Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Study of Synchrony Measures for the Early Detection of Alzheimer's Disease Based on Eeg

It has repeatedly been reported in the medical literature that the EEG signals of Alzheimer's disease (AD) patients are less synchronous than in age-matched control patients. This phenomenon, however , does at present not allow to reliably predict AD at an early stage, so-called mild cognitive impairment (MCI), due to the large variability among patients. In recent years, many novel techniques to quantify EEG synchrony have been developed; some of them are believed to be more sensitive to abnormalities in EEG synchrony than traditional measures such as the cross-correlation coefficient. In this paper, a wide variety of synchrony measures is investigated in the context of AD detection , including the cross-correlation coefficient, the mean-square and phase coherence function, Granger causality, the recently proposed corr-entropy coefficient and two novel extensions, phase synchrony indices derived from the Hilbert transform and time-frequency maps, information-theoretic divergence measures in time domain and time-frequency domain , state space based measures (in particular, non-linear interdependence measures and the S-estimator), and at last, the recently proposed stochastic-event synchrony measures. For the data set at hand, only two synchrony measures are able to convincingly distinguish MCI patients from age-matched control patients (p < 0.005), i.e., Granger causality (in particular, full-frequency directed transfer function) and stochastic event synchrony (in particular, the fraction of non-coincident activity). Combining those two measures with additional features may eventually yield a reliable diagnostic tool for MCI and AD.
Using rough sets in homophily based link prediction in online social networks

Online social networks are highly dynamic and sparse. One of the main problems in analyzing these networks is the problem of predicting the existence of links between users on these networks: Link prediction problem. Many researches have been conducted to predict links using variety of techniques like the decision tree and the logistic regression approaches. In this work, we will illustrate the use of rough set theory (RST) in predicting links over the Facebook social network based on homophilic features. Other classifiers are also employed in our work and compared to the rough set classifier.
On the Idempotence of the CPS Transformation

The CPS (continuation-passing style) transformation on typed lambda-terms has an interpretation in many areas of Computer Science, such as programming languages and type theory. Programming intuition suggests that in effect, it is idempotent, but this does not directly hold for all existing CPS transformations (Plotkin, Reynolds, Fischer, etc.). We rephrase the call-by-value CPS transformation to make it syntactically idempotent, modulo eta-reduction of the newly introduced continuation. Type-wise, iterating the transformation corresponds to refining the polymorphic domain of answers.
Evaluation of head pose estimation methods for a non-cooperative biometric system

Automatic head pose estimation is playing more and more important role in current computer vision applications. Precise localization of face landmark points together with subsequent analysis allow to determine person's gaze direction or its facial expression. Such solutions are widely used in driver assistance systems, intelligent environments like smart rooms, human-computer interfaces or identification systems. Surprisingly, in the literature, the tasks of determining face yaw or pitch angles and localizing landmark points are approached as separate problems. In this work, authors combine these two issues and analyze them together. Initially, a deep investigation of other researchers work is presented. On this basis, authors examine selected methods in the context of their application in non-cooperative recognition system. Experiments are conducted on a new, dedicated for this purpose multi-pose face dataset. Obtained results yield mean error of face yaw angle calculation of 3°.
Image-based adaptive visual servoing of 2D robots under Jacobian and dynamic friction uncertainties: theory and experiments

A globally convergent visual feedback control scheme is proposed for dynamical planar robot arms subject to uncertain camera, robot, analytical Jacobian and dynamic friction parameters. When complex friction arises, visual servoing suffers to drive the robot to the desired trajectories, in particular in slow motion and velocity reversals, which are typical motion regime in visual servoing due to the vision system properties. Moreover, dynamic friction is usually neglected in motion control and it is not the exception in visual servoing literature. In order to prove the theory described in this paper, the real-time OS, Linux-RTAI, is used to obtain experimental results of this controller on a direct-drive robot manipulator. Results suggest its excellent performance.
Opportunities for Evolutionary Music Composition

Traditional compositional techniques and many computer-assisted composition systems have been focused on the production of linear musical products. In an age where non-linear media are increasingly prominent there is a need to reassess these technologies in the light of new opportunities for making music with non-linear outcomes. This paper examines the current state of music making for non-linear media with a particular emphasis on evolutionary music and how and where it might be applied. In addition, some of the implications for computer-based tool design will be outlined.
ASKIT: Approximate Skeletonization Kernel-Independent Treecode in High Dimensions

