nils-herrmann/clean_scirepeval huggingface dataset:
Making privacy personal: Profiling social network users to inform privacy education and nudging

Social Network Sites (SNSs) offer a plethora of privacy controls, but users rarely exploit all of these mechanisms, nor do they do so in the same manner. We demonstrate that SNS users instead adhere to one of a small set of distinct privacy management strategies that are partially related to their level of privacy feature awareness. Using advanced Factor Analysis methods on the self-reported privacy behaviors and feature awareness of 308 Facebook users, we extrapolate six distinct privacy management strategies, including: Privacy Maximizers, Selective Sharers, Privacy Balancers, Self-Censors, Time Savers/Consumers, and Privacy Minimalists and six classes of privacy proficiency based on feature awareness, ranging from Novices to Experts. We then cluster users on these dimensions to form six distinct behavioral profiles of privacy management strategies and six awareness profiles for privacy proficiency. We further analyze these privacy profiles to suggest opportunities for training and education, interface redesign, and new approaches for personalized privacy recommendations.
Push-pull functional reactive programming

Functional reactive programming (FRP) has simple and powerful semantics, but has resisted efficient implementation. In particular, most past implementations have used demand-driven sampling, which accommodates FRP's continuous time semantics and fits well with the nature of functional programming. Consequently, values are wastefully recomputed even when inputs don't change, and reaction latency can be as high as the sampling period. This paper presents a way to implement FRP that combines data- and demand-driven evaluation, in which values are recomputed only when necessary, and reactions are nearly instantaneous. The implementation is rooted in a new simple formulation of FRP and its semantics and so is easy to understand and reason about. On the road to a new implementation, we'll meet some old friends (monoids, functors, applicative functors, monads, morphisms, and improving values) and make some new friends (functional future values, reactive normal form, and concurrent "unambiguous choice").
Beyond Fano's inequality: bounds on the optimal F-score, BER, and cost-sensitive risk and their implications

Fano’s inequality lower bounds the probability of transmission error through a communication channel. Applied to classification problems, it provides a lower bound on the Bayes error rate and motivates the widely used Infomax principle. In modern machine learning, we are often interested in more than just the error rate. In medical diagnosis, different errors incur different cost; hence, the overall risk is cost-sensitive. Two other popular criteria are balanced error rate (BER) and F-score. In this work, we focus on the two-class problem and use a general definition of conditional entropy (including Shannon’s as a special case) to derive upper/lower bounds on the optimal F-score, BER and cost-sensitive risk, extending Fano’s result. As a consequence, we show that Infomax is not suitable for optimizing F-score or cost-sensitive risk, in that it can potentially lead to low F-score and high risk. For cost-sensitive risk, we propose a new conditional entropy formulation which avoids this inconsistency. In addition, we consider the common practice of using a threshold on the posterior probability to tune performance of a classifier. As is widely known, a threshold of 0.5, where the posteriors cross, minimizes error rate—we derive similar optimal thresholds for F-score and BER.
A visual navigation system for autonomous flight of micro air vehicles

Many applications of unmanned aerial vehicles (UAVs) require the capability to navigate to some goal and to perform precise and safe landing. In this paper, we present a visual navigation system as an alternative pose estimation method for environments and situations in which GPS is unavailable. The developed visual odometer is an incremental procedure that estimates the vehicle's ego-motion by extracting and tracking visual features, using an onboard camera. For more robustness and accuracy, the visual estimates are fused with measurements from an Inertial Measurement Unit (IMU) and a Pressure Sensor Altimeter (PSA) in order to provide accurate estimates of the vehicle's height, velocity and position relative to a given location. These estimates are then exploited by a nonlinear hierarchical controller for achieving various navigation tasks such as take-off, landing, hovering, target tracking, etc. In addition to the odometer description, the paper presents validation results from autonomous flights using a small quadrotor UAV.
Vehicle detection and localization on bird's eye view elevation images using convolutional neural network

For autonomous vehicles, the ability to detect and localize surrounding vehicles is critical. It is fundamental for further processing steps like collision avoidance or path planning. This paper introduces a convolutional neural network- based vehicle detection and localization method using point cloud data acquired by a LIDAR sensor. Acquired point clouds are transformed into bird's eye view elevation images, where each pixel represents a grid cell of the horizontal x-y plane. We intentionally encode each pixel using three channels, namely the maximal, median and minimal height value of all points within the respective grid. A major advantage of this three channel representation is that it allows us to utilize common RGB image-based detection networks without modification. The bird's eye view elevation images are processed by a two stage detector. Due to the nature of the bird's eye view, each pixel of the image represent ground coordinates, meaning that the bounding box of detected vehicles correspond directly to the horizontal position of the vehicles. Therefore, in contrast to RGB-based detectors, we not just detect the vehicles, but simultaneously localize them in ground coordinates. To evaluate the accuracy of our method and the usefulness for further high-level applications like path planning, we evaluate the detection results based on the localization error in ground coordinates. Our proposed method achieves an average precision of 87.9% for an intersection over union (IoU) value of 0.5. In addition, 75% of the detected cars are localized with an absolute positioning error of below 0.2m.
Link Prediction across Aligned Networks with Sparse and Low Rank Matrix Estimation

