Miracle-dz/UniFAR-FTUs huggingface dataset:
Image Annotation Refinement Using Dynamic Weighted Voting Based on Mutual Information

Automatic image annotation is a promising solution to narrow the semantic gap between low-level content and high-level semantic concept, which has been an active research area in the fields of image retrieval, pattern recognition, and machine learning. However, even the most dedicated annotation algorithms are often unsatisfactory. Image annotation refinement has attracted much more attention recently. In this paper, a novel refinement algorithm using dynamic voting based on mutual information is proposed. Unlike the traditional refinement algorithm, the proposed algorithm adopts dynamic weighted voting to measure the dependence between the candidate annotations, which not only permits that the annotations with higher probabilities deny the annotations with lower probabilities, but also permits that the annotations with lower probabilities deny the annotations with higher probabilities. The proposed refinement algorithm adopts progressive method instead of iterative, which can significantly decrease the time cost of refining annotations. In order to further improve efficiency without sacrificing precision, we propose the block-based normalized cut algorithm to segment image. Experiments conducted on standard Washington Ground Truth Image Database demonstrate the effectiveness and efficiency of our proposed approach for refining image annotations.
An hybrid metaheuristic, an hybrid lower bound and a Tabu search for the two-machine flowshop total tardiness problem

The classical F2∥ΣTj problem is considered. A Tabu search and a matheuristic algorithm are proposed as upper bounds and a lower bound based on a partial relaxation of the MILP model is proposed. All these methods have been extensively tested on randomly generated instances. The results show the performances of the Tabu search algorithm. They also show the good performances of the matheuristic and of the lower bound, preferably for small instances (up to 100 jobs).
libgroupsig: An extensible C library for group signatures

One major need in the context of Privacy Enhancing Technologies (PETs) is to bridge theoretical proposals and practical implementations. In order to foster easy deployment of PETs, the crux is on proposing standard and well-defined programming interfaces. This need is not completely fulfilled in the case of group signatures. Group signatures are key cryptographic primitives to build up privacy respectful protocols and endorsing fair management of anonymity. To the best of our knowledge, currently there exists no abstract and unified programming interface definition for group signatures. In this work we address this matter and propose a programming interface definition enclosing the functionality of current group signatures schemes. Furthermore, for the sake of abstraction and generalization, we have also endowed our interface with the means to include new group signatures schemes. Finally, we have considered three well known group signature schemes to implement an open source library of the interface using C programming language. We have also performed an analysis of the software implementation with respect to different values of the key size and other parameters of the group signatures interface.
Stress and Emotion Classification using Jitter and Shimmer Features

In this paper, we evaluate the use of appended jitter and shimmer speech features for the classification of human speaking styles and of animal vocalization arousal levels. Jitter and shimmer features are extracted from the fundamental frequency contour and added to baseline spectral features, specifically Mel-frequency cepstral coefficients (MFCCs) for human speech and Greenwood function cepstral coefficients (GFCCs) for animal vocalizations. Hidden Markov models (HMMs) with Gaussian mixture models (GMMs) state distributions are used for classification. The appended jitter and shimmer features result in an increase in classification accuracy for several illustrative datasets, including the SUSAS dataset for human speaking styles as well as vocalizations labeled by arousal level for African elephant and Rhesus monkey species.
Functional Studies of Split Arabidopsis Ca2+/H+ Exchangers*

In plants, high capacity tonoplast cation/H+ antiport is mediated in part by a family of cation exchanger (CAX) transporters. Functional association between CAX1 and CAX3 has previously been shown. In this study we further examine the interactions between CAX protein domains through the use of nonfunctional halves of CAX transporters. We demonstrate that a protein coding for an N-terminal half of an activated variant of CAX1 (sCAX1) can associate with the C-terminal half of either CAX1 or CAX3 to form a functional transporter that may exhibit unique transport properties. Using yeast split ubiquitin, in planta bimolecular fluorescence complementation, and gel shift experiments, we demonstrate a physical interaction among the half proteins. Moreover, the half-proteins both independently localized to the same yeast endomembrane. Co-expressing variants of N- and C-terminal halves of CAX1 and CAX3 in yeast suggested that the N-terminal region mediates Ca2+ transport, whereas the C-terminal half defines salt tolerance phenotypes. Furthermore, in yeast assays, auto-inhibited CAX1 could be differentially activated by CAX split proteins. The N-terminal half of CAX1 when co-expressed with CAX1 activated Ca2+ transport, whereas co-expressing C-terminal halves of CAX variants with CAX1 conferred salt tolerance but no apparent Ca2+ transport. These findings demonstrate plasticity through hetero-CAX complex formation as well as a novel means to engineer CAX transport.
An automatic self-calibration approach for wide baseline stereo cameras using sea surface images

