nils-herrmann/clean_scirepeval huggingface dataset:
Low Space Harmonics Cancelation in Double-Layer Fractional Slot Winding Using Dual Multiphase Winding

One of the main drawbacks of nonoverlapped coils in fractional slot concentrated winding permanent magnet (PM) machines are the high eddy current losses in both rotor core and permanent magnets induced by the asynchronous harmonics of the armature reaction field. It has been shown in the literature that the reduction of low space harmonics can effectively reduce the rotor eddy current losses. This paper shows that employing a combined star-delta winding to a three-phase PM machine with fractional slot windings and with a number of slots equal to 12, or its multiples, yields a complete cancellation to the fundamental magneto-motive force (MMF) component, which significantly reduces the induced rotor eddy current. Besides, it offers a slight increase in machine torque density. A case study on the well-known 12-slot/10-pole PM machine is conducted to explore the proposed approach. With the same concept, the general n-phase PM machine occupying 4n slots and with a dual n-phase winding is then proposed. This configuration offers a complete cancelation of all harmonics below the torque producing MMF component. Hence, the induced eddy currents in both rotor core and magnets are significantly reduced. The winding connection and the required number of turns for both winding groups are also given. The concept is applied to a 20-slot/18-pole stator with a dual five-phase winding, where the stator winding is connected as a combined star/pentagon connection. The proposed concept is assessed through a simulation study based on 2-D finite element analysis.
Analyzing and Detecting Malicious Flash Advertisements

The amount of dynamic content on the web has been steadily increasing. Scripting languages such as JavaScript and browser extensions such as Adobe's Flash have been instrumental in creating web-based interfaces that are similar to those of traditional applications. Dynamic content has also become popular in advertising, where Flash is used to create rich, interactive ads that are displayed on hundreds of millions of computers per day. Unfortunately, the success of Flash-based advertisements and applications attracted the attention of malware authors, who started to leverage Flash to deliver attacks through advertising networks. This paper presents a novel approach whose goal is to automate the analysis of Flash content to identify malicious behavior. We designed and implemented a tool based on the approach, and we tested it on a large corpus of real-world Flash advertisements. The results show that our tool is able to reliably detect malicious Flash ads with limited false positives. We made our tool available publicly and it is routinely used by thousands of users.
Internet-based ICT adoption among SMEs: Demographic versus benefits, barriers, and adoption intention

The purpose of this paper is to investigate the demographic characteristics of small and medium enterprises (SMEs) with regards to their patterns of internet-based information and communications technology (ICT) adoption, taking into account the dimensions of ICT benefits, barriers, and subsequently adoption intention.  A questionnaire-based survey is used to collect data from 406 managers or owners of SMEs in Malaysia.  The results reveal that the SMEs would adopt internet-based ICT regardless of years of business start-up and internet experience. Some significant differences are spotted between manufacturing and service SMEs in terms of their demographic characteristics and internet-based ICT benefits, barriers, and adoption intention. Both the industry types express intention to adopt internet-based ICT, with the service-based SMEs demonstrating greater intention.  The paper focuses only on the SMEs in the southern region of Malaysia.  The findings offer valuable insights to the SMEs – in particular promoting internet-based ICT adoption for future business success.  This paper is perhaps one of the first to comprehensively investigate the relationship between demographic characteristics of SMEs and the various variables affecting their internet-based ICT adoption intention.
Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.

Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.
Experience and Management of Intravascular Injection with Facial Fillers: Results of a Multinational Survey of Experienced Injectors

Intravascular injection leading to skin necrosis or blindness is the most serious complication of facial injection with fillers. It may be underreported and the outcome of cases are unclear. Early recognitions of the symptoms and signs may facilitate prompt treatment if it does occur avoiding the potential sequelae of intravascular injection. To determine the frequency of intravascular injection among experienced injectors, the outcomes of these intravascular events, and the management strategies. An internet-based survey was sent to 127 injectors worldwide who act as trainers for dermal fillers globally. Of the 52 respondents from 16 countries, 71 % had ≥11 years of injection experience, and 62 % reported one or more intravascular injections. The most frequent initial signs were minor livedo (63 % of cases), pallor (41 %), and symptoms of pain (37 %). Mildness/absence of pain was a feature of 47 % of events. Hyaluronidase (5 to >500 U) was used immediately on diagnosis to treat 86 % of cases. The most commonly affected areas were the nasolabial fold and nose (39 % each). Of all the cases, only 7 % suffered moderate scarring requiring surface treatments. Uneventful healing was the usual outcome, with 86 % being resolved within 14 days. Intravascular injection with fillers can occur even at the hands of experienced injectors. It may not be always associated with immediate pain or other classical symptoms and signs. Prompt effective management leads to favorable outcomes, and will prevent catastrophic consequences such as skin necrosis. Intravascular injection leading to blindness may not be salvageable and needs further study. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
Volumetric stereo and silhouette fusion for image-based modeling

