nils-herrmann/clean_scirepeval huggingface dataset:
Two-Party Attribute-Based Key Agreement Protocol with Constant-Size Ciphertext and Key

1College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350117, Fujian, China 2College of Computer and Information, Hohai University, Nanjing 211100, Jiangsu, China 3Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, Nanjing University of Posts and Telecommunications, China 4Mathematics and Computer Science Department, Gannan Normal University, Ganzhou 341000, Jiangxi, China
Gender classification with KNN by extraction of Haar wavelet features from canny shape fingerprints

Fingerprints give a lot of information about various factors related to an individual. The main characteristic is that they are unique from person to person in many ways. The size, shape, pattern are some of the uniqueness factors seen, so they are area of research and study. Forensic science makes use of different evidences obtained out of which fingerprints are the one to be considered. Fingerprints play a vital role in getting details through the exact identification. Gender identification can also be done easily and efficiently through the fingerprints. Forensic anthropology has gender identification from fingerprints as an important part in order to identify the gender of a criminal and minimize the list of suspects search. Identification of fingerprints is studied and researched a lot in past and is continuously increasing day by day. The gender identification from fingerprints is carried in both spatial domain and frequency domain by applying different techniques. This paper studies frequency domain methods applied for gender identification from fingerprints. A survey of techniques show that DWT is widely used and also in combination with SVD and PCA for gender identification from fingerprints. An overall comparison of frequency domain techniques mainly focusing on DWT and its combinations is presented in this paper with a proposed canny edge detector and Haar DWT based fingerprint gender classification technique.
Hardware Implementation of a Performance and Energy-optimized Convolutional Neural Network for Seizure Detection

We present for the first time a μW-power convolutional neural network for seizure detection running on a low-power microcontroller. On a dataset of 22 patients a median sensitivity of 100% is achieved. With a false positive rate of 20.7 fp/h and a short detection delay of 3.4 s it is suitable for the application in an implantable closed-loop device.
[Importance-Performance Analysis for services management].

Performance Analysis (IPA) constitutes an indirect approximation to user's satisfaction measurement that allows to represent, in an easy and functional way, the main points and improvement areas of a specific product or service. Beginning from the importance and judgements concerning the performance that users grant to each prominent attributes of a service, it is possible to obtain a graphic divided into four quadrants in which recommendations for the organization economic resources management are included. Nevertheless, this tool has raised controversies since its origins, referred fundamentally to the placement of the axes that define the quadrants and the conception and measurement of the importance of attributes that compose the service. The primary goal of this article is to propose an alternative to the IPA representation that allows to overcome the limitations and contradictions derived from the original technique, without rejecting the classical graph. The analysis applies to data obtained in a survey about satisfaction with primary health care services of Galicia. Results will permit to advise to primary health care managers with a view toward the planning of future strategic actions.
TAX PROGRESSIVITY AND INCOME INEQUALITY

This paper examines whether income inequality is affected by the structural progressivity of national income tax systems. Using detailed personal income tax schedules for a large panel of countries, we develop and estimate comprehensive, time-varying measures of structural progressivity of national income tax systems over the 1981–2005 period. We find that while progressivity reduces observed inequality in reported gross and net income, it has a significantly smaller impact on true inequality, approximated by consumption-based measures of Gini. We show theoretically and empirically that, under specific conditions, tax progressivity may increase actual inequality, especially in countries with weak law and order and a large informal nontaxable sector. The paper discusses implications of these results for increasingly popular flat tax policies. The Kuznets hypothesis is also supported by the estimates.
A Controlled Trial of Selegiline, Alpha-Tocopherol, or Both as Treatment for Alzheimer's Disease

A BSTRACT Background There is evidence that medications or vitamins that increase the levels of brain catecholamines and protect against oxidative damage may reduce the neuronal damage and slow the progression of Alzheimer’s disease.  We conducted a double-blind, placebocontrolled, randomized, multicenter trial in patients with Alzheimer’s disease of moderate severity. A total of 341 patients received the selective monoamine oxidase inhibitor selegiline (10 mg a day), alphatocopherol (vitamin E, 2000 IU a day), both selegiline and alpha-tocopherol, or placebo for two years. The primary outcome was the time to the occurrence of any of the following: death, institutionalization, loss of the ability to perform basic activities of daily living, or severe dementia (defined as a Clinical Dementia Rating of 3).  Despite random assignment, the baseline score on the Mini–Mental State Examination was higher in the placebo group than in the other three groups, and this variable was highly predictive of the primary outcome (P 0.001). In the unadjusted analyses, there was no statistically significant difference in the outcomes among the four groups. In analyses that included the base-line score on the Mini–Mental State Examination as a covariate, there were significant delays in the time to the primary outcome for the patients treated with selegiline (median time, 655 days; P 0.012), alpha-tocopherol (670 days, P 0.001), or combination therapy (585 days, P 0.049), as compared with the placebo group (440 days).  In patients with moderately severe impairment from Alzheimer’s disease, treatment with selegiline or alpha-tocopherol slows the progression of disease. ( 1997;336:1216-22.
Seeing Through The Same Lens: Introspecting Guest Address Space At Native Speed

