Miracle-dz/UniFAR-FTUs huggingface dataset:
Cryptography and network security

William Stallings' Cryptography and Network Security: Principles and Practice, 5e is a practical survey of cryptography and network security with unmatched support for instructors and students. In this age of universal electronic connectivity, viruses and hackers, electronic eavesdropping, and electronic fraud, security is paramount. This text provides a practical survey of both the principles and practice of cryptography and network security. First, the basic issues to be addressed by a network security capability are explored through a tutorial and survey of cryptography and network security technology. Then, the practice of network security is explored via practical applications that have been implemented and are in use today. An unparalleled support package for instructors and students ensures a successful teaching and learning experience. The new edition has been updated to include coverage of the latest topics including expanded coverage of block cipher modes of operation, including authenticated encryption; revised and expanded coverage of AES; expanded coverage of pseudorandom number generation; new coverage of federated identity, HTTPS, Secure Shell (SSH) and wireless network security; completely rewritten and updated coverage of IPsec; and a new chapter on legal and ethical issues.
A novel pressure balanced microfluidic valve

A microfluidic valve capable of driving fluids under high (up to 10 atm) pressure is presented. The valve actuation is based on a principle of pressure compensation by means of an air chamber that supports a moving membrane. The air balances the fluid pressure, and then an electrostatic actuator can open the valve. This valve is designed with valve arrays in mind. In such configurations, it is useful for analysis, biology and medicine applications.
Dual frequency bi-orthogonally polarized antenna for GPS applications

The design, fabrication and testing of dual frequency bi-orthogonally polarized microstrip patch antenna structures for GPS receivers are studied. The considered configurations include orthogonally placed coplanar rectangular patches, orthogonally stacked rectangular patches and stacked square patches. The designed antennas are fabricated according to the optimized parameters, tuned in the presence of radomes and then measured for their bandwidth, input matching, radiation pattern, gain, and polarization characteristics. Comparisons of these different configurations are presented together with theoretical and experimental results.
Computation and action under bounded resources

We define and implement a model of rational action for automated reasoning systems that makes use of flexible approximation methods and inexpensive decision-theoretic procedures to determine how best to solve a problem under bounded computational resources. The model provides metareasoning techniques which enable a reasoning system to balance the costs of increased delays with the benefits of better results in a decision context. The decision-theoretic metareasoning techniques can be applied to a variety of computational tasks. We focus on the use of metalevel decision procedures to control complex probabilistic reasoning at the base level. The approach extends traditional decision analyses to autoepistemic models that represent knowledge about problem solving, in addition to knowledge about distinctions and relationships in the world. We found that it can be valuable to allocate a portion of costly reasoning resources to metalevel deliberation about the best way to use additional resources to solve a decision problem. After reviewing principles for applying multiattribute utility theory to the control of basic computational procedures, we describe how these principles can be used to control probabilistic reasoning. In particular, we examine techniques for controlling, at run time, the tradeoff between the complexity of detailed, accurate analyses and the tractability of less complex, yet less accurate probabilistic inference. We present the architecture and functionality of a system named Protos that embodies these principles for making high-stakes decisions under time pressure. We study the behavior of Protos on decision problems in critical-care medicine. Finally, we move beyond our focus on time constraints to consider the constraints on decision-theoretic reasoning posed by the cognitive limitations of people seeking insight from automated decision systems.
'Stealth' filtering with reduced order observations

Communications resources in modern automated systems have to be shared between different users and purposes. A process filtering task performed by a dedicated processor and integrating measurements delivered by a remote information source may in particular be subject to data flow limitations. The authors examine some problems related to these measurement data reductions in a linear filtering algorithm, focusing on linear observation compression schemes. A global approach gives a sufficient algebraic condition to admit reduced-order measurement vectors. Compression policies, dynamically optimized under specific criteria, are proposed. Remaining generic problems and possible extensions of such an approach are also discussed. <<ETX>>
Engineering the Knee Meniscus

Abstract The knee meniscus was once thought to be a vestigial tissue, but is now known to be instrumental in imparting stability, shock absorption, load transmission, and stress distribution within the knee joint. Unfortunately, most damage to the meniscus cannot be effectively healed by the body. Meniscus tissue engineering offers a possible solution to this problem by striving to create replacement tissue that may be implanted into a defect site. With a strong focus on structure-function relationships, this book details the essential anatomical, biochemical, and mechanical aspects of this versatile tissue and reviews current meniscus tissue engineering strategies and repair techniques. We have written this text such that undergraduate students, graduate students, and researchers will find it useful as a first foray into tissue engineering, a cohesive study of the meniscus, or a reference for meniscus engineering specifications. Table of Contents: Structure-Function Relationships of the Knee Meniscus / P.
To Improve the Data Security of Cloud in Cloud Computing by using Digital Signature with RSA Encryption Algorithms

