Miracle-dz/UniFAR-FTUs huggingface dataset:
Information Security Architecture-Context Aware Access Control Model for Educational Applications

Information is an asset for every organization. The constantly increasing nature of computers systems on the functioning of organizations results in concerns about the threats to the information usage. Security requirements approached at the organization level initiate the need for models that capture the organizational and distributed aspects of information usage. Such models have to express organization specific security policies and internal controls aiming to protect information against unauthorized access and modification and against usage of information for unintended purposes. Existing systems follows Role Based Access Control models (RBAC) which are application dependent and whether they address the problems posed by mobile devices such as note books, Personal Digital Assistants (PDA), is an open question. What today’s organization require is flexible, authentication on necessity, context aware access control and enforcement of dynamic authorization? In this paper we propose Context Aware Information Security Architecture to fulfill the organization’s security needs.
Error suppressing encode logic of FCDL in 6-bit flash A/D converter

A 6 bit, 320 Ms/s BiCMOS flash A/D converter was fabricated using a new Folded Cascoded Differential Logic (FCDL). This FCDL reduces sparkle code errors caused by comparator metastability, and improves encoder operation speed. The measured error rates of a chip implemented in a 0.7 /spl mu/m BiCMOS was less than 10/sup -10/ times/sample and the chip consumes only 505 mW.
FORMAL VERIFICATION OF PROGRAMS FOR ABSTRACT REGISTER MACHINES

Abstract register machines are an important formal model of computation widely used for the modeling of many classes of computational algorithms and the analysis of their complexity. Despite the signi cance of this model, formal veri cation of general-purpose programs for abstract register machines has not been properly covered in the existing literature. To ll this gap, we provide a formal speci cation for one version of abstract register machines the random-access machine invented by Aho, Hopcroft and Ullman. Executions of this machine are formalized by a transition system in the language of the veri cation system PVS. We also specify in PVS a simple search program for this architecture and its correctness property. The property is proved using the interactive proof checker of PVS. We were able to prove not only the functional correctness of the program, but also its time complexity, which shows the novelty of our approach.
Accelerating the analyses of 3‐way and 4‐way PARAFAC models utilizing multi‐dimensional wavelet compression

Parallel factor analysis (PARAFAC) is one of the most popular methods for evaluating multi‐way data sets, such as those typically acquired by hyphenated measurement techniques. One of the reasons for PARAFAC popularity is the ability to extract directly interpretable chemometric models with little a priori information and the capability to handle unknown interferents and missing values. However, PARAFAC requires long computation times that often prohibit sufficiently fast analyses for applications such as online sensing. An additional challenge faced by PARAFAC users is the handling and storage of very large, high‐dimensional data sets. Accelerating computations and reducing storage requirements in multi‐way analyses are the topics of this manuscript. This study introduces a data pre‐processing method based on multi‐dimensional wavelet transforms (WTs), which enables highly efficient data compression applied prior to data evaluation. Because multi‐dimensional WTs are linear, the intrinsic underlying linear data construction is preserved in the wavelet domain. In almost all studied examples, computation times for analyzing the much smaller, compressed data sets could be reduced so much that the additional effort for wavelet compression was more than recompensated. For 3‐way and 4‐way synthetic and experimental data sets, acceleration factors up to 50 have been achieved; these data sets could be compressed down to a few per cent of the original size. Despite the high compression, accurate and interpretable models were derived, which are in good agreement with conventionally determined PARAFAC models. This study also found that the wavelet type used for compression is an important factor determining acceleration factors, data compression ratios and model quality. Copyright © 2006 John Wiley & Sons, Ltd.
Microwave De-embedding : From Theory to Applications

