nils-herrmann/clean_scirepeval huggingface dataset:
A Research on Iron Loss of IPMSM With a Fractional Number of Slot Per Pole

In this paper, we investigated rotor iron loss of interior permanent magnet synchronous machine (IPMSM), which has distributed armature windings. In order to study the iron loss with the effect of slot-pole combination, two machines for high-speed operation such as electric vehicle were designed. One is fractional slot winding and the other is conventional one. In the analysis, we developed a new iron loss model of electrical machines for high-speed operation. The calculated iron loss was compared with the experimental data. It was clarified that the proposed method can estimate iron loss effectively at high-speed operation. Based on this newly proposed method, we analyzed iron loss of the two machines according to their driving conditions. From the analysis results, it was shown that rotor iron loss of machine employing fractional slot-winding is definitely large at load condition. In addition, it is interesting that the ratio (rotor iron loss/stator iron loss) becomes larger as the speed of the machines increase if the number of slot per pole is fractional.
Retrieval and classification of food images

Automatic food understanding from images is an interesting challenge with applications in different domains. In particular, food intake monitoring is becoming more and more important because of the key role that it plays in health and market economies. In this paper, we address the study of food image processing from the perspective of Computer Vision. As first contribution we present a survey of the studies in the context of food image processing from the early attempts to the current state-of-the-art methods. Since retrieval and classification engines able to work on food images are required to build automatic systems for diet monitoring (e.g., to be embedded in wearable cameras), we focus our attention on the aspect of the representation of the food images because it plays a fundamental role in the understanding engines. The food retrieval and classification is a challenging task since the food presents high variableness and an intrinsic deformability. To properly study the peculiarities of different image representations we propose the UNICT-FD1200 dataset. It was composed of 4754 food images of 1200 distinct dishes acquired during real meals. Each food plate is acquired multiple times and the overall dataset presents both geometric and photometric variabilities. The images of the dataset have been manually labeled considering 8 categories: Appetizer, Main Course, Second Course, Single Course, Side Dish, Dessert, Breakfast, Fruit. We have performed tests employing different representations of the state-of-the-art to assess the related performances on the UNICT-FD1200 dataset. Finally, we propose a new representation based on the perceptual concept of Anti-Textons which is able to encode spatial information between Textons outperforming other representations in the context of food retrieval and Classification.
Face Photo-Sketch Synthesis and Recognition

Today in Modern Society Face Recognition has gained much attention in the field of network multimedia access. After the 9/11 tragedy in India, the need for technologies for identification, detection and recognition of suspects has increased. One of the most common biometric recognition techniques is face recognition since face is the convenient way used by the people to identify each other. In this paper we are going to study a method for representing face which is based on the features which uses geometric relationship among the facial features like mouth, nose and eyes .Feature based face representation is done by independently matching templates of three facial regions i.e eyes, mouth and nose .Principal Component Analysis method which is also called Eigen faces is appearance based technique used widely for the dimensionality reduction and recorded a greater performance in face recognition. Here we are going to study about PCA followed by Feed Forward Neural Network called PCA-NN.
View Planning for Multi-View Stereo 3D Reconstruction Using an Autonomous Multicopter

Multi-view stereo algorithms are an attractive technique for the digital reconstruction of outdoor sites. Concerning the data acquisition process a vertical take off and landing UAV carrying a digital camera is a suitable platform in terms of mobility and flexibility in viewpoint placement. We introduce an automated UAV based data acquisition and outdoor site reconstruction system. A special focus is set on the problem of model based view planning using a coarse digital surface model (DSM) with minimal data preprocessing. The developed view planning heuristic considers a coverage, a maximum view angle and an overlapping constraint imposed by multi-view stereo reconstruction techniques. The time complexity of the algorithm is linear with respect to the size of the area of interest. We demonstrate the efficiency of the entire system in two scenarios, a building and a hillside. K. Schmid (B) · H. Hirschmüller · A. Dömel · I. Grixa · M. Suppa Department of Perception and Cognition, Robotics and Mechatronics Center (RMC), German Aerospace Center (DLR), Muenchner Str. 20, 82234 Wessling, Germany e-mail: korbinian.schmid@dlr.de G. Hirzinger Robotics and Mechatronics Center (RMC), German Aerospace Center (DLR), Muenchner Str. 20, 82234 Wessling, Germany
Coping theory and research: past, present, and future.

