nils-herrmann/clean_scirepeval huggingface dataset:
Diversifying query suggestions based on query documents

Many domain-specific search tasks are initiated by document-length queries, e.g., patent invalidity search aims to find prior art related to a new (query) patent. We call this type of search Query Document Search. In this type of search, the initial query document is typically long and contains diverse aspects (or sub-topics). Users tend to issue many queries based on the initial document to retrieve relevant documents. To help users in this situation, we propose a method to suggest diverse queries that can cover multiple aspects of the query document. We first identify multiple query aspects and then provide diverse query suggestions that are effective for retrieving relevant documents as well being related to more query aspects. In the experiments, we demonstrate that our approach is effective in comparison to previous query suggestion methods.
Sentiment analysis of commit comments in GitHub: an empirical study

Emotions have a high impact in productivity, task quality, creativity, group rapport and job satisfaction. In this work we use lexical sentiment analysis to study emotions expressed in commit comments of different open source projects and analyze their relationship with different factors such as used programming language, time and day of the week in which the commit was made, team distribution and project approval. Our results show that projects developed in Java tend to have more negative commit comments, and that projects that have more distributed teams tend to have a higher positive polarity in their emotional content. Additionally, we found that commit comments written on Mondays tend to a more negative emotion. While our results need to be confirmed by a more representative sample they are an initial step into the study of emotions and related factors in open source projects.
Human activity recognition in egocentric video using PNN, SVM, kNN and SVM+kNN classifiers

In recent years, the recognition of activity is a daring task which helps elderly people, disabled patients and so on. The aim of this paper is to design a system for recognizing the human activity in egocentric video. In this research work, the various textural features like gray level co-occurrence matrix and local binary pattern and point feature speeded up robust features are retrieved from activity videos which is a proposed work and classifiers like probabilistic neural network, support vector machine (SVM), k nearest neighbor (kNN) and proposed SVM+kNN classifiers are used to classify the activity. Here, multimodal egocentric activity dataset is chosen as input. The performance results showed that the SVM+kNN classifier outperformed other classifiers.
GROPING: Geomagnetism and cROwdsensing Powered Indoor NaviGation

Although a large number of WiFi fingerprinting based indoor localization systems have been proposed, our field experience with Google Maps Indoor (GMI), the only system available for public testing, shows that it is far from mature for indoor navigation. In this paper, we first report our field studies with GMI, as well as experiment results aiming to explain our unsatisfactory GMI experience. Then motivated by the obtained insights, we propose GROPING as a self-contained indoor navigation system independent of any infrastructural support. GROPING relies on geomagnetic fingerprints that are far more stable than WiFi fingerprints, and it exploits crowdsensing to construct floor maps rather than expecting individual venues to supply digitized maps. Based on our experiments with 20 participants in various floors of a big shopping mall, GROPING is able to deliver a sufficient accuracy for localization and thus provides smooth navigation experience.
An up to 16-year prospective study of 304 porcelain veneers.

This study aimed to prospectively analyze the outcomes of 304 feldspathic porcelain veneers prepared by the same operator, in 100 patients, that were in situ for up to 16 years.



A total of 304 porcelain veneers on incisors, canines, and premolars in 100 patients completed by one prosthodontist between 1988 and 2003 were sequentially included. Preparations were designed with chamfer margins, incisal reduction, and palatal overlap. At least 80% of each preparation was in enamel. Feldspathic porcelain veneers from refractory dies were etched (hydrofluoric acid), silanated, and cemented (Vision 2, Mirage Dental Systems). Outcomes were expressed as percentages (success, survival, unknown, dead, repair, failure). The results were statistically analyzed using the chi-square test and Kaplan-Meier survival estimation. Statistical significance was set at P < .05.



The cumulative survival for veneers was 96% +/- 1% at 5 to 6 years, 93% +/- 2% at 10 to 11 years, 91% +/- 3% at 12 to 13 years, and 73% +/- 16% at 15 to 16 years. The marked drop in survival between 13 and 16 years was the result of the death of 1 patient and the low number of veneers in that period. The cumulative survival was greater when different statistical methods were employed. Sixteen veneers in 14 patients failed. Failed veneers were associated with esthetics (31%), mechanical complications (31%), periodontal support (12.5%), loss of retention >2 (12.5%), caries (6%), and tooth fracture (6%). Statistically significantly fewer veneers survived as the time in situ increased.



