nils-herrmann/clean_scirepeval huggingface dataset:
A SEALANT for Inter-App Security Holes in Android

Android's communication model has a major security weakness: malicious apps can manipulate other apps into performing unintended operations and can steal end-user data, while appearing ordinary and harmless. This paper presents SEALANT, a technique that combines static analysis of app code, which infers vulnerable communication channels, with runtime monitoring of inter-app communication through those channels, which helps to prevent attacks. SEALANT's extensive evaluation demonstrates that (1) it detects and blocks inter-app attacks with high accuracy in a corpus of over 1,100 real-world apps, (2) it suffers from fewer false alarms than existing techniques in several representative scenarios, (3) its performance overhead is negligible, and (4) end-users do not find it challenging to adopt.
Requirement templates for distributed 3D graphics

Requirements are the basis upon which software architecture lies. As a consequence they should be expressed as precisely as possible in order to propose the best compromise between stakeholder needs and engineering constraints.
 While some measurements such as frame rate or latency are a widely known mean of expressing requirements in the 3D community, they often are loosely defined. This leads to software engineering decisions which exclude some of the most promising options.
 This paper proposes to adapt a non-functional requirements expression template used in general software architecture to the specific case of 3D based systems engineering. It shows that in the process some interesting proposals appear as a straightforward consequence of the better definition of the system to be built.
Optimal design of a novel 2-DOF compliant parallel micromanipulator for nanomanipulation

A new two-degrees-of-freedom (2-DOF) compliant parallel micromanipulator (CPM) utilizing flexure joints has been proposed for two-dimensional (2-D) nanomanipulation in this paper. The system is developed by a careful design and proper selection of electrical and mechanical components. Based upon the developed PRB model, both the position and velocity kinematic modelings have been performed in details, and the CPM's workspace area is determined analytically in view of the physical constraints imposed by pizeo-actuators and flexure hinges. Moreover, in order to achieve a maximum workspace subjected to the given dexterity indices, kinematic optimization of the design parameters has been carried out, which leads to a manipulator satisfying the requirement of this work. Simulation results reveal that the designed CPM can perform a high dexterous manipulation within its workspace.
Improved t-SNE based manifold dimensional reduction for remote sensing data processing

In our increasingly “data-abundant” society, remote sensing big data perform massive, high dimension and heterogeneity features, which could result in “dimension disaster” to various extent. It is worth mentioning that the past two decades have witnessed a number of dimensional reductions to weak the spatiotemporal redundancy and simplify the calculation in remote sensing information extraction, such as the linear learning methods or the manifold learning methods. However, the “crowding” and mixing when reducing dimensions of remote sensing categories could degrade the performance of existing techniques. Then in this paper, by analyzing probability distribution of pairwise distances among remote sensing datapoints, we use the 2-mixed Gaussian model(GMM) to improve the effectiveness of the theory of t-Distributed Stochastic Neighbor Embedding (t-SNE). A basic reducing dimensional model is given to test our proposed methods. The experiments show that the new probability distribution capable retains the local structure and significantly reveals differences between categories in a global structure.
Vehicle Routing Problem

— A set of customers with known locations and known requirements for some commodity, is to be supplied from a single depot by delivery vehicles o f known capacity. The problem of designing routes for these vehicles so as to minimise the cost of distribution is known as the vehicle routing problem ( VRP). In this paper we catégorise, discuss and extend both exact and approximate methods for solving VRP's, and we give some results on the properties offeasible solutions which help to reduce the computational effort invohed in solving such problems.
Rotation Detection in Chest Radiographs Based on Generalized Line Histogram of Rib-Orientations

We present a generalized line histogram technique to compute global rib-orientation for detecting rotated lungs in chest radiographs. We use linear structuring elements, such as line seed filters, as kernels to convolve with edge images, and extract a set of lines from the posterior rib-cage. After convolving kernels in all possible orientations in the range [0, π], we measure the angle for which the line histogram has maximum magnitude. This measure provides a good approximation of the global chest rib-orientation for each lung. A chest radiograph is said to be upright if the difference between the orientation angles of both lungs with respect to the horizontal axis, is negligible. We validate our method on sets of normal and abnormal images and argue that rib orientation can be used for rotation detection in chest radiographs as aid in quality control during image acquisition, and to discard images from training and testing data sets. In our test, we achieve a maximum accuracy of 90%.
Remembering the past to imagine the future: the prospective brain

