nils-herrmann/clean_scirepeval huggingface dataset:
APA CENTENNIAL FEATURE Classrooms : Goals , Structures , and Student Motivation

This article examines the classroom learning environment in relation to achievement goal theory of motivation. Classroom structures are described in terms of how they make different types of achievement goals salient and as a consequence elicit qualitatively different patterns of motivation. Task, evaluation and recognition, and authority dimensions of classrooms are presented as examples of structures that can influence children's orientation toward different achievement goals. Central to the thesis of this article is a perspective that argues for an identification of classroom structures that can contribute to a mastery orientation, a systematic analysis of these structures, and a determination of how these structures relate to each other. The ways in which interventions must address the independency among these structures are discussed in terms of how they influence student motivation.
Client-Server Computing in Mobile Environments

Recent advances in wireless data networking and portable information appliances have engendered a new paradigm of computing, called mobile computing, in which users carrying portable devices have access to data and information services regardless of their physical location or movement behavior. In the meantime, research addressing information access in mobile environments has proliferated. In this survey, we provide a concrete framework and categorization of the various ways of supporting mobile client-server computing for information access. We examine characteristics of mobility that distinguish mobile client-server computing from its traditional counterpart. We provide a comprehensive analysis of new paradigms and enabler concepts for mobile client-server computing, including mobile-aware adaptation, extended client-server model, and mobile data access. A comparative and detailed review of major research prototypes for mobile information access is also presented.
Using Extreme Programming in a Maintenance Environment

in Iona's engineering team, we often ignored good engineering processes and practices. As a result, the code base's overall health degenerated and was salvaged only through two successful reengineering efforts and a series of infrastructure projects designed to improve overall engineering practices. It wasn't until later that we realized how closely our efforts were tied to Extreme Programming. The problems When Iona moved from a start-up to a market-leading provider of middleware products in 1999, it faced four major problem areas: processes and practices, testing, code entropy, and team morale. An initial reengineering project that started in 1997 was concluding, and although it focused a tremendous amount of resources on reengi-neering, improving testing, and making necessary changes to the code base to comply with the latest version of the Corba specification , it did not sufficiently address the four problem areas; it merely skimmed the surface of what needed to be done. Processes and practices In early 1999, you could have asked two different engineers from the same Orbix team how they did their jobs, and each would have replied differently. This reflected the team's lack of process documentation and visibility and its failures to make process a part of each engineer's personal software practices. Also, there was no focus on process improvement. The junior engineers assigned to maintenance and enhancement had no experience with good engineering practices and had only a rudimentary understanding of process. To compound the problems, the maintenance and The authors review how efforts to introduce industry-level best practices led to Extreme Programming, which improved the team's ability to deliver quality support and product enhancements. D uring the 1990s, Iona Technologies' flagship product was a Corba-based middleware product called Orbix. We have since created a newer version of Orbix, but here we discuss how we developed and, more importantly, now maintain the older version, which represents the Corba specification's early evolution. We also review the inherent problems of code entropy due to specification instability and time-to-market pressures. Over the years, because of these pressures and the rapid growth reports from the field
Multiview Image Completion with Space Structure Propagation

We present a multiview image completion method that provides geometric consistency among different views by propagating space structures. Since a user specifies the region to be completed in one of multiview photographs casually taken in a scene, the proposed method enables us to complete the set of photographs with geometric consistency by creating or removing structures on the specified region. The proposed method incorporates photographs to estimate dense depth maps. We initially complete color as well as depth from a view, and then facilitate two stages of structure propagation and structure-guided completion. Structure propagation optimizes space topology in the scene across photographs, while structure-guide completion enhances, and completes local image structure of both depth and color in multiple photographs with structural coherence by searching nearest neighbor fields in relevant views. We demonstrate the effectiveness of the proposed method in completing multiview images.
Communication in Distributed Agile Development: A Case Study

Distributed software development is an increasingly important development approach for software companies as it brings tempting opportunities. Distributed development is already burdened with several problems and agile methods bring further challenges in the form of their reliance on verbal communication and volatile requirements. There is little empirical knowledge on distributed agile software development. We conducted two distributed agile software development case studies and compared our findings against existing recommendations about communication in distributed agile development. Our findings along with existing literature conclude that presented recommendations are worthwhile considering in distributed agile development, but with some caution. Our empirically based findings indicate that the role of a well-defined customer is the key recommendation. The lack of a well-defined customer able to meet responsibilities, as well as volatile requirements and inefficient communication, can cause severe problems even in small-scale distributed agile software development projects. Discussed recommendations are complemented with an additional recommendation.
A Fast Unified Model for Parsing and Sentence Understanding

