Miracle-dz/UniFAR-FTUs huggingface dataset:
OwlSight: Platform for Real-Time Detection and Visualization of Cyber Threats

Security reports published by leading companies reveal the growing number of cyber attacks. Thefts of money or sensitive data, harm the reputation of organizations and sabotage of national critical infrastructures are some of the motivations behind these attacks. The sophistication of these attacks is very high, creating major challenges to the detection and mitigation in useful time. In this context the development of systems to provide situational awareness, to detect cyber threats and alert them in real-time are very important to mitigate the impact of the attacks. In this paper we present a cyber threat platform targeted for real-time detection and visualization of cyber threats. The platform is composed by several building blocks and it is able to collect huge amounts of data from multiple sources, prepare and analyze the data and present the findings through a set of insightful dashboards. A version of the platform is already available and used in a real-context. It collects more than 107 million of malware events daily from different data sources and provides visualization and alerts in real-time for more than 2.7 million of infected unique IPs spread around the world.
INTERPOLATING HEAD RELATED TRANSFER FUNCTIONS

This paper describes the interpolation of head related transfer functions (HRTFs) for all directions. The interpolation of HRTFs enables us to reduce the number of measurements for new user’s HRTFs, and also reduce the data of HRTFs in auditory virtual systems. A linear interpolation and the spline interpolation are evaluated and the advantages of both methods are clarified. The interpolation methods are applied to HRTFs measured with two head-and-torso simulators. The objective results show that the linear interpolation is superior to the spline interpolation as the number of HRTFs used for interpolation is decreased. There is no significant difference between equally-spaced and optimal conditions for both methods. The HRTFs interpolated from 41 directions are evaluated subjectively. The subjective results show that there is no significant difference between the measured and interpolated HRTFs in azimuth and elevation.
Improving route prediction through user journey detection

The positioning datasets that underpin route prediction models arrive as time series or point process logs. However, their use for prediction requires them to be split into meaningful segments, conceptualised as travelling periods or 'journeys', to form a set of training inputs. Despite significant research into route prediction, this important pre-processing step has traditionally occurred in an ad-hoc fashion, using arbitrary connectivity or movement thresholds. There has been little consideration to date of the impact of this on prediction, a fact rectified in this work. Three methods for detection of journeys are evaluated using a dataset of labelled movement histories collected specifically for this investigation. We perform an exhaustive series of parameterisations of detection methods, optimising with respect to actual journeys specified by users. We find that using existing methods, GPS multipath artefacts introduce journey extraction error that raises concern for prediction applications. A new approach is presented that explicitly uses these effects to improve journey detection results.
Whole-School Inclusive Reform: "It's What's Best for Kids”

This qualitative case study investigates a school and university inclusive reform project, called the All Means All Project, and how it is understood and experienced by teachers and administrators at Kennedy School, a K-8 school in the northeast United States. The All Means All Project began when two university professors called into question the practice of placing students with disabilities in segregated special classrooms. Their goal was to create a collaborative, multi-dimensional approach to providing all students with access to rigorous academic instruction and promoting a sense of belonging through students’ full-time membership in general education classrooms. This reform was an immense undertaking because it required stakeholders to question their established policies and practices, engage in targeted professional development, and restructure the school’s staff and students in order to close two self-contained special education classrooms. With the support of the district superintendent, the director of special education, and Kennedy School administrators, the university professors began holding monthly, voluntary, afterschool meetings with school personnel. Because teachers and administrators are integral participants in this process, I sought to understand how they understood the project philosophically and how they experienced the reform process practically by utilizing participant observation and semi-structured interviews over a period of two years. Restructuring of this magnitude disrupted the existing order of business at Kennedy School and some stakeholders struggled to critically reflect upon the meanings they brought to this transition. Dominant cultural narratives about students, and about schooling, were challenged as many teachers and administrators sought to replace these narratives with counter narratives. This dissertation draws attention to the tendency for stakeholders to backslide into more familiar narratives when they feel challenged. Eventually Kennedy School stakeholders recognized benefits for students and for teachers based on their restructuring efforts. They created a mantra about whole-school inclusive reform, “It’s what’s best for kids! ” that seemed to sustain them through difficult days. Kennedy School stakeholders demonstrated the possibilities of what can happen when teachers and administrators are willing to take risks to educate all students. WHOLE-SCHOOL INCLUSIVE REFORM: “IT’S WHAT’S BEST FOR KIDS. ” By Nicole M. DeClouette B.A., University of California Berkeley, 1997 C.A. S. , Syracuse University, 2008 Dissertation Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Special Education. Syracuse University December 2012 Copyright 2012 Nicole Marie DeClouette All Rights Reserved
Development of an Arabic text-to-speech system

