nils-herrmann/clean_scirepeval huggingface dataset:
What to expect from teleconferencing.

Like other marvels of the electronic age, teleconferencing has been both oversold and underused. Though it has many potential uses, what managers know, or think they know, about it is generally based on misconceptions. Rather than relying only on vendors of teleconferencing, potential purchasers should first decide what their communication needs are, then choose the system that suits them best. These authors explain the new teleconferencing options and give guidelines for adapting them to a particular company.
Concealed object detection and segmentation over millimetric waves images

Millimetric Waves Images (MMW) are becoming more and more useful in the passive detection of threaten objects based on plastic substances as explosives or sharp/cutting weapons. Our goal is to achieve segmentation of the body and concealed threats dealing with the inherent problems of this type of images: noise, low resolution and intensity inhomogeneity. In this work we present the results of applying Iterative Steering Kernel Regression (ISKR) method for denoising and Local Binary Fitting (LBF) for segmentation in order to correctly segment bodies and threats over a database of 29 MMW images. These methods, which had not been tested in the literature with these type of images, are compared with previously applied state of the art methods. Experimental results show that the use of the proposed methods in MMW images improve the results that had been obtained before.
Connecting minds and sharing emotions through mimicry: A neurocognitive model of emotional contagion

During social interactions, people tend to automatically align with, or mimic their interactor's facial expressions, vocalizations, postures and other bodily states. Automatic mimicry might be implicated in empathy and affiliation and is impaired in several pathologies. Despite a growing body of literature on its phenomenology, the function and underlying mechanisms of mimicry remain poorly understood. The current review puts forward a new Neurocognitive Model of Emotional Contagion (NMEC), demonstrating how basic automatic mimicry can give rise to emotional contagion. We combine neurological, developmental and evolutionary insights to argue that automatic mimicry is a precursor to healthy social development. We show that (i) strong synchronization exists between people, (ii) that this resonates on different levels of processing and (iii) demonstrate how mimicry translates into emotional contagion. We conclude that our synthesized model, built upon integrative knowledge from various fields, provides a promising avenue for future research investigating the role of mimicry in human mental health and social development.
A Distribution-Based Clustering Algorithm for Mining in Large Spatial Databases

The problem of detecting clusters of points belonging to a spatial point process arises in many applications. In this paper , we introduce the new clustering algorithm DBCLASD (Distribution Based Clustering of LArge Spatial Databases) to discover clusters of this type. The results of experiments demonstrate that DBCLASD, contrary to partitioning algorithms such as CLARANS, discovers clusters of arbitrary shape. Furthermore, DBCLASD does not require any input parameters, in contrast to the clustering algorithm DBSCAN requiring two input parameters which may be difficult to provide for large databases. In terms of efficiency, DBCLASD is between CLARANS and DBSCAN, close to DBSCAN. Thus, the efficiency of DBCLASD on large spatial databases is very attractive when considering its nonparametric nature and its good quality for clusters of arbitrary shape.
Automatic Classification of Software Change Request Using Multi-label Machine Learning Methods

Automatic text classification of the software change request (CR) can be used for automating impact analysis, bug triage and effort estimation. In this paper, we focus on the automation of the process for assigning CRs to developers and present a solution that is based on automatic text classification of CRs. In addition our approach provides the list of source files, which are required to be modified and an estimate for the time required to resolve a given CR. To perform experiments, we downloaded the set of resolved CRs from the OSS project's repository for Mozilla. We labeled each CR with multiple labels i.e., the developer name, the list of source files, and the time spent to resolve the CR. To train the classifier, our approach applies the Problem Transformation and Algorithm Adaptation methods of multi-label machine learning to the multi-labeled CR data. With this approach, we have obtained precision levels up to 71.3% with 40.1% recall.
Development and analysis of U-core switched reluctance machine

Switched reluctance machines (SRMs) have seen a lot of interest due to their rugged and fault tolerant construction as well as their high efficiency over a wide speed range. The technology however suffers from torque ripple, acoustic noise and low torque density. Many concepts to address these disadvantages have been presented, but not all of them have been demonstrated practically. This paper presents a practical demonstration and assessment of a segmented U-core SRM, which copes with some of the disadvantages of the regular SRM. The U-core SRM has a segmented stator, with a short flux path and reduced flux reversal, reducing core losses. Due to an increased number of poles, torque density is increased and torque ripple reduced. A prototype is built and through a number of tests, the machine is mapped and all loss components are analysed. As a result of the analysis, an assessment is presented, which addresses the shortcomings of the U-core technology, as well as general considerations for SRMs. This allows for further development and improvement of the technology, and also contributes to improve the development and modelling of conventional SRMs.
An Efficient Representation for Filtrations of Simplicial Complexes

