nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Targetless Extrinsic Calibration of a 3D Lidar and Camera by Maximizing Mutual Information

This paper reports on a mutual information (MI) based algorithm for automatic extrinsic calibration of a 3D laser scanner and optical camera system. By using MI as the registration criterion, our method is able to work in situ without the need for any specific calibration targets, which makes it practical for in-field calibration. The calibration parameters are estimated by maximizing the mutual information obtained between the sensor-measured surface intensities. We calculate the Cramer-Rao-Lower-Bound (CRLB) and show that the sample variance of the estimated parameters empirically approaches the CRLB for a sufficient number of views. Furthermore, we compare the calibration results to independent ground-truth and observe that the mean error also empirically approaches to zero as the number of views are increased. This indicates that the proposed algorithm, in the limiting case, calculates a minimum variance unbiased (MVUB) estimate of the calibration parameters. Experimental results are presented for data collected by a vehicle mounted with a 3D laser scanner and an omnidirectional camera system.
CLORIFI: software vulnerability discovery using code clone verification

Software vulnerability has long been considered an important threat to the system safety. A vulnerability is often reproduced because of the frequent code reuse by programmers. Security patches are usually not propagated to all code clones; however, they could be leveraged to discover unknown vulnerabilities. Static code auditing approaches are frequently proposed to scan source codes for security flaws; unfortunately, these approaches generate too many false positives. While dynamic execution analysis methods can precisely report vulnerabilities, they are ineffective in path exploration, which limits them to scale to large programs. With the purpose of detecting vulnerability in a scalable way with more preciseness, in this paper, we propose a novel mechanism, called software vulnerability discovery using Code Clone Verification (CLORIFI), that scalably discovers vulnerabilities in real world programs using code clone verification. In the beginning, we use a fast and scalable syntax-based way to find code clones in program source codes based on released security patches. Subsequently, code clones are being verified using concolic testing to dramatically decrease the false positives. In addition, we mitigate the path explosion problem by backward sensitive data tracing in concolic execution. Experiments have been conducted with real-world open-source projects (recent Linux OS distributions and program packages). As a result, we found 7 real vulnerabilities out of 63 code clones from Ubuntu 14.04 LTS (Canonical, London, UK) and 10 vulnerabilities out of 40 code clones from CentOS 7.0 (The CentOS Project(community contributed)). Furthermore, we confirmed more code clone vulnerabilities in various versions of programs including Rsyslog (Open Source(Original author: Rainer Gerhards)), Apache (Apache Software Foundation, Forest Hill, Maryland, USA) and Firefox (Mozilla Corporation, Mountain View, California, USA). In order to evaluate the effectiveness of vulnerability verification in a systematic way, we also utilized Juliet Test Suite as measurement objects. The results show that CLORIFI achieves 98% accuracy with 0 false positives.
End-to-end Segmentation with Recurrent Attention Neural Network

Image segmentation quality depends heavily on the quality of the image. For many medical imaging modalities, image reconstruction is required to convert acquired raw data to images before any analysis. However, imperfect reconstruction with artifacts and loss of information is almost inevitable, which compromises the final performance of segmentation. In this study, we present a novel end-to-end deep learning framework that performs magnetic resonance brain image segmentation directly from the raw data. The end-toend framework consists a unique task-driven attention module that recurrently utilizes intermediate segmentation result to facilitate image-domain feature extraction from the raw data for segmentation, thus closely bridging the reconstruction and the segmentation tasks. In addition, we introduce a novel workflow to generate labeled training data for segmentation by exploiting imaging modality simulators and digital phantoms. Extensive experiment results show that the proposed method outperforms the state-of-the-art methods.
ExerLink: enabling pervasive social exergames with heterogeneous exercise devices

We envision that diverse social exercising games, or exergames, will emerge, featuring much richer interactivity with immersive game play experiences. Further, the recent advances of mobile devices and wireless networking will make such social engagement more pervasive - people carry portable exergame devices (e.g., jump ropes) and interact with remote users anytime, anywhere. Towards this goal, we explore the potential of using heterogeneous exercise devices as game controllers for a multi-player social exergame; e.g., playing a boat paddling game with two remote exercisers (one with a jump rope, and the other with a treadmill). In this paper, we propose a novel platform called ExerLink that converts exercise intensity to game inputs and intelligently balances intensity/delay variations for fair game play experiences. We report the design considerations and guidelines obtained from the design and development processes of game controllers. We validate the efficacy of game controllers and demonstrate the feasibility of social exergames with heterogeneous exercise devices via extensive human subject studies.
Algorithms for the Longest Common Subsequence Problem

