nils-herrmann/clean_scirepeval huggingface dataset:
Boosting Algorithms as Gradient Descent in Function

Much recent attention, both experimental and theoretical, has been focussed on classii-cation algorithms which produce voted combinations of classiiers. Recent theoretical work has shown that the impressive generalization performance of algorithms like AdaBoost can be attributed to the classiier having large margins on the training data. We present abstract algorithms for nding linear and convex combinations of functions that minimize arbitrary cost functionals (i.e functionals that do not necessarily depend on the margin). Many existing voting methods can be shown to be special cases of these abstract algorithms. Then, following previous theoretical results bounding the generalization performance of convex combinations of classiiers in terms of general cost functions of the margin, we present a new algorithm (DOOM II) for performing a gradient descent optimization of such cost functions. Experiments on several data sets from the UC Irvine repository demonstrate that DOOM II generally outperforms AdaBoost, especially in high noise situations. Margin distribution plots verify that DOOM II is willing tògive up' on examples that are too hard in order to avoid overrtting. We also show that the overrtting behavior exhibited by AdaBoost can be quantiied in terms of our proposed cost function.
Medical Image Enhancement Using Morphological Transformation

Medical imaging includes different modalities and processes to visualize the interior of human body for diagnostic and treatment purpose. However, one of the most common degradations in medical images is their poor contrast quality and noise. The existence of several objects and the close proximity of adjacent pixels values make the diagnostic process a daunting task. The idea of image enhancement techniques is to improve the quality of an image. In this study, morphological transform operation is carried out on medical images to enhance the contrast and quality. A disk shaped mask is used in Top-Hat and Bottom-Hat transform and this mask plays a vital role in the operation. Different types and sizes of medical images need different masks so that they can be successfully enhanced. The method shown in this study takes a mask of an arbitrary size and keeps changing its size until an optimum enhanced image is obtained from the transformation operation. The enhancement is achieved via an iterative exfoliation process. The results indicate that this method improves the contrast of medical images and can help with better diagnosis.
Flexible Sensing Electronics for Wearable/Attachable Health Monitoring.

Wearable or attachable health monitoring smart systems are considered to be the next generation of personal portable devices for remote medicine practices. Smart flexible sensing electronics are components crucial in endowing health monitoring systems with the capability of real-time tracking of physiological signals. These signals are closely associated with body conditions, such as heart rate, wrist pulse, body temperature, blood/intraocular pressure and blood/sweat bio-information. Monitoring such physiological signals provides a convenient and non-invasive way for disease diagnoses and health assessments. This Review summarizes the recent progress of flexible sensing electronics for their use in wearable/attachable health monitoring systems. Meanwhile, we present an overview of different materials and configurations for flexible sensors, including piezo-resistive, piezo-electrical, capacitive, and field effect transistor based devices, and analyze the working principles in monitoring physiological signals. In addition, the future perspectives of wearable healthcare systems and the technical demands on their commercialization are briefly discussed.
Substantial variation across geographic regions in the obesity prevalence among 6–8 years old Hungarian children (COSI Hungary 2016)

There have been previous representative nutritional status surveys conducted in Hungary, but this is the first one that examines overweight and obesity prevalence according to the level of urbanization and in different geographic regions among 6-8-year-old children. We also assessed whether these variations were different by sex.



This survey was part of the fourth data collection round of World Health Organization (WHO) Childhood Obesity Surveillance Initiative which took place during the academic year 2016/2017. The representative sample was determined by two-stage cluster sampling. A total of 5332 children (48.4% boys; age 7.54 ± 0.64 years) were measured from all seven geographic regions including urban (at least 500 inhabitants per square kilometer; n = 1598), semi-urban (100 to 500 inhabitants per square kilometer; n = 1932) and rural (less than 100 inhabitants per square kilometer; n = 1802) areas.



Using the WHO reference, prevalence of overweight and obesity within the whole sample were 14.2, and 12.7%, respectively. According to the International Obesity Task Force (IOTF) reference, rates were 12.6 and 8.6%. Northern Hungary and Southern Transdanubia were the regions with the highest obesity prevalence of 11.0 and 12.0%, while Central Hungary was the one with the lowest obesity rate (6.1%). The prevalence of overweight and obesity tended to be higher in rural areas (13.0 and 9.8%) than in urban areas (11.9 and 7.0%). Concerning differences in sex, girls had higher obesity risk in rural areas (OR = 2.0) but boys did not. Odds ratios were 2.0-3.4 in different regions for obesity compared to Central Hungary, but only among boys.



