nils-herrmann/clean_scirepeval huggingface dataset:
Audio-visual emotion recognition : A dynamic , multimodal approach

Designing systems able to interact with students in a natural manner is a complex and far from solved problem. A key aspect of natural interaction is the ability to understand and appropriately respond to human emotions. This paper details our response to the continuous Audio/Visual Emotion Challenge (AVEC'12) whose goal is to predict four affective signals describing human emotions. The proposed method uses Fourier spectra to extract multi-scale dynamic descriptions of signals characterizing face appearance, head movements and voice. We perform a kernel regression with very few representative samples selected via a supervised weighted-distance-based clustering, that leads to a high generalization power. We also propose a particularly fast regressor-level fusion framework to merge systems based on different modalities. Experiments have proven the efficiency of each key point of the proposed method and our results on challenge data were the highest among 10 international research teams. Key-words Affective computing, Dynamic features, Multimodal fusion, Feature selection, Facial expressions.
A Parallel Softmax Classification Algorithm Based on MapReduce

Softmax Classification algorithm is the generalization of Logistic classification algorithm on multi-classification problems. The traditional stand-alone training algorithm's efficiency is extremely low in the running condition of large amount of data. When substantially increasing the amount of data, the algorithm above will take a lot of time to update parameters. Although the Mahout in Hadoop has realized the Logistic regression, naive Bayesian classifier and classification algorithms, the Softmax classification algorithms has not. The core part of the implementation of parallel Softmax classification algorithm is to use Mapreduce to read the training data partially and use Map tasks and Reduce tasks to realize Parallel gradient descent algorithm, which could finally iteratively update algorithm parameters. Via correlative experiment, it can be proved that the Parallelized Softmax algorithm based on Mapreduce can shorten the process of iteration and the running time and improve training efficiency and precision.
Biomimetic design through natural language analysis to facilitate cross-domain information retrieval

Biomimetic, or biologically inspired, design uses analogous biological phenomena to develop solutions for engineering problems. Several instances of biomimetic design result from personal observations of biological phenomena. However, many engineers’ knowledge of biology may be limited, thus reducing the potential of biologically inspired solutions. Our approach to biomimetic design takes advantage of the large amount of biological knowledge already available in books, journals, and so forth, by performing keyword searches on these existing natural-language sources. Because of the ambiguity and imprecision of natural language, challenges inherent to natural language processing were encountered. One challenge of retrieving relevant cross-domain information involves differences in domain vocabularies, or lexicons. A keyword meaningful to biologists may not occur to engineers. For an example problem that involved cleaning, that is, removing dirt, a biochemist suggested the keyword “defend.” Defend is not an obvious keyword to most engineers for this problem, nor are the words defend and “clean0remove” directly related within lexical references. However, previous work showed that biological phenomena retrieved by the keyword defend provided useful stimuli and produced successful concepts for the clean0remove problem. In this paper, we describe a method to systematically bridge the disparate biology and engineering domains using natural language analysis. For the clean0remove example, we were able to algorithmically generate several biologically meaningful keywords, including defend, that are not obviously related to the engineering problem. We developed a method to organize and rank the set of biologically meaningful keywords identified, and confirmed that we could achieve similar results for two other examples in encapsulation and microassembly. Although we specifically address cross-domain information retrieval from biology, the bridging process presented in this paper is not limited to biology, and can be used for any other domain given the availability of appropriate domain-specific knowledge sources and references.
Automatic Open Knowledge Acquisition via Long Short-Term Memory Networks with Feedback Negative Sampling

Previous studies in Open Information Extraction (Open IE) are mainly based on extraction patterns. They manually define patterns or automatically learn them from a large corpus. However, these approaches are limited when grasping the context of a sentence, and they fail to capture implicit relations. In this paper, we address this problem with the following methods. First, we exploit long shortterm memory (LSTM) networks to extract higher-level features along the shortest dependency paths, connecting headwords of relations and arguments. The path-level features from LSTM networks provide useful clues regarding contextual information and the validity of arguments. Second, we constructed samples to train LSTM networks without the need for manual labeling. In particular, feedback negative sampling picks highly negative samples among non-positive samples through a model trained with positive samples. The experimental results show that our approach produces more precise and abundant extractions than state-of-the-art open IE systems. To the best of our knowledge, this is the first work to apply deep learning to Open IE.
Feature-Point Tracking by Optical Flow Discriminates Subtle Differences in Facial Expression