A filtration over a simplicial complex K is an ordering of the simplices of K such that all prefixes in the ordering are subcomplexes of K. Filtrations are at the core of Persistent Homology, a major tool in Topological Data Analysis. To represent the filtration of a simplicial complex, the entire filtration can be appended to any data structure that explicitly stores all the simplices of the complex such as the Hasse diagram or the recently introduced Simplex Tree [Algorithmica’14]. However, with the popularity of various computational methods that need to handle simplicial complexes, and with the rapidly increasing size of the complexes, the task of finding a compact data structure that can still support efficient queries is of great interest.
 This direction has been recently pursued for the case of maintaining simplicial complexes. For instance, Boissonnat et al. [Algorithmica’17] considered storing the simplices that are maximal with respect to inclusion and Attali et al. [IJCGA’12] considered storing the simplices that block the expansion of the complex. Nevertheless, so far there has been no data structure that compactly stores the filtration of a simplicial complex, while also allowing the efficient implementation of basic operations on the complex.
 In this article, we propose a new data structure called the Critical Simplex Diagram (CSD), which is a variant of the Simplex Array List [Algorithmica’17]. Our data structure allows one to store in a compact way the filtration of a simplicial complex and allows for the efficient implementation of a large range of basic operations. Moreover, we prove that our data structure is essentially optimal with respect to the requisite storage space. Finally, we show that the CSD representation admits fast construction algorithms for Flag complexes and relaxed Delaunay complexes.
Hybrid electric vehicle propulsion system architectures of the e-CVT type

There is now significant interest in hybrid electric vehicle (HEV) propulsion systems globally. Economics play a major role as evidenced by oil prices in North America pressing upwards of $100/Bbl coupled with a customer preference for full size crossover and sport utility vehicles. The situation in Oceania is milder, but emerging markets such as China are experiencing automotive sector growth rates of 37%/year. Europe remains least affected by hybrids since nearly 47% of all new vehicles sold are diesel fueled and have economy ratings on par with that of gasoline-electric hybrids. In the global economy there are presently some 57 Mil new vehicles manufactured each year. Toyota and Honda have projected that HEVs will be 10 % to 15 % of the U.S. market by 2009, with Toyota raising the bar further by stating they will produce 1 Mil hybrids a year in the 2012 time frame. Hybrid propulsion system types are only vaguely comprehended by the buying public, and to a large measure, even by technical professionals. This paper addresses this latter issue by presenting a summary of the globally accepted standard in hybrid power trains-the power split architecture, or more generically and in common usage, the electronic-continuously variable transmission
Discriminant Learning Analysis

Linear discriminant analysis (LDA) as a dimension reduction method is widely used in classification such as face recognition. However, it suffers from the small sample size (SSS) problem when data dimensionality is greater than the sample size, as in images where features are high dimensional and correlated. In this paper, we propose to address the SSS problem in the framework of statistical learning theory. We compute linear discriminants by regularized least squares regression, where the singularity problem is resolved. The resulting discriminants are complete in that they include both regular and irregular information. We show that our proposal and its nonlinear extension belong to the same framework where powerful classifiers such as support vector machines are formulated. In addition, our approach allows us to establish an error bound for LDA. Finally, our experiments validate our theoretical analysis results.
Variational Sequential Monte Carlo

Many recent advances in large scale probabilistic inference rely on variational methods. The success of variational approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member of this family that most closely approximates the exact posterior. In this paper we present a new approximating family of distributions, the variational sequential Monte Carlo (VSMC) family, and show how to optimize it in variational inference. VSMC melds variational inference (VI) and sequential Monte Carlo (SMC), providing practitioners with flexible, accurate, and powerful Bayesian inference. The VSMC family is a variational family that can approximate the posterior arbitrarily well, while still allowing for efficient optimization of its parameters. We demonstrate its utility on state space models, stochastic volatility models for financial data, and deep Markov models of brain neural circuits.
Service Quality Transformation and its Impact on Customer Satisfaction and Loyalty in Malaysian Retail Banking Sector

