nils-herrmann/clean_scirepeval huggingface dataset:
Sequenced event set pattern matching

Event pattern matching is a query technique where a sequence of input events is matched against a complex pattern that specifies constraints on extent, order, values, and quantification of matching events. The increasing importance of such query techniques is underpinned by a significant amount of research work, the availability of commercial products, and by a recent proposal to extend SQL for event pattern matching. The proposed SQL extension includes an operator PERMUTE, which allows to express patterns that match any permutation of a set of events. No implementation of this operator is known to the authors.
 In this paper, we study the sequenced event set pattern matching problem, which is the problem of matching a sequence of input events against a complex pattern that specifies a sequence of sets of events rather than a sequence of single events. Similar to the PERMUTE operator, events that match with a set specified in the pattern can occur in any permutation, whereas events that match with different sets have to be strictly consecutive, following the order of the sets in the pattern specification. We formally define the problem of sequenced event set pattern matching, propose an automaton-based evaluation algorithm, and provide a detailed analysis of its runtime complexity. An empirical evaluation with real-world data shows that our algorithm outperforms a brute force approach that uses existing techniques to solve the sequenced event set pattern matching problem, and it validates the results from our complexity analysis.
Finding question-answer pairs from online forums

Online forums contain a huge amount of valuable user generated content. In this paper we address the problem of extracting question-answer pairs from forums. Question-answer pairs extracted from forums can be used to help Question Answering services (e.g. Yahoo! Answers) among other applications. We propose a sequential patterns based classification method to detect questions in a forum thread, and a graph based propagation method to detect answers for questions in the same thread. Experimental results show that our techniques are very promising.
A Large DataBase of Hypernymy Relations Extracted from the Web

Hypernymy relations (those where an hyponym term shares a “isa” relationship with his hypernym) play a key role for many Natural Language Processing (NLP) tasks, e.g. ontology learning, automatically building or extending knowledge bases, or word sense disambiguation and induction. In fact, such relations may provide the basis for the construction of more complex structures such as taxonomies, or be used as effective background knowledge for many word understanding applications. We present a publicly available database containing more than 400 million hypernymy relations we extracted from the CommonCrawl web corpus. We describe the infrastructure we developed to iterate over the web corpus for extracting the hypernymy relations and store them effectively into a large database. This collection of relations represents a rich source of knowledge and may be useful for many researchers. We offer the tuple dataset for public download and an Application Programming Interface (API) to help other researchers programmatically query the
Improving the Ridge Based Fingerprint Recognition Method Using Sweat Pores

Among several biometric traits possible to be used for people identification, fingerprint is still the most used. Current automated fingerprint identification systems are based on ridge patterns and minutiae, classified as first and second level features, respectively. However, the development of new fingerprint sensors and the growing demand for more secure systems are leading to the use of additional discriminative fingerprint characteristics known as third level features, such as the sweat pores. Recent researches on fingerprint recognition have focused on fingerprint fragments, in which methods based only on first and second level features tend to obtain low recognition rates. This paper proposes a robust method developed for fingerprint recognition from fingerprint fragments based on ridges and sweat pores. We have extended a ridgebased fingerprint recognition method previously proposed in the literature, based on Hough Transform, by incorporating sweat pores information in the matching step. Experimental results showed that although the reduction of Equal Error Rate is modest, a significant improvement was observed when analyzing the FMR100 and FMR1000 metrics, which are more suitable for high security applications. For these two metrics, the proposed approach obtained a reduction superior to 10% of the rates, when compared to the original ridge-based approach. ; fingerprints; ridges; sweat pores
Axiomatic Analysis for Improving the Log-Logistic Feedback Model

Pseudo-relevance feedback (PRF) has been proven to be an effective query expansion strategy to improve retrieval performance. Several PRF methods have so far been proposed for many retrieval models. Recent theoretical studies of PRF methods show that most of the PRF methods do not satisfy all necessary constraints. Among all, the log-logistic model has been shown to be an effective method that satisfies most of the PRF constraints. In this paper, we first introduce two new PRF constraints. We further analyze the log-logistic feedback model and show that it does not satisfy these two constraints as well as the previously proposed "relevance effect" constraint. We then modify the log-logistic formulation to satisfy all these constraints. Experiments on three TREC newswire and web collections demonstrate that the proposed modification significantly outperforms the original log-logistic model, in all collections.
Robust 3D Object Tracking from Monocular Images Using Stable Parts