Current approaches to automated analysis have focused on a small set of prototypic expressions (e.g., joy or anger). Prototypic expressions occur infrequently in everyday life, however, and emotion expression is far more varied. To capture the full range of emotion expression, automated discrimination of fine-grained changes in facial expression is needed. We developed and implemented an optical-flow based approach (feature point tracking) that is sensitive to subtle changes in facial expression. In image sequences from 100 young adults, action units and action unit combinations in the brow and mouth regions were selected for analysis if they occurred a minimum of 25 times in the image database. Selected facial features were automatically tracked using a hierarchical algorithm for estimating optical flow. Images sequences were randomly divided into training and test sets. Feature point tracking demonstrated high concurrent validity with human coding using the Facial Action Coding System (FACS).
From knowledge theory to management practice: towards an integrated approach

This paper critically contrasts the three main schools of thought on knowledge ± namely, those that respectively conceptualize knowledge as situated in mind, process, and object ± and assesses the resulting implications for knowledge management (KM). Against the background of the existing diversity of de®nitions of KM an integrated and holistic view of the KM value chain is put forward. Within this theoretical framework ®ve main research streams (culture, knowledge location, awareness, evaluation, and absorption) are identi®ed with a view to devising a practical concept of KM applicable in a business context. With a focus on knowledge  ̄ow and detailed approaches to potential solutions, con ̄icts and compatibilities between existing business strategies and KM are examined. A conceptual model is devised to oer a holistic integration of the theoretical and practical themes in order to serve as a framework for developing a future research agenda for the development of theoretically grounded, yet practical, KM business tools and applications.
Earlier Identification of Children with Autism Spectrum Disorder: An Automatic Vocalisation-Based Approach

Autism spectrum disorder (ASD) is a neurodevelopmental disorder usually diagnosed in or beyond toddlerhood. ASD is defined by repetitive and restricted behaviours, and deficits in social communication. The early speech-language development of individuals with ASD has been characterised as delayed. However, little is known about ASD-related characteristics of pre-linguistic vocalisations at the feature level. In this study, we examined pre-linguistic vocalisations of 10-month-old individuals later diagnosed with ASD and a matched control group of typically developing individuals (N = 20). We segmented 684 vocalisations from parent-child interaction recordings. All vocalisations were annotated and signal-analytically decomposed. We analysed ASD-related vocalisation specificities on the basis of a standardised set (eGeMAPS) of 88 acoustic features selected for clinical speech analysis applications. 54 features showed evidence for a differentiation between vocalisations of individuals later diagnosed with ASD and controls. In addition, we evaluated the feasibility of automated, vocalisation-based identification of individuals later diagnosed with ASD. We compared linear kernel support vector machines and a 1-layer bidirectional long short-term memory neural network. Both classification approaches achieved an accuracy of 75% for subject-wise identification in a subject-independent 3-fold cross-validation scheme. Our promising results may be an important contribution en-route to facilitate earlier identification of ASD.
A Survey on Structure from Motion

The structure from motion (SfM) problem in computer vision is the problem of recovering the 3D structure of a stationary scene from a set of projective measurements, represented as a collection of 2D images, via estimation of motion of the cameras corresponding to these images. In essence, SfM involves the three main stages of (1) extraction of features in images (e.g., points of interest, lines, etc.) and matching of these features between images, (2) camera motion estimation (e.g., using relative pairwise camera poses estimated from the extracted features), (3) recovery of the 3D structure using the estimated motion and features (e.g., by minimizing the so-called reprojection error). This survey mainly focuses on the relatively recent developments in the literature pertaining to stages (2) and (3). More specifically, after touching upon the early factorization-based techniques for motion and structure estimation, we provide a detailed account of some of the recent camera location estimation methods in the literature, which precedes the discussion of notable techniques for 3D structure recovery. We also cover the basics of the simultaneous localization and mapping (SLAM) problem, which can be considered to be a specific case of the SfM problem. Additionally, a review of the fundamentals of feature extraction and matching (i.e., stage (1) above), various recent methods for handling ambiguities in 3D scenes, SfM techniques involving relatively uncommon camera models and image features, and popular sources of data and SfM software is included in our survey.
Anisotropic Huber-L1 Optical Flow

