nils-herrmann/clean_scirepeval huggingface dataset:
Characterizing DevOps by Hearing Multiple Voices

Recently, DevOps has emerged as an alternative for software organizations inserted into a dynamic market to handle daily software demands. As claimed, it intends to make the software development and operations teams to work collaboratively. However, it is hard to observe a shared understanding of DevOps, what potentially hinders the discussions in the literature and can confound observations when conducting empirical studies. Therefore, we performed a Multivocal Literature Review aiming at characterizing DevOps in multiple perspectives, including data sources from technical and gray literature. Grounded Theory procedures were used to rigorous analyze the collected data. It allowed us to achieve a grounded definition for DevOps, as well as to identify its recurrent principles, practices, required skills, potential benefits, challenges and what motivates the organizations to adopt it. Finally, we understand the DevOps movement has identified relevant issues in the state-of-the-practice. However, we advocate for the scientific investigations concerning the potential benefits and drawbacks as a consequence of adopting the suggested principles and practices.
An efficient chaotic water cycle algorithm for optimization tasks

Water cycle algorithm (WCA) is a new population-based meta-heuristic technique. It is originally inspired by idealized hydrological cycle observed in natural environment. The conventional WCA is capable to demonstrate a superior performance compared to other well-established techniques in solving constrained and also unconstrained problems. Similar to other meta-heuristics, premature convergence to local optima may still be happened in dealing with some specific optimization tasks. Similar to chaos in real water cycle behavior, this article incorporates chaotic patterns into stochastic processes of WCA to improve the performance of conventional algorithm and to mitigate its premature convergence problem. First, different chaotic signal functions along with various chaotic-enhanced WCA strategies (totally 39 meta-heuristics) are implemented, and the best signal is preferred as the most appropriate chaotic technique for modification of WCA. Second, the chaotic algorithm is employed to tackle various benchmark problems published in the specialized literature and also training of neural networks. The comparative statistical results of new technique vividly demonstrate that premature convergence problem is relieved significantly. Chaotic WCA with sinusoidal map and chaotic-enhanced operators not only can exploit high-quality solutions efficiently but can outperform WCA optimizer and other investigated algorithms.
Assured Cloud-Based Data Analysis with ClusterBFT

The shift to cloud technologies is a paradigm change that offers considerable financial and administrative gains. However governmental and business institutions wanting to tap into these gains are concerned with security issues. The cloud presents new vulnerabilities and is dominated by new kinds of applications, which calls for new security solutions. Intuitively, Byzantine fault tolerant (BFT) replication has many benefits to enforce integrity and availability in clouds. Existing BFT systems, however, are not suited for typical “data-flow processing” cloud applications which analyze large amounts of data in a parallelizable manner: indeed, existing BFT solutions focus on replicating single monolithic servers, whilst data-flow applications consist in several different stages, each of which may give rise to multiple components at runtime to exploit cheap hardware parallelism; similarly, BFT replication hinges on comparison of redundant outputs generated, which in the case of data-flow processing can represent huge amounts of data. In fact, current limits of data processing directly depend on the amount of data that can be processed per time unit. In this paper we present ClusterBFT, a system that secures computations being run in the cloud by leveraging BFT replication coupled with fault isolation. In short, ClusterBFT leverages a combination of variable-degree clustering, approximated and offline output comparison, smart deployment, and separation of duty, to achieve a parameterized tradeoff between fault tolerance and overhead in practice. We demonstrate the low overhead achieved with ClusterBFT when securing dataflow computations expressed in Apache Pig, and Hadoop. Our solution allows assured computation with less than 10 percent latency overhead as shown by our evaluation.
Improving Neural Program Synthesis with Inferred Execution Traces

The task of program synthesis, or automatically generating programs that are consistent with a provided specification, remains a challenging task in artificial intelligence. As in other fields of AI, deep learning-based end-to-end approaches have made great advances in program synthesis. However, compared to other fields such as computer vision, program synthesis provides greater opportunities to explicitly exploit structured information such as execution traces. While execution traces can provide highly detailed guidance for a program synthesis method, they are more difficult to obtain than more basic forms of specification such as input/output pairs. Therefore, we use the insight that we can split the process into two parts: infer traces from input/output examples, then infer programs from traces. Our application of this idea leads to state-of-the-art results in program synthesis in the Karel domain, improving accuracy to 81.3% from the 77.12% of prior work.
CHAPTER 1 A Snapshot in Time : Industrial – Organizational Psychology Today

