Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital watermarking using improved human visual system model

In digital watermarking, one aim is to insert the maximum possible watermark signal without significantly affecting image quality. Advantage can be taken of the masking effect of the eye to increase the signal strength in busy or high contrast image areas. The application of such a human visual system model to watermarking has been proposed by several authors. However if a simple contrast measurement is used, an objectionable ringing effect may become visible on connected directional edges. In this paper we describe a method which distinguishes between connected directional edges and high frequency textured areas, which have no preferred edge direction. The watermark gain on connected directional edges is suppressed, while the gain in high contrast textures is increased. Overall, such a procedure accommodates a more robust watermark for the same level of visual degradation because the watermark is attenuated where it is truly objectionable, and enhanced where it is not. Furthermore, some authors propose that the magnitude of a signal which can be imperceptibly placed in the presence of a reference signal can be described by a non-linear mapping of magnitude to local contrast. In this paper we derive a mapping function experimentally by determining the point of just noticeable difference between a reference image and a reference image with watermark.
Anti-CD 3 x EGFR bispecific antibody redirects cytokine-induced killer cells to glioblastoma in vitro and in vivo

The epidermal growth factor receptor (EGFR) is an attractive target for the immunotherapy of EGFR+ tumors. Adjuvant immunotherapy with cytokine-induced killer (CIK) cells may improve progression-free survival rates in patients suffering from cancer. In the present study, we examined the bispecific antibody anti-CD3 x anti-EGFR (EGFRBi-Ab) for its ability to redirect CIK cells to target EGFR-positive glioblastoma. The specific cytolytic activity of CIK cells armed with EGFRBi-Ab against U87MG-luc cells was evaluated by bioluminescent signal generated using luciferase reporter assay which did not alter the surface molecule expression or proliferation ability of U87MG cells. In contrast to unarmed CIK cells, increased cytotoxic activity of EGFRBi-armed CIK cells against the U87MG-luc target was observed at effector/ target (E/T) ratios of 5:1, 10:1, and 20:1. Moreover, EGFRBiarmed CIK cells secreted significantly higher levels of IFN-γ, TNF-α, and IL-2 than their unarmed CIK counterpart cells. Furthermore, in glioblastoma xenograft mice, infusion of the EGFRBi-armed CIK cells successfully inhibited the growth of glioblastoma tumors. The in vitro and in vivo antitumor effects of EGFRBi-armed CIK cells support their clinical use for treatment of glioblastoma in the future.
Behavioural correlates of an altered balance between synaptic and extrasynaptic GABAAergic inhibition in a mouse model

GABAA receptors mediate fast phasic inhibitory postsynaptic potentials and participate in slower tonic extrasynaptic inhibition. Thy1α6 mice with ectopic forebrain expression of GABAA receptor α6 subunits exhibit increased extrasynaptic GABAA receptor‐mediated background conductance and reduced synaptic GABAA receptor currents in hippocampal CA1 neurons [W. Wisden et al. (2002) Neuropharmacology 43, 530–549]. Here we demonstrate that isolated CA1 neurons of these mice showed furosemide‐sensitivity of GABA‐evoked currents, confirming the functional expression of α6 subunit. In addition, receptor autoradiography of the CA1 region of Thy1α6 brain sections revealed pharmacological features that are unique for α6βγ2 and α6β receptors. The existence of atypical α6β receptors was confirmed after completely eliminating GABAA receptors containing γ1, γ2, γ3 or δ subunits using serial immunoaffinity chromatography on subunit‐specific GABAA receptor antibodies. Behaviourally, the Thy1α6 mice showed normal features with slightly enhanced startle reflex and struggle‐escape behaviours. However, they were more sensitive to GABAA antagonists DMCM (shorter latency to writhing clonus) and picrotoxinin (shorter latency to generalized convulsions). Tiagabine, an antiepileptic GABA‐uptake inhibitor that increases brain GABA levels, delayed picrotoxinin‐induced convulsions at a low dose of 3.2 mg/kg in Thy1α6 mice, but not in control mice; however, the overall effect of higher tiagabine doses on the convulsion latency remained smaller in the Thy1α6 mice. Altered balance between extrasynaptic and synaptic receptors thus affects seizure sensitivity to GABAergic convulsants. Importantly, the increased extrasynaptic inhibition, even when facilitated in the presence of tiagabine, was not able fully to counteract enhanced seizure induction by GABAA antagonists.
Using Visual Velocity Detection to Achieve Synchronization in Imitation

