Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-Supervised Segmentation of Ultrasound Images Based on Patch Representation and Continuous Min Cut

Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature.
Asymptotics of Some Nonlinear Eigenvalue Problems for a MEMS Capacitor: Part I: Fold Point Asymptotics

Several nonlinear eigenvalue problems modeling the steady-state deflection of an elastic membrane associated with a MEMS capacitor under a constant applied voltage are analyzed using formal asymptotic methods. The nonlinear eigenvalue problems under consideration represent various regular and singular perturbations of the basic membrane nonlinear eigenvalue problem ∆u = λ/(1 + u) in Ω with u = 0 on ∂Ω, where Ω is a bounded two-dimensional domain. The following three perturbations of this basic problem are considered; the addition of a bending energy term of the form −δ∆u; the effect of a fringing-field where λ is replaced by λ `
Ranking programs using black box testing

We present an unbiased method for measuring the relative quality of different solutions to a programming problem. Our method is based on identifying possible bugs from program behaviour through black-box testing. The main motivation for such a method is its use in experimental evaluation of software development methods. We report on the use of our method in a small-scale such experiment, which was aimed at evaluating the effectiveness of property-based testing vs. unit testing in software development.
An Improved Point-in-Triangulation Localization Algorithm Based on Cosine Theorem

Self-localization of sensor nodes is one of the important technologies in wireless sensor networks. APIT is a range-free localization algorithm; it has the advantages of high localization accuracy and low communication overhead. However, the localization coverage of APIT is low, and when the proportion of anchor nodes is low, APIT is prone to OutToIn and InToOut error. To solve the above problems, this paper proposed a new point-in-triangulation test method based on cosine theorem according to APIT's basic idea to prevent OutToIn and InToOut error, and added RSSI model to locate the unknown nodes which are judged outside the triangles through trilateral measurement method to improve the localization coverage. Simulation results shows that compared with APIT algorithm, the new algorithm has higher localization accuracy and coverage.
Investigation on Implementation Method of Web Services Security Proxy

Security has been an essential problem in the development of Web Services, and traditional technology cannot satisfy the needs in this area any more. Through an analysis on the existing schemes, the Web service security implementation frame SP-WSS based on SOAP security proxy is put forward, which has such advantages as being transparent, flexible and easily imple- mented. Finally, United ID authentication will be also discussed in the paper.
Hexapedal robot for amphibious locomotion on ground and water

Many bio-inspired robots have been developed. Generally, these robots can drive in one environment. It is hard to drive in multiple environments. Therefore, in this study, we developed a robotic platform that can drive in two environments: on the water and the ground. We made a hexapedal robotic platform and analyzed its locomotion when the robot drove on the water and the ground. This analysis considered running speed and pitch motion when the robot drove in the two environments. In addition, we performed an experiment to compare the analyzed results. As a result, this research can be an example to help develop robotic platform amphibious locomotion.
Adaptive beamforming using a novel numerical optimisation algorithm

Currently, the uniform linear array (ULA) is the most commonly used antenna system for different wireless systems like commercial cellular systems. In this study, a ULA adaptive antenna that uses a novel numerical stochastic optimisation algorithm inspired from colonising weeds, designated as invasive weed optimisation (IWO), is introduced. Weeds are shown to be very robust and adaptive to changes in the environment. Thus, capturing their properties leads to a powerful optimisation algorithm. This optimisation algorithm is used for adaptive beamforming; the obtained results are compared with the results obtained from two other optimisation algorithms, that is, the least mean square and genetic algorithms. The reported results show that the IWO is very robust and effective in locating the optimal solution with higher precision and a lower cost function when compared with the other two algorithms. Other advantages of the IWO algorithm is its simplicity and fast convergence, which makes it a practical algorithm for adaptive beamforming.
Social Personality Evaluation Based on Prosodic and Acoustic Features