Software-based MMU emulation lies at the heart of outof-VM live memory introspection, an important technique in the cloud setting that applications such as live forensics and intrusion detection depend on. Due to the emulation, the software-based approach is much slower compared to native memory access by the guest VM. The slowness not only results in undetected transient malicious behavior, but also inconsistent memory view with the guest; both undermine the effectiveness of introspection. We propose the immersive execution environment (ImEE) with which the guest memory is accessed at native speed without any emulation. Meanwhile, the address mappings used within the ImEE are ensured to be consistent with the guest throughout the introspection session. We have implemented a prototype of the ImEE on Linux KVM. The experiment results show that ImEE-based introspection enjoys a remarkable speed up, performing several hundred times faster than the legacy method. Hence, this design is especially useful for realtime monitoring, incident response and high-intensity introspection.
A Linear Time Algorithm for Computing Exact Euclidean Distance Transforms of Binary Images in Arbitrary Dimensions

A sequential algorithm is presented for computing the exact Euclidean distance transform (DT) of a k-dimensional binary image in time linear in the total number of voxelsN. The algorithm, which is based on dimensionality reduction and partial Voronoi diagram construction, can be used for computing the DT for a wide class of distance functions, including the Lp and chamfer metrics. At each dimension level, the DT is computed by constructing the intersection of the Voronoi diagram whose sites are the feature voxels with each row of the image. This construction is performed efficiently by using the DT in the next lower dimension. The correctness and linear time complexity are demonstrated analytically and verified experimentally. The algorithm may be of practical value since it is relatively simple and easy to implement and it is relatively fast (not only does it run in OðNÞ time but the time constant is small). A simple modification of the algorithm computes the weighted Euclidean DT, which is useful for images with anisotropic voxel dimensions. A parallel version of the algorithm runs in OðN=pÞ time with
A process of knowledge discovery from web log data: Systematization and critical review

This paper presents a comprehensive survey of web log/usage mining based on over 100 research papers. This is the first survey dedicated exclusively to web log/usage mining. The paper identifies several web log mining sub-topics including specific ones such as data cleaning, user and session identification. Each sub-topic is explained, weaknesses and strong points are discussed and possible solutions are presented. The paper describes examples of web log mining and lists some major web log mining software packages.
Single-View Sketch Based Modeling

This paper presents a new sketch modeling system that is able to generate complex objects drawn from a unique viewpoint. The user draws the model in an iterative manner, adding simple parts to the existing object until completion. Each part is constructed from two construction lines (lines on the surface of the object that are planar and perpendicular to each other) whose orientation in the 3D space is uniquely determined by the system, and an optional silhouette. The system is then able to produce rough 3D reconstructions of drawings very easily by tracing over a sketch for example. Such models are perfectly suited to investigate their shade or shadow and they can be used as substitutes for more detailed models when the need for quick models is present. The user can also explore shapes directly on the system, refining the shape on the go in a oversketching way. The creation of models is very efficient, as the user models the shapes directly in the correct pose and orientation. Results show that the system is able to create complex objects without ever having to change the viewpoint.
Fractional Order PD Path-Following Control of an AR. Drone Quadrotor

In this paper a fractional order proportional-derivative (FO-PD) control strategy is presented and applied to AR. Drone quadrotor system. The controller parameters are calculated based on specifying a certain gain crossover frequency, a phase margin and a robustness to gain variations. Its performance is compared against two other integer order controllers; i) Extended Prediction Self-Adaptive Control (EPSAC) approach to Model Predictive Control (MPC) ii) Integer order PD controller. The closed loop control simulations applied on the AR. Drone system indicate the proposed controller outperforms the integer order PD control. Additionally, the proposed controller has less complexity but similar performance as MPC based control.
Antifungal Activity in Ethanolic Extracts of Carica papaya L. cv. Maradol Leaves and Seeds

