nils-herrmann/clean_scirepeval huggingface dataset:
Using web corpus statistics for program analysis

Several program analysis tools - such as plagiarism detection and bug finding - rely on knowing a piece of code's relative semantic importance. For example, a plagiarism detector should not bother reporting two programs that have an identical simple loop counter test, but should report programs that share more distinctive code. Traditional program analysis techniques (e.g., finding data and control dependencies) are useful, but do not say how surprising or common a line of code is. Natural language processing researchers have encountered a similar problem and addressed it using an n-gram model of text frequency, derived from statistics computed over text corpora.
 We propose and compute an n-gram model for programming languages, computed over a corpus of 2.8 million JavaScript programs we downloaded from the Web. In contrast to previous techniques, we describe a code n-gram as a subgraph of the program dependence graph that contains all nodes and edges reachable in n steps from the statement. We can count n-grams in a program and count the frequency of n-grams in the corpus, enabling us to compute tf-idf-style measures that capture the differing importance of different lines of code. We demonstrate the power of this approach by implementing a plagiarism detector with accuracy that beats previous techniques, and a bug-finding tool that discovered over a dozen previously unknown bugs in a collection of real deployed programs.
Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines

To allow the hidden units of a restricted Boltzmann machine to model the transformation between two successive images, Memisevic and Hinton (2007) introduced three-way multiplicative interactions that use the intensity of a pixel in the first image as a multiplicative gain on a learned, symmetric weight between a pixel in the second image and a hidden unit. This creates cubically many parameters, which form a three-dimensional interaction tensor. We describe a low-rank approximation to this interaction tensor that uses a sum of factors, each of which is a three-way outer product. This approximation allows efficient learning of transformations between larger image patches. Since each factor can be viewed as an image filter, the model as a whole learns optimal filter pairs for efficiently representing transformations. We demonstrate the learning of optimal filter pairs from various synthetic and real image sequences. We also show how learning about image transformations allows the model to perform a simple visual analogy task, and we show how a completely unsupervised network trained on transformations perceives multiple motions of transparent dot patterns in the same way as humans.
Treatment of keratosis pilaris with 810-nm diode laser: a randomized clinical trial.

Keratosis pilaris (KP) is a common skin disorder of follicular prominence and erythema that typically affects the proximal extremities, can be disfiguring, and is often resistant to treatment. Shorter-wavelength vascular lasers have been used to reduce the associated erythema but not the textural irregularity.



To determine whether the longer-wavelength 810-nm diode laser may be effective for treatment of KP, particularly the associated skin roughness/bumpiness and textural irregularity.



We performed a split-body, rater-blinded, parallel-group, balanced (1:1), placebo-controlled randomized clinical trial at a dermatology outpatient practice of an urban academic medical center from March 1 to October 1, 2011. We included all patients diagnosed as having KP on both arms and Fitzpatrick skin types I through III. Of the 26 patients who underwent screening, 23 met our enrollment criteria. Of these, 18 patients completed the study, 3 were lost to or unavailable for follow-up, and 2 withdrew owing to inflammatory hyperpigmentation after the laser treatment.



Patients were randomized to receive laser treatment on the right or left arm. Each patient received treatment with the 810-nm pulsed diode laser to the arm randomized to be the treatment site. Treatments were repeated twice, for a total of 3 treatment visits spaced 4 to 5 weeks apart.



The primary outcome measure was the difference in disease severity score, including redness and roughness/bumpiness, with each graded on a scale of 0 (least severe) to 3 (most severe), between the treated and control sites. Two blinded dermatologists rated the sites at 12 weeks after the initial visit.



At follow-up, the median redness score reported by the 2 blinded raters for the treatment and control sides was 2.0 (interquartile range [IQR], 1-2; P = .11). The median roughness/bumpiness score was 1.0 (IQR, 1-2) for the treatment sides and 2.0 (IQR, 1-2) for the control sides, a difference of 1 (P = .004). The median overall score combining erythema and roughness/bumpiness was 3.0 (IQR, 2-4) for the treatment sides and 4.0 (IQR, 3-5) for the control sides, a difference of 1 (P = .005).



