nils-herrmann/clean_scirepeval huggingface dataset:
A High Torque Density Vernier PM Machines for Hybrid Electric Vehicle Applications

This paper proposes a novel vernier permanent magnet (VPM) machine for hybrid electric vehicle (HEV) applications. The main features of the proposed machine are concentrated armature windings with short and non- overlapping end-winding, and the permanent magnets (PMs) are on both sides of rotor and stator. Unlike conventional motors, this machine operates with flux modulation principles, and the electromagnetic torque is produced by the interaction between the armature magneto motive force (MMF) and the exciting fields by stator and rotor PMs. In this paper, the electromagnetic performance is simulated by the finite element analysis (FEA). The results show that the torque density is about 33% higher than an existing interior permanent magnet (IPM) machines.
The Pfam protein families database: towards a more sustainable future

In the last two years the Pfam database (http://pfam.xfam.org) has undergone a substantial reorganisation to reduce the effort involved in making a release, thereby permitting more frequent releases. Arguably the most significant of these changes is that Pfam is now primarily based on the UniProtKB reference proteomes, with the counts of matched sequences and species reported on the website restricted to this smaller set. Building families on reference proteomes sequences brings greater stability, which decreases the amount of manual curation required to maintain them. It also reduces the number of sequences displayed on the website, whilst still providing access to many important model organisms. Matches to the full UniProtKB database are, however, still available and Pfam annotations for individual UniProtKB sequences can still be retrieved. Some Pfam entries (1.6%) which have no matches to reference proteomes remain; we are working with UniProt to see if sequences from them can be incorporated into reference proteomes. Pfam-B, the automatically-generated supplement to Pfam, has been removed. The current release (Pfam 29.0) includes 16 295 entries and 559 clans. The facility to view the relationship between families within a clan has been improved by the introduction of a new tool.
Integrating Grid-Based and Topological Maps for Mobile Robot Navigation

Research on mobile robot navigation has produced two major paradigms for mapping indoor environments: grid-based and topological. While grid-based methods produce accurate metric maps, their complexity often prohibits efficient planning and problem solving in large-scale indoor environments. Topological maps, on the other hand, can be used much more efficiently, yet accurate and consistent topological maps are considerably difficult to learn in large-scale environments. This paper describes an approach that integrates both paradigms: grid-based and topological. Grid-based maps are learned using artificial neural networks and Bayesian integration. Topological maps are generated on top of the grid-based maps, by partitioning the latter into coherent regions. By combining both paradigms—grid-based and topological—, the approach presented here gains the best of both worlds: accuracy/consistency and efficiency. The paper gives results for autonomously operating a mobile robot equipped with sonar sensors in populated multi-room envi-
Face anti-spoofing via hybrid convolutional neural network

Face anti-spoofing techniques have been developed several years since the face recognition systems were successfully applied. Existing approaches in this topic merely use the entire region of human face. However, different facial parts always have different structures and the full-face model maybe weaken the discrepancy of the specific parts. So training the specific model for each facial part can improve the performance of anti-spoofing. Motivated by this, in this paper, we propose a new method of face anti-spoofing leveraging hybrid convolutional neural network (CNN) for facial parts. The main procedure of our work can be summarized as follows: (i) We divide the face into several parts; (ii) Based on different parts, training the corresponding CNN model of each part, which will constitute the hybrid CNN; (iii) Concatenating the last layer of the hybrid model to train a SVM classifier; (iv) By the SVM to distinguish the real and fake faces. We tested the effectiveness of our method on two public available databases, Replay-Attack and CASIA, and the experiments show our proposed method can obtain satisfactory results with respect to the state-of-the- art methods.
Principles for Learning Controllable TTS from Annotated and Latent Variation

For building flexible and appealing high-quality speech synthesisers, it is desirable to be able to accommodate and reproduce fine variations in vocal expression present in natural speech. Synthesisers can enable control over such output properties by adding adjustable control parameters in parallel to their text input. If not annotated in training data, the values of these control inputs can be optimised jointly with the model parameters. We describe how this established method can be seen as approximate maximum likelihood and MAP inference in a latent variable model. This puts previous ideas of (learned) synthesiser inputs such as sentence-level control vectors on a more solid theoretical footing. We furthermore extend the method by restricting the latent variables to orthogonal subspaces via a sparse prior. This enables us to learn dimensions of variation present also within classes in coarsely annotated speech. As an example, we train an LSTM-based TTS system to learn nuances in emotional expression from a speech database annotated with seven different acted emotions. Listening tests show that our proposal successfully can synthesise speech with discernible differences in expression within each emotion, without compromising the recognisability of synthesised emotions compared to an identical system without learned nuances.
Directed Graph Embedding