Cloud computing has showed up as a popular design in managing world to back up managing large volumetric details using cluster of commodity computer systems. It is the newest effort in offering and managing computing as a service. Cloud Computing has become a boon for an IT industry nowadays. It is like a next stage platform in the evolution of Internet. It provides a platform with an enhanced and efficient way to store data in the cloud i.e. server with different range of capabilities and application. It provides an easy way of accessing one’s personal file or data and use application without installing it on machines by just having Internet access. We can have efficient computing by centralized data storage, processing and bandwidth. Example: Yahoo, Gmail, Amazon etc. are good cloud service providers. So all we need is to have Internet access then we can send mail and can access our account from any part of the world. The server and the email management software is installed on the cloud and managed by service providers. Providing an easy access to work and business still it has a major problem and threat i.e. “DATA SECURITY”. In this Research Paper, we have tried to assess Cloud Storage Methodology and Data Security in cloud by the Implementation of digital signature with RSA algorithm.
A hybrid intelligent system architecture for utility demand forecasting

An architectural framework is proposed for the design and construction of hybrid load forecasting systems for electric utilities. This framework consists of the intelligent techniques of artificial neural networks, fuzzy logic, knowledge-based and case-based reasoning. The knowledge-based system is the core of the integration since it is used to supervise the operations of the other intelligent techniques. Experts can also represent their knowledge in rules to refine and validate the results obtained from the other modules of neural networks and case-based reasoning. The framework was implemented on an object oriented real-time expert system shell G2 with General Diagnostic Assistant (GDA) and NeurOn-Line. In this environment, the intelligent techniques are encapsulated in blocks, which communicate with each other via data paths. The blocks can interact with rules in the knowledge base via rule-terminals. Procedures can be invoked by rules.
VLSI neural systems and circuits

ULSI technology can potentially fabricate 10/sup 9/ devices on a single die. Numerous applications in the areas of vision, speech, sensor processing, nonlinear control, and learning exist for such integrated circuits, but first new fault-tolerant architectures need to be developed. One approach is to design systems that are inspired by the human brain. These architectures operate in a highly parallel fashion to solve extremely complex problems with a high degree of fault tolerance. A description is given of the pulsed neural cell, which is a compact analog neural circuit. The pulsed neural cell performs four-quadrant multiplication, refreshes the stored analog voltages, and allows linear and nonlinear outputs. This cell allows the building of massive and highly complex neural implementations. <<ETX>>
Automatic aircraft recognition and identification

Aircraft recognition remains a challenging problem despite a great deal of effort to automate the recognition process. The majority of the aircraft recognition methods assume the successful isolation of the aircraft silhouette from the background, and only a few have actually addressed real world concerns, such as occlusion, clutter and shadows. This thesis presents an automatic aircraft recognition system, which shows improved performance with complex images. This system assumes from the start that the image could possibly be degraded, contain occlusions, clutter, camouflage, shadows and blurring. It is designed to tolerate and overcome the degradations at various analysis stages. The first part of the thesis focuses on the generic aircraft recognition problem using a generic description of aircraft parts and the geometric relationships that exist among them. The system implements line groupings in a hierarchical fashion, progressively leading towards a generic aircraft structure. A voting scheme is used to consolidate line groupings belonging to an aircraft while discouraging the formation of spurious line groupings. The aircraft identification process is carried out in the second part of the thesis, where the generically recognised aircraft is matched to model candidates. Model matching is carried out via pixellevel silhouette boundary matching. The system is tested on numerous real aircraft, scaled-down model aircraft and non-aircraft images with adverse image conditions. The developed system achieves a recognition rate of 84% at a false alarm rate of 7% on real aircraft images, and an correct matching rate of about 90% and a false matching rate of 7% on the generically recognised aircraft from model aircraft images.
Mastering musical meaning: images as interpretive resources in multimodal music texts