We present a fast algorithm for kernel summation problems in high-dimensions. These problems appear in computational physics, numerical approximation, non-parametric statistics, and machine learning. In our context, the sums depend on a kernel function that is a pair potential defined on a dataset of points in a high-dimensional Euclidean space. A direct evaluation of the sum scales quadratically with the number of points. Fast kernel summation methods can reduce this cost to linear complexity, but the constants involved do not scale well with the dimensionality of the dataset. The main algorithmic components of fast kernel summation algorithms are the separation of the kernel sum between near and far field (which is the basis for pruning) and the efficient and accurate approximation of the far field. We introduce novel methods for pruning and approximating the far field. Our far field approximation requires only kernel evaluations and does not use analytic expansions. Pruning is not done using bounding boxes but rather combinatorially using a sparsified nearest-neighbor graph of the input. The time complexity of our algorithm depends linearly on the ambient dimension. The error in the algorithm depends on the low-rank approximability of the far field, which in turn depends on the kernel function and on the intrinsic dimensionality of the distribution of the points. The error of the far field approximation does not depend on the ambient dimension. We present the new algorithm along with experimental results that demonstrate its performance. We report results for Gaussian kernel sums for 100 million points in 64 dimensions, for one million points in 1000 dimensions, and for problems in which the Gaussian kernel has a variable bandwidth. To the best of our knowledge, all of these experiments are impossible or prohibitively expensive with existing fast kernel summation methods.
Optical Simulation of Light Management in CIGS Thin-Film Solar Cells Using Finite Element Method

In this paper we present an optical simulation of light management in Cu(In,Ga)Se2 thin-film solar cells with reduced absorber layer thickness, with the goal of absorption enhancement in the absorber layer. The light management was achieved by texturing of the substrate layer, and the conformal growth of all the following layers was assumed. Two texturing shapes have been explored: triangular and convex, with different periods and height aspect ratios. The simulations have shown that significant enhancement of absorption within the absorber layer can be achieved using the proposed geometry. The results showed that the triangular textures with small periods (100–200 nm) and high aspect ratios have the most prominent effect on the enhancement of absorption within the absorber layer, although they are difficult to achieve experimentally.
Joint Transceiver Design for Amplify-and-Forward Multiuser MIMO Relay Communication Systems with Source-Destination Links

In this paper, we generalize the iterative algorithms proposed by Khandaker to amplify-and-forward multiuser Multiple-Input Multiple-Output (MIMO) relay communication systems with the source-destination links. Compared with the conventional algorithms, where the source-destination links are ignored, the evolved Tri-Step method and Bi-Step method are derived for the new system model with the source-destination links for more spatial diversity gain. From the theoretical derivation of the Minimum Mean-Squared Error (MMSE) of the signal waveform estimation at the destination node, we show that the existence of the source-destination links bring benefits no matter how weak they are. Numerical examples demonstrate that the evolved algorithms perform much better than the original algorithms and other existing ones in terms of both MSE and Bit-Error-Rate (BER). Index Terms—MIMO relay, MMSE, multiuser, direct links
Success Factors for an Information Systems Projects Team Creating New Context

This paper aims to investigate the factors that influence cohesion and success of an IS project team. Based on the literature regarding project team success and IS, a complex framework has been adopted in order to develop the research model for the study. Consequently, the research model will incorporate variables increasing the stability and chance of success of the IS project team. The case study and the conclusions are based on the interpretation of the results of a survey and a number of semi-structured interviews with members of an IS project team that were and still are involved in many IS projects at the University of Craiova, between 1999 and 2008. Our research findings show that different approaches to IS project team members dictated by their different specializations (skills and competencies) create a new context of team complexity and team cognition, which might affect IS in different ways.
Cross-Layer Optimization Using MIMO System for Wireless Networks

Multimedia transmissions over wireless networks need continuous media play out. In order to meet best Quality of Services, higher layers data depend on lower layer parameters. That is readily known as cross-layer design. Our cross-layer design model optimizes the physical layer, MAC layer and application layer. Video summary frames are transmitted via MIMO transmit/receive diversity with AMC at the physical layer and Payload and data rate adaptation at the MAC link layer and source coding at the application layer. We, first model the finite-state Markov chain (FSMC) for the physical layer service and based on that, we then characterize the Lagrangian relaxation and Dynamic programming to find the optimal shortest path. The source coding as well as retransmission requests are based on the channel-state information (CSI) and the system controller is implemented in all the three physical, MAC layer and application layers. The numerical results revealed that our proposed cross-layer design can efficiently achieve the effective throughput and distortion gain.
Do citizens vote for parties, policies or the expected winner in proportional representation systems? Evidence from four different countries using a multiple-type model

