nils-herrmann/clean_scirepeval huggingface dataset:
Adjustable bipod flexures for mounting mirrors in a space telescope.

A new mirror mounting technique applicable to the primary mirror in a space telescope is presented. This mounting technique replaces conventional bipod flexures with flexures having mechanical shims so that adjustments can be made to counter the effects of gravitational distortion of the mirror surface while being tested in the horizontal position. Astigmatic aberration due to the gravitational changes is effectively reduced by adjusting the shim thickness, and the relation between the astigmatism and the shim thickness is investigated. We tested the mirror interferometrically at the center of curvature using a null lens. Then we repeated the test after rotating the mirror about its optical axis by 180° in the horizontal setup, and searched for the minimum system error. With the proposed flexure mount, the gravitational stress at the adhesive coupling between the mirror and the mount is reduced by half that of a conventional bipod flexure for better mechanical safety under launch loads. Analytical results using finite element methods are compared with experimental results from the optical interferometer. Vibration tests verified the mechanical safety and optical stability, and qualified their use in space applications.
Dynamic Supernodes in Sparse Cholesky Update/Downdate and Triangular Solves

The supernodal method for sparse Cholesky factorization represents the factor L as a set of supernodes, each consisting of a contiguous set of columns of L with identical nonzero pattern. A conventional supernode is stored as a dense submatrix. While this is suitable for sparse Cholesky factorization where the nonzero pattern of L does not change, it is not suitable for methods that modify a sparse Cholesky factorization after a low-rank change to A (an update/downdate, Ā = A ± WWT). Supernodes merge and split apart during an update/downdate. Dynamic supernodes are introduced which allow a sparse Cholesky update/downdate to obtain performance competitive with conventional supernodal methods. A dynamic supernodal solver is shown to exceed the performance of the conventional (BLAS-based) supernodal method for solving triangular systems. These methods are incorporated into CHOLMOD, a sparse Cholesky factorization and update/downdate package which forms the basis of x = A\b MATLAB when A is sparse and symmetric positive definite.
Touché: enhancing touch interaction on humans, screens, liquids, and everyday objects

Touché proposes a novel Swept Frequency Capacitive Sensing technique that can not only detect a touch event, but also recognize complex configurations of the human hands and body. Such contextual information significantly enhances touch interaction in a broad range of applications, from conventional touchscreens to unique contexts and materials. For example, in our explorations we add touch and gesture sensitivity to the human body and liquids. We demonstrate the rich capabilities of Touché with five example setups from different application domains and conduct experimental studies that show gesture classification accuracies of 99% are achievable with our technology.
"...No one Can Hack My Mind": Comparing Expert and Non-Expert Security Practices

The state of advice given to people today on how to stay safe online has plenty of room for improvement. Too many things are asked of them, which may be unrealistic, time consuming, or not really worth the effort. To improve the security advice, our community must find out what practices people use and what recommendations, if messaged well, are likely to bring the highest benefit while being realistic to ask of people. In this paper, we present the results of a study which aims to identify which practices people do that they consider most important at protecting their security online. We compare self-reported security practices of non-experts to those of security experts (i.e., participants who reported having five or more years of experience working in computer security). We report on the results of two online surveys—one with 231 security experts and one with 294 MTurk participants—on what the practices and attitudes of each group are. Our findings show a discrepancy between the security practices that experts and non-experts report taking. For instance, while experts most frequently report installing software updates, using two-factor authentication and using a password manager to stay safe online, non-experts report using antivirus software, visiting only known websites, and changing passwords frequently.
Job Scheduling in Multiprogrammed Parallel Systems

Scheduling in the context of parallel systems is often thought of in terms of assigning tasks in a program to processors, so as to minimize the makespan. This formulation assumes that the processors are dedicated to the program in question. But when the parallel system is shared by a number of users, this is not necessarily the case. In the context of multiprogrammed parallel machines, scheduling refers to the execution of threads from competing programs. This is an operating system issue, involved with resource allocation, not a program development issue. Scheduling schemes for multiprogrammed parallel systems can be classi ed as one or two leveled. Single-level scheduling combines the allocation of processing power with the decision of which thread will use it. Two level scheduling decouples the two issues: rst, processors are allocated to the job, and then the job's threads are scheduled using this pool of processors. The processors of a parallel system can be shared in two basic ways, which are relevant for both one-level and two-level scheduling. One approach is to use time slicing, e.g. when all the processors in the system (or all the processors in the pool) service a global queue of ready threads. The other approach is to use space slicing, and partition the processors statically or dynamically among the di erent jobs. As these approaches are orthogonal to each other, it is also possible to combine them in various ways; for example, this is often done in gang scheduling. Systems using the various approaches are described, and the implications of the di erent mechanisms are discussed. The goals of this survey are to describe the many di erent approaches within a uni ed framework based on the mechanisms used to achieve multiprogramming, and at the same time document commercial systems that have not been described in the open literature.
Signals and Comonads

