nils-herrmann/clean_scirepeval huggingface dataset:
Acquiring temporal constraints between relations

We consider the problem of automatically acquiring knowledge about the typical temporal orderings among relations (e.g., actedIn(person, film) typically occurs before wonPrize (film, award)), given only a database of known facts (relation instances) without time information, and a large document collection. Our approach is based on the conjecture that the narrative order of verb mentions within documents correlates with the temporal order of the relations they represent. We propose a family of algorithms based on this conjecture, utilizing a corpus of 890m dependency parsed sentences to obtain verbs that represent relations of interest, and utilizing Wikipedia documents to gather statistics on narrative order of verb mentions. Our proposed algorithm, GraphOrder, is a novel and scalable graph-based label propagation algorithm that takes transitivity of temporal order into account, as well as these statistics on narrative order of verb mentions. This algorithm achieves as high as 38.4% absolute improvement in F1 over a random baseline. Finally, we demonstrate the utility of this learned general knowledge about typical temporal orderings among relations, by showing that these temporal constraints can be successfully used by a joint inference framework to assign specific temporal scopes to individual facts.
Adaptive visualization of search results: Bringing user models to visual analytics

18 March 2009  20 April 2009  Adaptive visualization is a new approach at the crossroads of user modeling and information visualization. Taking into account information about a user, adaptive visualization attempts to provide user-adapted visual presentation of information. This paper proposes Adaptive VIBE, an approach for adaptive visualization of search results in an intelligence analysis context. Adaptive VIBE extends the popular VIBE visualization framework by infusing user model terms as reference points for spatial document arrangement and manipulation. We explored the value of the proposed approach using data obtained from a user study. The result demonstrated that user modeling and spatial visualization technologies are able to reinforce each other, creating an enhanced level of user support. Spatial visualization amplifies the user model's ability to separate relevant and non-relevant documents, whereas user modeling adds valuable reference points to relevance-based spatial visualization. Information Visualization (2009) 8, 167--179. doi:10.1057/ivs.2009.12
Anger and Its Direction in Collaborative Software Development

Recent research has provided evidence that software developers experience a wide range of emotions. We argue that among those emotions anger deserves special attention as it can serve as an onset for tools supporting collaborative softwaredevelopment. This, however, requires a fine-grained model of the anger emotion, able to distinguish between anger directed towards self, others, and objects. Detecting anger towards self could be useful to support developers experiencing difficulties, detection of anger towards others might be helpful for community management, detecting anger towards objects might be helpful to recommend and prioritize improvements. As a first step towards automatic identification of anger direction, we built a classifier for anger direction, based on a manually annotated gold standard of 723 sentences that were obtained by mining comments in Apache issue reports.
A survey study of the association between mobile phone use and daytime sleepiness in California high school students

Mobile phone use is near ubiquitous in teenagers. Paralleling the rise in mobile phone use is an equally rapid decline in the amount of time teenagers are spending asleep at night. Prior research indicates that there might be a relationship between daytime sleepiness and nocturnal mobile phone use in teenagers in a variety of countries. As such, the aim of this study was to see if there was an association between mobile phone use, especially at night, and sleepiness in a group of U.S. teenagers.



A questionnaire containing an Epworth Sleepiness Scale (ESS) modified for use in teens and questions about qualitative and quantitative use of the mobile phone was completed by students attending Mountain View High School in Mountain View, California (n = 211).



Multivariate regression analysis indicated that ESS score was significantly associated with being female, feeling a need to be accessible by mobile phone all of the time, and a past attempt to reduce mobile phone use. The number of daily texts or phone calls was not directly associated with ESS. Those individuals who felt they needed to be accessible and those who had attempted to reduce mobile phone use were also ones who stayed up later to use the mobile phone and were awakened more often at night by the mobile phone.



The relationship between daytime sleepiness and mobile phone use was not directly related to the volume of texting but may be related to the temporal pattern of mobile phone use.
Botnet spam campaigns can be long lasting: evidence, implications, and analysis