This article presents a unified framework that allows us to disentangle to what extent agents in proportional representation (PR) systems engage in the different strategies that the available voting models have separately suggested: ‘party’, ‘coalitional’, ‘bandwagon’ and ‘other’ voting (i.e. neither of the previous three). Results using data from multiple countries reveal that at least 75% of agents cast a sincere party vote. Around 10% of voters try to affect policymaking by casting a coalitional vote. Since most coalitional agents use their vote to ‘push’ coalitions away from the centre, extreme parties are the most benefited. Hence, strategic coalitional voting may increase rather than preclude fragmentation of a party system in PR contexts. Another 5% of voters support the expected winner regardless of their own party and coalition preferences. Finally, 5–10% of voters fall into the category of other types. The characteristics and motivations of each type are uncovered. Political sophistication increases the likelihood of sincere and coalitional voting. On the other hand, dissatisfaction with parties voted for in the past increases other voting. In particular, recent growth of European right-wing nationalist parties is shown to rely more on other voting and less on sincere and coalitional support.
College Students’ Psychological Needs and Intrinsic Motivation to Learn: An Examination of Self-Determination Theory

Over the last several decades, instructional communication scholars have studied and measured student motivation as an important learning outcome. Unfortunately, this research has lacked theoretical guidance and has treated student motivation as a construct that varies only in quantity, ignoring existing theory that suggests student motivation is best understood as a construct that differs in quality (i.e., intrinsic motivation). To create two new measures that incorporate theoretical explanations of student motivation, three studies (N = 1,067) were undertaken using self-determination theory (SDT) to operationalize students’ intrinsic motivation as a product of basic psychological need satisfaction. In the first two studies, the Student Psychological Needs Scale and the Intrinsic Motivation to Learn Scale were developed and validated. In the third study, parallel mediation analyses supported SDT’s prediction that the fulfillment of students’ psychological needs (i.e., autonomy, competence, relatedness) would mediate the relationship between personalized education practices and intrinsic motivation to learn.
Forensic-by-Design Framework for Cyber-Physical Cloud Systems

As businesses continue to offer customers and employees increased access, improved software functionality, and continued improvements in supply chain management opportunities, it raises the risk of cyber-physical attacks on cyber-physical cloud systems (CPCS). In this article, the authors discuss the challenges associated with a CPCS attack and highlight the need for forensic-by-design, prior to presenting their conceptual CPCS forensic-by-design model. The six factors of the framework are discussed, namely, risk management principles and practices, forensic readiness principles and practices, incident handling principles and practices, laws and regulation, CPCS hardware and software requirements, and industry-specific requirements. Future research topics are also identified.
Immune Suppression in Tumors as a Surmountable Obstacle to Clinical Efficacy of Cancer Vaccines

Human tumors are usually not spontaneously eliminated by the immune system and therapeutic vaccination of cancer patients with defined antigens is followed by tumor regressions only in a small minority of the patients. The poor vaccination effectiveness could be explained by an immunosuppressive tumor microenvironment. Because T cells that infiltrate tumor metastases have an impaired ability to lyse target cells or to secrete cytokine, many researchers are trying to decipher the underlying immunosuppressive mechanisms. We will review these here, in particular those considered as potential therapeutic targets. A special attention will be given to galectins, a family of carbohydrate binding proteins. These lectins have often been implicated in inflammation and cancer and may be useful targets for the development of new anti-cancer therapies.
A Multiphase Shape Optimization Problem for Eigenvalues: Qualitative Study and Numerical Results

We consider the multiphase shape optimization problem $$\min\Big\{\sum_{i=1}^h\lambda_1(\Omega_i)+\alpha|\Omega_i|:\ \Omega_i\ \hbox{open},\ \Omega_i\subset D,\ \Omega_i\cap\Omega_j=\emptyset\Big\},$$ where $\alpha>0$ is a given constant and $ D\subset\Bbb{R}^2$ is a bounded open set with Lipschitz boundary. We give some new results concerning the qualitative properties of the optimal sets and the regularity of the corresponding eigenfunctions. We also provide numerical results for the optimal partitions.
Effect of Secondary Path Amplitude Estimation Errors on the Performance of ANC-Motivated Algorithms for Open-Fitting Hearing Aids

Over the past years the usage of open-fitting hearing aids has been steadily increasing, due to the fact that they largely alleviate occlusion-related problems. However, existing noise reduction (NR) schemes such as the multichannel Wiener filter (MWF) [1] do not take into account the ambient noise leaking through the open fitting, therefore typically leading to a degraded noise reduction performance. To provide information about this leakage and hence improve the performance of NR algorithms, an internal (so-called error) microphone can be incorporated in the ear mould.
A better indicator for genre classification: Topic word or surface text feature: A case study of recognition of brief biography

