nils-herrmann/clean_scirepeval huggingface dataset:
An Axiomatic Basis for Computer Programming

In this paper an attempt is made to explore the logical foundations of computer programming by use of techniques which were first applied in the study of geometry and have later been extended to other branches of mathematics. This involves the elucidation of sets of axioms and rules of inference which can be used in proofs of the properties of computer programs. Examples are given of such axioms and rules, and a formal proof of a simple theorem is displayed. Finally, it is argued that important advantage, both theoretical and practical, may follow from a pursuance of these topics.
Unsupervised Keyphrase Extraction with Multipartite Graphs

We propose an unsupervised keyphrase extraction model that encodes topical information within a multipartite graph structure. Our model represents keyphrase candidates and topics in a single graph and exploits their mutually reinforcing relationship to improve candidate ranking. We further introduce a novel mechanism to incorporate keyphrase selection preferences into the model. Experiments conducted on three widely used datasets show significant improvements over state-of-the-art graph-based models.
Selecting a Project 's Methodology

The punch line is that having multiple methodologies is appropriate and necessary. We can differentiate them according to staff size and system criticality (more dimensions exist, but these two serve well initially). For any point in the size–criticality space, methodology designers select a scope of concerns, prioritizing some quality of the project. Based on these choices, the project team selects how light or heavy a methodology they want for their project.  To help software development teams handle the selection, I describe several principles that underlie methodology design. I have successfully used these principles on several software development projects, one of which I describe in some detail in this article. Under standards, we find notations, such as the drawing and programming languages selected ; policies, such as incremental-development use; and conventions—the standards the project team determines. Team values, the least obvious element in the above list, are what the team strives for in terms of how it communicates and works. Different team values promote different methodologies. Some companies have methodologies that cover from the initial sales call through maintenance and all roles for which project funds pay . Most books containing what people call methodologies have mainly just the designer or programmer in mind, showing a few techniques and drawing standards. Fitting these techniques and standards into a methodology framework, we understand why software developers get frustrated when they see authors call these things methodologies. The individual design focus Selecting a Project's Methodology Alistair Cockburn, Humans and Technology This article describes a framework for methodology differentiation, principles for methodology selection, and project experiences using these ideas. H ow do we determine the need for various processes or methodologies, and what helps us choose the appropriate one for our project? To answer these questions, we need to get to the bottom of the controversy over methodolo-gies and discover the dimensions along which they vary. process diversity
Consensus photometric stereo

This paper describes a photometric stereo method that works with a wide range of surface reflectances. Unlike previous approaches that assume simple parametric models such as Lambertian reflectance, the only assumption that we make is that the reflectance has three properties; monotonicity, visibility, and isotropy with respect to the cosine of light direction and surface orientation. In fact, these properties are observed in many non-Lambertian diffuse reflectances. We also show that the monotonicity and isotropy properties hold specular lobes with respect to the cosine of the surface orientation and the bisector between the light direction and view direction. Each of these three properties independently gives a possible solution space of the surface orientation. By taking the intersection of the solution spaces, our method determines the surface orientation in a consensus manner. Our method naturally avoids the need for radiometrically calibrating cameras because the radiometric response function preserves these three properties. The effectiveness of the proposed method is demonstrated using various simulated and real-world scenes that contain a variety of diffuse and specular surfaces.
Towards automatic generation of vulnerability-based signatures

In this paper we explore the problem of creating vulnerability signatures. A vulnerability signature matches all exploits of a given vulnerability, even polymorphic or metamorphic variants. Our work departs from previous approaches by focusing on the semantics of the program and vulnerability exercised by a sample exploit instead of the semantics or syntax of the exploit itself. We show the semantics of a vulnerability define a language which contains all and only those inputs that exploit the vulnerability. A vulnerability signature is a representation (e.g., a regular expression) of the vulnerability language. Unlike exploit-based signatures whose error rate can only be empirically measured for known test cases, the quality of a vulnerability signature can be formally quantified for all possible inputs. We provide a formal definition of a vulnerability signature and investigate the computational complexity of creating and matching vulnerability signatures. We also systematically explore the design space of vulnerability signatures. We identify three central issues in vulnerability-signature creation: how a vulnerability signature represents the set of inputs that may exercise a vulnerability, the vulnerability coverage (i.e., number of vulnerable program paths) that is subject to our analysis during signature creation, and how a vulnerability signature is then created for a given representation and coverage. We propose new data-flow analysis and novel adoption of existing techniques such as constraint solving for automatically generating vulnerability signatures. We have built a prototype system to test our techniques. Our experiments show that we can automatically generate a vulnerability signature using a single exploit which is of much higher quality than previous exploit-based signatures. In addition, our techniques have several other security applications, and thus may be of independent interest
Gain-scheduled wheel slip control in automotive brake systems