Feldspathic porcelain veneers, when bonded to enamel substrate, offer a predictable long-term restoration with a low failure rate. The statistical methods used to calculate the cumulative survival can markedly affect the apparent outcome and thus should be clearly defined in outcome studies.
Composition of Word Representations Improves Semantic Role Labelling

State-of-the-art semantic role labelling systems require large annotated corpora to achieve full performance. Unfortunately, such corpora are expensive to produce and often do not generalize well across domains. Even in domain, errors are often made where syntactic information does not provide sufficient cues. In this paper, we mitigate both of these problems by employing distributional word representations gathered from unlabelled data. While straight-forward word representations of predicates and arguments improve performance, we show that further gains are achieved by composing representations that model the interaction between predicate and argument, and capture full argument spans.
An empirical study of the effects of test-suite reduction on fault localization

Fault-localization techniques that utilize information about all test cases in a test suite have been presented. These techniques use various approaches to identify the likely faulty part(s) of a program, based on information about the execution of the program with the test suite. Researchers have begun to investigate the impact that the composition of the test suite has on the effectiveness of these fault-localization techniques. In this paper, we present the first experiment on one aspect of test-suite composition--test-suite reduction. Our experiment studies the impact of the test-suite reduction on the effectiveness of fault-localization techniques. In our experiment, we apply 10 test-suite reduction strategies to test suites for eight subject programs. We then measure the differences between the effectiveness of four existing fault-localization techniques on the unreduced and reduced test suites. We also measure the reduction in test-suite size of the 10 test-suite reduction strategies. Our experiment shows that fault-localization effectiveness varies depending on the test-suite reduction strategy used, and it demonstrates the trade-offs between test-suite reduction and fault-localization effectiveness.
Building a Book Recommender system using time based content filtering

Recommender System are new generation internet tool that help user in navigating through information on the internet and receive information related to their preferences. Although most of the time recommender systems are applied in the area of online shopping and entertainment domains like movie and music, yet their applicability is being researched upon in other area as well. This paper presents an overview of the Recommender Systems which are currently working in the domain of online book shopping. This paper also proposes a new book recommender system that combines user choices with not only similar users but other users as well to give diverse recommendation that change over time. The overall architecture of the proposed system is presented and its implementation with a prototype design is described. Lastly, the paper presents empirical evaluation of the system based on a survey reflecting the impact of such diverse recommendations on the user choices.  Recommender system; Collaborative filtering; Content filtering; Data mining; Time; Book
Wideband, low loss and compact 3D LTCC balun with asymmetric structure for millimeter wave applications

A 3D LTCC (low temperature co-fired ceramic) millimeter wave balun using asymmetric structure was investigated in this paper. The proposed balun consists of embedded multilayer microstrip and CPS (coplanar strip) lines. It was designed at 40GHz. The measured insertion loss of the back-to-back balanced transition is -1.14dB, thus the estimated insertion loss of each device is -0.57dB including the CPS line loss. The 10dB return loss bandwidth of the unbalanced back-to-back transition covers the frequency range of 17.3/spl sim/46.6GHz (91.7%). The area occupied by this balun is 0.42 /spl times/ 0.066/spl lambda//sub 0/ (2.1 /spl times/ 0.33mm/sup 2/). The high performances have been achieved using the low loss and relatively high dielectric constant of LTCC (/spl epsiv//sub r/=5.4, tan/spl delta/=0.0015 at 35GHz) and a 3D stacked configuration. This balun can be used as a transition of microstrip-to-CPS and vice-versa and insures also an impedance transformation from 50 to 110 Ohm for an easy integration with a high input impedance antenna. This is the first reported 40 GHz wideband 3D LTCC balun using asymmetric structure to balance the output amplitude and phase difference.
Predicting Legislative Roll Calls from Text

We develop several predictive models linking legislative sentiment to legislative text. Our models, which draw on ideas from ideal point estimation and topic models, predict voting patterns based on the contents of bills and infer the political leanings of legislators. With supervised topics, we provide an exploratory window into how the language of the law is correlated with political support. We also derive approximate posterior inference algorithms based on variational methods. Across 12 years of legislative data, we predict specific voting patterns with high accuracy.
Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition

We propose a simple two-step approach for speeding up convolution layers within large convolutional neural networks based on tensor decomposition and discriminative fine-tuning. Given a layer, we use non-linear least squares to compute a low-rank CP-decomposition of the 4D convolution kernel tensor into a sum of a small number of rank-one tensors. At the second step, this decomposition is used to replace the original convolutional layer with a sequence of four convolutional layers with small kernels. After such replacement, the entire network is fine-tuned on the training data using standard backpropagation process. We evaluate this approach on two CNNs and show that it yields larger CPU speedups at the cost of lower accuracy drops compared to previous approaches. For the 36-class character classification CNN, our approach obtains a 8.5x CPU speedup of the whole network with only minor accuracy drop (1% from 91% to 90%). For the standard ImageNet architecture (AlexNet), the approach speeds up the second convolution layer by a factor of 4x at the cost of 1% increase of the overall top-5 classification error.
The (Un)reliability of saliency methods

