nils-herrmann/clean_scirepeval huggingface dataset:
Quantification of food intake using food image analysis

Measuring free-living peoples’ food intake represents methodological and technical challenges. The Remote Food Photography Method (RFPM) involves participants capturing pictures of their food selection and plate waste and sending these pictures to the research center via a wireless network, where they are analyzed by Registered Dietitians to estimate food intake. Initial tests indicate that the RFPM is reliable and valid, though the efficiency of the method is limited due to the reliance on human raters to estimate food intake. Herein, we describe the development of a semi-automated computer imaging application to estimate food intake based on pictures captured by participants.
A Video-Based Real-Time Vehicle Counting System Using Adaptive Background Method

This paper presents a video-based solution for real time vehicle detection and counting system, using a surveillance camera mounted on a relatively high place to acquire the traffic video stream.The two main methods applied in this system are: the adaptive background estimation and the Gaussian shadow elimination. The former allows a robust moving detection especially in complex scenes. The latter is based on color space HSV, which is able to deal with different size and intensity shadows. After these two operations, it obtains an image with moving vehicle extracted, and then operation counting is effected by a method called virtual detector.
Interference Management in Femtocells

Increase in system capacity and data rates can be achieved efficiently in a wireless system by getting the transmitter and receiver closer to each other. Femtocells deployed in the macrocell significantly improve the indoor coverage and provide better user experience. The femtocell base station called as Femtocell Access Point (FAP) is fully user deployed and hence reduces the infrastructure, maintenance and operational cost of the operator while at the same time providing good Quality of Service (QoS) to the end user and high network capacity gains. However, the mass deployment of femtocell faces a number of challenges, among which interference management is of much importance, as the fundamental limits of capacity and achievable data rates mainly depends on the interference faced by the femtocell network. To cope with the technical challenges including interference management faced by the femtocells, researchers have suggested a variety of solutions. These solutions vary depending on the physical layer technology and the specific scenarios considered. Furthermore, the cognitive capabilities, as a functionality of femtocell have also been discussed in this survey. This article summarises the main concepts of femtocells that are covered in literature and the major challenges faced in its large scale deployment. The main challenge of interference management is discussed in detail with its types in femtocells and the solutions proposed over the years to manage interference have been summarised. In addition an overview of the current femtocell standardisation and the future research direction of femtocells have also been provided.
Robust Articulated-ICP for Real-Time Hand Tracking

We present a robust method for capturing articulated hand motions in realtime using a single depth camera. Our system is based on a realtime registration process that accurately reconstructs hand poses by fitting a 3D articulated hand model to depth images. We register the hand model using depth, silhouette, and temporal information. To effectively map low-quality depth maps to realistic hand poses, we regularize the registration with kinematic and temporal priors, as well as a data-driven prior built from a database of realistic hand poses. We present a principled way of integrating such priors into our registration optimization to enable robust tracking without severely restricting the freedom of motion. A core technical contribution is a new method for computing tracking correspondences that directly models occlusions typical of single-camera setups. To ensure reproducibility of our results and facilitate future research, we fully disclose the source code of our implementation.
On Skyline Groups

We formulate and investigate the novel problem of finding the skyline k-tuple groups from an n-tuple dataset - i.e., groups of k tuples which are not dominated by any other group of equal size, based on aggregate-based group dominance relationship. The major technical challenge is to identify effective anti-monotonic properties for pruning the search space of skyline groups. To this end, we show that the anti-monotonic property in the well-known Apriori algorithm does not hold for skyline group pruning. We then identify order-specific property which applies to SUM, MIN, and MAX and weak candidate-generation property which applies to MIN and MAX only. Experimental results on both real and synthetic datasets verify that the proposed algorithms achieve orders of magnitude performance gain over a baseline method.
Time series cluster kernel for learning similarities between multivariate time series with missing data

Similarity-based approaches represent a promising direction for time series analysis. However, many such methods rely on parameter tuning, and some have shortcomings if the time series are multivariate (MTS), due to dependencies between attributes, or the time series contain missing data. In this paper, we address these challenges within the powerful context of kernel methods by proposing the robust time series cluster kernel (TCK). The approach taken leverages the missing data handling properties of Gaussian mixture models (GMM) augmented with informative prior distributions. An ensemble learning approach is exploited to ensure robustness to parameters by combining the clustering results of many GMM to form the final kernel. We evaluate the TCK on synthetic and real data and compare to other state-of-the-art techniques. The experimental results demonstrate that the TCK is robust to parameter choices, provides competitive results for MTS without missing data and outstanding results for missing data.
Inferring Logical Forms From Denotations