In this paper, we propose the Directed Graph Embedding (DGE) method that embeds vertices on a directed graph into a vector space by considering the link structure of graphs. The basic idea is to preserve the locality property of vertices on a directed graph in the embedded space. We use the transition probability together with the stationary distribution of Markov random walks to measure such locality property. It turns out that by exploring the directed links of the graph using random walks, we can get an optimal embedding on the vector space that preserves the local affinity which is inherent in the directed graph. Experiments on both synthetic data and real-world Web page data are considered. The application of our method to Web page classification problems gets a significant improvement comparing with state-of-art methods.
Face to Face Collaborative AR on Mobile Phones

Mobile phones are an ideal platform for augmented reality. In this paper we describe how they can also be used to support face to face collaborative AR gaming. We have created a custom port of the ARToolKit library to the Symbian mobile phone operating system and then developed a sample collaborative AR game based on this. We describe the game in detail and user feedback from people who have played the game. We also provide general design guidelines that could be useful for others who are developing mobile phone collaborative AR applications.
Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise

Modern machine learning-based approaches to computer vision require very large databases of hand labeled images. Some contemporary vision systems already require on the order of millions of images for training (e.g., Omron face detector ). New Internet-based services allow for a large number of labelers to collaborate around the world at very low cost. However, using these services brings interesting theoretical and practical challenges: (1) The labelers may have wide ranging levels of expertise which are unknown a priori, and in some cases may be adversarial; (2) images may vary in their level of difficulty; and (3) multiple labels for the same image must be combined to provide an estimate of the actual label of the image. Probabilistic approaches provide a principled way to approach these problems. In this paper we present a probabilistic model and use it to simultaneously infer the label of each image, the expertise of each labeler, and the difficulty of each image. On both simulated and real data, we demonstrate that the model outperforms the commonly used “Majority Vote” heuristic for inferring image labels, and is robust to both noisy and adversarial labelers.
Mapping CMMI Project Management Process Areas to SCRUM Practices

Over the past years, the capability maturity model (CMM) and capability maturity model integration (CMMI) have been broadly used for assessing organizational maturity and process capability throughout the world. However, the rapid pace of change in information technology has caused increasing frustration to the heavyweight plans, specifications, and other documentation imposed by contractual inertia and maturity model compliance criteria. In light of that, agile methodologies have been adopted to tackle this challenge. The aim of our paper is to present mapping between CMMI and one of these methodologies, Scrum. It shows how Scrum addresses the Project Management Process Areas of CMMI. This is useful for organizations that have their plan-driven process based on the CMMI model and are planning to improve its processes toward agility or to help organizations to define a new project management framework based on both CMMI and Scrum practices.
Secure routing protocol design for UAV Ad hoc NETworks

UAV Ad hoc NETworks (UAANETs) can be defined as a new form of ad hoc networks in which nodes are Unmanned Aerial Vehicles (UAVs) and Ground Control Station (GCS). Compared to the usual Mobile Ad hoc NETwork (MANET), this new network paradigm has some unique features and brings specific challenges such as node mobility degree, network connectivity patterns, delay-sensitive applications and network security. Indeed, from routing point of view, none of the several UAANET routing protocols proposed in the literature have been designed with security in mind. This lack of consideration can make the certification of UAANETs difficult to obtain. In this paper, we present our vision of such a challenge and the research that we are conducting. The aim is to propose an original secure routing protocol for UAANETs using a Model Driven Development (MDD) approach which will ease the certification of final UAV products. The first preliminary results concerning our secure-routing protocol design will be presented. This paper describes our ongoing research which will provide secure communications for UAV ad hoc networks at the end of the SUANET (Secure Uav Ad-hoc NETwork) project.
The DEXMART hand: Mechatronic design and experimental evaluation of synergy-based control for human-like grasping

