nils-herrmann/clean_scirepeval huggingface dataset:
Privacy Tipping Points in Smartphones Privacy Preferences

The aim of this research was to understand what affects people's privacy preferences in smartphone apps. We ran a four-week study in the wild with 34 participants. Participants were asked to answer questions, which were used to gather their personal context and to measure their privacy preferences by varying app name and purpose of data collection. Our results show that participants shared the most when no information about data access or purpose was given, and shared the least when both of these details were specified. When just one of either purpose or the requesting app was shown, participants shared less when just the purpose was specified than when just the app name was given. We found that the purpose for data access was the predominant factor affecting users' choices. In our study the purpose condition vary from being not specified, to vague to be very specific. Participants were more willing to disclose data when no purpose was specified. When a vague purpose was shown, participants became more privacy-aware and were less willing to disclose their information. When specific purposes were shown participants were more willing to disclose when the purpose for requesting the information appeared to be beneficial to them, and shared the least when the purpose for data access was solely beneficial to developers.
Practical Black-Box Attacks on Deep Neural Networks Using Efficient Query Mechanisms

Existing black-box attacks on deep neural networks (DNNs) have largely focused on transferability, where an adversarial instance generated for a locally trained model can “transfer” to attack other learning models. In this paper, we propose novel Gradient Estimation black-box attacks for adversaries with query access to the target model’s class probabilities, which do not rely on transferability. We also propose strategies to decouple the number of queries required to generate each adversarial sample from the dimensionality of the input. An iterative variant of our attack achieves close to 100% attack success rates for both targeted and untargeted attacks on DNNs. We carry out a thorough comparative evaluation of black-box attacks and show that Gradient Estimation attacks achieve attack success rates similar to state-of-the-art white-box attacks on the MNIST and CIFAR-10 datasets. We also apply the Gradient Estimation attacks successfully against real-world classifiers hosted by Clarifai. Further, we evaluate black-box attacks against state-of-theart defenses based on adversarial training and show that the Gradient Estimation attacks are very effective even against these defenses.
Reciprocal rank fusion outperforms condorcet and individual rank learning methods

Reciprocal Rank Fusion (RRF), a simple method for combining the document rankings from multiple IR systems, consistently yields better results than any individual system, and better results than the standard method Condorcet Fuse. This result is demonstrated by using RRF to combine the results of several TREC experiments, and to build a meta-learner that ranks the LETOR 3 dataset better than any previously reported method
xDGP: A Dynamic Graph Processing System with Adaptive Partitioning

Many real-world systems, such as social networks, rely on mining efficiently large graphs, with hundreds of millions of vertices and edges. This volume of information requires partitioning the graph across multiple nodes in a distributed system. This has a deep effect on performance, as traversing edges cut between partitions incurs a significant performance penalty due to the cost of communication. Thus, several systems in the literature have attempted to improve computational performance by enhancing graph partitioning, but they do not support another characteristic of realworld graphs: graphs are inherently dynamic, their topology evolves continuously, and subsequently the optimum partitioning also changes over time. In this work, we present the first system that dynamically repartitions massive graphs to adapt to structural changes. The system optimises graph partitioning to prevent performance degradation without using data replication. The system adopts an iterative vertex migration algorithm that relies on local information only, making complex coordination unnecessary. We show how the improvement in graph partitioning reduces execution time by over 50%, while adapting the partitioning to a large number of changes to the graph in three real-world scenarios.
Hybrides Steuerungs- und Regelungskonzept für das hochautomatisierte Fahren auf Autobahnen

Zusammenfassung Es wird eine neuartige hybride Systemarchitektur für kontinuierliche Steuerungsund Regelungssysteme mit diskreten Entscheidungsfindungsprozessen vorgestellt. Die Funktionsweise wird beispielhaft für das hochautomatisierte Fahren auf Autobahnen und den Nothalteassistenten dargestellt. Da für einen zukünftigen Einsatz derartiger Systeme deren Robustheit entscheidend ist, wurde diese bei der Entwicklung des Ansatzes in den Mittelpunkt gestellt.  An innovative hybrid system structure for continuous control systems with discrete decisionmaking processes is presented. The functionality is demonstrated on a highly automated driving system on freeways and on the emergency stop assistant. Due to the fact that the robustness will be a determining factor for future usage of these systems, the presented structure focuses on this feature.
A critical conduction mode bridgeless flyback converter