We start by def ining conven t ions and t e rmino logy that will be used th roughou t this paper . String C = clc~ ... cp is a subsequence of string A = aja2 "'" am if there is a mapp ing F : {1, 2 . . . . , p} ~ {1, 2, ... , m} such that F(i) = k only if c~ = ak and F is a m o n o t o n e strictly increasing funct ion (i .e. F(i) = u, F(]) = v, and i < j imply that u < v). C can be fo rmed by delet ing m p (not necessari ly ad jacen t ) symbols f rom A . F o r example , " c o u r s e " is a subsequence of " c o m p u t e r sc ience . " Str ing C is a c o m m o n s ubs equence of strings A and B if C is a s u b s e q u e n c e of A and also a subsequence of B. String C is a longest c o m m o n subsequence (abbrev ia ted LCS) of string A and B if C is a c o m m o n subsequence of A and B of maximal length , i .e. there is no c o m m o n subsequence of A and B that has grea te r length. Th roughou t this paper , we assume that A and B are strings of lengths m and n , m _< n , that have an LCS C of (unknown) length p . We assume that the symbols that may appea r in these strings c o m e f rom some a lphabet of size t . A symbol can be s tored in m e m o r y by using log t bits, which we assume will fit in one word of memory . Symbols can be c o m p a r e d (a -< b?) in one t ime unit . The n u m b e r of di f ferent symbols that actual ly appear in string B is def ined to be s (which must be less than n and t). The longest c o m m o n s u b s e q u e n c e prob lem has been solved by using a recurs ion re la t ionship on the length of the solut ion [7, 12, 16, 21]. These are general ly appl icable a lgor i thms that take O ( m n ) t ime for any input strings o f lengths m and n even though the lower bound on t ime of O ( m n ) need not apply to all inputs . We present a lgor i thms that , depend ing on the na ture of the Input, may not requ i re quadra t ic t ime to r ecove r an LCS. The first a lgor i thm is appl icable in the genera l case and requi res O ( p n + n log n) t ime. T h e second a lgor i thm requi res t ime b o u n d e d by O((m + 1 p )p log n). In the c o m m o n special case where p is close to m , this a lgor i thm takes t ime
The State of Solving Large Incomplete-Information Games, and Application to Poker

ion algorithms take as input a description of the game and output a smaller but strategically similar — or even equivalent — game. The abstraction algorithms discussed here work with any finite number of players and do not assume a zerosum game. Information Abstraction The most popular kind of abstraction is information abstraction. The game is abstracted so that the agents cannot distinguish some of the states that they can distinguish in the actual game. For example in an abstracted poker hand, an agent is not able to observe all the nuances of the cards that she would normally observe. Lossless Information Abstraction. It turns out that it is possible to do lossless information abstraction, which may seem like an oxymoron at first. The method I will describe (Gilpin and Sandholm 2007b) is for a class of games that we call games with ordered signals. It is structured, but still general enough to capture a wide range of strategic situations. A game with ordered signals consists of a finite number of rounds. Within a round, the players play a game on a directed tree (the tree can be different in different rounds). The only uncertainty players face stems from private signals the other players have received and from the unknown future signals. In other words, players observe each Articles 14 AI MAGAZINE Nash equilibrium Nash equilibrium Original game
Using Part-of-Speech N-grams for Sensitive-Text Classification

Freedom of Information legislations in many western democracies, including the United Kingdom (UK) and the United States of America (USA), state that citizens have typically the right to access government documents. However, certain sensitive information is exempt from release into the public domain. For example, in the UK, FOIA Exemption 27 (International Relations) excludes the release of Information that might damage the interests of the UK abroad. Therefore, the process of reviewing government documents for sensitivity is essential to determine if a document must be redacted before it is archived, or closed until the information is no longer sensitive. With the increased volume of digital government documents in recent years, there is a need for new tools to assist the digital sensitivity review process. Therefore, in this paper we propose an automatic approach for identifying sensitive text in documents by measuring the amount of sensitivity in sequences of text. Using government documents reviewed by trained sensitivity reviewers, we focus on an aspect of FOIA Exemption 27 which can have a major impact on international relations, namely, information supplied in confidence. We show that our approach leads to markedly increased recall of sensitive text, while achieving a very high level of precision, when compared to a baseline that has been shown to be effective at identifying sensitive text in other domains.
Calibrate Multiple Consumer RGB-D Cameras for Low-Cost and Efficient 3D Indoor Mapping