Classification based on topic (content) rather than genre (form) prevails in the text data mining and search engine circle. To simplify this work, a BOW (Bag of Words) strategy, counting topic-related words as features, is comprehensively utilized to make a final decision. Indeed, texts can be categorized by expression styles rather than their themes. Brief biography is a typical text class which differs from others in its genre. In order to differentiate biographical data from other texts quickly and effectively, we constructed two knowledge bases and compared their performances, i.e. topic keyword (content) and simple linguistic features (form). The results showed that the latter form-based approach is superior to its former counterpart. A further discussion is elaborated at the end of this paper.
Modeling the hemodynamic response in single‐trial functional MRI experiments

Today, most studies of cognitive processes using functional MRI (fMRI) experiments adopt a single‐trial design. Highly flexible stimulation paradigms require new statistical models in which not only the activation amount but also the time course of the measured hemodynamic response is analyzed. Most previous approaches have been based on a linear regression context and have introduced hemodynamic model functions to improve the signal detection. In this report a nonlinear regression context is derived, from which shape parameters for the hemodynamic response are obtained per trial and per region of interest. These parameters allow the investigation of stimulus‐induced shape variations of the hemodynamic response. By embedding the estimation into a robust statistical framework and rigorously analyzing the spatiotemporal interactions in the fMRI data, it is possible to derive statistically valid descriptions of single hemodynamic responses. The model, estimation algorithm, validation, and an example analysis from a single‐trial fMRI study are reported. Magn Reson Med 42:787–797, 1999. © 1999 Wiley‐Liss, Inc.
Private Information Retrieval from Coded Databases with Colluding Servers

We present a general framework for Private Information Retrieval (PIR) from arbitrary coded databases, that allows one to adjust the rate of the scheme according to the suspected number of colluding servers. If the storage code is a generalized Reed-Solomon code of length n and dimension k, we design PIR schemes which simultaneously protect against t colluding servers and provide PIR rate 1-(k+t-1)/n, for all t between 1 and n-k. This interpolates between the previously studied cases of t=1 and k=1 and asymptotically achieves the known capacity bounds in both of these cases, as the size of the database grows.
A distributed information fusion method for localization based on Pareto optimization

To overcome the limitations of specific positioning techniques for mobile wireless nodes and achieve a high accuracy, the fusion of heterogeneous sensor information is an appealing strategy. In this paper, the problem of optimal fusion of ranging information typically provided by Ultra-Wideband radio with speed and absolute orientation information is addressed. A new distributed recursive estimation method is proposed. The method does not assume any motion model of mobile nodes and is based on a Pareto optimization. The challenging part of the new estimator is the characterization of the statistical information needed to model the optimization problem. The proposed estimator is validated by Monte Carlo simulations, and the performance is compared to several Kalman-based filters commonly employed for localization and sensor fusion. Much better performance is achieved, but at the price of an increased computational complexity.
Remarks on global a priori estimates for the nonlinear Schr

We present a unified approach for obtaining global a priori estimates for solutions of nonlinear defocusing Schrodinger equations with defocusing nonlinearities. The estimates are produced by contracting the local momentum conservation law with appropriate vector fields. The corresponding law is written for defocusing equations of tensored solutions. In particular, we obtain a new estimate in two dimensions. We bound the restricted L 4 t L 4 γ Strichartz norm of the solution on any curve γ in ℝ 2 . For the specific case of a straight line we upgrade this estimate to a weighted Strichartz estimate valid in the full plane.
Spectral Watermarking : A Spread Spectrum Watermark Embedded In the DCT Domain

Digital watermarking is a technology being developed to ensure security and protection of multimedia data. The purpose of digital watermarking is not to restrict use of multimedia resources, but rather to facilitate data authentication and copyright protection. In this paper, we discuss the general requirements on data hiding and digital watermarking. It includes topics in spatial watermarking; spread spectrum watermarking, spectral watermarking, and a brief survey of currently available commercial and public domain software are provided. The Improved technique is robust to image/video compression, and one can recover the hidden data without requiring the availability of the host signal. The new scheme enables signature images to be as much as 25% of the host image data, and hence could be used both in digital watermarking as well as in image data embedding.
Differential Fault Analysis on High Throughput AES

: Cryptography plays a key role in ensuring the privacy and integrity of data. Cryptanalysis is the study of how to crack encryption algorithms and their implementations. Differential cryptanalysis is a plain text attack which occurs in the cryptographic system. Testing can control and observe the internal states of the system. While testing in the cryptographic system, by observing the input and output bit changes, the third parties may shift out key. To reduce the observability and to increase the security, Robust Secure Scan design (RSS) is proposed. RSS design encrypts the content during scan chain operation. By adding pipelining process in the S Box and transforming the S Box transformation into combinational design, 50% high throughput is achieved over the standard Encryption algorithm. By this method the security is much improved as it incorporates RSS design, which involves double encryption process.