As we write this chapter, the field of industrial– organizational psychology in the United States has survived its third attempt at a name change. To provide a little perspective, the moniker industrial psychology became popular after World War I, and described a field that was characterized by ability testing and vocational assessment (Koppes, 2003). The current label, industrial– organizational (I-O) psychology, was made official in 1973. The addition of organizational reflected the growing influence of social psychologists and organizational development consultants, as well as the intellectual and social milieu of the period (see Highhouse, 2007). The change to I-O psychology was more of a compromise than a solution—which may have succeeded only to the extent that everyone was equally dissatisfied. The first attempt to change this clunky label, therefore, occurred in 1976. Popular alternatives at the time were personnel psychology , business psychology , and psychology of work . The leading contender, however, was organizational psychology because, according to then-future APA Division 14 president Arthur MacKinney, “all of the Division’s work is grounded in organizational contexts” (MacKinney 1976, p. 2). The issue stalled before ever making it
Estimation of suspended sediment concentrations using Terra MODIS: an example from the Lower Yangtze River, China.

Traditional measurements of suspended sediment concentrations (SSC) through in-situ sampling in rivers are expensive and time-consuming to perform. Thus, these methods cannot provide continuous SSC records. Although remote sensing has been used for SSC estimation, little research has been undertaken on inland rivers, especially for highly turbid rivers like the Yangtze. Previous studies have proposed Landsat TM/ETM+ Band 4 as a spectral SSC indicator for the Yangtze, but its limitation on temporal resolution is insufficient for the study of dynamic changes of sediment. This paper presents a method of estimating SSC of the Yangtze at Jiujiang using time-series satellite data of high temporal resolution Terra MODIS. It was found that differences in water reflectance between Band 2 and Band 5 could provide relatively accurate SSC estimation even when in-situ atmospheric conditions were unknown. After cross-validation, mean absolute relative error (ARE) and relative root mean square error (RRMSE) were relatively low (i.e., 25.5% and 36.5%, respectively). This empirical relationship was successfully applied to the estimation of SSC at Datong in the Lower Yangtze River, although the SSC values were generally underestimated. This study suggests that Terra MODIS could be used to estimate SSC in large turbid rivers, although some influencing factors require further study to improve the accuracy of SSC estimation.
Holographic localization of passive UHF RFID transponders

In this paper a method for holographic localization of passive UHF-RFID transponders is presented. It is shown how persons or devices that are equipped with a RFID reader and that are moving along a trajectory can be enabled to locate tagged objects reliably. The localization method is based on phase values sampled from a synthetic aperture by a RFID reader. The calculated holographic image is a spatial probability density function that reveals the actual RFID tag position. Experimental results are presented which show that the holographically measured positions are in good agreement with the real position of the tag. Additional simulations have been carried out to investigate the positioning accuracy of the proposed method depending on different distortion parameters and measuring conditions. The effect of antenna phase center displacement is briefly discussed and measurements are shown that quantify the influence on the phase measurement.
Modified genetic algorithm for solving n-queens problem

Genetic algorithm is applicable to a wide range of constraint satisfaction problems such as n-queens problem. In the absence of specialized solution for a particular problem, genetic algorithm would be efficient. But holism and random choices cause problem for genetic algorithm in searching large state spaces. So, the efficiency of this algorithm would be demoted when the size of state space of the problem grows exponentially. In this paper, we attempt to cover this weakness by using local search algorithm like minimal conflicts algorithm. Minimal conflicts algorithm is trying to provide partial view for genetic algorithm by locally searching the state space. This may cause genetic algorithm to take longer steps toward the solution. Modified genetic algorithm, is the result of collaboration between genetic algorithm and minimal conflicts algorithm. Comparing the results of applying standard genetic algorithm and modified genetic algorithm on n-queens problem in section VI, indicates the amount of performance improvement.
Progress and application of through glass via (TGV) technology

Glass provides many opportunities for advanced packaging. The most obvious advantage is given by the material properties. As an insulator, glass has low electrical loss, particularly at high frequencies. The relatively high stiffness and ability to adjust the coefficient of thermal expansion gives advantages to manage warp in glass core substrates and bonded stacks for both through glass vias (TGV) and carrier applications. Glass also gives advantages for developing cost effective solutions. Glass forming processes allow the potential to form both in panel format as well as at thicknesses as low as 100 um, giving opportunities to optimize or eliminate current manufacturing methods. As the industry adopts glass solutions, significant advancements have been made in downstream processes such as glass handling and via/surface metallization. Of particular interest is the ability to leverage tool sets and processes for panel fabrication to enable cost structures desired by the industry. By utilizing the stiffness and adjustable CTE of glass substrates, as well as continuously reducing via size that can be made in a panel format, opportunities to manufacture glass TGV substrates in a panel format increase. We will provide an update on advancements in these areas as well as handling techniques to achieve desired process flows. We will also provide the latest demonstrations of electrical, thermal and mechanical reliability.
A Piece of My Mind: A Sentiment Analysis Approach for Online Dispute Detection

