nils-herrmann/clean_scirepeval huggingface dataset:
The Impact of Timing on the Salience of Smartphone App Privacy Notices

In a series of experiments, we examined how the timing impacts the salience of smartphone app privacy notices. In a web survey and a field experiment, we isolated different timing conditions for displaying privacy notices: in the app store, when an app is started, during app use, and after app use. Participants installed and played a history quiz app, either virtually or on their phone. After a distraction or delay they were asked to recall the privacy notice's content. Recall was used as a proxy for the attention paid to and salience of the notice. Showing the notice during app use significantly increased recall rates over showing it in the app store. In a follow-up web survey, we tested alternative app store notices, which improved recall but did not perform as well as notices shown during app use. The results suggest that even if a notice contains information users care about, it is unlikely to be recalled if only shown in the app store.
Healthcare fraud detection : A survey and a clustering model incorporating Geolocation information

Health care has become a major expenditure in the US since 1980. Both the size of the health care sector and the enormous volume of money involved make it an attractive fraud target. Therefore, effective fraud detection is important for reducing the cost of health care services. In order to achieve more effective fraud detection, many researchers have attempted to develop sophisticated antifraud approaches incorporating data mining, machine learning or other methods. This introduce some preliminary knowledge of U.S. health care system and its fraudulent behaviors, analyzes the characteristics of health care data, and reviews and compares currently proposed fraud detection approaches using health care data in the literature as well as their corresponding data preprocess methods. Also a novel health care fraud detection method including geo-location information is proposed.
Improvements from a Flipped Classroom May Simply Be the Fruits of Active Learning

The "flipped classroom" is a learning model in which content attainment is shifted forward to outside of class, then followed by instructor-facilitated concept application activities in class. Current studies on the flipped model are limited. Our goal was to provide quantitative and controlled data about the effectiveness of this model. Using a quasi-experimental design, we compared an active nonflipped classroom with an active flipped classroom, both using the 5-E learning cycle, in an effort to vary only the role of the instructor and control for as many of the other potentially influential variables as possible. Results showed that both low-level and deep conceptual learning were equivalent between the conditions. Attitudinal data revealed equal student satisfaction with the course. Interestingly, both treatments ranked their contact time with the instructor as more influential to their learning than what they did at home. We conclude that the flipped classroom does not result in higher learning gains or better attitudes compared with the nonflipped classroom when both utilize an active-learning, constructivist approach and propose that learning gains in either condition are most likely a result of the active-learning style of instruction rather than the order in which the instructor participated in the learning process.
SpamGuru: An Enterprise Anti-Spam Filtering System

Spam-reduction techniques have developed rapidly ov er the last few years, as spam volumes have increased. We believe that no one anti-spam soluti on is the “right” answer, and that the best approac h is a multifaceted one, combining various forms of filtering w ith infrastructure changes, financial changes, lega l recourse, and more, to provide a stronger barrier to spam tha n can be achieved with one solution alone. SpamGur u addresses the part of this multi-faceted approach t hat can be handled by technology on the recipient’s side, using plug-in tokenizers and parsers, plug-in classificat ion modules, and machine-learning techniques to ach ieve high hit rates and low false-positive rates.
Federal Reserve Bank of St . Louis Working Paper Series Asset Allocation under Multivariate Regime Switching

This paper studies asset allocation decisions in the presence of regime switching in asset returns. We find evidence that four separate regimes characterized as crash, slow growth, bull and recovery states are required to capture the joint distribution of stock and bond returns. Optimal asset allocations vary considerably across these states and change over time as investors revise their estimates of the state probabilities. In the crash state, buy-and-hold investors allocate more of their portfolio to stocks the longer their investment horizon, while the optimal allocation to stocks declines as a function of the investment horizon in bull markets. The joint effects of learning about state probabilities and predictability of asset returns from the dividend yield give rise to a non-monotonic relationship between the investment horizon and the demand for stocks. Welfare costs from ignoring regime switching can be substantial even after accounting for parameter uncertainty. Out-of-sample forecasting experiments confirm the economic importance of accounting for the presence of regimes in asset returns.
Dither for Smoothing Relay Feedback Systems

Dither signals provide an effective way to compensate for nonlinearities in control systems. The seminal works by Zames and Shneydor, and more recently, by Mossaheb, present rigorous tools for systematic design of dithered systems. Their results rely, however, on a Lipschitz assumption relating to nonlinearity, and thus, do not cover important applications with discontinuities. This paper presents initial results on how to analyze and design dither in nonsmooth systems. In particular, it is shown that a dithered relay feedback system can be approximated by a smoothed system. Guidelines are given for tuning the amplitude and the period time of the dither signal, in order to stabilize the nonsmooth system.
On Modern Deep Learning and Variational Inference

