Miracle-dz/UniFAR-FTUs huggingface dataset:
Asynchronous Invariant Digital Image Watermarking in Radon Field for Resistant Encrypted Watermark

With the rapid evolution of processing multimedia technologies (text, audio, image and video) and its internet application (wide and easy transmission of digital multimedia contents), the copyright protection has been receiving an increasing attention. Among the existing strategies to protect multimedia online, digital watermarking provides a promising way of protecting data online from illegal manipulation and duplication. In this paper, a new image of watermarking scheme is presented. It performs imperceptible watermarking of color image in the radon domain. The proposed algorithm can resist to geometrical attacks. Experimental results show that the proposed watermarking approach not only can meet the demand on invisibility and robustness of the watermark, but also presents a good performance compared to other proposed methods considered in the comparative study. A mathematical study is developed to demonstrate how and why this approach is robust against geometric transforms.
Learning non-linear ranking functions for web search using probabilistic model building GP

Ranking the set of search results according to their relevance to a user query is an important task in an Information Retrieval (IR) systems such as a Web Search Engine. Learning the optimal ranking function for this task is a challenging problem because one must consider complex non-linear interactions between numerous factors such as the novelty, authority, contextual similarity, etc. of thousands of documents that contain the user query. We model this task as a non-linear ranking problem, for which we propose Rank-PMBGP, an efficient algorithm to learn an optimal non-linear ranking function using Probabilistic Model Building Genetic Programming. We evaluate the proposed method using the LETOR dataset, a standard benchmark dataset for training and evaluating ranking functions for IR. In our experiments, the proposed method obtains a Mean Average Precision (MAP) score of 0.291, thereby significantly outperforming a non-linear baseline approach that uses Genetic Programming.
Registration of 3D Reconstruction Point Clouds Based on the Assistant Cameras Array System

Three-dimensional reconstruction plays an important role in the real practice. Since the three-dimensional reconstruction technique based on structured light is seriously restricted by the light conditions, the three-dimensional reconstruction technique based on passive optical binocular vision is widely applied with the superiority of unlimited by the environment conditions. However, the precision of the reconstruction is not satisfying. The point cloud registration technique of three-dimensional reconstruction can greatly improve the drawback, but the traditional ICP algorithm endures a low iteration speed owing to large iterations and the precision is still unsatisfying. This paper presents a point cloud registration technique algorithm of three-dimensional reconstruction based on assistant camera array system, which creatively takes advantage of run length encoding to do the corner recognition, and can acquire accurate registration results without iteration. Experimental results indicate a greater improvement in accuracy and speed compared with ICP algorithm.
OBSERVATION ON TRAINING NEURAL NETWORK FOR DIAGNOSING SCHIZOPHRENIA

Artificial neural networks may be deployed to diagnose illnesses including mental illness. One such mental illness is schizophrenia which is characterized by persistent delusions, hallucinations, disorganized speech, highly disorganized or catatonic behaviour and negative symptoms. It is commonly believed that one or two hidden layers in a neural network are sufficient to classify data, and that more hidden layers may be avoided because of longer times taken for the network to converge. However, we demonstrate that beyond a certain size of the hidden layer(s), it is harmful to deploy more than one layer because not only will it take longer for the network parameters to converge, the classification performance deteriorates sharply with more than one hidden layers.
An ISOCAM survey through gravitationally lensing galaxy clusters. II. The properties of infrared galaxies in the A2218 field

We observed the cluster Abell 2218 (z=0.175), with ISOCAM on board the Infrared Space Observatory, using two filters with reference wavelengths of 6.7 and 14.3 microns. We detected 67 extragalactic mid-infrared sources. Using the ``GRASIL'' models of Silva et al. (1998) we have obtained acceptable and well constrained fits to the observed spectral energy distributions (SEDs) of 41 of these sources, for which additional optical/near-infrared magnitudes, and, in some cases, redshifts, were available. We have then determined the total infrared luminosities and star formation rates (SFRs) of these 41 sources, of which 27 are cluster members and 14 are field galaxies at a median redshift z~0.6. The SEDs of most of the ISOCAM cluster members are best fit by models with negligible star formation in the last ~1 Gyr. The 7 faintest cluster members have a slightly higher, but still very mild, star-formation activity. The ISOCAM-selected cluster galaxies appear to be an unbiased subsample of the overall cluster population. If A2218 is undergoing a merger, as suggested by some optical and X-ray analyses, then this merger does not seem to affect the mid-infrared properties of its galaxies. The SEDs of most ISOCAM-selected field sources are best fit by models with significant ongoing (and recent) star formation, resembling those of massive star-forming spirals or (post)starburst galaxies. Their median SFR is 22 solar masses per year. Eight of the 14 field sources are Luminous IR Galaxies. (Abridged)
Examining the Externalities of Welfare Reform: TANF and Crime