A rapidly growing number of recent studies show that imagining the future depends on much of the same neural machinery that is needed for remembering the past. These findings have led to the concept of the prospective brain; an idea that a crucial function of the brain is to use stored information to imagine, simulate and predict possible future events. We suggest that processes such as memory can be productively re-conceptualized in light of this idea.
Modified Ninhydrin Reagent for the Detection of Amino Acids on TLC

Ninhydrin is the most well known spray reagent for identification of amino acids. Spring with Ninhydrin as a non-specific reagent is well-known and widely used for its remarkable high sensitivity. Using Ninhydrin reagent alone to detect amino acid on thin layer chromatography (TLC) paper is not advisable due to its lower sensitivity. A new spray reagent, Stannus chloride solution (SnCl2) has been used to detect amino acids on filter paper (whitman paper 14) and TLC paper, silica Gel, 60 F254 TLC Aluminum Sheet 20 x 20 cm MerckGermany. Al so, modified TLC pre-staining method was used, which only consisted of 3 steps: spotting, separating and color. The improved method was rapid and inexpensive and the results obtained were clear and reliable. In addition it is suitable for screening different amino acid.
A Survey of Utility-based Privacy-Preserving Data Transformation Methods

As a serious concern in data publishing and analysis, privacy preserving data processing has received a lot of attention. Privacy preservation often leads to information loss. Consequently, we want to minimize utility loss as long as the privacy is preserved. In this chapter, we survey the utility-based privacy preservation methods systematically. We first briefly discuss the privacy models and utility measures, and then review four recently proposed methods for utilitybased privacy preservation. We first introduce the utility-based anonymization method for maximizing the quality of the anonymized data in query answering and discernability. Then we introduce the top-down specialization (TDS) method and the progressive disclosure algorithm (PDA) for privacy preservation in classification problems. Last, we introduce the anonymized marginal method, which publishes the anonymized projection of a table to increase the utility and satisfy the privacy requirement.
Cloaked websites: propaganda, cyber-racism and epistemology in the digital era

This article analyzes cloaked websites, which are sites published by individuals or groups who conceal authorship in order to disguise deliberately a hidden political agenda. Drawing on the insights of critical theory and the Frankfurt School, this article examines the way in which cloaked websites conceal a variety of political agendas from a range of perspectives. Of particular interest here are cloaked white supremacist sites that disguise cyber-racism. The use of cloaked websites to further political ends raises important questions about knowledge production and epistemology in the digital era. These cloaked sites emerge within a social and political context in which it is increasingly difficult to parse fact from propaganda, and this is a particularly pernicious feature when it comes to the cyber-racism of cloaked white supremacist sites. The article concludes by calling for the importance of critical, situated political thinking in the evaluation of cloaked websites.
Blockholders and Corporate Governance

This paper reviews the theoretical and empirical literature on the channels through which blockholders (large shareholders) engage in corporate governance. In classical models, blockholders exert governance through direct intervention in a firm’s operations, otherwise known as “voice.” These theories have motivated empirical research on the determinants and consequences of activism. More recent models show that blockholders can govern through an alternative mechanism known as “exit”—selling their shares if the manager underperforms. These theories give rise to new empirical studies on the two-way relationship between blockholders and financial markets, linking corporate finance with asset pricing. Blockholders may also worsen governance by extracting private benefits of control or pursuing objectives other than firm value maximization. I highlight the empirical challenges in identifying causal effects of and on blockholders as well as the typical strategies attempted to achieve identification. I close with directions for future research.  A nn u. R ev . F in . E co n. 2 01 4. 6: 23 -5 0. D ow nl oa de d fr om w w w .a nn ua lr ev ie w s. or g A cc es s pr ov id ed b y U ni ve rs ity o f Pe nn sy lv an ia o n 01 /0 7/ 15 . F or p er so na l u se o nl y.
Particle swarm optimization for generating interpretable fuzzy reinforcement learning policies

Fuzzy controllers are efficient and interpretable system controllers for continuous state and action spaces. To date, such controllers have been constructed manually or trained automatically either using expert-generated problem-specific cost functions or incorporating detailed knowledge about the optimal control strategy. Both requirements for automatic training processes are not found in most real-world reinforcement learning (RL) problems. In such applications, online learning is often prohibited for safety reasons because it requires exploration of the problem’s dynamics during policy training. We introduce a fuzzy particle swarm reinforcement learning (FPSRL) approach that can construct fuzzy RL policies solely by training parameters on world models that simulate real system dynamics. These world models are created by employing an autonomous machine learning technique that uses previously generated transition samples of a real system. To the best of our knowledge, this approach is the first to relate self-organizing fuzzy controllers to model-based batch RL. FPSRL is intended to solve problems in domains where online learning is prohibited, system dynamics are relatively easy to model from previously generated default policy transition samples, and it is expected that a relatively easily interpretable control policy exists. The efficiency of the proposed approach with problems from such domains is demonstrated using three standard RL benchmarks, i.e., mountain car, cart-pole balancing, and cart-pole swing-up. Our experimental results demonstrate high-performing, interpretable fuzzy policies.
Learning Synchronous Grammars for Semantic Parsing with Lambda Calculus

