nils-herrmann/clean_scirepeval huggingface dataset:
Image Interpolation and Resampling

This chapter presents a survey of interpolation and resampling techniques in the context of exact, separable interpolation of regularly sampled data. In this context, the traditional view of interpolation is to represent an arbitrary continuous function as a discrete sum of weighted and shifted synthesis functions—in other words, a mixed convolution equation. An important issue is the choice of adequate synthesis functions that satisfy interpolation properties. Examples of finite-support ones are the square pulse (nearest-neighbor interpolation), the hat function (linear interpolation), the cubic Keys' function, and various truncated or windowed versions of the sinc function. On the other hand, splines provide examples of infinite-support interpolation functions that can be realized exactly at a finite, surprisingly small computational cost. We discuss implementation issues and illustrate the performance of each synthesis function. We also highlight several artifacts that may arise when performing interpolation, such as ringing, aliasing, blocking and blurring. We explain why the approximation order inherent in the synthesis function is important to limit these interpolation artifacts, which motivates the use of splines as a tunable way to keep them in check without any significant cost penalty. I. I NTRODUCTION Interpolation is a technique that pervades many an application. Interpolation is almost never the goal in itself, yet it affects both the desired results and the ways to obtain them. Notwithstanding its nearly universal relevance, some authors give it less importance than it deserves, perhaps because considerations on interpolation are felt as being paltry when compared to the description of a more inspiring grand scheme of things of some algorithm or method. Due to this indifference, it appears as if the basic principles that underlie interpolation might be sometimes cast aside, or even misunderstood. The goal of this chapter is to refresh the notions encountered in classical interpolation, as well as to introduce the reader to more general approaches. 1.1. Definition What is interpolation? Several answers coexist. One of them defines interpolation as an informed estimate of the unknown . We prefer the following—admittedly less concise—definition: modelbased recovery of continuous data from discrete data within a known range of abscissa. The reason for this preference is to allow for a clearer distinction between interpolation and extrapolation. The former postulates the existence of a known range where the model applies, and asserts that the deterministicallyrecovered continuous data is entirely described by the discrete data, while the latter authorizes the use of the model outside of the known range, with the implicit assumption that the model is "good" near data samples, and possibly less good elsewhere. Finally, the three most important hypothesis for interpolation are:
Fake News: Fundamental Theories, Detection Strategies and Challenges

The explosive growth of fake news and its erosion to democracy, justice, and public trust increased the demand for fake news detection. As an interdisciplinary topic, the study of fake news encourages a concerted effort of experts in computer and information science, political science, journalism, social science, psychology, and economics. A comprehensive framework to systematically understand and detect fake news is necessary to attract and unite researchers in related areas to conduct research on fake news. This tutorial aims to clearly present (1) fake news research, its challenges, and research directions; (2) a comparison between fake news and other related concepts (e.g., rumors); (3) the fundamental theories developed across various disciplines that facilitate interdisciplinary research; (4) various detection strategies unified under a comprehensive framework for fake news detection; and (5) the state-of-the-art datasets, patterns, and models. We present fake news detection from various perspectives, which involve news content and information in social networks, and broadly adopt techniques in data mining, machine learning, natural language processing, information retrieval and social search. Facing the upcoming 2020 U.S. presidential election, challenges for automatic, effective and efficient fake news detection are also clarified in this tutorial.
Code Hot Spot: A tool for extraction and analysis of code change history

Commercial software development teams have limited time available to focus on improvements to their software. These teams need a way to quickly identify areas of the source code that would benefit from improvement, as well as quantifiable data to defend the selected improvements to management. Past research has shown that mining configuration management systems for change information can be useful in determining faulty areas of the code. We present a tool named Code Hot Spot, which mines change records out of Microsoft's TFS configuration management system and creates a report of hot spots. Hot spots are contiguous areas of the code that have higher values of metrics that are indicators of faulty code. We present a study where we use this tool to study projects at ABB to determine areas that need improvement. The resulting data have been used to prioritize areas for additional code reviews and unit testing, as well as identifying change prone areas in need of refactoring.
Toward whole-session relevance: exploring intrinsic diversity in web search