Synchronization and coordination are important mechanisms involved in imitation and social interaction. In this paper, we study different methods to improve the reactivity of agents to changes in their environment in different coordination tasks. In a robot synchronization task, we compare the differences between using only position detection or velocity detection. We first test an existing position detection approach, and then we compare the results with those obtained using a novel method that takes advantage of visual detection of velocity. We test and discuss the applicability of these two methods in several coordination scenarios, to conclude by seeing how to combine the advantages of both methods.
Development of a novel test method for skin safety verification of physical assistant robots

The study mainly concerns a novel blister generation method for the establishment of a safety verification test. This test system focuses on the skin friction trauma, which has been found in the operators of physical assistant robots due to their misuses or overlong operating, and can even deteriorate into serious dermatopathya without preventing and prompt treatment. In the present study, first, we prove that blisters can be generated in the porcine skin. With the histological section of the porcine skin getting micro blisters, the symbols of porcine skin's blister in the dermis-epidermis structure are identified with those of human blisters, and summarized for applying this substitutive material to help researchers avoid the ethical controversy of adopting human subjects. Second, the characteristics of blister generation in the porcine skin were further confirmed with a series of time-gradient tests. After a condition-matching calculation, these characteristics are verified to play a role on extending that of human blister in previous studies and we integrate our results with the previous human blister generation data and clarify an inherently safe condition region in the shear stress - time relationship. Third, with this novel method, we successfully complete a safety validation test on a “stand up and sit down” activity of a user wearing a physical assistant robot, which finally proves our findings are of practical usability in regard to a possible physical stress hazard.
Assigning Course Supervision to School Principals from Educational Supervisors: Effects on Teachers

The inspectional structure of the National Education System in Turkey changed with a regulation which came into forced in 2015, which passed school principals the responsibility for teachers’ course control. The primary purpose of this study is to reveal teacher opinions on the new implementation which emerged from the change in the supervisory system. The study group consists of branch teachers for this study under the qualitative research method. Results indicate that supervision conducted by both supervisors and principals is not perfect. However, when considered generally, teachers find the supervision carried out by the principals to be positive. The results emphasize that there should be a standard for the supervisions conducted by principals and ethical codes should be set for objective/unbiased teacher supervision. It was also observed that course supervision competences of school principals are not at the desired levels; thus, school principals should be trained on supervision.
Psychology and Economics Jean Tirole Self-Confidence : Intrapersonal Strategies

This paper analyzes the self-identi.cation process and its role in motivation. We build a model of self-con.dence where people have imperfect knowledge about their ability, which in most tasks is a complement to e¤ort in determining performance. Higher self-con.dence thus enhances motivation, and this creates incentives for the manipulation of self-perception. An individual su¤ering from time-inconsistency may thus want to enhance the self-con.dence of his future selves, so as to limit their procrastination. The bene.ts of con.dence-maintenance must, however, be traded o¤ against the risks of overcon.dence (inappropriate tasks being pursued). Moreover, rational inference implies that the individual cannot systematically fool himself. A . rst application of the model is self-handicapping: to avoid a negative inference about their ability, people may deliberately impair their performance, or choose overambitious tasks. Another application is selective memory or awareness management: people are (endogenously) more likely to remember or consciously acknowledge their successes than their failures. This, in turn, helps explain the widely documented prevalence of self–serving beliefs –that is, the fact that most people have overoptimistic assessments of their own abilities and other desirable traits. We analyze the workings of this “psychological immune system” and show that it typically leads to multiple equilibria in cognitive strategies, self con.dence, and behavior. Moreover, while active self-esteem maintenance can improve ex-ante welfare, it can also be self-defeating. Systematically “looking on the bright side”, avoiding “negative” thoughts and people, etc., can thus be bene.cial in certain environments; but in other circumstances one can only lose by playing such games with oneself, and it would be better to always “accept who you are” and “be honest with yourself ”.
An integrated classification method for brain computer interface system

