nils-herrmann/clean_scirepeval huggingface dataset:
Spherical Random Features for Polynomial Kernels

Compact explicit feature maps provide a practical framework to scale kernel methods to large-scale learning, but deriving such maps for many types of kernels remains a challenging open problem. Among the commonly used kernels for nonlinear classification are polynomial kernels, for which low approximation error has thus far necessitated explicit feature maps of large dimensionality, especially for higher-order polynomials. Meanwhile, because polynomial kernels are unbounded, they are frequently applied to data that has been normalized to unit `2 norm. The question we address in this work is: if we know a priori that data is normalized, can we devise a more compact map? We show that a putative affirmative answer to this question based on Random Fourier Features is impossible in this setting, and introduce a new approximation paradigm, Spherical Random Fourier (SRF) features, which circumvents these issues and delivers a compact approximation to polynomial kernels for data on the unit sphere. Compared to prior work, SRF features are less rank-deficient, more compact, and achieve better kernel approximation, especially for higher-order polynomials. The resulting predictions have lower variance and typically yield better classification accuracy.
Hand gesture recognition using a neural network shape fitting technique

A new method for hand gesture recognition that is based on a hand gesture fitting procedure via a new Self-Growing and Self-Organized Neural Gas (SGONG) network is proposed. Initially, the region of the hand is detected by applying a color segmentation technique based on a skin color filtering procedure in the YCbCr color space. Then, the SGONG network is applied on the hand area so as to approach its shape. Based on the output grid of neurons produced by the neural network, palm morphologic characteristics are extracted. These characteristics, in accordance with powerful finger features, allow the identification of the raised fingers. Finally, the hand gesture recognition is accomplished through a likelihood-based classification technique. The proposed system has been extensively tested with success. & 2009 Elsevier Ltd. All rights reserved.
The Augmented Reality Marketing : A Merger of Marketing and Technology in Tourism

This chapter elaborates on augmented reality marketing (ARM) as a digital marketing campaign and a strategic trend in tourism and hospitality. The computer assisted augmenting of perception by means of additional interactive information levels in real time is known as augmented reality. Augmented reality marketing is a constructed worldview on a device with blend of reality and added or augmented themes interacting with five sense organs and experiences. The systems and approaches of marketing are integrating with technological applications in almost all sectors of economies and in all phases of a business’s value delivery network. Trends in service sector marketing provide opportunities in generating technology led tourism marketing campaigns. Also, the adoption, relevance and significance of technology in tourism and hospitality value delivery network can hardly be ignored. Many factors are propelling the functionalities of diverse actors in tourism. This paper explores the use of technology at various phases of tourism and hospitality marketing, along with the role of technology in enhancing consumer experience and value addition. It further supports the view that technology is aiding in faster diffusion of tourism products, relates destinations or attractions and thus benefiting the entire society. The augmented reality in marketing can create effective and enjoyable interactive experience by engaging the customer through a rich and rewarding experience of virtually plus reality. Such a tool has real potential in marketing in tourism and hospitality sector. Thus, this study discusses the ARM as a promising trend in tourism and hospitality and how this will meet future needs of tourism and hospitality products or offerings. The Augmented Reality Marketing: A Merger of Marketing and Technology in Tourism
EyeSee: beyond reality with Microsoft HoloLens

Head-mounted Augmented Reality (AR) devices allow overlaying digital information on the real world, where objects may be out of view. Visualizing these out-of-view objects is useful under certain scenarios. To address this, we developed EyeSee360[1] in our previous work. However, our implementation of EyeSee360 was limited to video-see-through devices. These devices suffer from a delayed looped camera image and are decreasing the human field-of-view. In this demo, we present our EyeSee360 transferred to optical-see-through Augmented Reality to overcome these limitations.
Community health information system focusing Pakistan

During past years, significant progress has been taken placed to address health care in areas of HIV / AIDS, Malaria, Dengue, Swine Flu, Cancer, Hepatitis etc. However, all working systems which were in market do not perform overall statistics about disease situation. They are neither universal nor sufficiently as broad as they should be to aware public about disease. Progress and development in health care system at national level could not meet to the requirements of population. Sometimes, progress has stopped, and other times they are in progress. The health systems can deliver impressive services equitably, efficiently and effectively in critical situations to achieve the required goals. Today, there are many health systems working in international market and they target different multi-national group of people, they incorporate attention and take initiative to providing a strengthen support to different countries. Community Health Information System is a web based application, which can improve the productivity of health care through delivering an integrated evidence-based and effective health information in real-time. System can provide useful and efficient data containing wide-range of health KPIs and health determinants - to general public, patients, health professionals and policy makers. Community Health Information System, focused on gathering data and information from all over Pakistan. This system is mainly designed for the current health situation regarding 3rd world countries, e.g.: Pakistan.
Text mining: Challenges and future directions