The calibration process of a stereo rig is normally done by making two cameras view one special designed calibration pattern (such as checkerboard) at the same time to estimate the relative rotation between two cameras. Multiple image pairs from different perspectives are required for better estimation accuracy. Stereo rig with wide baseline is necessary when accurate depth estimation for distant object is desired. However, in order to make calibration pattern to be viewed from both left and right cameras, the wider the baseline the bigger the calibration pattern should be. Manpower even equipment such as crane is required to operate such a big checkerboard for the calibration purpose only. In contrast to the traditional stereo calibration method using calibration pattern, we propose a self-calibration approach that can easily estimate cameras' rotation matrices for stereo rig with very wide baseline using sea horizon and a point at infinite distance. Compared with conventional techniques which require expensive equipments or manpower, our approach only needs a pair of sea images, moreover, special calibration patterns and feature correspondences are not required. As a result, it is flexible and very easy to implement. Real world experiments demonstrate the feasibility of our approach.
De novo MECP2 frameshift mutation in a boy with moderate mental retardation, obesity and gynaecomastia

Rett syndrome (RTT) is an X‐linked neurodevelopmental disorder caused by mutations in the MECP2 gene, with apparent lethality in male embryos. However, recent studies indicate that mutations in the MECP2 gene can cause congenital encephalopathy, an Angelman‐like phenotype and even nonspecific mental retardation in males.
The Scope of Cooperation: Values and Incentives

What explains the range of situations in which individuals cooperate? This paper studies a model where individuals respond to incentives but are also influenced by norms of good conduct inherited from earlier generations. Parents rationally choose what values to transmit to their offspring, and this choice is influenced by the spatial patterns of external enforcement and of likely future transactions. The equilibrium displays strategic complementarities between values and current behavior, which reinforce the effects of changes in the external environment. Values evolve gradually over time, and if the quality of legal enforcement is chosen under majority rule, there is path dependence: adverse initial conditions may lead to a unique equilibrium where legal enforcement remains weak and individual values discourage cooperation.
Literature programming – tool support for authoring and reviewing a scientiﬁc paper

Authoring or reviewing a scientiﬁc paper is tedious to avoid or to locate presentational errors. Errors such as spelling, grammar can be checked by existing tools, whereas structural errors for concepts are harder to detect. Converting a technical paper into a program, the “lit-erature programming” proposed in this paper allows existing program analysis tools to be reused for detecting and resolving some of its writing problems. For example, a simple C/C++ parser can be reused to check type errors that can not be captured by spelling and grammatical checkers; redundancies and false dependencies can be exposed and removed by the restructuring tool we developed for C/C++ programs. In general, an analogy between the literature (in terms of papers) and the software (in terms of programs) is made to reuse software engineering tools for literature programming (writing/reviewing/studying). The work has been applied to a recently published ICSE paper, showing a promising direction of software engineering-aided literature programming.
Round-Trip Modeling Using OPM/PL

In this work we present OPM/PL, a suite of modeling tools based on the Object-Process Methodology (OPM) and implemented in Prolog. OPM/PL includes tools for model authoring, querying and visualization, as well as code parsing and code generation. OPM/PL aims at integrating the information from the source code, models and requirements of a software project into a single database with a uniform format. This approach extends the role of a modeling tool to include a representation of the code and its relations to the model. The OPM/PL database contains both model elements and information from code parsing. This combined representation allows developers to flesh out a model from existing code, and update the code according changes in the model. Thus, OPM/PL provides a system for round-trip modeling.
Clinical Risk Prediction by Exploring High-Order Feature Correlations

Clinical risk prediction is one important problem in medical informatics, and logistic regression is one of the most widely used approaches for clinical risk prediction. In many cases, the number of potential risk factors is fairly large and the actual set of factors that contribute to the risk is small. Therefore sparse logistic regression is proposed, which can not only predict the clinical risk but also identify the set of relevant risk factors. The inputs of logistic regression and sparse logistic regression are required to be in vector form. This limits the applicability of these models in the problems when the data cannot be naturally represented vectors (e.g., medical images are two-dimensional matrices). To handle the cases when the data are in the form of multi-dimensional arrays, we propose HOSLR: High-Order Sparse Logistic Regression, which can be viewed as a high order extension of sparse logistic regression. Instead of solving one classification vector as in conventional logistic regression, we solve for K classification vectors in HOSLR (K is the number of modes in the data). A block proximal descent approach is proposed to solve the problem and its convergence is guaranteed. Finally we validate the effectiveness of HOSLR on predicting the onset risk of patients with Alzheimer's disease and heart failure.
Application of a modified two-point backward difference to sequential event detection in surface electromyography