Three treatments with the 810-nm diode laser may induce significant improvements in skin texture and roughness/bumpiness in KP patients with Fitzpatrick skin types I through III, but baseline erythema is not improved. Complete treatment of erythema and texture in KP may require diode laser treatment combined with other laser or medical modalities that address redness.
Distance Fields for Rapid Collision Detection in Physically Based Modeling

In this paper we address the problem of rapid distance computation between rigid objects and highly deformable objects, which is important in the context of physically based modeling of e.g hair or clothing. Our method is in particular useful when modeling deformable objects with particle systems—the most common approach to simulate such objects. We combine some already known techniques about distance fields into an algorithm for rapid collision detection. Only the rigid objects of an environment are represented by distance fields. In the context of proximity queries, which are essential for proper collision detection, this representation has two main advantages: First, any given boundary representation can be approximated quite easily, no high-degree polynomials or complicated approximation algorithms are needed. Second, the evaluation of distances and normals needed for collision response is extremely fast and independent of the complexity of the object. In the course of the paper we propose a simple, but fast algorithm for partial distance field computation. The sources are triangular meshes. Then, we present our approach for collision detection in detail. Examples from an interactive cloth animation system show the advantages of our approach in practice. We conclude that our method allows real-time animations of complex deformable objects in non-trivial environments on standard PC hardware.
Assert Use in GitHub Projects

Asserts have long been a strongly recommended (if non-functional) adjunct to programs. They certainly don't add any user-evident feature value; and it can take quite some skill and effort to devise and add useful asserts. However, they are believed to add considerable value to the developer. Certainly, they can help with automated verification; but even in the absence of that, claimed advantages include improved understandability, maintainability, easier fault localization and diagnosis, all eventually leading to better software quality. We focus on this latter claim, and use a large dataset of asserts in C and C++ programs to explore the connection between asserts and defect occurrence. Our data suggests a connection: functions with asserts do have significantly fewer defects. This indicates that asserts do play an important role in software quality; we therefore explored further the factors that play a role in assertion placement: specifically, process factors (such as developer experience and ownership) and product factors, particularly interprocedural factors, exploring how the placement of assertions in functions are influenced by local and global network properties of the callgraph. Finally, we also conduct a differential analysis of assertion use across different application domains.
Molecular MIMO: From Theory to Prototype

In diffusion-based molecular communication, information transport is governed by diffusion through a fluid medium. The achievable data rates for these channels are very low compared to the radio-based communication system, since diffusion can be a slow process. To improve the data rate, a novel multiple-input multiple-output (MIMO) design for molecular communication is proposed that utilizes multiple molecular emitters at the transmitter and multiple molecular detectors at the receiver (in RF communication these all correspond to antennas). Using particle-based simulators, the channel's impulse response is obtained and mathematically modeled. These models are then used to determine interlink interference (ILI) and intersymbol interference (ISI). It is assumed that when the receiver has incomplete information regarding the system and the channel state, low complexity symbol detection methods are preferred since the receiver is small and simple. Thus, four detection algorithms are proposed-adaptive thresholding, practical zero forcing with channel models excluding/including the ILI and ISI, and Genie-aided zero forcing. The proposed algorithms are evaluated extensively using numerical and analytical evaluations.
Cryptography - an introduction to computer security

Want to get experience? Want to get any ideas to create new things in your life? Read cryptography an introduction to computer security now! By reading this book as soon as possible, you can renew the situation to get the inspirations. Yeah, this way will lead you to always think more and more. In this case, this book will be always right for you. When you can observe more about the book, you will know why you need this.
Design and validation of the RiceWrist-S exoskeleton for robotic rehabilitation after incomplete spinal cord injury

Robotic devices are well-suited to provide high intensity upper limb therapy in order to induce plasticity and facilitate recovery from brain and spinal cord injury. In order to realize gains in functional independence, devices that target the distal joints of the arm are necessary. Further, the robotic device must exhibit key dynamic properties that enable both high dynamic transparency for assessment, and implementation of novel interaction control modes that significantly engage the participant. In this paper, we present the kinematic design, dynamical characterization, and clinical validation of the RiceWrist-S, a serial robotic mechanism that facilitates rehabilitation of the forearm in pronation-supination, and of the wrist in flexion-extension and radial-ulnar deviation. The RiceWrist-Grip, a grip force sensing handle, is shown to provide grip force measurements that correlate well with those acquired from a hand dynamometer. Clinical validation via a single case study of incomplete spinal cord injury rehabilitation for an individual with injury at the C3-5 level showed moderate gains in clinical outcome measures. Robotic measures of movement smoothness also captured gains, supporting our hypothesis that intensive upper limb rehabilitation with the RiceWrist-S would show beneficial outcomes. This work was supported in part by grants from Mission Connect, a project of the TIRR Foundation, the National Science Foundation Graduate Research Fellowship Program under Grant No. 0940902, NSF CNS-1135916, and H133P0800007-NIDRRARRT. A.U. Pehlivan, F. Sergi, A. Erwin, and M. K. O’Malley are with the Mechatronics and Haptic Interfaces Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX 77005. F. Sergi is also with the department of PM&R, Baylor College of Medicine, Houston, TX 77030. N. Yozbatiran and G. E. Francisco are with the Department of PM&R and UTHealth Motor Recovery Lab, University of Texas Health Science Center at Houston, TX 77030 (e-mails: aliutku@rice.edu, fabs@rice.edu, ace7@rice.edu, Nuray.Yozbatiran@uth.tmc.edu, Gerard.E.Francisco@uth.tmc.edu, and omalleym@rice.edu)
Financial Forecasting Using Support Vector Machines