In recent decades, personality as a long term paralinguistic information has attracted more and more researchers. The main idea of the personality refers to the characteristics which acts as interactions between persons and the social occasions This paper proposes an approach for the automatic prediction of the Big-Five personality traits and 30 sub dimensions the listeners attribute to a speaker they don't know. The experiments are performed over a corpus of 1031 speech clips (337 identities in total) annotated not only Big-Five personality traits, but also all 30 sub-dimensions by using The Revised NEO Personality Inventory (NEO PI-R). The results show that it is possible to predict some particular sub-dimension with high accuracy (more than 75%) whether a person is perceived to be in the higher or lower part of the scales corresponding to each of the 30 sub dimensions, these sub dimensions give personality more accurate descriptions to lay the foundation for a more diversified personality classification
Safety and efficacy of dabigatran versus warfarin in patients undergoing catheter ablation of atrial fibrillation: a systematic review and meta-analysis

Background Dabigatran etexilate, a new thrombin inhibitor, has been shown to be comparable to warfarin in patients with atrial fibrillation (AF). However, there is a limited body of evidence on the efficacy and safety of using dabigatran among patients undergoing AF catheter ablation. Objective A random effects meta-analysis was performed of controlled trials comparing dabigatran and warfarin in paroxysmal/persistent AF patients undergoing catheter ablation. Methods Data sources included Medline, Embase, and Cochrane (from inception to April 2013). Three independent reviewers selected studies comparing warfarin to dabigatran. Descriptive and quantitative information was extracted from each selected study, regarding periprocedural all cause mortality, thromboembolic events and major bleeding, as well as modalities of periprocedural anticoagulation bridging. Results After a detailed screening of 228 search results, 14 studies were identified enrolling a total of 4782 patients (1823 treated with dabigatran and 2959 with warfarin). No deaths were reported. No significant differences were found between patients treated with dabigatran and warfarin as regards thromboembolic events (0.55% dabigatran vs 0.17% warfarin; risk ratios (RR)=1.78, 95% CI 0.66 to 4.80; p=0.26) and major bleeding (1.48% dabigatran vs 1.35% warfarin; RR=1.07, 95% CI 0.51 to 2.26; p=0.86). No difference was found between the 110 mg twice daily and 150 mg twice daily dabigatran dosages concerning major bleeding (0% vs 1.62%, respectively; RR=0.19, 95% CI 0.01 to 3.18; p=0.25) and thromboembolism (0% vs 0.40%, respectively; RR=0.72, 95% CI 0.04 to 12.98; p=0.82). Conclusions In the specific setting of AF catheter ablation, this first pooled analysis suggests that patients treated with dabigatran have a similar incidence of thromboembolic events and major bleeding compared to warfarin, with low event rates overall.
Mouse Usage Biometrics in eLearning Systems: Detection of Impersonation and User Profiling

Biometrics could be used to suppress impersonation in e-learning systems and thus to improve credibility of exams taken at home. Mouse usage characteristics are cheap and widely accessible alternative to other forms of biometrics reaching promising results in identity verification task. The paper provides preliminary results of cheating detection method based on mouse usage data gathered in an e-learning system. Mouse path is analyzed in deeper by decomposition to arcs and straight segments in order to study single-intent moves and to reveal curve characteristics. The paper also describes preliminary results of predicting learning styles of students according to the characteristics of computer mouse usage patterns for further recommendation of suitable materials.
Segregation in swarms of e-puck robots based on the Brazil nut effect

When a mixture of particles with different attributes undergoes vibration, a segregation pattern is often observed. For example, in muesli cereal packs, the largest particles---the Brazil nuts---tend to end up at the top. For this reason, the phenomenon is known as the Brazil nut effect. In previous research, an algorithm inspired by this effect was designed to produce segregation patterns in swarms of simulated agents that move on a horizontal plane. In this paper, we adapt this algorithm for implementation on robots with directional vision. We use the e-puck robot as a platform to test our implementation. In a swarm of e-pucks, different robots mimic disks of different sizes (larger than their physical dimensions). The motion of every robot is governed by a combination of three components: (i) attraction towards a point, which emulates the effect of a gravitational pull, (ii) random motion, which emulates the effect of vibration, and (iii) repulsion from nearby robots, which emulates the effect of collisions between disks. The algorithm does not require robots to discriminate between other robots; yet, it is capable of forming annular structures where the robots in each annulus represent disks of identical size. We report on a set of experiments performed with a group of 20 physical e-pucks. The results obtained in 100 trials of 20 minutes each show that the percentage of incorrectly-ordered pairs of disks from different groups decreases as the size ratio of disks in different groups is increased. In our experiments, this percentage was, on average, below 0.5% for size ratios from 3.0 to 5.0. Moreover, for these size ratios, all segregation errors observed were due to mechanical failures that caused robots to stop moving.
Real-time Parameterized Locomotion Generation Abstract Real-time Parameterized Locomotion Generation Ozet Gerç Ek Zamanli Parametr ˙ Ik Gezme Hareket ˙ I T ¨ Uret ˙ Ilmes ˙ I