Tree-structured neural networks exploit valuable syntactic parse information as they interpret the meanings of sentences. However, they suffer from two key technical problems that make them slow and unwieldy for large-scale NLP tasks: they usually operate on parsed sentences and they do not directly support batched computation. We address these issues by introducing the Stackaugmented Parser-Interpreter Neural Network (SPINN), which combines parsing and interpretation within a single treesequence hybrid model by integrating treestructured sentence interpretation into the linear sequential structure of a shift-reduce parser. Our model supports batched computation for a speedup of up to 25× over other tree-structured models, and its integrated parser can operate on unparsed data with little loss in accuracy. We evaluate it on the Stanford NLI entailment task and show that it significantly outperforms other sentence-encoding models.
A Fast Quantum Mechanical Algorithm for Database Search

were proposed in the early 1980’s [Benioff80] and shown to be at least as powerful as classical computers an important but not surprising result, since classical computers, at the deepest level, ultimately follow the laws of quantum mechanics. The description of quantum mechanical computers was formalized in the late 80’s and early 90’s [Deutsch85][BB92] [BV93] [Yao93] and they were shown to be more powerful than classical computers on various specialized problems. In early 1994, [Shor94] demonstrated that a quantum mechanical computer could efficiently solve a well-known problem for which there was no known efficient algorithm using classical computers. This is the problem of integer factorization, i.e. testing whether or not a given integer, N, is prime, in a time which is a finite power of o (logN) .
ntology matching with semantic verification ves

Automated Semantic Matching of Ontologies with Verification (ASMOV) is a novel algorithm that uses lexical and structural characteristics of two ontologies to iteratively calculate a similarity measure between them, derives an alignment, and then verifies it to ensure that it does not contain semantic inconsistencies. In this paper, we describe the ASMOV algorithm, and then present experimental results that measure vailable online xxx eywords: ntology ntology alignment ntology matching its accuracy using the OAEI 2008 tests, and that evaluate its use with two different thesauri: WordNet, and the Unified Medical Language System (UMLS). These results show the increased accuracy obtained by combining lexical, structural and extensional matchers with semantic verification, and demonstrate the advantage of using a domain-specific thesaurus for the alignment of specialized ontologies.
Gray and color image contrast enhancement by the curvelet transform

We present in this paper a new method for contrast enhancement based on the curvelet transform. The curvelet transform represents edges better than wavelets, and is therefore well-suited for multiscale edge enhancement. We compare this approach with enhancement based on the wavelet transform, and the Multiscale Retinex. In a range of examples, we use edge detection and segmentation, among other processing applications, to provide for quantitative comparative evaluation. Our findings are that curvelet based enhancement out-performs other enhancement methods on noisy images, but on noiseless or near noiseless images curvelet based enhancement is not remarkably better than wavelet based enhancement.
Diffusion Adaptation Strategies for Distributed Optimization and Learning Over Networks

We propose an adaptive diffusion mechanism to optimize global cost functions in a distributed manner over a network of nodes. The cost function is assumed to consist of a collection of individual components. Diffusion adaptation allows the nodes to cooperate and diffuse information in real-time; it also helps alleviate the effects of stochastic gradient noise and measurement noise through a continuous learning process. We analyze the mean-square-error performance of the algorithm in some detail, including its transient and steady-state behavior. We also apply the diffusion algorithm to two problems: distributed estimation with sparse parameters and distributed localization. Compared to well-studied incremental methods, diffusion methods do not require the use of a cyclic path over the nodes and are robust to node and link failure. Diffusion methods also endow networks with adaptation abilities that enable the individual nodes to continue learning even when the cost function changes with time. Examples involving such dynamic cost functions with moving targets are common in the context of biological networks.
Towards Extracting Faithful and Descriptive Representations of Latent Variable Models