In a Music listening examination in the final year of secondary schooling in New South Wales, Australia, successful students use a range of images – graphic notation, diagrams, tables and graphs – as well as language, to build meaning about the concepts of music. Little is known about the ways in which language and image can be deployed as interpretive resources to construct musical knowledge in this high stakes examination. To address this gap, theories of Systemic Functional Linguistics and Systemic Functional Multimodal Discourse Analysis are utilised to make the disciplinary and semiotic requirements of this examination clearer. In this article, five types of images are described and classified, analysing ways in which images can construct disciplinary meanings about concepts of music, principles of composition, and providing a range of perspectives on musical time. Analysis of affordances of images and language, as well as intersemiosis, provides some insights into the hidden curriculum of Music as well as the value of images as a significant interpretive resource.
BioPSE Case Study: Modeling, Simulation, and Visualization of Three Dimensional Mouse Heart Propagation

The Bioelectric Problem Solving Environment (BioPSE) is a flexible software architecture, specifically designed for the interactive investigation of large-scale bioelectic field problems. CARDIOWAVE is a high-performance software system designed for the simulation of cardiac propagation. By bridging together the BioPSE and CARDIOWAVE software systems, we have developed an integrated system for the modeling, simulation, and visualization of three dimensional mouse heart propagation. K eywords: List 5-8 keywords (e.g. BioPSE; CARDIOWAVE; Mouse Heart; Problem Solving Environment)
REAL HUMAN FACE DETECTION FOR SURVEILLANCE SYSTEM USING HETEROGENEOUS SENSORS

Face detection algorithms are used to detect the human in various industry fields. A typical face detection algorithm such as Haar Feature-based Cascade Classifier gives us an easier way to detect human face. It consists of several classifiers which contain complicated arithmetic operations. Several classifiers constitute the cascade which can detect each element of human face. The more cascades are contained in the algorithm to detect elements of human face, the more it takes a time to detect human face. The previous cascade hardly recognize real human, since previous cascade processes only one source from image source. In this paper, we present a new cascade method for human face detection which exploits several classifiers for data not only from image source but also various heterogeneous sensors. Cascades consist of various sensors based on tuple data type could be operated quickly. It provides more accuracy of real human face detection, reduces the number of classifier for high speed processing in real-time.
Collaborative concept mapping activities in a classroom scenario

The aim of this study is to test collaborative concept mapping activities using computers in a classroom scenario and to evaluate the possibilities that Elkar-CM offers for collaboratively learning non-technical topics. Elkar-CM is a multi-lingual and multi-media software program designed for drawing concept maps (CMs) collaboratively. Concept mapping is a widely accepted technique that promotes meaningful learning. Graphically representing concepts of the learning domain and relationships between them helps students integrate new knowledge into their current cognitive structure. This study was carried out with Social Education degree students at the University of the Basque Country (UPV/EHU). The experiment included two learning activities. First, all students collaboratively constructed in the classroom a CM on the subject of Moral Development. Second, students were organised into groups to complete the CM generated in the first part.
ESTIMATION OF EGG WEIGHT BY MACHINE VISION AND NEURAL NETWORKS TECHNIQUES

Egg weight measurement is one of the most important parameters in marketing this product. Information regarding egg weight is not only vital for grading systems based merely on weight, but it is also necessary for assessing quality indices such as yolk-albumen ratio, shell thickness and hatchability. In the present study a machine vision system combined with artificial neural network technique was used for estimating egg weight. The system hardware consists of a CCD camera, a capture video, an illumination system and a mirror. As an egg is introduced into the frame, two perpendicular images are created. These images are then processed in MATLAB and pixel data corresponding to each image edge is extracted. Once the center of gravity of each image edge is obtained, twelve size features can be calculated for each image. These features are then classified into three categories named as input vectors (1-3). Each input vector along with its real weight data (measured) is exported to three parallel training algorithms of a Multi Layer Perceptron (MLP) Network. The training algorithms are variable learning rate (MLP-GDX), resilient back propagation (MLP-RP) and scaled conjugate gradient (MLP-SCG). These training algorithms were optimized to estimate egg weight. Evaluation results showed that MLP-SCG was superior to the other two algorithms in estimating egg weight at high accuracy (R=.96). In other words, MLP-CSG was capable of egg weight estimation with an absolute error of no more than 2.3g for the average egg size of 60 g.
Automatic System for Providing Security Services in the Internet of Things applications over Wireless Sensor Networks