A channel of communication for both human brain and computer system is provided via a system called Brain Computer Interface (BCI). The vital aim of BCI research is to develop a system that helps the disabled people to interact with other persons and allows their interaction with the external environments or as an additional man-machine interaction channel for healthy users. Different techniques have been developed in the literature for the classification of brain signals. The purpose of this work is to deveolp a novel method of analyzing the EEG signals. We have used high pass filter to remove artifacts, DWT algorithms for feature extraction and features like Mean Absolute Value, Root Mean Square, and Simple Square Integral are used. The neural network algorithm is used to find the correct class label for EEG signal after clustering the feature vectors using K-Nearest Neighbor algorithm. It has been depicted from results that the proposed integrated technique outperforms a better performance than methods mentioned in literature.
Fuzzy C-Means Two-Level Variable Weighting Clustering Algorithm For Multiview Data

In Clustering, the multiview data is considered with two levels of variables as the view differences and the importance of individual variables in each view are taken into account. Existing system TW kmeans, an automated two-level variable weighting clustering algorithm for multiview data, which simultaneously compute weights for views and individual variables. In TW k-means, a view weight is assigned to each view to identify the compactness of the view and a variable weight is assigned to each variable to identify the importance of the variable. Both view and variable weights are used in the distance function to determine the clusters of objects. The two level variable clustering improves the accuracy for multiview data. Proposed a Fuzzy c-means clustering method which is more efficient in handling outlier points. The previous method excludes outlier points by giving an extremely small membership values in existing clusters while fuzzy c-means algorithm tends give an outsized membership values. FCM algorithm combined with two-level variable weighting clustering algorithm for multiview data. Experimental results show that combining FCM Clustering algorithm with twolevel variable weighting clustering improves the clustering accuracy of the multiview data than the TW k-means clustering.
User-priority guided Min-Min scheduling algorithm for load balancing in cloud computing

Cloud computing is emerging as a new paradigm of large-scale distributed computing. In order to utilize the power of cloud computing completely, we need an efficient task scheduling algorithm. The traditional Min-Min algorithm is a simple, efficient algorithm that produces a better schedule that minimizes the total completion time of tasks than other algorithms in the literature [7]. However the biggest drawback of it is load imbalanced, which is one of the central issues for cloud providers. In this paper, an improved load balanced algorithm is introduced on the ground of Min-Min algorithm in order to reduce the makespan and increase the resource utilization (LBIMM). At the same time, Cloud providers offer computer resources to users on a pay-per-use base. In order to accommodate the demands of different users, they may offer different levels of quality for services. Then the cost per resource unit depends on the services selected by the user. In return, the user receives guarantees regarding the provided resources. To observe the promised guarantees, user-priority was considered in our proposed PA-LBIMM so that user's demand could be satisfied more completely. At last, the introduced algorithm is simulated using Matlab toolbox. The simulation results show that the improved algorithm can lead to significant performance gain and achieve over 20% improvement on both VIP user satisfaction and resource utilization ratio.
Writer-adaptation for on-line handwritten character recognition