Current research on web search has focused on optimizing and evaluating single queries. However, a significant fraction of user queries are part of more complex tasks [20] which span multiple queries across one or more search sessions [26,24]. An ideal search engine would not only retrieve relevant results for a user's particular query but also be able to identify when the user is engaged in a more complex task and aid the user in completing that task [29,1]. Toward optimizing whole-session or task relevance, we characterize and address the problem of intrinsic diversity (ID) in retrieval [30], a type of complex task that requires multiple interactions with current search engines. Unlike existing work on extrinsic diversity  that deals with ambiguity in intent across multiple users, ID queries often have little ambiguity in intent but seek content covering a variety of aspects on a shared theme. In such scenarios, the underlying needs are typically exploratory, comparative, or breadth-oriented in nature. We identify and address three key problems for ID retrieval: identifying authentic examples of ID tasks from post-hoc analysis of behavioral signals in search logs; learning to identify initiator queries that mark the start of an ID search task; and given an initiator query, predicting which content to prefetch and rank.
HyperDex: a distributed, searchable key-value store

Distributed key-value stores are now a standard component of high-performance web services and cloud computing applications. While key-value stores offer significant performance and scalability advantages compared to traditional databases, they achieve these properties through a restricted API that limits object retrieval---an object can only be retrieved by the (primary and only) key under which it was inserted. This paper presents HyperDex, a novel distributed key-value store that provides a unique search primitive that enables queries on secondary attributes. The key insight behind HyperDex is the concept of hyperspace hashing in which objects with multiple attributes are mapped into a multidimensional hyperspace. This mapping leads to efficient implementations not only for retrieval by primary key, but also for partially-specified secondary attribute searches and range queries. A novel chaining protocol enables the system to achieve strong consistency, maintain availability and guarantee fault tolerance. An evaluation of the full system shows that HyperDex is 12-13x faster than Cassandra and MongoDB for finding partially specified objects. Additionally, HyperDex achieves 2-4x higher throughput for get/put operations.
Lifted Relational Neural Networks: Efficient Learning of Latent Relational Structures

We propose a method to combine the interpretability and expressive power of firstorder logic with the effectiveness of neural network learning. In particular, we introduce a lifted framework in which first-order rules are used to describe the structure of a given problem setting. These rules are then used as a template for constructing a number of neural networks, one for each training and testing example. As the different networks corresponding to different examples share their weights, these weights can be efficiently learned using stochastic gradient descent. Our framework provides a flexible way for implementing and combining a wide variety of modelling constructs. In particular, the use of first-order logic allows for a declarative specification of latent relational structures, which can then be efficiently discovered in a given data set using neural network learning. Experiments on 78 relational learning benchmarks clearly demonstrate the effectiveness of the framework.
Analysis of Error in Depth Perception with Vergence and Spatially Varying Sensing

In stereo vision the depth of a 3-D point is estimated based based and feature-based techniques. Area-based methods on the position of its projections on the left and right images. correspond brightness patterns in two images [14, 24]. The image plane of cameras that produces the images consists These algorithms have several drawbacks which are of discrete pixels. This discretization of images generates uncerpointed out in [12, 18]. Feature-based methods match featainty in estimation of the depth at each 3-D point. In this tures such as edges [3, 9, 11, 13, 15, 21], and linear edge paper, we investigate the effect of vergence and spatially varysegments [1, 23]. Finally, the depth of each point is obtained ing resolution on the depth estimation error. First, vergence is using triangulation. studied when pairs of stereo images with uniform resolution Cameras have an image plane which consists of a number are used. Then the problem is studied for a stereo system similar of discrete picture elements (pixels). In general, these pixto that of humans, in which cameras have high resolution in els are uniformly arranged in a two-dimensional array acthe center and nonlinearly decreasing resolution toward the periphery. In this paper we are only concerned with error in cording to certain industrial standards. The projection of depth perception, assuming that stereo matching is already each 3-D point in the scene is approximated to the nearest done. the resulting error is referred to as discretization
A new rating scale for Alzheimer's disease.

