nils-herrmann/clean_scirepeval huggingface dataset:
Preventing man-in-the-middle attack in Diffie-Hellman key exchange protocol

The acceleration in developments in communication technology has led to a consequent increase in the vulnerability of data due to penetration attacks. These attacks often came from outside where non-qualified companies develop IT projects. Cryptography can offer high levels of security but has recently shown vulnerabilities such as the man-in-the-middle (MITM) attack in areas of key exchange protocols, especially in the Diffie-Hellman (DH) protocol. Firstly, this paper presents an overview of MITM attacks targeted at the DH protocol then discusses some of the shortcomings of current defenses. A proposed method to secure DH, which helps secure systems against MITM attacks, is then presented. This method involves the use of Geffe generation of binary sequences. The use of Geffe generator offers high levels of randomness. Data hashed and encrypted using this proposed method will be so difficult to intercept and decrypt without the appropriate keys. This offers high levels of security and helps prevent MITM attacks.
An Overview of Virtual Simulation and Serious Gaming for Surgical Education and Training

The rising popularity of video games has seen a recent push towards the application of serious games to medical education and training. With their ability to engage players/learners for a specific purpose, serious games provide an opportunity to acquire cognitive and technical surgical skills outside the operating room thereby optimizing operating room exposure with live patients. However, before the application of serious games for surgical education and training becomes more widespread, there are a number of open questions and issues that must be addressed including the relationship between fidelity, multi-modal cue interaction, immersion, and knowledge transfer and retention. In this chapter we begin with a brief overview of alternative medical/surgical educational methods, followed by a discussion of serious games and their application to surgical education, fidelity, multi-modal cue interaction and their role within a virtual simulations/serious games. The chapter ends with a description of the serious games surgical cognitive education and training framework (SCETF) and concluding remarks.
Design and Modelling of Omnibola © , A Spherical Mobile Robot

This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. A spherical mobile robot called Omnibola © is introduced and analyzed in this article. Some advantages of this kind of robot compared with typical wheeled robots are described. Its geometry and its features are presented, emphasizing on those which make it different from other ball-shaped robots. A mathematical model has been developed to have a tool to study our robot dynamics. We conducted some experiments to confirm that model results are similar to experimental results observed in the real robot. Either in experiments or in simulations, the robot's behavior is quite oscillatory. Because of this, a simple control law is proposed to stabilize that oscillatory motion.
SPARSKIT: a basic tool kit for sparse matrix computations - Version 2

This paper presents the main features of a tool package for manipulating and working with sparse matrices. One of the goals of the package is to provide basic tools to facilitate exchange of software and data between researchers in sparse matrix computations. Our starting point is the Harwell/Boeing collection of matrices for which we provide a number of tools. Among other things the package provides programs for converting data structures, printing simple statistics on a matrix, plotting a matrix profile, performing basic linear algebra operations with sparse matrices and so on. *RIACS, Mail Stop 230-5, NASA Ames Research Center, Moffett Field, CA 94035. This work was supported in part by the NAS Systems Division, via Cooperative Agreement NCC 2-387 between NASA and the University Space Research Association (USRA) and in part by the Department of Energy under grant DE-FG02-85ER25001.
Device-to-device communications: The physical layer security advantage

In systems that allow device-to-device (D2D) communications, user pairs in close proximity communicate directly without using an access point (AP) as an intermediary. D2D communications leads to improved throughput, reduced power consumption and interference, and more flexible resource allocation. We show that the D2D paradigm also provides significantly improved security at the physical layer, by reducing exposure of the information to eavesdroppers from two relatively high-power transmissions to a single low-power hop. We derive the secrecy outage probability (SOP) for the D2D and cellular systems, and compare performance for D2D scenarios in the presence of a multi-antenna eavesdropper. The cellular approach is only seen to have an advantage in certain cases when the AP has a large number of antennas and perfect channel state information.
Comparison of different image formats using LSB Steganography

Nowadays secure and safe transmission of data is a very important aspect for all internet users. In the data securing process the use of encryption algorithms almost make it very difficult for an eavesdropper to get access to the user's data. It helps to ensure the privacy of a company/user from others. Keeping in view the importance of steganography for secure data transmission this work incorporates the use of LSB substitution method. Simulation results show the feasibility of the proposed method. The data has been successfully embedded into different formats of images without any perceptible distortion in the image.
Accurate nonrigid 3D human body surface reconstruction using commodity depth sensors

