nils-herrmann/clean_scirepeval huggingface dataset:
Parallel Programming Paradigms and Frameworks in Big Data Era

With Cloud Computing emerging as a promising new approach for ad-hoc parallel data processing, major companies have started to integrate frameworks for parallel data processing in their product portfolio, making it easy for customers to access these services and to deploy their programs. We have entered the Era of Big Data. The explosion and profusion of available data in a wide range of application domains rise up new challenges and opportunities in a plethora of disciplines—ranging from science and engineering to biology and business. One major challenge is how to take advantage of the unprecedented scale of data—typically of heterogeneous nature—in order to acquire further insights and knowledge for improving the quality of the offered services. To exploit this new resource, we need to scale up and scale out both our infrastructures and standard techniques. Our society is already data-rich, but the question remains whether or not we have the conceptual tools to handle it. In this paper we discuss and analyze opportunities and challenges for efficient parallel data processing. Big Data is the next frontier for innovation, competition, and productivity, and many solutions continue to appear, partly supported by the considerable enthusiasm around the MapReduce paradigm for large-scale data analysis. We review various parallel and distributed programming paradigms, analyzing how they fit into the Big Data era, and present modern emerging paradigms and frameworks. To better support practitioners interesting in this domain, we end with an analysis of on-going research challenges towards the truly fourth generation data-intensive science.
Compact Design of a Hydraulic Driving Robot for Intraoperative MRI-Guided Bilateral Stereotactic Neurosurgery

In this letter, we present an intraoperative magnetic resonance imaging (MRI)-guided robot for bilateral stereotactic procedures. Its compact design enables robot's operation within the constrained space of standard imaging head coil. magnetic resonance (MR) safe and high-performance hydraulic transmissions are incorporated. A maximum stiffness coefficient of 24.35 N/mm can be achieved with transmission fluid preloaded at 2 bar. Sufficient targeting accuracy (average within ≤1.73 mm) has been demonstrated in a simulated needle insertion task of deep brain stimulation. A novel MR-based wireless tracking technique is adopted. It is capable of offering real-time and continuous (30–40 Hz) three-dimensional (3-D) localization of robotic instrument under the proper MR tracking sequence. It outperforms the conventional method of using low-contrast passive fiducials that can only be revealed in the MR image domain. Two wireless tracking units/markers are integrated with the robot, which are two miniaturized coil circuits in size of 1.5 × 5 × 0.2 mm3, fabricated on flexible thin films. A navigation test was performed under the standard MRI settings in order to visualize the 3-D localization of the robotic instrument. MRI compatibility test was also carried out to prove the minimal interference to MR images of the presented hydraulic robotic platform.
How do we read algorithms? A case study

The authors report on an experiment that explored the way subjects view an algorithm, written in Pascal, and the graphical representation of its behavior. They designed the experiment to examine the influence of programming experience on how subjects view a short but complex algorithm, and they analyzed the scan patterns of eye movement for subjects at two experience levels. They describe the experimental procedure in detail, and discuss the results. An important finding was that all subjects needed numerous fixations in most areas of the algorithm and spent more time viewing relevant slide areas than did subjects in studies using simple text. This supports the hypothesis that viewing strategies for algorithms differ from those for prose.<<ETX>>
A multi-label Hyperspectral image classification method with deep learning features

Hyperspectral image (HSI) classification is an important application of HSI analysis, which aims at assigning a class label to each pixel. However, considering that mixed pixels commonly exist in HSI, assigning a unique label to each pixel is imprecise. To better analysis the scene imaged in an HSI, we propose a multi-label hyperspectral image classification approach based on deep learning in this study. First, stacked denoising autoencoder (SDAE) method is used to extract deep features for each pixel without supervision, which can well represent the nonlinearity of the mixed pixels in a high dimensional feature space. Then, multi-label logistic regression method assigns each pixel multi labels. Experimental results on the synthetic data, real hyperspectral data and down-sampling hyperspectral data demonstrate the effectiveness of the proposed method.
A Study of Attribute-based Proxy Re-encryption Scheme in Cloud Environments