Methods that use latent representations of data, such as matrix and tensor factorization or deep neural methods, are becoming increasingly popular for applications such as knowledge base population and recommendation systems. These approaches have been shown to be very robust and scalable but, in contrast to more symbolic approaches, lack interpretability. This makes debugging such models difficult, and might result in users not trusting the predictions of such systems. To overcome this issue we propose to extract an interpretable proxy model from a predictive latent variable model. We use a socalled pedagogical method, where we query our predictive model to obtain observations needed for learning a descriptive model. We describe two families of (presumably more) descriptive models, simple logic rules and Bayesian networks, and show how members of these families provide descriptive representations of matrix factorization models. Preliminary experiments on knowledge extraction from text indicate that even though Bayesian networks may be more faithful to a matrix factorization model than the logic rules, the latter are possibly more useful for interpretation and debugging.
Understanding Consumer Decision Making for Complex Choices: The Effects of Individual and Contextual Factors

E-commerce consumers are facing increasingly complex purchasing decisions. Due to their cognitive capacity, however, consumers may not always achieve their goal of optimal product choice. Existing research has focused on providing aids to consumers to help them make rational and conscious information choices for complex purchasing decisions. Recently, a dual-process Unconscious Thought Theory (UTT) has suggested otherwise. It shows that due to the limited cognitive resources, unconscious information processing may outperform conscious information processing for complex decisions. Drawing on the UTT, the study proposes that strategically designed interventions would interact with other contextual and individual factors in consumer information processing, and ultimately lead to superior consumer choice under certain choice environment. An experiment was conducted to test the research model. Focusing on the unconscious information processing in online shopping, this study has important implications for Web-specific human-computer interaction research and e-commerce
Deep Semantic Feature Matching

Estimating dense visual correspondences between objects with intra-class variation, deformations and background clutter remains a challenging problem. Thanks to the breakthrough of CNNs there are new powerful features available. Despite their easy accessibility and great success, existing semantic flow methods could not significantly benefit from these without extensive additional training. We introduce a novel method for semantic matching with pre-trained CNN features which is based on convolutional feature pyramids and activation guided feature selection. For the final matching we propose a sparse graph matching framework where each salient feature selects among a small subset of nearest neighbors in the target image. To improve our method in the unconstrained setting without bounding box annotations we introduce novel object proposal based matching constraints. Furthermore, we show that the sparse matching can be transformed into a dense correspondence field. Extensive experimental evaluations on benchmark datasets show that our method significantly outperforms existing semantic matching methods.
Neuromorphic Artificial Touch for Categorization of Naturalistic Textures

We implemented neuromorphic artificial touch and emulated the firing behavior of mechanoreceptors by injecting the raw outputs of a biomimetic tactile sensor into an Izhikevich neuronal model. Naturalistic textures were evaluated with a passive touch protocol. The resulting neuromorphic spike trains were able to classify ten naturalistic textures ranging from textiles to glass to BioSkin, with accuracy as high as 97%. Remarkably, rather than on firing rate features calculated over the stimulation window, the highest achieved decoding performance was based on the precise spike timing of the neuromorphic output as captured by Victor Purpura distance. We also systematically varied the sliding velocity and the contact force to investigate the role of sensing conditions in categorizing the stimuli via the artificial sensory system. We found that the decoding performance based on the timing of neuromorphic spike events was robust for a broad range of sensing conditions. Being able to categorize naturalistic textures in different sensing conditions, these neurorobotic results pave the way to the use of neuromorphic tactile sensors in future real-life neuroprosthetic applications.
Attention-Fused Deep Matching Network for Natural Language Inference

Natural language inference aims to predict whether a premise sentence can infer another hypothesis sentence. Recent progress on this task only relies on a shallow interaction between sentence pairs, which is insufficient for modeling complex relations. In this paper, we present an attention-fused deep matching network (AF-DMN) for natural language inference. Unlike existing models, AF-DMN takes two sentences as input and iteratively learns the attention-aware representations for each side by multi-level interactions. Moreover, we add a selfattention mechanism to fully exploit local context information within each sentence. Experiment results show that AF-DMN achieves state-of-the-art performance and outperforms strong baselines on Stanford natural language inference (SNLI), multigenre natural language inference (MultiNLI), and Quora duplicate questions datasets.
An evidential model of distributed reputation management