Bioactive compounds from vegetal sources are a potential source of natural antifungic. An ethanol extraction was used to obtain bioactive compounds from Carica papaya L. cv. Maradol leaves and seeds of discarded ripe and unripe fruit. Both, extraction time and the papaya tissue flour:organic solvent ratio significantly affected yield, with the longest time and highest flour:solvent ratio producing the highest yield. The effect of time on extraction efficiency was confirmed by qualitative identification of the compounds present in the lowest and highest yield extracts. Analysis of the leaf extract with phytochemical tests showed the presence of alkaloids, flavonoids and terpenes. Antifungal effectiveness was determined by challenging the extracts (LE, SRE, SUE) from the best extraction treatment against three phytopathogenic fungi: Rhizopus stolonifer, Fusarium spp. and Colletotrichum gloeosporioides. The leaf extract exhibited the broadest action spectrum. The MIC50 for the leaf extract was 0.625 mg ml−1 for Fusarium spp. and >10 mg ml−1 for C. gloeosporioides, both equal to approximately 20% mycelial growth inhibition. Ethanolic extracts from Carica papaya L. cv. Maradol leaves are a potential source of secondary metabolites with antifungal properties.
Computer-Based Cognitive Training Versus Paper and Pencil Training : Which is more Effective ? A Randomized Controlled Trial in People with Mild Cognitive Impairment

Traditional Cognitive Training with paper-pencil tasks (PPCT) and Computer-Based Cognitive Training (C-BCT) both are effective for people with Mild Cognitive Impairment (MCI). The aim of this study is to evaluate the efficacy of a C-BCT program versus a PPCT one.  One hundred and twenty four (n=124) people with amnesic & multiple domains MCI (aMCImd) diagnosis were randomly assigned in two groups, a PPCT group (n=65), and a C-BCT (n=59). The groups were matched at baseline in age, gender, education, cognitive and functional performance. Both groups attended 48 weekly 1-hour sessions of attention and executive function training for 12 months. Neuropsychological assessment was performed at baseline and 12 months later.  At the follow up, the PPCT group was better than the C-BCT group in visual selective attention (p≤ 0.022). The C-BCT group showed improvement in working memory (p=0.042) and in speed of switching of attention (p=0.012), while the PPCT group showed improvement in general cognitive function (p=0.005), learning ability (p=0.000), delayed verbal recall (p=0.000), visual perception (p=0.013) and visual memory (p=0.000), verbal fluency (p=0.000), visual selective attention (p=0.021), speed of switching of attention (p=0.001), visual selective attention/multiple choices (p=0.010) and Activities of Daily Living (ADL) as well (p=0.001).  Both C-BCT and PPCT are beneficial for people with aMCImd concerning cognitive functions. However, the administration of a traditional PPCT program seems to affect a greater range of cognitive abilities and transfer the primary cognitive benefit in real life.
"They're all going out to something weird": Workflow, Legacy and Metadata in the Music Production Process

In this paper we use results from two ethnographic studies of the music production process to examine some key issues regarding how work is currently accomplished in studio production environments. These issues relate in particular to workflows and how metadata is adapted to the specific needs of specific parts of the process. We find that there can be significant tensions between how reasoning is applied to metadata at different stages of production and that this can lead to overheads where metadata has to be either changed or created anew to make the process work. On the basis of these findings we articulate some of the potential solutions we are now examining. These centre in particular upon the notions of Digital/Dynamic Musical Objects and flexible metadata shells.
Body contouring using 635-nm low level laser therapy.

Noninvasive body contouring has become one of the fastest-growing areas of esthetic medicine. Many patients appear to prefer nonsurgical less-invasive procedures owing to the benefits of fewer side effects and shorter recovery times. Increasingly, 635-nm low-level laser therapy (LLLT) has been used in the treatment of a variety of medical conditions and has been shown to improve wound healing, reduce edema, and relieve acute pain. Within the past decade, LLLT has also emerged as a new modality for noninvasive body contouring. Research has shown that LLLT is effective in reducing overall body circumference measurements of specifically treated regions, including the hips, waist, thighs, and upper arms, with recent studies demonstrating the long-term effectiveness of results. The treatment is painless, and there appears to be no adverse events associated with LLLT. The mechanism of action of LLLT in body contouring is believed to stem from photoactivation of cytochrome c oxidase within hypertrophic adipocytes, which, in turn, affects intracellular secondary cascades, resulting in the formation of transitory pores within the adipocytes' membrane. The secondary cascades involved may include, but are not limited to, activation of cytosolic lipase and nitric oxide. Newly formed pores release intracellular lipids, which are further metabolized. Future studies need to fully outline the cellular and systemic effects of LLLT as well as determine optimal treatment protocols.
Analyzing Skin Lesions in Dermoscopy Images Using Convolutional Neural Networks

In this paper, we discuss the problem of automatic skin lesion analysis, specifically melanoma detection and semantic segmentation. We accomplish this by using deep learning techniques to perform classification on publicly available dermoscopic images. Skin cancer, of which melanoma is a type, is the most prevalent form of cancer in the US and more than four million cases are diagnosed in the US every year. In this work, we present our efforts towards an accessible, deep learning-based system that can be used for skin lesion classification, thus leading to an improved melanoma screening system. For classification, a deep convolutional neural network architecture is first implemented over the raw images. In addition, hand-coded features such as 166-D color histogram distribution, edge histogram and Multiscale Color local binary patterns are extracted from the images and presented to a random forest classifier. The average of the outputs from the two mentioned classifiers is taken as the final classification result. The classification task achieves an accuracy of 80.3%, AUC score of 0.69 and a precision score of 0.81. For segmentation, we implement a convolutional-deconvolutional architecture and the segmentation model achieves a Dice coefficient of 73.5%.
Eigenspace-based anomaly detection in computer systems

