Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Approach for Nonuniformity Correction in Infrared Focal Plane Array

In this paper, we propose a new scene-based nonuniformity correction technique for infrared focal plane arrays. Our work is based on the use of two well-known scene-based methods, namely, adaptive and interframe registration-based exploiting pure translation motion model between frames. The two approaches have their benefits and drawbacks, which make them extremely effective in certain conditions and not adapted for others. Following on that, we developed a method robust to various conditions, which may slow or affect the correction process by elaborating a decision criterion that adapts the process to the most effective technique to ensure fast and reliable correction. In addition to that, problems such as bad pixels and ghosting artifacts are also dealt with to enhance the overall quality of the correction. The performance of the proposed technique is investigated and compared to the two state-of-the-art techniques cited above.
Predicting the Learning Rate of Gradient Descent for Accelerating Matrix Factorization

Matrix Factorization (MF) has become the predominant technique in recommender systems. The model parameters are usually learned by means of numerical methods, such as gradient descent. The learning rate of gradient descent is typically set to lower values in order to ensure that the algorithm will not miss a local optimum. As a consequence, the algorithm may take several iterations to converge. Ideally, one wants to find the learning rate that will lead to a local optimum in the first iterations, but that is very difficult to achieve given the high complexity of the search space. Starting with an exploratory analysis on several recommender systems datasets, we observed that there is an overall linear relationship between the learning rate and the number of iterations needed until convergence. Another key observation is that this relationship holds across the different recommender datasets chosen. From this, we propose to use simple linear regression models for predicting, for an unknown dataset, a good learning rate to start with. The idea is to estimate a learning rate that will get us as close as possible to a local optimal in the first iteration, without overshooting it. We evaluate our approach on 8 real-world recommender datasets and compare it against the standard learning algorithm, that uses a fixed learning rate, and adaptive learning rate strategies from the literature. We show that, for some datasets, we can reduce the number of iterations up to 40% when compared to the standard approach.
The effect of various infertility treatments on the risk of preterm birth.

BACKGROUND The high incidence of preterm birth (<37 weeks gestation) is a major concern in assisted reproductive technology. The objective of this study was to compare the risk of preterm birth from singleton pregnancies following either low technology treatment (intrauterine insemination and donor insemination) or high technology treatment (IVF, ICSI and gamete intra-Fallopian transfer) with that of naturally conceived pregnancies. METHODS Three cohorts of pregnancies resulting from either low or high technology treatment or from natural conception were included in the study. A number of potential risk factors were adjusted for. RESULTS The incidence of very preterm birth (<32 weeks of gestation) was not significantly increased in the low technology treatment group (1.0 versus 1.3% in controls) but was significantly higher in the high technology treatment group (5.2%, P < 0.001). In spontaneous, elective Caesarean section (CS) and induced delivery onset, the risk of preterm birth increased gradually from the controls to the low technology treatment group to the high technology treatment group, while for an emergency CS the risk of preterm birth was very high in both treatment groups. CONCLUSIONS The overall incidence of preterm birth increased significantly from the controls to the low technology treatment group and to the high technology treatment group. Logistic regression analysis showed that younger and older age, previous perinatal death and emergency CS were associated with an increased risk, while a previous live birth reduced the risk. The length of the infertile period did not seem to affect the risk in any of the treatment groups.
Confiance, métacognition et perception

Resume – Les probabilites subjectives ont un role central dans la prise de decision. Si les modeles theoriques et les donnees experimentales sont relativement silencieux en economie sur la facon dont se forment ces croyances lors du processus decisionnel, il n’est pas de meme en sciences cognitives. Nous proposons ici une revue de litterature de l’etude de la metacognition au travers de modeles computationnels de detection du signal. Cette methodologie est ensuite importee a la decision non perceptive et nous montrons comment son utilisation ouvre de nouvelles pistes de recherche dans l’etude des croyances subjectives en economie experimentale.
A new B2B platform based on cloud computing

With the fast development of Internet and its data scale, B2B (Business to Business), whose speed and high availability advantage is based on Internet, is eroding more and more market share of traditional business. In recent years, the new data processing technologies, such as cloud computing, assure the enhancement of computer's computing capacity; it has become possible for researchers to process and analyze the massive business data, including large scale customer data, and large scale commodity information. A new B2B platform frame, based on cloud computing with less running time and better response efficiency, is proposed here to promote transaction handle efficiency. By adoption of complex network theory and cloud computing technology, the new platform has been evaluated to decrease transaction handle time, and make more benefits.
Accurate Estimate of the Cross-Validated Prediction Error Variance in Bayes Classifiers