Saliency methods aim to explain the predictions of deep neural networks. These methods lack reliability when the explanation is sensitive to factors that do not contribute to the model prediction. We use a simple and common pre-processing step —adding a constant shift to the input data— to show that a transformation with no effect on the model can cause numerous methods to incorrectly attribute. In order to guarantee reliability, we posit that methods should fulfill input invariance, the requirement that a saliency method mirror the sensitivity of the model with respect to transformations of the input. We show, through several examples, that saliency methods that do not satisfy input invariance result in misleading attribution.
Loose-limbed People: Estimating 3D Human Pose and Motion Using Non-parametric Belief Propagation

We formulate the problem of 3D human pose estimation and tracking as one of inference in a graphical model. Unlike traditional kinematic tree representations, our model of the body is a collection of loosely-connected body-parts. In particular, we model the body using an undirected graphical model in which nodes correspond to parts and edges to kinematic, penetration, and temporal constraints imposed by the joints and the world. These constraints are encoded using pair-wise statistical distributions, that are learned from motion-capture training data. Human pose and motion estimation is formulated as inference in this graphical model and is solved using Particle Message Passing (PaMPas). PaMPas is a form of non-parametric belief propagation that uses a variation of particle filtering that can be applied over a general graphical model with loops. The loose-limbed model and decentralized graph structure allow us to incorporate information from “bottom-up” visual cues, such as limb and head detectors, into the inference process. These detectors enable automatic initialization and aid recovery from transient tracking failures. We illustrate the method by automatically tracking people in multi-view imagery using a set of calibrated cameras and present quantitative evaluation using the HumanEva dataset.
Metacognitive regulation of text learning: on screen versus on paper.

Despite immense technological advances, learners still prefer studying text from printed hardcopy rather than from computer screens. Subjective and objective differences between on-screen and on-paper learning were examined in terms of a set of cognitive and metacognitive components, comprising a Metacognitive Learning Regulation Profile (MLRP) for each study media. Participants studied expository texts of 1000-1200 words in one of the two media and for each text they provided metacognitive prediction-of-performance judgments with respect to a subsequent multiple-choice test. Under fixed study time (Experiment 1), test performance did not differ between the two media, but when study time was self-regulated (Experiment 2) worse performance was observed on screen than on paper. The results suggest that the primary differences between the two study media are not cognitive but rather metacognitive--less accurate prediction of performance and more erratic study-time regulation on screen than on paper. More generally, this study highlights the contribution of metacognitive regulatory processes to learning and demonstrates the potential of the MLRP methodology for revealing the source of subjective and objective differences in study performance among study conditions.
Privacy Preserving Policy-Based Content Sharing in Public Clouds

An important problem in public clouds is how to selectively share documents based on fine-grained attribute-based access control policies (acps). An approach is to encrypt documents satisfying different policies with different keys using a public key cryptosystem such as attribute-based encryption, and/or proxy re-encryption. However, such an approach has some weaknesses: it cannot efficiently handle adding/revoking users or identity attributes, and policy changes; it requires to keep multiple encrypted copies of the same documents; it incurs high computational costs. A direct application of a symmetric key cryptosystem, where users are grouped based on the policies they satisfy and unique keys are assigned to each group, also has similar weaknesses. We observe that, without utilizing public key cryptography and by allowing users to dynamically derive the symmetric keys at the time of decryption, one can address the above weaknesses. Based on this idea, we formalize a new key management scheme, called broadcast group key management (BGKM), and then give a secure construction of a BGKM scheme called ACV-BGKM. The idea is to give some secrets to users based on the identity attributes they have and later allow them to derive actual symmetric keys based on their secrets and some public information. A key advantage of the BGKM scheme is that adding users/revoking users or updating acps can be performed efficiently by updating only some public information. Using our BGKM construct, we propose an efficient approach for fine-grained encryption-based access control for documents stored in an untrusted cloud file storage.
Self-Organised Middleware Architecture for the Internet-of-Things

