmental and structural monitoring that utilizes the powerful computing and storage capability of datacenters to analyze the mass data collected by the wireless intelligent sensor network deployed in civil environment. The system also benefits from smartphone and social network platform to setup the spatial and population models, which enables faster evacuation and better resource allocation.
Secure Fragment Allocation in a Distributed Storage System with Heterogeneous Vulnerabilities

There is a growing demand for large-scale distributed storage systems to support resource sharing and fault tolerance. Although heterogeneity issues of distributed systems have been widely investigated, little attention has yet been paid to security solutions designed for distributed storage systems with heterogeneous vulnerabilities. This fact motivates us to investigate a fragment allocation scheme called S-FAS to improve security of a distributed system where storage sites have a wide variety of vulnerabilities. In the S-FAS approach, we integrate file fragmentation with the secret sharing technique in a distributed storage system with heterogeneous vulnerabilities. Storage sites in a distributed systems are classified into a variety of different server types based on vulnerability characteristics. Given a file and a distributed system, S-FAS allocates fragments of the file to as many different types of nodes as possible in the system. Data confidentiality is preserved because fragments of a file are allocated to multiple storage nodes. We develop storage assurance and dynamic assurance models to evaluate the quality of security offered by S-FAS. Analysis results show that fragment allocations made by S-FAS lead to enhanced security because of the consideration of heterogeneous vulnerabilities in distributed storage systems.
Phase-Reconfigurable Shuffle Optimization for Hadoop MapReduce

Hadoop MapReduce is a leading open source framework that supports the realization of the Big Data revolution and serves as a pioneering platform in ultra large amount of information storing and processing. However, tuning a MapReduce system has become a difficult task because a large number of parameters restrict its performance, many of which are related with shuffle, a complicated phase between map and reduce functions, including sorting, grouping, and HTTP transferring. During shuffle phase, a large mount of time is spent on disk I/O due to the low speed of data throughput. In this paper, we build a mathematical model to judge the computing complexity of different operating orders within map-side shuffle, so that a faster execution can be achieved through reconfiguring the order of sorting and grouping. Furthermore, a three-dimensional exploring space of the performance is expanded, with which, some sampled features during shuffle stage, such as key number, spilling file number, and the variances of intermediate results, are collected to support the evaluation of computing complexity of each operating order. Thus, an optimized reconfiguration of map-side shuffle architecture can be achieved within Hadoop without extra disk I/O induced. Comparing with the original Hadoop implementation, the results show that our reconfigurable architecture gains up to <inline-formula><tex-math notation="LaTeX">$2.37\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>2</mmlmath><inline-graphic xlink:href="qiu-ieq1-2459707.gif"/></alternatives></inline-formula> speedup to finish the map-side shuffle work.
Security protection and checking for embedded system integration against buffer overflow attacks via hardware/software

With more embedded systems networked, it becomes an important problem to effectively defend embedded systems against buffer overflow attacks. Due to the increasing complexity and strict requirements, off-the-shelf software components are widely used in embedded systems, especially for military and other critical applications. Therefore, in addition to effective protection, we also need to provide an approach for system integrators to efficiently check whether software components have been protected. In this paper, we propose the HSDefender (Hardware/Software Defender) technique to perform protection and checking together. Our basic idea is to design secure call instructions so systems can be secured and checking can be easily performed. In the paper, we classify buffer overflow attacks into two categories and provide two corresponding defending strategies. We analyze the HSDefender technique with respect to hardware cost, security, and performance. We experiment with our HSDefender technique on the simplescalar/ARM simulator with benchmarks from MiBench, an embedded benchmark suite. The results show that our HSDefender technique can defend a system against more types of buffer overflow attacks with less overhead compared with the previous work.
Dynamic temperature-aware task scheduling based on sliding window model for MPSoCs

As the power density of modern chips increases drastically, chips are prone to overheating. Thermal hot spots increase cooling costs, negatively impact reliability and degrade performance. A valid task scheduling can reduce chip's average temperature and temperature variations. We propose a dynamic temperature-aware task scheduling policy based on sliding window model. This scheduling policy calculates the probability of task allocation for each core according to current and historical temperatures of the core, and then the one with the maximal probability is chosen to execute the ready task. If multiple cores have the same probability, the scheduler gives priority to the core that has the minimal average temperature of neighbor units. The experimental results show that this scheduling policy can reduce hot spots, decrease spatial and temporal temperature variations of all units, and thus achieve a relatively lower average temperature and more balanced temperature distribution.
Optimizing Scheduling in Embedded CMP Systems with Phase Change Memory