1Department of Computer Science, School of Engineering and Applied Science, Institute for Computer Graphics, The George Washington University, 800 22nd Street NW Suite 3400, Washington, DC 20052, USA 2Department of Epidemiology and Biostatistics, Milken Institute School of Public Health, The George Washington University, 800 22nd Street NW Suite 7680, Washington, DC 20052, USA 3Department of Computer Science, School of Engineering and Applied Science, and Department of Pediatrics, School of Medicine and Health Sciences, Institute for Computer Graphics, The George Washington University, 800 22nd Street NW Suite 5830, Washington, DC 20052, USA
Estimating Rotational Iron Losses in an Induction Machine

We present an assessment of the effect of rotational losses on an induction machine. The assessment provides an estimate of the iron losses in an induction machine by three methods, all of which rely on the output data of a two-dimensional finite-element method: 1) calculating iron losses as if they were produced by a purely alternating field; 2) calculating the iron losses by adding the losses produced by the orthogonal components of the flux density, as if the losses produced by these components were independent phenomena; 3) applying a correction factor based on experimental data to improve the rotational loss calculation. The correction factor is a function of the peak flux density value and the ratio of the major to the minor axis of the flux density loci. The third method represents the main contribution of this paper to the field and is explained in detail. Finally, a discussion of the results addresses two aspects: the location where rotational fields occur, and their impact on the total loss calculation.
Decision making regarding Smith-Petersen vs. pedicle subtraction osteotomy vs. vertebral column resection for spinal deformity.

Author experience and literature review.



To investigate and discuss decision-making on when to perform a Smith-Petersen osteotomy as opposed to a pedicle subtraction procedure and/or a vertebral column resection.



Articles have been published regarding Smith-Petersen osteotomies, pedicle subtraction procedures, and vertebral column resections. Expectations and complications have been reviewed. However, decision-making regarding which of the 3 procedures is most useful for a particular spinal deformity case is not clearly investigated.



Discussed in this manuscript is the author's experience and the literature regarding the operative options for a fixed coronal or sagittal deformity.



There are roles for Smith-Petersen osteotomy, pedicle subtraction, and vertebral column resection. Each has specific applications and potential complications.



As the magnitude of resection increases, the ability to correct deformity improves, but also the risk of complication increases. Therein, an understanding of potential applications and complications is helpful.
Transcriptome-wide Identification of RNA-Binding Protein and MicroRNA Target Sites by PAR-CLIP

RNA transcripts are subject to posttranscriptional gene regulation involving hundreds of RNA-binding proteins (RBPs) and microRNA-containing ribonucleoprotein complexes (miRNPs) expressed in a cell-type dependent fashion. We developed a cell-based crosslinking approach to determine at high resolution and transcriptome-wide the binding sites of cellular RBPs and miRNPs. The crosslinked sites are revealed by thymidine to cytidine transitions in the cDNAs prepared from immunopurified RNPs of 4-thiouridine-treated cells. We determined the binding sites and regulatory consequences for several intensely studied RBPs and miRNPs, including PUM2, QKI, IGF2BP1-3, AGO/EIF2C1-4 and TNRC6A-C. Our study revealed that these factors bind thousands of sites containing defined sequence motifs and have distinct preferences for exonic versus intronic or coding versus untranslated transcript regions. The precise mapping of binding sites across the transcriptome will be critical to the interpretation of the rapidly emerging data on genetic variation between individuals and how these variations contribute to complex genetic diseases.
Supporting Undergraduate Computer Architecture Students Using a Visual MIPS64 CPU Simulator

The topics of computer architecture are always taught using an Assembly dialect as an example. The most commonly used textbooks in this field use the MIPS64 Instruction Set Architecture (ISA) to help students in learning the fundamentals of computer architecture because of its orthogonality and its suitability for real-world applications. This paper shows how to use the EduMIPS64 visual CPU Simulator as a supporting tool for teaching the standard topics covered by an undergraduate course in computer architecture. The proposed approach is first compared to other similar works in the field, then after a short description of the simulator, the paper focuses on how it can be used for teaching specific topics in an undergraduate computer architecture course. This discussion is then followed by a quantitative assessment of the suitability of the simulator by means of a survey compiled by students themselves; the results show that EduMIPS64 is suitable for the purpose for which it was built-that is, supporting the learning process of computer architecture topics.
Random Features for Large-Scale Kernel Machines