A relationship between the variance of the prediction error committed by the Bayes classifier and the mean prediction error was established by experiments in emotional speech classification within a cross-validation framework in a previous work. This paper theoretically justifies the validity of the aforementioned relationship. Furthermore, it proves that the new estimate of the variance of the prediction error, treated as a random variable itself, exhibits a much smaller variance than the usual estimate obtained by cross- validation even for a small number of repetitions. Accordingly, we claim that the proposed estimate is more accurate than the usual, straightforward, estimate of the variance of the prediction error obtained by applying cross-validation.
Recursive projected sparse matrix recovery (ReProSMR) with application in real-time video layer separation

In this work, we propose an online algorithm Recursive Projected Sparse Matrix Recovery (ReProSMR) for recovering a time sequence of sparse matrix St and a time sequence of dense matrix Lt from their sum Mt = Lt + St when Lts' lies in a slowly changing low dimensional tensor subspace. A key application where this problem occurs is in video layer separation where the goal is to separate a video sequence into a slowly changing low dimensional background sequence and a sparse foreground sequence. Mathematically, a 2D image can be thought of as a second order tensor. ReProSMR is a modification of Recursive Projected Compressive Sensing (ReProCS) based on tensor PCA. Experimental comparisons demonstrating the advantages and computation gain of ReProSMR are shown for both simulated and real videos.
Computer-based course evaluation: an extended technology acceptance model

Abstract This study explored the antecedents of students’ intention to participate in computer-based course evaluation. The study proposed an extended Technology Acceptance Model (TAM) that integrated subjective norm and perceived relevance into TAM. Based on the responses from 149 business school students in Macao, the study identified the direct and indirect effects of subjective norm and perceived relevance on perceived ease of use, perceived usefulness and students’ intention to participate in computer-based course evaluation. The results of structural equation modelling showed that subjective norm had a positive, strong and significant effect on perceived relevance that in turn affected perceived ease of use and perceived usefulness. Besides, subjective norm and perceived usefulness had direct effects on students’ intention to participate in computer-based course evaluation. Thus, subjective norm played a major role in affecting Chinese students’ participation in course evaluation.
Multimodal Retrieval With Asymmetrically Weighted Regularized Canonical Correlation Analysis

Joint modeling of language and vision has been drawing increasing interest. A multimodal data representation allowing for bidirectional retrieval of images by sentences and vice versa is a key aspect of this modeling. In this paper we show that a cross-view mapping of the search space to the query space achieves state of the art performance in bidirectional retrieval using off the shelf features. We show that this search to query mapping can be implemented by a simple asymmetric weighting of the canonical correlation weights, where the canonical weights of the search space are weighted by the canonical correlations. We revisit regulariza- tion for Canonical Correlation Analysis (CCA) within the framework of spectral filtering and the Bjork Golub algorithm. We present our regularized CCA algo- rithms within two spectral filtering regularization families: Tikhonov regulariza- tion and truncated SVD (T-SVD)regularization . T-SVD regularization is new to the best of our knowledge in the context of CCA and is faster to cross-validate than Tikhonov regularized CCA at the price of a small loss in accuracy. In light of the spectral filtering interpretation of the regularizer, we devise a fast method to choose the regularization parameter in the Tikhonov regularized CCA, using the regularization path of T-SVD CCA and the singular values of the data matrices.
A framework for PC applications with portable and scalable FPGA accelerators

This paper presents a novel framework for implementing portable and scalable data-intensive applications on reconfigurable hardware. Instead of using expensive “reconfigurable supercomputers”, we focus our work on standard PCs and PCI-Express extension cards featuring Field-Programmable Gate Arrays (FPGAs) and memory. In our framework, we exploit task-level parallelism by manually partitioning applications into several parallel tasks using a communication API for data streams. This also allows pure software implementations on PCs without FPGA cards. If an FPGA accelerator is present, the same API calls transfer data between the PC's CPU and the FPGA. Then, the tasks implemented in hardware can exploit instruction-level and pipelining parallelsims as well. Furthermore, the framework consists of hardware implementation rules which enable portable and scalable designs. Device specific hardware wrappers hide the FPGA's and board's idiosyncrasies from the application developer. We also present a new method to automatically select a task's optimal degree of parallelism on an FPGA for a given hardware platform, i. e. to generate a hardware design which uses the available communication bandwidth between the PC and the FPGA optimally. Experimental results show the feasibility of our approach.
HCI framework for human centric converged network service

Converged network is motivated by service. All network capabilities can only be represented through service. Current service utilizes all advantage of network, in order to provide rich service experience. However, there is some gap between developer and end users, as developers didn't fully understand users' requirement. So service development can't catch up with user's increasing requirement. This paper introduces HCI approach in service development, trying to use cognitive model of interaction to improve quality of experience. The goal is to provide a framework and some principles to make human centric service much easier and quicker. So detail cognitive model mapping will be discussed, and a general framework will also be mentioned. As the work going, this framework will be applied it in a real network setting.
Hong Kong men with low incomes have worse health-related quality of life as judged by SF-36 scores.