Users' addiction to online social networks is discovered to be highly correlated with their social connections in the networks. Dense social connections can effectively help online social networks retain their active users and improve the social network services. Therefore, it is of great importance to make a good prediction of the social links among users. Meanwhile, to enjoy more social network services, users nowadays are usually involved in multiple online social networks simultaneously. Formally, the social networks which share a number of common users are defined as the "aligned networks".With the information transferred from multiple aligned social networks, we can gain a more comprehensive knowledge about the social preferences of users in the pre-specified target network, which will benefit the social link prediction task greatly. However, when transferring the knowledge from other aligned source networks to the target network, there usually exists a shift in information distribution between different networks, namely domain difference. In this paper, we study the social link prediction problem of the target network, which is aligned with multiple social networks concurrently. To accommodate the domain difference issue, we project the features extracted for links from different aligned networks into a shared lower-dimensional feature space. Moreover, users in social networks usually tend to form communities and would only connect to a small number of users. Thus, the target network structure has both the low-rank and sparse properties. We propose a novel optimization framework, SLAMPRED, to combine both these two properties aforementioned of the target network and the information of multiple aligned networks with nice domain adaptations. Since the objective function is a linear combination of convex and concave functions involving nondifferentiable regularizers, we propose a novel optimization method to iteratively solve it. Extensive experiments have been done on real-world aligned social networks, and the experimental results demonstrate the effectiveness of the proposed model.
Optimal resistance of stress grading system in converter-fed rotating machines

Excessive heat generation and occurrence of partial discharge have been observed in end-turn stress grading (SG) system in form-wound machines under PWM voltage. In this paper, multi-winding stress grading (SG) system is proposed as a method to change resistance of SG per length. Although the maximum field at the edge of stator and CAT are in a trade-off relationship, analytical results suggest that we can suppress field and excessive heat generation at both stator and CAT edges by multi-winding of SG and setting the length of CAT appropriately. This is also experimentally confirmed by measuring potential distribution of model bar-coil and observing partial discharge and temperature rise.
A low-fat vegan diet improves glycemic control and cardiovascular risk factors in a randomized clinical trial in individuals with type 2 diabetes.

We sought to investigate whether a low-fat vegan diet improves glycemic control and cardiovascular risk factors in individuals with type 2 diabetes.



Individuals with type 2 diabetes (n = 99) were randomly assigned to a low-fat vegan diet (n = 49) or a diet following the American Diabetes Association (ADA) guidelines (n = 50). Participants were evaluated at baseline and 22 weeks.



Forty-three percent (21 of 49) of the vegan group and 26% (13 of 50) of the ADA group participants reduced diabetes medications. Including all participants, HbA(1c) (A1C) decreased 0.96 percentage points in the vegan group and 0.56 points in the ADA group (P = 0.089). Excluding those who changed medications, A1C fell 1.23 points in the vegan group compared with 0.38 points in the ADA group (P = 0.01). Body weight decreased 6.5 kg in the vegan group and 3.1 kg in the ADA group (P < 0.001). Body weight change correlated with A1C change (r = 0.51, n = 57, P < 0.0001). Among those who did not change lipid-lowering medications, LDL cholesterol fell 21.2% in the vegan group and 10.7% in the ADA group (P = 0.02). After adjustment for baseline values, urinary albumin reductions were greater in the vegan group (15.9 mg/24 h) than in the ADA group (10.9 mg/24 h) (P = 0.013).