Reliable detection of onset and termination of muscle contraction is an essential task in the analysis of surface electromyographic signals. An event detection method that can be used for sequential detection of both onset and termination of muscle contraction is described. The method builds on the techniques of envelope detection, two-point backward difference and threshold based decision making. Therefore, fast conventional digital signal processing techniques can be used in its implementation. Because the method is computationally efficient, it can be employed in both real-time and non-real-time applications. This text discusses the architecture of the method, considers the practical aspects of its implementation, analyses its computational complexity and evaluates its performance on the grounds of experimental results.
Model Combination for Correcting Preposition Selection Errors

Many grammatical error correction approaches use classifiers with specially-engineered features to predict corrections. A simpler alternative is to use n-gram language model scores. Rozovskaya and Roth (2011) reported that classifiers outperformed a language modeling approach. Here, we report a more nuanced result: a classifier approach yielded results with higher precision while a language modeling approach provided better recall. Most importantly, we found that a combined approach using a logistic regression ensemble outperformed both a classifier and a language modeling approach.
Simulation of a Jackson tandem network using state-dependent importance sampling

This paper considers importance sampling as a tool for rare-event simulation. The focus is on estimating the probability of overflow in the downstream queue of a Jackson two-node tandem queue. It is known that in this setting 'traditional' state-independent importance-sampling distributions perform poorly. We therefore concentrate on developing a state-dependent change of measure that is provably asymptotically efficient. More specific contributions are the following. (i) We concentrate on the probability of the second queue exceeding a certain predefined threshold before the system empties. Importantly, we identify an asymptotically efficient importance-sampling distribution for any initial state of the system. (ii) The choice of the importance-sampling distribution is backed up by appealing heuristics that are rooted in large-deviations theory. (iii) Our method for proving asymptotic efficiency is substantially more straightforward than some that have been used earlier.
Quantum Biophysical Semeiotics Bedside Diagnosing Inherited Real Risk of Brain Disorders : Boezio ' s Sign " . Neurodegenerative Disease Primary Prevention

This is an overview of ‘Quantum Biophysical Semeiotics’ (QBS) signs for the diagnosis of neurodegenerative disorders, even potential or initial and symptomless, such as Alzheimer’s Disease (AD), Parkinson’s Disease (PD), Senile Dementia (SD), Multiple Sclerosis (MS), Amyothrophyc Lateral Sclerosis (ALS), Niemann-Pick Type ABC Disease (NPD). This article highlights the aspecific sign termed ‘Inherited Real Risk’ of brain disorder, which provides useful information regarding differential diagnosis between brain cancer and other cerebral diseases, both in clinical and pre-clinical stages. The ‘Inherited Real Risk’ of brain disorder need then to be integrated with other specific signs, according to different trigger-points related to different cerebral disorders: in this paper, Boezio's sign is illustrated in order to verify if in a given patient the congenital risk of Amyotrophic Lateral Sclerosis (ALS) is real. In addition to that, taking into account the recursive genetic effects underlying neurodegenerative pathologies, an early QBS diagnosis allows a primary and pre-primary prevention, of such ominous diseases. QBS is an extension of medical semeiotics. It is grounded on a multidisciplinary approach that involves chemistry and biology, genetics and neuroscience, chaos theory and quantum physics. It is based on the method of ‘Auscultatory Percussion’, through which by means of the common stethoscope, it is possible to listen to the signs that the body gives us when appropriately stimulated. The stimuli are used to induce auscultatory percussion reflexes in precise and well defined biological systems of the human body, thus giving local qualitative information on their physiological and pathological condition, no matter whether potential, actively developing but still clinically silent (that is not yet evident through usual clinical, laboratory and radiological investigations) or in acute or chronic phases.
Antenatal antioxidant treatment with melatonin to decrease newborn neurodevelopmental deficits and brain injury caused by fetal growth restriction

Fetal intrauterine growth restriction (IUGR) is a serious pregnancy complication associated with increased rates of perinatal morbidity and mortality, and ultimately with long‐term neurodevelopmental impairments. No intervention currently exists that can improve the structure and function of the IUGR brain before birth. Here, we investigated whether maternal antenatal melatonin administration reduced brain injury in ovine IUGR. IUGR was induced in pregnant sheep at 0.7 gestation and a subset of ewes received melatonin via intravenous infusion until term. IUGR, IUGR + melatonin (IUGR + MLT) and control lambs were born naturally, neonatal behavioral assessment was used to examine neurological function and at 24 hr after birth the brain was collected for the examination of neuropathology. Compared to control lambs, IUGR lambs took significantly longer to achieve normal neonatal lamb behaviors, such as standing and suckling. IUGR brains showed widespread cellular and axonal lipid peroxidation, and white matter hypomyelination and axonal damage. Maternal melatonin administration ameliorated oxidative stress, normalized myelination and rescued axonopathy within IUGR lamb brains, and IUGR + MLT lambs demonstrated significant functional improvements including a reduced time taken to attach to and suckle at the udder after birth. Based on these observations, we began a pilot clinical trial of oral melatonin administration to women with an IUGR fetus. Maternal melatonin was not associated with adverse maternal or fetal effects and it significantly reduced oxidative stress, as evidenced by reduced malondialdehyde levels, in the IUGR + MLT placenta compared to IUGR alone. Melatonin should be considered for antenatal neuroprotective therapy in human IUGR.
Extended Kalman Filter based loosely coupled INS/GPS integration scheme using FPGA and DSP