Traditional indoor laser scanning trolley/backpacks with multi-laser scanner, panorama cameras, and an inertial measurement unit (IMU) installed are a popular solution to the 3D indoor mapping problem. However, the cost of those mapping suits is quite expensive, and can hardly be replicated by consumer electronic components. The consumer RGB-Depth (RGB-D) camera (e.g., Kinect V2) is a low-cost option for gathering 3D point clouds. However, because of the narrow field of view (FOV), its collection efficiency and data coverages are lower than that of laser scanners. Additionally, the limited FOV leads to an increase of the scanning workload, data processing burden, and risk of visual odometry (VO)/simultaneous localization and mapping (SLAM) failure. To find an efficient and low-cost way to collect 3D point clouds data with auxiliary information (i.e., color) for indoor mapping, in this paper we present a prototype indoor mapping solution that is built upon the calibration of multiple RGB-D sensors to construct an array with large FOV. Three time-of-flight (ToF)-based Kinect V2 RGB-D cameras are mounted on a rig with different view directions in order to form a large field of view. The three RGB-D data streams are synchronized and gathered by the OpenKinect driver. The intrinsic calibration that involves the geometry and depth calibration of single RGB-D cameras are solved by homography-based method and ray correction followed by range biases correction based on pixel-wise spline line functions, respectively. The extrinsic calibration is achieved through a coarse-to-fine scheme that solves the initial exterior orientation parameters (EoPs) from sparse control markers and further refines the initial value by an iterative closest point (ICP) variant minimizing the distance between the RGB-D point clouds and the referenced laser point clouds. The effectiveness and accuracy of the proposed prototype and calibration method are evaluated by comparing the point clouds derived from the prototype with ground truth data collected by a terrestrial laser scanner (TLS). The overall analysis of the results shows that the proposed method achieves the seamless integration of multiple point clouds from three Kinect V2 cameras collected at 30 frames per second, resulting in low-cost, efficient, and high-coverage 3D color point cloud collection for indoor mapping applications.
Intelligent Development Environment and Software Knowledge Graph

Software intelligent development has become one of the most important research trends in software engineering. In this paper, we put forward two key concepts — intelligent development environment (IntelliDE) and software knowledge graph — for the first time. IntelliDE is an ecosystem in which software big data are aggregated, mined and analyzed to provide intelligent assistance in the life cycle of software development. We present its architecture and discuss its key research issues and challenges. Software knowledge graph is a software knowledge representation and management framework, which plays an important role in IntelliDE. We study its concept and introduce some concrete details and examples to show how it could be constructed and leveraged.
LexIt: A Computational Resource on Italian Argument Structure

The aim of this paper is to introduce LexIt, a computational framework for the automatic acquisition and exploration of distributional information about Italian verbs, nouns and adjectives, freely available through a web interface at the address http://sesia.humnet.unipi.it/lexit. LexIt is the first large-scale resource for Italian in which subcategorization and semantic selection properties are characterized fully on distributional ground: in the paper we describe both the process of data extraction and the evaluation of the subcategorization frames extracted with LexIt.
Actively Transfer Domain Knowledge

When labeled examples are not readily available, active learning and transfer learning are separate efforts to obtain labeled examples for inductive learning. Active learning asks domain experts to label a small set of examples, but there is a cost incurred for each answer. While transfer learning could borrow labeled examples from a different domain without incurring any labeling cost, there is no guarantee that the transferred examples will actually help improve the learning accuracy. To solve both problems, we propose a framework to actively transfer the knowledge across domains, and the key intuition is to use the knowledge transferred from other domain as often as possible to help learn the current domain, and query experts only when necessary. To do so, labeled examples from the other domain (out-of-domain) are examined on the basis of their likelihood to correctly label the examples of the current domain (in-domain). When this likelihood is low, these out-of-domain examples will not be used to label the in-domain example, but domain experts are consulted to provide class label. We derive a sampling error bound and a querying bound to demonstrate that the proposed method can effectively mitigate risk of domain difference by transferring domain knowledge only when they are useful, and query domain experts only when necessary. Experimental studies have employed synthetic datasets and two types of real world datasets, including remote sensing and text classification problems. The proposed method is compared with previously proposed transfer learning and active learning methods. Across all comparisons, the proposed approach can evidently outperform the transfer learning model in classification accuracy given different out-of-domain datasets. For example, upon the remote sensing dataset, the proposed approach achieves an accuracy around 94.5%, while the comparable transfer learning model drops to less than 89% in most cases. The software and datasets are available from the authors.
Unsupervised Interpretable Pattern Discovery in Time Series Using Autoencoders

We study the use of feed-forward convolutional neural networks for the unsupervised problem of mining recurrent temporal patterns mixed in multivariate time series. Traditional convolutional autoencoders lack interpretability for two main reasons: the number of patterns corresponds to the manually-fixed number of convolution filters, and the patterns are often redundant and correlated. To recover clean patterns, we introduce different elements in the architecture, including an adaptive rectified linear unit function that improves patterns interpretability, and a group-lasso regularizer that helps automatically finding the relevant number of patterns. We illustrate the necessity of these elements on synthetic data and real data in the context of activity mining in videos.
Wavelet Feature Based Confusion Character Sets for Gujarati Script

