Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Part Structure in the Perceptual Localization of a Shape

The process of object localization may be accomplished with respect to a particular reference location, such as the center of gravity, COG (eg Vishwanath and Kowler, 2003 Vision Research 43 1637 – 1653). Here, we investigated how part structure affects an object's reference location. The reference location was evaluated with a measure of the illusory displacement of an internal target element embedded within a larger object (Morgan et al, 1990 Vision Research 30 1793 – 1810). To examine whether the reference location is different for shapes with part structure, two shapes were tested: circle (small and large; no part structure) and bell (shape with two parts, one larger than the other). Results were examined with respect to two predictions: either the location of an object is based on its shape as a whole, disregarding part structure (ie a single, overall COG), or the parts are processed separately (different COGs). With the circles, the results showed a systematic illusory displacement of the internal target toward the COG. With the bell, the illusion was significantly weaker than with both circles—even though the main part of the bell had the same size as the small circle, and its horizontal axis had the same extent as the large circle. Moreover, the distance judgments for the bell were consistent with a (weaker) reference point being located at the COG of the larger part, rather than at the COG of the entire bell. These results show that the part structure of a shape plays a role in the representation of its location, and that for complex shapes the perceived location of an embedded element depends more on the parts within which it is embedded, rather than on the whole shape.
Predictive exploration considering previously mapped environments

The ability to explore an unknown environment is an important prerequisite for building truly autonomous robots. The central decision that a robot needs to make when exploring an unknown environment is to select the next view point(s) for gathering observations. In this paper, we consider the problem of how to select view points that support the underlying mapping process. We propose a novel approach that makes predictions about the structure of the environments in the unexplored areas by relying on maps acquired previously. Our approach seeks to find similarities between the current surroundings of the robot and previously acquired maps stored in a database in order to predict how the environment may expand in the unknown areas. This allows us to predict potential future loop closures early. This knowledge is used in the view point selection to actively close loops and in this way reduce the uncertainty in the robot's belief. We implemented and tested the proposed approach. The experiments indicate that our method improves the ability of a robot to explore challenging environments and improves the quality of the resulting maps.
Predictive value of qualitative assessment of general movements for motor developmental outcome (cerebral palsy) in full-term infants with cerebral damage

Objective To study the predictive value of qualitative assessment of general movements(GMs)for motor developmental outcome(cerebral palsy)in full-term infants with cerebral damage.Methods A total of 55full-term infants with brain damage were retrospectively included in the Rehabilitation Department of Children's Hospital of Fudan University.All of them received GM assessments at least once during fidgety-movement period and their motor development outcome were determined after 1-year-old by their neurological examination and results of Peabody Developmental Motor Scale-2.Sensitivity,specificity,positive predictive value and negative predictive value of GMs assessment for cerebral palsy were calculated.Results Among the 55infants with brain damage,20infants didn't show fidgety movements while 35did.After 1-year-old,19children were diagnosed with cerebral palsy while 36children were not.The predictive values of GMs assessment for cerebral palsy were as following:sensitivity 89%,specificity 92%,positive predictive value 85%,negative predictive value 94%. Conclusions The qualitative assessment of GMs has a good predictive value for long-term motor developmental outcome(cerebral palsy)in full-term infants with brain damage.As a new method of non-invasive,nonintrusive neurological assessment,the assessment of general movements is suitable for wide application in follow-ups among full-term infants with brain damage.
A NEW APPROACH FOR DAM MONITORING AND SURVEILLANCE USING BLIND SOURCE SEPARATION

Usually, dam monitoring systems are based on both boundary conditions (temperature, rainfall, water level, etc.) and structural responses (displacements, rota- tions, pore pressures, etc.). Statistical analysis tools are widely used to compare the cur- rent response of the dam with a whole set of recorded data, in order to determine eventual unwanted behaviors. The main drawback of this approach is that the structural response quantities are related to the external loads using analytical functions, whose parameters do not have physical meaning. In this paper, a new approach, based on Blind Source Separation (BSS) to nd out the contributions of the external loads: air temperature and hydrostatic pressure, structure deformation and identify the irreversible component in structural response, is presented. Finally, it presented a case study whose purpose is to assess the separation of the contributions due to the external loads mentioned above with- out a priori knowledge of the generator phenomena or of the propagation environment, and use only the crest displacements of a concrete dam.
Catching up with the Keynesians