This study explores the relationship between welfare policy variation in the United States following the introduction of Temporary Assistance for Needy Families (TANF) and its relationship with various types of crime. While early studies of the effects of welfare assistance on crime consistently found a negative association, more recent examinations have complicated these findings. Nearly all prior research focuses on Aid for Families with Dependent Children or early years of TANF. Examining a longer time-series and using propensity score weighting to model the tendencies of states to select into more stringent welfare regimes, we find a strong association between states with greater levels of welfare restrictiveness and higher rates of violent crime. There is mixed evidence that this relationship also exists with property crimes.
Identification of Anti-prostate Cancer Targets for Resveratrol: an Inverse Screening Approach

Chemoprevention of cancer through phytochemicals has become a powerful weapon against cancer. The intake of bioactive components in our diet reduces the risk of cancer. Resveratrol has proved as one of the promising phytochemical derived from grape skin against cancer. In the present study the role of resveratrol in prostate cancer prevention was taken into consideration. The putative therapeutic targets for resveratrol were obtained from the PharmMapper Server with their fit scores. The targets obtained were classified as anti- prostate cancer, anti-inflammatory, Signal transducing modulators, anti-oxidant, anti-proliferative and anti- angiogenesis based on their therapeutic action using TTD (Therapeutic Target Database) and PDTD (Potential Drug Target Database). Our component resveratrol showed to be an important molecule which can be helpful in the drug discovery against prostate cancer.
Modelling of complex signals using gaussian processes

In complex-valued signal processing, estimation algorithms require complete knowledge (or accurate estimation) of the second order statistics, this makes Gaussian processes (GP) well suited for modelling complex signals, as they are designed in terms of covariance functions. Dealing with bivariate signals using GPs require four covariance matrices, or equivalently, two complex matrices. We propose a GP-based approach for modelling complex signals, whereby the second-order statistics are learnt through maximum likelihood; in particular, the complex GP approach allows for circularity coefficient estimation in a robust manner when the observed signal is corrupted by (circular) white noise. The proposed model is validated using climate signals, for both circular and noncircular cases. The results obtained open new possibilities for collaboration between the complex signal processing and Gaussian processes communities towards an appealing representation and statistical description of bivariate signals.
Body Mass Index: A Risk Factor for Retinopathy in Type 2 Diabetic Patients

The aim of the study was to investigate whether body mass index (BMI) independently or in correlation with other risk factors is associated with diabetic retinopathy (DR) progression. The study included 545 patients with type 2 diabetes. According to DR status, they were divided into three groups: group 1 (no retinopathy; n = 296), group 2 (mild/moderate nonproliferative DR; n = 118), and group 3 (severe/very severe NPDR or proliferative DR; n = 131). Patients without DR were younger than those with signs of retinopathy at time of diabetes onset whilst diabetes duration was longer in groups with severe NPDR and PDR. DR progression was correlated with diabetes duration, BMI, HbA1c, hypertension, and cholesterol. Statistical analyses showed that the progression of retinopathy increased significantly with higher BMI (gr. 1: 26.50 ± 2.70, gr. 2: 28.11 ± 3.00, gr. 3: 28.69 ± 2.50; P < 0.01). We observed a significant deterioration of HbA1c and a significant increase in cholesterol and hypertension with an increase in BMI. Correlation between BMI and triglycerides was not significant. Thus, BMI in correlation with HbA1c cholesterol and hypertension appears to be associated with the progression of DR in type 2 diabetes and may serve as a predictive factor for the development of this important cause of visual loss in developed countries.
Predicting Individual Differences in Student Learning via Collaborative Filtering