In today's world, the amount of stored information has been enormously increasing day by day which is generally in the unstructured form and cannot be used for any processing to extract useful information, so several techniques such as summarization, classification, clustering, information extraction and visualization are available for the same which comes under the category of text mining. Text Mining can be defined as a technique which is used to extract interesting information or knowledge from the text documents. Text mining, also known as text data mining or knowledge discovery from textual databases, refers to the process of extracting interesting and non-trivial patterns or knowledge from text documents. Regarded by many as the next wave of knowledge discovery, text mining has very high commercial values.
Understanding Bland Altman analysis

In a contemporary clinical laboratory it is very common to have to assess the agreement between two quantitative methods of measurement. The correct statistical approach to assess this degree of agreement is not obvious. Correlation and regression studies are frequently proposed. However, correlation studies the relationship between one variable and another, not the differences, and it is not recommended as a method for assessing the comparability between methods.  In 1983 Altman and Bland (B&A) proposed an alternative analysis, based on the quantification of the agreement between two quantitative measurements by studying the mean difference and constructing limits of agreement.  The B&A plot analysis is a simple way to evaluate a bias between the mean differences, and to estimate an agreement interval, within which 95% of the differences of the second method, compared to the first one, fall. Data can be analyzed both as unit differences plot and as percentage differences plot.  The B&A plot method only defines the intervals of agreements, it does not say whether those limits are acceptable or not. Acceptable limits must be defined a priori, based on clinical necessity, biological considerations or other goals.  The aim of this article is to provide guidance on the use and interpretation of Bland Altman analysis in method comparison studies.
Wide-residual-inception networks for real-time object detection

Since convolutional neural network (CNN) models emerged, several tasks in computer vision have actively deployed CNN models for feature extraction. However, the conventional CNN models have a high computational cost and require high memory capacity, which is impractical and unaffordable for commercial applications such as real-time on-road object detection on embedded boards or mobile platforms. To tackle this limitation of CNN models, this paper proposes a wide-residual-inception (WR-Inception) network, which constructs the architecture based on a residual inception unit that captures objects of various sizes on the same feature map, as well as shallower and wider layers, compared to state-of-the-art networks like ResNets. To verify the proposed networks, this paper conducted two experiments; one is a classification task on CIFAR-10/100 and the other is an on-road object detection task using a Single-Shot Multi-box Detector (SSD) on the KITTI dataset. WR-Inception achieves comparable accuracy on CIFAR-10/100, with test errors at 4.82% and 23.12%, respectively, which outperforms 164-layer Pre-ResNets. In addition, the detection experiments demonstrate that the WR-Inception-based SSD outperforms ResNet-101 — based SSD on KITTI. Besides, WR-Inception-based SSD achieves 16 frames per seconds, which is 3.85 times faster than ResNet-101-based SSD. We could expect WR-Inception to be used for real application systems.
Pathological synchronization in Parkinson's disease: networks, models and treatments

Parkinson's disease is a common and disabling disorder of movement owing to dopaminergic denervation of the striatum. However, it is still unclear how this denervation perverts normal functioning to cause slowing of voluntary movements. Recent work using tissue slice preparations, animal models and in humans with Parkinson's disease has demonstrated abnormally synchronized oscillatory activity at multiple levels of the basal ganglia-cortical loop. This excessive synchronization correlates with motor deficit, and its suppression by dopaminergic therapies, ablative surgery or deep-brain stimulation might provide the basic mechanism whereby diverse therapeutic strategies ameliorate motor impairment in patients with Parkinson's disease. This review is part of the INMED/TINS special issue, Physiogenic and pathogenic oscillations: the beauty and the beast, based on presentations at the annual INMED/TINS symposium (http://inmednet.com/).
Inverse Nyquist Stability Criterion for Grid-Tied Inverters

A new impedance-based stability criterion was proposed for a grid-tied inverter system based on a Norton equivalent circuit of the inverter [18]. As an extension of the work in , this paper shows that using a Thévenin representation of the inverter can lead to the same criterion in . Further, this paper shows that the criterion proposed by Middlebrook can still be used for the inverter systems. The link between the criterion in [18] and the original criterion is the inverse Nyquist stability criterion. The criterion in [18] is easier to be used. Because the current feedback controller and the phase-locked loop of the inverter introduce poles at the origin and right-half plane to the output impedance of the inverter. These poles do not appear in the minor loop gain defined in [18] but in the minor loop gain defined by Middlebrook. Experimental systems are used to verify the proposed analysis.
Millimeter-Wave 5G Antennas for Smartphones: Overview and Experimental Demonstration

