nils-herrmann/clean_scirepeval huggingface dataset:
High Accuracy Optical Flow Estimation Based on a Theory for Warping

We study an energy functional for computing optical flow that combines three assumptions: a brightness constancy assumption, a gradient constancy assumption, and a discontinuity-preserving spatio-temporal smoothness constraint. In order to allow for large displacements, linearisations in the two data terms are strictly avoided. We present a consistent numerical scheme based on two nested fixed point iterations. By proving that this scheme implements a coarse-to-fine warping strategy, we give a theoretical foundation for warping which has been used on a mainly experimental basis so far. Our evaluation demonstrates that the novel method gives significantly smaller angular errors than previous techniques for optical flow estimation. We show that it is fairly insensitive to parameter variations, and we demonstrate its excellent robustness under noise. In Proc. 8th European Conference on Computer Vision, Springer LNCS 3024, T. Pajdla and J. Matas (Eds.), vol. 4, pp. 25-36, Prague, Czech Republic, May 2004  Received The Longuet-Higgins Best Paper Award.
The usability of everyday technology: emerging and fading opportunities

Current work in the field of usability tends to focus on snapshots of use as the basis for evaluating designs. However, giving due consideration to the fact that everyday use of technology involves a process of evolution, we set out to investigate how the design of the technology may be used to support this. Based on a long-term empirical study of television use in the homes of two families, we illustrate how use continuously develops in a complex interplay between the users' expectations---as they are formed and triggered by the design---and the needs and context of use per se. We analyze the empirical data from the perspective of activity theory. This framework serves to highlight how use develops, and it supports our analysis and discussion about how design, the users' backgrounds, previous experience, and needs, and the specific context of use supports or hinders the development of use. Moreover, we discuss how the characteristics of the home settings, in which the televisions studied were situated, represent a challenge to usability work. The concluding discussion leads to a set of hypotheses relevant to designers and researchers who wish to tackle some of the aspects of usability of particular importance to development in the use of home technology.
Audio-Visual Speech Recognition Using Bimodal-Trained Bottleneck Features for a Person with Severe Hearing Loss

In this paper, we propose an audio-visual speech recognition system for a person with an articulation disorder resulting from severe hearing loss. In the case of a person with this type of articulation disorder, the speech style is quite different from those of people without hearing loss that a speaker-independent acoustic model for unimpaired persons is hardly useful for recognizing it. The audio-visual speech recognition system we present in this paper is for a person with severe hearing loss in noisy environments. Although feature integration is an important factor in multimodal speech recognition, it is difficult to integrate efficiently because those features are different intrinsically. We propose a novel visual feature extraction approach that connects the lip image to audio features efficiently, and the use of convolutive bottleneck networks (CBNs) increases robustness with respect to speech fluctuations caused by hearing loss. The effectiveness of this approach was confirmed through word-recognition experiments in noisy environments, where the CBN-based feature extraction method outperformed the conventional methods.
Through the eyes of love: reality and illusion in intimate relationships.

This article reviews the research literature and theory concerned with accuracy of judgments in romantic relationships. We initially propose a model of cognition in (romantic) relationships that distinguishes between 2 forms of accuracy: mean-level bias and tracking accuracy. We then report the results of meta-analyses of research on heterosexual, romantic relationships, which used external benchmarks and reported levels of tracking accuracy (98 studies) and/or mean-level bias (48 studies). The results revealed robust overall effect sizes for both tracking accuracy (r = .47) and positive mean-level bias (r = .09). As expected, the effects were substantial and positive for tracking accuracy across 6 judgmental categories, whereas signed mean-level bias was negative for the interaction attributions (e.g., love, communication). The results showed, as expected, that these 2 forms of accuracy were independent-the 2 kinds of effect size derived from the same set of 38 studies were uncorrelated. As expected, gender, relationship length, and relationship evaluations moderated mean-level bias across studies but (unexpectedly) not for tracking accuracy. In the Discussion we evaluate the prior model in light of the findings, other research, moderating variables (such as self-esteem), the role of projection, the early stages of mate selection, metacognition, and the rationality and nature of motivated cognition. We conclude that our model, findings, and analyses help to resolve the apparent paradox that love is both riven with illusions and rooted in reality, and support both evolutionary and social psychological approaches to understanding cognition in romantic relationships.
Parents' perceptions of health and physical activity needs of children with Down syndrome.