This paper presents a volumetric stereo and silhouette fusion algorithm for acquiring high quality models from multiple calibrated photographs. Our method is based on computing and merging depth maps. Different from previous methods of this category, the silhouette information is also applied in our algorithm to recover the shape information on the textureless and occluded areas. The proposed algorithm starts by computing visual hull using a volumetric method in which a novel projection test method is proposed for visual hull octree construction. Then, the depth map of each image is estimated by an expansion-based approach that returns a 3D point cloud with outliers and redundant information. After generating an oriented point cloud from stereo by rejecting outlier, reducing scale, and estimating surface normal for the depth maps, another oriented point cloud from silhouette is added by carving the visual hull octree structure using the point cloud from stereo to restore the textureless and occluded surfaces. Finally, Poisson Surface Reconstruction approach is applied to convert the oriented point cloud both from stereo and silhouette into a complete and accurate triangulated mesh model. The proposed approach has been implemented and the performance of the approach is demonstrated on several real data sets, along with qualitative comparisons with the state-of-the-art image-based modeling techniques according to the Middlebury benchmark.
Visualizing the Behavior of Object-Oriented Systems

Numerous classes, complex inheritance and containment hierarchies, and diverse patterns of dynamic interaction all contribute to difficulties in understanding, reusing, debugging, and tuning large object-oriented systems. To help overcome these difficulties, we introduce novel views of the behavior of object-oriented systems and an architecture for creating and animating these views. We describe platform-independent techniques for instrumenting object-oriented programs, a language-independent protocol for monitoring their execution, and a structure for decoupling the execution of a subject program from its visualization. Case studies involving tuning and debugging of real systems are presented to demonstrate the benefits of visualization. We believe that visualization will prove to be a valuable tool for object-oriented software development.
Progress in Tourism Management Progress in information technology and tourism management : 20 years on and 10 years after the Internet — The state of eTourism research

This paper reviews the published articles on eTourism in the past 20 years. Using a wide variety of sources, mainly in the tourism literature, this paper comprehensively reviews and analyses prior studies in the context of Internet applications to tourism. The paper also projects future developments in eTourism and demonstrates critical changes that will influence the tourism industry structure. A major contribution of this paper is its overview of the research and development efforts that have been endeavoured in the field, and the challenges that tourism researchers are, and will be, facing.
Deep Convolutional Neural Networks and Data Augmentation for Acoustic Event Detection

We propose a novel method for Acoustic Event Detection (AED). In contrast to speech, sounds coming from acoustic events may be produced by a wide variety of sources. Furthermore, distinguishing them often requires analyzing an extended time period due to the lack of a clear sub-word unit. In order to incorporate the long-time frequency structure for AED, we introduce a convolutional neural network (CNN) with a large input field. In contrast to previous works, this enables to train audio event detection end-to-end. Our architecture is inspired by the success of VGGNet  and uses small, 3×3 convolutions, but more depth than previous methods in AED. In order to prevent over-fitting and to take full advantage of the modeling capabilities of our network, we further propose a novel data augmentation method to introduce data variation. Experimental results show that our CNN significantly outperforms state of the art methods including Bag of Audio Words (BoAW) and classical CNNs, achieving a 16% absolute improvement.
Teacher Quality and Student Achievement : A Review of State Policy Evidence by

........................................................................................................................... 4 Introduction ...................................................................................................................... 5 Differences in State Policies Regarding Teaching .......................................................... 14 Trends in Student Achievement: Policy Hypotheses ...................................................... 17 A National View of Teacher Qualifications and Student Achievement ............................. 27 Analysis of Policy Relationships...................................................................................... 32 Conclusions and Implications.......................................................................................... 38 Endnotes ......................................................................................................................... 40 References ...................................................................................................................... 41
Evaluation of decision trees: a multi-criteria approach

