nils-herrmann/clean_scirepeval huggingface dataset:
The amputee mobility predictor: an instrument to assess determinants of the lower-limb amputee's ability to ambulate.

To describe the development of the Amputee Mobility Predictor (AMP) instrument designed to measure ambulatory potential of lower-limb amputees with (AMPPRO) and without (AMPnoPRO) the use of a prosthesis, and to test its reliability and validity.



Measurement study using known groups method and concurrence with existing measures.



Academic medical center.



A convenience sample of 191 lower-limb amputee subjects who had completed prosthetic training, 24 in the reliability study (mean age +/- standard deviation, 68.3+/-17.9y, range, 28-99y) and 167 in the validity study (mean age, 54.8+/-18.6y; range, 18-100y).



Not applicable.



Intra- and interrater reliability; construct validity by known groups method; concurrent validity by comparisons with 6-minute walk test, Comorbidity Index, age, and time since amputation; predictive validity by comparison with 6-minute walk test after controlling for other factors.



Interrater reliability was.99 for subjects tested with and without their prosthesis; intrarater reliability was.96 and.97. Both the AMPnoPRO (P<.0001) and the AMPPRO scores (P<.0001) distinguished among the 4 Medicare functional classification levels. The AMP correlated strongly with 6-minute walk scores (AMPnoPRO r=.69, P<.0001; AMPPRO r=.82, P<.0001) and the amputee activity survey (AMPnoPRO r=.67, P<.0001; AMPPRO r=.77, P<.0001), and negatively correlated with age (AMPnoPRO r=-.69, P<.0001; AMPPRO r=.56, P<.0001) and comorbidity (AMPnoPRO r=-.43, P<.0001; AMPPRO r=.38, P<.0001).



The AMP with and without a prosthesis are reliable and valid measures for the assessment of functional ambulation in lower-limb amputee subjects.
Sturdy childhood predictors of adult antisocial behaviour: replications from longitudinal studies.

Results are compared in studies of 4 male cohorts - 1 all white, 1 all black, and 2 racially representative of the population - growing up in different eras, followed past varying portions of their adult lives, living in different parts of the US. Despite sample differences and differences in sources of information and in the variables used to measure both childhood predictors and adult outcomes, some striking replications appear with respect to childhood predictors of adult antisocial behaviour. All types of antisocial behaviour in childhood predict a high level of antisocial behaviour in adulthood and each kind of adult antisocial behaviour is predicted by the number of childhood antisocial behaviours, indicating that adult and childhood antisocial behaviour both form syndromes and that these syndromes are closely interconnected. Also confirmed across studies are: (1) adult antisocial behaviour virtually requires childhood antisocial behaviour; (2) most antisocial children do not become antisocial adults; (3) the variety of antisocial behaviour in childhood is a better predictor of adult antisocial behaviour than is any particular behaviour; (4) adult antisocial behaviour is better predicted by childhood behaviour than by family background or social class of rearing; (5) social class makes little contribution to the prediction of serious adult antisocial behaviour.
Convolutional neural networks for SAR image segmentation

Segmentation of Synthetic Aperture Radar (SAR) images has several uses, but it is a difficult task due to a number of properties related to SAR images. In this article we show how Convolutional Neural Networks (CNNs) can easily be trained for SAR image segmentation with good results. Besides this contribution we also suggest a new way to do pixel wise annotation of SAR images that replaces a human expert manual segmentation process, which is both slow and troublesome. Our method for annotation relies on 3D CAD models of objects and scene, and converts these to labels for all pixels in a SAR image. Our algorithms are evaluated on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset which was released by the Defence Advanced Research Projects Agency during the 1990s. The method is not restricted to the type of targets imaged in MSTAR but can easily be extended to any SAR data where prior information about scene geometries can be estimated.
Machine learning based malware classification for Android applications using multimodal image representations

The popularity of smartphones usage especially Android mobile platform has increased to 80% of share in global smartphone operating systems market, as a result of which it is on the top in the attacker's target list. The fact of having more private data and low security assurance letting the attacker to write several malware programs in different ways for smartphone, and the possibility of obfuscating the malware detection applications through different coding techniques is giving more energy to attacker. Several approaches have been proposed to detect malwares through code analysis which are now severely facing the problem of code obfuscation and high computation requirement. We propose a machine learning based method to detect android malware by analyzing the visual representation of binary formatted apk file into Grayscale, RGB, CMYK and HSL. GIST feature from malware and benign image dataset were extracted and used to train machine learning algorithms. Initial experimental results are encouraging and computationally effective. Among machine learning algorithms Random Forest have achieved highest accuracy of 91% for grayscale image, which can be further improved by tuning the various parameters.
Misinformation and Its Correction: Continued Influence and Successful Debiasing.