This paper investigates the importance of antecedents of customer loyalty such as customer satisfaction and service quality dimensions. It addresses whether the dimensions of service quality impacts customer satisfaction eventually leading to customer loyalty. The Structural Equation modeling technique was used in this analysis to determine customer satisfaction and customer loyalty. The results suggest that empathy and assurance are the key factors in delivering customer satisfaction, whereas, tangibility, reliability and responsiveness are found to least impacting customer satisfaction. The relationship between customer satisfaction and customer loyalty is also found to be very significant. Therefore, the bank managers should focus their resources on improving customer care and assuring the services provided to the bank customers will eventually create customer loyalty towards their banks.
DHA: Lidar and Vision data Fusion-based On Road Object Classifier

In this paper, we first extract three different kinds of high-level features from LIDAR point cloud, and combine them into the DHA (Depth, Height and Angle) channels. Integrated with the traditional RGB image from camera, we build a rich feature-based road object classifier by training a deep convolutional neural network model with six-channel (RGBDHA) data. Subsequently, this deep convolution neural network is fed by the integration of spacial and RGB information. With additional upsampled LIDAR data, the classifier reaches higher accuracy than single RGB image base methods. Several simulations on the famous autonomous vehicle benchmark of KITTI show that our fusion-based classifier outperforms RGB-based approaches about 15% and reaches average accuracy of 96%.
K-Means Cluster Analysis for Image Segmentation

Does K-Means reasonably divides the data into k groups is an important question that arises when one works on Image Segmentation? Which color space one should choose and how to ascertain that the k we determine is valid? The purpose of this study was to explore the answers to aforementioned questions. We perform K-Means on a number of 2-cluster, 3-cluster and k-cluster color images (k>3) in RGB and L*a*b* feature space. Ground truth (GT) images have been used to accomplish validation task. Silhouette analysis supports the peaks for given k-cluster image. Model accuracy in RGB space falls between 30% and 55% while in L*a*b* color space it ranges from 30% to 65%. Though few images used, but experimentation proves that K-Means significantly segment images much better in L*a*b* color space as compared to RGB feature space.
Capacitive power transfer for contactless charging

The simplicity and low cost of capacitive interfaces makes them very attractive for wireless charging stations. Major benefits include low electromagnetic radiation and the amenability of combined power and data transfer over the same interface. We present a capacitive power transfer circuit using series resonance that enables efficient high frequency, moderate voltage operation through soft-switching. An included analysis predicts fundamental limitations on the maximum achievable efficiency for a given amount of coupling capacitance and is used to find the optimum circuit component values and operating point. Automatic tuning loops ensure the circuit operates at the optimum frequency and maximum efficiency over a wide range of coupling capacitance and load conditions. An example interface achieves near 80% efficiency at 3.7 W with only 63pF of coupling capacitance. An automatic tuning loop adjusts the frequency from 4.2 MHz down to 4MHz to allow for 25% variation in the nominal coupling capacitance. The duty cycle is also automatically adjusted to maintain over 70% efficiency for light loads down to 0.3 W.
Microscale microbial fuel cells: Advances and challenges.

The next generation of sustainable energy could come from microorganisms; evidence that it can be seen with the given rise of Electromicrobiology, the study of microorganisms' electrical properties. Many recent advances in electromicrobiology stem from studying microbial fuel cells (MFCs), which are gaining acceptance as a future alternative "green" energy technology and energy-efficient wastewater treatment method. MFCs are powered by living microorganisms with clean and sustainable features; they efficiently catalyse the degradation of a broad range of organic substrates under natural conditions. There is also increasing interest in photosynthetic MFCs designed to harness Earth's most abundant and promising energy source (solar irradiation). Despite their vast potential and promise, however, MFCs and photosynthetic MFCs have not yet successfully translated into commercial applications because they demonstrate persistent performance limitations and bottlenecks associated with scaling up. Instead, microscale MFCs have received increasing attention as a unique platform for various applications such as powering small portable electronic elements in remote locations, performing fundamental studies of microorganisms, screening bacterial strains, and toxicity detection in water. Furthermore, the stacking of miniaturized MFCs has been demonstrated to offer larger power densities than a single macroscale MFC in terms of scaling up. In this overview, we discuss recent achievements in microscale MFCs as well as their potential applications. Further scientific and technological challenges are also reviewed.
Change from baseline and analysis of covariance revisited.

