nils-herrmann/clean_scirepeval huggingface dataset:
Accuracy of Clinical Judgment of the Chin-Down Posture for Dysphagia During the Clinical/Bedside Assessment as Corroborated by Videofluoroscopy in Adults with Acute Stroke

Speech-language pathologists tend to rely on the noninstrumental swallowing evaluation in making recommendations about a patient’s diet and management plan. The present study was designed to examine the sensitivity and specificity of the accuracy of using the chin-down posture during the clinical/bedside swallowing assessment. In 15 patients with acute stroke and clinically suspected oropharyngeal dysphagia, the correlation between clinical and videofluoroscopic findings was examined. Results identified that there is a difference in outcome prediction using the chin-down posture during the clinical/bedside assessment of swallowing compared to assessment by videofluoroscopy. Results are discussed relative to statistical and clinical perspectives, including site of lesion and factors to be considered in the design of an overall treatment plan for a patient with disordered swallowing.
Living amidst large wildlife: livestock and crop depredation by large mammals in the interior villages of Bhadra Tiger Reserve, South India.

Conflict with humans over livestock and crops seriously undermines the conservation prospects of India's large and potentially dangerous mammals such as the tiger (Panthera tigris) and elephant (Elephas maximus). This study, carried out in Bhadra Tiger Reserve in south India, estimates the extent of material and monetary loss incurred by resident villagers between 1996 and 1999 in conflicts with large felines and elephants, describes the spatiotemporal patterns of animal damage, and evaluates the success of compensation schemes that have formed the mainstay of loss-alleviation measures. Annually each household lost an estimated 12% (0.9 head) of their total holding to large felines, and approximately 11% of their annual grain production (0.82 tonnes per family) to elephants. Compensations awarded offset only 5% of the livestock loss and 14% of crop losses and were accompanied by protracted delays in the processing of claims. Although the compensation scheme has largely failed to achieve its objective of alleviating loss, its implementation requires urgent improvement if reprisal against large wild mammals is to be minimized. Furthermore, innovative schemes of livestock and crop insurance need to be tested as alternatives to compensations.
Forward-Looking Synthetic Aperture Radar (FLoSAR): The Array Approach

We consider a system aimed at improving the resolution of a conventional airborne radar, looking in the forward direction, by forming an end-fire synthetic array along the airplane line of flight. The system is designed to operate even in slant (non-horizontal) flight trajectories, and it allows imaging along the line of flight. By using the array theory, we analyze system geometry and ambiguity problems, and analytically evaluate the achievable resolution and the required pulse repetition frequency. Processing computational burden is also analyzed, and finally some simulation results are provided.
Performance Anomaly Detection and Resolution for Autonomous Clouds

Fundamental properties of cloud computing such as resource sharing and on-demand self-servicing is driving a growing adoption of the cloud for hosting both legacy and new application services. A consequence of this growth is that the increasing scale and complexity of the underlying cloud infrastructure as well as the fluctuating service workloads is inducing performance incidents at a higher frequency than ever before with far-reaching impact on revenue, reliability, and reputation. Hence, effectively managing performance incidents with emphasis on timely detection, diagnosis and resolution has thus become a necessity rather than luxury. While other aspects of cloud management such as monitoring and resource management are experiencing greater automation, automated management of performance incidents remains a major concern. Given the volume of operational data produced by cloud datacenters and services, this thesis focus on how data analytics techniques can be used in the aspect of cloud performance management. In particular, this work investigates techniques and models for automated performance anomaly detection and prevention in cloud environments. To familiarize with developments in the research area, we present the outcome of an extensive survey of existing research contributions addressing various aspects of performance problem management in diverse systems domains. We discuss the design and evaluation of analytics models and algorithms for detecting performance anomalies in real-time behaviour of cloud datacenter resources and hosted services at different resolutions. We also discuss the design of a semi-supervised machine learning approach for mitigating performance degradation by actively driving quality of service from undesirable states to a desired target state via incremental capacity optimization. The research methods used in this thesis include experiments on real virtualized testbeds to evaluate aspects of proposed techniques while other aspects are evaluated using performance traces from real-world datacenters. Insights and outcomes from this thesis can be used by both cloud and service operators to enhance the automation of performance problem detection, diagnosis and resolution. They also have the potential to spur further research in the area while being applicable in related domains such as Internet of Things (IoT), industrial sensors as well as in edge and mobile clouds.
A Comparative Study Of Indian And Western Music Forms