We present an algorithm for estimating the pose of a rigid object in real-time under challenging conditions. Our method effectively handles poorly textured objects in cluttered, changing environments, even when their appearance is corrupted by large occlusions, and it relies on grayscale images to handle metallic environments on which depth cameras would fail. As a result, our method is suitable for practical Augmented Reality applications including industrial environments. At the core of our approach is a novel representation for the 3D pose of object parts: We predict the 3D pose of each part in the form of the 2D projections of a few control points. The advantages of this representation is three-fold: We can predict the 3D pose of the object even when only one part is visible; when several parts are visible, we can easily combine them to compute a better pose of the object; the 3D pose we obtain is usually very accurate, even when only few parts are visible. We show how to use this representation in a robust 3D tracking framework. In addition to extensive comparisons with the state-of-the-art, we demonstrate our method on a practical Augmented Reality application for maintenance assistance in the ATLAS particle detector at CERN.
Robust stabilization of a class of uncertain nonlinear systems via fuzzy control: quadratic stabilizability, H∞ control theory, and linear matrix inequalities

This paper presents stability analysis for a class of uncertain nonlinear systems and a method for designing robust fuzzy controllers to stabilize the uncertain nonlinear systems. First, a stability condition for Takagi and Sugeno’s fuzzy model is given in terms of Lyapunov stability theory. Next, new stability conditions for a generalized class of uncertain systems are derived from robust control techniques such as quadratic stabilization, H w control theory, and linear matrix inequalities. The derived stability conditions are used to analyze the stability of Takagi and Sugeno’s fuzzy control systems with uncertainty which can be regarded as a generalized class of uncertain nonlinear systems. The design method employs the so-called parallel distributed compensation. Important issues for the stability analysis and design are remarked. Finally, three design examples of fuzzy controllers for stabilizing nonlinear systems and uncertain nonlinear systems are presented.
GBLENDER: towards blending visual query formulation and query processing in graph databases

Given a graph database D and a query graph g, an exact subgraph matching query asks for the set S of graphs in D that contain g as a subgraph. This type of queries find important applications in several domains such as bioinformatics and chemoinformatics, where users are generally not familiar with complex graph query languages. Consequently, user-friendly visual interfaces which support query graph construction can reduce the burden of data retrieval for these users. Existing techniques for subgraph matching queries built on top of such visual framework are designed to optimize the time required in retrieving the result set S from D, assuming that the whole query graph has been constructed. This leads to sub-optimal system response time as the query processing is initiated only after the user has finished drawing the query graph.
 In this paper, we take the first step towards exploring a novel graph query processing paradigm, where instead of processing a query graph after its construction, it interleaves visual query construction and processing to improve system response time. To realize this, we present an algorithm called GBLENDER that prunes false results and prefetches partial query results by exploiting the latency offered by the visual query formulation. It employs a novel action-aware indexing scheme that exploits users' interaction characteristics with visual interfaces to support efficient retrieval. Extensive experiments on both real and synthetic datasets demonstrate the effectiveness and efficiency of our solution.
A Pattern for Fog Computing

Fog Computing is a new variety of the cloud computing paradigm that brings virtualized cloud services to the edge of the network to control the devices in the IoT. We present a pattern for fog computing which describes its architecture, including its computing, storage and networking services. Fog computing is implemented as an intermediate platform between end devices and cloud computing data centers. The recent popularity of the Internet of Things (IoT) has made fog computing a necessity to handle a variety of devices. It has been recognized as an important platform to provide efficient, location aware, close to the edge, cloud services. Our model includes most of the functionality found in current fog architectures.
From TagME to WAT: a new entity annotator

In this paper we propose a novel entity annotator for texts which hinges on TagME's algorithmic technology, currently the best one available. The novelty is twofold: from the one hand, we have engineered the software in order to be modular and more efficient; from the other hand, we have improved the annotation pipeline by re-designing all of its three main modules: spotting, disambiguation and pruning. In particular, the re-design has involved the detailed inspection of the performance of these modules by developing new algorithms which have been in turn tested over all publicly available datasets (i.e. AIDA, IITB, MSN, AQUAINT, and the one of the ERD Challenge). This extensive experimentation allowed us to derive the best combination which achieved on the ERD development dataset an F1 score of 74.8%, which turned to be 67.2% F1 for the test dataset. This final result was due to an impressive precision equal to 87.6%, but very low recall 54.5%. With respect to classic TagME on the development dataset the improvement ranged from 1% to 9% on the D2W benchmark, depending on the disambiguation algorithm being used. As a side result, the final software can be interpreted as a flexible library of several parsing/disambiguation and pruning modules that can be used to build up new and more sophisticated entity annotators. We plan to release our library to the public as an open-source project.
L0 sparse graphical modeling