This paper proposes a new isolated bridgeless AC-DC power factor correction (PFC) converter. The proposed circuit consists of dual flyback converters which provide high power factor (PF). By eliminating input bridge diodes, the number of conducting components is reduced. Therefore, conduction losses are decreased and efficiency can be further improved. Critical conduction mode (CRM) operation decreases the switching losses of switch components. Thus, operational modes of CRM are analyzed and sensing configurations are also presented to address some of the challenge points such as zero crossing detection (ZCD) circuit and sensing circuits of the bridgeless converter. Using a transformer allows for more flexible voltage gain design and, thus, a single-stage isolated PFC. The proposed circuit is verified with a 75W (12V/6.4A) experimental prototype in discontinuous conduction mode (DCM) and CRM.
Computation of Octanol-Water Partition Coefficients by Guiding an Additive Model with Knowledge

We have developed a new method, i.e., XLOGP3, for logP computation. XLOGP3 predicts the logP value of a query compound by using the known logP value of a reference compound as a starting point. The difference in the logP values of the query compound and the reference compound is then estimated by an additive model. The additive model implemented in XLOGP3 uses a total of 87 atom/group types and two correction factors as descriptors. It is calibrated on a training set of 8199 organic compounds with reliable logP data through a multivariate linear regression analysis. For a given query compound, the compound showing the highest structural similarity in the training set will be selected as the reference compound. Structural similarity is quantified based on topological torsion descriptors. XLOGP3 has been tested along with its predecessor, i.e., XLOGP2, as well as several popular logP methods on two independent test sets: one contains 406 small-molecule drugs approved by the FDA and the other contains 219 oligopeptides. On both test sets, XLOGP3 produces more accurate predictions than most of the other methods with average unsigned errors of 0.24-0.51 units. Compared to conventional additive methods, XLOGP3 does not rely on an extensive classification of fragments and correction factors in order to improve accuracy. It is also able to utilize the ever-increasing experimentally measured logP data more effectively.
Latent Dirichlet Allocation for Text

Latent Dirichlet Allocation (LDA) is an unsupervised, statistical approach to document modeling that discovers latent semantic topics in large collections of text documents. LDA posits that words carry strong semantic information, and documents discussing similar topics will use a similar group of words. Latent topics are thus discovered by identifying groups of words in the corpus that frequently occur together within documents. In this way, LDA models documents as a random mixture over latent topics, with each topic being characterized by its own particular distribution over words. In this report, we show that LDA is not only useful in the text domain, but also in the image and music domain. In particular, we discuss algorithms that extend LDA to accomplish tasks like document classification for text, object localization for images, and automatic harmonic analysis for music. For each domain, we also emphasize approaches that go beyond LDA’s traditional bag-of-words representation to achieve more realistic models that incorporate order information.
Simulator Sickness Depends on Frequency of the Simulator Motion Mismatch: An Observation

In this study we describe a new approach to relate simulator sickness ratings with the main frequency component of the simulator motion mismatch, that is, the computed difference between the time histories of simulator motion and vehicle motion, respectively. During two driving simulator experiments in the TNO moving-base driving simulatorthat were performed for other reasons than the purpose of this studywe collected simulator sickness questionnaires from in total 58 subjects. The main frequency component was computed by means of the power spectrum density of the computed mismatch signal. We hypothesized that simulator sickness incidence depends on this frequency component, in a similar way as the incidence of real motion sickness, such as sea sickness, depends on motion frequency. The results show that the simulator sickness ratings differed between both driving simulator experiments. The experiment with its main frequency component of the mismatch signal of 0.08 Hz had significantly higher simulator sickness incidence than the experiment with its main frequency at 0.46 Hz. Since the experimental design differed between both experiments, we cannot exclusively attribute the difference in sickness ratings to the frequency component, but the observation does suggest that quantitative analysis of the mismatch between the motion profiles of the simulator and the vehicle may greatly improve our understanding of the causal mechanism of simulator sickness.
Retargeting Technical Documentation to Augmented Reality

We present a system which automatically transfers printed technical documentation, such as handbooks, to three-dimensional Augmented Reality. Our system identifies the most frequent forms of instructions found in printed documentation, such as image sequences, explosion diagrams, textual annotations and arrows indicating motion. The analysis of the printed documentation works automatically, with minimal user input. The system only requires the documentation itself and a CAD model or 3D scan of the object described in the documentation. The output is a fully interactive Augmented Reality application, presenting the information from the printed documentation in 3D, registered to the real object.
Securing Encryption Algorithms against DPA at the Logic Level: Next Generation Smart Card Technology

