Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliability evaluation on weighted graph metrics of fNIRS brain networks.

Background Resting-state fNIRS (R-fNIRS) imaging data has proven to be a valuable technique to quantitatively characterize functional architectures of human brain network. However, whether the brain network metrics derived using weighted brain network model is test-retest (TRT) reliable remains largely unknown. Methods Here, we firstly constructed weighted brain networks on a group of 18 participants, and then applied graph-theory approach to quantify topological parameters of each weighted network. The intraclass correlation coefficient (ICC) was further applied to quantify the TRT reliability of network metrics. Results We found that the reliability of the weighted network metrics is threshold-sensitive, and most of these network metrics showed fair to excellent reliability. Specifically, the global network metrics, e.g., clustering coefficient, path length, local efficiency and global efficiency were of excellent level reliability (ICC >0.75) on both HbO and HbR signals. The nodal network metrics, e.g., degree and efficiency, generally also showed excellent level reliability on both HbO and HbR signals, and the reliability of these two metrics was better than that of nodal betweenness. Conclusions Overall, these findings demonstrated that most weighted network metrics derived from fNIRS are TRT reliable and can be used for brain network research.
Advances and Prospects of Nicardipine Effects in Attenuation of Hydroxy-Daunorubicin Induced Acute Cardiotoxicity in rats.

Hydroxy daunorubicin is a cytotoxic antibiotic which is being used for a wide range of tumors but its prolonged use has lead to cardiomyopathy in numerous cases. Present study was designed to investigate the role of nicardipine- L-type calcium channel blocker, used for the management of hypertension and angina pectoris against the hydroxydaunorubicin induced cardiotoxicity. Sixty Wale-Sprague male rats were used in this study, ranging from 300gm-350gm in weight . The animals were randomly divided into three groups, twenty rats in each group. Group I: (control): received physiological saline (4ml/kg/day), orally for ten consecutive days. Group II (Hydroxydaunorubicin group): received a single dose hydroxydaunorubicin (20mg/kg) intraperitoneally. Group III (pretreatment group): received nicardipine (0.1mg/kg) intraperitoneally daily, starting six days before hydroxydaunorubicin (20mg/kg) and continued for four consecutive days. At the end of the experiment, blood samples were collected for cardiac biomarker determination. The animals were dissected and hearts were taken for histopathological examination. Hydroxydaunorubicin has shown to increase cardiac Troponin I, LDH, MDA, and interleukin-17 significantly (p<0.05) compared to the control group while as pretreatment with nicardipine showed a significant reduction in cardiac biomarkers significantly p<0.05.
Automatic Segmentation of Product Bottle Label Based on GrabCut Algorithm

In this paper, we propose a method to build an accurate initial trimap for the GrabCut algorithm without the need for human interaction. First, we identify a rough candidate for the label region of a bottle by applying a saliency map to find a salient area from the image. Then, the Hough Transformation method is used to detect the left and right borders of the label region, and the k-means algorithm is used to localize the upper and lower borders of the label of the bottle. These four borders are used to build an initial trimap for the GrabCut method. Finally, GrabCut segments accurate regions for the label. The experimental results for 130 wine bottle images demonstrated that the saliency map extracted a rough label region with an accuracy of 97.69% while also removing the complex background. The Hough transform and projection method accurately drew the outline of the label from the saliency area, and then the outline was used to build an initial trimap for GrabCut. Finally, the GrabCut algorithm successfully segmented the bottle label with an average accuracy of 92.31%. Therefore, we believe that our method is suitable for product label recognition systems that automatically segment product labels. Although our method achieved encouraging results, it has some limitations in that unreliable results are produced under conditions with varying illumination and reflections. Therefore, we are in the process of developing preprocessing algorithms to improve the proposed method to take into account variations in illumination and reflections.
Cryptography Challenges of Cloud Computing for E-Government Services