We investigate the novel task of online dispute detection and propose a sentiment analysis solution to the problem: we aim to identify the sequence of sentence-level sentiments expressed during a discussion and to use them as features in a classifier that predicts the DISPUTE/NON-DISPUTE label for the discussion as a whole. We evaluate dispute detection approaches on a newly created corpus of Wikipedia Talk page disputes and find that classifiers that rely on our sentiment tagging features outperform those that do not. The best model achieves a very promising F1 score of 0.78 and an accuracy of 0.80.
Increasing SCADA System Availability by Fault Tolerance Techniques

In SCADA systems, there are many RTUs (Remote Terminal Units) are used for field data collection as well as sending data to master node through the communication system. In such case master node represents the collected data and enables manager to handle the remote controlling activities. The RTU is nothing but the unit of data acquisition in standalone manner. The processor used in RTU is vulnerable to random faults due to harsh environment around RTUs. Faults may lead to the failure of RTU unit and hence it becomes inaccessible for information acquisition. For long running methods, fault tolerance is major concern and research problem since from last two decades. Using the SCADA systems increase the problem of fault tolerance is becoming servered. To handle the faults in oreder to perform the message passing through all the layers of communication system fo the SCADA that time need the efficient fault tolerance. The faults like RTU, message passing layer faults in communication system etc. SCADA is nothing but one of application of MPI. The several techniques for the fault tolerance has been described for MPI which are utilized in different applications such as SCADA. The goal of this paper is to present the study over the different fault tolerance techniques which can be used to optimize the SCADA system availability by mitigating the faults in RTU devices and communication systems.
NoC Based Multiplier-Less Constant Geometry FFT Architecture

This paper reports the architecture of a Network-on-Chip (NoC) based constant geometry FFT (CGFFT). The twiddle factor complex multiplications have been realized using new distributed arithmetic (NEDA) blocks. The architecture, which contains four processing elements (PEs) that are mapped by considering the data flow between the stages of FFT, has been mapped on to a 2 × 2 mesh NoC. The known traffic pattern and known data source-destination locations have reduced the latency of the router by minimizing the buffer size. The architecture has been implemented using Xilinx Kintex7 FPGA and has been simulated using a cycle-accurate SystemC based simulator.
Face detection and recognition for home service robots with end-to-end deep neural networks

This paper proposes an effective end-to-end face detection and recognition framework based on deep convolutional neural networks for home service robots. We combine the state-of-the-art region proposal based deep detection network with the deep face embedding network into an end-to-end system, so that the detection and recognition networks can share the same deep convolutional layers, enabling significant reduction of computation through sharing convolutional features. The detection network is robust to large occlusion, and scale, pose, and lighting variations. The recognition network does not require explicit face alignment, which enables an effective training strategy to generate a unified network. A practical robot system is also developed based on the proposed framework, where the system automatically asks for a minimum level of human supervision when needed, and no complicated region-level face annotation is required. Experiments are conducted over WIDER and LFW benchmarks, as well as a personalized dataset collected from an office setting, which demonstrate state-of-the-art performance of our system.
Design of a GPS-based solar tracker system for a vertical solar still

Compared with horizontal solar still, vertical solar still has better condensation but lower evaporation. To increase the evaporation, it needs a control system that moves a vertical solar still to follow the azimuth angle of the sun. This paper presents a design of a GPS-based solar tracker system, that can moves the vertical solar still follow the azimuth angle of the sun. Furthermore, to determine the effect of this solar tracker system, research was done by comparing the power of 2 solar cells placed in different positions. The first solar cell was placed upright and rotated with the solar tracker system. The second solar cell was placed horizontally. The result showed that the power generated by the first solar cell is greater than the second solar cell.
Managing for Stakeholders : The Role of Stakeholder-Based Management in Project Success

Today, project management practices play a key role in different industries and sectors. Project management is promoted as an organizational strategic component that leads innovation, creates value and turns vision into reality. Despite the importance of projects and project management their high rate of failures and challenges is a major concern of both industry and academia. Among the reasons that affect project outcomes, stakeholder influential attributes and more importantly, their understanding and effective utilization and management are identified as the key to project success. This study utilizes the body of knowledge developed in the field of project management and uses stakeholder theory combined with a number of complementary theories to achieve its goals and objectives. The study moves beyond the traditional power-based frameworks employing six key influential attributes to examine their direct and mediating effects on project success. The quantitative survey data are analyzed using SEM statistical techniques and procedures to produce research results. The research results have led to the development of a new typology of stakeholder influential attributes (TSIA) and a stakeholder-based project management model (SBPMM) that aid managing for stakeholders’ strategy and principle.
Healthcare data breaches : Biometric technology to the rescue