A core problem in learning semantic parsers from denotations is picking out consistent logical forms—those that yield the correct denotation—from a combinatorially large space. To control the search space, previous work relied on restricted set of rules, which limits expressivity. In this paper, we consider a much more expressive class of logical forms, and show how to use dynamic programming to efficiently represent the complete set of consistent logical forms. Expressivity also introduces many more spurious logical forms which are consistent with the correct denotation but do not represent the meaning of the utterance. To address this, we generate fictitious worlds and use crowdsourced denotations on these worlds to filter out spurious logical forms. On the WIKITABLEQUESTIONS dataset, we increase the coverage of answerable questions from 53.5% to 76%, and the additional crowdsourced supervision lets us rule out 92.1% of spurious logical forms.
The Role of Self-compassion in Romantic Relationships

This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.
Relational learning via collective matrix factorization

Relational learning is concerned with predicting unknown values of a relation, given a database of entities and observed relations among entities. An example of relational learning is movie rating prediction, where entities could include users, movies, genres, and actors. Relations encode users' ratings of movies, movies' genres, and actors' roles in movies. A common prediction technique given one pairwise relation, for example a #users x #movies ratings matrix, is low-rank matrix factorization. In domains with multiple relations, represented as multiple matrices, we may improve predictive accuracy by exploiting information from one relation while predicting another. To this end, we propose a collective matrix factorization model: we simultaneously factor several matrices, sharing parameters among factors when an entity participates in multiple relations. Each relation can have a different value type and error distribution; so, we allow nonlinear relationships between the parameters and outputs, using Bregman divergences to measure error. We extend standard alternating projection algorithms to our model, and derive an efficient Newton update for the projection. Furthermore, we propose stochastic optimization methods to deal with large, sparse matrices. Our model generalizes several existing matrix factorization methods, and therefore yields new large-scale optimization algorithms for these problems. Our model can handle any pairwise relational schema and a wide variety of error models. We demonstrate its efficiency, as well as the benefit of sharing parameters among relations.
Zero-knowledge from secure multiparty computation

We present a general construction of a zero-knowledge proof for an NP relation R(x,w) which only makes a black-box use of a secure protocol for a related multi-partyfunctionality f. The latter protocol is only required to be secure against a small number of "honest but curious" players. As an application, we can translate previous results on the efficiency of secure multiparty computation to the domain of zero-knowledge, improving over previous constructions of efficient zero-knowledge proofs. In particular, if verifying R on a witness of length m can be done by a circuit C of size s, and assuming one-way functions exist, we get the following types of zero-knowledge proof protocols.
 Approaching the witness length. If C has constant depth over ∧,∨,⊕, - gates of unbounded fan-in, we get a zero-knowledge protocol with communication complexity m·poly(k)·polylog(s), where k is a security parameter. Such a protocol can be implemented in either the standard interactive model or, following a trusted setup, in a non-interactive model.
 "Constant-rate" zero-knowledge. For an arbitrary circuit C of size s and a bounded fan-in, we geta zero-knowledge protocol with communication complexity O(s)+poly(k). Thus, for large circuits, the ratio between the communication complexity and the circuit size approaches a constant. This improves over the O(ks) complexity of the best previous protocols.
Concolic Execution on Small-Size Binaries: Challenges and Empirical Study

Concolic execution has achieved great success in many binary analysis tasks. However, it is still not a primary option for industrial usage. A well-known reason is that concolic execution cannot scale up to large-size programs. Many research efforts have focused on improving its scalability. Nonetheless, we find that, even when processing small-size programs, concolic execution suffers a great deal from the accuracy and scalability issues. This paper systematically investigates the challenges that can be introduced even by small-size programs, such as symbolic array and symbolic jump. We further verify that the proposed challenges are non-trivial via real-world experiments with three most popular concolic execution tools: BAP, Triton, and Angr. Among a set of 22 logic bombs we designed, Angr can solve only four cases correctly, while BAP and Triton perform much worse. The results imply that current tools are still primitive for practical industrial usage. We summarize the reasons and release the bombs as open source to facilitate further study.
Absorptive Capacity and Information Systems Research: Review, Synthesis, and Directions for Future Research