A wheel slip controller is developed and experimentally tested in a car equipped with electromechanical brake actuators and a brake-by-wire ABS syst em. A gain scheduling approach is taken, where the vehicle speed is viewed as a slowly time-varying parameter and the model is linearized about the nominal wheel slip. Gain matrices for the different operating conditions are designed using an LQR approach. The stability and robustness of the controller are dem onstrate via Lyapunov theory, frequency analysis and experiments using a test vehicle.
Tiled convolutional neural networks

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient based learning applied to document recognition. Proceeding of the IEEE, 1998.  H. Lee, R. Grosse, R. Ranganath, and A.Y. Ng. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In ICML, 2009.  M.A. Ranzato, K. Jarrett, K. Kavukcuoglu and Y. LeCun. What is the best multi-stage architecture for object recognition? In ICCV, 2009.  A. Hyvarinen and P. Hoyer. Topographic independent component analysis as a model of V1 organization and receptive fields. Neural Computation, 2001.  A. Hyvarinen, J. Hurri, and P. Hoyer. Natural Image Statistics. Springer, 2009.  K. Kavukcuoglu, M. Ranzato, R. Fergus, Y. LeCun. Learning invariant features through topographic filter maps . In CVPR, 2009.  K. Gregor, Y. LeCun. Emergence of Complex-Like Cells in a Temporal Product Network with Local Receptive Fields.  2010.
Pilot Contamination and Precoding in Multi-Cell TDD Systems

This paper considers a multi-cell multiple antenna system with precoding used at the base stations for downlink transmission. Channel state information (CSI) is essential for precoding at the base stations. An effective technique for obtaining this CSI is time-division duplex (TDD) operation where uplink training in conjunction with reciprocity simultaneously provides the base stations with downlink as well as uplink channel estimates. This paper mathematically characterizes the impact that uplink training has on the performance of such multi-cell multiple antenna systems. When non-orthogonal training sequences are used for uplink training, the paper shows that the precoding matrix used by the base station in one cell becomes corrupted by the channel between that base station and the users in other cells in an undesirable manner. This paper analyzes this fundamental problem of pilot contamination in multi-cell systems. Furthermore, it develops a new multi-cell MMSE-based precoding method that mitigates this problem. In addition to being linear, this precoding method has a simple closed-form expression that results from an intuitive optimization. Numerical results show significant performance gains compared to certain popular single-cell precoding methods.
Coalescing in Temporal Databases

Coalescing is a unary operator applicable to temporal datab ases; it is similar to duplicate elimination in conventional databases. Tuples in a temporal relation that agr ee on the explicit attribute values and that have adjacent or overlapping time periods are candidates for coalescing. Uncoalesced relations can arise in many ways, e.g., via a projection or union operator, or by not enforcing coalesci ng on update or insertion. In this paper we show how semantically superfluous coalescing can be eliminated. We t hen turn to efficiently performing coalescing. We provide a variety of iterative and non-iterative approache s, via SQL and embedded SQL, that require no changes to the DBMS, demonstrating that coalescing can be formulate d in SQL-89. Detailed performance studies show that all such approaches are quite expensive. We propose a sp ctrum of coalescing algorithms within a DBMS, based on nested-loop, explicit partitioning, explicit sor ting, temporal sorting, and combined explicit/temporal sorting, as well as their hybrid variants. These algorithms are empirically compared, paying particular attention to attribute skew and timestamp distributions. The experim nts show that coalescing can be implemented with reasonable efficiency, and with modest development cost.
A data-oriented (and beyond) network architecture

The Internet has evolved greatly from its original incarnation. For instance, the vast majority of current Internet usage is data retrieval and service access, whereas the architecture was designed around host-to-host applications such as telnet and ftp. Moreover, the original Internet was a purely transparent carrier of packets, but now the various network stakeholders use middleboxes to improve security and accelerate applications. To adapt to these changes, we propose the Data-Oriented Network Architecture (DONA), which involves a clean-slate redesign of Internet naming and name resolution.
KNN Matting