OBJECTIVE To analyse the association between income and health-related quality of life using the Medical Outcome Study Short Form 36 (SF-36) Chinese version in Hong Kong Chinese working population. DESIGN Cross-sectional observation study. SETTING A commercial company in Hong Kong. PARTICIPANTS All clerical and administrative staff of a commercial company was invited to participate; 876 of the 1003 staff agreed. The subjects were categorised into three income groups according to monthly income in Hong Kong dollars (low, < or =10,000; middle, >10,000-25,000; high, >25,000). The mean age of the 288 men and 588 women was 34.9 (standard deviation, 7.9; median, 34.0; range, 18-71) years. MAIN OUTCOME MEASURES SF-36 scores on health-related quality of life. RESULTS The distribution of income was 30% in high-, 54.8% in middle-, and 15.2% in low-income groups. Women had similar SF-36 scores among different income groups. In men, for most variables there was a significant positive linear correlation between income and SF-36 scores. CONCLUSION Low income is associated with a worse health-related quality of life in Hong Kong Chinese men.
A More Agile Approach to Embedded System Development

Given the plethora of advantages attributed to agile methodologies, why are there only infrequent reports of their application in the embedded systems world? We believe this has two principal causes. First, embedded applications go through a life cycle that differs from desktop life cycles, so that desktop application production processes must be significantly altered to meet the demands of this new domain. Second, without providing for full life-cycle tool support, no change to embedded production processes will ever succeed, and again desktop tools must be adapted to meet the new environment's constraints. Here, the authors discuss their thoughts on an XP-inspired embedded life cycle and their successes in developing, extending, and using embedded test-driven development frameworks for customers (Matlab-Fit and Embedded-FitNesse) and developers (MUnit and Embedded-Unit).
Magnetization Transfer Prepared Gradient Echo MRI for CEST Imaging

Chemical exchange saturation transfer (CEST) is an emerging MRI contrast mechanism that is capable of noninvasively imaging dilute CEST agents and local properties such as pH and temperature, augmenting the routine MRI methods. However, the routine CEST MRI includes a long RF saturation pulse followed by fast image readout, which is associated with high specific absorption rate and limited spatial resolution. In addition, echo planar imaging (EPI)-based fast image readout is prone to image distortion, particularly severe at high field. To address these limitations, we evaluated magnetization transfer (MT) prepared gradient echo (GRE) MRI for CEST imaging. We proved the feasibility using numerical simulations and experiments in vitro and in vivo. Then we optimized the sequence by serially evaluating the effects of the number of saturation steps, MT saturation power (B1), GRE readout flip angle (FA), and repetition time (TR) upon the CEST MRI, and further demonstrated the endogenous amide proton CEST imaging in rats brains (n = 5) that underwent permanent middle cerebral artery occlusion. The CEST images can identify ischemic lesions in the first 3 hours after occlusion. In summary, our study demonstrated that the readily available MT-prepared GRE MRI, if optimized, is CEST-sensitive and remains promising for translational CEST imaging.
PAPR Reduction Scheme for MISO and MIMO OFDM Systems with Limited Feedback

For multiple-input single-output (MISO) and multiple-input multiple output (MIMO) beamforming (BF) orthogonal frequency division multiplexing (OFDM) systems employing cluster-based codebook index feedback (CCIF), a new peak-to-average power ratio (PAPR) reduction method is proposed. In the proposed method, a dummy signal is generated in null space of the channel and superimposed on the original signal to reduce the PAPR. First, we modify the conventional method to apply to the MISO/MIMO OFDM system with CCIF based BF. From the observation that the conventional method will severely degrade the bit error rate (BER) performance, weight functions for the dummy signal across the subcarriers are proposed. The proposed weight functions are found by both nonlinear optimal and simple suboptimal manners. Through simulation results, it is shown that the proposed scheme can make the BER loss be negligible at the expense of some loss in PAPR reduction gain. Key-Words: OFDM, MIMO, PAPR reduction, transmit beamforming, limited feedback.
Document classification with a weighted frequency pattern tree algorithm