This paper summarizes recent activities carried out for the development of an innovative anthropomorphic robotic hand called the DEXMART Hand. The main goal of this research is to face the problems that affect current robotic hands by introducing suitable design solutions aimed at achieving simplification and cost reduction while possibly enhancing robustness and performance. While certain aspects of the DEXMART Hand development have been presented in previous papers, this paper is the first to give a comprehensive description of the final hand version and its use to replicate humanlike grasping. In this paper, particular emphasis is placed on the kinematics of the fingers and of the thumb, the wrist architecture, the dimensioning of the actuation system, and the final implementation of the position, force and tactile sensors. The paper focuses also on how these solutions have been integrated into the mechanical structure of this innovative robotic hand to enable precise force and displacement control of the whole system. Another important aspect is the lack of suitable control tools that severely limits the development of robotic hand applications. To address this issue, a new method for the observation of human hand behavior during interaction with common day-to-day objects by means of a 3D computer vision system is presented in this work together with a strategy for mapping human hand postures to the robotic hand. A simple control strategy based on postural synergies has been used to reduce the complexity of the grasp planning problem. As a preliminary evaluation of the DEXMART Hand’s capabilities, this approach has been adopted in this paper to simplify and speed up the transfer of human actions to the robotic hand, showing its effectiveness in reproducing human-like grasping.
DIKIW : Data , Information , Knowledge , Intelligence , Wisdom and their Interrelationships

DIKW hierarchy is the model used for discussion of data, information, knowledge, wisdom and their interrelationships. However, definitions of data, information, and knowledge are entrapped in a logical fallacy known as circular definition. There are some agreements in the definitions without the fallacy. The missing ‘Intelligence’ in DIKW plays a major role between knowledge and wisdom. This gives rise to a revised DIKW model here named DIKIW.
River flow estimation using adaptive neuro fuzzy inference system

Accurate estimation of River flow changes is a quite important problem for a wise and sustainable use. Such a problem is crucial to the works and decisions related to the water resources and management. In this study, an adaptive network-based fuzzy inference system (ANFIS) approach was used to construct a River flow forecasting system. In particular, the applicability of ANFIS as an estimation model for River flow was investigated. To illustrate the applicability and capability of the ANFIS, the River Great Menderes, located the west of Turkey and the most important water resource of Great Menderes Catchment’s, was chosen as a case study area. The advantage of this method is that it uses the input–output data sets. Totally 5844 daily data sets collected in 1985–2000 years were used to estimate the River flow. The models having various input structures were constructed and the best structure was investigated. In addition four various training/testing data sets were constructed by cross validation methods and the best data set was investigated. The performance of the ANFIS models in training and testing sets were compared with the observations and also evaluated. The results indicated that the ANFIS can be applied successfully and provide high accuracy and reliability for River flow estimation.
Testing of Copper Pillar Bumps for Wafer Sort

Using a copper pillar interconnect in flip chip packaging provides a lead-free solution that is more reliable, and also scalable to very fine pitch. Vertical probe card technology, also called buckling beam technology, was used in characterization of wafer probe process and electrical contact on solder bumps and copper pillars at 150 μm pitch arrays. Probe contact was investigated by modeling the scrub, penetration or deformation on a bump under various conditions of wafer probe and experimentally tested on wafers on copper pillars, solder bumps of different metallurgies or sheet wafers. A single-probe contact test system was devised to study the contact behavior of a probe on a bump. Various probe tip geometries including wedge, pointed and flat, were studied. Probing procedures were investigated for achieving reliable electrical contact for large pitch area array bumps as well as fine pitch, 50 μm, copper pillar array bumps using two different wafer test technologies.
Satellite Image Classification Methods and Techniques: A Review

Satellite image classification process involves grouping the image pixel values into meaningful categories. Several satellite image classification methods and techniques are available. Satellite image classification methods can be broadly classified into three categories 1) automatic 2) manual and 3) hybrid. All three methods have their own advantages and disadvantages. Majority of the satellite image classification methods fall under first category. Satellite image classification needs selection of appropriate classification method based on the requirements. The current research work is a study on satellite image classification methods and techniques. The research work also compares various researcher&apos;s comparative results on satellite image classification methods.
Calciphytoliths (calcium oxalate crystals) analysis for the identification of decayed tea plants (Camellia sinensis L.)