This paper describes an automatically determination process of the security services for products and services on the Internet of Things. This process has as inputs the service context, the legislative diversity and the information involved among others. Considering the resources limitations in a Wireless Sensor Networks and the already mentioned inputs, it is possible to find the best solution to apply in each specific case. We will introduce the "Utility Matrix" as a main concept to link all interests of stakeholders regarding their security needs and the legal imperatives. The final solution has been implemented with an expert system. The process outputs are composed by several products as a security policy for the service; a protected data certification, and an effective tool to simulate and evaluate impacts over new services or when service conditions or laws change. Challenges to research over new technical solutions needs can also be obtained. This proposal will connect the Industrial, Judicial and Technological areas working together to obtain trustworthy certifications for all stakeholders. The results have been evaluated in a real scenario made up of a Wireless Sensor Network, over middleware service oriented platform in the framework of ? AWARE project? and the expert system connected to the platform in order to configure the security services.
Intelligent adaptive control using multiple models

We develop a new intelligent adaptive control algorithm that is applicable to systems with large parameters variations and multiple operating modes. It uses a set of models, called the dynamic model bank, that guides the adaptation process. The dynamic model bank summarizes the parameters of the models that successfully approximate the plant. The model bank is automatically created and updated and does not call for an initial set of models. It uses a soft switching mechanism that provides a smooth transition from an interpolative to a pure "hard" switching scheme between the models in the bank. We also demonstrate the advantage of using this approach on several examples considering the control of systems with large parameter variations.
Complex atom transform‐based scheme for detection of subsynchronous oscillations

Summary A novel scheme based on the complex atom transform (CAT) for detection of subsynchronous oscillations (SSO) is proposed in this paper. SSO components and the fundamental are closely spaced in frequency domain causing the possibility of aliasing. Some conventional SSO detection methods share the problems of low mode resolution and slow dynamic response. The CAT has flexible window in time–frequency domain. By adjusting the window of the CAT, the scheme can achieve high multi-mode resolution, desirable accuracy and fast dynamic response when analyzing multi-mode SSO waveforms to facilitate multi-mode SSO suppression. First step of the scheme is to use the CAT to obtain oscillation frequencies and envelope curves of SSO waveforms. The least squares (LS) method is then deployed to measure initial oscillation amplitude and damping ratio from the envelope curve. The effectiveness and correctness of the proposed scheme are verified with simulations using both an artificially generated signal and the IEEE First Benchmark Model. Compared with the conventional methods, such as the continuous wavelet transform (CWT), the scheme has higher mode resolution, more desirable accuracy and faster dynamic response. In addition, the scheme is simple and the computation burden is low; thus, it is easy to be implemented for online application. Moreover, the CAT-based scheme can also be used for synchrophasor measurements; hence, the scheme can be integrated into phasor measurement units (PMU), which are installed in generator terminals. Copyright © 2013 John Wiley & Sons, Ltd.
Enhanced Anonymous Tag Based Credentials

A scheme for anonymous tag based anonymous credentials is enhanced. Different from existing zero knowledge proof based anonymous credential schemes that require numbers of challenges and responses between verifiers and credential holders, the anonymous tag based scheme requires only small number of challenges and responses between verifiers and credential holders. This is because the scheme in this paper can be easily made sound even in environments where verifiers also may behave dishonestly. However, the original scheme has probabilistic features, i.e. verifiers must generate dummy fake challenges; therefore overheads for managing anonymous systems cannot be reduced to the minimum. The enhanced anonymous tag based scheme excludes these probabilistic features from interactions between verifiers and credential holders. The scheme also corrects several design errors included in the original scheme.
Using natural language as knowledge representation in an intelligent tutoring system

Knowledge used in an intelligent tutoring system to teach students is usually acquired from authors who are experts in the domain. A problem is that they cannot directly add and update knowledge if they don’t learn formal language used in the system. Using natural language to represent knowledge can allow authors to update knowledge easily. This thesis presents a new approach to use unconstrained natural language as knowledge representation for a physics tutoring system so that non-programmers can add knowledge without learning a new knowledge representation. This approach allows domain experts to add not only problem statements, but also background knowledge such as commonsense and domain knowledge including principles in natural language. Rather than translating into a formal language, natural language representation is directly used in inference so that domain experts can understand the internal process, detect knowledge bugs, and revise the knowledgebase easily. In authoring task studies with the new system based on this approach, it was shown that the size of added knowledge was small enough for a domain expert to add, and converged to near zero as more problems were added in one mental model test. After entering the no-new-knowledge state in the test, 5 out of 13 problems (38 percent) were automatically solved by the system without adding new knowledge.
Comparison of Two Sensitivity Analysis Methods in a Tumor Growth Model

The aim of this work is to compare two different sensitivity analysis methods, used to investigate the influence of the input parameters of a tumor model on its outcome. The first method uses a Monte Carlo approach to produce Scatterplots and the second uses the Elementary Effects method. Numerical experiments are conducted to highlight the main strengths of each approach.