We report on an automated runtime anomaly detection method at the application layer of multi-node computer systems. Although several network management systems are available in the market, none of them have sufficient capabilities to detect faults in multi-tier Web-based systems with redundancy. We model a Web-based system as a weighted graph, where each node represents a "service" and each edge represents a dependency between services. Since the edge weights vary greatly over time, the problem we address is that of anomaly detection from a time sequence of graphs.In our method, we first extract a feature vector from the adjacency matrix that represents the activities of all of the services. The heart of our method is to use the principal eigenvector of the eigenclusters of the graph. Then we derive a probability distribution for an anomaly measure defined for a time-series of directional data derived from the graph sequence. Given a critical probability, the threshold value is adaptively updated using a novel online algorithm.We demonstrate that a fault in a Web application can be automatically detected and the faulty services are identified without using detailed knowledge of the behavior of the system.
An Exploratory Study on Issues and Challenges of Agile Software Development with Scrum

An Exploratory Study on Issues and Challenges of Agile Software Develop m nt with Scrum by Juyun Joey Cho, Doctor of Philosophy Utah State University, 2010 Major Professor: Dr. David H. Olsen Department: Management Information Systems The purpose of this dissertation was to explore critical issues and challenges t hat might arise in agile software development processes with Scrum. It a lso sought to provide management guidelines to help organizations avoid and overcome barriers in adopting the Scrum method as a future software development method. A qualitative researc h method design was used to capture the knowledge of practitioners and scrutinize the Scrum software development process in its natural settings. An in-depth case s tudy was conducted in two organizations where the Scrum method was fully integrated in every aspect of two organizations’ software development processes. One organizat ion provides large-scale and mission-critical applications and the other provides smalland mediumscale applications. Differences between two organizations provided useful c ontrasts for the data analysis. Data were collected through an email survey, observations, documents, and semistructured face-to-face interviews. The email survey was used to re fine interview
Centrality measures in spatial networks of urban streets.

We study centrality in urban street patterns of different world cities represented as networks in geographical space. The results indicate that a spatial analysis based on a set of four centrality indices allows an extended visualization and characterization of the city structure. A hierarchical clustering analysis based on the distributions of centrality has a certain capacity to distinguish different classes of cities. In particular, self-organized cities exhibit scale-free properties similar to those found in nonspatial networks, while planned cities do not.
Subtyping of Breast Cancer by Immunohistochemistry to Investigate a Relationship between Subtype and Short and Long Term Survival: A Collaborative Analysis of Data for 10,159 Cases from 12 Studies

Immunohistochemical markers are often used to classify breast cancer into subtypes that are biologically distinct and behave differently. The aim of this study was to estimate mortality for patients with the major subtypes of breast cancer as classified using five immunohistochemical markers, to investigate patterns of mortality over time, and to test for heterogeneity by subtype.



We pooled data from more than 10,000 cases of invasive breast cancer from 12 studies that had collected information on hormone receptor status, human epidermal growth factor receptor-2 (HER2) status, and at least one basal marker (cytokeratin [CK]5/6 or epidermal growth factor receptor [EGFR]) together with survival time data. Tumours were classified as luminal and nonluminal tumours according to hormone receptor expression. These two groups were further subdivided according to expression of HER2, and finally, the luminal and nonluminal HER2-negative tumours were categorised according to expression of basal markers. Changes in mortality rates over time differed by subtype. In women with luminal HER2-negative subtypes, mortality rates were constant over time, whereas mortality rates associated with the luminal HER2-positive and nonluminal subtypes tended to peak within 5 y of diagnosis and then decline over time. In the first 5 y after diagnosis the nonluminal tumours were associated with a poorer prognosis, but over longer follow-up times the prognosis was poorer in the luminal subtypes, with the worst prognosis at 15 y being in the luminal HER2-positive tumours. Basal marker expression distinguished the HER2-negative luminal and nonluminal tumours into different subtypes. These patterns were independent of any systemic adjuvant therapy.



The six subtypes of breast cancer defined by expression of five markers show distinct behaviours with important differences in short term and long term prognosis. Application of these markers in the clinical setting could have the potential to improve the targeting of adjuvant chemotherapy to those most likely to benefit. The different patterns of mortality over time also suggest important biological differences between the subtypes that may result in differences in response to specific therapies, and that stratification of breast cancers by clinically relevant subtypes in clinical trials is urgently required.