Agarwal, R., Krudys, G., and Tanniru, M. 1997. “Infusing Learning into the Information Systems Organization,” European Journal of Information Systems (6:1), pp. 25-40. Alavi, M., and Leidner, D. E. 2001.  Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues,” MIS Quarterly (25:1), pp. 107-136. Andersen, T. J. 2001. “Information Technology, Strategic Decision Making Approaches and Organizational Performance in Different Industrial Settings,” Journal of Strategic Information Systems (10:2), pp. 101-119. Andersen, T. J., and Segars, A. H. 2001. “The Impact of IT on Decision Structure and Firm Performance: Evidence from the Textile and Apparel Industry,” Information & Management (39:2), pp. 85-100. Andersson, M., Lindgren, R., and Henfridsson, A. 2008. “Architectural Knowledge in Inter-Organizational IT Innovation,” Journal of Strategic Information Systems (17:1), pp. 19-38. Armstrong, C. P., and Sambamurthy, V. 1999. “Information Technology Assimilation  The Influence of Senior Leadership and IT Infrastructures,” Information Systems Research (10:4), pp. 304-327. Auer, T. 1998. “Quality of IS Use,” European Journal of Information Systems (7:3), pp. 192-201. Bassellier, G., Benbasat, I., and Reich, B. H. 2003. “The Influence of Business Managers’ IT Competence on Championing IT,” Information Systems Research (14:4), pp. 317-336.
Fingerprint attack against touch-enabled devices

Oily residues left by tapping fingers on a touch screen may breach user privacy. In this paper, we introduce the fingerprint attack against touch-enabled devices. We dust the touch screen surface to reveal fingerprints, and use an iPhone camera to carefully photograph fingerprints while striving to remove the virtual image of the phone from the fingerprint image. We then sharpen the fingerprints in an image via various image processing techniques and design effective algorithms to automatically map fingerprints to a keypad in order to infer tapped passwords. Extensive experiments were conducted on iPad, iPhone and Android phone and the results show that the fingerprint attack is effective and efficient in inferring passwords from fingerprint images. To the best of our knowledge, we are the first using fingerprint powder on touch screen and inferring passwords from fingerprints. Video at http://www.youtube.com/watch?v=vRUbJIcV9vg shows the dusting process on iPhone and video at http://www.youtube.com/watch?v=6jS6KroER3Y shows the dusting process on iPad. After dusting, password characters for login are clearly disclosed.
Sexuality before and after male-to-female sex reassignment surgery.

The sexual behaviors and attitudes of male-to-female (MtF) transsexuals have not been investigated systematically. This study presents information about sexuality before and after sex reassignment surgery (SRS), as reported by 232 MtF patients of one surgeon. Data were collected using self-administered questionnaires. The mean age of participants at time of SRS was 44 years (range, 18-70 years). Before SRS, 54% of participants had been predominantly attracted to women and 9% had been predominantly attracted to men. After SRS, these figures were 25% and 34%, respectively.Participants' median numbers of sexual partners before SRS and in the last 12 months after SRS were 6 and 1, respectively. Participants' reported number of sexual partners before SRS was similar to the number of partners reported by male participants in the National Health and Social Life Survey (NHSLS). After SRS, 32% of participants reported no sexual partners in the last 12 months, higher than reported by male or female participants in the NHSLS. Bisexual participants reported more partners before and after SRS than did other participants. 49% of participants reported hundreds of episodes or more of sexual arousal to cross-dressing or cross-gender fantasy (autogynephilia) before SRS; after SRS, only 3% so reported. More frequent autogynephilic arousal after SRS was correlated with more frequent masturbation, a larger number of sexual partners, and more frequent partnered sexual activity. 85% of participants experienced orgasm at least occasionally after SRS and 55% ejaculated with orgasm.
Latent Tensor Transfer Learning for RGB-D Action Recognition