Phase Change Memory (PCM) is emerging as one of the most promising alternative technology to the Dynamic RAM (DRAM) when building large-scale main memory systems. Even though the PCM is easy to scale, it encounters serious endurance problems. Writes are the primary wear mechanism in the PCM. The PCM can perform 108 to 109 times of writes before it cannot be programmed reliably. In addition, the PCM has high write latency. To prolong the lifetime of the PCM as the main memory and enhance the performance, we propose a Scratch Pad Memory (SPM) based memory mechanism and an Integer Linear Programming (ILP) memory activities scheduling algorithm to reduce the redundant write operations in the PCM. The idea of our approach is to share the data copies among the SPMs, instead of writing back to the PCM main memory each time a modify occurs. Our experimental results show that the ILP scheduling can generate the optimal schedule of memory activities with minimum write operations, reducing the number of write by up to 61%.
Flexible Consistency for Distributed Storage Systems

Consistency is a critical issue in distributed storage systems. Data in distributed storage systems are replicated across multiple storage nodes. When a copy of data is modified, all other copies need to be updated to prevent clients from reading stale data. However, such strong consistency requires the system to wait for all replicas to finish updates before returning to the client. Thus performance penalty arises. To improve performance, many systems use eventual consistency to allow stale data existing in the system at certain time. In this paper, we propose a flexible consistency to take advantage of both types of consistency. Our work classifies data by their read and write patterns and makes dynamic adjustment of consistency online. The evaluation shows our flexible consistency performs well, especially for write-heavy applications.
On harmonic fixed-priority scheduling of periodic real-time tasks with constrained deadlines

It is well known that a harmonic task set, i.e., task periods are integer multiples of each other, can better utilize a processor to achieve high system utilization. However, the current definition of harmonic task set is limited only to tasks with deadlines equal to their periods. In this paper, we extend the concept of “harmonic task set” to tasks with constrained deadlines, i.e., deadlines less than or equal to their periods. We show that a harmonic task set with constrained deadlines has a better schedulability than the non-harmonic one with the same task utilization. We employ this characteristic for task partitioning on multi-core platform, and our extensive experimental results show that, by taking the task harmonic relationship into consideration, our partitioning approach can greatly improve the schedulability of real-time tasks on multi-core platforms.
Low-Power Low-Latency Data Allocation for Hybrid Scratch-Pad Memory

This letter aims at developing new memory architecture to overcome the daunting memory wall and energy wall issues in multicore embedded systems. We propose a new heterogeneous scratch-pad memory (SPM) architecture that is configured with SRAM, MRAM, and Z-RAM. Based on this architecture, we propose two algorithms: a dynamic programming (MDPDA) and a genetic algorithm (AGADA) to allocate data to different memory banks, therefore, reducing memory access cost in terms of power consumption and latency. Extensive experiments are performed to show the merits of the hybrid SPM memory architecture and the effectiveness of the proposed algorithms.
Jamming ACK Attack to Wireless Networks and a Mitigation Approach

In many medium access control (MAC) schemes for wireless networks, an Acknowledgment (ACK) packet is transmitted from the data receiver to the data sender to announce the successful reception of the data packet. Such a protocol requirement may become a system weakness when malicious nodes attack these wireless networks. In this paper, we demonstrate the effects of such a Jamming ACK (JACK) attack to networks employing the popular carrier sense multiple access with collision avoidance (CSMA/CA) scheme in IEEE 802.11 DCF. Our study shows that a JACK attacker can easily disrupt the traffic flow between two wireless nodes when it sends out JACK packets at the right time. The benefits of such a JACK attack include low energy consumption by the attacker, attack stealthiness, and great damage to the victim nodes. To mitigate the effects of JACK attacks, we propose in this paper an extended network allocation vector (ENAV) scheme. Our analysis and simulations show that the ENAV scheme recovers a significant portion of the lost throughput and reduces the energy drainage of the attacked nodes to 40%.
Integrated Quality Mesh Generation for Poisson Surface Reconstruction in HPC Applications

Finite Element Analysis (FEA) is a numerical method for solving engineering problem. Implementations of FEA are common high performance computing (HPC) applications. As a typical application of FEA, surface reconstruction of large model, proves to be a computationally expensive task as well. Poisson Surface Reconstruction, a cutting-edge surface reconstruction algorithm, is a commonly used method for efficiently solving linear systems and it creates watertight surfaces from oriented point sets. Poisson Surface Reconstruction leverages an octree-based Marching Cubes (MC) method for isosurface extraction. Hierarchical octree structure avoids unnecessary cells visiting and prevents cracks arising. In this paper, we integrate several quality improved methods with MC and coordinate unrelated components, obtaining a better quality mesh implementation. We improve mesh quality based on an extended lookup table and modify the connectivity of some fundamental patterns in MC, which effectively remove the reconstruction holes, thus improving overall surface quality. As for the relative value between each vertex and the average isovalue, the extended table explicitly differentiates between “strictly larger” and “equal to”. Newly introduced patterns in MC statistically prevent poor quality triangles production. Moreover, a decision making algorithm is proposed to eliminate ambiguity problems. We adapt SnapMC algorithm to avoid non manifold triangles to a certain extent. Comparisons with traditional Poisson algorithm and Smooth Signed Distance (SSD) highlight the capability in quality mesh generation and efficacy in handling high computational demand.