Attribute-based proxy re-encryption (ABPRE) scheme is one of the proxy cryptography, which can delegate the reencryption capability to the proxy and re-encrypt the encrypted data by using the re-encryption key. ABPRE extending the traditional proxy cryptography and attributes plays an important role. In ABPRE, users are identified by attributes, and the access policy is designed to control the user’s access. Using ABPRE can have these advantages: (i) The proxy can be delegated to execute the re-encryption operation, which reduces the computation overhead of the data owner; (ii) The authorized user just uses his own secret key to decrypt the encrypted data, and he doesn’t need to store an additional decryption key for deciphering; (iii) The sensitive information cannot be revealed to the proxy in re-encryption, and the proxy only complies to the data owner’s command. In this paper, we survey two various access policy attribute-based proxy re-encryption schemes and analyze these schemes. Thereafter, we list the comparisons of them by some criteria.
Massive MIMO and mmWave for 5G Wireless HetNet: Potential Benefits and Challenges

There has been active research worldwide to develop the next-generation, i.e., fifth-generation (5G), wireless network. The 5G network is expected to support a significantly large amount of mobile data traffic and a huge number of wireless connections and achieve better costand energy-efficiency as well as quality of service (QoS) in terms of communication delay, reliability, and security. To this end, the 5G wireless network should exploit the potential of new developments, including superdense and heterogeneous deployment of cells and massive antenna arrays [i.e., massive multiple-input, multiple-output (MIMO) technologies] and utilization of higher frequencies, particularly millimeter-wave (mmWave) frequencies. This article discusses the potential benefits and challenges of the 5G wireless heterogeneous network (HetNet) that incorporates massive MIMO and mmWave technologies.
Modeling social interestingness in conversational stories

Telling stories about our daily lives is one of the most ubiquitous, consequential and seamless ways in which we socialize. Current narrative generation methods mostly require specification of a priori knowledge or comprehensive domain models, which are not generalizable across contexts. Hence, such approaches do not lend themselves well to new and unpredictable domains of observation and interaction, in which social stories usually occur. In this paper, we describe a methodology for categorizing event descriptions as being socially interesting. The event sequences are drawn from crowd-sourced Plot Graphs. The models include low-level natural language and higher-level features. The results from classification and regression tasks look promising overall, indicating that general metrics of social interestingness of stories could be modeled for sociable agents.
How GaN Power Transistors Drive High-Performance Lidar: Generating ultrafast pulsed power with GaN FETs

Light detection and ranging (lidar) is a versatile light-based remote sensing technology that recently has been the subject of great attention. It has shown up in a number of media venues and has even led to public debate about the engineering choices of a well-known electric car company, Tesla Motors . While this article is not going to enter the fray, it will provide some background on lidar and discuss its strong connection to power electronics technologies.
CellDesigner: a process diagram editor for gene-regulatory and biochemical networks

Systems biology is characterized by synergistic integration of theory, computational modeling, and experiment. Though software infrastructure is one of the most critical components of systems biology research, there has been no common infrastructure or standard to enable integration of computational resources. To solve this problem, the Systems Biology Markup Language (SBML)  and Systems Biology Workbench (SBW) have been developed. A number of simulation and analysis software packages already support SBML and SBW, or are in the process to support it. An identification of logic and dynamics of gene-regulatory and biochemical networks is a major challenge of systems biology. We believe that such network building tools and simulation environments using standardized technologies play an important role in software platform of systems biology. As one of the approaches, we have developed CellDesigner , which is a process diagram editor for gene-regulatory and biochemical networks.
Nerve root sedimentation sign: evaluation of a new radiological sign in lumbar spinal stenosis.

Retrospective case-referent study.



To assess whether the new sedimentation sign discriminates between nonspecific low back pain (LBP) and symptomatic lumbar spinal stenosis (LSS).



In the diagnosis of LSS, radiologic findings do not always correlate with clinical symptoms, and additional diagnostic signs are needed. In patients without LSS, we observe the sedimentation of lumbar nerve roots to the dorsal part of the dural sac on supine magnetic resonance image scans. In patients with symptomatic and morphologic central LSS, this sedimentation is rarely seen. We named this phenomenon "sedimentation sign" and defined the absence of sedimenting nerve roots as positive sedimentation sign for the diagnosis of LSS.



This study included 200 patients. Patients in the LSS group (n = 100) showed claudication with or without LBP and leg pain, a cross-sectional area <80 mm, and a walking distance <200 m; patients in the LBP group (n = 100) had LBP, no leg pain, no claudication, a cross-sectional area of the dural sac >120 mm, and a walking distance >1000 m. The frequency of a positive sedimentation sign was compared between the 2 groups, and intraobserver and interobserver reliability were assessed in a random subsample (n = 20).