Graphical models are well established in providing compact conditional probability descriptions of complex multivariable interactions. In the Gaussian case, graphical models are determined by zeros in the precision or concentration matrix, i.e. the inverse of the covariance matrix. Hence, there has been much recent interest in sparse precision matrices in areas such as statistics, machine learning, computer vision, pattern recognition and signal processing. In this paper we propose a simple new algorithm for constructing a sparse estimator for the precision matrix from multivariate data where the sparsity is enforced by an l0 penalty. We compare and test the quality of our method on a synthetic graphical model.
Stochastic Vehicle Routing Problem: A Literature Survey

The purpose of this paper is to develop structural classi ̄cation of Stochastic Vehicle Routing Problem (SVRP) by di®erent domains and attributes. This research used a systematic review and meta-analysis on SVRP literatures. This includes browsing relevant researches and publications, screening related articles, identifying domains, attributes and categorising the articles based on the identi ̄ed domains and attributes. The ̄ndings of the study show clear di®erences on the number of studies under each domain and attribute. Most studied attributes are stochastic customer demand, capacitated vehicle, synthesis data and objective function with cost minimization. Whereas the least studied are maximisation objective function, stochastic service time, and an applied model using stochastic with recurs. The research helps to summarise and map a comprehensive survey on SVRP literatures so that various contributions in the  ̄eld are organised in a manner that provide a clear view for the readers and identify future research directions.
Automatic ad format selection via contextual bandits

Visual design plays an important role in online display advertising: changing the layout of an online ad can increase or decrease its effectiveness, measured in terms of click-through rate (CTR) or total revenue. The decision of which lay- out to use for an ad involves a trade-off: using a layout provides feedback about its effectiveness (exploration), but collecting that feedback requires sacrificing the immediate reward of using a layout we already know is effective (exploitation). To balance exploration with exploitation, we pose automatic layout selection as a contextual bandit problem. There are many bandit algorithms, each generating a policy which must be evaluated. It is impractical to test each policy on live traffic. However, we have found that offline replay (a.k.a. exploration scavenging) can be adapted to provide an accurate estimator for the performance of ad layout policies at Linkedin, using only historical data about the effectiveness of layouts. We describe the development of our offline replayer, and benchmark a number of common bandit algorithms.
Brachial–ankle pulse wave velocity: an index of central arterial stiffness?

Brachial–ankle pulse wave velocity (baPWV) is a promising technique to assess arterial stiffness conveniently. However, it is not known whether baPWV is associated with well-established indices of central arterial stiffness. We determined the relation of baPWV with aortic (carotid-femoral) PWV, leg (femoral-ankle) PWV, and carotid augmentation index (AI) by using both cross-sectional and interventional approaches. First, we studied 409 healthy adults aged 18–76 years. baPWV correlated significantly with aortic PWV (r=0.76), leg PWV (r=0.76), and carotid AI (r=0.52). A stepwise regression analysis revealed that aortic PWV was the primary independent correlate of baPWV, explaining 58% of the total variance in baPWV. Additional 23% of the variance was explained by leg PWV. Second, 13 sedentary healthy men were studied before and after a 16-week moderate aerobic exercise intervention (brisk walking to jogging; 30–45 min/day; 4–5 days/week). Reductions in aortic PWV observed with the exercise intervention were significantly and positively associated with the corresponding changes in baPWV (r=0.74). A stepwise regression analysis revealed that changes in aortic PWV were the only independent correlate of changes in baPWV (β=0.74), explaining 55% of the total variance. These results suggest that baPWV may provide qualitatively similar information to those derived from central arterial stiffness although some portions of baPWV may be determined by peripheral arterial stiffness.
Comprehension-Guided Referring Expressions

We consider generation and comprehension of natural language referring expression for objects in an image. Unlike generic image captioning which lacks natural standard evaluation criteria, quality of a referring expression may be measured by the receivers ability to correctly infer which object is being described. Following this intuition, we propose two approaches to utilize models trained for comprehension task to generate better expressions. First, we use a comprehension module trained on human-generated expressions, as a critic of referring expression generator. The comprehension module serves as a differentiable proxy of human evaluation, providing training signal to the generation module. Second, we use the comprehension model in a generate-and-rerank pipeline, which chooses from candidate expressions generated by a model according to their performance on the comprehension task. We show that both approaches lead to improved referring expression generation on multiple benchmark datasets.
The neural response to facial attractiveness.