Overweight and obesity are emerging problems in Hungary. Remarkable differences were observed in the prevalence of obesity by geographic regions. These variations can only be partly explained by geographic characteristics.
Real-Time Seamless Single Shot 6D Object Pose Prediction

We propose a single-shot approach for simultaneously detecting an object in an RGB image and predicting its 6D pose without requiring multiple stages or having to examine multiple hypotheses. Unlike a recently proposed single-shot technique for this task [10] that only predicts an approximate 6D pose that must then be refined, ours is accurate enough not to require additional post-processing. As a result, it is much faster - 50 fps on a Titan X (Pascal) GPU - and more suitable for real-time processing. The key component of our method is a new CNN architecture inspired by [27, 28] that directly predicts the 2D image locations of the projected vertices of the object's 3D bounding box. The object's 6D pose is then estimated using a PnP algorithm. For single object and multiple object pose estimation on the LINEMOD and OCCLUSION datasets, our approach substantially outperforms other recent CNN-based approaches [10, 25] when they are all used without postprocessing. During post-processing, a pose refinement step can be used to boost the accuracy of these two methods, but at 10 fps or less, they are much slower than our method.
Biquad Filter Using the Current Feedback Operational Amplifier

A new MOS-C bandpass-low-pass filter using the current feedback operational amplifier (CFOA) is presented. The filter employs two CFOA’s, eight MOS transistors operating in the nonsaturation region, and two grounded capacitors. The proposed MOS-C filter has the advantage of independent control ofQ and !o. PSpice simulation results for the proposed filter are given.
Getting even for customer mistreatment: the role of moral identity in the relationship between customer interpersonal injustice and employee sabotage.

Research on the "dark side" of organizational behavior has determined that employee sabotage is most often a reaction by disgruntled employees to perceived mistreatment. To date, however, most studies on employee retaliation have focused on intra-organizational sources of (in)justice. Results from this field study of customer service representatives (N = 358) showed that interpersonal injustice from customers relates positively to customer-directed sabotage over and above intra-organizational sources of fairness. Moreover, the association between unjust treatment and sabotage was moderated by 2 dimensions of moral identity (symbolization and internalization) in the form of a 3-way interaction. The relationship between injustice and sabotage was more pronounced for employees high (vs. low) in symbolization, but this moderation effect was weaker among employees who were high (vs. low) in internalization. Last, employee sabotage was negatively related to job performance ratings.
Smart ITS Sensor for the Transportation Planning Based on IoT Approaches Using Serverless and Microservices Architecture

Currently, there are many challenges in the transportation scope that researchers are attempting to resolve, and one of them is transportation planning. The main contribution of this paper is the design and implementation of an ITS (Intelligent Transportation Systems) smart sensor prototype that incorporates and combines the Internet of Things (IoT) approaches using the Serverless and Microservice Architecture, to help the transportation planning for Bus Rapid Transit (BRT) systems. The ITS smart sensor prototype can detect several Bluetooth signals of several devices (e.g., from mobile phones) that people use while travelling by the BRT system (e.g., in Bogota city). From that information, the ITS smart-sensor prototype can create an O/D (origin/destiny) matrix for several BRT routes, and this information can be used by the Administrator Authorities (AA) to produce a suitable transportation planning for the BRT systems. In addition, this information can be used by the center of traffic management and the AA from ITS cloud services using the Serverless and Microservice architecture.
ANALYZING THE USE OF AN ADVANCE BOOKING CURVE IN FORECASTING HOTEL RESERVATIONS

Although there is considerable interest in the advance bookings model as a forecasting method in the hotel industry, there has been little research analyzing the use of an advance booking curve in forecasting hotel reservations. The mainstream of advance booking models reviewed in the literature uses only the bookings-on-hand data on a certain day and ignores the previous booking data. This empirical study analyzes the entire booking data set for one year provided by the Hotel ICON in Hong Kong, and identifies the trends and patterns in the data. The analysis demonstrates the use of an advance booking curve in forecasting hotel reservations at property level.
Recent Advances in Friction Stir Welding – Process , Weldment Structure and Properties

Friction stir welding is a refreshing approach to the joining of metals. Although originally intended for aluminium alloys, reach of FSW has now extended to a variety of materials including steels and polymers. This review deals with the fundamental understanding of the process and its metallurgical consequences. The focus is on heat generation, heat transfer and plastic flow during welding, elements of tool design, understanding defect formation and the structure and properties of the welded materials. 1 Progress in Materials Science 53 (2008) 980-1023
Decoding-History-Based Adaptive Control of Attention for Neural Machine Translation