A positive sedimentation sign was identified in 94 patients in the LSS group (94%; 95% confidence interval, 90%-99%) but none in the LBP group (0%; 95% confidence interval, 0%-4%). Reliability was kappa = 1.0 (intraobserver) and kappa = 0.93 (interobserver), respectively. There was no difference in the detection of the sign between segmental levels L1-L5 in the LSS group.



A positive sedimentation sign exclusively and reliably occurs in patients with LSS, suggesting its usefulness in clinical practice. Future accuracy studies will address its sensitivity and specificity. If they confirm the sign's high specificity, a positive sedimentation sign can rule in LSS, and, with a high sensitivity, a negative sedimentation sign can rule out LSS.
Modeling by shortest data description

It is known that the usual criteria resulting from such, should we dare to say, ad hoc principles as the least squares, the minimum prediction error, and the maximum likelihood principles, cannot be used to estimate the order nor other structure parameters. This very fact seems to suggest that these time-honored principles are successful only insofar as they happen to coincide with one or another facet of some more powerful and comprehensive principle. The shortest description length of individual recursively definable objects was studied by Kolmogorov[1] and others, and it gives rise to the algorithmic notion of entropy. In statistical estimation, however, the data always includes a random element, and the original notion of entropy of random variables turns out to be the appropriate one to use. In a peripheral way we also invoke a simple combinatorial entropy notion which, however, need not cause any con-
Deep Analysis of Binary Code to Recover Program Structure

Title of dissertation: DEEP ANALYSIS OF BINARY CODE TO RECOVER PROGRAM STRUCTURE Khaled ElWazeer, Doctor of Philosophy, 2014 Dissertation directed by: Professor Rajeev Barua Department of Electrical and Computer Engineering Reverse engineering binary executable code is gaining more i nterest in the research community. Agencies as diverse as anti-virus companies, se curity consultants, code forensics consultants, law-enforcement agencies and nati onal security agencies routinely try to understand binary code. Engineers also often need to d ebug, optimize or instrument binary code during the software development process. In this dissertation, we present novel techniques to extend he capabilities of existing binary analysis and rewriting tools to be more scalab le, handling a larger set of stripped binaries with better and more understandable outp uts as well as ensuring correct recovered intermediate representation (IR) from binar ies such that any modified or rewritten binaries compiled from this representation work correctly. In the first part of the dissertation, we present techniques t o recover accurate function boundaries from stripped executables. Our techniques as opposed to current techniques ensure complete live executable code coverage, high quality recovered code, and functional behavior for most application binaries. We use s tatic and dynamic based techniques to remove as much spurious code as possible in a safe ma nner that does not hurt code coverage or IR correctness. Next, we present static tec hniques to recover correct prototypes for the recovered functions. The recovered prot otypes include the complete set of all arguments and returns. Our techniques ensure corr ct behavior of rewritten binaries for both internal and external functions. Finally, we present scalable and precise techniques to reco ver l cal variables for every function obtained as well as global and heap variables . Different techniques are represented for floating point stack allocated variables an d memory allocated variables. Data type recovery techniques are presented to declare mean ingful data types for the detected variables. Our data type recovery techniques can rec over integer, pointer, structural and recursive data types. We discuss the correctness of the r ecovered representation. The evaluation of all the methods proposed is conducted on Se condWrite, a binary rewriting framework developed by our research group. An imp ortant metric in the evaluation is to be able to recompile the IR with the recovered info rmation and run it producing the same answer that is produced when running the original ex ecutable. Another metric is the analysis time. Some other metrics are proposed to meas ure the quality of the IR with respect to the IR with source code information availabl e. DEEP ANALYSIS OF BINARY CODE TO RECOVER PROGRAM STRUCTURE
Computing Education in Korea - Current Issues and Endeavors

Computer education has been provided for a long period of time in Korea. Starting as a vocational program, the content of computer education for students evolved to include content on computer literacy, Information Communication Technology (ICT) literacy, and brand-new computer science. While a new curriculum related to computer science was established in 2007, the range of computer education being provided in Korean schools has been repeatedly reduced. To identify the cause of this recent phenomenon, we review the computer education environment using the Darmstadt model, including educational systems, curricula, and teaching environments. Then we examine what factors affected the decline of computer education. The major causes of failure are found to be the absence of policy and a comprehensive evaluation method. These causes have led to a reduction in the selection ratio of computer-related subjects and in the number of students taking computer classes. Based on this understanding, we bring some fundamental message for establishing robust computer science education.
Dynamic Memory Networks for Visual and Textual Question Answering

Neural network architectures with memory and attention mechanisms exhibit certain reasoning capabilities required for question answering. One such architecture, the dynamic memory network (DMN), obtained high accuracy on a variety of language tasks. However, it was not shown whether the architecture achieves strong results for question answering when supporting facts are not marked during training or whether it could be applied to other modalities such as images. Based on an analysis of the DMN, we propose several improvements to its memory and input modules. Together with these changes we introduce a novel input module for images in order to be able to answer visual questions. Our new DMN+ model improves the state of the art on both the Visual Question Answering dataset and the bAbI-10k text question-answering dataset without supporting fact supervision.
Are leader stereotypes masculine? A meta-analysis of three research paradigms.