This paper presents the first empirical results to our knowledge on learning synchronous grammars that generate logical forms. Using statistical machine translation techniques, a semantic parser based on a synchronous context-free grammar augmented with λoperators is learned given a set of training sentences and their correct logical forms. The resulting parser is shown to be the bestperforming system so far in a database query domain.
Allergic contact cheilitis caused by olaflur in toothpaste.

Allergic contact cheilitis caused by olaflur in toothpaste Anton de Groot1, Ron Tupker2, Diny Hissink3 and Marjolijn Woutersen4 1Acdegroot Publishing, 8351 HV Wapserveen, The Netherlands, 2Department of Dermatology, Sint Antonius Hospital, 3435 CM Nieuwegein, The Netherlands, 3Consumer and Safety Division, Netherlands Food and Consumer Product Safety Authority, 3511 GG Utrecht, The Netherlands, and 4Centre for Safety of Substances and Products, National Institute for Public Health and the Environment, 3721 MA Bilthoven, The Netherlands
Unveiling the Biometric Potential of Finger-Based ECG Signals

The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG biometric system, that uses signals collected at the fingers, through a minimally intrusive 1-lead ECG setup recurring to Ag/AgCl electrodes without gel as interface with the skin. The collected signal is significantly more noisy than the ECG acquired at the chest, motivating the application of feature extraction and signal processing techniques to the problem. Time domain ECG signal processing is performed, which comprises the usual steps of filtering, peak detection, heartbeat waveform segmentation, and amplitude normalization, plus an additional step of time normalization. Through a simple minimum distance criterion between the test patterns and the enrollment database, results have revealed this to be a promising technique for biometric applications.
History of circumcision, medical conditions, and sexual activity and risk of penile cancer.

Epidemiological evidence suggests lack of neonatal circumcision as the strongest risk factor for penile cancer, but the role of sexually transmitted diseases in the etiology of penile cancer has remained unclear.



To further clarify risk factors for penile cancer, we examined the role of circumcision, personal characteristics and habits (such as smoking), sexually transmitted diseases, past sexual activity, and medical conditions of the penis.



A population-based, case-control study was conducted in western Washington state and in the province of British Columbia. We interviewed 110 men with penile cancer diagnosed from January 1979 to July 1990 and 355 control subjects from the general population, frequency matched to case subjects on age and date of diagnosis. Tumor tissue from 67 case subjects was tested for human papillomavirus (HPV) DNA by polymerase chain reaction. Results of blood tests from 69 case subjects and 208 control subjects were available for study.  Relative to men circumcised at birth, the risk for penile cancer was 3.2 times greater among men who were never circumcised and 3.0 times greater among men who were circumcised after the neonatal period. For current smokers, the risk was 2.8 times that of men who never smoked. The risk among men reporting a history of genital warts was 5.9 times that of men reporting no such history. Of 67 tumors tested for HPV DNA, 49% were positive; the majority of these positive tumors (70%) were type 16, which has been associated with anogenital carcinoma. Relative risks (RRs) associated with a reported history of penile rash or penile tear were 9.4 and 3.9, respectively. Among men not circumcised at birth, RRs associated with presence of smegma and difficulty in retracting the foreskin were 2.1 and 3.5, respectively. Twenty-eight percent of case subjects, compared with only 10% of control subjects, reported 30 or more sexual partners, and men with HPV-positive tumors were more likely to report a greater number of sexual partners.



These results suggest that the absence of neonatal circumcision and potential resulting complications are associated with penile cancer. Additionally, medical conditions of the penis, sexual activity, infection with HPV, and smoking may increase the risk for penile cancer.