We propose a novel discipline for programming stream functions and for the semantic description of stream manipulation languages based on the observation that both general and causal stream functions can be characterized as coKleisli arrows of comonads. This seems to be a promising application for the old, but very little exploited idea that if monads abstract notions of computation of a value, comonads ought to be useable as an abstraction of notions of value in a context. We also show that causal partial-stream functions can be described in terms of a combination of a comonad and a monad.
SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-Grained Recognition

Most convolutional neural networks (CNNs) lack midlevel layers that model semantic parts of objects. This limits CNN-based methods from reaching their full potential in detecting and utilizing small semantic parts in recognition. Introducing such mid-level layers can facilitate the extraction of part-specific features which can be utilized for better recognition performance. This is particularly important in the domain of fine-grained recognition. In this paper, we propose a new CNN architecture that integrates semantic part detection and abstraction (SPDACNN) for fine-grained classification. The proposed network has two sub-networks: one for detection and one for recognition. The detection sub-network has a novel top-down proposal method to generate small semantic part candidates for detection. The classification sub-network introduces novel part layers that extract features from parts detected by the detection sub-network, and combine them for recognition. As a result, the proposed architecture provides an end-to-end network that performs detection, localization of multiple semantic parts, and whole object recognition within one framework that shares the computation of convolutional filters. Our method outperforms state-of-theart methods with a large margin for small parts detection (e.g. our precision of 93.40% vs the best previous precision of 74.00% for detecting the head on CUB-2011). It also compares favorably to the existing state-of-the-art on finegrained classification, e.g. it achieves 85.14% accuracy on CUB-2011.
Folliculitis decalvans: the use of dermatoscopy as an auxiliary tool in
clinical diagnosis*

Folliculitis decalvans is an inflammatory presentation of cicatrizing alopecia characterized by inflammatory perifollicular papules and pustules. It generally occurs in adult males, predominantly involving the vertex and occipital areas of the scalp. The use of dermatoscopy in hair and scalp diseases improves diagnostic accuracy. Some trichoscopic findings, such as follicular tufts, perifollicular erythema, crusts and pustules, can be observed in folliculitis decalvans. More research on the pathogenesis and treatment options of this disfiguring disease is required for improving patient management.
Detecting Deceptive Opinion Spam Using Human Computation

Websites that encourage consumers to research, rate, and review products online have become an increasingly important factor in purchase decisions. This increased importance has been accompanied by a growth in deceptive opinion spam fraudulent reviews written with the intent to sound authentic and mislead consumers. In this study, we pool deceptive reviews solicited through crowdsourcing with actual reviews obtained from product review websites. We then explore several humanand machine-based assessment methods to spot deceptive opinion spam in our pooled review set. We find that the combination of humanbased assessment methods with easily-obtained statistical information generated from the review text outperforms detection methods using human assessors alone.
Nasal Tip Plasty Using Various Techniques in Rhinoplasty

Rhinoplasty is one of the most common aesthetic surgical procedures in Korea today. However, simple augmentation rhinoplasty results often failed to satisfy the high expectations of patients. As a result, many procedures have been developed to improve the appearance of the nasal tip and nasal projection. However, the characteristics of Korean nasal tips including the bulbous appearance (attributable to the thickness of the skin), flared nostrils, and restriction of the nasal tip attributable to an underdeveloped medical crus of the alar cartilage and a short columella have made such procedures difficult. Currently, most plastic surgeons perform rhinoplasty simultaneously with various nasal tip plasty techniques to improve the surgical results. An important part of an aesthetically pleasing result is to ensure an adequate nasal tip positioned slightly higher than the proper dorsum, with the two tip defining points in close proximity to each other, giving the nose a triangular shape from the caudal view. From June 2002 to November 2003, the authors performed rhinoplasty with simultaneous nasal tip plasty using various techniques according to the tip status of 55 patients (25 deviated noses, 9 broad noses, 15 low noses, and 6 secondary cleft lip and nose deformities). The surgery included realignment of alar cartilage by resection and suture, fibroareolar and subcutaneous tissue resection, tip graft, and columellar strut. The postoperative results over an average period of 10 months were entirely satisfactory. There were no patient complaints, nor complications resulting from the procedures. Good nasal tip projection, natural columellar appearance, and improvement of the nasolabial angle were achieved for most patients. In conclusion, rhinoplasty with simultaneous nasal tip plasty, achieved by a variety of techniques according to patients’ tip status, is an effective method for improving the appearance of the nose and satisfying the desires of the patients.
Framework for SCADA cyber-attack dataset creation