REAL-TIME PARAMETERIZED LOCOMOTION GENERATION Muzaffer Akbay M.S. in Computer Engineering Supervisor: Assoc. Prof. Dr. Uğur Güdükbay September, 2008 Reuse and blending of captured motions for creating realistic motions of human body is considered as one of the challenging problems in animation and computer graphics. Locomotion (walking, running and jogging) is one of the most common types of daily human motion. Based on blending of multiple motions, we propose a two-stage approach for generating locomotion according to userspecified parameters, such as linear and angular velocities. Starting from a large dataset of various motions, we construct a motion graph of similar short motion segments. This process includes the selection of motions according to a set of predefined criteria, the correction of errors on foot positioning, pre-adjustments, motion synchronization, and transition partitioning. In the second stage, we generate an animation according to the specified parameters by following a path on the graph during run-time, which can be performed in real-time. Two different blending techniques are used at this step depending on the number of the input motions: blending based on scattered data interpolation and blending based on linear interpolation. Our approach provides an expandable and efficient motion generation system, which can be used for real time applications.
Analysis and prediction of β-turn types using multinomial logistic regression and artificial neural network

Motivation: So far various statistical and machine learning techniques applied for prediction of β-turns. The majority of these techniques have been only focused on the prediction of β-turn location in proteins. We developed a hybrid approach for analysis and prediction of different types of β-turn. Results: A two-stage hybrid model developed to predict the β-turn types I, II, IV and VIII. Multinomial logistic regression was initially used for the first time to select significant parameters in prediction of β-turn types using a selfconsistency test procedure. The extracted parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in β-turn sequence. The most significant parameters were then selected using multinomial logistic regression model. Among these, the occurrences of glutamine, histidine, glutamic acid and arginine, respectively, in positions i, i+1, i+2 and i+3 of β-turn sequence had an overall relationship with 5 β-turn types. A neural network model was then constructed and fed by the parameters selected by multinomial logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains by nine fold cross-validation. It has been observed that the hybrid model gives a Matthews correlation coefficient (MCC) of 0.235, 0.473, 0.103 and 0.124, respectively, for β-turn types I, II, IV and VIII which are best among previously reported results. Our model also distinguished the different types of βturn in the embedded binary logit comparisons which have not carried out so far. Availability: Available on request from the authors. Contact: parviz@modares.ac. ir
Time-Variant Characterization and Compensation of Wideband Circuits

Many wideband circuits use interleaving to extend bandwidth leaving them with a cyclically time-variant output. This paper describes a technique for characterization of these types of circuits based on least-squares estimation of the time-varying response of circuits. Applying the methodology to a 90-nm, 12-GS/s 8-bit digitally-equalized DAC, shows significant timing varying behavior. Furthermore, using the data obtained from characterization to construct linear time-variant compensation improves the DAC signal-to-distortion ratio by at least 6 dB.
A logistic map based PN sequence generator for direct-sequence spread-spectrum modulation system

In this paper, a pseudo noise (PN) sequence is generated using the features of one-dimensional chaotic system, such as a logistic map. The logistic map based scheme is applicable for use with wireless networks because the chaotic nature makes it resilient against stochastic variations and requires simplistic measures to generate the sequence. The proposed PN sequence generation method is used as spreading sequence in a Direct-Sequence Spread Spectrum Modulation (DS-SSM) in additive white Gaussian noise (AWGN) and Rayleigh channels. The method is found to be reliable and is robust against random variations observed in wireless channels. Also, the performance of the scheme is compared against that of existing techniques. Results from a comparative analysis indicate that the proposed method generally yields enhanced performance in a range of conditions considered for the work.
Evaluation of valuable user generated content on social news web sites