Cloud computing is the popular technology and seems to be very promising for the future trends. In the cloud computing, programs and data are stored and sometimes processed in cloud. So data and programs can easy be accessible anywhere, anytime. Cloud computing is also a very applicable solution for e-government services due to cost effectiveness and efficiency of services. While it eliminates the need of maintaining costly computing facilities by companies and institutes, one of the barriers for cloud adoption is still security concerns. Out of cloud computing is depended upon internet, safety topics such as, confidentiality, authentication, privacy, and data securities are the main concerns. In the cloud, users should search encryption capabilities for preserving and retaining their data. In the same time, they have to protect the functionality of the underlying cloud applications. There are many encryption algorithms to encrypt the data. However, there are new varieties of cyber-attacks developed that threaten the data in the cloud infrastructure. These attacks allow the encrypted data to be re-encrypted, making it incomprehensible by the original owner of the data so that it becomes impossible for the user to decrypt it. This article is written to allow users to understand the working steps and challenges of the algorithms, comparisons of the algorithms and possible solutions for the resent threats in the cloud environment related with cryptography for e-government applications.
Recent developments and application of metabolomics in cancer diseases

Metabolomics studies provide useful information about health and disease status. Metabolite based investigations on various cancers is a powerful approach to diagnosis, prognosis and therapy of cancer diseases. Recently by using advanced analytical techniques such as NMR and MS and its hyphenation methods, global metabolic profiling of diseases has been possible. It is predictable that international contributions and software developments in the future will lead to accurate instrumental analysis based on a large number of human samples that finally will improve validation methods and reach this field from the research phase to the clinical phase. In this review, we also discussed the latest developments in analytical methods, application of data analysis, investigation of useful databases and the curent application of metabolomics in cancer diseases that have led to the identification of related biomarkers. In continuation, we listed biomarkers involved in cancer diseases that have been published during recent years.
Analysis of Security and Cryptographic Approaches to Provide Secret and Verifiable Electronic Voting

Electronic voting systems are inextricably bound to security and cryptographic techniques. Over the last decades, countless techniques have been proposed to face the dangers of electronic voting systems with mathematical precision. Certainly, the majority of these works address secrecy and verifiability. In this chapter, security and cryptographic techniques are analyzed with respect to those security properties that can be evaluated on the basis of these techniques: secrecy, fairness, integrity, and verifiability. Furthermore, the chapter discusses their adequacy to ensure further relevant properties like eligibility and uniqueness, and evaluates security and cryptographic techniques with respect to the costs that come along with their real-world application. The authors conclude the chapter with a summary of the evaluation results, which can serve as guideline for decision-makers.
An Introduction to Linear Programming and Game Theory: Thie/An Introduction

Introduction to Linear Programming and Game Theory, Third Edition includes various additions as well as improvements that have been developed over the last decade, and the most significant addition to the text involves technology. It features an introduction, discussion, and utilization of Solver, a spreadsheet software package that solves mathematical programming problems. PRT Simplex, a computer application for learning the simplex method, has been developed by co-author Gerard Keough and was designed to be used with this book.
Requirement Capture, Formal Description and Verification of an Invoicing System

The Invoicing case study is a typical business system proposed by Henri Habrias as a common example for a contest on the capacity of particular formal methods to capture requirements from the client. For this, the case study is informally described by half a page of English text. In this report, we use the formal description technique LOTOS for requirement capture, formal description and verification of the Invoicing case study. First, we analyse and interpret the informal requirements of the case study using the LOTOS approach for description of systems. This leads to a set of twenty questions about the informal description. By answering to these questions, we obtain a high-level specification architecture that can be formalised. Then, we present the formal description of the case study in LOTOS and, for comparison, in E-LOTOS, the new version of LOTOS currently being standardized. Since LOTOS allows a balance to be struck between process-oriented and data-oriented modeling, descriptions in both styles are given. After that, we verify the LOTOS descriptions by model-checking using the CADP (CAESAR/ALDEBARAN) toolbox. The underlying Labelled Transition System (LTS) models corresponding to various scenarios are generated using the CAESAR compiler. We push further the analysis of the case study by formalizing in temporal logic six properties of the system. We verify these properties on the LTS models using the XTL model-checker. Finally, we study the equivalence of the process-oriented and data-oriented descriptions using the ALDEBARAN tool.
Design of safe control laws for the locomotion of biped robots