Cyber-security research and development for SCADA is being inhibited by the lack of available SCADA attack datasets. This paper presents a modular dataset generation framework for SCADA cyber-attacks, to aid the development of attack datasets. The presented framework is based on requirements derived from related prior research, and is applicable to any standardised or proprietary SCADA protocol. We instantiate our framework and validate the requirements using a Python implementation. This paper provides experiments of the framework's usage on a state-of-the-art DNP3 critical infrastructure test-bed, thus proving framework's ability to generate SCADA cyber-attack datasets.
Hashtag Graph Based Topic Model for Tweet Mining

Mining topics in Twitter is increasingly attracting more attention. However, the shortness and informality of tweets leads to extreme sparse vector representation with a large vocabulary, which makes the conventional topic models (e.g., Latent Dirichlet Allocation) often fail to achieve high quality underlying topics. Luckily, tweets always show up with rich user-generated hash tags as keywords. In this paper, we propose a novel topic model to handle such semi-structured tweets, denoted as Hash tag Graph based Topic Model (HGTM). By utilizing relation information between hash tags in our hash tag graph, HGTM establishes word semantic relations, even if they haven't co-occurred within a specific tweet. In addition, we enhance the dependencies of both multiple words and hash tags via latent variables (topics) modeled by HGTM. We illustrate that the user-contributed hash tags could serve as weakly-supervised information for topic modeling, and hash tag relation could reveal the semantic relation between tweets. Experiments on a real-world twitter data set show that our model provides an effective solution to discover more distinct and coherent topics than the state-of-the-art baselines and has a strong ability to control sparseness and noise in tweets.
Understanding and Predicting Interestingness of Videos

The amount of videos available on the Web is growing explosively. While some videos are very interesting and receive high rating from viewers, many of them are less interesting or even boring. This paper conducts a pilot study on the understanding of human perception of video interestingness, and demonstrates a simple computational method to identify more interesting videos. To this end we first construct two datasets of Flickr and YouTube videos respectively. Human judgements of interestingness are collected and used as the groundtruth for training computational models. We evaluate several off-the-shelf visual and audio features that are potentially useful for predicting interestingness on both datasets. Results indicate that audio and visual features are equally important and the combination of both modalities shows very promising results.
An Efficient Black-box Technique for Defeating Web Application Attacks

Over the past few years, injection vulnerabilities have become the primary target for remote exploits. SQL injection, command injection, and cross-site scripting are some of the popular attacks that exploit these vulnerabilities. Taint-tracking has emerged as one of the most promising approaches for defending against these exploits, as it supports accurate detection (and prevention) of popular injection attacks. However, practical deployment of tainttracking defenses has been hampered by a number of factors, including: (a) high performance overheads (often over 100%), (b) the need for deep instrumentation, which has the potential to impact application robustness and stability, and (c) specificity to the language in which an application is written. In order to overcome these limitations, we present a new technique in this paper called taint inference. This technique does not require any source-code or binary instrumentation of the application to be protected; instead, it operates by intercepting requests and responses from this application. For most web applications, this interception may be achieved using network layer interposition or library interposition. We then develop a class of policies called syntaxand taint-aware policies that can accurately detect and/or block most injection attacks. An experimental evaluation shows that our techniques are effective in detecting a broad range of attacks on applications written in multiple languages (including PHP, Java and C), and impose low performance overheads (below 5%).
Segment Graph Based Image Filtering: Fast Structure-Preserving Smoothing

In this paper, we design a new edge-aware structure, named segment graph, to represent the image and we further develop a novel double weighted average image filter (SGF) based on the segment graph. In our SGF, we use the tree distance on the segment graph to define the internal weight function of the filtering kernel, which enables the filter to smooth out high-contrast details and textures while preserving major image structures very well. While for the external weight function, we introduce a user specified smoothing window to balance the smoothing effects from each node of the segment graph. Moreover, we also set a threshold to adjust the edge-preserving performance. These advantages make the SGF more flexible in various applications and overcome the "halo" and "leak" problems appearing in most of the state-of-the-art approaches. Finally and importantly, we develop a linear algorithm for the implementation of our SGF, which has an O(N) time complexity for both gray-scale and high dimensional images, regardless of the kernel size and the intensity range. Typically, as one of the fastest edge-preserving filters, our CPU implementation achieves 0.15s per megapixel when performing filtering for 3-channel color images. The strength of the proposed filter is demonstrated by various applications, including stereo matching, optical flow, joint depth map upsampling, edge-preserving smoothing, edges detection, image abstraction and texture editing.
Prostate cancer diagnosis using deep learning with 3D multiparametric MRI