The history of tea is poorly known, mainly due to the questionable identification of decayed tea plants in archaeological samples. This paper attempts to test the utility of calciphytoliths (calcium oxalate crystals) for the identification of tea in archaeological samples. It provides the first survey of the macropatterns of calciphytoliths in several species of Theaceae and common non-Theaceae plants. Crystals were extracted from 45 samples of tea, Theaceae and common non-Theaceae plants, and detected microscopically between crossed polarizers. In tea plants, druse and trichome base are the most distinctive crystals. Druses have the smallest diameter (11.65 ± 3.64 μm), and trichome bases have four distinctive straight and regular cracks, similar to a regular extinction cross. The results provide morphological criteria for distinguishing tea from other plants, specifically the presence of identifiable druses together with calcified trichome bases. The implications are significant for understanding the history of tea and plant exploitation, especially for plants for which the preservation of macrofossils is poor.
Bluetooth 5: A Concrete Step Forward toward the IoT

Six years after the adoption of the standard 4, the Bluetooth Special Interest Group (SIG) has officially released the main features of Bluetooth 5. It is a major development in short-range wireless communication technology. As stated by the SIG, the new standard will change the way people approach the Internet of Things (IoT) significantly. In this article, the future IoT scenarios and use cases that justify the push for Bluetooth 5 are introduced. A set of new technical features that are included in Bluetooth 5.0 are presented, and their advantages and drawbacks are described.
Knowledge-Rich Word Sense Disambiguation Rivaling Supervised Systems

One of the main obstacles to highperformance Word Sense Disambiguation (WSD) is the knowledge acquisition bottleneck. In this paper, we present a methodology to automatically extend WordNet with large amounts of semantic relations from an encyclopedic resource, namely Wikipedia. We show that, when provided with a vast amount of high-quality semantic relations, simple knowledge-lean disambiguation algorithms compete with state-of-the-art supervised WSD systems in a coarse-grained all-words setting and outperform them on gold-standard domain-specific datasets.
Dimension Reduction and Data Visualization Using Neural Networks

The problem of visual presentation of multidimensional data is discussed. The projection methods for dimension reduction are reviewed. The chapter deals with the artificial neural networks that may be used for reducing dimension and data visualization, too. The stress is put on combining the selforganizing map (SOM) and Sammon mapping and on the neural network for Sammon’s mapping SAMANN. Large scale applications are discussed: environmental data analysis, statistical analysis of curricula, comparison of schools, analysis of the economic and social conditions of countries, analysis of data on the fundus of eyes and analysis of physiological data on men’s health.
Gender and Helping Behavior : A Meta-Analytic Review of the Social Psychological Literature

According to our social-role theory of gender and helping, the male gender role fosters helping that is heroic and chivalrous, whereas the female gender role fosters helping that is nurturant and caring. In social psychological studies, helping behavior has been examined in the context of short-term encounters with strangers. This focus has tended to exclude from the research literature those helping behaviors prescribed by the female gender role, because they are displayed primarily in long-term, close relationships. In contrast, the helping behaviors prescribed by the male gender role have been generously represented in research findings because they are displayed in relationships with strangers as well as in close relationships. Results from our meta-analytic review of sex differences in helping behavior indicate that in general men helped more than women and women received more help than men. Nevertheless, sex differences in helping were extremely inconsistent across studies and were successfully predicted by various attributes of the studies and the helping behaviors. These predictors were interpreted in terms of several aspects of our social-role theory of gender and helping.
Protocol considerations for a prefix-caching proxy for multimedia streams

The increasing popularity of multimedia streaming applications introduces new challenges in content distribution. Web-initiated multimedia streams typically experience high start-up delay, due to large protocol overheads and the poor delay, throughput, and loss properties of the Internet. Internet service providers can improve performance by caching the initial segment (the pre x) of popular streams at proxies near the requesting clients. The proxy can initiate transmission to the client while simultaneously requesting the remainder of the stream from the server. This paper analyzes the challenges of realizing a pre x-caching service in the context of the IETF's Real-Time Streaming Protocol (RTSP), a multimedia streaming protocol that derives from HTTP. We describe how to exploit existing RTSP features, such as the Range header, and how to avoid several round-trip delays by caching protocol information at the proxy. Based on our experiences, we propose extensions to RTSP that would ease the development of new multimedia proxy services. In addition, we discuss how caching the partial contents of multimedia streams introduces new challenges in cache coherency and feedback control. Then, we brie y present our preliminary implementation of pre x caching on a Linux-based PC, and describe how the proxy interoperates with the RealNetworks server and client.
Information-theoretical label embeddings for large-scale image classification

We present a method for training multi-label, massively multi-class image classification models, that is faster and more accurate than supervision via a sigmoid cross-entropy loss (logistic regression). Our method consists in embedding high-dimensional sparse labels onto a lower-dimensional dense sphere of unit-normed vectors, and treating the classification problem as a cosine proximity regression problem on this sphere. We test our method on a dataset of 300 million high-resolution images with 17,000 labels, where it yields considerably faster convergence, as well as a 7% higher mean average precision compared to logistic regression.