A new rating instrument, the Alzheimer's Disease Assessment Scale, was designed specifically to evaluate the severity of cognitive and noncognitive behavioral dysfunctions characteristic of persons with Alzheimer's disease. Item descriptions, administration procedures, and scoring are outlined. Twenty-seven subjects with Alzheimer's disease and 28 normal elderly subjects were rated on 40 items. Twenty-one items with significant intraclass correlation coefficients for interrater reliability (range, .650-.989) and significant Spearman rank-order correlation coefficients for test-retest reliability (range, .514-1) constitute the final scale. Subjects with Alzheimer's disease had significantly more cognitive and noncognitive dysfunction than the normal elderly subjects.
Search, interrupted: understanding and predicting search task continuation

Many important search tasks require multiple search sessions to complete. Tasks such as travel planning, large purchases, or job searches can span hours, days, or even weeks. Inevitably, life interferes, requiring the searcher either to recover the "state" of the search manually (most common), or plan for interruption in advance (unlikely). The goal of this work is to better understand, characterize, and automatically detect search tasks that will be continued in the near future. To this end, we analyze a query log from the Bing Web search engine to identify the types of intents, topics, and search behavior patterns associated with long-running tasks that are likely to be continued. Using our insights, we develop an effective prediction algorithm that significantly outperforms both the previous state-of-the-art method, and even the ability of human judges, to predict future task continuation. Potential applications of our techniques would allow a search engine to pre-emptively "save state" for a searcher (e.g., by caching search results), perform more targeted personalization, and otherwise better support the searcher experience for interrupted search tasks.
Engineering Design via Surrogate Modelling - A Practical Guide

Engineering design problems requiring the construction of a cheap-to-evaluate 'surrogate' model f that emulates the expensive response of some black box f come in a variety of forms, but they can generally be distilled down to the following template. Here ffx is some continuous quality, cost or performance metric of a product or process defined by a k-vector of design variables x ∈ D ⊂ R k. In what follows we shall refer to D as the design space or design domain. Beyond the assumption of continuity, the only insight we can gain into f is through discrete observations or samples x ii → y ii = ffx ii i = 1 n. These are expensive to obtain and therefore must be used sparingly. The task is to use this sparse set of samples to construct an approximation f , which can then be used to make a cheap performance prediction for any design x ∈ D. Much of this book is made up of recipes for constructing f , given a set of samples. Excepting a few pathological cases, the mathematical formulations of these modelling approaches are well-posed, regardless of how the sampling plan X = x 1 x 2 x nn determines the spatial arrangement of the observations we have built them upon. Some models do require a minimum number n of data points but, once we have passed this threshold, we can use them to build an unequivocally defined surrogate. However, a well-posed model does not necessarily generalize well, that is it may still be poor at predicting unseen data, and this feature does depend on the sampling plan X. For example, measuring the performance of a design at the extreme values of its parameters may leave a great deal of interesting behaviour undiscovered, say, in the centre of the design space. Equally, spraying points liberally in certain parts of the inside of the domain, forcing the surrogate model to make far-reaching extrapolations elsewhere, may lead us to (false) global conclusions based on patchy, local knowledge of the objective landscape. Of course, we do not always have a choice in the matter. We may be using data obtained by someone else for some other purpose or the available observations may come from a variety of external sources and we may not be able to add to them. The latter situation often occurs in conceptual design, where we …
Learning to rank for robust question answering

This paper aims to solve the problem of improving the ranking of answer candidates for factoid based questions in a state-of-the-art Question Answering system. We first provide an extensive comparison of 5 ranking algorithms on two datasets -- from the Jeopardy quiz show and a medical domain. We then show the effectiveness of a cascading approach, where the ranking produced by one ranker is used as input to the next stage. The cascading approach shows sizeable gains on both datasets. We finally evaluate several rank aggregation techniques to combine these algorithms, and find that Supervised Kemeny aggregation is a robust technique that always beats the baseline ranking approach used by Watson for the Jeopardy competition. We further corroborate our results on TREC Question Answering datasets.
Postal address block location in real time

The CEDAR real-time address block location system, which determines candidates for the location of the destination address from a scanned mail piece image, is described. For each candidate destination address block (DAB), the address block location (ABL) system determines the line segmentation, global orientation, block skew, an indication of whether the address appears to be handwritten or machine printed, and a value indicating the degree of confidence that the block actually contains the destination address. With 20-MHz Sparc processors, the average time per mail piece for the combined hardware and software system components is 0.210 seconds. The system located 89.0% of the addresses as the top choice. Recent developments in the system include the use of a top-down segmentation tool, address syntax analysis using only connected component data, and improvements to the segmentation refinement routines. This has increased top choice performance to 91.4%.<<ETX>>
Flash memories: Successes and challenges