Both a low-fat vegan diet and a diet based on ADA guidelines improved glycemic and lipid control in type 2 diabetic patients. These improvements were greater with a low-fat vegan diet.
An elementary social information foraging model

User interfaces and information systems have become increasingly social in recent years, aimed at supporting the decentralized, cooperative production and use of content. A theory that predicts the impact of interface and interaction designs on such factors as participation rates and knowledge discovery is likely to be useful. This paper reviews a variety of observed phenomena in social information foraging and sketches a framework extending Information Foraging Theory towards making predictions about the effects of diversity, interference, and cost-of-effort on performance time, participation rates, and utility of discoveries.
Automatic Registration of 2-D with 3-D Imagery in Urban Environments

We are building a system that can automatically acquire 3D range scans and 2D images to build geometrically correct, texture mapped 3D models of urban environments. This paper deals with the problem of automatically registering the 3D range scans with images acquired at other times and with unknown camera calibration and location. The method involves the utilization of parallelism and orthogonality constraints that naturally exist in urban environments. We present results for building a texture mapped 3-D model of an urban
End-to-End Text Recognition with Hybrid HMM Maxout Models

The problem of detecting and recognizing text in natural scenes has proved to be more challenging than its counterpart in documents, with most of the previous work focusing on a single part of the problem. In this work, we propose new solutions to the character and word recognition problems and then show how to combine these solutions in an end-to-end text-recognition system. We do so by leveraging the recently introduced Maxout networks along with hybrid HMM models that have proven useful for voice recognition. Using these elements, we build a tunable and highly accurate recognition system that beats state-of-theart results on all the sub-problems for both the ICDAR 2003 and SVT benchmark
Influence of magnet size and rotor topology on the air-gap flux density of a radial flux PMSM

When designing the rotor of a radial flux permanent magnet synchronous machine (PMSM), one key part is the sizing of the permanent magnets (PM) in the rotor to produce the required air-gap flux density. This paper focuses on the effect that different coefficients have on the air-gap flux density of four radial flux PMSM rotor topologies. A direct connection is shown between magnet volume and flux producing magnet area with the aid of static finite element model simulations of the four rotor topologies. With this knowledge, the calculation of the flux producing magnet area can be done with ease once the minimum magnet volume has been determined. This technique can also be applied in the design of line-start PMSM rotors where the rotor area is limited.
Time Domain Audio Features for Chainsaw Noise Detection Using WSNs

In this paper, we present an analysis of low complexity signal processing algorithms capable of identifying special noises, such as the sounds of forest machinery (used for forestry, logging). Our objective is to find methods that are able to detect internal combustion engines in rural environment, and are also easy to implement on low power devices of WSNs (wireless sensor networks). In this context, we review different methods for detecting illegal logging, with an emphasis on autocorrelation and TESPAR audio techniques. The processing of extracted audio features is to be solved with limited memory and processor resources typical for low cost sensors modes. The representation of noise models is also considered with different archetypes. Implementations of the proposed methods were tested not by simulations but on sensor nodes equipped with an omnidirectional microphone and a low power microcontroller. Our results show that high recognition rate can be achieved using time domain algorithms and highly energy efficient and inexpensive architectures.
CloudDB: One Size Fits All Revived

We present a data management platform in the cloud, CloudDB. The guiding principle of CloudDB’s design is establishing data independence for the applications that need to use diverse underlying data stores that are optimized for varying workload needs and characteristics. The applications should not have to be aware of the physical organization of the data and how the data is accessed. Ideally, an application only needs a logical specification of the data access layer and the data access requests are handled in a declarative way. CloudDB hosts variety of specialized databases that deliver high performance, scalability, and cost efficiency for varying application needs. CloudDB’s API layer is designed in such a way to give data independence to the higher level applications. The goal is to let the clients use just a simple, standard, and uniform language API to access data management functions as a service.
Newborn and infant footprint crease pattern extraction

Medical staff around the world must take reasonable steps to identify newborns and infants clearly, so as to prevent mix-ups, and to ensure the correct medication reaches the correct child. Footprints are frequently taken despite verification with footprints being challenging due to strong noise. The noise is introduced by the tininess of the structures, movement during capture, and the infant's rapid growth. In this article we address the image processing part of the problem and introduce a novel algorithm for the extraction of creases from infant footprints. The algorithm uses directional filtering on different resolution levels, morphological processing, and block-wise crease line reconstruction. We successfully test our method on noise-affected infant footprints taken from the same infants at different ages.
A fuzzy relative of the k-medoids algorithm with application to web document and snippet clustering

This paper presents new algorithms (fuzzy e-methods (FCMdd) and fuzzy c trimmed medoids (FCTMdd)) for fuzzy clustering of relational data. The objective functions are based on selecting c representative objects (medoids) from the data set in such a way that the total dissimilarity within each cluster is minimized. A comparison of FCMdd with the relational fuzzy c-means algorithm shows that FCMdd is much faster. We present examples of applications of these algorithms to web document and snippet clustering.
A Serious Game for Clinical Assessment of Cognitive Status: Validation Study

We propose the use of serious games to screen for abnormal cognitive status in situations where it may be too costly or impractical to use standard cognitive assessments (eg, emergency departments). If validated, serious games in health care could enable broader availability of efficient and engaging cognitive screening.