Music in India has very ancient roots. Indian classical music is considered to be one of the oldest musical traditions in the world but compared to Western music very little work has been done in the areas of genre recognition, classification, automatic tagging, comparative studies etc. In this work, we investigate the structural differences between Indian and Western music forms and compare the two forms of music in terms of harmony, rhythm, microtones, timbre and other spectral features. To capture the temporal and static structure of the spectrogram, we form a set of global and local frame-wise features for 5genres of each music form. We then apply Adaboost classification and GMM based Hidden Markov Models for four types of feature sets and observe that Indian Music performs better as compared to Western Music. We have achieved a best accuracy of 98.0% and 77.5% for Indian and Western musical genres respectively. Our comparative analysis indicates that features that work well with one form of music may not necessarily perform well with the other form. The results obtained on Indian Music Genres are better than the previous state-of-the-art.
Evenly Cascaded Convolutional Networks

We introduce Evenly Cascaded convolutional Network (ECN), a neural network taking inspiration from the cascade algorithm of wavelet analysis. ECN employs two feature streams - a low-level and high-level steam. At each layer these streams interact, such that low-level features are modulated using advanced perspectives from the high-level stream. ECN is evenly structured through resizing feature map dimensions by a consistent ratio, which removes the burden of ad-hoc specification of feature map dimensions. ECN produces easily interpretable features maps, a result whose intuition can be understood in the context of scale-space theory. We demonstrate that ECN’s design facilitates the training process through providing easily trainable shortcuts. We report new state-of-the-art results for small networks, without the need for additional treatment such as pruning or compression - a consequence of ECN’s simple structure and direct training. A 6-layered ECN design with under 500k parameters achieves 95.24% and 78.99% accuracy on CIFAR-10 and CIFAR-100 datasets, respectively, outperforming the current state-of-the-art on small parameter networks, and a 3 million parameter ECN produces results competitive to the state-of-the-art.
An algorithm of CUDA-based Poisson surface reconstruction

CUDA is a new general-purpose C language interface to GPU developed by NVIDIA. It makes full use of parallel of GPU and has been widely used now. 3D model reconstruction is a traditional and common technique which has been widely used in engineering experiments, CAD and computer graphics. In this paper, we present an algorithm of CUDA-based Poisson surface reconstruction. Our algorithm makes full use of parallel of GPU and runs entirely on GPU and is ten times faster than previous CPU algorithm.
Self-Previewing Gestures and the Gesture-and-Effect Model: Experimentation with Responsive Visual Feedback for New and Unlearned Interactions

s on human factors in computing systems (pp. 815–828). ACM New York, NY, USA. Hudlicka, E. (1997). Summary of knowledge elicitation techniques for requirements analysis (Course material for human computer interaction). Worcester Polytechnic Institute. Kaptelinin, V., & Nardi, B. (2012). Affordances in HCI: Toward a mediated action perspective. In Proceedings of CHI '12 (pp. 967–976).
A Framework for the Classi fi cation of Joint Hypermobility and Related Conditions

2017 Wil In the last decade, growing attention has been placed on joint hypermobility and related disorders. The new nosology for Ehlers–Danlos syndrome (EDS), the best-known and probably the most common of the disorders featuring joint hypermobility, identifies more than 20 different types of EDS, and highlights the need for a single set of criteria to substitute the previous ones for the overlapping EDS hypermobility type and joint hypermobility syndrome. Joint hypermobility is a feature commonly encountered in many other disorders, both genetic and acquired, and this finding is attracting the attention of an increasing number of medical and non-medical disciplines. In this paper, the terminology of joint hypermobility and related disorders is summarized. Different types of joint hypermobility, its secondary musculoskeletal manifestations and a simplified categorization of genetic syndromes featuring joint hypermobility are presented. The concept of a spectrum of pathogenetically related manifestations of joint hypermobility intersecting the categories of pleiotropic syndromes with joint hypermobility is introduced. A group of hypermobility spectrum disorders is proposed as diagnostic labels for patients with symptomatic joint hypermobility but not corresponding to any other syndromes with joint hypermobility.
Transforming Experience: The Potential of Augmented Reality and Virtual Reality for Enhancing Personal and Clinical Change