What are the neural correlates of attractiveness? Using functional MRI (fMRI), the authors addressed this question in the specific context of the apprehension of faces. When subjects judged facial beauty explicitly, neural activity in a widely distributed network involving the ventral occipital, anterior insular, dorsal posterior parietal, inferior dorsolateral, and medial prefrontal cortices correlated parametrically with the degree of facial attractiveness. When subjects were not attending explicitly to attractiveness, but rather were judging facial identity, the ventral occipital region remained responsive to facial beauty. The authors propose that this region, which includes the fusiform face area (FFA), the lateral occipital cortex (LOC), and medially adjacent regions, is activated automatically by beauty and may serve as a neural trigger for pervasive effects of attractiveness in social interactions.
Vision based robot localization by ground to satellite matching in GPS-denied situations

This paper studies the problem of matching images captured from an unmanned ground vehicle (UGV) to those from a satellite or high-flying vehicle. We focus on situations where the UGV navigates in remote areas with few man-made structures. This is a difficult problem due to the drastic change in perspective between the ground and aerial imagery and the lack of environmental features for image comparison. We do not rely on GPS, which may be jammed or uncertain. We propose a two-step approach: (1) the UGV images are warped to obtain a bird's eye view of the ground, and (2) this view is compared to a grid of satellite locations using whole-image descriptors. We analyze the performance of a variety of descriptors for different satellite map sizes and various terrain and environment types. We incorporate the air-ground matching into a particle-filter framework for localization using the best-performing descriptor. The results show that vision-based UGV localization from satellite maps is not only possible, but often provides better position estimates than GPS estimates, enabling us to improve the location estimates of Google Street View.
Newer Understanding of Specific Anatomic Targets in the Aging Face as Applied to Injectables: Aging Changes in the Craniofacial Skeleton and Facial Ligaments.

Logical correction of aging contour changes of the face is based on understanding its structure and the processes involved in the aging appearance. Aging changes are seen at all tissue levels between the skin and bone although the relative contribution of each component to the overall change of facial appearance has yet to be satisfactorily determined. Significantly, the facial skeleton changes profoundly with aging as a consequence of significant resorption of the bones of dental origin in particular. The resultant loss of skeletal projection gives the visual impression of descent while the reduced ligamentous support leads to laxity of the overlying soft tissues. Understanding the specific changes of the face with aging is fundamental to achieving optimum correction and safe use of injectables for facial rejuvenation.
Using the weak inversion region to optimize input stage design of CMOS op amps

Operation of MOS devices in the strong, moderate, and weak inversion regions is considered. The advantages of designing the input differential stage of a CMOS op amp to operate in the weak or moderate inversion region are presented. These advantages include higher voltage gain, less distortion, and ease of compensation. Specific design guidelines are presented to optimize amplifier performance. Simulations that demonstrate the expected improvements are given.
Trust Me: Social Games are Better than Social Icebreakers at Building Trust

Interpersonal trust is one of the key components of efficient teamwork. Research suggests two main approaches for trust formation: personal information exchange (e.g., social icebreakers), and creating a context of risk and interdependence (e.g., trust falls). However, because these strategies are difficult to implement in an online setting, trust is more difficult to achieve and preserve in distributed teams. In this paper, we argue that games are an optimal environment for trust formation because they can simulate both risk and interdependence. Results of our online experiment show that a social game can be more effective than a social task at fostering interpersonal trust. Furthermore, trust formation through the game is reliable, but trust depends on several contingencies in the social task. Our work suggests that gameplay interactions do not merely promote impoverished versions of the rich ties formed through conversation; but rather engender genuine social bonds.
STAR: A System for Ticket Analysis and Resolution

In large scale and complex IT service environments, a problematic incident is logged as a ticket and contains the ticket summary (system status and problem description). The system administrators log the step-wise resolution description when such tickets are resolved. The repeating service events are most likely resolved by inferring similar historical tickets. With the availability of reasonably large ticket datasets, we can have an automated system to recommend the best matching resolution for a given ticket summary. In this paper, we first identify the challenges in real-world ticket analysis and develop an integrated framework to efficiently handle those challenges. The framework first quantifies the quality of ticket resolutions using a regression model built on carefully designed features. The tickets, along with their quality scores obtained from the resolution quality quantification, are then used to train a deep neural network ranking model that outputs the matching scores of ticket summary and resolution pairs. This ranking model allows us to leverage the resolution quality in historical tickets when recommending resolutions for an incoming incident ticket. In addition, the feature vectors derived from the deep neural ranking model can be effectively used in other ticket analysis tasks, such as ticket classification and clustering. The proposed framework is extensively evaluated with a large real-world dataset.