Accurately identifying spam campaigns launched by a large number of bots in a botnet allows for accurate spam campaign signature generation and hence is critical to defeating spamming botnets. The straight-forward approach of clustering all spam containing the same label such as an URL into a campaign can be easily defeated by techniques such as simple obfuscations of URLs. In this paper, we perform a comprehensive study of content-agnostic characteristics of spam campaigns, e.g. duration and source-network distribution of spammers, in order to ascertain whether and how they can assist the simple label-based clustering methods in identifying campaigns and generating campaign signatures. In particular, from a five-month trace collected by a relay sinkhole, we manually identified and then analyzed seven URL-based botnet spam campaigns consisting of 52 million spam messages sent over 2.09 million SMTP connections originated from over 150,000 non-proxy spamming hosts and destined to about 200,000 end domains. Our analysis shows that the spam campaigns, when observed from large destination domains, exhibit durations far longer than the five-day period as reported in a recent study. We analyze the implications of this finding on spam campaign signature generation. We further study other characteristics of these long-lasting campaigns. Our analysis reveals several new findings regarding workload distribution, sending patterns, and coordination among the spamming machines.
Automatic classification of documents by formality

This paper addresses the task of classifying documents into formal or informal style. We studied the main characteristics of each style in order to choose features that allowed us to train classifiers that can distinguish between the two styles. We built our data set by collecting documents for both styles, from different sources. We tested several classification algorithms, namely Decision Trees, Naïve Bayes, and Support Vector Machines, to choose the classifier that leads to the best classification results. We performed attribute selection in order to determine the contribution of each feature to our model.
10 kV and 15 kV silicon carbide power MOSFETs for next-generation energy conversion and transmission systems

Advanced high-voltage (10 kV-15 kV) silicon carbide (SiC) power MOSFETs described in this paper have the potential to significantly impact the system performance, size, weight, high-temperature reliability, and cost of next-generation energy conversion and transmission systems. In this paper, we report our recently developed 10 kV/20 A SiC MOSFETs with a chip size of 8.1 × 8.1 mm2 and a specific on-resistance (RON, SP) of 100 MΩ-cm2 at 25 °C. We also developed 15 kV/10 A SiC power MOSFETs with a chip size of 8 × 8 mm2 and a RON, SP of 204 mQ cm2 at 25 °C. To our knowledge, this 15 kV SiC MOSFET is the highest voltage rated unipolar power switch. Compared to the commercial 6.5 kV Silicon (Si) IGBTs, these 10 kV and 15 kV SiC MOSFETs exhibit extremely low switching losses even when they are switched at 2-3× higher voltage. The benefits of using these 10 kV and 15 kV SiC MOSFETs include simplifying from multilevel to two-level topology and removing the need for time-interleaving by improving the switching frequency from a few hundred Hz for Si based systems to ≥ 10 kHz for hard-switched SiC based systems.
Counting Everyday Objects in Everyday Scenes

We are interested in counting the number of instances of object classes in natural, everyday images. Previous counting approaches tackle the problem in restricted domains such as counting pedestrians in surveillance videos. Counts can also be estimated from outputs of other vision tasks like object detection. In this work, we build dedicated models for counting designed to tackle the large variance in counts, appearances, and scales of objects found in natural scenes. Our approach is inspired by the phenomenon of subitizing &#x2013; the ability of humans to make quick assessments of counts given a perceptual signal, for small count values. Given a natural scene, we employ a divide and conquer strategy while incorporating context across the scene to adapt the subitizing idea to counting. Our approach offers consistent improvements over numerous baseline approaches for counting on the PASCAL VOC 2007 and COCO datasets. Subsequently, we study how counting can be used to improve object detection. We then show a proof of concept application of our counting methods to the task of Visual Question Answering, by studying the how many? questions in the VQA and COCO-QA datasets.
Retinal Vessels Segmentation Techniques and Algorithms : A Survey

Retinal vessels identification and localization aim to separate the different retinal vasculature structure tissues, either wide or narrow ones, from the fundus image background and other retinal anatomical structures such as optic disc, macula, and abnormal lesions. Retinal vessels identification studies are attracting more and more attention in recent years due to non-invasive fundus imaging and the crucial information contained in vasculature structure which is helpful for the detection and diagnosis of a variety of retinal pathologies included but not limited to: Diabetic Retinopathy (DR), glaucoma, hypertension, and Age-related Macular Degeneration (AMD). With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting retinal vessels are becoming more and more crucial and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for retinal vessels segmentation techniques. Firstly, a brief introduction to retinal fundus photography and imaging modalities of retinal images is given. Then, the preprocessing operations and the state of the art methods of retinal vessels identification are introduced. Moreover, the evaluation and validation of the results of retinal vessels segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for retinal vessels identification techniques.
Decision support with belief functions theory for seabed characterization