This paper proposes a method to compensate RGB-D images from the original target RGB images by transferring the depth knowledge of source data. Conventional RGB databases (e.g., UT-Interaction database) do not contain depth information since they are captured by the RGB cameras. Therefore, the methods designed for {RGB} databases cannot take advantage of depth information, which proves useful for simplifying intra-class variations and background subtraction. In this paper, we present a novel transfer learning method that can transfer the knowledge from depth information to the RGB database, and use the additional source information to recognize human actions in RGB videos. Our method takes full advantage of 3D geometric information contained within the learned depth data, thus, can further improve action recognition performance. We treat action data as a fourth-order tensor (row, column, frame and sample), and apply latent low-rank transfer learning to learn shared subspaces of the source and target databases. Moreover, we introduce a novel cross-modality regularizer that plays an important role in finding the correlation between RGB and depth modalities, and then more depth information from the source database can be transferred to that of the target. Our method is extensively evaluated on public by available databases. Results of two action datasets show that our method outperforms existing methods.
PLAG: Practical Landmark Allocation for Cloud Geolocation

Knowing the physical location of files in a cloud system is of a great importance for any user, as is it can affect the whole service drastically. However, pinpointing the exact coordinates for the location of a server is very challenging. Providers prefer not to share the location of their data centers with public for security reasons, and this fact also adds to the complexity of this concept. Researchers have recently developed delay based schemes for cloud data geolocation, some of which use proprietary landmarks for location verification. Unfortunately, such landmark-based schemes are often impractical due to high cost and latency. In this paper, we have developed a practical scheme for landmark allocation in cloud data geolocation. We augment existing approaches with a new landmark allocation modification to get the same or often better accuracy, while decreasing the cost considerably. Our approach improves the existing state of the art by introducing the concept of publicly distributed landmarks for all delay based geolocation techniques.
3D Gaze Estimation using Eye Vergence

We propose a fast and robust method to estimate the 3D gaze position based on the eye vergence information extracted from eye-tracking data. This method is specially designed for Point-of-Regard (PoR) estimation in non-virtual environments with the aim to make it applicable to the study of human visual attention deployment in natural scenarios. Our approach starts with a calibration step at different depth distances in order to achieve the best depth approximation. In addition, we investigate the distance range, for which state-of-the-art eyetracking technology allows 3D gaze estimation based on eye vergence. Our method provides a mean accuracy of 1.2◦ at a working distance between 200 mm and 400 mm from the user without requiring calibrated lights
Human abilities: emotional intelligence.

Emotional intelligence (EI) involves the ability to carry out accurate reasoning about emotions and the ability to use emotions and emotional knowledge to enhance thought. We discuss the origins of the EI concept, define EI, and describe the scope of the field today. We review three approaches taken to date from both a theoretical and methodological perspective. We find that Specific-Ability and Integrative-Model approaches adequately conceptualize and measure EI. Pivotal in this review are those studies that address the relation between EI measures and meaningful criteria including social outcomes, performance, and psychological and physical well-being. The Discussion section is followed by a list of summary points and recommended issues for future research.
Optical Flow Guided TV-L1 Video Interpolation and Restoration

The ability to generate intermediate frames between two given images in a video sequence is an essential task for video restoration and video post-processing. In addition, restoration requires robust denoising algorithms, must handle corrupted frames and recover from impaired frames accordingly. In this paper we present a unified framework for all these tasks. In our approach we use a variant of the TV-L denoising algorithm that operates on image sequences in a space-time volume. The temporal derivative is modified to take the pixels’ movement into account. In order to steer the temporal gradient in the desired direction we utilize optical flow to estimate the velocity vectors between consecutive frames. We demonstrate our approach on impaired movie sequences as well as on benchmark datasets where the ground-truth is known.
A Monte Carlo algorithm for fast projective clustering

We propose a mathematical formulation for the notion of optimal projective cluster, starting from natural requirements on the density of points in subspaces. This allows us to develop a Monte Carlo algorithm for iteratively computing projective clusters. We prove that the computed clusters are good with high probability. We implemented a modified version of the algorithm, using heuristics to speed up computation. Our extensive experiments show that our method is significantly more accurate than previous approaches. In particular, we use our techniques to build a classifier for detecting rotated human faces in cluttered images.
Protecting people from phishing: the design and evaluation of an embedded training email system

Phishing attacks, in which criminals lure Internet users to websites that impersonate legitimate sites, are occurring with increasing frequency and are causing considerable harm to victims. In this paper we describe the design and evaluation of an embedded training email system that teaches people about phishing during their normal use of email. We conducted lab experiments contrasting the effectiveness of standard security notices about phishing with two embedded training designs we developed. We found that embedded training works better than the current practice of sending security notices. We also derived sound design principles for embedded training systems.