This paper describes a design method to secure encryption algorithms against Differential Power Analysis at the logic level. The method employs logic gates with a power consumption, which is independent of the data signals, and therefore the technique removes the foundation for DPA. In a design experiment, a fundamental component of the DES algorithm has been implemented. Detailed transistor level simulations show a perfect security whenever the layout parasitics are not taken into account.
Benchmarking face morphing forgery detection: Application of stirtrace for impact simulation of different processing steps

We analyze StirTrace towards benchmarking face morphing forgeries and extending it by additional scaling functions for the face biometrics scenario. We benchmark a Benford's law based multi-compression-anomaly detection approach and acceptance rates of morphs for a face matcher to determine the impact of the processing on the quality of the forgeries. We use 2 different approaches for automatically creating 3940 images of morphed faces. Based on this data set, 86614 images are created using StirTrace. A manual selection of 183 high quality morphs is used to derive tendencies based on the subjective forgery quality. Our results show that the anomaly detection seems to be able to detect anomalies in the morphing regions, the multi-compression-anomaly detection performance after the processing can be differentiated into good (e.g. cropping), partially critical (e.g. rotation) and critical results (e.g. additive noise). The influence of the processing on the biometric matcher is marginal.
Printed Monopole Slot Antenna for Internal Multiband Mobile Phone Antenna

A new internal multiband mobile phone antenna formed by two printed monopole slots of different lengths cut at the edge of the system ground plane of the mobile phone is presented. The antenna can generate two wide bands centered at about 900 and 2100 MHz to cover the GSM850/GSM900/DCS/PCS/UMTS bands and the 2.4-GHz WLAN band. Further, the antenna has a simple planar structure and occupies a small area of only. It is also promising to bend the antenna into an L shape to reduce its volume occupied inside the mobile phone. Good radiation characteristics are obtained over the two wide operating bands.
A life-cycle perspective on online community success

Using the information systems lifecycle as a unifying framework, we review online communities research and propose a sequence for incorporating success conditions during initiation and development to increase their chances of becoming a successful community, one in which members participate actively and develop lasting relationships. Online communities evolve following distinctive lifecycle stages and recommendations for success are more or less relevant depending on the developmental stage of the online community. In addition, the goal of the online community under study determines the components to include in the development of a successful online community. Online community builders and researchers will benefit from this review of the conditions that help online communities succeed.
A hierarchical approach to manipulation with diverse actions

We define the Diverse Action Manipulation (DAMA) problem in which we are given a mobile robot, a set of movable objects, and a set of diverse, possibly non-prehensile manipulation actions, and the goal is to find a sequence of actions that moves each of the objects to a goal configuration. We show that the DAMA problem can be framed as a multi-modal planning problem and describe a hierarchical algorithm that takes advantage of this multi-modal nature. We also extend our earlier forward search sampling algorithm to a bi-directional version. We give results on a complicated manipulation domain and demonstrate that both new algorithms are significantly more efficient than the original, and that the hierarchical algorithm is usually much more efficient than the forward or bi-directional searches.
Convolutional Matrix Factorization for Document Context-Aware Recommendation

Sparseness of user-to-item rating data is one of the major factors that deteriorate the quality of recommender system. To handle the sparsity problem, several recommendation techniques have been proposed that additionally consider auxiliary information to improve rating prediction accuracy. In particular, when rating data is sparse, document modeling-based approaches have improved the accuracy by additionally utilizing textual data such as reviews, abstracts, or synopses. However, due to the inherent limitation of the bag-of-words model, they have difficulties in effectively utilizing contextual information of the documents, which leads to shallow understanding of the documents. This paper proposes a novel context-aware recommendation model, convolutional matrix factorization (ConvMF) that integrates convolutional neural network (CNN) into probabilistic matrix factorization (PMF). Consequently, ConvMF captures contextual information of documents and further enhances the rating prediction accuracy. Our extensive evaluations on three real-world datasets show that ConvMF significantly outperforms the state-of-the-art recommendation models even when the rating data is extremely sparse. We also demonstrate that ConvMF successfully captures subtle contextual difference of a word in a document. Our implementation and datasets are available at http://dm.postech.ac.kr/ConvMF.
Usable gestures for blind people: understanding preference and performance

Despite growing awareness of the accessibility issues surrounding touch screen use by blind people, designers still face challenges when creating accessible touch screen interfaces. One major stumbling block is a lack of understanding about how blind people actually use touch screens. We conducted two user studies that compared how blind people and sighted people use touch screen gestures. First, we conducted a gesture elicitation study in which 10 blind and 10 sighted people invented gestures to perform common computing tasks on a tablet PC. We found that blind people have different gesture preferences than sighted people, including preferences for edge-based gestures and gestures that involve tapping virtual keys on a keyboard. Second, we conducted a performance study in which the same participants performed a set of reference gestures. We found significant differences in the speed, size, and shape of gestures performed by blind people versus those performed by sighted people. Our results suggest new design guidelines for accessible touch screen interfaces.
Social selection and peer influence in an online social network.