Data mining (DM) techniques are being increasingly used in many modern organizations to retrieve valuable knowledge structures from organizational databases, including data warehouses. An important knowledge structure that can result from data mining activities is the decision tree (DT) that is used for the classi3cation of future events. The induction of the decision tree is done using a supervised knowledge discovery process in which prior knowledge regarding classes in the database is used to guide the discovery. The generation of a DT is a relatively easy task but in order to select the most appropriate DT it is necessary for the DM project team to generate and analyze a signi3cant number of DTs based on multiple performance measures. We propose a multi-criteria decision analysis based process that would empower DM project teams to do thorough experimentation and analysis without being overwhelmed by the task of analyzing a signi3cant number of DTs would o7er a positive contribution to the DM process. We also o7er some new approaches for measuring some of the performance criteria. ? 2003 Elsevier Ltd. All rights reserved.
Semantic data extraction over MQTT for IoTcentric wireless sensor networks

The emergence of the paradigm of Internet of Things (IoT) has necessitated the development of machine-to-machine (M2M) protocols geared towards wireless sensor network interfacing to the Internet and implementing machine learning algorithms over the cloud. This paper discusses the viability of the MQ Telemetry Transport (MQTT) protocol for such applications. This paper introduces MQTT along with its merits and demerits and suitability towards IoT applications. Then it outlines an implementation of a typical IoT application involving ubiquitous sensing, M2M communication, cloud computing and semantic data extraction. The results of this experiment are then analyzed. Finally, the paper looks at future improvements in the proposed architecture for widespread use.
No-fault-found and intermittent failures in electronic products

This paper reviews the possible causes and effects for no-fault-found observations and intermittent failures in electronic products and summarizes them into cause and effect diagrams. Several types of intermittent hardware failures of electronic assemblies are investigated, and their characteristics and mechanisms are explored. One solder joint intermittent failure case study is presented. The paper then discusses when no-fault-found observations should be considered as failures. Guidelines for assessment of intermittent failures are then provided in the discussion and conclusions.
Joint Semantic Synthesis and Morphological Analysis of the Derived Word

Much like sentences are composed of words, words themselves are composed of smaller units. For example, the English word questionably can be analyzed as question+able+ly. However, this structural decomposition of the word does not directly give us a semantic representation of the word’s meaning. Since morphology obeys the principle of compositionality, the semantics of the word can be systematically derived from the meaning of its parts. In this work, we propose a novel probabilistic model of word formation that captures both the analysis of a word w into its constituent segments and the synthesis of the meaning of w from the meanings of those segments. Our model jointly learns to segment words into morphemes and compose distributional semantic vectors of those morphemes. We experiment with the model on English CELEX data and German DErivBase (Zeller et al., 2013) data. We show that jointly modeling semantics increases both segmentation accuracy and morpheme F1 by between 3% and 5%. Additionally, we investigate different models of vector composition, showing that recurrent neural networks yield an improvement over simple additive models. Finally, we study the degree to which the representations correspond to a linguist’s notion of morphological productivity.
Novel Polarization-Reconfigurable Converter Based on Multilayer Frequency-Selective Surfaces

In this paper, a novel polarization-reconfigurable converter (PRC) is proposed based on a multilayer frequency-selective surface (MFSS). First, the MFSS is designed using the square patches and the grid lines array to determine the operational frequency and bandwidth, and then the corners of the square patches are truncated to produce the phase difference of 90° between the two orthogonal linear components for circular polarization performance. To analyze and synthesize the PRC array, the operational mechanism is described in detail. The relation of the polarization states as a function of the rotating angle of the PRC array is summarized from the principle of operation. Therefore, the results show that the linear polarization (LP) from an incident wave can be reconfigured to LP, right- and left-hand circular polarizations by rotating the free-standing converter screen. The cell periods along x- and y-directions are the same, and their total height is 6 mm. The fractional bandwidth of axial ratio (AR) less than 3 dB is more than 15% with respect to the center operating frequency of 10 GHz at normal incidence. Simultaneously, the AR characteristics of different incidence angles for oblique incidence with TE and TM polarizations show that the proposed PRC has good polarization and angle stabilities. Moreover, the general design procedure and method is presented. Finally, a circularly shaped PRC array using the proposed PRC element based on the MFSS design is fabricated and measured. The agreement between the simulated and measured results is excellent.
Multi-modal sensor registration for vehicle perception via deep neural networks