Document classification can be defined as the task of automatically categorizing collections of electronic documents into their annotated classes, based on their contents. It is an important problem in Data mining. Due to the exponential growth of documents in the Internet and the emergent need to organize them, developing an efficient document classification method to automatically manipulate web documents is of great importance and has received an ever-increased attention in the recent years. However, the existing approaches to text classification treat documents primarily as a bag of words, where all the information about the document is gathered based on the presence of individual words in the document, and not in what order or context those words appear in a sentence. In this paper we investigate the possibility of adopting the FP-tree, a data structure used in itemset mining, for the representation of training documents in text classification while preserving sentence information. Comparison between our method and other conventional document classification algorithms is conducted on several corpora. The experimental results indicate that our proposed algorithm yields much better performance than other conventional algorithms, especially the ones with primarily disjoint classification categories.
GoalBit: the first free and open source peer-to-peer streaming network

In this paper we present the GoalBit platform. GoalBit is the first open source peer-to-peer system for the distribution of real-time video streams through the Internet. The main advantage of a P2P architecture is the possibility of exploiting the available bandwidth unused by the set of user' hosts connected to the network. The main difficulty is that these hosts are typically highly dynamic, they continuously enter and leave the network. To deal with this problem, we use a mesh connectivity approach (bittorrent-like) where the stream is decomposed into several pieces sent by different peers to each client. Nowadays, the GoalBit platform is used by operators and by final users to broadcast their live contents. In this paper we explain by the first time the Goalbit architecture, its protocol, and how the content is packetized. To illustrate its potential, we present some empirical results measured in a popular final user channel, with more than 60 peers concurrently connected.
Evaluation of V2V and V2I mesh prototypes based on a wireless sensor network

Wireless vehicular communications have been identified as one of the most important Intelligent Transportation System technologies to be developed in the coming years, from a safety as well as an energy efficiency point of view. Several standards definitions, hardware developments, research projects, use cases definitions and field operational tests are currently under deployment in order to accelerate the market availability of this technology. Nevertheless, though the possible applications of vehicular communications are defined, there is a lack of availability of completely functional communication technology to support these applications. Issues like the adaptation of communication technologies to run in a massive high speed mobile environment, the behaviour of Vehicle Ad-hoc Networks (VANET) in multihop mesh mode or routing algorithms for reliable V2V and V2I data transmission are currently open research areas. In this paper we present an evaluation of V2V and V2I prototypes based on wireless sensor mesh networks, tested in real vehicles with real communication hardware. This paper opens up a frame to solve some of the present deficiencies of vehicular communications and enable the possibility of testing safety applications in real VANET situations.
Indoor Positioning for Smartphones Using Asynchronous Ultrasound Trilateration

Modern smartphones are a great platform for Location Based Services (LBS). While outdoor LBS for smartphones has proven to be very successful, indoor LBS for smartphones has not yet fully developed due to the lack of an accurate positioning technology. In this paper we present an accurate indoor positioning approach for commercial off-the-shelf (COTS) smartphones that uses the innate ability of mobile phones to produce ultrasound, combined with Time-Difference-of-Arrival (TDOA) asynchronous trilateration. We evaluate our indoor positioning approach by describing its strengths and weaknesses, and determine its absolute accuracy. This is accomplished through a range of experiments that involve variables such as position of control point microphones, position of phone within the room, direction speaker is facing and presence of user in the signal path. Test results show that our Lok8 (locate) mobile positioning system can achieve accuracies better than 10 cm in a real-world environment.
A psychologically plausible and computationally effective approach to learning syntax

Computational learning of natural language is often attempted without using the knowledge available from other research areas such as psychology and linguistics. This can lead to systems that solve problems that are neither theoretically or practically useful. In this paper we present a system CLL which aims to learn natural language syntax in a way that is both computationally effective and psychologically plausible. This theoretically plausible system can also perform the practically useful task of unsupervised learning of syntax. CLL has then been applied to a corpus of declarative sentences from the Penn Treebank (Marcus et al., 1993; Marcus et al., 1994) on which it has been shown to perform comparatively well with respect to much less psychologically plausible systems, which are significantly more supervised and are applied to somewhat simpler problems.
Picking the one that does not fit - A matter of logical proportions

Quiz or tests about reasoning capabilities often pertain to the perception of similarity and dissimilarity between situations. Thus, one may be asked to complete a series of entities $A$, $B$, $C$ by an appropriate $X$, or to pick the one that does not fit in a list. It has been shown that the first problem can receive a solution by solving analogical proportion equations between the representations of the entities in a logical setting, where we assume that $X$ should be such that $A$ is to $B$ as $C$ is to $X$. In this paper, we focus on the second problem, and we show that it can be properly handled by means of heterogeneous proportions that are the logical dual of the homogeneous proportions involved in the modeling of analogical proportions and related proportions. Thus, the formal setting of logical proportions, to which homogeneous and heterogeneous proportions belong, provides an appropriate framework for handling the two problems in a coherent way. As it already exists for homogeneous proportions, a particular multiple-valued logic extension of heterogeneous proportions is discussed (indeed being an intruder in a group may be a matter of degree).