Indic script recognition is a difficult task due to the large number of symbols that result from concatenation of vowel modifiers to basic consonants and the conjunction of consonants with modifiers etc. Recognition of Gujarati script is a less studied area and no attempt is made so far to constitute confusion sets of Gujarati glyphs. In this paper, we present confusion sets of glyphs in printed Gujarati. Feature vector made up of Daubechies D4 wavelet coefficients were subjected to two different classifiers, giving more than 96% accuracy for a larger set of symbols. Novel application of GR neural-net architecture allows for fast building of a classifier for the large character data set. The combined approach of wavelet feature extraction and GRNN classification has given the highest recognition accuracy reported on this script.
Deep, Skinny Neural Networks are not Universal Approximators

In order to choose a neural network architecture that will be effective for a particular modeling problem, one must understand the limitations imposed by each of the potential options. These limitations are typically described in terms of information theoretic bounds, or by comparing the relative complexity needed to approximate example functions between different architectures. In this paper, we examine the topological constraints that the architecture of a neural network imposes on the level sets of all the functions that it is able to approximate. This approach is novel for both the nature of the limitations and the fact that they are independent of network depth for a broad family of activation functions.
Scaling and criticality in a stochastic multi-agent model of a financial market

Financial prices have been found to exhibit some universal characteristics that resemble the scaling laws characterizing physical systems in which large numbers of units interact. This raises the question of whether scaling in finance emerges in a similar way — from the interactions of a large ensemble of market participants. However, such an explanation is in contradiction to the prevalent ‘efficient market hypothesis’ in economics, which assumes that the movements of financial prices are an immediate and unbiased reflection of incoming news about future earning prospects. Within this hypothesis, scaling in price changes would simply reflect similar scaling in the ‘input’ signals that influence them. Here we describe a multi-agent model of financial markets which supports the idea that scaling arises from mutual interactions of participants. Although the ‘news arrival process’ in our model lacks both power-law scaling and any temporal dependence in volatility, we find that it generates such behaviour as a result of interactions between agents.
A method for indexing Web pages using Web bots

Exploring the content of web pages for automatic indexing is of fundamental importance for efficient e-commerce and other applications of the Web. It enables users, including customers and businesses, to locate the best sources for their use. Today’s search engines use one of two approaches to indexing web pages. They either (i) analyze the frequency of the words (after filtering out common or meaningless words) appearing in the entire or a part (typically, a title, an abstract or the first 300 words) of the text of the target web page, or (ii) they use sophisticated algorithms to take into account associations of words in the indexed web page. In both cases only words appearing in the web page in question are used in analysis. Often, to increase relevance of the selected terms to the potential searches, the indexing is refined by human processing. To identify so called “authority” or “expert” pages, some search engines use the structure of the links between pages to identify pages that are often referenced by other pages. Analyzing the density, direction and clustering of links, this method is capable of identifying the pages that are likely to contain valuable information. It is analogous to a well known citation analysis method developed in library sciences and used by such publications as the Science Citation Index. A slightly different approach is used in the Google Search Engine implementation which assigns to each page a score that depends on frequency with which this page is visited by web surfers. The basic difference between the existing methods and the one discussed here is that these methods rely on a structure of web page linkages that lead from or to the indexed page. In contrast, our method uses the content of the pages linked to or from the indexed page for indexing. So our method uses a structure of words used by the linked pages, whereas the current methods use the structure of the connections between linked pages. In this paper we propose and demonstrate usage of a new method based on bots which analyze content of the pages linked to or from the page of interest. We analyze the similarity of the word usage at the different link distance from the page of interest and demonstrate that a structure of words used by the linked pages enables more efficient indexing and search.
Ranking-Based Emotion Recognition for Experimental Music

Emotion recognition is an open problem in Affective Computing the field. Music emotion recognition (MER) has challenges including variability of musical content across genres, the cultural background of listeners, reliability of ground truth data, and the modeling human hearing in computational domains. In this study, we focus on experimental music emotion recognition. First, we present a music corpus that contains 100 experimental music clips and 40 music clips from 8 musical genres. The dataset (the music clips and annotations) is publicly available at: http://metacreation.net/project/emusic/. Then, we present a crowdsourcing method that we use to collect ground truth via ranking the valence and arousal of music clips. Next, we propose a smoothed RankSVM (SRSVM) method. The evaluation has shown that the SRSVM outperforms four other ranking algorithms. Finally, we analyze the distribution of perceived emotion of experimental music against other genres to demonstrate the difference between genres.