The use of Support Vector Machines (SVMs) is studied in financial forecasting by comparing it with a multi-layer perceptron trained by the Back Propagation (BP) algorithm. SVMs forecast better than BP based on the criteria of Normalised Mean Square Error (NMSE), Mean Absolute Error (MAE), Directional Symmetry (DS), Correct Up (CP) trend and Correct Down (CD) trend. S&P 500 daily price index is used as the data set. Since there is no structured way to choose the free parameters of SVMs, the generalisation error with respect to the free parameters of SVMs is investigated in this experiment. As illustrated in the experiment, they have little impact on the solution. Analysis of the experimental results demonstrates that it is advantageous to apply SVMs to forecast the financial time series.
Jointly Learning to Parse and Perceive: Connecting Natural Language to the Physical World

This paper introduces Logical Semantics with Perception (LSP), a model for grounded language acquisition that learns to map natural language statements to their referents in a physical environment. For example, given an image, LSP can map the statement “blue mug on the table” to the set of image segments showing blue mugs on tables. LSP learns physical representations for both categorical (“blue,” “mug”) and relational (“on”) language, and also learns to compose these representations to produce the referents of entire statements. We further introduce a weakly supervised training procedure that estimates LSP’s parameters using annotated referents for entire statements, without annotated referents for individual words or the parse structure of the statement. We perform experiments on two applications: scene understanding and geographical question answering. We find that LSP outperforms existing, less expressive models that cannot represent relational language. We further find that weakly supervised training is competitive with fully supervised training while requiring significantly less annotation effort.
Edge Computing: Vision and Challenges

The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.
Computing Productivity: Firm-Level Evidence

We explore the effect of computerization on productivity and output growth using data from 527 large U.S. firms over 19871994. We find that computerization makes a contribution to measured productivity and output growth in the short term (using 1-year differences) that is consistent with normal returns to computer investments. However, the productivity and output contributions associated with computerization are up to 5 times greater over long periods (using 5- to 7-year differences). The results suggest that the observed contribution of computerization is accompanied by relatively large and time-consuming investments in complementary inputs, such as organizational capital, that may be omitted in conventional calculations of productivity. The large long-run contribution of computers and their associated complements that we uncover may partially explain the subsequent investment surge in computers in the late 1990s.
A Feasible Nonconvex Relaxation Approach to Feature Selection

Variable selection problems are typically addressed under a penalized optimization framework. Nonconvex penalties such as the minimax concave plus (MCP) and smoothly clipped absolute deviation (SCAD), have been demonstrated to have the properties of sparsity practically and theoretically. In this paper we propose a new nonconvex penalty that we call exponential-type penalty. The exponential-type penalty is characterized by a positive parameter, which establishes a connection with the l0 and l1 penalties. We apply this new penalty to sparse supervised learning problems. To solve to resulting optimization problem, we resort to a reweighted l1 minimization method. Moreover, we devise an efficient method for the adaptive update of the tuning parameter. Our experimental results are encouraging. They show that the exponential-type penalty is competitive with MCP and SCAD.
Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS)

Recently it was shown that the problem of Maximum Inner Product Search (MIPS) is efficient and it admits provably sub-linear hashing algorithms. Asymmetric transformations before hashing were the key in solving MIPS which was otherwise hard. In [18], the authors use asymmetric transformations which convert the problem of approximate MIPS into the problem of approximate near neighbor search which can be efficiently solved using hashing. In this work, we provide a different transformation which converts the problem of approximate MIPS into the problem of approximate cosine similarity search which can be efficiently solved using signed random projections. Theoretical analysis show that the new scheme is significantly better than the original scheme for MIPS. Experimental evaluations strongly support the theoretical findings.
Fog Orchestration for Internet of Things Services