The case for preferring analysis of covariance (ANCOVA) to the simple analysis of change scores (SACS) has often been made. Nevertheless, claims continue to be made that analysis of covariance is biased if the groups are not equal at baseline. If the required equality were in expectation only, this would permit the use of ANCOVA in randomized clinical trials but not in observational studies. The discussion is related to Lord's paradox. In this note, it is shown, however that it is not a necessary condition for groups to be equal at baseline, not even in expectation, for ANCOVA to provide unbiased estimates of treatment effects. It is also shown that although many situations can be envisaged where ANCOVA is biased it is very difficult to imagine circumstances under which SACS would then be unbiased and a causal interpretation could be made.
WorkerRank: Using Employer Implicit Judgements to Infer Worker Reputation

In online labor marketplaces two parties are involved; employers and workers. An employer posts a job in the marketplace to receive applications from interested workers. After evaluating the match to the job, the employer hires one (or more workers) to accomplish the job via an online contract. At the end of the contract, the employer can provide his worker with some rating that becomes visible in the worker online profile. This form of explicit feedback guides future hiring decisions, since it is indicative of worker true ability. In this paper, first we discuss some of the shortcomings of the existing reputation systems that are based on the end-of-contract ratings. Then we propose a new reputation mechanism that uses Bayesian updates to combine employer implicit feedback signals in a link-analysis approach. The new system addresses the shortcomings of existing approaches, while yielding better signal for the worker quality towards hiring decision.
The Effect of Organizational Climate on Counterproductive Behaviors : An Empirical Study on the Employees of Manufacturing Enterprises

This study investigates the impact of organizational climate on counterproductive behaviors. In organizational behavior studies, organizational climate is suggested as an important determinant or precursor of counterproductive behaviors. However, in the literature, there are limited studies on the relationship between counterproductive behaviors and organizational climate. In this respect, significant findings have been collected as a result of a research conducted on 204 employees of two enterprises in the field of metal and solar energy. Based on the findings, significant and negative relationships have been observed between counterproductive behaviors and dimensions of organizational climate such as reward, warmth, support/commitment, organizational structure and organizational standards. Moreover, warmth relationship environment, support/commitment and organizational standards dimensions are found out to have effect on counterproductive behaviors.
Smart Library: Identifying Books on Library Shelves Using Supervised Deep Learning for Scene Text Reading

Physical library collections are valuable and long standing resources for knowledge and learning. However, managing and finding books or other volumes on a large collection of bookshelves often leads to tedious manual work, especially for large collections where books or others might be missing or misplaced. Recently, deep neural-based models have been successful in detecting and recognizing text in images taken from natural scenes. Based on this, we investigate deep learning for facilitating book management. This task introduces further challenges including image distortion and varied lighting conditions. We present a library inventory building and retrieval system based on scene text reading. We specifically design our text recognition model using rich supervision to accelerate training and achieve state-of-the- art performance on several benchmark datasets. Our proposed system has the potential to greatly reduce the amount of manual labor required for managing book inventories.
A Corpus of Gesture-Annotated Dialogues for Monologue-to-Dialogue Generation from Personal Narratives

Story-telling is a fundamental and prevalent aspect of human social behavior. In the wild, stories are told conversationally in social settings, often as a dialogue and with accompanying gestures and other nonverbal behavior. This paper presents a new corpus, the STORY DIALOGUE WITH GESTURES (SDG) corpus, consisting of 50 personal narratives regenerated as dialogues, complete with annotations of gesture placement and accompanying gesture forms. The corpus includes dialogues generated by human annotators, gesture annotations on the human generated dialogues, videos of story dialogues generated from this representation, video clips of each gesture used in the gesture annotations, and annotations of the original personal narratives with a deep representation of story called a STORY INTENTION GRAPH. Our long term goal is the automatic generation of story co-tellings as animated dialogues from the STORY INTENTION GRAPH. We expect this corpus to be a useful resource for researchers interested in natural language generation, intelligent virtual agents, generation of nonverbal behavior, and story and narrative representations.
AfRob: The affordance network ontology for robots

AfNet, The Affordance Network is an open affordance computing initiative that provides affordance knowledge ontologies for common household articles in terms of affordance features using surface forms termed as afbits (affordance bits). AfNet currently offers 68 base affordance features (25 structural, 10 material, 33 grasp), providing over 200 object category definitions in terms of 4000 afbits. Symbol grounding algorithms for these affordance features enable recognition of objects in visual (RGB-D) data. While AfNet is built as a generic visual knowledge ontology for recognition, it is well suited for deployment on domestic robots. In this paper, we describe AfRob, an extension of AfNet for robotic applications. AfRob builds upon AfNet by imbibing semantic context and mapping for holistic recognition and manipulation of objects in domestic environments. AfRob also offers modules to enable robots to interact and grasp objects through the generation of grasp affordances. The paper also details the inference mechanisms that adapt AfNet for robots in domestic contexts. Results demonstrate the efficiency of the affordance driven approach to holistic visual processing.