Research on Text-to-speech technology has received the interest of professional researchers in many languages which is a consequence of wide range of applications where Text-To-Speech is implemented. However, Arabic language, spoken by millions of people as an official language in 24 different countries, gained less attention compared with other languages despite the fact that it has a religious value for more than 1.6 billion Muslim worldwide. These facts exhibit the need for a high quality, small size, and completely free Arabic TTS with the ability of future improvements. The vowelized written text of Arabic language carries the pronunciation rules with limited exceptions, so rule-based system with an exception dictionary for words that fail with those letter-to-phoneme rules may be a much more reasonable approach. This paper is a development of a rule-based text- to- speech Hybrid synthesis system which is a combination formant and concatenation techniques with acceptable naturalness. The simulation results of the system shows good quality in handling word, phrase, and sentence level compared to other available Arabic TTS systems. The accuracy of the overall system is 96%. Further improvements need to be done for stressed syllable position and intonation.
80 HORMONAL AND GENDER INFLUENCES ON MOOD REGULATION

Within the past 20 years, the putative role of gender and gonadal steroids in mood regulation has been transformed from the staple of stereotype to a critical locus of research in clinical neuroscience. This transformation reflects the impact of explosive advances in molecular endocrinology and basic neuroscience, both of which suggest the myriad and dramatic neuroregulatory effects of gonadal steroids. Although direct isomorphs between basic mechanisms and clinical observations are for the most part absent, our burgeoning knowledge of the cellular and central nervous system effects of gonadal steroids is offering new models for understanding the relevance of gender and gonadal steroids in mood regulation. In this chapter, we review some of the major findings in reproductive neuroscience, emphasize the context dependency of many of these findings, and suggest that similar contextual effects underlie the inability to demonstrate uniform effects of gender or gonadal steroids on mood and behavior. Reproductive hormones have played a central historical role in the development of our understanding of the effects of hormones on brain and behavior. More than 2,000 years ago, Aristotle, in his biological treatise Historia Animalium, observed that castration of immature male birds prevents the development of characteristic male singing and sexual behavior (1). One hundred fifty years ago, Berthold (2) successfully transplanted testes in castrated roosters and reversed their hypogonadal symptoms, demonstrating that reproductive organs possess factors that can dramatically alter physiology and behavior. These observations culminated in the claims by nineteenth century organotherapists (e.g.,
Oxidative Stress and Metabolic Pathologies: From an Adipocentric Point of View

Oxidative stress plays a pathological role in the development of various diseases including diabetes, atherosclerosis, or cancer. Systemic oxidative stress results from an imbalance between oxidants derivatives production and antioxidants defenses. Reactive oxygen species (ROS) are generally considered to be detrimental for health. However, evidences have been provided that they can act as second messengers in adaptative responses to stress. Obesity represents a major risk factor for deleterious associated pathologies such as type 2 diabetes, liver, and coronary heart diseases. Many evidences regarding obesity-induced oxidative stress accumulated over the past few years based on established correlations of biomarkers or end-products of free-radical-mediated oxidative stress with body mass index. The hypothesis that oxidative stress plays a significant role in the development of metabolic disorders, especially insulin-resistance state, is supported by several studies where treatments reducing ROS production reverse metabolic alterations, notably through improvement of insulin sensitivity, hyperlipidemia, or hepatic steatosis. In this review, we will develop the mechanistic links between oxidative stress generated by adipose tissue in the context of obesity and its impact on metabolic complications development. We will also attempt to discuss potential therapeutic approaches targeting obesity-associated oxidative stress in order to prevent associated-metabolic complications.
On the Relationship Between Markov chain Monte Carlo Methods for Model Uncertainty