Effective teaching requires an understanding of a student’s knowledge state—what material the student has and has not mastered and what material is fragile and easily lost. To facilitate automated teaching, our goal is to construct models that infer the knowledge state of individual students for specific elements of knowledge. The challenge to inference is that the available evidence is quite weak. For example, suppose that a student solved four out of five specific long-division problems correctly on a quiz; how well would you expect the student to do on a particular long-division problem assigned a month later? To overcome the sparsity of observations, we use a collaborative filtering approach that leverages information about a population of students studying a population of items (elements of knowledge) to infer how well a specific student has learned a specific item. We extend item-response theory, a traditional class of models that recover latent traits of students and items, to address the facts that knowledge state is nonstationary and that both observations and predictions may span a broad range of time. This extension is based on a psychological model of memory that can take into account dynamic information about study history. We evaluate three alternative models whose latent variables are determined either via maximum likelihood estimation or a hierarchical Bayesian approach. We show for two different student-learning data sets that, when we combine multiple weak sources of information from the population, we can make strong inferences about an individual student’s knowledge and performance.
A Distributed Utility-Maximizing Algorithm for Data Collection in Mobile Crowd Sensing

Mobile crowd sensing harnesses the data sensing capability of individual smartphones, underpinning a variety of valuable knowledge discovery, environment monitoring and decision making applications. It is a central issue for a mobile crowd sensing system to maximize the utility of sensing data collection at a given cost of resource consumption at each smartphone. However, it is particularly challenging. On the one hand, the utility of sensing data from a smartphone is usually dependent on its context which is random and varies over time. On the other hand, because of the marginal effect, the sensing decision of a smartphone is also dependent on decisions of other smartphones. Little work has explored the utility maximization problem of sensing data collection. This paper proposes a distributed algorithm for maximizing the utility of sensing data collection when the smartphone cost is constrained. The design of the algorithm is inspired by stochastic network optimization technique and distributed correlated scheduling. It does not require any priori knowledge of smartphone contexts in the future, and hence sensing decisions can be made by individual smartphone. Rigorous theoretical analysis show that the proposed algorithm can achieve a time average utility that is within O(1/V ) of the theoretical optimum.
Within-patient correlation influence on defibrillation outcome prediction using a Gaussian Mixture Model

In this study, we examine whether it is reliable to use all successive shocks from one patient for the development of an outcome predictor model to discriminate “Success” versus “No success”. Vector of predictors v are extracted from time and non-linear dynamics domains and a Gaussian Mixture Model-based bayesian classifier, with probability density estimated by the Expectation-Maximization algorithm, is applied in order to detect shocks with “Success” according to the probability P(v/Success). A cross-validation analysis is performed independently on 136 first shocks (Group1) and 382 second and later shocks (Group2). At 5 s post-shock, an Organized Rhythm (OR) is considered as “Success” and Ventricular Fibrillation (VF) is defined as “No success”. A decrease in performance of discrimination of OR versus VF between Group1 and Group2 is observed with an Area Under the ROC Curve of 0.82 and 0.65, respectively. This corroborates the current hypothesis that within-patient correlation affects defibrillation outcome prediction accuracy.
Pole and Crossarm Identification in Distribution Power Line Images

Society is getting more and more dependent of the energy industry. Therefore it is producers, distributors and consumers interest that the energy system is maintained accordingly. Nowadays, only 20% of a distribution grid is covered by preventive inspections. This work present an initial step in a system of preventively inspect a network using an Unmanned Aerial Vehicle that will acquire low altitude images of the distribution system network and an intelligent image analysis is used to look for irregularities in the network. Here, a method for identifying poles and cross arms using neural networks is presented. Also, measurements done in the image database allowed to define a somewhat non-conventional set of entries increasing the hit hate by more than 20%. The results justify, once more, the advantages of exploring the available knowledge around a problem.
The Effects of Congressional Staff Networks in the US House of Representatives

Standard accounts of legislative behavior typically neglect the activities of professional staff, who are treated as extensions of the elected officials they serve. However, staff appear to have substantial independent effects on observed levels of legislator productivity and policy preferences. In this article, we use a novel data set of comprehensive longitudinal employment records from the US House of Representatives to estimate the effects of congressional staff on legislative behavior. Specifically, results from a series of heteroskedastic Bayesian spatial autoregressive models indicate that members of Congress who exchange important staff members across congresses are more similar in their legislative effectiveness and voting patterns than we would otherwise expect. These findings suggest that scholars should reconsider the role of staff in the legislative process.
Literary authorship attribution with phrase-structure fragments

We present a method of authorship attribution and stylometry that exploits hierarchical information in phrase-structures. Contrary to much previous work in stylometry, we focus on content words rather than function words. Texts are parsed to obtain phrase-structures, and compared with texts to be analyzed. An efficient tree kernel method identifies common tree fragments among data of known authors and unknown texts. These fragments are then used to identify authors and characterize their styles. Our experiments show that the structural information from fragments provides complementary information to the baseline trigram model.
Adaptive Application for Mobile Network Environment