Bayesian modelling and variational inference are rooted in Bayesian statistics, and easily benefit from the vast literature in the field. In contrast, deep learning lacks a solid mathematical grounding. Instead, empirical developments in deep learning are often justified by metaphors, evading the unexplained principles at play. It is perhaps astonishing then that most modern deep learning models can be cast as performing approximate variational inference in a Bayesian setting. This mathematically grounded result, studied in Gal and Ghahramani  for deep neural networks (NNs), is extended here to arbitrary deep learning models. The implications of this statement are profound: we can use the rich Bayesian statistics literature with deep learning models, explain away many of the curiosities with these, combine results from deep learning into Bayesian modelling, and much more. We demonstrate the practical impact of the framework with image classification by combining Bayesian and deep learning techniques, obtaining new state-of-the-art results, and survey open problems to research. These stand at the forefront of a new and exciting field combining modern deep learning and Bayesian techniques.
An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem With Various QoS Requirements

Grid computing is increasingly considered as a promising next-generation computational platform that supports wide-area parallel and distributed computing. In grid environments, applications are always regarded as workflows. The problem of scheduling workflows in terms of certain quality of service (QoS) requirements is challenging and it significantly influences the performance of grids. By now, there have been some algorithms for grid workflow scheduling, but most of them can only tackle the problems with a single QoS parameter or with small-scale workflows. In this frame, this paper aims at proposing an ant colony optimization (ACO) algorithm to schedule large-scale workflows with various QoS parameters. This algorithm enables users to specify their QoS preferences as well as define the minimum QoS thresholds for a certain application. The objective of this algorithm is to find a solution that meets all QoS constraints and optimizes the user-preferred QoS parameter. Based on the characteristics of workflow scheduling, we design seven new heuristics for the ACO approach and propose an adaptive scheme that allows artificial ants to select heuristics based on pheromone values. Experiments are done in ten workflow applications with at most 120 tasks, and the results demonstrate the effectiveness of the proposed algorithm.
Smart City Components Architicture

The research is essentially to modularize the structure of utilities and develop a system for following up the activities electronically on the city scale. The GIS operational platform will be the base for managing the infrastructure development components with the systems interoperability for the available city infrastructure related systems. The concentration will be on the available utility networks in order to develop a comprehensive, common, standardized geospatial data models. The construction operations for the utility networks such as electricity, water, Gas, district cooling, irrigation, sewerage and communication networks; are need to be fully monitored on daily basis, in order to utilize the involved huge resources and man power. These resources are allocated only to convey the operational status for the construction and execution sections that used to do the required maintenance. The need for a system that serving the decision makers for following up these activities with a proper geographical representation will definitely reduce the operational cost for the long term.
Permanent magnetic system design for the wall-climbing robot

This paper presents the design of the permanent magnetic system for the wall climbing robot with permanent magnetic tracks. A proposed wall climbing robot with permanent magnetic adhesion mechanism for inspecting the oil tanks is briefly put forward, including the mechanical system architecture. The permanent magnetic adhesion mechanism and the tracked locomotion mechanism are employed in the robot system. By static and dynamic force analysis of the robot, design parameters about adhesion mechanism are derived. Two types of the structures of the permanent magnetic units are given in the paper. The analysis of those two types of structure is also detailed. Finally, two wall climbing robots equipped with those two different magnetic systems are discussed and the experiments are included in the paper.
Compact Hybrid Integrated 100-Gb/s Transmitter Optical Sub-Assembly Using Optical Butt-Coupling Between EADFB Lasers and Silica-Based AWG Multiplexer

A compact hybrid-integrated 100 Gb/s (4 lane × 25.78125 Gb/s) transmitter optical sub-assembly (TOSA) has been developed for a 100 Gb/s transceiver for 40-km transmission over a single-mode fiber. The TOSA has a simple configuration in which four electro-absorption modulator-integrated distributed feedback (EADFB) lasers are directly attached to the input waveguide end-face of a silica-based arrayed waveguide grating (AWG) multiplexer without bulk lenses. To achieve a high optical butt coupling efficiency between the EADFB lasers and the AWG multiplexer, we integrated a laterally tapered spot-size converter (SSC) for the EADFB laser and employed a waveguide with a high refractive index difference of 2.0% for the AWG multiplexer. By optimizing the laterally tapered SSC structure, we achieved a butt-coupling loss of less than 3 dB, which is an improvement of around 2 dB compared with a laser without an SSC structure. We also developed an ultracompact AWG multiplexer, which was 6.7 mm × 3.5 mm in size with an insertion loss of less than 1.9 dB. We achieved this by using a Mach-Zehnder interferometer-synchronized configuration to obtain a low loss and wide flat-top transmission filter spectra. The TOSA body size was 19.9 mm (L) × 6.0 mm (W) × 5.8 mm (H). Error-free operation was demonstrated for a 40-km transmission when all the lanes were driven simultaneously with a low EA modulator driving voltage of 1.5 V at an operating temperature of 55 °C.
Data Augmentation via Levy Processes