The objective of this work is to demonstrate the feasibility of a game-based cognitive assessment delivered on tablet technology to a clinical sample and to conduct preliminary validation against standard mental status tools commonly used in elderly populations.



We carried out a feasibility study in a hospital emergency department to evaluate the use of a serious game by elderly adults (N=146; age: mean 80.59, SD 6.00, range 70-94 years). We correlated game performance against a number of standard assessments, including the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and the Confusion Assessment Method (CAM).



After a series of modifications, the game could be used by a wide range of elderly patients in the emergency department demonstrating its feasibility for use with these users. Of 146 patients, 141 (96.6%) consented to participate and played our serious game. Refusals to play the game were typically due to concerns of family members rather than unwillingness of the patient to play the game. Performance on the serious game correlated significantly with the MoCA (r=-.339, P <.001) and MMSE (r=-.558, P <.001), and correlated (point-biserial correlation) with the CAM (r=.565, P <.001) and with other cognitive assessments.



This research demonstrates the feasibility of using serious games in a clinical setting. Further research is required to demonstrate the validity and reliability of game-based assessments for clinical decision making.
The moral obligation to create children with the best chance of the best life.

According to what we call the Principle of Procreative Beneficence (PB),couples who decide to have a child have a significant moral reason to select the child who, given his or her genetic endowment, can be expected to enjoy the most well-being. In the first part of this paper, we introduce PB,explain its content, grounds, and implications, and defend it against various objections. In the second part, we argue that PB is superior to competing principles of procreative selection such as that of procreative autonomy.In the third part of the paper, we consider the relation between PB and disability. We develop a revisionary account of disability, in which disability is a species of instrumental badness that is context- and person-relative.Although PB instructs us to aim to reduce disability in future children whenever possible, it does not privilege the normal. What matters is not whether future children meet certain biological or statistical norms, but what level of well-being they can be expected to have.
Fully integrated wideband high-current rectifiers for inductively powered devices

This paper describes the design and implementation of fully integrated rectifiers in BiCMOS and standard CMOS technologies for rectifying an externally generated RF carrier signal in inductively powered wireless devices, such as biomedical implants, radio-frequency identification (RFID) tags, and smartcards to generate an on-chip dc supply. Various full-wave rectifier topologies and low-power circuit design techniques are employed to decrease substrate leakage current and parasitic components, reduce the possibility of latch-up, and improve power transmission efficiency and high-frequency performance of the rectifier block. These circuits are used in wireless neural stimulating microsystems, fabricated in two processes: the University of Michigan's 3-/spl mu/m 1M/2P N-epi BiCMOS, and the AMI 1.5-/spl mu/m 2M/2P N-well standard CMOS. The rectifier areas are 0.12-0.48 mm/sup 2/ in the above processes and they are capable of delivering >25mW from a receiver coil to the implant circuitry. The performance of these integrated rectifiers has been tested and compared, using carrier signals in 0.1-10-MHz range.
Mobile Cloud Forensics: An Analysis of Seven Popular Android Apps

Using the evidence collection and analysis methodology for Android devices proposed by Martini, Do and Choo (2015), we examined and analyzed seven popular Android cloud-based apps. Firstly, we analyzed each app in order to see what information could be obtained from their private app storage and SD card directories. We collated the information and used it to aid our investigation of each app’s database files and AccountManager data. To complete our understanding of the forensic artefacts stored by apps we analyzed, we performed further analysis on the apps to determine if the user’s authentication credentials could be collected for each app based on the information gained in the initial analysis stages. The contributions of this research include a detailed description of artefacts, which are of general forensic interest, for each app analyzed.
Learning a Deep Compact Image Representation for Visual Tracking

In this paper, we study the challenging problem of tracking the trajectory of a moving object in a video with possibly very complex background. In contrast to most existing trackers which only learn the appearance of the tracked object online, we take a different approach, inspired by recent advances in deep learning architectures, by putting more emphasis on the (unsupervised) feature learning problem. Specifically, by using auxiliary natural images, we train a stacked denoising autoencoder offline to learn generic image features that are more robust against variations. This is then followed by knowledge transfer from offline training to the online tracking process. Online tracking involves a classification neural network which is constructed from the encoder part of the trained autoencoder as a feature extractor and an additional classification layer. Both the feature extractor and the classifier can be further tuned to adapt to appearance changes of the moving object. Comparison with the state-of-the-art trackers on some challenging benchmark video sequences shows that our deep learning tracker is more accurate while maintaining low computational cost with real-time performance when our MATLAB implementation of the tracker is used with a modest graphics processing unit (GPU).