The seabed characterization from sonar images is a very hard task because of the produced data and the unknown environment, even for an human expert. In this work we propose an original approach in order to combine binary classifiers arising from different kinds of strategies such as one-versus-one or one-versus-rest, usually used in the SVM-classification. The decision functions coming from these binary classifiers are interpreted in terms of belief functions in order to combine these functions with one of the numerous operators of the belief functions theory. Moreover, this interpretation of the decision function allows us to propose a process of decisions by taking into account the rejected observations too far removed from the learning data, and the imprecise decisions given in unions of classes. This new approach is illustrated and evaluated with a SVM in order to classify the different kinds of sediment on image sonar.
Order preserving hashing for approximate nearest neighbor search

In this paper, we propose a novel method to learn similarity-preserving hash functions for approximate nearest neighbor (NN) search. The key idea is to learn hash functions by maximizing the alignment between the similarity orders computed from the original space and the ones in the hamming space. The problem of mapping the NN points into different hash codes is taken as a classification problem in which the points are categorized into several groups according to the hamming distances to the query. The hash functions are optimized from the classifiers pooled over the training points. Experimental results demonstrate the superiority of our approach over existing state-of-the-art hashing techniques.
Nonnegative Sparse PCA

We describe a nonnegative variant of the ”Sparse PCA” problem. The goal is to create a low dimensional representation from a collection of points which on the one hand maximizes the variance of the projected points and on the other uses only parts of the original coordinates, and thereby creating a sparse representation. What distinguishes our problem from other Sparse PCA formulations is that the projection involves only nonnegative weights of the original coordinates — a desired quality in various fields, including economics, bioinformatics and computer vision. Adding nonnegativity contributes to sparseness, where it enforces a partitioning of the original coordinates among the new axes. We describe a simple yet efficient iterative coordinate-descent type of scheme which converges to a local optimum of our optimization criteria, giving good results on large real world datasets.
Maximum Margin Multiclass Nearest Neighbors

We develop a general framework for marginbased multicategory classification in metric spaces. The basic work-horse is a marginregularized version of the nearest-neighbor classifier. We prove generalization bounds that match the state of the art in sample size n and significantly improve the dependence on the number of classes k. Our point of departure is a nearly Bayes-optimal finite-sample risk bound independent of k. Although k-free, this bound is unregularized and non-adaptive, which motivates our main result: Rademacher and scale-sensitive margin bounds with a logarithmic dependence on k. As the best previous risk estimates in this setting were of order √ k, our bound is exponentially sharper. From the algorithmic standpoint, in doubling metric spaces our classifier may be trained on n examples in O(n log n) time and evaluated on new points in O(log n) time.
Flock: Hybrid Crowd-Machine Learning Classifiers

We present hybrid crowd-machine learning classifiers: classification models that start with a written description of a learning goal, use the crowd to suggest predictive features and label data, and then weigh these features using machine learning to produce models that are accurate and use human-understandable features. These hybrid classifiers enable fast prototyping of machine learning models that can improve on both algorithm performance and human judgment, and accomplish tasks where automated feature extraction is not yet feasible. Flock, an interactive machine learning platform, instantiates this approach. To generate informative features, Flock asks the crowd to compare paired examples, an approach inspired by analogical encoding. The crowd's efforts can be focused on specific subsets of the input space where machine-extracted features are not predictive, or instead used to partition the input space and improve algorithm performance in subregions of the space. An evaluation on six prediction tasks, ranging from detecting deception to differentiating impressionist artists, demonstrated that aggregating crowd features improves upon both asking the crowd for a direct prediction and off-the-shelf machine learning features by over 10%. Further, hybrid systems that use both crowd-nominated and machine-extracted features can outperform those that use either in isolation.
A framework for feature selection in clustering.

We consider the problem of clustering observations using a potentially large set of features. One might expect that the true underlying clusters present in the data differ only with respect to a small fraction of the features, and will be missed if one clusters the observations using the full set of features. We propose a novel framework for sparse clustering, in which one clusters the observations using an adaptively chosen subset of the features. The method uses a lasso-type penalty to select the features. We use this framework to develop simple methods for sparse K-means and sparse hierarchical clustering. A single criterion governs both the selection of the features and the resulting clusters. These approaches are demonstrated on simulated data and on genomic data sets.
Numerical instabilities in topology optimization : A survey on procedures dealing with checkerboards , mesh-dependencies and local minima