Large-scale Internet of Things (IoT) services such as healthcare, smart cities, and marine monitoring are pervasive in cyber-physical environments strongly supported by Internet technologies and fog computing. Complex IoT services are increasingly composed of sensors, devices, and compute resources within fog computing infrastructures. The orchestration of such applications can be leveraged to alleviate the difficulties of maintenance and enhance data security and system reliability. However, efficiently dealing with dynamic variations and transient operational behavior is a crucial challenge within the context of choreographing complex services. Furthermore, with the rapid increase of the scale of IoT deployments, the heterogeneity, dynamicity, and uncertainty within fog environments and increased computational complexity further aggravate this challenge. This article gives an overview of the core issues, challenges, and future research directions in fog-enabled orchestration for IoT services. Additionally, it presents early experiences of an orchestration scenario, demonstrating the feasibility and initial results of using a distributed genetic algorithm in this context.
Fast personalized PageRank on MapReduce

In this paper, we design a fast MapReduce algorithm for Monte Carlo approximation of personalized PageRank vectors of all the nodes in a graph. The basic idea is very efficiently doing single random walks of a given length starting at each node in the graph. More precisely, we design a MapReduce algorithm, which given a graph G and a length », outputs a single random walk of length » starting at each node in G. We will show that the number of MapReduce iterations used by our algorithm is optimal among a broad family of algorithms for the problem, and its I/O efficiency is much better than the existing candidates. We will then show how we can use this algorithm to very efficiently approximate all the personalized PageRank vectors. Our empirical evaluation on real-life graph data and in production MapReduce environment shows that our algorithm is significantly more efficient than all the existing algorithms in the MapReduce setting.
A Laser-Aided Inertial Navigation System (L-INS) for human localization in unknown indoor environments

This paper presents a novel 3D indoor Laser-aided Inertial Navigation System (L-INS) for the visually impaired.  position and orientation (pose) of the person and a 3D map of the environment. The IMU measurements are integrated to obtain pose estimates, which are subsequently corrected using line-to-plane correspondences between linear segments in the laser-scan data and orthogonal structural planes of the building. Exploiting the orthogonal building planes ensures fast and efficient initialization and estimation of the map features while providing human-interpretable layout of the environment. The L-INS is experimentally validated by a person traversing a multistory building, and the results demonstrate the reliability and accuracy of the proposed method for indoor localization and mapping.
A pneumatic tactile alerting system for the driving environment

Sensory overloaded environments present an opportunity for innovative design in the area of Human-Machine Interaction. In this paper we study the usefulness of a tactile display in the automobile environment. Our approach uses a simple pneumatic pump to produce pulsations of varying frequencies on the driver's hands through a car steering wheel fitted with inflatable pads. The goal of the project is to evaluate the effectiveness of such a system in alerting the driver of a possible problem, when it is used to augment the visual display presently used in automobiles. A steering wheel that provides haptic feedback using pneumatic pockets was developed to test our hypothesis. The steering wheel can pulsate at different frequencies. The system was tested in a simple multitasking paradigm on several subjects and their reaction times to different stimuli were measured and analyzed. For these experiments, we found that using a tactile feedback device lowers reaction time significantly and that modulating frequency of vibration provides extra information that can reduce the time necessary to identify a problem.
What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment

We propose an end-to-end, domainindependent neural encoder-aligner-decoder model for selective generation, i.e., the joint task of content selection and surface realization. Our model first encodes a full set of over-determined database event records via an LSTM-based recurrent neural network, then utilizes a novel coarse-to-fine aligner to identify the small subset of salient records to talk about, and finally employs a decoder to generate free-form descriptions of the aligned, selected records. Our model achieves the best selection and generation results reported to-date (with 59% relative improvement in generation) on the benchmark WEATHERGOV dataset, despite using no specialized features or linguistic resources. Using an improved k-nearest neighbor beam filter helps further. We also perform a series of ablations and visualizations to elucidate the contributions of our key model components. Lastly, we evaluate the generalizability of our model on the ROBOCUP dataset, and get results that are competitive with or better than the state-of-the-art, despite being severely data-starved.