We are interested in a general alpha matting approach for the simultaneous extraction of multiple image layers; each layer may have disjoint segments for material matting not limited to foreground mattes typical of natural image matting. The estimated alphas also satisfy the summation constraint. Our approach does not assume the local colorline model, does not need sophisticated sampling strategies, and generalizes well to any color or feature space in any dimensions. Our matting technique, aptly called KNN matting, capitalizes on the nonlocal principle by usingK nearest neighbors (KNN) in matching nonlocal neighborhoods, and contributes a simple and fast algorithm giving competitive results with sparse user markups. KNN matting has a closed-form solution that can leverage on the preconditioned conjugate gradient method to produce an efficient implementation. Experimental evaluation on benchmark datasets indicates that our matting results are comparable to or of higher quality than state of the art methods.
CNTFET-Based Design of Ternary Logic Gates and Arithmetic Circuits

This paper presents a novel design of ternary logic gates using carbon nanotube (CNT) FETs (CNTFETs). Ternary logic is a promising alternative to the conventional binary logic design technique, since it is possible to accomplish simplicity and energy efficiency in modern digital design due to the reduced circuit overhead such as interconnects and chip area. A resistive-load CNTFET-based ternary logic design has been proposed to implement ternary logic based on CNTFET. In this paper, a novel design technique for ternary logic gates based on CNTFETs is proposed and compared with the existing resistive-load CNTFET logic gate designs. Especially, the proposed ternary logic gate design technique combined with the conventional binary logic gate design technique provides an excellent speed and power consumption characteristics in datapath circuit such as full adder and multiplier. Extensive simulation results using SPICE are reported to show that the proposed ternary logic gates consume significantly lower power and delay than the previous resistive-load CNTFET gates implementations. In realistic circuit application, the utilization of the proposed ternary gates combined with binary gates results in over 90% reductions in terms of the power delay product.
Mining StackOverflow to turn the IDE into a self-confident programming prompter

Developers often require knowledge beyond the one they possess, which often boils down to consulting sources of information like Application Programming Interfaces (API) documentation, forums, Q&A websites, etc. Knowing what to search for and how is non- trivial, and developers spend time and energy to formulate their problems as queries and to peruse and process the results. We propose a novel approach that, given a context in the IDE, automatically retrieves pertinent discussions from Stack Overflow, evaluates their relevance, and, if a given confidence threshold is surpassed, notifies the developer about the available help. We have implemented our approach in Prompter, an Eclipse plug-in. Prompter has been evaluated through two studies. The first was aimed at evaluating the devised ranking model, while the second was conducted to evaluate the usefulness of Prompter.
Vehicle License Plate Recognition Based on Text-line Construction and Multilevel RBF Neural Network

License plate localization and character segmentation and recognition are the research hotspots of vehicle license plate recognition (VLPR) technology. A new method to VLPR is presented in this paper. In license plate localization section, Otsu binarization is operated to get the plate-candidates regions, and a text-line is constructed from the candidate regions. According to the text-line construction result and the characteristics of the license plate character arrangement, the license plate location will be determined. And then the locally optimal adaptive binarization is utilized to make more accurate license plate localization. After the license plate localization, the segment method of vertical projection information with prior knowledge is used to slit characters and the statistical features are extracted. Then the multilevel classification RBF neural network is used to recognize characters using the feature vector as input. The results show that this method can recognize characters precisely and improve the ability of license plate character recognition effectively.
Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health

Mental illness is one of the most pressing public health issues of our time. While counseling and psychotherapy can be effective treatments, our knowledge about how to conduct successful counseling conversations has been limited due to lack of large-scale data with labeled outcomes of the conversations. In this paper, we present a large-scale, quantitative study on the discourse of text-message-based counseling conversations. We develop a set of novel computational discourse analysis methods to measure how various linguistic aspects of conversations are correlated with conversation outcomes. Applying techniques such as sequence-based conversation models, language model comparisons, message clustering, and psycholinguistics-inspired word frequency analyses, we discover actionable conversation strategies that are associated with better conversation outcomes.
Statistical mechanics of complex networks

Complex networks describe a wide range of systems in nature and society. Frequently cited examples include the cell, a network of chemicals linked by chemical reactions, and the Internet, a network of routers and computers connected by physical links. While traditionally these systems have been modeled as random graphs, it is increasingly recognized that the topology and evolution of real networks are governed by robust organizing principles. This article reviews the recent advances in the field of complex networks, focusing on the statistical mechanics of network topology and dynamics. After reviewing the empirical data that motivated the recent interest in networks, the authors discuss the main models and analytical tools, covering random graphs, small-world and scale-free networks, the emerging theory of evolving networks, and the interplay between topology and the network’s robustness against failures and attacks.
OpenFlow-Based Server Load Balancing Gone Wild