For agents to function effectively in large and open networks, they must ensure that their correspondents, i.e., the agents they interact with, are trustworthy. Since no central authorities may exist, the only way agents can find trustworthy correspondents is by collaborating with others to identify those whose past behavior has been untrustworthy. In other words, finding trustworthy correspondents reduces to the problem of distributed reputation management.Our approach adapts the mathematical theory of evidence to represent and propagate the ratings that agents give to their correspondents. When evaluating the trustworthiness of a correspondent, an agent combines its local evidence (based on direct prior interactions with the correspondent) with the testimonies of other agents regarding the same correspondent. We experimentally studied this approach to establish that some important properties of trust are captured by it.
Private Release of Graph Statistics using Ladder Functions

Protecting the privacy of individuals in graph structured data while making accurate versions of the data available is one of the most challenging problems in data privacy. Most efforts to date to perform this data release end up mired in complexity, overwhelm the signal with noise, and are not effective for use in practice. In this paper, we introduce a new method which guarantees differential privacy. It specifies a probability distribution over possible outputs that is carefully defined to maximize the utility for the given input, while still providing the required privacy level. The distribution is designed to form a 'ladder', so that each output achieves the highest 'rung' (maximum probability) compared to less preferable outputs. We show how our ladder framework can be applied to problems of counting the number of occurrences of subgraphs, a vital objective in graph analysis, and give algorithms whose cost is comparable to that of computing the count exactly. Our experimental study confirms that our method outperforms existing methods for counting triangles and stars in terms of accuracy, and provides solutions for some problems for which no effective method was previously known. The results of our algorithms can be used to estimate the parameters of suitable graph models, allowing synthetic graphs to be sampled.
Compressive phase retrieval via generalized approximate message passing

In this paper, we propose a novel approach to compressive phase retrieval based on loopy belief propagation and, in particular, on the generalized approximate message passing (GAMP) algorithm. Numerical results show that the proposed PR-GAMP algorithm has excellent phase-transition behavior, noise robustness, and runtime. In particular, for successful recovery of synthetic Bernoulli-circular-Gaussian signals, PR-GAMP requires ≈4 times the number of measurements as a phase-oracle version of GAMP and, at moderate to large SNR, the NMSE of PR-GAMP is only ≈3 dB worse than that of phase-oracle GAMP. A comparison to the recently proposed convex-relation approach known as “CPRL” reveals PR-GAMP's superior phase transition and orders-of-magnitude faster runtimes, especially as the problem dimensions increase. When applied to the recovery of a 65k-pixel grayscale image from 32k randomly masked magnitude measurements, numerical results show a median PR-GAMP runtime of only 13.4 seconds.
A Case Study in Understanding OSPF and BGP Interactions Using Efficient Experiment Design

In this paper, we analyze the two dominant inter- and intradomain routing protocols in the Internet: Open Shortest Path Forwarding (OSPFv2) and Border Gateway Protocol (BGP4). Specifically, we investigate interactions between these two routing protocols as well as overall (i.e. both OSPF and BGP) stability and dynamics. Our analysis is based on large-scale simulations of OSPF and BGP, and careful design of experiments (DoE) to perform an efficient search for the best parameter settings of these two routing protocols
Robot dynamics and control

This chapter presents an introduction to the dynamics and control of robot manipulators. We derive the equations of motion for a general open-chain manipulator and, using the structure present in the dynamics , construct control laws for asymptotic tracking of a desired trajectory. In deriving the dynamics, we will make explicit use of twists for representing the kinematics of the manipulator and explore the role that the kinematics play in the equations of motion. We assume some familiarity with dynamics and control of physical systems.
Geometry images

Surface geometry is often modeled with irregular triangle meshes. The process of remeshing refers to approximating such geometry using a mesh with (semi)-regular connectivity, which has advantages for many graphics applications. However, current techniques for remeshing arbitrary surfaces create only semi-regular meshes. The original mesh is typically decomposed into a set of disk-like charts, onto which the geometry is parametrized and sampled. In this paper, we propose to remesh an arbitrary surface onto a completely regular structure we call a geometry image. It captures geometry as a simple 2D array of quantized points. Surface signals like normals and colors are stored in similar 2D arrays using the same implicit surface parametrization --- texture coordinates are absent. To create a geometry image, we cut an arbitrary mesh along a network of edge paths, and parametrize the resulting single chart onto a square. Geometry images can be encoded using traditional image compression algorithms, such as wavelet-based coders.