For the first time to the best of our knowledge, this paper provides an overview of millimeter-wave (mmWave) 5G antennas for cellular handsets. Practical design considerations and solutions related to the integration of mmWave phased-array antennas with beam switching capabilities are investigated in detail. To experimentally examine the proposed methodologies, two types of mesh-grid phased-array antennas featuring reconfigurable horizontal and vertical polarizations are designed, fabricated, and measured at the 60 GHz spectrum. Afterward the antennas are integrated with the rest of the 60 GHz RF and digital architecture to create integrated mmWave antenna modules and implemented within fully operating cellular handsets under plausible user scenarios. The effectiveness, current limitations, and required future research areas regarding the presented mmWave 5G antenna design technologies are studied using mmWave 5G system benchmarks.
Internet and Mobile Phone Addiction among High School Students: A Cross Sectional Study from Iran

Addiction to internet and mobile phone may be affecting all aspect of student’s life. Knowledge about prevalence and related factors of internet and mobile phone addiction is necessary for planning for prevention and treatment. This study was conducted to evaluate the prevalence of internet and mobile phone addiction among Iranian students.  This cross sectional study conducted from Jun to April 2015 in Rasht Iran. With using stratified sampling method, 581 high school students from two region of Rasht in North of Iran were recruited as the subjects for this study. Data were collected with using demographics questionnaire, Cell phone Overuse Scale (COS), and the Internet Addiction Test (IAT). Analysis was performed using Statistical Package for Social Sciences (SPSS) 17 21 version.  Of the 581 students, who participate in present study, 53.5% were female and the rest were male. The mean age of students was 16.28±1.01 years. The mean score of IAT was 42.03±18.22. Of the 581 students, 312 (53.7%), 218 (37.5%) and 51 (8.8%) showed normal, mild and moderate level of internet addiction. The mean score of COS was 55.10±19.86.Of the 581 students, 27(6/4%), 451(6/77) and 103 (7/17) showed low, moderate and high level of mobile phone addiction.  according to finding of present study, rate of mobile phone and internet addiction were high among Iranian students. Health care authorities should pay more attention to these problems.
Relationship between core stability, functional movement, and performance.

The purpose of this study was to determine the relationship between core stability, functional movement, and performance. Twenty-eight healthy individuals (age = 24.4 ± 3.9 yr, height = 168.8 ± 12.5 cm, mass = 70.2 ± 14.9 kg) performed several tests in 3 categories: core stability (flexion [FLEX], extension [EXT], right and left lateral [LATr/LATl]), functional movement screen (FMS) (deep squat [DS], trunk-stability push-up [PU], right and left hurdle step [HSr/HSl], in-line lunge [ILLr/ILLl], shoulder mobility [SMr/SMl], active straight leg raise [ASLRr/ASLRl], and rotary stability [RSr/RSl]), and performance tests (backward medicine ball throw [BOMB], T-run [TR], and single leg squat [SLS]). Statistical significance was set at p ≤ 0.05. There were significant correlations between SLS and FLEX (r = 0.500), LATr (r = 0.495), and LATl (r = 0.498). The TR correlated significantly with both LATr (r = 0.383) and LATl (r = 0.448). Of the FMS, BOMB was significantly correlated with HSr (r = 0.415), SMr (r = 0.388), PU (r = 0.407), and RSr (r = 0.391). The TR was significantly related with HSr (r = 0.518), ILLl (r = 0.462) and SMr (r = 0.392). The SLS only correlated significantly with SMr (r = 0.446). There were no significant correlations between core stability and FMS. Moderate to weak correlations identified suggest core stability and FMS are not strong predictors of performance. In addition, existent assessments do not satisfactorily confirm the importance of core stability on functional movement. Despite the emphasis fitness professionals have placed on functional movement and core training for increased performance, our results suggest otherwise. Although training for core and functional movement are important to include in a fitness program, especially for injury prevention, they should not be the primary emphasis of any training program.
Automated Test Input Generation for Android: Are We There Yet? (E)

Like all software, mobile applications ("apps") must be adequately tested to gain confidence that they behave correctly. Therefore, in recent years, researchers and practitioners alike have begun to investigate ways to automate apps testing. In particular, because of Android's open source nature and its large share of the market, a great deal of research has been performed on input generation techniques for apps that run on the Android operating systems. At this point in time, there are in fact a number of such techniques in the literature, which differ in the way they generate inputs, the strategy they use to explore the behavior of the app under test, and the specific heuristics they use. To better understand the strengths and weaknesses of these existing approaches, and get general insight on ways they could be made more effective, in this paper we perform a thorough comparison of the main existing test input generation tools for Android. In our comparison, we evaluate the effectiveness of these tools, and their corresponding techniques, according to four metrics: ease of use, ability to work on multiple platforms, code coverage, and ability to detect faults. Our results provide a clear picture of the state of the art in input generation for Android apps and identify future research directions that, if suitably investigated, could lead to more effective and efficient testing tools for Android.
Aspect of Blame in Tweets: A Deep Recurrent Neural Network Approach