A novel deep learning architecture (XmasNet) based on convolutional neural networks was developed for the classification of prostate cancer lesions, using the 3D multiparametric MRI data provided by the PROSTATEx challenge. End-to-end training was performed for XmasNet, with data augmentation done through 3D rotation and slicing, in order to incorporate the 3D information of the lesion. XmasNet outperformed traditional machine learning models based on engineered features, for both train and test data. For the test data, XmasNet outperformed 69 methods from 33 participating groups and achieved the second highest AUC (0.84) in the PROSTATEx challenge. This study shows the great potential of deep learning for cancer imaging.
AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes

We present AutoExtend, a system to learn embeddings for synsets and lexemes. It is flexible in that it can take any word embeddings as input and does not need an additional training corpus. The synset/lexeme embeddings obtained live in the same vector space as the word embeddings. A sparse tensor formalization guarantees efficiency and parallelizability. We use WordNet as a lexical resource, but AutoExtend can be easily applied to other resources like Freebase. AutoExtend achieves state-of-the-art performance on word similarity and word sense disambiguation tasks.
Accurate Viscous Free Surfaces for Buckling, Coiling, and Rotating Liquids

We present a fully implicit Eulerian technique for simulating free surface viscous liquids which eliminates artifacts in previous approaches, efficiently supports variable viscosity, and allows the simulation of more compelling viscous behaviour than previously achieved in graphics. Our method exploits a variational principle which automatically enforces the complex boundary condition on the shear stress at the free surface, while giving rise to a simple discretization with a symmetric positive definite linear system. We demonstrate examples of our technique capturing realistic buckling, folding and coiling behavior. In addition, we explain how to handle domains whose boundary comprises both ghost fluid Dirichlet and variational Neumann parts, allowing correct behaviour at free surfaces and solid walls for both our viscous solve and the variational pressure projection of Batty et al. [BBB07].
Community of inquiry as a theoretical framework to foster "epistemic engagement" and "cognitive presence" in online education

In this paper, several recent theoretical conceptions of technology-mediated education are examined and a study of 2159 online learners is presented. The study validates an instrument designed to measure teaching, social, and cognitive presence indicative of a community of learners within the community of inquiry (CoI) framework [Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a textbased environment: Computer conferencing in higher education. The Internet and Higher Education, 2, 1–19; Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical thinking, cognitive presence, and computer conferencing in distance education.  Results indicate that the survey items cohere into interpretable factors that represent the intended constructs. Further it was determined through structural equation modeling that 70% of the variance in the online students’ levels of cognitive presence, a multivariate measure of learning, can be modeled based on their reports of their instructors’ skills in fostering teaching presence and their own abilities to establish a sense of social presence. Additional analysis identifies more details of the relationship between learner understandings of teaching and social presence and its impact on their cognitive presence. Implications for online teaching, policy, and faculty development are discussed. ! 2008
MRI Brain Image Segmentation based on Thresholding

Medical Image processing is one of the most challenging topics in research field. The main objective of image segmentation is to extract various features of the image that are used for analysing, interpretation and understanding of images. Medical Resonance Image plays a major role in Medical diagnostics. Image processing in MRI of brain is highly essential due to accurate detection of the type of brain abnormality which can reduce the chance of fatal result. This paper outlines an efficient image segmentation technique that can distinguish the pathological tissues such as edema and tumour from the normal tissues such as White Matter (WM), Grey Matter (GM), and Cerebrospinal Fluid (CSF). Thresholding is simpler and most commonly used techniques in image segmentation. This technique can be used to detect the contour of the tumour in brain.
Effect of selected yogic practices on the management of hypertension.

On the basis of medical officers diagnosis, thirty three (N = 33) hypertensives, aged 35-65 years, from Govt. General Hospital, Pondicherry, were examined with four variables viz, systolic and diastolic blood pressure, pulse rate and body weight. The subjects were randomly assigned into three groups. The exp. group-I underwent selected yoga practices, exp. group-II received medical treatment by the physician of the said hospital and the control group did not participate in any of the treatment stimuli. Yoga imparted in the morning and in the evening with 1 hr/session. day-1 for a total period of 11-weeks. Medical treatment comprised drug intake every day for the whole experimental period. The result of pre-post test with ANCOVA revealed that both the treatment stimuli (i.e., yoga and drug) were effective in controlling the variables of hypertension.