The authors have designed a writer-adaptable character recognition system for online characters entered on a touch terminal. It is based on a Time Delay Neural Network (TDNN) that is pre-trained on examples from many writers to recognize digits and uppercase letters. The TDNN without its last layer serves as a preprocessor for an optimal hyperplane classifier that can be easily retrained to peculiar writing styles. This combination allows for fast writer-dependent learning of new letters and symbols. The system is memory and speed efficient. <<ETX>>
The α 1 AT and TIMP-1 Gene Polymorphism in the Development of Asthma

Asthma has been an inflammatory disorder accompanied by tissue remodeling and protease-antiprotease imbalance in lungs. The SNPs of alpha-1 antitrypsin (α 1 AT) and tissue inhibitor of metalloproteinase-1 (TIMP-1) genes were studied for their association with asthma. Genotyping of α 1 AT and TIMP-1 genes was performed in 202 asthmatics and 204 controls. Serum levels of α 1AT, TIMP-1 and cytokines were estimated to find if the interplay between genotypes and cellular biomarkers determines the pathogenesis of asthma. The analysis of results showed significantly low level of α 1AT in the serum of asthmatics as compared to controls (P = 0.001), whereas cytokines were elevated in patients. No significant difference was observed in the concentration of TIMP-1 in patients and controls. Genotyping led to the identification of 3 SNPs (Val213Ala, Glu363Lys, and Glu376Asp) in α 1 AT gene. The novel SNP Glu363Lys of α 1 AT was found to be associated with asthma (P = 0.001). The analysis of TIMP-1 gene showed the occurrence of seven SNPs, including a novel intronic SNP at base G3774A. The allele frequency of G3774A and Phe124Phe was significantly higher in asthmatics as compared to controls. Thus, the SNP Glu363Lys of α 1 AT and G3774A and Phe124Phe of TIMP-1 could be important genetic markers for use in better management of the disease.
A New Method for a Piezoelectric Energy Harvesting System Using a Backtracking Search Algorithm-Based PI Voltage Controller

This paper presents a new method for a vibration-based piezoelectric energy harvesting system using a backtracking search algorithm (BSA)-based proportional-integral (PI) voltage controller. This technique eliminates the exhaustive conventional trial-and-error procedure for obtaining optimized parameter values of proportional gain (Kp), and integral gain (Ki) for PI voltage controllers. The generated estimate values of Kp and Ki are executed in the PI voltage controller that is developed through the BSA optimization technique. In this study, mean absolute error (MAE) is used as an objective function to minimize output error for a piezoelectric energy harvesting system (PEHS). The model for the PEHS is designed and analyzed using the BSA optimization technique. The BSA-based PI voltage controller of the PEHS produces a significant improvement in minimizing the output error of the converter and a robust, regulated pulse-width modulation (PWM) signal to convert a MOSFET switch, with the best response in terms of rise time and settling time under various load conditions.
An MCMC Approach to Solving Hybrid Factored MDPs

Hybrid approximate linear programming (HALP) has recently emerged as a promising framework for solving large factored Markov decision processes (MDPs) with discrete and continuous state and action variables. Our work addresses its major computational bottleneck - constraint satisfaction in large structured domains of discrete and continuous variables. We analyze this problem and propose a novelMarkov chainMonte Carlo (MCMC) method for finding the most violated constraint of a relaxed HALP. This method does not require the discretization of continuous variables, searches the space of constraints intelligently based on the structure of factored MDPs, and its space complexity is linear in the number of variables. We test the method on a set of large control problems and demonstrate improvements over alternative approaches.
Flexible aircraft reduced-order LPV model generation from a set of large-scale LTI models

In the civilian aeronautical industry, flexible aircraft models are often built and validated at frozen flight and mass configurations. Unfortunately, these medium(large)-scale models derived from high fidelity numerical tools are generally not well adapted for simulation, control and analysis. In this paper, a methodology to derive a reduced-order Linear Parameter Varying (LPV) model from a set of medium(large)-scale Linear Time Invariant (LTI) models describing a given system at frozen configurations is described. The proposed methodology is in three steps: (i) first, local model approximation is applied using recent advances in SVD-Krylov methods, (ii) then, an appropriate base change is applied to allow interpolation, (iii) and finally, an LPV model is derived and converted into a Linear Fractional Representation (LFR) of suitable size for analysis and control purposes. Results are thoroughly assessed on a set of industrial aeroelastic aircraft models.
A multimedia cognitive-based information retrieval system