The widespread prevalence and persistence of misinformation in contemporary societies, such as the false belief that there is a link between childhood vaccinations and autism, is a matter of public concern. For example, the myths surrounding vaccinations, which prompted some parents to withhold immunization from their children, have led to a marked increase in vaccine-preventable disease, as well as unnecessary public expenditure on research and public-information campaigns aimed at rectifying the situation. We first examine the mechanisms by which such misinformation is disseminated in society, both inadvertently and purposely. Misinformation can originate from rumors but also from works of fiction, governments and politicians, and vested interests. Moreover, changes in the media landscape, including the arrival of the Internet, have fundamentally influenced the ways in which information is communicated and misinformation is spread. We next move to misinformation at the level of the individual, and review the cognitive factors that often render misinformation resistant to correction. We consider how people assess the truth of statements and what makes people believe certain things but not others. We look at people's memory for misinformation and answer the questions of why retractions of misinformation are so ineffective in memory updating and why efforts to retract misinformation can even backfire and, ironically, increase misbelief. Though ideology and personal worldviews can be major obstacles for debiasing, there nonetheless are a number of effective techniques for reducing the impact of misinformation, and we pay special attention to these factors that aid in debiasing. We conclude by providing specific recommendations for the debunking of misinformation. These recommendations pertain to the ways in which corrections should be designed, structured, and applied in order to maximize their impact. Grounded in cognitive psychological theory, these recommendations may help practitioners-including journalists, health professionals, educators, and science communicators-design effective misinformation retractions, educational tools, and public-information campaigns.
Reference table based k-anonymous private blocking

Privacy Preserving Record Linkage is an emerging field of research which attempts to deal with the classical linkage problem from a privacy preserving point of view. In this paper we propose a novel approach for performing Privacy Preserving Blocking in order to minimize the computational cost of Privacy Preserving Record Linkage. We achieve this without compromising privacy by using Nearest Neighbors clustering, a well-known clustering algorithm and by using a reference table. A reference table is a publicly known table the contents of which are used as intermediate references. The combination of Nearest Neighbors and a reference table offers our approach k-anonymity characteristics.
Ten Simple Rules for Developing Usable Software in Computational Biology

The rise of high-throughput technologies in molecular biology has led to a massive amount of publicly available data. While computational method development has been a cornerstone of biomedical research for decades, the rapid technological progress in the wet lab makes it difficult for software development to keep pace. Wet lab scientists rely heavily on computational methods, especially since more research is now performed in silico. However, suitable tools do not always exist, and not everyone has the skills to write complex software. Computational biologists are required to close this gap, but they often lack formal training in software engineering. To alleviate this, several related challenges have been previously addressed in the Ten Simple Rules series, including reproducibility , effectiveness [2], and open-source development of software [3, 4]. Here, we want to shed light on issues concerning software usability. Usability is commonly defined as “a measure of interface quality that refers to the effectiveness, efficiency, and satisfaction with which users can perform tasks with a tool” . Considering the subjective nature of this topic, a broad consensus may be hard to achieve. Nevertheless, good usability is imperative for achieving wide acceptance of a software tool in the community. In many cases, academic software starts out as a prototype that solves one specific task and is not geared for a larger user group. As soon as the developer realizes that the complexity of the problems solved by the software could make it widely applicable, the software will grow to meet the new demands. At least by this point, if not sooner, usability should become a priority. Unfortunately, efforts in scientific software development are constrained by limited funding, time, and rapid turnover of group members. As a result, scientific software is often poorly documented, non-intuitive, non-robust with regards to input data and parameters, and hard to install. For many use cases, there is a plethora of tools that appear very similar and make it difficult for the user to select the one that best fits their needs. Not surprisingly, a substantial fraction of these tools are probably abandonware; i.e., these are no longer actively developed or supported in spite of their potential value to the scientific community. To our knowledge, software development as part of scientific research is usually carried out by individuals or small teams with no more than two or three members. Hence, the responsibility of designing, implementing, testing, and documenting the code rests on few shoulders. Additionally, there is pressure to produce publishable results or, at least, to contribute analysis work to ongoing projects. Consequently, academic software is typically released as a prototype. We acknowledge that such a tool cannot adhere to and should not be judged by the standards
Identifying HTTPS-Protected Netflix Videos in Real-Time