Individuals with Down syndrome typically have low fitness levels and obesity despite data that indicate physiological gains from physical activity and exercise interventions. Low fitness levels and obesity in individuals with Down syndrome may be related to sedentary lifestyles, social and recreational opportunities, or low motivation to be physically active. These causal influences on the overall health of individuals with Down syndrome may be related to parental or caregiver support. Through this study, parents of children with Down syndrome from preschool to adolescent ages were interviewed about their perceptions of the health and physical activity needs of their children. Data from four focus groups indicated the following most salient themes: (1) all parents believed participation in physical activity has immediate and long-term positive health impacts on their child with Down syndrome, and most of the parents thought their child would benefit from being more physically active, (2) most parents observed that their child participated in physical activities primarily for social reasons, most notably to be with their peers with or without Down syndrome or to be with their sibling(s), and that without such motivation their child would choose sedentary activities, (3) parents of teenagers identified a need for their child to learn an individual sport to have sporting opportunities that do not require ability-matched teammates and opponents, and (4) parents recognised their need for help from physical activity specialists through either parent education regarding home-based physical activity programmes or an increase in appropriate community-based physical activity programmes for their child with Down syndrome. The interview data suggest future research should evaluate the outcomes of long-term individualised home-based physical activity interventions for children with Down syndrome. Additionally, educators, recreation specialists, and therapists should assist children and youth with their acquisition of skills used in individual and dual sports.
A Unified Generative Adversarial Framework for Image Generation and Person Re-identification

Person re-identification (re-id) aims to match a certain person across multiple non-overlapping cameras. It is a challenging task because the same person's appearance can be very different across camera views due to the presence of large pose variations. To overcome this issue, in this paper, we propose a novel unified person re-id framework by exploiting person poses and identities jointly for simultaneous person image synthesis under arbitrary poses and pose-invariant person re-identification. The framework is composed of a GAN based network and two Feature Extraction Networks (FEN), and enjoys following merits. First, it is a unified generative adversarial model for person image generation and person re-identification. Second, a pose estimator is utilized into the generator as a supervisor in the training process, which can effectively help pose transfer and guide the image generation with any desired pose. As a result, the proposed model can automatically generate a person image under an arbitrary pose. Third, the identity-sensitive representation is explicitly disentangled from pose variations through the person identity and pose embedding. Fourth, the learned re-id model can have better generalizability on a new person re-id dataset by using the synthesized images as auxiliary samples. Extensive experimental results on four standard benchmarks including Market-1501 [69], DukeMTMC-reID [40], CUHK03 [23], and CUHK01  demonstrate that the proposed model can perform favorably against state-of-the-art methods.
Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation

In this paper, we address the problem of unsupervised domain transfer learning in which no labels are available in the target domain. We use a transformation matrix to transfer both the source and target data to a common subspace, where each target sample can be represented by a combination of source samples such that the samples from different domains can be well interlaced. In this way, the discrepancy of the source and target domains is reduced. By imposing joint low-rank and sparse constraints on the reconstruction coefficient matrix, the global and local structures of data can be preserved. To enlarge the margins between different classes as much as possible and provide more freedom to diminish the discrepancy, a flexible linear classifier (projection) is obtained by learning a non-negative label relaxation matrix that allows the strict binary label matrix to relax into a slack variable matrix. Our method can avoid a potentially negative transfer by using a sparse matrix to model the noise and, thus, is more robust to different types of noise. We formulate our problem as a constrained low-rankness and sparsity minimization problem and solve it by the inexact augmented Lagrange multiplier method. Extensive experiments on various visual domain adaptation tasks show the superiority of the proposed method over the state-of-the art methods. The MATLAB code of our method will be publicly available at http://www.yongxu.org/lunwen.html.
Persuasive System Design Does Matter: A Systematic Review of Adherence to Web-Based Interventions

Although web-based interventions for promoting health and health-related behavior can be effective, poor adherence is a common issue that needs to be addressed. Technology as a means to communicate the content in web-based interventions has been neglected in research. Indeed, technology is often seen as a black-box, a mere tool that has no effect or value and serves only as a vehicle to deliver intervention content. In this paper we examine technology from a holistic perspective. We see it as a vital and inseparable aspect of web-based interventions to help explain and understand adherence.



This study aims to review the literature on web-based health interventions to investigate whether intervention characteristics and persuasive design affect adherence to a web-based intervention.



We conducted a systematic review of studies into web-based health interventions. Per intervention, intervention characteristics, persuasive technology elements and adherence were coded. We performed a multiple regression analysis to investigate whether these variables could predict adherence.