Attention-based sequence-to-sequence model has proved successful in Neural Machine Translation (NMT). However, the attention without consideration of decoding history, which includes the past information in the decoder and the attention mechanism, often causes much repetition. To address this problem, we propose the decoding-history-based Adaptive Control of Attention (ACA) for the NMT model. ACA learns to control the attention by keeping track of the decoding history and the current information with a memory vector, so that the model can take the translated contents and the current information into consideration. Experiments on Chinese-English translation and the EnglishVietnamese translation have demonstrated that our model significantly outperforms the strong baselines. The analysis shows that our model is capable of generating translation with less repetition and higher accuracy. The code will be available at
High Altitude Wind Energy Generation Using Controlled Power Kites

This paper presents simulation and experimental results regarding a new class of wind energy generators, denoted as KiteGen, which employ power kites to capture high altitude wind power. A realistic kite model, which includes the kite aerodynamic characteristics and the effects of line weight and drag forces, is used to describe the system dynamics. Nonlinear model predictive control techniques, together with an efficient implementation based on set membership function approximation theory, are employed to maximize the energy obtained by KiteGen, while satisfying input and state constraints. Two different kinds of KiteGen are investigated through numerical simulations, the yo-yo configuration and the carousel configuration, respectively. For each configuration, a generator with the same kite and nominal wind characteristics is considered. A novel control strategy for the carousel configuration, with respect to previous works, is also introduced. The simulation results show that the power generation potentials of the yo-yo and carousel configurations are very similar. Thus, the choice between these two configurations for further development of a medium-to-large scale generator will be made on the basis of technical implementation problems and of other indexes like construction costs and generated power density with respect to land occupation. Experimental data, collected using the small-scale KiteGen prototype built at Politecnico di Torino, are compared to simulation results. The good matching between simulation and real measured data increases the confidence with the presented simulation results, which show that energy generation with controlled power kites can represent a quantum leap in wind power technology, promising to obtain renewable energy from a source largely available almost everywhere, with production costs lower than those of fossil sources.
Analyzing evolution of research topics with NEViewer: a new method based on dynamic co-word networks

Understanding the evolution of research topics is crucial to detect emerging trends in science. This paper proposes a new approach and a framework to discover the evolution of topics based on dynamic co-word networks and communities within them. The NEViewer software was developed according to this approach and framework, as compared to the existing studies and science mapping software tools, our work is innovative in three aspects: (a) the design of a longitudinal framework based on the dynamics of co-word communities; (b) it proposes a community labelling algorithm and community evolution verification algorithms; (c) and visualizes the evolution of topics at the macro and micro level respectively using alluvial diagrams and coloring networks. A case study in computer science and a careful assessment was implemented and demonstrating that the new method and the software NEViewer is feasible and effective.
Mental stress as consequence and cause of vision loss: the dawn of psychosomatic ophthalmology for preventive and personalized medicine

The loss of vision after damage to the retina, optic nerve, or brain has often grave consequences in everyday life such as problems with recognizing faces, reading, or mobility. Because vision loss is considered to be irreversible and often progressive, patients experience continuous mental stress due to worries, anxiety, or fear with secondary consequences such as depression and social isolation. While prolonged mental stress is clearly a consequence of vision loss, it may also aggravate the situation. In fact, continuous stress and elevated cortisol levels negatively impact the eye and brain due to autonomous nervous system (sympathetic) imbalance and vascular dysregulation; hence stress may also be one of the major causes of visual system diseases such as glaucoma and optic neuropathy. Although stress is a known risk factor, its causal role in the development or progression of certain visual system disorders is not widely appreciated. This review of the literature discusses the relationship of stress and ophthalmological diseases. We conclude that stress is both consequence and cause of vision loss. This creates a vicious cycle of a downward spiral, in which initial vision loss creates stress which further accelerates vision loss, creating even more stress and so forth. This new psychosomatic perspective has several implications for clinical practice. Firstly, stress reduction and relaxation techniques (e.g., meditation, autogenic training, stress management training, and psychotherapy to learn to cope) should be recommended not only as complementary to traditional treatments of vision loss but possibly as preventive means to reduce progression of vision loss. Secondly, doctors should try their best to inculcate positivity and optimism in their patients while giving them the information the patients are entitled to, especially regarding the important value of stress reduction. In this way, the vicious cycle could be interrupted. More clinical studies are now needed to confirm the causal role of stress in different low vision diseases to evaluate the efficacy of different anti-stress therapies for preventing progression and improving vision recovery and restoration in randomized trials as a foundation of psychosomatic ophthalmology.
Normalisation and Analysis of Social Media Texts ( NormSoMe )