If a document is about travel, we may expect that short snippets of the document should also be about travel. We introduce a general framework for incorporating these types of invariances into a discriminative classifier. The framework imagines data as being drawn from a slice of a Lévy process. If we slice the Lévy process at an earlier point in time, we obtain additional pseudo-examples, which can be used to train the classifier. We show that this scheme has two desirable properties: it preserves the Bayes decision boundary, and it is equivalent to fitting a generative model in the limit where we rewind time back to 0. Our construction captures popular schemes such as Gaussian feature noising and dropout training, as well as admitting new generalizations.
Neurons in the orbitofrontal cortex encode economic value

Economic choice is the behaviour observed when individuals select one among many available options. There is no intrinsically ‘correct’ answer: economic choice depends on subjective preferences. This behaviour is traditionally the object of economic analysis and is also of primary interest in psychology. However, the underlying mental processes and neuronal mechanisms are not well understood. Theories of human and animal choice have a cornerstone in the concept of ‘value’. Consider, for example, a monkey offered one raisin versus one piece of apple: behavioural evidence suggests that the animal chooses by assigning values to the two options. But where and how values are represented in the brain is unclear. Here we show that, during economic choice, neurons in the orbitofrontal cortex (OFC) encode the value of offered and chosen goods. Notably, OFC neurons encode value independently of visuospatial factors and motor responses. If a monkey chooses between A and B, neurons in the OFC encode the value of the two goods independently of whether A is presented on the right and B on the left, or vice versa. This trait distinguishes the OFC from other brain areas in which value modulates activity related to sensory or motor processes. Our results have broad implications for possible psychological models, suggesting that economic choice is essentially choice between goods rather than choice between actions. In this framework, neurons in the OFC seem to be a good candidate network for value assignment underlying economic choice.
Walking Trajectory Planning on Stairs Using Virtual Slope for Biped Robots

In this paper, a “virtual slope method” for walking trajectory planning on stairs for biped robots is proposed. In conventional methods for walking on stairs, there are two problems about the zero-moment point (ZMP). One is a ZMP equation problem, and the other is a ZMP definition problem in a double-support phase. First, a ZMP equation on stairs is different from that on flat ground. Therefore, the same trajectory generation as flat ground cannot be implemented. This problem is defined as a “ZMP equation problem.” Second, the ZMP cannot be defined in the double-support phase on stairs because contact points of the feet do not constitute a plane. The ZMP can be defined only on the plane. This problem is defined as a “ZMP definition problem.” These two problems are solved concurrently by the virtual slope method. It is the method that regards the stairs as a virtual slope. In walking trajectory planning on a slope of the constant gradient, the two problems about the ZMP do not exist. Additionally, a trajectory planning procedure based on the virtual slope method is explained. The validity of the proposed method is confirmed by some simulations and experiments.
Autonomy infused teleoperation with application to brain computer interface controlled manipulation

Robot teleoperation systems face a common set of challenges including latency, low-dimensional user commands, and asymmetric control inputs. User control with Brain–Computer Interfaces (BCIs) exacerbates these problems through especially noisy and erratic low-dimensional motion commands due to the difficulty in decoding neural activity. We introduce a general framework to address these challenges through a combination of computer vision, user intent inference, and arbitration between the human input and autonomous control schemes. Adjustable levels of assistance allow the system to balance the operators capabilities and their perception of control authority. Additionally, a custom servo controller design allow for safe interactions of the robotic arm with the environment. We present experimental results demonstrating significant performance improvement using our shared-control assistance framework on adapted rehabilitation benchmarks with two subjects at various timepoints relative to their implantation with intracortical BCIs. Our results indicate that shared assistance mitigates perceived user difficulty in using a seven-degree of freedom robotic arm as a prosthetic and enables successThis is one of several papers published in Autonomous Robots comprising the “Special Issue on Robotics Science and Systems”. B Katharina Muelling kmuelling@nrec.ri.cmu.edu 1 Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA 2 Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA 3 Department of Physical Medicine and Rehabilitation, Univeristy of Pittsburgh, Pittsburgh, PA, USA 4 VA Pittsburgh Healthcare System, Pittsburgh, PA, USA 5 Center for the Neural Basis of Cognition, Pittsburgh, PA, USA ful performance on previously infeasible tasks.We showcase the extensibility of our architecture with applications to quality-of-life tasks such as opening a door, pouring liquids from containers, and manipulation with objects previously unknown to the system in densely cluttered environments.
Hearing and saying. The functional neuro-anatomy of auditory word processing.