GPS provides an accurate position and velocity information but with a slower update rate, whereas INS provides position, velocity and attitude information with a higher update rate. However, INS alone cannot provide proper solutions due to the increasing error characteristics of the accelerometer and gyros of the inertial sensor. To get an accurate navigation solution, it is necessary to integrate INS with GPS. This paper discusses integration of INS/GPS systems using Extended Kalman Filter (EKF). TMS320vc33 floating point DSP is used for INS and EKF computations. A FPGA (Field Programmable Gate Array) based GPS data acquisition system and Dual Port RAM (DPRAM) have been designed and used for the presented architecture.
SICOP: identifying significant co-interaction patterns

SUMMARY Interactions between various types of molecules that regulate crucial cellular processes are extensively investigated by high-throughput experiments and require dedicated computational methods for the analysis of the resulting data. In many cases, these data can be represented as a bipartite graph because it describes interactions between elements of two different types such as the influence of different experimental conditions on cellular variables or the direct interaction between receptors and their activators/inhibitors. One of the major challenges in the analysis of such noisy datasets is the statistical evaluation of the relationship between any two elements of the same type. Here, we present SICOP (significant co-interaction patterns), an implementation of a method that provides such an evaluation based on the number of their common interaction partners, their so-called co-interaction. This general network analytic method, proved successful in diverse fields, provides a framework for assessing the significance of this relationship by comparison with the expected co-interaction in a suitable null model of the same bipartite graph. SICOP takes into consideration up to two distinct types of interactions such as up- or downregulation. The tool is written in Java and accepts several common input formats and supports different output formats, facilitating further analysis and visualization. Its key features include a user-friendly interface, easy installation and platform independence. AVAILABILITY The software is open source and available at cna.cs. uni-kl.de/SICOP under the terms of the GNU General Public Licence (version 3 or later).
Analyzing Social Network Structures in the Iterated Prisoner's Dilemma with Choice and Refusal

The Iterated Prisoner's Dilemma with Choice and Refusal (IPD/CR) is an extension of the Iterated Prisoner's Dilemma with evolution that allows players to choose and to refuse their game partners. From individual behaviors, behavioral population structures emerge. In this report, we examine one particular IPD/CR environment and document the social network methods used to identify population behaviors found within this complex adaptive system. In contrast to the standard homogeneous population of nice cooperators, we have also found metastable populations of mixed strategies within this environment. In particular, the social networks of interesting populations and their evolution are examined.
High doses of caffeine reduce in vivo osteogenic activity in prepubertal rats

Caffeine adversely affects endochondral ossification during fetal skeletal growth, and results in increased incidence of delayed and abnormal fetal skeletal development. Chronic caffeine intake also decreases growth hormone secretion. Thus, it is conceivable that caffeine may disrupt bone growth during the peripubertal period. This study aimed to investigate the impact of high‐caffeine consumption on bone growth throughout puberty. A total of 51 male rats (21 days old) were divided randomly into three groups: a control group and two groups fed caffeine via gavage with 120 and 180 mg kg−1 day−1 for 4 weeks. After death, the final length and weight of leg bones were measured, and the tibia processed for histomorphometric analysis. Caffeine caused a significant decrease in body mass gain. This was accompanied with proportional decreases in lean body mass and body fat. In addition, bone mass and osteogenic activity in vivo were assessed using dual‐energy X‐ray absorptiometry and 18F‐NaF positron emission tomography. The results showed significant decreases of bone mass and in vivo osteogenic activity in the caffeine‐fed groups. Rats fed with caffeine showed a significantly shorter and lighter tibia and femur and the vertebral column compared with controls. In addition, caffeine does not increase the width of the growth plates (GPs), it slows the rate at which the GP closes due to a slower rate of growth. These results demonstrated that caffeine altered osteogenic activity, leading to delayed peripubertal longitudinal bone growth and maturation. Given that osteogenic cells undergo dynamic changes in metabolic activity and that the pubertal growth spurt is mainly stimulated by growth hormone/insulin‐like growth factor‐1 and sex steroids during pubertal development, caffeine could suppress ossification by interfering with both physiological changes in hormonal secretion and osteogenic activity during this critical period. Further study will be needed to investigate the cellular/molecular mechanism by which caffeine affects osteogenesis using in vitro experimental models.