In this essay in honor of Donald Oken, I emphasize coping as a key concept for theory and research on adaptation and health. My focus will be the contrasts between two approaches to coping, one that emphasizes style—that is, it treats coping as a personality characteristic—and another that emphasizes process—that is, efforts to manage stress that change over time and are shaped by the adaptational context out of which it is generated. I begin with an account of the style and process approaches, discuss their history briefly, set forth the principles of a process approach, describe my own efforts at measurement, and define coping and its functions from a process standpoint. This is followed by a digest of major generalizations that resulted from coping process research. The essay concludes with a discussion of special issues of coping measurement, in particular, the limitations of both coping style and process approaches and how these limitations might be dealt with. There has been a prodigious volume of coping research in the last decade or two, which I can only touch on very selectively. In this essay, I also ignore a host of important developmental issues that have to do with the emergence of coping and its cognitive and motivational bases in infants, as well as a growing literature on whether, how, and why the coping process changes with aging.
SVM based feature set analysis in dynamic malayalam handwritten character recognition

Dynamic or Online handwritten character recognition is a challenging field in Human Computer Interfaces. The classification success rate of current techniques decreases when the dataset involves the similarity and complexity in stroke styles, number of strokes and stroke characteristics variations. Malayalam is a complex south Indian language spoken about 35 million people especially in Kerala and Lakshadweep islands. In this paper, a classification scheme based on support vector machines (SVM) is proposed to improve the accuracy in classification and recognition of online malayalam handwritten characters. SVM Classifier is a popular one in academy as well as in industry. This Classifiers are more suitable in a real world applicative problem, if we have major concern on the speed of recognition per character. The contribution of various features towards the accuracy in recognition is analyzed. Performance for different kernels of SVM are also studied. A graphical user interface has developed for reading and displaying the character. Different writing styles are taken for each of the 44 alphabets. Various features are extracted and used for classification after the preprocessing of input data samples. Feature Selection is carried out by choosing of different combinations of extracted features versus accuracy. Highest recognition accuracy of 97% is obtained for the best selected features in SVM with polynomial kernel. Recognition speed of a single stroke is obtained 0.52 secs.
Sparse Matrix Multiplication on CAM Based Accelerator

Sparse matrix multiplication is an important component of linear algebra computations. In this paper, an architecture based on Content Addressable Memory (CAM) and Resistive Content Addressable Memory (ReCAM) is proposed for accelerating sparse matrix by sparse vector and matrix multiplication in CSR format. Using functional simulation, we show that the proposed ReCAM-based accelerator exhibits two orders of magnitude higher power efficiency as compared to existing sparse matrix-vector multiplication implementations.
Video Encryption Based on OpenCV

This paper uses the open source computer vision library (OpenCV for short) as basic library and calls library functions to achieve real-time video capture and output. We narrate the process of making the matrix interpretable as an image through analyzing the data structure of IplImage and meaning of its member variable. The library functions to read video take the video frame for unit and the storage form of video frame in memory is the same as the ordinary image, so we use Arnold transformation to scramble each frame of video and accomplish video encryption.Ultimately play the processed video frames successively and achieve video encryption based on OpenCV simply.
A Robust Deep Model for Improved Classification of AD/MCI Patients

Accurate classification of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI), plays a critical role in possibly preventing progression of memory impairment and improving quality of life for AD patients. Among many research tasks, it is of a particular interest to identify noninvasive imaging biomarkers for AD diagnosis. In this paper, we present a robust deep learning system to identify different progression stages of AD patients based on MRI and PET scans. We utilized the dropout technique to improve classical deep learning by preventing its weight coadaptation, which is a typical cause of overfitting in deep learning. In addition, we incorporated stability selection, an adaptive learning factor, and a multitask learning strategy into the deep learning framework. We applied the proposed method to the ADNI dataset, and conducted experiments for AD and MCI conversion diagnosis. Experimental results showed that the dropout technique is very effective in AD diagnosis, improving the classification accuracies by 5.9% on average as compared to the classical deep learning methods.
Mobile application testing matrix and challenges