After more than a year of research and development, Netflix recently upgraded their infrastructure to provide HTTPS encryption of video streams in order to protect the privacy of their viewers. Despite this upgrade, we demonstrate that it is possible to accurately identify Netflix videos from passive traffic capture in real-time with very limited hardware requirements. Specifically, we developed a system that can report the Netflix video being delivered by a TCP connection using only the information provided by TCP/IP headers. To support our analysis, we created a fingerprint database comprised of 42,027 Netflix videos. Given this collection of fingerprints, we show that our system can differentiate between videos with greater than 99.99% accuracy. Moreover, when tested against 200 random 20-minute video streams, our system identified 99.5% of the videos with the majority of the identifications occurring less than two and a half minutes into the video stream.
Improved Consistent Sampling, Weighted Minhash and L1 Sketching

We propose a new Consistent Weighted Sampling method, where the probability of drawing identical samples for a pair of inputs is equal to their Jaccard similarity. Our method takes deterministic constant time per non-zero weight, improving on the best previous approach which takes expected constant time. The samples can be used as Weighted Minhash for efficient retrieval and compression (sketching) under Jaccard or L1 metric. A method is presented for using simple data statistics to reduce the running time of hash computation by two orders of magnitude. We compare our method with the random projection method and show that it has better characteristics for retrieval under L1. We present a novel method of mapping hashes to short bit-strings, apply it to Weighted Minhash, and achieve more accurate distance estimates from sketches than existing methods, as long as the inputs are sufficiently distinct. We show how to choose the optimal number of bits per hash for sketching, and demonstrate experimental results which agree with the theoretical analysis.
Polyhedral Extraction Tool

We present a new library for extracting a polyhedral model from C source. The library is based on clang, the LLVM C frontend, and isl, a library for manipulating quasi-affine sets and relations. The use of clang for parsing the C code brings advanced diagnostics and full support for C99. The use of isl allows for an easy construction and a powerful and compact representation of the polyhedral model. Besides allowing arbitrary piecewise quasi-affine index expressions and conditions, the library also supports some data dependent constructs and has special treatment for unsigned integers. The library has been successfully used to obtain polyhedral models for use in an equivalence checker, a tool for constructing polyhedral process networks, a parallelizer targeting GPUs and an interactive polyhedral environment.
Eyewitness Identification Procedures : Recommendations for Lineups and Photospreads

There is increasing evidence that false eyewitness identification is the primary cause of the conviction of innocent people. In 1996, the American Psychology/Law Society and Division 41 of the American Psychological Association appointed a subcommittee to review scientific evidence and make recommendations regarding the best procedures for constructing and conducting lineups and photospreads. Three important themes from the scientific literature relevant to lineup methods were identified and reviewed, namely relative-judgment processes, the lineups-as-experiments analogy, and confidence malleability. Recommendations are made that double-blind lineup testing should be used, that eyewitnesses should be forewarned that the culprit might not be present, that distractors should be selected based on the eyewitness’s verbal description of the perpetrator, and that confidence should be assessed and recorded at the time of identification. The potential costs and benefits of these recommendations are discussed.
How to separate between Machine-Printed/Handwritten and Arabic/Latin Words?

This paper gathers some contributions to script and its nature identification. Different sets of features have been employed successfully for discriminating between handwritten and machine-printed Arabic and Latin scripts. They include some well established features, previously used in the literature, and new structural features which are intrinsic to Arabic and Latin scripts. The performance of such features is studied towards this paper. We also compared the performance of five classifiers: Bayes (AODEsr), k-Nearest Neighbor (k-NN), Decision Tree (J48), Support Vector Machine (SVM) and Multilayer perceptron (MLP) used to identify the script at word level. These classifiers have been chosen enough different to test the feature contributions. Experiments have been conducted with handwritten and machine-printed words, covering a wide range of fonts. Experimental results show the capability of the proposed features to capture differences between scripts and the effectiveness of the three classifiers. An average identification precision and recall rates of 98.72% was achieved, using a set of 58 features and AODEsr classifier, which is slightly better than those reported in similar works.
A Provably-Secure ECC-Based Authentication Scheme for Wireless Sensor Networks