1 I n t r o d u c t i o n The topology optimization method for continuum structures (Bends0e and Kikuchi 1988; or BendsCe 1995, for an overview) has reached a level of maturi ty where it is being applied to many industrial problems and it has widespread academic use, not only for structural optimization problems but also in material, mechanism, electromagnetics and other coupled field design problems. Despite its level of matureness, there still exist a number of problems concerning convergence, checkerboards and mesh-dependence which are subject to debate in the topology optimization community. In this paper, we seek to summarize the current knowledge on these problems and to discuss methods with which they can be avoided. The topology optimization problem consists in finding the subdomain f2s with limited volume V*, included in a predetermined design domain ~2, that optimizes a given objective function f (e.g. compliance). Finding the optimal topology corresponds to finding the connectedness, shape and number of holes such that the objective function is extremized. Introducing a density function p defined on $2, taking the value 1 in ~s and 0 elsewhere, the nested version of the problem can be written as mins.t, p t2f(P) p dz <_ V*, } p ( x ) = 0 o r 1, g x E f 2 , (1) i.e. wherever the displacement function u (or in general, the state function) appears, it has been eliminated through the implicit dependence defined by the equilibrium equation. Typically the topology optimization problem is treated by discretizing (1) by dividing ~ into N finite elements. Usually one approximates the density as element-wise constant and thereby the discretized density can be represented by the N-vector p. Taking p to be constant in each element is practical since integrations over elements can be performed with p outside the integral sign, and consequently one can operate with simple scalings of the usual element stiffness matrices. The discretized 0-1 topology optimization problem becomes
XFT: Practical Fault Tolerance beyond Crashes

Despite years of intensive research, Byzantine fault-tolerant (BFT) systems have not yet been adopted in practice. This is due to additional cost of BFT in terms of resources, protocol complexity and performance, compared with crash fault-tolerance (CFT). This overhead of BFT comes from the assumption of a powerful adversary that can fully control not only the Byzantine faulty machines, but at the same time also the message delivery schedule across the entire network, effectively inducing communication asynchrony and partitioning otherwise correct machines at will. To many practitioners, however, such strong attacks appear irrelevant. In this paper, we introduce cross fault tolerance or XFT, a novel approach to building reliable and secure distributed systems and apply it to the classical state-machine replication (SMR) problem. In short, an XFT SMR protocol provides the reliability guarantees of widely used asynchronous CFT SMR protocols such as Paxos and Raft, but also tolerates Byzantine faults in combination with network asynchrony, as long as a majority of replicas are correct and communicate synchronously. This allows the development of XFT systems at the price of CFT (already paid for in practice), yet with strictly stronger resilience than CFT — sometimes even stronger than BFT itself. As a showcase for XFT, we present XPaxos, the first XFT SMR protocol, and deploy it in a geo-replicated setting. Although it offers much stronger resilience than CFT SMR at no extra resource cost, the performance of XPaxos matches that of the state-of-the-art CFT protocols.
The Quantized kd-Tree: Efficient Ray Tracing of Compressed Point Clouds

Both ray tracing and point-based representations provide means to efficiently display very complex 3D models. Computational efficiency has been the main focus of previous work on ray tracing point-sampled surfaces. For very complex models efficient storage in the form of compression becomes necessary in order to avoid costly disk access. However, as ray tracing requires neighborhood queries, existing compression schemes cannot be applied because of their sequential nature. This paper introduces a novel acceleration structure called the quantized kd-tree, which offers both efficient traversal and storage. The gist of our new representation lies in quantizing the kd-tree splitting plane coordinates. We show that the quantized kd-tree reduces the memory footprint up to 18 times, not compromising performance. Moreover, the technique can also be employed to provide LOD (level-of-detail) to reduce aliasing problems, with little additional storage cost
Rumor Identification and Belief Investigation on Twitter

Social media users spend several hours a day to read, post and search for news on microblogging platforms. Social media is becoming a key means for discovering news. However, verifying the trustworthiness of this information is becoming even more challenging. In this study, we attempt to address the problem of rumor detection and belief investigation on Twitter. Our definition of rumor is an unverifiable statement, which spreads misinformation or disinformation. We adopt a supervised rumors classification task using the standard dataset. By employing the Tweet Latent Vector (TLV) feature, which creates a 100-d vector representative of each tweet, we increased the rumor retrieval task precision up to 0.972. We also introduce the belief score and study the belief change among the rumor posters between 2010 and 2016.
A survey on search-based software design

Search-based approaches to software design are investigated. Software design is considered from a wide view, including topics that can also be categorized under software maintenance or re-engineering. Search-based approaches have been used in research from high architecture level design to software clustering and finally software refactoring. Enhancing and predicting software quality with search-based methods is also taken into account as a part of the design process. The choices regarding fundamental decisions, such as representation and fitness function, when using in meta-heuristic search algorithms, are emphasized and discussed in detail. Ideas for future research directions are also given.