During life, many personal changes occur. These include changing house, school, work, and even friends and partners. However, the daily experience shows clearly that, in some situations, subjects are unable to change even if they want to. The recent advances in psychology and neuroscience are now providing a better view of personal change, the change affecting our assumptive world: (a) the focus of personal change is reducing the distance between self and reality (conflict); (b) this reduction is achieved through (1) an intense focus on the particular experience creating the conflict or (2) an internal or external reorganization of this experience; (c) personal change requires a progression through a series of different stages that however happen in discontinuous and non-linear ways; and (d) clinical psychology is often used to facilitate personal change when subjects are unable to move forward. Starting from these premises, the aim of this paper is to review the potential of virtuality for enhancing the processes of personal and clinical change. First, the paper focuses on the two leading virtual technologies - augmented reality (AR) and virtual reality (VR) - exploring their current uses in behavioral health and the outcomes of the 28 available systematic reviews and meta-analyses. Then the paper discusses the added value provided by VR and AR in transforming our external experience by focusing on the high level of personal efficacy and self-reflectiveness generated by their sense of presence and emotional engagement. Finally, it outlines the potential future use of virtuality for transforming our inner experience by structuring, altering, and/or replacing our bodily self-consciousness. The final outcome may be a new generation of transformative experiences that provide knowledge that is epistemically inaccessible to the individual until he or she has that experience, while at the same time transforming the individual's worldview.
Core-Oxidized Amorphous Cobalt Phosphide Nanostructures: An Advanced and Highly Efficient Oxygen Evolution Catalyst.

We demonstrated a high-yield and easily reproducible synthesis of a highly active oxygen evolution reaction (OER) catalyst, "the core-oxidized amorphous cobalt phosphide nanostructures". The rational formation of such core-oxidized amorphous cobalt phosphide nanostructures was accomplished by homogenization, drying, and annealing of a cobalt(II) acetate and sodium hypophosphite mixture taken in the weight ratio of 1:10 in an open atmosphere. Electrocatalytic studies were carried out on the same mixture and in comparison with commercial catalysts, viz., Co3O4-Sigma, NiO-Sigma, and RuO2-Sigma, have shown that our catalyst is superior to all three commercial catalysts in terms of having very low overpotential (287 mV at 10 mA cm-2), lower Tafel slope (0.070 V dec-1), good stability upon constant potential electrolysis, and accelerated degradation tests along with a significantly higher mass activity of 300 A g-1 at an overpotential of 360 mV. The synergism between the amorphous CoxPy shell with the Co3O4 core is attributed to the observed enhancement in the OER performance of our catalyst. Moreover, detailed literature has revealed that our catalyst is superior to most of the earlier reports.
Principles for Information Visualization Spreadsheets

preadsheets have proven highly successful for interacting with numerical data, such as applying algebraic operations, defining data propagation relationships, manipulating rows or columns, and exploring " what-if " scenarios. Spread-sheet techniques have recently been extended from numeric domains to other domains. 1,2 Here we present a spreadsheet approach to displaying and exploring information visu-alizations, with large, abstract, multidimensional data sets that are visually represented in multiple ways. We illustrate how spread-sheet techniques provide a struc-tured, intuitive, and powerful interface for investigating information visualizations.  Here we refocus the discussion to illustrate principles that make the spreadsheet approach powerful. These principles show how we can perform many user tasks easily in the visu-alization spreadsheet that prove much more difficult using other approaches. The visualization spreadsheet's benefit comes from enabling users to build multiple visual representations of several data sets, perform operations on these visu-alizations together or separately, and compare and contrast them visually. These operations are becoming ever more important as we realize certain interaction capabilities are critical, such as exploring different views of the data interactively, applying operations like rotation or data filtering to a group of views, and comparing two or more related data sets. These operations fit naturally into a spreadsheet environment. These benefits derive from the way spreadsheets span a range of user interactions. On the one hand, spreadsheets directly benefit end users, because the direct manipulation interface makes it easy to view, navigate, and interact with the data. On the other hand, spreadsheets provide a flexible and easy-to-learn environment for user programming. The success of spreadsheet–based structured interaction eliminates many of the stumbling blocks in traditional programming environments. Spreadsheet developers create templates that enable end users to reliably repeat often-needed computations without the effort of redevelopment or coding. Users do not have to worry about the data dependencies between data sets or memory management. These programming idiosyncrasies are taken care of automatically. By providing a natural environment to explore and apply operations on data, visualization spreadsheets easily enable the exploration of data sets. What is a visualization spreadsheet? Based on our experiences and drawing on others' past work, 1-3 we define the spreadsheet paradigm's characteristics as follows: s The tabular layout lets users view collections of visu-alizations simultaneously. Cells can handle large data sets instead of a few numbers. s …
Setting an attention region for convolutional neural networks using region selective features, for recognition of materials within glass vessels