Social news websites have gained significant popularity in the last few years. The participants of such websites are not only allowed to share news links but also to annotate, to evaluate and to comment them. To quantify interestingness and attractiveness of the user generated content in respect to the original link source we introduce the User Generated Content add-on (UGC+) index. Based on the definition of UGC+ we also propose a concept for comparing groups of links filtered by different properties, e.g. authorship or topic-categories. We apply the proposed measure on the Spanish Digg-clone Menéame.
A Multiobjective Simultaneous Learning Framework for Clustering and Classification

Traditional pattern recognition involves two tasks: clustering learning and classification learning. Clustering result can enhance the generalization ability of classification learning, while the class information can improve the accuracy of clustering learning. Hence, both learning methods can complement each other. To fuse the advantages of both learning methods together, many existing algorithms have been developed in a sequential fusing way by first optimizing the clustering criterion and then the classification criterion associated with the obtained clustering results. However, such kind of algorithms naturally fails to achieve the simultaneous optimality for two criteria, and thus has to sacrifice either the clustering performance or the classification performance. To overcome that problem, in this paper, we present a multiobjective simultaneous learning framework (MSCC) for both clustering and classification learning. MSCC utilizes multiple objective functions to formulate the clustering and classification problems, respectively, and more importantly, it employs the Bayesian theory to make these functions all only dependent on a set of the same parameters, i.e., clustering centers which play a role of the bridge connecting the clustering and classification learning. By simultaneously optimizing the clustering centers embedded in these functions, not only the effective clustering performance but also the promising classification performance can be simultaneously attained. Furthermore, from the multiple Pareto-optimality solutions obtained in MSCC, we can get an interesting observation that there is complementarity to great extent between clustering and classification learning processes. Empirical results on both synthetic and real data sets demonstrate the effectiveness and potential of MSCC.
Mixing traffic in a buffered banyan network

A model of Banyan networks is developed to account for their operational as well as topological characteristics. The model incorporates a description of the states of individual switching nodes and explicitly captures the interconnection patterns between stages. Accordingly, the performance of a network can be evaluated via an iterative procedure. Using the model, we analyzed one class of nonuniform traffic pattern referred to as “point-to-point” traffic that has particular significance to mixed voice, data, and video applications. The results obtained indicate that even a single dedicated channel (with point-to-point traffic) can significantly limit the throughput of the background uniform traffic. The paper concludes with several switch design strategies for switching point-to-point traffic using a self-routing switching network.
Firm-Specific Factors as Predictors of Future Returns for Romanian Common Stocks: Empirical Evidence

This paper examines the relationship between stock returns and firm-specific ratios on the Romanian stock market for the period 2002 to 2008. We investigate the explanatory power on future stock returns of market beta, financial leverage, book-to-market equity, size, the earnings-to-price ratio, ROA and ROE. We find that size has the most significant effect in capturing variations in stock returns over the whole period. The negative relationship between size and returns is persistent and the size effect is present on Bucharest Stock Exchange. E/P ratio has a strong positive impact on stock returns and it remains statistically significant through different regression models. Most of the variation in stock returns is captured by size and E/P ratio together. B/M ratio has a significant positive relationship with returns in the univariate regression, but losses its significance in the multivariate setting. Results indicate that beta lacks explanatory power even when its impact is examined alone in the regression analysis and therefore the use of beta as a measure of systematic risk on the Romanian market is unsupported.Also, the year effect is significant on the Romanian market, and the two-way fixed effects model that allows for the intercept to vary both across companies and in time provides a better explanation of the future stock returns on the Bucharest Stock Exchange.
Assessment of knowledge ability towards decision-making in information systems from managers perspective

Enterprise resource planning (ERP) / information systems are maintained the business-process integration, which provides past records for decision-making. By integrating the information of different departments, the ERP systems are capable for real time management of internal indispensable data that can solve certain problems with an appropriate decision. The decision-making capabilities are necessary for managers to perk up the quality of fabrication, sales, forecasting and business development. In this paper, the corporate managers' experiences are focused in view of the financial module towards the selected ERP systems. In order to do an empirical study, a group of financial managers and experts are involved from textile industries. A quantitative research (questionnaire) technique is adopted to investigate the non-functional requirements and users' understanding. During experimental analysis, we found that the selected ERP systems are unproductive to provide an appropriate knowledge for decision-making. Therefore, an acquisition of tacit knowledge would be required to improve the decision-support capabilities of ERP systems.