We want a biped robot to walk safely in a crowd. This involves two aspects: balance and collision avoidance. The first implies avoiding kinematic and dynamical failures of the unstable walking dynamics of the robot; the second refers to avoiding collisions with people. We want to be able to solve both problems not only now but also in the future. We can ensure balance indefinitely by entering in a cyclic walk or by making the robot stop after a couple of steps. Nonetheless, we cannot give a comparable guarantee in collision avoidance for many reasons: impossibility of having absolute knowledge of where people are moving, kinematic/dynamical limitations of the robot, adversarial crowd motion, etc. We address this limitation with a standard strategy for crowd navigation, known as passive safety, that allows us to formulate a unified Model Predictive Control approach for balance and collision avoidance in which we require the robot to stop safely in finite time. In addition, we define a novel safe navigation strategy based on the premise of avoiding collisions for as long as possible that minimizes their occurrence and severity. We propose a lexicographic formulation that produces motions that comply with such premise.We increase the degrees of freedom of the locomotion of a biped robot by allowing the duration and orientation of its steps to vary online. This introduces nonlinearities in the constraints of the optimization problems we solve. We approximate these nonlinear constraints with safe linear constraints so that satisfying the latter implies satisfying the former. We propose a novel method (Safe Sequential Quadratic Programming) that ensures feasible Newton iterates in the solution of nonlinear problems based on this redefinition of constraints.We make a series of simulations of a biped robot walking in a crowd to evaluate the performance of our proposed controllers. We are able to attest the reduction in the number and in the severity of collisions with our proposed navigation strategy in comparison with passive safety, specially when there is uncertainty in the motion of people. We show typical behaviors of the robot that arise when we allow the online variation of the duration and orientation of the steps and how it further improves collision avoidance. We report the computational cost of our proposed numerical method for nonlinear problems in comparison with a standard method. We show that we only need one Newton iteration to arrive to a feasible solution but that the CPU time is dependent on the amount of active set factorizations needed to arrive to the optimal active set.
EFFECTS OF MIDDLE-DISTANCE TRIATHLON RACE ON SERUM BIOCHEMICAL PARAMETERS

For the purpose of examining relative impact of the middle-distance triathlon race (Swim : 2 .0 km, Bike : 90.0 km, Run : 21.0 km), blood samples were taken on the day before, immediately after , and the day following the race from triathletes who participated in a middle-distance triathlon race at Tokunoshima, Kagoshima in June 1993. White blood cell count was significantly increased immediately after the race.It was recovered on the next day to the value observed before the race . Serum creatine phosphokinase activity was elevated significantly immediately after the competition . It continued to rise up to the level of about 4 times higher than the pre-race value. In terms of renal function, serum creatinine concentration showed a significant increase immediately after the competition. However, it recovered to normal range by the day after the competition. In addition, serum urea nitrogen concentration was also significantly increased after the competition . It remained at the high level until the day after the competition. In conclusion, it might be suggested that the middle-distance triathlon gives a significant impact on human body, even though it is quntitatively less than the long-distance type (Swim : 3.9 km, Bike :180.2 km, Run : 42.2 km) . (Jpn. J. Phys. Fitness Sports Med. 1996, 45: 429-440) key words: Middle-distance triathlon, Serum enzymes activity
Real-time Teleoperation of Flexible Beveled-tip Needle Insertion using Haptic Force Feedback and 3D Ultrasound Guidance