This paper examines the role for tax policies in productivity-shock driven economies with "catching-up-with-the-Joneses" utility functions. The optimal tax policy is shown to affect the economy counter-cyclically via procyclical taxes, i.e., "cooling down" the economy with higher taxes when it is "overheating" in booms and "stimulating" the economy with lower taxes in recessions to keep consumption up. Thus, models with catching-up-with-the-Joneses utility functions call for traditional Keynesian demand management policies. Parameter values from Campbell and Cochrane (1995) are also used to illustrate that the necessary labor taxes can be very high, in the order of 50 percent. However, Campbell and Cochrane's nonlinear version of the aspiration level in the catching-up-with-the-Joneses preferences has the additional implication that consumption bunching can be welfare enhancing.
Integration and prediction difficulty in Hindi sentence comprehension: Evidence from an eye-tracking corpus

This is the first attempt at characterizing reading difficulty in Hindi using naturally occurring sentences. We created the Potsdam-Allahabad Hindi Eyetracking Corpus by recording eye-movement data from 30 participants at the University of Allahabad, India. The target stimuli were 153 sentences selected from the beta version of the Hindi-Urdu treebank. We find that word- or low-level predictors (syllable length, unigram and bigram frequency) affect first-pass reading times, regression path duration, total reading time, and outgoing saccade length. An increase in syllable length results in longer fixations, and an increase in word unigram and bigram frequency leads to shorter fixations. Longer syllable length and higher frequency lead to longer outgoing saccades. We also find that two predictors of sentence comprehension difficulty, integration and storage cost, have an effect on reading difficulty. Integration cost (Gibson, 2000) was approximated by calculating the distance (in words) between a dependent and head; and storage cost (Gibson, 2000), which measures difficulty of maintaining predictions, was estimated by counting the number of predicted heads at each point in the sentence. We find that integration cost mainly affects outgoing saccade length, and storage cost affects total reading times and outgoing saccade length. Thus, word-level predictors have an effect in both early and late measures of reading time, while predictors of sentence comprehension difficulty tend to affect later measures. This is, to our knowledge, the first demonstration using eye-tracking that both integration and storage cost influence reading difficulty.
The application of soft-switching and frequency synchronization technologies in electromagnetic heater design

In order to solve the electromagnetic heaters' problems of high switching losses and resonant frequency drifting in harsh industrial environments, in this paper, we introduce the soft-switching technology to make the IGBT work on the condition of zero-current turning on and zero-voltage turning off to reduce switching losses, and the IGBT drivers auto adjust the driving frequency to achieve frequency synchronization and adapt the drifting of resonant frequency, in this way, the electromagnetic heaters' adaptability and thermal efficiency can be improved.
Benefits of a Working Memory Training Program for Inattention in Daily Life: A Systematic Review and Meta-Analysis

Background Many common disorders across the lifespan feature impaired working memory (WM). Reported benefits of a WM training program include improving inattention in daily life, but this has not been evaluated in a meta-analysis. This study aimed to evaluate whether one WM training method has benefits for inattention in daily life by conducting a systematic review and meta-analysis. Methods We searched Medline and PsycINFO, relevant journals and contacted authors for studies with an intervention and control group reporting post-training estimates of inattention in daily life. To reduce the influence of different WM training methods on the findings, the review was restricted to trials evaluating the Cogmed method. A meta-analysis calculated the pooled standardised difference in means (SMD) between intervention and control groups. Results A total of 622 studies were identified and 12 studies with 13 group comparisons met inclusion criteria. The meta-analysis showed a significant training effect on inattention in daily life, SMD=-0.47, 95% CI -0.65, -0.29, p<. 00001. Subgroup analyses showed this significant effect was observed in groups of children and adults as well as users with and without ADHD, and in studies using control groups that were active and non-adaptive, wait-list and passive as well as studies using specific or general measures. Seven of the studies reported follow-up assessment and a meta-analysis showed persisting training benefits for inattention in daily life, SMD=-0.33, 95% CI -0.57 -0.09, p=. 006. Additional meta-analyses confirmed improvements after training on visuospatial WM, SMD=0.66, 95% CI 0.43, 0.89, p<. 00001, and verbal WM tasks, SMD=0.40, 95% CI 0.18, 0.62, p=. 0004. Conclusions Benefits of a WM training program generalise to improvements in everyday functioning. Initial evidence shows that the Cogmed method has significant benefits for inattention in daily life with a clinically relevant effect size.
Long range audio and audio-visual event detection using a laser Doppler vibrometer

Association of audio events with video events presents a challenge to a typical camera-microphone approach in order to capture AV signals from a large distance. Setting up a long range microphone array and performing geo-calibration of both audio and video sensors is difficult. In this work, in addition to a geo-calibrated electro-optical camera, we propose to use a novel optical sensor - a Laser Doppler Vibrometer (LDV) for real-time audio sensing, which allows us to capture acoustic signals from a large distance, and to use the same geo-calibration for both the camera and the audio (via LDV). We have promising preliminary results on association of the audio recording of speech with the video of the human speaker.
Stereoscopic System for 3-D Seabed Mosaic Reconstruction