The neural systems involved in hearing and repeating single words were investigated in a series of experiments using PET. Neuropsychological and psycholinguistic studies implicate the involvement of posterior and anterior left perisylvian regions (Wernicke's and Broca's areas). Although previous functional neuroimaging studies have consistently shown activation of Wernicke's area, there has been only variable implication of Broca's area. This study demonstrates that Broca's area is involved in both auditory word perception and repetition but activation is dependent on task (greater during repetition than hearing) and stimulus presentation (greater when hearing words at a slow rate). The peak of frontal activation in response to hearing words is anterior to that associated with repeating words; the former is probably located in Brodmann's area 45, the latter in Brodmann's area 44 and the adjacent precentral sulcus. As Broca's area activation is more subtle and complex than that in Wernicke's area during these tasks, the likelihood of observing it is influenced by both the study design and the image analysis technique employed. As a secondary outcome from the study, the response of bilateral auditory association cortex to 'own voice' during repetition was shown to be the same as when listening to "other voice' from a prerecorded tape.
A Native and Adaptive Approach for Unified Processing of Linked Streams and Linked Data

In this paper we address the problem of scalable, native and adaptive query processing over Linked Stream Data integrated with Linked Data. Linked Stream Data consists of data generated by stream sources, e.g., sensors, enriched with semantic descriptions, following the standards proposed for Linked Data. This enables the integration of stream data with Linked Data collections and facilitates a wide range of novel applications. Currently available systems use a “black box” approach which delegates the processing to other engines such as stream/event processing engines and SPARQL query processors by translating to their provided languages. As the experimental results described in this paper show, the need for query translation and data transformation, as well as the lack of full control over the query execution, pose major drawbacks in terms of efficiency. To remedy these drawbacks, we present CQELS (Continuous Query Evaluation over Linked Streams), a native and adaptive query processor for unified query processing over Linked Stream Data and Linked Data. In contrast to the existing systems, CQELS uses a “white box” approach and implements the required query operators natively to avoid the overhead and limitations of closed system regimes. CQELS provides a flexible query execution framework with the query processor dynamically adapting to the changes in the input data. During query execution, it continuously reorders operators according to some heuristics to achieve improved query execution in terms of delay and complexity. Moreover, external disk access on large Linked Data collections is reduced with the use of data encoding and caching of intermediate query results. To demonstrate the efficiency of our approach, we present extensive experimental performance evaluations in terms of query execution time, under varied query types, dataset sizes, and number of parallel queries. These results show that CQELS outperforms related approaches by orders of magnitude.
K-d Trees for Semidynamic Point Sets

A <italic>K</italic>-d tree represents a set of <italic>N</italic> points in <italic>K</italic>-dimensional space. Operations on a <italic>semidynamic</italic> tree may delete and undelete points, but may not insert new points. This paper shows that several operations that require <italic>&Ogr;</italic>(log <italic>N</italic>) expected time in general <italic>K</italic>-d trees may be performed in constant expected time in semidynamic trees. These operations include deletion, undeletion, nearest neighbor searching, and fixed-radius near neighbor searching (the running times of the first two are proved, while the last two are supported by experiments and heuristic arguments). Other new techniques can also be applied to general <italic>K</italic>-d trees: simple sampling reduces the time to build a tree from <italic>&Ogr;</italic>(<italic>KN</italic> log <italic>N</italic>) to <italic>&Ogr;</italic>(<italic>KN</italic> + <italic>N</italic> log <italic>N</italic>), and more advanced sampling builds a robust tree in the same time. The methods are straightforward to implement, and lead to a data structure that is significantly faster and less vulnerable to pathological inputs than ordinary <italic>K</italic>-d trees.
Co-Segmentation of 3D Shapes via Subspace Clustering

We present a novel algorithm for automatically co-segmenting a set of shapes from a common family into consistent parts. Starting from over-segmentations of shapes, our approach generates the segmentations by grouping the primitive patches of the shapes directly and obtains their correspondences simultaneously. The core of the algorithm is to compute an affinity matrix where each entry encodes the similarity between two patches, which is measured based on the geometric features of patches. Instead of concatenating the different features into one feature descriptor, we formulate co-segmentation into a subspace clustering problem in multiple feature spaces. Specifically, to fuse multiple features, we propose a new formulation of optimization with a consistent penalty, which facilitates both the identification of most similar patches and selection of master features for two similar patches. Therefore the affinity matrices for various features are sparsity-consistent and the similarity between a pair of patches may be determined by part of (instead of all) features. Experimental results have shown how our algorithm jointly extracts consistent parts across the collection in a good manner.