Needle insertion procedures can greatly benefit from robotic systems to improve their accuracy and success rate. However, a fully automated system is usually not desirable and the clinicians need to be included in the control loop. In this paper we present a teleoperation framework for beveledtip flexible needle steering that enables the user to directly and intuitively control the trajectory of the needle tip via a haptic interface. The 6 degrees of freedom of the needle base are used to perform several automatic safety and targeting tasks in addition to the one controlled by the user. Real-time visual feedback is provided by a 3D ultrasound probe and used to track the 3D location of the needle and of a spherical target. Several haptic force feedback are compared as well as two different levels of mix between automated and user-controlled tasks. A validation of the framework is conducted in gelatin phantom and a mean targeting accuracy of 2.5 mm is achieved. The results show that providing an adequate haptic guidance to the user can reduce the risks of damage to the tissues while still letting the surgeon in control of the tip trajectory.
Diseño de un coprocesador hardware para segmentación de huellas dactilares

Mariano Lopez Dept. d’Enginyeria Electronica E.P.S. d’Enginyeria de Vilanova i la Geltru Universitat Politecnica de Catalunya 08800 Vilanova i la Geltru lopezg@eel.upc. edu Enrique Canto, Nicolau Canellas, Maria Dolores Palomera, Mariano Fons, Francesc Fons Dept. d’Enginyeria Electronica, Electrica i Automatica E.T. Superior d’Enginyeria Universitat Rovira i Virgili, 43007 Tarragona ecanto@etse.urv. es, ncanyell@etse.urv. es, lolap@tinet.fut. es, mariano.fons@estudiants.urv. es, francesc.fons@estudiants.urv. es
State of Education for Library and Information Science in Nigerian Private Universities

Private Universities in Nigeria are established, financed, and managed by individuals, corporate establishments, and religious organizations for the development of human resources in the country. This article adopts a descriptive approach to the present state of education for library and information science (LIS) in Nigerian Private Universities. It traces the historical development of legally recognized universities (federal, state, and private) in the country, which currently stands at 117. It also traced the development of librarianship education in Nigeria. Out of the 117 universities, 45 are private with only 2 offering LIS, thereby contributing to inadequate number of professional librarians in the nation. It outlines the factors causing the neglect of LIS by these private universities, which include wrong notion and perception of LIS by societal members, poor visibility of the professional association of librarians in the country, and poor state of the nation's information infrastructure. Recommendations and strategies to establish and run a department of LIS for the training of librarians in these private universities are given. If adopted, they will lead to the production of more proactive contemporary librarians for the nation and the world at large.
Etude PROGENE, projet français d'analyse génétique du cancer de la prostate familial : recrutement et analyse

Buts : Initier une etude de liaison genetique afin de localiser un (plusieurs) gene (s) de predisposition au cancer de la prostate (CaP) hereditaire. En effet, differentes etudes epidemiologiques ont mis en evidence une possible agregation familiale dans environ 25% des cas. Une etude de segregation familiale [14] a par ailleurs montre qu'une predisposition genetique, a transmission autosomique dominante et a penetrance elevee (88% a 85 ans) pourrait etre a l'origine de 9% de l'ensemble des CaP. Methodes : Une collecte nationale de familles avec au moins 2 CaP a permis, 1) d'identifier les familles a forme hereditaire, 2) de constituer une banque d'ADN constitutionnel apres prelevement sanguin de sujets appartenant a ces familles, et 3) d'effectuer une etude de simulation d'analyse de liaison genetique, en prealable au genotypage microsatellite. Resultats : De juillet 1994 a septembre 1995, nous avons inclus 67 familles (180 CaP). Actuellement, 45 autres familles sont en cours d'inclusion. Parmi ces 67 familles, 24 (89 CaP, 54 vivants) repondaient a au moins un des criteres de l'etude de CARTER [14] pour les formes hereditaires de CaP familial. Deux familles ont ete egalement retenues car les 3 porteurs de CaP sont des parents du second degre. Au total 26 familles sont porteuses d'une forme hereditaire, dont 18 (73 CaP, 46 vivants) sont considerees informatives pour une etude de liaison genetique (lod score = 4 pour θ = 0,01 avec un marqueur a 8 alleles). L'ADN constitutionnel de 271 individus de ces familles informatives a ete extrait a partir de cellules circulantes obtenues de prelevements sanguins, les lymphocytes immortalises, et le genotypage initie pour 216 marqueurs microsatellites repartis sur tout le genome, en moyenne tous les 20 cM. Conclusion : Alors que le recrutement nous a permis d'identifier de nombreuses familles informatives pour un risque herite de CaP, l'etude predictive suggere une forte probabilite de localiser un gene de predisposition par analyse de liaison genetique. Il deviendra alors possible d'identifier au sein des familles concernees les sujets porteurs de l'anomalie genetique, et donc a haut risque de CaP. Enfin, la mise en evidence d'un locus permettra d'envisager le clonage et l'identification du (des) gene (s) implique (s).
Deep Learning Approaches for Data Augmentation and Classification of Breast Masses using Ultrasound Images