1,2 Computer Studies Department , Faculty of Science, The Polytechnic, Ibadan Oyo State, Nigeria. ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Patient identification is the foundation of effective healthcare: the correct care needs to be delivered to the correct patient. However, relying on manual identification processes such as demographic searches and social security numbers often results in patient misidentification hence, the needs for electronic medical records (EMR). . It was discovered that many medical systems switching to electronic health records in order to explore the advantages of electronic medical records (EMR) creates new problems by producing more targets for medical data to be hacked. Hackers are believed to have gained access to up to 80 million records that contained Social Security numbers, birthdays, postal addresses, and e-mail addresses.
Cryptographic Public Verification of Data Integrity for Cloud Storage Systems

Cloud storage services allow users to outsource their data to cloud servers to save on local data storage costs. However, unlike using local storage devices, users don't physically own the data stored on cloud servers and can't be certain about the integrity of the cloud-stored data. Many public verification schemes have been proposed to allow a third-party auditor to verify the integrity of outsourced data. However, most of these schemes assume that the auditors are honest and reliable, so are vulnerable to malicious auditors. Moreover, in most of these schemes, an external adversary could modify the outsourced data and tamper with the interaction messages between the cloud server and the auditor, thus invalidating the outsourced data integrity verification. This article proposes an efficient and secure public verification of data integrity scheme that protects against external adversaries and malicious auditors. The proposed scheme adopts a random masking technique to protect against external adversaries, and requires users to audit auditors' behaviors to prevent malicious auditors from fabricating verification results. It uses Bitcoin to construct unbiased challenge messages to thwart collusion between malicious auditors and cloud servers. A performance analysis demonstrates that the proposed scheme is efficient in terms of the user's auditing overhead.
Chameleon: An Adaptive Wear Balancer for Flash Clusters

NAND flash-based Solid State Devices (SSDs) offer the desirable features of high performance, energy efficiency, and fast growing capacity. Thus, the use of SSDs is increasing in distributed storage systems. A key obstacle in this context is that the natural unbalance in distributed I/O workloads can result in wear imbalance across the SSDs in a distributed setting. This, in turn can have significant impact on the reliability, performance, and lifetime of the storage deployment. Extant load balancers for storage systems do not consider SSD wear imbalance when placing data, as the main design goal of such balancers is to extract higher performance. Consequently, data migration is the only common technique for tackling wear imbalance, where existing data is moved from highly loaded servers to the least loaded ones. In this paper, we explore an innovative holistic approach, Chameleon, that employs data redundancy techniques such as replication and erasure-coding, coupled with endurance-aware write offloading, to mitigate wear level imbalance in distributed SSD-based storage. Chameleon aims to balance the wear among different flash servers while meeting desirable objectives of: extending life of flash servers; improving I/O performance; and avoiding bottlenecks. Evaluation with a 50 node SSD cluster shows that Chameleon reduces the wear distribution deviation by 81% while improving the write performance by up to 33%.
Highly Efficient Algorithms for AES Key Retrieval in Cache Access Attacks

Leakage of information between two processes sharing the same processor cache has been exploited in many novel approaches targeting various cryptographic algorithms. The software implementation of AES is an specially attractive target since it makes extensive use of cache-resident table lookups. We consider two attack scenarios where either the plaintext or ciphertext is known. We employ a multi-threaded spy process and ensure that each time slice provided to the victim (running AES) is small enough so that it makes a very limited number of table accesses. We design and implement a suite of algorithms to deduce the 128-bit AES key using as input the set of (unordered) cache line numbers captured by the spy threads in an access-driven cache-based side channel attack. Our algorithms are expressed using simple relational algebraic operations and run in under a minute. Above all, our attack is highly efficient - we demonstrate recovery of the full AES key given only about 6 - 7 blocks of plaintext or ciphertext (theoretically even a single block would suffice). This is a substantial improvement over previous cache-based side channel attacks that require between 100 and a million encryptions. Moreover, our attack supports varying cache hit/miss observation granularities, does not need frequent interruptions of the victim and will work even if the victim makes up to 60 cache accesses before being interrupted. Finally, we develop analytic models to estimate the number of encryptions/decryptions required as a function of access granularity and compare model results with those obtained from our experiments.