This article considers Markov chain computational methods for incorporating uncertainty about the dimension of a parameter when performing inference within a Bayesian setting. A general class of methods is proposed for performing such computations, based upon a product space representation of the problem which is similar to that of Carlin and Chib. It is shown that all of the existing algorithms for incorporation of model uncertainty into Markov chain Monte Carlo (MCMC) can be derived as special cases of this general class of methods. In particular, we show that the popular reversible jump method is obtained when a special form of Metropolis–Hastings (M–H) algorithm is applied to the product space. Furthermore, the Gibbs sampling method and the variable selection method are shown to derive straightforwardly from the general framework. We believe that these new relationships between methods, which were until now seen as diverse procedures, are an important aid to the understanding of MCMC model selection procedures and may assist in the future development of improved procedures. Our discussion also sheds some light upon the important issues of “pseudo-prior” selection in the case of the Carlin and Chib sampler and choice of proposal distribution in the case of reversible jump. Finally, we propose efficient reversible jump proposal schemes that take advantage of any analytic structure that may be present in the model. These proposal schemes are compared with a standard reversible jump scheme for the problem of model order uncertainty in autoregressive time series, demonstrating the improvements which can be achieved through careful choice of proposals.
A Viable System Model to Analyze an Organization ' s Metasystem

Organisations in any economy are rapidly changing as they considered being complex and dynamic systems and they are incorporated in a complex environment which establishes a need for a new methodology to help the analysts and the banks in making better decisions in assessing these organisations. This paper used a systems thinking approach by constructing accurate models to illustrate how problems could be identified in an early stage. The Viable System Model shows that it could provides the analyst with a deeper insight into the organizational structure of any firm and helps in identifies problems that need to be addressed.
A-Type and B-Type Natriuretic Peptides in Cardiac Surgical Procedures

This study was performed to determine the secretion pattern and prognostic value of A-type (ANP) and B-type (BNP) natriuretic peptide in patients undergoing cardiac surgical procedures. We measured ANP and BNP in patients undergoing coronary artery bypass grafting (CABG) with (n = 28) or without (n = 32) ventricular dysfunction and in patients undergoing mitral (n = 21) or aortic (n = 24) valve replacement, respectively. Postoperative mortality was recorded up to 730 days after operation. ANP, but not BNP, concentrations were closely associated with volume reloading of the heart after aortic cross-clamp in all patients. The secretion pattern of BNP during surgery was much less uniform. BNP, but not ANP, concentrations correlated with aortic cross-clamp time (r2 = 0.32; P = 0.006) and postoperative troponin I concentrations (r2 = 0.22; P = 0.0009) in bypass patients, and preoperative BNP increases were associated with a more frequent postoperative (2-yr) mortality in these patients. Markedly increased preoperative BNP concentrations in mitral (3-fold) and aortic (14-fold) valve disease patients did not further increase during cardiopulmonary surgery. The data suggest that ANP is primarily influenced by intravascular volume reloading of the heart after cross-clamp, whereas the secretion of BNP is related to other factors, such as duration of ischemia and long-term left ventricular pressure and/or excessive intravascular volume. BNP, but not ANP, was shown to be a mortality risk predictor in patients undergoing CABG.
Using UML models for performance calculation

UML models will quite often lack some of the detail required for performance estimation. This paper explores some ideas to automatically remove layers of indirection and abstraction that hinder straightforward performance evaluation of UML models. Using UML makes the information accessible (since it is a widely know language) and reduces the extra effort (and overhead) needed for performance engineering. Much information normally present in UML models can be used for performance engineering, and adding extra information is relatively straightforward. Using these ideas in a CASE tool allows automatic performance calculations based on models at varying levels of detail . And by breaking-down performance requirements at each design refinement they become tangible and more meaningful during development and testing.
INSTRUMENTAL VARIABLE ESTIMATION IN BINARY MEASUREMENT ERROR MODELS by

J. S. Buzas Department of Statistics North Carolina State University Raleigh, NC 27695 We describe an approach to instrumental variable estimation in binary regression measurement error models. The method entails constructing an approximate mean model for the binary response as a function of the measured predictor, the instrument and any covariates in the model. Estimates are obtained by exploiting relationships between various regression parameters, just as in linear instrumental variable estimation. In the course of deriving the approximate mean model we present an alternative characterization of instrumental variable estimation in linear measurement error models.
An improved progressive mesh and streaming transmission strategy

The transmission of large scale Web 3D scene is one of the most valuable problems for the researchers in computer graphics. As the increase of the size of Web 3D scene data and its complicity organizations, the limited bandwidth of current network can hardly meet the requirement in transmissions of these data, so it become a bottleneck in Virtual Reality, Video Games, Digital Museum, ect. This paper introduces an improved algorithm which is proposed to get better performance of progressive mesh and streaming transmission data. By using this method we can generate smaller size of base model and the respective streaming information. In addition, we decimate some of data by using Huffman encoding to get the final binary file, as while, the data was encrypted.
Collision Free Intelligent Bloom Join Filters