We included 101 articles on 83 interventions. The typical web-based intervention is meant to be used once a week, is modular in set-up, is updated once a week, lasts for 10 weeks, includes interaction with the system and a counselor and peers on the web, includes some persuasive technology elements, and about 50% of the participants adhere to the intervention. Regarding persuasive technology, we see that primary task support elements are most commonly employed (mean 2.9 out of a possible 7.0). Dialogue support and social support are less commonly employed (mean 1.5 and 1.2 out of a possible 7.0, respectively). When comparing the interventions of the different health care areas, we find significant differences in intended usage (p=.004), setup (p<.001), updates (p<.001), frequency of interaction with a counselor (p<.001), the system (p=.003) and peers (p=.017), duration (F=6.068, p=.004), adherence (F=4.833, p=.010) and the number of primary task support elements (F=5.631, p=.005). Our final regression model explained 55% of the variance in adherence. In this model, a RCT study as opposed to an observational study, increased interaction with a counselor, more frequent intended usage, more frequent updates and more extensive employment of dialogue support significantly predicted better adherence.



Using intervention characteristics and persuasive technology elements, a substantial amount of variance in adherence can be explained. Although there are differences between health care areas on intervention characteristics, health care area per se does not predict adherence. Rather, the differences in technology and interaction predict adherence. The results of this study can be used to make an informed decision about how to design a web-based intervention to which patients are more likely to adhere.
Holographic reduced representations

Associative memories are conventionally used to represent data with very simple structure: sets of pairs of vectors. This paper describes a method for representing more complex compositional structure in distributed representations. The method uses circular convolution to associate items, which are represented by vectors. Arbitrary variable bindings, short sequences of various lengths, simple frame-like structures, and reduced representations can be represented in a fixed width vector. These representations are items in their own right and can be used in constructing compositional structures. The noisy reconstructions extracted from convolution memories can be cleaned up by using a separate associative memory that has good reconstructive properties.
MorphoSys: An Integrated Reconfigurable System for Data-Parallel and Computation-Intensive Applications

Hartej Singh, Ming-Hau Lee, Guangming Lu, Fadi J. Kurdahi, Nader Bagherzadeh, University of California, Irvine, CA 92697 and Eliseu M. C. Filho, Federal University of Rio de Janeiro,  This paper introduces MorphoSys, a reconfigurable computing system developed to investigate the effectiveness of combining reconfigurable hardware with general-purpose processors for word-level, computation-intensive applications. MorphoSys is a coarse-grain, integrated reconfigurable system-on-chip targeted at high-throughput and data-parallel applications. It comprises of a reconfigurable array of processing cells, a modified RISC processor core and an efficient memory interface unit. This paper describes the MorphoSys architecture, including the reconfigurable processing array, the control processor and data and configuration memories. The suitability of MorphoSys for the target application domain is then illustrated with examples such as video compression, data encryption and target recognition. Performance evaluation of these applications indicates improvements of up to an order of magnitude on MorphoSys in comparison with other systems.
Neuropsychology and clinical neuroscience of persistent post-concussive syndrome.

On the mild end of the acquired brain injury spectrum, the terms concussion and mild traumatic brain injury (mTBI) have been used interchangeably, where persistent post-concussive syndrome (PPCS) has been a label given when symptoms persist for more than three months post-concussion. Whereas a brief history of concussion research is overviewed, the focus of this review is on the current status of PPCS as a clinical entity from the perspective of recent advances in the biomechanical modeling of concussion in human and animal studies, particularly directed at a better understanding of the neuropathology associated with concussion. These studies implicate common regions of injury, including the upper brainstem, base of the frontal lobe, hypothalamic-pituitary axis, medial temporal lobe, fornix, and corpus callosum. Limitations of current neuropsychological techniques for the clinical assessment of memory and executive function are explored and recommendations for improved research designs offered, that may enhance the study of long-term neuropsychological sequelae of concussion.
Fabrication of extended-release patient-tailored prednisolone tablets via fused deposition modelling (FDM) 3D printing.

Rapid and reliable tailoring of the dose of controlled release tablets to suit an individual patient is a major challenge for personalized medicine. The aim of this work was to investigate the feasibility of using a fused deposition modelling (FDM) based 3D printer to fabricate extended release tablet using prednisolone loaded poly(vinyl alcohol) (PVA) filaments and to control its dose. Prednisolone was loaded into a PVA-based (1.75 mm) filament at approximately 1.9% w/w via incubation in a saturated methanolic solution of prednisolone. The physical form of the drug was assessed using differential scanning calorimetry (DSC) and X-ray powder diffraction (XRPD). Dose accuracy and in vitro drug release patterns were assessed using HPLC and pH change flow-through dissolution test. Prednisolone loaded PVA filament demonstrated an ability to be fabricated into regular ellipse-shaped solid tablets using the FDM-based 3D printer. It was possible to control the mass of printed tablet through manipulating the volume of the design (R(2) = 0.9983). On printing tablets with target drug contents of 2, 3, 4, 5, 7.5 and 10mg, a good correlation between target and achieved dose was obtained (R(2) = 0.9904) with a dose accuracy range of 88.7-107%. Thermal analysis and XRPD indicated that the majority of prednisolone existed in amorphous form within the tablets. In vitro drug release from 3D printed tablets was extended up to 24h. FDM based 3D printing is a promising method to produce and control the dose of extended release tablets, providing a highly adjustable, affordable, minimally sized, digitally controlled platform for producing patient-tailored medicines.
Classification of Multiple-Sentence Questions