Disentangling the effects of selection and influence is one of social science's greatest unsolved puzzles: Do people befriend others who are similar to them, or do they become more similar to their friends over time? Recent advances in stochastic actor-based modeling, combined with self-reported data on a popular online social network site, allow us to address this question with a greater degree of precision than has heretofore been possible. Using data on the Facebook activity of a cohort of college students over 4 years, we find that students who share certain tastes in music and in movies, but not in books, are significantly likely to befriend one another. Meanwhile, we find little evidence for the diffusion of tastes among Facebook friends-except for tastes in classical/jazz music. These findings shed light on the mechanisms responsible for observed network homogeneity; provide a statistically rigorous assessment of the coevolution of cultural tastes and social relationships; and suggest important qualifications to our understanding of both homophily and contagion as generic social processes.
Sorting Things Out: Classification and Its Consequences

Bowker GC and Star SL. 389 pages. Cambridge, MA, and London: MIT Pr; 1999. $29.95. ISBN 0262024616.  Field of medicine: Public health and medical informatics.  Hardcover book (softcover also available). Audience: Physicians and nonphysicians involved in developing or setting policy for classification systems, nomenclatures, or vocabularies.  To discuss the idea that classifications and standardizations have direct impact on social and political aspects of human interaction.  The authors organize their presentation into an introductory chapter that frames the issues, followed by three sections (classification and large-scale infrastructures, classification and biography, and classification and work practice) providing specific examples, and a conclusion section. The authors use the International Classification of Diseases, 9th revision, race classification under apartheid in South Africa, and the Nursing Intervention Classification as primary examples. An extensive bibliography of more than 300 references, a name index, and a subject index follow the text.  The authors present fascinating history and insights into the development of various classification systems. In addition, they identify issues that arise during the creation of any classification system, such as the need to compromise between providing granular classifications that satisfy needs specific to a time and place. Finally, the authors draw attention to the implications of choices made in the development of some important classification systems. These implications bear on moral judgments, financial effects, and political gains or losses.  The authors' writing style hinder the reader's ability to access the interesting information and to understand the implications of choices made in developing classification systems. While the overall organization of the book is clear, the themes and ideas do not flow well. Sentences require repeated readings, and a dictionary at your side would be helpful, given the authors' frequent use of unfamiliar words. These failings obscure interesting and valuable facts and viewpoints. Related readings: Svenonius'The Intellectual Foundation of Information Organization (MIT Pr; 2000) and Aitchison and colleagues'Thesaurus Construction and Use: A Practical Manual (Fitzroy Dearborn; 2000).
Combining Customer Attribute and Social Network Mining for Prepaid Mobile Churn Prediction

Customer churn, i.e., losing a customer to the competition, is a major problem in mobile telecommunications. This paper investigates the added value of combining regular tabular data mining with social network mining, leveraging the graph formed by communications between customers. We extend classical tabular churn datasets with predictors derived from social network neighborhoods. We also extend traditional social network spreading activation models with information from classical tabular churn models. Experiments show that in the second approach the combination of tabular and social network mining improves results, but overall the traditional tabular churn models score best.
Analysis of Artificial Neural Network for Financial Time Series Forecasting

Financial forecasting has been challenging problem due to its high non-linearity and high volatility. An Artificial Neural Network (ANN) can model flexible linear or non-linear relationship among variables. ANN can be configured to produce desired set of output based on set of given input. In this paper we attempt at analyzing the usefulness of artificial neural network for forecasting financial data series with use of different algorithms such as backpropagation, radial basis function etc. With their ability of adapting non-linear and chaotic patterns, ANN is the current technique being used which offers the ability of predicting financial data more accurately. "A x-y-1 network topology is adopted because of x input variables in which variable y was determined by the number of hidden neurons during network selection with single output." Both x and y were changed.