A larger study would allow examination of interrelationships of circumcision, infection with HPV, and smoking as risk factors.
Learning First-Order Horn Clauses from Web Text

There are nearly an unbounded number of classes, objects and relations Missing  Many useful facts are never explicitly stated No Negative Examples: Labeling positive and negative examples for all interesting relations is impractical Learning First-Order Horn Clauses from Web Text Stefan Schoenmackers Oren Etzioni Daniel S. Weld Jesse Davis Turing Center, University of Washington Katholieke Universiteit Leuven
Morphological Tagging Based on Averaged Perceptron

Czech (like other Slavic languages) is well known for its complex morphology. Text processing (e.g., automatic translation, syntactic analysis...) usually requires unambiguous selection of grammatical categories (so called morphological tag) for every word in a text. Morphological tagging consists of two parts – assigning all possible tags to every word in a text and selecting the right tag in a given context. Project Morče attempts to solve the second part, usually called disambiguation. Using a statistical method based on the combination of a Hidden Markov Model and the AveragedAveraged Perceptron algorithm, a number of experiments have been made exploring different parameter settings of the algorithm in order to obtain the best success rate possible. Final accuracy of Morče on data from PDT 2.0 was 95.431% (results of March 2006). So far, it is the best result for a standalone tagger.
Parasitology meets ecology on its own terms: Margolis et al. revisited.

We consider 27 population and community terms used frequently by parasitologists when describing the ecology of parasites. We provide suggestions for various terms in an attempt to foster consistent use and to make terms used in parasite ecology easier to interpret for those who study free-living organisms. We suggest strongly that authors, whether they agree or disagree with us, provide complete and unambiguous definitions for all parameters of their studies.
Amazon Food Review Classification using Deep Learning and Recommender System

In this paper we implemented different models to solve the review usefulness classification problem. Both feed-forward neural network and LSTM were able to beat the baseline model. Performances of the models are evaluated using 0-1 loss and F-1 scores. In general, LSTM outperformed feed-forward neural network, as we trained our own word vectors in that model, and LSTM itself was able to store more information as it processes sequence of words. Besides, we built a recommender system using the user-item-rating data to further investigate this dataset and intended to make connection with review classification. The performance of recommender system is measured by RMSE in rating predictions.
Performance Measurements of Virtual Reality Systems: Quantifying the Timing and Positioning Accuracy

We propose the very first non-intrusive measurement methodology for quantifying the performance of commodity Virtual Reality (VR) systems. Our methodology considers the VR system under test as a black-box and works with any VR applications. Multiple performance metrics on timing and positioning accuracy are considered, and detailed testbed setup and measurement steps are presented. We also apply our methodology to several VR systems in the market, and carefully analyze the experiment results. We make several observations: (i) 3D scene complexity affects the timing accuracy the most, (ii) most VR systems implement the dead reckoning algorithm, which incurs a non-trivial correction latency after incorrect predictions, and (iii) there exists an inherent trade-off between two positioning accuracy metrics: precision and sensitivity.
Smart parking using IoT technology

One of the main problems in many big and crowded cities is finding parking spaces for vehicles. With IoT technology and mobile applications, in this paper, we propose a design and development of a real smart parking system that can provide more than just information about vacant spaces but also help user to locate the space where the vehicle can be parked in order to reduce traffics in the parking area. Moreover, we use computer vision to detect vehicle plate number in order to monitor the vehicles in the parking area for enhancing security and also to help user find his/her car when he/she forgets where the car is parked. In our system, we also design the payment process using mobile payment in order to reduce time and remove bottleneck of the payment process at the entry/exit gate of the parking area.
Wound debridement: a clinical update.

This article aims to improve nurses' knowledge of wound debridement through a review of different techniques and the related physiology of wound healing. Debridement has long been an established component of effective wound management. However, recent clinical developments have widened the choice of methods available. This article provides an overview of the physiology of wounds, wound bed preparation, methods of debridement and the important considerations for the practitioner in implementing effective, informed and patient-centred wound care.
BITE: Bitcoin Lightweight Client Privacy using Trusted Execution

Decentralized blockchains offer attractive advantages over traditional payments such as the ability to operate without a trusted authority and increased user privacy. However, the verification of blockchain payments requires the user to download and process the entire chain which can be infeasible for resource-constrained devices, such as mobile phones. To address such concerns, most major blockchain systems support lightweight clients that outsource most of the computational and storage burden to full blockchain nodes. However, such payment verification methods leak considerable information about the underlying clients, thus defeating user privacy that is considered one of the main goals of decentralized cryptocurrencies. In this paper, we propose a new approach to protect the privacy of lightweight clients in blockchain systems like Bitcoin. Our main idea is to leverage commonly available trusted execution capabilities, such as SGX enclaves. We design and implement a system called Bite where enclaves on full nodes serve privacy-preserving requests from lightweight clients. As we will show, naive serving of client requests from within SGX enclaves still leaks user information. Bite therefore integrates several privacy preservation measures that address external leakage as well as SGX side-channels. We show that the resulting solution provides strong privacy protection and at the same time improves the performance of current lightweight clients.