To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. Our randomized features are designed so that the inner products of the transformed data are approximately equal to those in the feature space of a user specified shift-invariant kernel. We explore two sets of random features, provide convergence bounds on their ability to approximate various radial basis kernels, and show that in large-scale classification and regression tasks linear machine learning algorithms that use these features outperform state-of-the-art large-scale kernel machines.
Literature Review on Automatic Text Summarization: Single and Multiple Summarizations

The online information available on world wide web is in enormous amount. Search engines like Google, Yahoo were developed to retrieve information from the databases. But actual results were not obtained as the electronic information is increasing day by day. Thus automatic summarization came into demand. Automatic summarization gathers several documents as input and provides the shorter summarized version as output which is informative, unambiguous, save valuable time. Research was done on a single document and moved towards multiple documents. This review categorizes single and multiple summarization methods.
Pronoun Translation and Prediction with or without Coreference Links

The Idiap NLP Group has participated in both DiscoMT 2015 sub-tasks: pronounfocused translation and pronoun prediction. The system for the first sub-task combines two knowledge sources: grammatical constraints from the hypothesized coreference links, and candidate translations from an SMT decoder. The system for the second sub-task avoids hypothesizing a coreference link, and uses instead a large set of source-side and target-side features from the noun phrases surrounding the pronoun to train a pronoun predictor.
Effects of domain on measures of semantic relatedness

Measures of semantic relatedness have been used in a variety of applications in information retrieval and language technology, such as measuring document similarity and cohesion of text. Definitions of such measures have ranged from using distance-based calculations over WordNet or other taxonomies to statistical distributional metrics over document collections such as Wikipedia or the Web. Existing measures do not explicitly consider the domain associations of terms when calculating relatedness: This article demonstrates that domain matters. We construct a data set of pairs of terms with associated domain information and extract pairs that are scored nearly identical by a sample of existing semantic-relatedness measures. We show that human judgments reliably score those pairs containing terms from the same domain as significantly more related than cross-domain pairs, even though the semantic-relatedness measures assign the pairs similar scores. We provide further evidence for this result using a machine learning setting by demonstrating that domain is an informative feature when learning a metric. We conclude that existing relatedness measures do not account for domain in the same way or to the same extent as do human judges.
RPython: a step towards reconciling dynamically and statically typed OO languages

Although the C-based interpreter of Python is reasonably fast, implementations on the CLI or the JVM platforms offers some advantages in terms of robustness and interoperability. Unfortunately, because the CLI and JVM are primarily designed to execute statically typed, object-oriented languages, most dynamic language implementations cannot use the native bytecodes for common operations like method calls and exception handling; as a result, they are not able to take full advantage of the power offered by the CLI and JVM.
 We describe a different approach that attempts to preserve the flexibility of Python, while still allowing for efficient execution. This is achieved by limiting the use of the more dynamic features of Python to an initial, bootstrapping phase. This phase is used to construct a final RPython (Restricted Python) program that is actually executed. RPython is a proper subset of Python, is statically typed, and does not allow dynamic modification of class or method definitions; however, it can still take advantage of Python features such as mixins and first-class methods and classes.
 This paper presents an overview of RPython, including its design and its translation to both CLI and JVM bytecode. We show how the bootstrapping phase can be used to implement advanced features, like extensible classes and generative programming. We also discuss what work remains before RPython is truly ready for general use, and compare the performance of RPython with that of other approaches.
A Quadratic-Complexity Observability-Constrained Unscented Kalman Filter for SLAM

This paper addresses two key limitations of the unscented Kalman filter (UKF) when applied to the simultaneous localization and mapping (SLAM) problem: the cubic computational complexity in the number of states and the inconsistency of the state estimates. To address the first issue, we introduce a new sampling strategy for the UKF, which has constant computational complexity. As a result, the overall computational complexity of UKF-based SLAM becomes of the same order as that of the extended Kalman filter (EKF)-based SLAM, i.e., quadratic in the size of the state vector. Furthermore, we investigate the inconsistency issue by analyzing the observability properties of the linear-regression-based model employed by the UKF. Based on this analysis, we propose a new algorithm, termed observability-constrained (OC)-UKF, which ensures the unobservable subspace of the UKF's linear-regression-based system model is of the same dimension as that of the nonlinear SLAM system. This results in substantial improvement in the accuracy and consistency of the state estimates. The superior performance of the OC-UKF over other state-of-the-art SLAM algorithms is validated by both Monte-Carlo simulations and real-world experiments.
Learning based digital matting