This paper presents an inexpensive framework for 3-D seabed mosaic reconstruction, based on an asynchronous stereo vision system when simplifying motion assumptions are used. In order to achieve a metric reconstruction some knowledge about the scene is recovered by a simple and reliable calibration step. The major issue in calibration come from the asynchronism that complicate the proper frames selection. To overcome this problem a stereo frames selection based on epipolar gap evaluation (EGE) is proposed. Stereo disparity maps are evaluated by using both local and global approaches. To deal with brightness constancy model violation, zero-mean normalized cross-correlation is used as similarity measure in local approach, whereas a histogram equalization is necessary in global approach in order to improve min-cut based algorithms. Experimental results validate the proposed framework, allowing to define 3-D mosaics having visual quality similar to those obtained by using specialized hardware.
Evolutionary medical image registration using automatic parameter tuning

Image registration is a fundamental step in combining information from multiple images in medical imaging, computer vision and image processing. In this paper, we configure a recent evolutionary algorithm for medical image registration, r-GA, with an offline automatic parameter tuning technique. In addition, we demonstrate the use of automatic tuning to compare different registration algorithms, since it allows to consider results that are not affected by the ability and efforts invested by the designers in configuring the different algorithms, a crucial task that strongly impacts their performance. Our experimental study is carried out on a large dataset of brain MRI, on which we compare the performance of r-GA with four classic IR techniques. Our results show that all algorithms benefit from the automatic tuning process and indicate that r-GA performs significantly better than the competitors.
Automatic Generation of Animatable 3 D Personalized Model Based on Multiview Images

We propose a fully-automatic 3D model generating method of individual people from multi-view images. An automatically constructed 3D model can be deformed and animated by controlling the joint. An animatable 3D personalized model of individual people is generated by transferring the joint-skeleton structure and appearance of a generic 3D human model to an individual 3D volumetric model reconstructed from multi-view images. We automatically estimate the joint position of individual people by computing a weight function consisting of the kinematic joint-skeleton structure of the generic 3D human model and anthropometric information. Our generic 3D human model approximates the whole body using sweep surfaces. The vertices on the object boundary are bound to the sweep surfaces and follow their deformation. Thus, the animatable 3D individual model can be generated by transferring the sweep surface of the generic 3D model to the individual 3D volumetric model. Keywords-Personalized modeling; animatable model; jointskeleton; sweep surface; transfering
Attention Driven Face Recognition, Learning from Human Vision System

This paper proposes a novel face recognition algorithm inspired by Human Visual System (HVS). Firstly, we learn where people look by recording observers’ eye movements when they are viewing face images. We find that the observers are consistent in the regions fixated and such fixated regions are selected as the salient regions. Secondly, we represent the face images by four scales of Local Binary Gabor Patterns (LGBPs) for the salient regions whereas one scale LGBPs for the others, inspired by the fact that fovea of HVS has a higher spatial acuity than the periphery. Thirdly, we integrate the global information of face images in face recognition. The experimental results demonstrate that the proposed method learning from human beings is comparable with those learned with machine learning algorithms, which shows that the characteristics of the HVS provide valuable insights into face recognition.
A formal security proof for the ZRTP Protocol

When some agents want to communicate through a media stream (for example voice or video), the Real Time Protocol (RTP) is used. This protocol does not provide encryption, so it is necessary to use Secure RTP (SRTP) to secure the communication. In order for this to work, the agents need to agree on key material and ZRTP provides them with a procedure to perform this task: it is a key agreement protocol, which relies on a Diffie-Hellman exchange to generate SRTP session parameters, providing confidentiality and protecting against Man-in-the-Middle attacks even without a public key infrastructure or endpoint certificates. This is an analysis of the protocol performed with ProVerif, which tests security properties of ZRTP; in order to perform the analysis, the protocol has been modeled in the applied π-calculus.
A learning perspective on selfish behavior in games

Computer systems increasingly involve the interaction of multiple self-interested agents. The designers of these systems have objectives they wish to optimize, but by allowing selfish agents to interact in the system, they lose the ability to directly control behavior. What is lost by this lack of centralized control? What are the likely outcomes of selfish behavior? In this work, we consider learning dynamics as a tool for better classifying and understanding outcomes of selfish behavior in games. In particular, when such learning algorithms exist and are efficient, we propose "regret-minimization" as a criterion for self-interested behavior and study the system-wide effects in broad classes of games when players achieve this criterion. In addition, we present a general transformation from offline approximation algorithms for linear optimization problems to online algorithms that achieve low regret.
Learning Semantically Coherent and Reusable Kernels in Convolution Neural Nets for Sentence Classification