The adoption of smart phones and the usages of mobile applications are increasing rapidly. Consequently, within limited time-range, mobile Internet usages have managed to take over the desktop usages particularly since the first smart phone-touched application released by iPhone in 2007. This paper is proposed to provide solution and answer the most demandable questions related to mobile application automated and manual testing limitations. Moreover, Mobile application testing requires agility and physically testing. Agile testing is to detect bugs through automated tools, whereas the compatibility testing is more to ensure that the apps operates on mobile OS (Operation Systems) as well as on the different real devices. Moreover, we have managed to answer automated or manual questions through two mobile application case studies MES (Mobile Exam System) and MLM (Mobile Lab Mate) by creating test scripts for both case studies and our experiment results have been discussed and evaluated on whether to adopt test on real devices or on emulators? In addition to this, we have introduced new mobile application testing matrix for the testers and some enterprises to obtain knowledge from.
Stock market prediction: A big data approach

The Stock market process is full of uncertainty and is affected by many factors. Hence the Stock market prediction is one of the important exertions in finance and business. There are two types of analysis possible for prediction, technical and fundamental. In this paper both technical and fundamental analysis are considered. Technical analysis is done using historical data of stock prices by applying machine learning and fundamental analysis is done using social media data by applying sentiment analysis. Social media data has high impact today than ever, it can aide in predicting the trend of the stock market. The method involves collecting news and social media data and extracting sentiments expressed by individual. Then the correlation between the sentiments and the stock values is analyzed. The learned model can then be used to make future predictions about stock values. It can be shown that this method is able to predict the sentiment and the stock performance and its recent news and social data are also closely correlated.
Introduction to Autonomous Mobile Robots, Second Edition

No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher. Introduction to autonomous mobile robots.-2nd ed. / Roland Siegwart, Illah R. Nourbakhsh, and Da-vide Scaramuzza. p. cm.-(Intelligent robotics and autonomous agents series) Includes bibliographical references and index. Robotics has achieved its greatest success to date in the world of industrial manufacturing. Robot arms, or manipulators, comprise a $ 2 billion industry. Bolted at its shoulder to a specific position in the assembly line, the robot arm can move with great speed and accuracy to perform repetitive tasks such as spot welding and painting (figure 1.1). In the electronics industry, manipulators place surface-mounted components with superhuman precision, making the portable telephone and laptop computer possible. Yet, for all of their successes, these commercial robots suffer from a fundamental disadvantage: lack of mobility. A fixed manipulator has a limited range of motion that depends Figure 1.1 Picture of auto assembly plant-spot welding robot of KUKA and a parallel robot Delta of SIG Dem-aurex SA (invented at EPFL [296]) during packaging of chocolates. 2 Chapter 1 on where it is bolted down. In contrast, a mobile robot would be able to travel throughout the manufacturing plant, flexibly applying its talents wherever it is most effective. This book focuses on the technology of mobility: how can a mobile robot move unsu-pervised through real-world environments to fulfill its tasks? The first challenge is locomo-tion itself. How should a mobile robot move, and what is it about a particular locomotion mechanism that makes it superior to alternative locomotion mechanisms? Hostile environments such as Mars trigger even more unusual locomotion mechanisms (figure 1.2). In dangerous and inhospitable environments, even on Earth, such teleoperated systems have gained popularity (figures 1.3-1.6). In these cases, the low-level complexities of the robot often make it impossible for a human operator to control its motions directly. The human performs localization and cognition activities but relies on the robot's control scheme to provide motion control. For example, Plustech's walking robot provides automatic leg coordination while the human operator chooses an overall direction of travel (figure 1.3). Figure 1.6 depicts an underwater vehicle that controls three propellers to stabilize the robot submarine autonomously in spite of underwater turbulence and water currents while the operator chooses position goals for the submarine to achieve. …
Memory Errors: The Past, the Present, and the Future

Memory error exploitations have been around for over 25 years and still rank among the top 3 most dangerous software errors. Why haven’t we been able to stop them? Given the host of security measures on modern machines, are we less vulnerable than before, and can we expect to eradicate memory error problems in the near future? In this paper, we present a quarter century worth of memory errors: attacks, defenses, and statistics. A historical overview provides insight in past trends and developments, while an investigation of real-world vulnerabilities and exploits allows us to speculate on the significance of memory errors in the foreseeable future.
Impact of Customer Relationship Management on Customer Loyalty , Customer Retention and Customer Profitability for Hotelier Sector