In operation research, there is no single method available for solving all optimization problems. Hence a number of techniques have been developed for solving different types of optimization problems. Optimization is the act of obtaining the best result under given circumstances. The ultimate goal of optimization is either to minimize the efforts required or to maximize the desired benefit (5). Query Optimization is one of the optimization problems in database management system. It is a process of determining the most efficient way to execute a given query by considering the possible query plans. The approach suggested in the paper is mainly focused on join operation of the query. Previous work done was based on semi-join approach for query optimization but a semi-join needs more local processing such as projection and higher data transmission. To improve the previous approach, the filter based approach is utilized. The evaluation of filter is done by considering the collisions occurred, using perfect hash function and using sets of filters. Paper focuses on importance of optimization and Intelligent Bloom Join filter approach for data reduction in query optimization. Keywords—Optimization; Query Optimization; Bloom Join; Bloom Filter.
Object classification using CNN for video traffic detection system

Recently, a lot of research on the use of big data is made, and this paper was aimed to perform classification experiments using CNN for the detected object collected from traffic detectors. In addition the experimental results were compared with the HOG descriptor that is commonly used in existing pedestrian and object classification and wavelet, texture and descriptor that are used in the road surface condition classification. According to the results after applied to the collected RVFTe-10 data, the performances of HOG SVM and CNN were excellent by showing 99.9% and 99.5% respectively.
Identification of Mimotope Peptides Which Bind to the Mycotoxin Deoxynivalenol-Specific Monoclonal Antibody

ABSTRACT Monoclonal antibody 6F5 (mAb 6F5), which recognizes the mycotoxin deoxynivalenol (DON) (vomitoxin), was used to select for peptides that mimic the mycotoxin by employing a library of filamentous phages that have random 7-mer peptides on their surfaces. Two phage clones selected from the random peptide phage-displayed library coded for the amino acid sequences SWGPFPF and SWGPLPF. These clones were designated DONPEP.2 and DONPEP.12, respectively. The results of a competitive enzyme-linked immunosorbent assay (ELISA) suggested that the two phage displayed peptides bound to mAb 6F5 specifically at the DON binding site. The amino acid sequence of DONPEP.2 plus a structurally flexible linker at the C terminus (SWGPFPFGGGSC) was synthesized and tested to determine its ability to bind to mAb 6F5. This synthetic peptide (designated peptide C430) and DON competed with each other for mAb 6F5 binding. When translationally fused with bacterial alkaline phosphatase, DONPEP.2 bound specifically to mAb 6F5, while the fusion protein retained alkaline phosphatase activity. The potential of using DONPEP.2 as an immunochemical reagent in a DON immunoassay was evaluated with a DON-spiked wheat extract. When peptide C430 was conjugated to bovine serum albumin, it elicited antibody specific to peptide C430 but not to DON in both mice and rabbits. In an in vitro translation system containing rabbit reticulocyte lysate, synthetic peptide C430 did not inhibit protein synthesis but did show antagonism toward DON-induced protein synthesis inhibition. These data suggest that the peptides selected in this study bind to mAb 6F5 and that peptide C430 binds to ribosomes at the same sites as DON.
Catheter ablation of frequent ventricular tachycardia after interventional left ventricular restoration with the Revivent‐Transcatheter™‐system

The Revivent‐TC™‐system is able to restore LV volumes in patients with severe ischemic cardiomyopathy. We are presenting a case report of successful implantation of the Revivent‐TC™‐system, but postprocedural development of sustained VT. This case report is presenting one way to successfully treat patients with postprocedural frequent VT.
BUILDING AN ADVANCE DOMAIN ONTOLOGY MODEL OF INFORMATION SCIENCE (OIS)

The paper describes the process of modelling domain knowledge of Information Science (IS) by creating an Ontology of Information Science domain (OIS). It also reports on the life cycle of the ontology building process using Methontology, based on the IEEE standard for development software life cycle process, which mainly consists of: specification, conceptualization, formalization, implementation, maintenance and evaluation. The information resource used in acquisition and evaluation has been obtained from Information Science. The conceptualization consists of identifying IS concepts and grouping them into a hierarchy tree based on a faceted classification scheme. The OIS ontology is formalized by using the ontology editor Protege to generate the ontology code. The achieved result is OIS ontology which has fourteen facets: Actors, Method, Practice, Studies, Mediator, Kinds, Domains, Resources, Legislation, Philosophy & Theories, Societal, Tool, Time and Space. The model is evaluated using ontology quality criteria to check the ontology’s usefulness, and how it could be transferred into application ontology for Information Science education.