TV regularization is an L1 penalization of the flow gradient magnitudes, and due to the tendency of the L1 norm to favor sparse solutions (i.e. lots of ‘zeros’), the fill-in effect caused by the regularizer leads to piecewise constant solutions in weakly textured areas. This effect, known as ‘staircasing’ in a 1D setting, can be reduced significantly by using a quadratic penalization for small gradient magnitudes while sticking to linear penalization for larger magnitudes to maintain the discontinuity preserving properties known from TV. A comparison of isotropic TV and isotropic Huber regularity is shown in Fig. 1 by means of rendering the disparities u1 of the Dimetrodon dataset. The color coded flow (cf. Fig. 1(a)) is superimposed as texture. Based on the two observations that motion discontinuities often occur along object boundaries and that in turn object boundaries often coincide
Realization of three-dimensional walking of a cheetah-modeled bio-inspired quadruped robot

Adaptability of quadruped animals is not solely reached by brain control, but by the interaction between its body, environment, and control. Especially, morphology of the body is supposed to contribute largely to the adaptability. We have tried to understand quadrupedal locomotion by building a bio-inspired quadruped robot named “Pneupard”, which has a feline-like muscular-skeletal structure. In our previous study, we successfully realized alternative gait of hindlimbs by reflex control based on the sole touch information, which is called an unloading rule, and that of forelimbs as well. In this paper, we finally connect forelimbs and hindlimbs by a rigid spine, and conduct 3D walking experiments only with the simple unloading rule. Through several preliminary experiments, we realize that the touch information on the sole is the most critical for stable 3D walking.
Evaluating the benefits of augmented reality for task localization in maintenance of an armored personnel carrier turret

We present the design, implementation, and user testing of a prototype augmented reality application to support military mechanics conducting routine maintenance tasks inside an armored vehicle turret. Our prototype uses a tracked head-worn display to augment a mechanic's natural view with text, labels, arrows, and animated sequences designed to facilitate task comprehension, location, and execution. A within-subject controlled user study examined professional military mechanics using our system to complete 18 common tasks under field conditions. These tasks included installing and removing fasteners and indicator lights, and connecting cables, all within the cramped interior of an armored personnel carrier turret. An augmented reality condition was tested against two baseline conditions: an untracked headworn display with text and graphics and a fixed flat panel display representing an improved version of the laptop-based documentation currently employed in practice. The augmented reality condition allowed mechanics to locate tasks more quickly than when using either baseline, and in some instances, resulted in less overall head movement. A qualitative survey showed mechanics found the augmented reality condition intuitive and satisfying for the tested sequence of tasks.
Supply Chain Sourcing Under Asymmetric Information

We study a supply chain with two suppliers competing over a contract to supply components to a manufacturer. One of the suppliers is a big company for whom the manufacturer’s business constitutes a small part of his business. The other supplier is a small company for whom the manufacturer’s business constitutes a large portion of his business. We analyze the problem from the perspective of the big supplier and address the following questions: What is the optimal contracting strategy that the big supplier should follow? How does the information about the small supplier’s production cost affect the profits and contracting decision? How does the existence of the small supplier affect profits? By studying various information scenarios regarding the small supplier’s and the manufacturer’s production cost, we show, for example, that the big supplier benefits when the small supplier keeps its production cost private. We quantify the value of information for the big supplier and the manufacturer. We also quantify the cost (value) of the alternative-sourcing option for the big supplier (the manufacturer). We determine when an alternative-sourcing option has more impact on profits than information. We conclude with extensions and numerical examples to shed light on how system parameters affect this supply chain.
AudioDAQ: turning the mobile phone's ubiquitous headset port into a universal data acquisition interface

We present AudioDAQ, a new platform for continuous data acquisition using the headset port of a mobile phone. AudioDAQ differs from existing phone peripheral interfaces by drawing all necessary power from the microphone bias voltage, encoding all data as analog audio, and leveraging the phone's built-in voice memo application (or a custom application) for continuous data collection. These properties make the AudioDAQ design more universal, so it works across a broad range of phones including sophisticated smart phones and simpler feature phones, enables simple analog peripherals without requiring a microcontroller, requires no hardware or software modifications on the phone itself, uses significantly less power than prior approaches, and allows continuous data capture over an extended period of time. The AudioDAQ design is efficient because it draws all necessary power from the microphone bias voltage, and it is general because this voltage and a voice memo application are present on most mobile phones in use today. We show the viability of our architecture by evaluating an end-to-end system that can capture EKG signals continuously for hours and send the data to the cloud for storage, processing, and visualization.
Optimizing rule placement in software-defined networks for energy-aware routing