Conventional QA systems cannot answer to the questions composed of two or more sentences. Therefore, we aim to construct a QA system that can answer such multiple-sentence questions. As the first stage, we propose a method for classifying multiple-sentence questions into question types. Specifically, we first extract the core sentence from a given question text. We use the core sentence and its question focus in question classification. The result of experiments shows that the proposed method improves F-measure by 8.8% and accuracy by 4.4%.
The use of socially assistive robots in the design of intelligent cognitive therapies for people with dementia

Currently the 2 percent growth rate for the world's older population exceeds the 1.2 rate for the world's population as a whole. By 2050, the number of individuals over the age 85 is projected to be three times more than there is today. Most of these individuals will need physical, emotional, and cognitive assistance. In this paper, we present a new adaptive robotic system based on the socially assistive robotics (SAR) technology that tries to provide a customized help protocol through motivation, encouragements, and companionship to users suffering from cognitive changes related to aging and/or Alzheimer's disease. Our results show that this approach can engage the patients and keep them interested in interacting with the robot, which, in turn, increases their positive behavior.
Protecting Your Right: Verifiable Attribute-Based Keyword Search with Fine-Grained Owner-Enforced Search Authorization in the Cloud

Search over encrypted data is a critically important enabling technique in cloud computing, where encryption-before-outsourcing is a fundamental solution to protecting user data privacy in the untrusted cloud server environment. Many secure search schemes have been focusing on the single-contributor scenario, where the outsourced dataset or the secure searchable index of the dataset are encrypted and managed by a single owner, typically based on symmetric cryptography. In this paper, we focus on a different yet more challenging scenario where the outsourced dataset can be contributed from multiple owners and are searchable by multiple users, i.e., multi-user multi-contributor case. Inspired by attribute-based encryption (ABE), we present the first attribute-based keyword search scheme with efficient user revocation (ABKS-UR) that enables scalable fine-grained (i.e., file-level) search authorization. Our scheme allows multiple owners to encrypt and outsource their data to the cloud server independently. Users can generate their own search capabilities without relying on an always online trusted authority. Fine-grained search authorization is also implemented by the owner-enforced access policy on the index of each file. Further, by incorporating proxy re-encryption and lazy re-encryption techniques, we are able to delegate heavy system update workload during user revocation to the resourceful semi-trusted cloud server. We formalize the security definition and prove the proposed ABKS-UR scheme selectively secure against chosen-keyword attack. To build confidence of data user in the proposed secure search system, we also design a search result verification scheme. Finally, performance evaluation shows the efficiency of our scheme.
DakNet: rethinking connectivity in developing nations

DakNet provides extraordinarily low-cost digital communication, letting remote villages leapfrog past the expense of traditional connectivity solutions and begin development of a full-coverage broadband wireless infrastructure. What is the basis for a progressive, market-driven migration from e-governance to universal broadband connectivity that local users will pay for? DakNet, an ad hoc network that uses wireless technology to provide asynchronous digital connectivity, is evidence that the marriage of wireless and asynchronous service may indeed be the beginning of a road to universal broadband connectivity. DakNet has been successfully deployed in remote parts of both India and Cambodia at a cost two orders of magnitude less than that of traditional landline solutions.
Optimisation of quasi-resonant induction cookers

In this paper, the optimisation possibilities of a quasi-resonant induction cooker are investigated. Simulations show that main losses of the induction cooker occur in the heating coil and the RC-IGBT (power switch). The performance of the coil can be improved mainly by minimising the coil resistance. The IGBT-optimisation is based on the reduction of tail current in soft switching mode. The tail current is reduced by using lifetime engineering and reduction of the IGBT-thickness. The losses of the optimised IGBT are about 30% lower. As a result, the cooling system of the IGBT can be smaller and cheaper. Together with some optimisations of the circuit the overall losses of the system could be improved by almost 10% or 1 percentage point.