ABSTRACF - The next generation of information retrieval technology will essentially be represented by distributed multimedia systems. The main objective of these systems is to integrate multiple databases, to provide efficient multimedia information retrieval and transparent services for the end user. In this paper, a model and some operations in our cognitivebased multimedia environment will be given. We propose a new methodology for multimedia objects manipulation. Some elements of a general object-oriented multimedia data model called AMCIRS has been described in [11]. In this paper, we intend to extend our AMCIRS model and to address some basic issues such as; search and query based access, the integration of multimedia and knowledge-based systems, support for dynamic user cognition correction.
Surface Reconstruction through Poisson Disk Sampling

This paper intends to generate the approximate Voronoi diagram in the geodesic metric for some unbiased samples selected from original points. The mesh model of seeds is then constructed on basis of the Voronoi diagram. Rather than constructing the Voronoi diagram for all original points, the proposed strategy is to run around the obstacle that the geodesic distances among neighboring points are sensitive to nearest neighbor definition. It is obvious that the reconstructed model is the level of detail of original points. Hence, our main motivation is to deal with the redundant scattered points. In implementation, Poisson disk sampling is taken to select seeds and helps to produce the Voronoi diagram. Adaptive reconstructions can be achieved by slightly changing the uniform strategy in selecting seeds. Behaviors of this method are investigated and accuracy evaluations are done. Experimental results show the proposed method is reliable and effective.
Spatio-temporal outlier detection in large databases

Outlier detection is one of the major data mining methods. This paper proposes a three-step approach to detect spatio-temporal outliers in large databases. These steps are clustering, checking spatial neighbors, and checking temporal neighbors. In this paper, we introduce a new outlier detection algorithm to find small groups of data objects that are exceptional when compared with rest large amount of data. In contrast to the existing outlier detection algorithms, new algorithm has the ability of discovering outliers according to the non-spatial, spatial and temporal values of the objects. In order to demonstrate the new algorithm, this paper also presents an example application using a data warehouse
Effort constraints in adaptive feedforward control

Adaptive algorithms are presented for constrained multichannel feedforward control. They minimize the sum of squared error signals while limiting the value of either the sum of squared control signals (total effort) or the mean square value of each individual control signal (individual efforts). It is shown that the exact least squares solutions for these problems have a simple form, which can be approached with an adaptive algorithm. The properties of the resulting algorithm are illustrated by simulation.
MINUET : MUSICAL INTERFERENCE UNMIXING ESTIMATION TECHNIQUE

We propose a noise cancellation technique that performs robustly in the presence of poor channel estimates and channel synchronization errors. The technique is based on the assumption that the signals have a sparse representation in a chosen signal basis, in this case, the time-frequency domain. Moreover, we assume the components of the signal of interest that contain a majority of its power overlap with components of the interference signal containing negligible power. In case of speech mixed with music, this occurs because in the time-frequency domain both music and speech are sparse and the large magnitude coefficients rarely overlap. The robustness of the technique to channel estimation and synchronization errors is demonstrated experimentally on speech/music mixtures.
Improved delay measurement method in FPGA based on transition probability

The ability to measure delay of arbitrary circuits on FPGA offers many opportunities for on-chip characterisation and optimisation. This paper describes an improved delay measurement method by monitoring the transition probability at the output nodes as the operating frequency is swept. The new method uses optimised test vector generation to improve the accuracy of the test method. It is effectively demonstrated on a 4th order IIR filter circuit implemented on an Altera Cyclone III FPGA.