Presently, middleware technologies abound for the Internet-of-Things (IoT), directed at hiding the complexity of underlying technologies and easing the use and management of IoT resources. The middleware solutions of today are capable technologies, which provide much advanced services and that are built using superior architectural models, they however fail short in some important aspects: existing middleware do not properly activate the link between diverse applications with much different monitoring purposes and many disparate sensing networks that are of heterogeneous nature and geographically dispersed. Then, current middleware are unfit to provide some system-wide global arrangement (intelligence, routing, data delivery) emerging from the behaviors of the constituent nodes, rather than from the coordination of single elements, i.e. self-organization. This paper presents the SIMPLE self-organized and intelligent middleware platform. SIMPLE middleware innovates from current state-of-research exactly by exhibiting self-organization properties, a focus on data-dissemination using multi-level subscriptions processing and a tiered networking approach able to cope with many disparate, widespread and heterogeneous sensing networks (e.g. WSN). In this way, the SIMLE middleware is provided as robust zero-configuration technology, with no central dependable system, immune to failures, and able to efficiently deliver the right data at the right time, to needing applications.
SMEPP: A Secure Middleware For Embedded P2P

The increasing presence of embedded devices with internet access capabilities constitutes a new challenge in software development. These devices are now cooperating in a distributed manner towards what has been called as “Internet of Things”. In this new scenario the client-server model is sometimes not adequate and dynamic ad-hoc networks are more common than before. However, security poses as a hard issue as these systems are extremely vulnerable. In this paper, we introduce SMEPP project, which aims at developing a middleware designed for P2P systems with a special focus on embedded devices and security. SMEPP is designed to be deployed in a wide range of devices. It tries to ease the development of applications hiding platforms details and other aspects such as scalability, adaptability and interoperability. A full implementation of this middleware is already available that incorporates security features specially designed for low-resource devices. Moreover, we describe two business applications being developed using this middleware in the context of “Digital Home” and “Environmental Monitoring in Industrial Environments”.
THE RIPPLE EFFECT : EMOTIONAL CONTAGION AND ITS INFLUENCE ON GROUP BEHAVIORi

Group emotional contagion, the transfer of moods among people in a group, and its influence on work group dynamics was examined in a laboratory study of managerial decision making using multiple, convergent measures of mood, individual attitudes, behavior, and group-level dynamics. Using a 2 times 2 experimental design, with a trained confederate enacting mood conditions, the predicted effect of emotional contagion was found among group members, using both outside coders' ratings of participants' mood and participants' selfreported mood. No hypothesized differences in contagion effects due to the degree of pleasantness of the mood expressed and the energy level with which it was conveyed were found. There was a significant influence of emotional contagion on individual-level attitudes and group processes. As predicted, the positive emotional contagion group members experienced improved cooperation, decreased conflict, and increased perceived task performance. Theoretical implications and practical ramifications of emotional contagion in groups and organizations are discussed. Disciplines Human Resources Management | Organizational Behavior and Theory This journal article is available at ScholarlyCommons: http://repository.upenn.edu/mgmt_papers/72 THE RIPPLE EFFECT: EMOTIONAL CONTAGION AND ITS INFLUENCE ON GROUP BEHAVIOR SIGAL G. BARSADE School of Management Yale University Box 208200 New Haven, CT 06520-8200 Telephone: (203) 432-6159 Fax: (203) 432-9994 E-mail: sigal.barsade@yale.edu August 2001 Revise and Resubmit, ASQ; Comments Welcome i I would like to thank my mentor Barry Staw, Charles O’Reilly, JB, Ken Craik, Batia Wiesenfeld, Jennifer Chatman,  Linda Johanson, and three anonymous ASQ reviewers who have helped lead to positive emotional and cognitive contagion.
Schubert Varieties and Distances between Subspaces of Different Dimensions

We resolve a basic problem regarding subspace distances that has arisen considerably often in applications: How could one define a notion of distance between two linear subspaces of different dimensions in a way that generalizes the usual Grassmann distance between equidimensional subspaces? We show that a natural solution is given by the distance of a point to a Schubert variety within the Grassmannian. Aside from reducing to the usual Grassmann distance when the subspaces are equidimensional, this distance is intrinsic and does not depend on any embedding into a larger ambient space. Furthermore, it can be written down as concrete expressions involving principal angles, and is efficiently computable in numerically stable ways. Our results are also largely independent of the Grassmann distance — if desired, it may be substituted by any other common distances between subspaces. Central to our approach to these problems is a concrete algebraic geometric view of the Grassmannian that parallels the differential geometric perspective that is now well-established in applied and computational mathematics. A secondary goal of this article is to demonstrate that the basic algebraic geometry of Grassmannian can be just as accessible and useful to practitioners.