Since the entrance of strategies oriented to marketing relational in Hotelier Sector, the traditional way of travel agents and other representatives arranging hospitality services for hotel and travel reservations has changed. The strategies oriented to customer relationship management are a relatively new area of specialty loyalty marketing in the hotel and hotelier sector, with advancements being made constantly. The use of this type of strategy can allow hoteliers or companies to tailor special guest programs, services and promotions based on hotel guest preferences. The hotel can use the data collected in a program to identify the needs of particular customers across hotel chains to be able to use marketing that can be targeted at specific groups of people. It also gives hoteliers the opportunity to evaluate frequent guest programs, personalize their services and perform trend analysis. A program based in marketing relational is typically run by hotels and companies to collect guest information and transaction data for use and examining to allow hoteliers to see target groups that should be marketed too. Based on these transactions hotels are able to create and manage guest loyalty programs and reward schemes. This research approach is to appraise the impact of customer relationship management on customer profitability as mediated by customer loyalty and customer retention within the hotelier sector of Mexico, and specifically for those hoteliers classified like of three stars. A sample of 100 hotels three stars was interviewed as respondents in this study. The objective of the study was to find the impact relationship between effective customer relationship implementation, customer loyalty, and customer retention and customer profitability. The findings of the study add value to hotels three stars in Mexico, and provide some invaluable statistical results essential for hotel managers and owners to successfully enhance customer loyalty, customer retention and customer profitability. By applying a questionnaire that count with seven blocks different one each other, the results of our study identified and provide path analysis of the relevant systems; and enumerated among the relevant system, those critical inter-component relationships within this highly competitive industry. This study’s findings add to the body of knowledge and enable the managers of this sector to implement customer relationship management in the best shape possible, to match it with Mexican market-needs thereby creating more loyal and repeat clientele.
Automotive radome design - fishnet structure for 79 GHz

Metamaterials are considered as an option for quasi-optical matching of automotive radar radomes, lowering transmission loss and minimizing reflections. This paper shows a fishnet structure design for the 79 GHz band which is suitable for this type of matching and which exhibits a negative index of refraction. The measured transmission loss is 0.9 dB at 79 GHz. A tolerance study concerning copper plating, substrate permittivity, oblique incidence, and polarization is shown. Quasi-optical measurements were done in the range of 60 – 90 GHz, which agree with simulated results.
A Frequent Pattern Mining Algorithm for Feature Extraction of Customer Reviews

Online shoppers often have different idea about the same product. They look for the product features that are consistent with their goal. Sometimes a feature might be interesting for one, while it does not make that impression for someone else. Unfortunately, identifying the target product with particular features is a tough task which is not achievable with existing functionality provided by common websites. In this paper, we present a frequent pattern mining algorithm to mine a bunch of reviews and extract product features. Our experimental results indicate that the algorithm outperforms the old pattern mining techniques used by previous researchers.
A study on intelligent personalized push notification with user history

In mobile application, the push notification is an important function to make users consume an item (eg. movie, novel). If a mobile application sends too many notifications to users without considering their preferences, it can cause them to turn off the notification or even deleting the mobile application. Thus, we develop a framework that selects a set of users who are likely to react to the push notification. To the best of our knowledge, we first apply recommendation techniques (i.e., collaborative filtering and content-based models) to the problem of discovering the target of push notification.
Combining Psychodynamic Psychotherapy and Pharmacotherapy.

Many patients with depression, anxiety disorders, and other psychiatric disorders are treated with combinations of psychodynamic psychotherapy and medication. Whether this is better than monotherapy is an empirical question that requires much more extensive research than is currently available. When medications were first introduced to treat psychiatric illnesses, some psychopharmacologists insisted that it heralded a new area of "biological psychiatry" that would ultimately render psychotherapy obsolete. Psychodynamic theorists and practitioners, on the other hand, argued that psychopharmacology offered only a superficial approach to treatment. Fortunately, these battles are now largely supplanted by the belief that whatever treatment offers the patient the best outcome should be employed, regardless of the therapist's theoretical outlook. This should motivate more extensive study of the value of combination treatment. So far, the few studies that have been done suggest that the combination of psychodynamic psychotherapy and medication may be superior for the treatment of mood and anxiety disorders, but most of these studies have small sample sizes and involve only short-term psychotherapy. An examination of the neuroscience of mood and anxiety disorders and of the mechanism of action of psychodynamic psychotherapy and of antidepressant medication suggests several routes by which the two treatment modalities could be synergistic: stimulation of hippocampal neurogenesis; epigenetic regulation of gene expression; dendritic remodeling; enhanced prefrontal cortical control of limbic system activity; and action at specific neurohormonal and neurotransmitter targets. The evidence for each of these mechanisms is reviewed with an eye toward potential experiments that might be relevant to them.