Flash memory grew from a simple concept in the early 1980s to a technology that generated close to $23 billion in worldwide revenue in 2007, and this represents one of the many success stories in the semiconductor industry. This success was made possible by the continuous innovation of the industry along many different fronts. In this paper, the history, the basic science, and the successes of flash memories are briefly presented. Flash memories have followed the Moore’s Law scaling trend for which finer line widths, achieved by improved lithographic resolution, enable more memory bits to be produced for the same silicon area, reducing cost per bit. When looking toward the future, significant challenges exist to the continued scaling of flash memories. In this paper, I discuss possible areas that need development in order to overcome some of the size-scaling challenges. Innovations are expected to continue in the industry, and flash memories will continue to follow the historical trend in cost reduction of semiconductor memories through the rest of this decade.
Example-directed synthesis: a type-theoretic interpretation

Input-output examples have emerged as a practical and user-friendly specification mechanism for program synthesis in many environments. While example-driven tools have demonstrated tangible impact that has inspired adoption in industry, their underlying semantics are less well-understood: what are "examples" and how do they relate to other kinds of specifications? This paper demonstrates that examples can, in general, be interpreted as refinement types. Seen in this light, program synthesis is the task of finding an inhabitant of such a type. This insight provides an immediate semantic interpretation for examples. Moreover, it enables us to exploit decades of research in type theory as well as its correspondence with intuitionistic logic rather than designing ad hoc theoretical frameworks for synthesis from scratch. We put this observation into practice by formalizing synthesis as proof search in a sequent calculus with intersection and union refinements that we prove to be sound with respect to a conventional type system. In addition, we show how to handle negative examples, which arise from user feedback or counterexample-guided loops. This theory serves as the basis for a prototype implementation that extends our core language to support ML-style algebraic data types and structurally inductive functions. Users can also specify synthesis goals using polymorphic refinements and import monomorphic libraries. The prototype serves as a vehicle for empirically evaluating a number of different strategies for resolving the nondeterminism of the sequent calculus---bottom-up theorem-proving, term enumeration with refinement type checking, and combinations of both---the results of which classify, explain, and validate the design choices of existing synthesis systems. It also provides a platform for measuring the practical value of a specification language that combines "examples" with the more general expressiveness of refinements.
The SALSA Corpus: a German Corpus Resource for Lexical Semantics

This paper describes the SALSA corpus, a large German corpus manually annotated with role-semantic information, based on the syntactically annotated TIGER newspaper corpus (Brants et al., 2002). The first release, comprising about 20,000 annotated predicate instances (about half the TIGER corpus), is scheduled for mid-2006. In this paper we discuss the frame-semantic annotation framework and its cross-lingual applicability, problems arising from exhaustive annotation, strategies for quality control, and possible applications.
The first years in an L 2-speaking environment : A comparison of Japanese children and adults learning American English

This study examined Japanese speakers’ learning of American English during their first years of immersion in the United States (U.S.). Native Japanesespeaking (NJ) children (n=16) and adults (n=16) were tested on two occasions, averaging 0.5 (T1) and 1.6 years (T2) after arrival in the U.S. Age-matched groups of native English-speaking children (n=16) and adults (n=16) also participated. The NJ adults’ scores for segmental perception and production were higher than the NJ children’s at T1. The NJ children’s foreign accent scores and pronunciation of English fricatives improved significantly between T1 and T2, whereas the NJ adults’ scores did not. These findings suggest that adults may have an advantage over children initially but that children may improve in production more quickly than adults when immersed in an L2-speaking environment.
Stereo-Camera-Based Urban Environment Perception Using Occupancy Grid and Object Tracking

This paper deals with environment perception for automobile applications. Environment perception comprises measuring the surrounding field with onboard sensors such as cameras, radar, lidars, etc., and signal processing to extract relevant information for the planned safety or assistance function. Relevant information is primarily supplied using two well-known methods, namely, object based and grid based. In the introduction, we discuss the advantages and disadvantages of the two methods and subsequently present an approach that combines the two methods to achieve better results. The first part outlines how measurements from stereo sensors can be mapped onto an occupancy grid using an appropriate inverse sensor model. We employ the Dempster-Shafer theory to describe the occupancy grid, which has certain advantages over Bayes' theorem. Furthermore, we generate clusters of grid cells that potentially belong to separate obstacles in the field. These clusters serve as input for an object-tracking framework implemented with an interacting multiple-model estimator. Thereby, moving objects in the field can be identified, and this, in turn, helps update the occupancy grid more effectively. The first experimental results are illustrated, and the next possible research intentions are also discussed.