This meta-analysis examined the extent to which stereotypes of leaders are culturally masculine. The primary studies fit into 1 of 3 paradigms: (a) In Schein's (1973) think manager-think male paradigm, 40 studies with 51 effect sizes compared the similarity of male and leader stereotypes and the similarity of female and leader stereotypes; (b) in Powell and Butterfield's (1979) agency-communion paradigm, 22 studies with 47 effect sizes compared stereotypes of leaders' agency and communion; and (c) in Shinar's (1975) masculinity-femininity paradigm, 7 studies with 101 effect sizes represented stereotypes of leadership-related occupations on a single masculinity-femininity dimension. Analyses implemented appropriate random and mixed effects models. All 3 paradigms demonstrated overall masculinity of leader stereotypes: (a) In the think manager-think male paradigm, intraclass correlation = .25 for the women-leaders similarity and intraclass correlation = .62 for the men-leaders similarity; (b) in the agency-communion paradigm, g = 1.55, indicating greater agency than communion; and (c) in the masculinity-femininity paradigm, g = 0.92, indicating greater masculinity than the androgynous scale midpoint. Subgroup and meta-regression analyses indicated that this masculine construal of leadership has decreased over time and was greater for male than female research participants. In addition, stereotypes portrayed leaders as less masculine in educational organizations than in other domains and in moderate- than in high-status leader roles. This article considers the relation of these findings to Eagly and Karau's (2002) role congruity theory, which proposed contextual influences on the incongruity between stereotypes of women and leaders. The implications for prejudice against women leaders are also considered.
Representation Learning of Entities and Documents from Knowledge Base Descriptions

In this paper, we describe TextEnt, a neural network model that learns distributed representations of entities and documents directly from a knowledge base (KB). Given a document in a KB consisting of words and entity annotations, we train our model to predict the entity that the document describes and map the document and its target entity close to each other in a continuous vector space. Our model is trained using a large number of documents extracted from Wikipedia. The performance of the proposed model is evaluated using two tasks, namely fine-grained entity typing and multiclass text classification. The results demonstrate that our model achieves stateof-the-art performance on both tasks. The code and the trained representations are made available online for further academic research.
Crisis prevention: how to gear up your board.

Today's critics of corporate boardrooms have plenty of ammunition. The two crucial responsibilities of boards-oversight of long-term company strategy and the selection, evaluation, and compensation of top management--were reduced to damage control during the 1980s. Walter Salmon, a longtime director, notes that while boards have improved since he began serving on them in 1961, they haven't kept pace with the need for real change. Based on over 30 years of boardroom experience, Salmon recommends against government reform of board practices. But he does prescribe a series of incremental changes as a remedy. To begin with, he suggests limiting the size of boards and increasing the number of outside directors on them. In fact, according to Salmon, only three insiders belong on a board: the CEO, the COO, and the CFO. Changing how committees function is also necessary for gearing up today's boards. The audit committee, for example, can periodically review "high-exposure areas" of a business, perhaps helping to prevent embarrassing drops in future profits. Compensation committees can structure incentive compensation for executives to emphasize long-term rather than short-term performance. And nominating committees should be responsible for finding new, independent directors--not the CEO. In general, boards as a whole must spot problems early and blow the whistle, exercising what Salmon calls, "constructive dissatisfaction." On a revitalized board, directors have enough confidence in the process to vigorously challenge one another, including the company's chief executive.
Analysis of Neural Cryptography

In this paper we analyse the security of a new key exchange protocol proposed in , which is based on mutually learning neural networks. This is a new potential source for public key cryptographic schemes which are not based on number theoretic functions, and have small time and memory complexities. In the first part of the paper we analyse the scheme, explain why the two parties converge to a common key, and why an attacker using a similar neural network is unlikely to converge to the same key. However, in the second part of the paper we show that this key exchange protocol can be broken in three different ways, and thus it is completely insecure.
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.