We cast some new insights into solving the digital matting problem by treating it as a semi-supervised learning task in machine learning. A local learning based approach and a global learning based approach are then produced, to fit better the scribble based matting and the trimap based matting, respectively. Our approaches are easy to implement because only some simple matrix operations are needed. They are also extremely accurate because they can efficiently handle the nonlinear local color distributions by incorporating the kernel trick, that are beyond the ability of many previous works. Our approaches can outperform many recent matting methods, as shown by the theoretical analysis and comprehensive experiments. The new insights may also inspire several more works.
Running head : SOCIAL NETWORKING SITE USE Psychological Predictors of Young Adults ' Use of Social Networking Sites

Psychological predictors of young adults': use of social networking sites.  The authors would like to thank Shari Walsh for assistance in the design of the study and Eric Livingston for assistance in data collection.  Young people are increasingly using social networking sites (SNSs), like Myspace and Facebook, to engage with others. The use of SNSs can have both positive and negative effects on the individual; however, few research studies identify the types of people who frequent these Internet sites. This study sought to predict young adults' use of SNSs and addictive tendency towards the use of SNSs from their personality characteristics and levels of self-esteem. University students (N = 201), aged 17 to 24 years, reported their use of SNSs and addictive tendencies for SNSs use, and completed the NEO Five-Factor Personality Inventory 1 and the Coopersmith Self-Esteem Inventory. 2 Multiple regression analyses revealed that, as a group, the personality and self-esteem factors significantly predicted both level of SNS use and addictive tendency but did not explain a large amount of variance in either outcome measure. The findings indicated that extraverted and unconscientious individuals reported higher levels of both SNS use and addictive tendencies. Future research should attempt to identify which other psychosocial characteristics explain young people's level of use and propensity for addictive tendencies for these popular Internet sites. Social networking site use 3 The proliferation of social networking sites (SNSs) has created a phenomenon that engages millions of Internet users around the world, especially young people. Given the popularity of these sites and their importance in young people's lives to facilitate communication and relationships, it is important to understand the factors influencing SNS use, especially at higher levels, and to identify those who may be prone to developing addictive tendencies towards new communication technologies. 5 As with other communication technologies, 6,7 a useful starting point may be to examine the role of personality traits and self-esteem on young people's SNS use. Researchers have confirmed repeatedly that the five-factor model of personality adequately accounts for and explains personality by taking the approach that personality consists of five traits; openness to experience (pursuing and appreciating all types of experience), conscientiousness (control, regulation and direction of goals and impulses), extraversion (amount and intensity of interpersonal interactions), agreeableness (the type of interactions a person prefers to have with others), and neuroticism (degree of emotional adjustment and instability). 8 Self esteem …
Tradeoffs in Neural Variational Inference Thesis

Humans understand the world on an operational and causal level, allowing us to make accurate predictions about the consequences of our actions, and enabling us to plan. What would it take to endow artificial intelligence with similar capabilities? An important component is the ability to learn from observations, that is, to translate data into a model that has a higher level representation. Such representation abstracts away irrelevant variation but preserves important characteristics of the observation, thus enabling data-efficient learning for recognition, classification, and causal modeling. In machine learning, the field of unsupervised learning addresses the challenge of learning models from observations. One recent advancement in this field has been the development of Variational Auto-Encoders (VAE) (, [54]). Since its inception, there have been several suggested improvements. While these works compare their results to the original VAE work, a thorough comparison between these improvements is lacking. In this work, we present a thorough comparison between several selected improvements. We compare the variational log likelihood between these models on three datasets of varying complexity: MSRC-12 (), MNIST (), and celebA ([39]). While variational lower bounds are often presented, most work often neglects to discuss model complexity and the associated training times and convergence rates. We compare the modeling times for these various approaches to provide practical guidelines regarding the trade-offs between the variational lower bound achieved and the run time required for training. Finally, we contribute an extensive code repository implementing these state of the art machine learning techniques on the datasets mentioned. These are developed using the Chainer ([66]) framework. These implemented models are also available for use on other datasets.