Today’s data centers host online services on multiple servers, with a front-end load balancer directing each client request to a particular replica. Dedicated load balancers are expensive and quickly become a single point of failure and congestion. The OpenFlow standard enables an alternative approach where the commodity network switches divide traffic over the server replicas, based on packet-handling rules installed by a separate controller. However, the simple approach of installing a separate rule for each client connection (or “microflow”) leads to a huge number of rules in the switches and a heavy load on the controller. We argue that the controller should exploit switch support for wildcard rules for a more scalable solution that directs large aggregates of client traffic to server replicas. We present algorithms that compute concise wildcard rules that achieve a target distribution of the traffic, and automatically adjust to changes in load-balancing policies without disrupting existing connections. We implement these algorithms on top of the NOX OpenFlow controller, evaluate their effectiveness, and propose several avenues for further research.
THE INSTITUTIONAL FOUNDATIONS OF PUBLIC POLICY : A TRANSACTIONS APPROACH WITH APPLICATION TO ARGENTINA *

Public policies are the outcomes of complex intertemporal exchanges among politicians. The basic institutional characteristics of a country constitute the framework within which those transactions are accomplished. We develop a transactions theory to understand the ways in which political institutions affect the transactions that political actors are able to undertake, and hence the policies that emerge. We argue that Argentina is a case in which the functioning of political institutions has been such that it prevented the capacity to undertake efficient intertemporal political exchanges. We use positive political theory and transaction cost economics to explain the workings of Argentine political institutions, and to show how that maps into low-quality policies.
Learning Diverse Image Colorization

Colorization is an ambiguous problem, with multiple viable colorizations for a single grey-level image. However, previous methods only produce the single most probable colorization. Our goal is to model the diversity intrinsic to the problem of colorization and produce multiple colorizations that display long-scale spatial co-ordination. We learn a low dimensional embedding of color fields using a variational autoencoder (VAE). We construct loss terms for the VAE decoder that avoid blurry outputs and take into account the uneven distribution of pixel colors. Finally, we build a conditional model for the multi-modal distribution between grey-level image and the color field embeddings. Samples from this conditional model result in diverse colorization. We demonstrate that our method obtains better diverse colorizations than a standard conditional variational autoencoder (CVAE) model, as well as a recently proposed conditional generative adversarial network (cGAN).
Improved Statistical Machine Translation Using Monolingually-Derived Paraphrases

Untranslated words still constitute a major problem for Statistical Machine Translation (SMT), and current SMT systems are limited by the quantity of parallel training texts. Augmenting the training data with paraphrases generated by pivoting through other languages alleviates this problem, especially for the so-called “low density” languages. But pivoting requires additional parallel texts. We address this problem by deriving paraphrases monolingually, using distributional semantic similarity measures, thus providing access to larger training resources, such as comparable and unrelated monolingual corpora. We present what is to our knowledge the first successful integration of a collocational approach to untranslated words with an end-to-end, state of the art SMT system demonstrating significant translation improvements in a low-resource setting.
Haptic Feedback for Virtual Reality 1

Haptic feedback is a crucial sensorial modality in virtual reality interactions. Haptics means both force feedback (simulating object hardness, weight, and inertia) and tactile feedback (simulating surface contact geometry, smoothness, slippage, and temperature). Providing such sensorial data requires desk-top or portable special-purpose hardware called haptic interfaces. Modeling physical interactions involves precise collision detection, real-time force computation, and high control-loop bandwidth. This results in a large computation load which requires multi-processor parallel processing on networked computers. Applications for haptics-intensive VR simulations include CAD model design and assembly. Improved technology (wearable computers, novel actuators, haptic toolkits) will increase the use of force/tactile feedback in future VR simulations.
Analyzing Stability in Wide-Area Network Performance

The Internet is a very large scale, complex, dynamical system that is hard to model and analyze. In this paper, we develop and analyze statistical models for the observed end-to-end network performance based on extensive packet-level traces (consisting of approximately 1.5 billion packets) collected from the primary Web site for the Atlanta Summer Olympic Games in 1996. We find that observed mean throughputs for these transfers measured over 60 million complete connections vary widely as a function of end-host location and time of day, confirming that the Internet is characterized by a large degree of heterogeneity. Despite this heterogeneity, we find (using best-fit linear regression techniques) that we can express the throughput for Web transfers to most hosts as a random variable with a log-normal distribution. Then, using observed throughput as the control parameter, we attempt to quantify the spatial (statistical similarity across neighboring hosts) and temporal (persistence over time) stability of network performance. We find that Internet hosts that are close to each other often have almost identically distributed probability distributions of throughput. We also find that throughputs to individual hosts often do not change appreciably for several minutes. Overall, these results indicate that there is promise in protocol mechanisms that cache and share network characteristics both within a single host and amongst nearby hosts.