We present a language-independent method for automatic diacritic restoration. The method focuses on low computational resource usage, making it suitable for mobile devices. We train a decision tree classifier on character-based features without involving a dictionary. Since our features require at most a few characters of context, this approach can be applied to very short text segments such as tweets and text messages. We test the method on a Hungarian web corpus and on Hungarian Facebook comments. It achieves state-of-the-art results on web data and over 92% on Facebook comments. A C++ implementation for Hungarian diacritics is publicly available, support for other languages is under development.
Orbital Angular Momentum (OAM) Generation by Cylinder Dielectric Resonator Antenna for Future Wireless Communications

In this paper, we demonstrate that the solid cylinder dielectric resonator antenna (CDRA) is a high radiation efficient device to generate orbital angular momentum (OAM) waves with different OAM modes at different frequencies. Theoretical formulations of its radiation fields are deduced in the cylindrical coordinate system, which shows that all components of the electric and magnetic fields have the azimuth dependence of  $e^{jm\varphi }$ . A full-wave simulation on a practical size CDRA is then implemented to validate the theoretical derivations. The simulation results show that different separated OAM modes can be obtained at different frequencies. Specifically, the higher order OAM modes are generated at higher electron magnetic modes, which makes CDRA a promising method to overcome the narrow band and multiband issues existing in the design of current OAM devices. Furthermore, a maximum radiation efficiency of 97.1% at 13.3 GHz, and a minimum radiation efficiency of 72.4% at 19.45 GHz are obtained in our simulation, which is a high radiation efficiency feature at the OAM resonant frequencies.
OCR accuracy improvement on document images through a novel pre-processing approach

Digital camera and mobile document image acquisition are new trends arising in the world of Optical Character Recognition and text detection. In some cases, such process integrates many distortions and produces poorly scanned text or text-photo images and natural images, leading to an unreliable OCR digitization. In this paper, we present a novel nonparametric and unsupervised method to compensate for undesirable document image distortions aiming to optimally improve OCR accuracy. Our approach relies on a very efficient stack of document image enhancing techniques to recover deformation of the entire document image. First, we propose a local brightness and contrast adjustment method to effectively handle lighting variations and the irregular distribution of image illumination. Second, we use an optimized greyscale conversion algorithm to transform our document image to greyscale level. Third, we sharpen the useful information in the resulting greyscale image using Un-sharp Masking method. Finally, an optimal global binarization approach is used to prepare the final document image to OCR recognition. The proposed approach can significantly improve text detection rate and optical character recognition accuracy. To demonstrate the efficiency of our approach, an exhaustive experimentation on a standard dataset is presented.
Experimental economics for web mining

This paper offers a step towards research infrastructure, which makes data from experimental economics efficiently usable for analysis of web data. We believe that regularities of human behavior found in experimental data also emerge in real world web data. A format for data from experiments is suggested, which enables its publication as open data. Once standardized datasets of experiments are available on-line, web mining can take advantages from this data. Further, the questions about t he order of causalities arisen from web data analysis can inspire new experiment setups.
Security Requirements Analysis for the IoT

Due to the rapid growth of network infrastructure and sensor, the age of the IoT (internet of things) that can be implemented into the smart car, smart home, smart building, and smart city is coming. IoT is a very useful ecosystem that provides various services (e.g., amazon echo); however, at the same time, risk can be huge too. Collecting information to help people could lead serious information leakage, and if IoT is combined with critical control system (e.g., train control system), security attack would cause loss of lives. Furthermore, research on IoT security requirements is insufficient now. Therefore, this paper focuses on IoT security, and its requirements. First, we propose basic security requirements of IoT by analyzing three basic characteristics (i.e., heterogeneity, resource constraint, dynamic environment). Then, we suggest six key elements of IoT (i.e., IoT network, cloud, user, attacker, service, platform) and analyze their security issues for overall security requirements. In addition, we evaluate several IoT security requirement researches.
The relevance of the philosophical 'mind-body problem' for the status of psychosomatic medicine: a conceptual analysis of the biopsychosocial model.

Psychosomatic medicine, with its prevailing biopsychosocial model, aims to integrate human and exact sciences with their divergent conceptual models. Therefore, its own conceptual foundations, which often remain implicit and unknown, may be critically relevant. We defend the thesis that choosing between different metaphysical views on the 'mind-body problem' may have important implications for the conceptual foundations of psychosomatic medicine, and therefore potentially also for its methods, scientific status and relationship with the scientific disciplines it aims to integrate: biomedical sciences (including neuroscience), psychology and social sciences. To make this point, we introduce three key positions in the philosophical 'mind-body' debate (emergentism, reductionism, and supervenience physicalism) and investigate their consequences for the conceptual basis of the biopsychosocial model in general and its 'psycho-biological' part ('mental causation') in particular. Despite the clinical merits of the biopsychosocial model, we submit that it is conceptually underdeveloped or even flawed, which may hamper its use as a proper scientific model.