The ability to simultaneously leverage multiple modes of sensor information is critical for perception of an automated vehicle's physical surroundings. Spatio-temporal alignment of registration of the incoming information is often a prerequisite to analyzing the fused data. The persistence and reliability of multi-modal registration is therefore the key to the stability of decision support systems ingesting the fused information. LiDAR-video systems like on those many driverless cars are a common example of where keeping the LiDAR and video channels registered to common physical features is important. We develop a deep learning method that takes multiple channels of heterogeneous data, to detect the misalignment of the LiDAR-video inputs. A number of variations were tested on the Ford LiDAR-video driving test data set and will be discussed. To the best of our knowledge the use of multi-modal deep convolutional neural networks for dynamic real-time LiDAR-video registration has not been presented.
Learning to Rewrite Queries

It is widely known that there exists a semantic gap between web documents and user queries and bridging this gap is crucial to advance information retrieval systems. The task of query rewriting, aiming to alter a given query to a rewrite query that can close the gap and improve information retrieval performance, has attracted increasing attention in recent years. However, the majority of existing query rewriters are not designed to boost search performance and consequently their rewrite queries could be sub-optimal. In this paper, we propose a learning to rewrite framework that consists of a candidate generating phase and a candidate ranking phase. The candidate generating phase provides us the flexibility to reuse most of existing query rewriters; while the candidate ranking phase allows us to explicitly optimize search relevance. Experimental results on a commercial search engine demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the important components of the proposed framework.
Human-like social skills in dogs?

Domestic dogs are unusually skilled at reading human social and communicative behavior--even more so than our nearest primate relatives. For example, they use human social and communicative behavior (e.g. a pointing gesture) to find hidden food, and they know what the human can and cannot see in various situations. Recent comparisons between canid species suggest that these unusual social skills have a heritable component and initially evolved during domestication as a result of selection on systems mediating fear and aggression towards humans. Differences in chimpanzee and human temperament suggest that a similar process may have been an important catalyst leading to the evolution of unusual social skills in our own species. The study of convergent evolution provides an exciting opportunity to gain further insights into the evolutionary processes leading to human-like forms of cooperation and communication.
UbiHeld: ubiquitous healthcare monitoring system for elderly and chronic patients

Once the person's identity is established, the most important aspects of ubiquitous healthcare monitoring of elderly and chronic patients are location, activity, physiological and psychological parameters. Since smartphones have become the most pervasive computing platform today, it is only a logical extension to use the same in healthcare domain for bringing ubiquity. Besides smartphone, skeleton based activity detection and localization using depth sensor like Kinect make ubiquitous monitoring effective without compromising privacy to a large extent. Finally sensing mental condition is made possible by analysis of the subject's social network feed. This paper presents an end-to-end healthcare monitoring system code named UbiHeld (Ubiquitous Healthcare for Elderly) using the techniques mentioned above and an IoT (Internet of Things) based back-end platform.
Unfolded recurrent neural networks for speech recognition

We introduce recurrent neural networks (RNNs) for acoustic modeling which are unfolded in time for a fixed number of time steps. The proposed models are feedforward networks with the property that the unfolded layers which correspond to the recurrent layer have time-shifted inputs and tied weight matrices. Besides the temporal depth due to unfolding, hierarchical processing depth is added by means of several non-recurrent hidden layers inserted between the unfolded layers and the output layer. The training of these models: (a) has a complexity that is comparable to deep neural networks (DNNs) with the same number of layers; (b) can be done on frame-randomized minibatches; (c) can be implemented efficiently through matrix-matrix operations on GPU architectures which makes it scalable for large tasks. Experimental results on the Switchboard 300 hours English conversational telephony task show a 5% relative improvement in word error rate over state-of-the-art DNNs trained on FMLLR features with i-vector speaker adaptation and hessianfree sequence discriminative training.