A smart-card-based user authentication scheme for wireless sensor networks (in short, a SUA-WSN scheme) is designed to restrict access to the sensor data only to users who are in possession of both a smart card and the corresponding password. While a significant number of SUA-WSN schemes have been suggested in recent years, their intended security properties lack formal definitions and proofs in a widely-accepted model. One consequence is that SUA-WSN schemes insecure against various attacks have proliferated. In this paper, we devise a security model for the analysis of SUA-WSN schemes by extending the widely-accepted model of Bellare, Pointcheval and Rogaway (2000). Our model provides formal definitions of authenticated key exchange and user anonymity while capturing side-channel attacks, as well as other common attacks. We also propose a new SUA-WSN scheme based on elliptic curve cryptography (ECC), and prove its security properties in our extended model. To the best of our knowledge, our proposed scheme is the first SUA-WSN scheme that provably achieves both authenticated key exchange and user anonymity. Our scheme is also computationally competitive with other ECC-based (non-provably secure) schemes.
Service-Oriented E-Learning Platforms: From Monolithic Systems to Flexible Services

The learning management system (LMS) has dominated Internet-based education for the past two decades. However, the traditional LMS is failing to keep pace with advances in Internet technologies and social interactions online. To support technological diversity, current frameworks such as the e-learning framework (ELF), the IMS abstract framework, and the open knowledge initiative (OKI) have defined the initial steps toward service-oriented e-learning platforms. Next-generation platforms will be based on these service-oriented visions. The authors discuss LMS evolution and present core challenges that must be addressed to achieve information interoperability in next-generation e-learning platforms
Inducing a Lexicon of Abusive Words - a Feature-Based Approach

We address the detection of abusive words. The task is to identify such words among a set of negative polar expressions. We propose novel features employing information from both corpora and lexical resources. These features are calibrated on a small manually annotated base lexicon which we use to produce a large lexicon. We show that the word-level information we learn cannot be equally derived from a large dataset of annotated microposts. We demonstrate the effectiveness of our (domain-independent) lexicon in the crossdomain detection of abusive microposts.
Accumulators with Applications to Anonymity-Preserving Revocation

Membership revocation is essential for cryptographic applications, from traditional PKIs to group signatures and anonymous credentials. Of the various solutions for the revocation problem that have been explored, dynamic accumulators are one of the most promising. We propose Braavos, a new, RSA-based, dynamic accumulator. It has optimal communication complexity and, when combined with efficient zero-knowledge proofs, provides an ideal solution for anonymous revocation. For the construction of Braavos we use a modular approach: we show how to build an accumulator with better functionality and security from accumulators with fewer features and weaker security guarantees. We then describe an anonymous revocation component (ARC) that can be instantiated using any dynamic accumulator. ARC can be added to any anonymous system, such as anonymous credentials or group signatures, in order to equip it with a revocation functionality. Finally, we implement ARC with Braavos and plug it into Idemix, the leading implementation of anonymous credentials. This work resolves, for the first time, the problem of practical revocation for anonymous credential systems.
BANNER: An Executable Survey of Advances in Biomedical Named Entity Recognition

There has been an increasing amount of research on biomedical named entity recognition, the most basic text extraction problem, resulting in significant progress by different research teams around the world. This has created a need for a freely-available, open source system implementing the advances described in the literature. In this paper we present BANNER, an open-source, executable survey of advances in biomedical named entity recognition, intended to serve as a benchmark for the field. BANNER is implemented in Java as a machine-learning system based on conditional random fields and includes a wide survey of the best techniques recently described in the literature. It is designed to maximize domain independence by not employing brittle semantic features or rule-based processing steps, and achieves significantly better performance than existing baseline systems. It is therefore useful to developers as an extensible NER implementation, to researchers as a standard for comparing innovative techniques, and to biologists requiring the ability to find novel entities in large amounts of text.
A flexible architecture integrating monitoring and analytics for managing large-scale data centers

To effectively manage large-scale data centers and utility clouds, operators must understand current system and application behaviors. This requires continuous, real-time monitoring along with on-line analysis of the data captured by the monitoring system, i.e., integrated monitoring and analytics -- Monalytics [28]. A key challenge with such integration is to balance the costs incurred and associated delays, against the benefits attained from identifying and reacting to, in a timely fashion, undesirable or non-performing system states. This paper presents a novel, flexible architecture for Monalytics in which such trade-offs are easily made by dynamically constructing software overlays called Distributed Computation Graphs (DCGs) to implement desired analytics functions. The prototype of Monalytics implementing this flexible architecture is evaluated with motivating use cases in small scale data center experiments, and a series of analytical models is used to understand the above trade-offs at large scales. Results show that the approach provides the flexibility needed to meet the demands of autonomic management at large scale with considerably better performance/cost than traditional and brute force solutions.