Convolutional neural networks have emerged as the leading method for the classification and segmentation of images. In some cases, it is desirable to focus the attention of the net on a specific region in the image; one such case is the recognition of the contents of transparent vessels, where the vessel region in the image is already known. This work presents a valve filter approach for focusing the attention of the net on a region of interest (ROI). In this approach, the ROI is inserted into the net as a binary map. The net uses a different set of convolution filters for the ROI and background image regions, resulting in a different set of features being extracted from each region. More accurately, for each filter used on the image, a corresponding valve filter exists that acts on the ROI map and determines the regions in which the corresponding image filter will be used. This valve filter effectively acts as a valve that inhibits specific features in different image regions according to the ROI map. In addition, a new data set for images of materials in glassware vessels in a chemistry laboratory setting is presented. This data set contains a thousand images with pixel-wise annotation according to categories ranging from filled and empty to the exact phase of the material inside the vessel. The results of the valve filter approach and fully convolutional neural nets (FCN) with no ROI input are compared based on this data set.
A Computer Vision-Inspired Deep Learning Architecture for Virtual Metrology Modeling With 2-Dimensional Data

The rise of industry 4.0 and data-intensive manufacturing makes advanced process control (APC) applications more relevant than ever for process/production optimization, related costs reduction, and increased efficiency. One of the most important APC technologies is virtual metrology (VM). VM aims at exploiting information already available in the process/system under exam, to estimate quantities that are costly or impossible to measure. Machine learning (ML) approaches are the foremost choice to design VM solutions. A serious drawback of traditional ML methodologies is that they require a features extraction phase that generally limits the scalability and performance of VM solutions. Particularly, in presence of multi-dimensional data, the feature extraction process is based on heuristic approaches that may capture features with poor predictive power. In this paper, we exploit modern deep learning (DL)-based technologies that are able to automatically extract highly informative features from the data, providing more accurate and scalable VM solutions. In particular, we exploit DL architectures developed in the realm of computer vision to model data that have both spatial and time evolution. The proposed methodology is tested on a real industrial dataset related to etching, one of the most important semiconductor manufacturing processes. The dataset at hand contains optical emission spectroscopy data and it is paradigmatic of the feature extraction problem in VM under examination.
Methods of isolating extracellular vesicles impact down-stream analyses of their cargoes.

Viable tumor cells actively release vesicles into the peripheral circulation and other biologic fluids, which exhibit proteins and RNAs characteristic of that cell. Our group demonstrated the presence of these extracellular vesicles of tumor origin within the peripheral circulation of cancer patients and proposed their utility for diagnosing the presence of tumors and monitoring their response to therapy in the 1970s. However, it has only been in the past 10 years that these vesicles have garnered interest based on the recognition that they serve as essential vehicles for intercellular communication, are key determinants of the immunosuppressive microenvironment observed in cancer and provide stability to tumor-derived components that can serve as diagnostic biomarkers. To date, the clinical utility of extracellular vesicles has been hampered by issues with nomenclature and methods of isolation. The term "exosomes" was introduced in 1981 to denote any nanometer-sized vesicles released outside the cell and to differentiate them from intracellular vesicles. Based on this original definition, we use "exosomes" as synonymous with "extracellular vesicles." While our original studies used ultracentrifugation to isolate these vesicles, we immediately became aware of the significant impact of the isolation method on the number, type, content and integrity of the vesicles isolated. In this review, we discuss and compare the most commonly utilized methods for purifying exosomes for post-isolation analyses. The exosomes derived from these approaches have been assessed for quantity and quality of specific RNA populations and specific marker proteins. These results suggest that, while each method purifies exosomal material, there are pros and cons of each and there are critical issues linked with centrifugation-based methods, including co-isolation of non-exosomal materials, damage to the vesicle's membrane structure and non-standardized parameters leading to qualitative and quantitative variability. The down-stream analyses of these resulting varying exosomes can yield misleading results and conclusions.
Personalized Approach Based on SVM and ANN for Detecting Credit Card Fraud

A novel personalized approach has recently been presented to prevent credit card fraud. This new approach proposes to prevent fraud before initial use of a new card, even users without any real transaction data. This approach shows potential, nevertheless, there are some problems needed solving. A main issue is how to predict accurately with only few data, since it collects quasi-real transaction data via an online questionnaire system and thus respondents are commonly unwilling to spend too much time to reply questionnaires. This study employs both support vector machines (SVM) and artificial neural networks (ANN) to investigate the time-varying fraud problem. The performance of ANN is compared with that from SVM. Results show that SVM and ANN are comparable in training but ANN can have highest training accuracy. However, ANN seems to overfit training data and thus has worse performance of predicting the future data when data number is small