In a mobile network environment, a Mobile Router (MR) has multiple communication interfaces to the Internet. It switches between or simultaneously uses these interfaces, causing network conditions within the MR to change. If the network conditions were shared with between nodes behind the MR (MNN) and their correspondent nodes (CN), it would be possible for applications to change its behavior accordingly. In this research, an application that has this ability is called Adaptive Application. We propose a new architecture of Adaptive Application, which allows allocating IDs to communication interfaces of MR and distributing those IDs to both CN and MNN. MNN and CN can be informed of the interfaces currently used on the MR. In a mobile network environment, a communication interface is a big determining factor in network conditions. This thus allows applications to adapt to network conditions. We also expose evaluation results obtained from a videoconference system that implements the proposed architecture. This application reacts to changes in the currently used interface and dynamically modifies its transmission rate. Finally, we conclude that dynamic adaptability of applications to the current network condition could be effective.
Source Phrase Segmentation and Translation for Japanese-English Translation Using Dependency Structure

There are various approaches to statistical machine translation (SMT). In particular, phrase-based SMT (PBSMT) is used as a de facto standard for many language pairs because it works robustly across languages and it is easy to implement. However, the results of PBSMT can include ungrammatical sentences, since it typically does not take syntactic structure into account. To overcome this problem, we propose a linguistically motivated approach based on segmenting a source phrase using a dependency structure and translating each phrase with PBSMT. This paper presents the results of our method on JapaneseEnglish translation and discusses potential improvements.
Secrecy for Bounded Security Protocols: Disequality Tests and an Intruder with Existentials Lead to Undecidability

The complexity of the initial secrecy problem for protocols in restricted form, with bounded length messages, unbounded number of protocol sessions, bounded existentials, disequality tests and an intruder with existentials remained open. In this paper, we prove that the problem above is undecidable, using exactly the same setting and formalism as in the paper in that the problem was initially stated.To our knowledge, up to now there is no proof of undecidability of the open problem mentioned above that constructs a protocol (well-founded) in restricted form in multiset rewriting formalism.
Analysis of a deeply saturated sensorless PMSynRel drive for an automotive application

This paper presents an method of mapping feasible region for a sensorless control PMSynRel drive using the resulting position error signal. Thanks to the rotor anisotropy, the rotor position can be detected at low speeds by means of injecting high frequency voltage, either a pulsating voltage vector in the estimated synchronous frame or a rotating vector in the stator frame, on top of the fundamental excitation. However, the resulting position error signal is degraded and distorted by magnetic saturation (including cross-saturation) and spatial inductance harmonics. Therefore, the feasible region is used to find the stable operating points. Instead of mapping the inductances, which is time consuming, position error signal is used to map the feasible sensorless control region. A prediction method of the maximum position estimation error is also presented by taking the spatial inductance harmonics into account. After that, some techniques are presented to compensate the cross-saturation and slot harmonic effects in order to improve the sensorless control quality. Additionally, it is shown how saturation in the rotor bridges affects the initial polarity detection required during startup.
A passive walking strategy for a biped robot with a large mass torso by a spring and a damper

This paper presents a control system for a biped robot with a large mass torso by extension of previous work in the field of CPG (Central Pattern Generator). The algorithm takes advantage of the passive dynamics of walking, assisting only when necessary with an intermittent oscillator driving the hip joint. However, a biped robot with a large mass torso cannot walk stably only by the CPG controller and a simple feedback control. We speculate about inherent reasons of falling based on dynamics principles and propose a method to solve this problem. This proposed method is that each leg of the model is connected to a torso by a virtual spring. Furthermore a virtual damper with the variable damper coefficient is attached between a torso and a stance leg. It makes the horizontal moving of the torso easy by shortening the step length and swinging the torso. The results from simulation indicate our controller has a good effect on the biped robot walking with a large mass torso.
ADVANCES IN THE APPLICATION OF MATHEMATICAL MORPHOLOGY IN SPATIAL DATA PROCESSING AND ANALYSIS

Mathematical morphology originated in 1960s. It uses a structuring element to measure and extract corresponding objects in images. The purpose is to analyze and identify these images. Mathematical morphology can be widely applied in geoscience because not only the research object but also the data format in geoscience research is so consistent with mathematical morphology. This article sets out from the theory of mathematical morphology and probes the cardinal application in geoscience. Aiming at the complexity of earth observation data, we discussed the suitability of mathematical morphology in geoscience research. The prospect of application of mathematical morphology in geoscience was expected. * Corresponding author.