The state-of-the-art CNN models give good performance on sentence classification tasks. The purpose of this work is to empirically study desirable properties such as semantic coherence, attention mechanism and reusability of CNNs in these tasks. Semantically coherent kernels are preferable as they are a lot more interpretable for explaining the decision of the learned CNN model. We observe that the learned kernels do not have semantic coherence. Motivated by this observation, we propose to learn kernels with semantic coherence using clustering scheme combined with Word2Vec representation and domain knowledge such as SentiWordNet. We suggest a technique to visualize attention mechanism of CNNs for decision explanation purpose. Reusable property enables kernels learned on one problem to be used in another problem. This helps in efficient learning as only a few additional domain specific filters may have to be learned. We demonstrate the efficacy of our core ideas of learning semantically coherent kernels and leveraging reusable kernels for efficient learning on several benchmark datasets. Experimental results show the usefulness of our approach by achieving performance close to the state-of-the-art methods but with semantic and reusable properties.
Verb-based Semantic Modelling and Analysis of Textual Requirements

Automated machine analysis of natural language requirements poses several challenges. Complex requirements such as functional requirements and use cases are hard to parse and analyze, the language itself is un-constrained, the flow of requirements may be haphazard, and one requirement may contradict another - to name a few challenges. In this paper, we present a lightweight semantic modeling technique through natural language processing to filter requirements and create a semi-formal semantic network of requirement sentences. We employ novel techniques of classification of verbs used in requirements, semantic role labeling, discourse identification, and a few verb entailment and dependency relationships to generate a lightweight semantic network and critique the requirements. We discuss the design of the model and some early results obtained from analyzing real-life industrial requirements.
Evaluating effects of structural holds on pointing and dragging performance with flexible displays

In this paper, we present a study of the effects of structural holds and rigidity of a flexible display on touch pointing and dragging performance. We discuss an observational study in which we collected common holds used when pointing on a mockup paper display. We also measured the force patterns each hold generated within the display surface. We analyzed this data to produce 3 force zones in the display for each of the four most frequently observed holds: the grip zone, rigid zone, and the flexible zone. We report on an empirical evaluation in which we compared the efficiency of pointing and dragging operations between holds, and between structural zones within holds, using a real flexible Lumalive display. Results suggest that structural force distributions in a flexible display affect the Index of Performance of both pointing and dragging tasks, irrespective of hold, with rigid parts of the display yielding a 12% average performance gain over flexible areas.
A Trusted Control Model of Cloud Storage

Cloud storage service brings about not only low cost and scalability, but also great challenges in the field of data security and privacy protection. Security has been regarded as one of the greatest problems in the field of cloud storage service. Aiming at the present problem, a trusted control model of cloud storage (TCMCS) is presented. With the using of cipher-text access control and integrity verification, TCMCS makes the users' data safe. Considering the security and efficiency in data sharing, we introduced the trusted third party (TTP). Theoretic analysis and simulation show that data security can be preserved while retaining prefect performance in data processing.
Optimal MIMO-WLAN Location for Transmitter in Indoor Environment Using Particle Swarm Optimizer

ABSTRACT The optimal antenna location for maximizing the channel capacity is searched by particle swarm optimizer (PSO). There are two different receiver locations considered in the simulation. The receivers are located with uniform intervals distribution (i) on the tables; (ii) in the whole indoor environment. Numerical results have shown that our proposed method is effect to find the optimal location for transmitting antenna to maximizing the channel capacity.
Handbook of Research on Effective Electronic Gaming in Education

This chapter explores prospective computer teachers’ perceptions of and experiences in goal-based scenario (GBS) centered 3D educational game development process. Twenty-six pre-service computer teachers who enrolled in a Design, Development and Evaluation of Educational Software undergraduate course formed the sample of this case study, and they, in groups, developed GBS-centered 3D educational games. The data were collected through GBS evaluation checklists, interviews, and formative evaluations. The findings indicated that the pre-service teachers preferred GBS-centered educational games to traditional educational games. They declared that the most important feature of educational games was their contribution to motivation, attention, and retention. Although the majority of the groups developed their educational games in line with GBS, they had difficulty creating a realistic scenario and mission. Unlike what the literature indicates, one of the group’s formative evaluation results showed that while the second graders prefer realism, the sixth graders prefer more fantasy in the scenario.