Breast classification and detection using ultrasound imaging is considered a significant step in computer-aided diagno-sis systems. Over the previous decades, researchers have proved the opportunities to automate the initial tumor classification and detection. The shortage of popular datasets of ultrasound images of breast cancer prevents researchers from obtaining a good performance of the classification algorithms. Traditional augmentation approaches are firmly limited, especially in tasks where the images follow strict standards, as in the case of medical datasets. Therefore besides the traditional augmentation, we use a new methodology for data augmentation using Generative Adversarial Network (GAN). We achieved higher accuracies by integrating traditional with GAN-based augmentation. This paper uses two breast ultrasound image datasets obtained from two various ultrasound systems. The first dataset is our dataset which was collected from Baheya Hospital for Early Detection and Treatment of Women’s Cancer, Cairo (Egypt), we name it (BUSI) referring to Breast Ultrasound Images (BUSI) dataset. It contains 780 images (133 normal, 437 benign and 210 malignant). While the Dataset (B) is obtained from related work and it has 163 images (110 benign and 53 malignant). To overcome the shortage of public datasets in this field, BUSI dataset will be publicly available for researchers. Moreover, in this paper, deep learning approaches are proposed to be used for breast ultrasound classification. We examine two different methods: a Convolutional Neural Network (CNN) approach and a Transfer Learning (TL) approach and we compare their performance with and without augmentation. The results confirm an overall enhancement using augmentation methods with deep learning classification methods (especially transfer learning) when evaluated on the two datasets.
Identificação e classificação de entidades mencionadas em galego

Automatic named entity recognition and classification are important tasks for many natural language processing applications, such as machine translation, information extraction or question-answering systems. This paper describes the adaptation and implementation of several open-source systems for the identification and classification of the following named entities in Galician: (i) dates, (ii) numerals, (iii) quantities and (iv) proper nouns. Analysis of the first three types of named entities is performed with the software FreeLing, using finite-state automata. For the proper noun recognition task, two methods were compared: (i) finite-state automata and (ii) machine learning models. Finally, the semantic classification of proper nouns was carried out with a rulebased system that takes advantage of automatically obtained resources. This paper shows some evaluations for each tool, all available under free licenses.
Low Power Schmitt Trigger

The Schmitt Trigger is a comparator circuit that incorporates positive feedback. Noise is being ignored by CMOS Schmitt Trigger as the hysteresis in a Schmitt Trigger circuit offers a better noise margin and noise stable operation. And the simulation has been done on Tanner EDA tool at TSMC 130nm technology with 1 V supply voltage. TSPICE simulation results of the circuit confirm the effectiveness of the approach. Proposed Schmitt Trigger is designed by using less transistor count and a capacitor which results in less average power consumption with decrease in area. Delay is also decreased by using only one PMOS as because delay is more concentrated to PMOS due to less mobility of PMOS compare to NMOS. Keywords: CMOS Schmitt Trigger, Delay, Low power consumption