Twitter as an information dissemination tool has proved to be instrumental in generating user curated content in short spans of time. Tweeting usually occurs when reacting to events, speeches, about a service or product. This in some cases comes with its fair share of blame on varied aspects in reference to say an event. Our work in progress details how we plan to collect the informal texts, clean them and extract features for blame detection. We are interested in augmenting Recurrent Neural Networks (RNN) with selfdeveloped association rules in getting the most out of the data for training and evaluation. We aim to test the performance of our approach using human-induced terror-related tweets corpus. It is possible tailoring the model to fit natural disaster scenarios.
Review: Polymeric-Based 3D Printing for Tissue Engineering

Three-dimensional (3D) printing, also referred to as additive manufacturing, is a technology that allows for customized fabrication through computer-aided design. 3D printing has many advantages in the fabrication of tissue engineering scaffolds, including fast fabrication, high precision, and customized production. Suitable scaffolds can be designed and custom-made based on medical images such as those obtained from computed tomography. Many 3D printing methods have been employed for tissue engineering. There are advantages and limitations for each method. Future areas of interest and progress are the development of new 3D printing platforms, scaffold design software, and materials for tissue engineering applications.
Deep Reinforcement Learning for Pairs Trading Georgia Institute of Technology

In this work, we § designed and implemented a DRL-based agent to make pair-trading decisions; § utilized a model-free reinforcement learning approach, policy gradient; § fitted the policy function with recurrent neural networks (RNNs) and variations (i.e., bi-directional RNNs, long short-term memory networks (LSTM), and bidirectional LSTM; § fetched 15-year stock price data from Yahoo Finance and § selected the pair (SCG vs WEC) for experiments via co-integration tests.
STag: A Stable Fiducial Marker System

In this paper, we propose STag, a fiducial marker system that provides stable pose estimation. The outer square border of the marker is used for detection and pose estimation. This is followed by a novel pose refinement step using the inner circular border. The refined pose is more stable and robust across viewing conditions compared to the state of the art. In addition, the lexicographic generation algorithm is adapted for fiducial markers, and libraries with various sizes are created. This makes the system suitable for applications that require many unique markers, or few unique markers with high occlusion resistance. The edge segment-based detection algorithm is of low complexity, and returns few false candidates. These features are demonstrated with experiments on real images, including comparisons with the state of the art fiducial marker systems.
Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets

Following detection and tracking of traffic actors, prediction of their future motion is the next critical component of a self-driving vehicle (SDV), allowing the SDV to move safely and efficiently in its environment. This is particularly important when it comes to vulnerable road users (VRUs), such as pedestrians and bicyclists. We present a deep learning method for predicting VRU movement where we rasterize high-definition maps and actor’s surroundings into bird’s-eye view image used as input to convolutional networks. In addition, we propose a fast architecture suitable for real-time inference, and present an ablation study of rasterization choices.
An overview of wireless IoT protocol security in the smart home domain

While the application of IoT in smart technologies becomes more and more proliferated, the pandemonium of its protocols becomes increasingly confusing. More seriously, severe security deficiencies of these protocols become evident, as time-to-market is a key factor, which satisfaction comes at the price of a less thorough security design and testing. This applies especially to the smart home domain, where the consumer-driven market demands quick and cheap solutions. This paper presents an overview of IoT application domains and discusses the most important wireless IoT protocols for smart home, which are KNX-RF, EnOcean, Zigbee, Z-Wave and Thread. Finally, it describes the security features of said protocols and compares them with each other, giving advice on whose protocols are more suitable for a secure smart home.
DeepLogic: Towards End-to-End Differentiable Logical Reasoning.

Combining machine learning with logic-based expert systems in order to get the best of both worlds are becoming increasingly popular. However, to what extent machine learning can already learn to reason over rule-based knowledge is still an open problem. In this paper, we explore how symbolic logic, defined as logic programs at a character level, is learned to be represented in a high-dimensional vector space using RNNbased iterative neural networks to perform reasoning. We create a new dataset that defines 12 classes of logic programs exemplifying increased level of complexity of logical reasoning and train the networks in an end-to-end fashion to learn whether a logic program entails a given query. We analyse how learning the inference algorithm gives rise to representations of atoms, literals and rules within logic programs and evaluate against increasing lengths of predicate and constant symbols as well as increasing steps of multi-hop reasoning.