Software-defined Networks (SDN), in particular OpenFlow, is a new networking paradigm enabling innovation through network programmability. Over past few years, many applications have been built using SDN such as server load balancing, virtual-machine migration, traffic engineering and access control. In this paper, we focus on using SDN for energy-aware routing (EAR). Since traffic load has a small influence on power consumption of routers, EAR allows to put unused links into sleep mode to save energy. SDN can collect traffic matrix and then computes routing solutions satisfying QoS while being minimal in energy consumption. However, prior works on EAR have assumed that the table of OpenFlow switch can hold an infinite number of rules. In practice, this assumption does not hold since the flow table is implemented with Ternary Content Addressable Memory (TCAM) which is expensive and power-hungry. In this paper, we propose an optimization method to minimize energy consumption for a backbone network while respecting capacity constraints on links and rule space constraints on routers. In details, we present an exact formulation using Integer Linear Program (ILP) and introduce efficient greedy heuristic algorithm. Based on simulations, we show that using this smart rule space allocation, it is possible to save almost as much power consumption as the classical EAR approach.
Learning in Artificial Neural Networks: A Statistical Perspective

The premise of this article is that learning procedures used to train artificial neural networks are inherently statistical techniques. It follows that statistical theory can provide considerable insight into the properties, advantages, and disadvantages of different network learning methods. We review concepts and analytical results from the literatures of mathematical statistics, econometrics, systems identification, and optimization theory relevant to the analysis of learning in artificial neural networks. Because of the considerable variety of available learning procedures and necessary limitations of space, we cannot provide a comprehensive treatment. Our focus is primarily on learning procedures for feedforward networks. However, many of the concepts and issues arising in this framework are also quite broadly relevant to other network learning paradigms. In addition to providing useful insights, the material reviewed here suggests some potentially useful new training methods for artificial neural networks.
Interacting with Paper on the Digital Desk

real world can be overcome in VR, and physical tools can. be m a d e obsolete by more flexible, virtual alternatives'.. Physical tools can be hard to replace , however. At one time it seemed that paper might become obsolete, for example, and visionaries predicted the "paperlless office" would dominate within a few years. But the trouble is that people like paper. It is easier to read than a screen , it is cheap, universally accepted , tactile, and portable. According to some studies, paperwork in the office has increased by a l~actor of six since 1970, and is now growing at 20% annually [14]. Like electronic documents, paper has properties that people just cannot seem to give up, making it resilient in the face of computer-based alternatives . Consequently, we have two desks: one for "paper pushing" and the other for "pixel pushing." Although activities on the two desk.s are often related, the two are quite isolated from each other. Printers and scanners provide a way for documents to move back ;and forth between desk-tops, but this conversion process is inconvenient. Wang's Freestyle system , for example, was. a "paper-less office" system that attempted to coexist with partially paper-based processes. A key factor necessary for adoption of that system was to minimize the printing and scanning re-RoE.ALL o quired, because too many of these tasks would cause the process to revert entirely back to paper. Trade-offs between electronic and paper documents can make the choice of medium difficult, but imagine if we did not have to choose, and we had a space where documents could be both paper and electronic at the same time. Instead of putting the user in the virtual world of the computer , we could do the opposite: add the computer to the real world of the user and create a Computer Augmented Environment for paper (see guest editors' introduction.). Instead of replacing paper with computers, we could enhance paper with computation. The Xerox PaperWorks product [8] takes a step in this direction with its fax-based paper user interface (UI) to a storage and retrieval system. With this system, ordinary paper forms are enhanced to control a PC through a fax machine. These paper documents gain some properties of electronic documents, but fax machines are slow compared to computer screens. Response time is limited by the delay it takes to scan and print a …
Practical Stabilization of a Skid-steering Mobile Robot - A Kinematic-based Approach

This paper presents kinematic control problem of skid-steering mobile robot using practical smooth and time-varying stabilizer. The stability result is proved using Lyapunov analysis and takes into account both input signal saturation and uncertainty of kinematics. In order to ensure stable motion of the robot the condition of permissible velocities is formulated according to dynamic model and wheel-surface interaction. Theoretical considerations are illustrated by simulation results
An extended online purchase intention model for middle-aged online users

This article focuses on examining the determinants and mediators of the purchase intention of nononline purchasers between ages 31 and 60 who mostly have strong purchasing power. It propose anew online purchase intention model by integrating the technology acceptance model with additional determinants and adding habitual online usage as a new mediator. Based on a sample of more than 300 middle-aged non-online purchasers, beyond some situationally-specific predictor variables, online purchasing attitude and habitual online usage are key mediators. Personal awareness of security only affects habitual online usage, t indicating a concern of middle-aged users. Habitual online usage is a