This groundbreaking book is the first to give an introduction to microwave de-embedding, showing how it is the cornerstone for waveform engineering. The authors of each chapter clearly explain the theoretical concepts, providing a foundation that supports linear and non-linear measurements, modelling and circuit design. Recent developments and future trends in the field are covered throughout, including successful strategies for low-noise and power amplifier design. This book is a must-have for those wishing to understand the full potential of the microwave de-embedding concept to achieve successful results in the areas of measurements, modelling, and design at high frequencies. With this book you will learn: The theoretical background of high-frequency de-embedding for measurements, modelling, and design Details on applying the de-embedding concept to the transistor’s linear, non-linear, and noise behaviour The impact of de-embedding on low-noise and power amplifier design The recent advances and future trends in the field of high-frequency de-embedding Presents the theory and practice of microwave de-embedding, from the basic principles to recent advances and future trends Written by experts in the field, all of whom are leading researchers in the area Each chapter describes theoretical background and gives experimental results and practical applications Includes forewords by Giovanni Ghione and Stephen Maas
Two sides of the same coin: Session Types and Game Semantics

Game semantics and session types are two formalisations of the same concept: message-passing open programs following certain protocols . Game semantics represents protocols as games , and programs as strategies ; while session types specify protocols, and well-typed π -calculus processes model programs. Giving faithful models of the π -calculus and giving a precise description of strategies as a programming language are two difficult problems. In this paper, we show how these two problems can be tackled at the same time by building an accurate game semantics model of the session π -calculus. Our main contribution is to fill a semantic gap between the synchrony of the (session) π -calculus and the asynchrony of game semantics, by developing an event-structure based game semantics for synchronous concurrent computation. This model supports the first truly concurrent fully abstract (for barbed congruence) interpretation of the synchronous (session) π -calculus. We further strengthen this correspondence, establishing finite definability of asynchronous strategies by the internal session π -calculus. As an application of these results, we propose a faithful encoding of synchronous strategies into asynchronous strategies by call-return protocols, which induces automatically an encoding at the level of processes. Our results bring session types and game semantics into the same picture, proposing the session calculus as a programming language for strategies, and strategies as a very accurate model of the session calculus. We implement a prototype which computes the interpretation of session processes as synchronous strategies.
Mind-Based Technologies. A Framework for Producing Personalized Psychological Effects

Mind-Based Technologies are technologies that can facilitate immediate psychological effects, such as emotion, cognition, presence and flow in their users as they are consuming multimodal information. With personalization technologies one may vary the content and way of presenting information to enable targeted and personalized psychological effects. The key to producing such effects is to be able to model the users and predict how they may react to a particular content and way of presenting information (modalities, visual layouts, ways of interaction, user interface controls) within a certain context, task, communication device and user interface. Despite obvious complexities empirical evidence suggests that the way of presenting information to users with certain psychological profiles has predictable effects. Hence, one may propose new types of system designs that would tend to produce desired types of psychological effects for individual users by varying the way of presenting information. One possible application area of Mind-Based Technologies is decision-making systems for military use.
Reliably mapping a robot's environment using fast vision and local, but not global, metric data

A robot operating in an initially unknown environment must, in general, acquire and maintain information about that environment in order to perform tasks which involve repeated returns to particular locations, or reliable traversal of particular routes. Many practical applications of mobile robots in industrial and office environments require these capabilities. A basic prerequisite for this mapping functionality is the ability to reliably determine where the obstacles in the environment actually are. This thesis describes a system which provides reliable mapping functionality in a mobile robot, obtaining reliable information from cheap and readily available sensors without requiring great computational resources. The basic sensor used to detect obstacles in the environment is a camera. Using assumptions about the environment which are quite reliable in common office environments, the system is able to reliably detect and estimate the range to visible obstacles at very little computational expense. The system uses this data to build up a graph-structured representation of the environment. While the system uses local metric information, it does not assign a single set of global coordinates to any of the obstacles it detects, for two reasons. Firstly, assignment of global coordinates consistent with all observations may be impossible. For instance, if there are ramps in the environment, the measured distances between obstacles will not be consistent with any embedding into a plane. Secondly, global coordinates are not actually required for any task the robot must actually perform. However, purely local metric data is reliable enough to be of substantial use, and there are sometimes few alternatives to using it. Finally, the thesis describes parallels between the internal operations of the navigation system and what is believed to be a spatial memory system operating in the brains (specifically, the hippocampi) of rodents, and discusses how more a "brain-like" system might have certain advantages over the one described here. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
Predicting student performance with neural networks

In recent years, Neural Networks have seen widespread and succesful implementations in a wide range of data mining applications, often surpassing other classifiers. This study aims to investigate if Neural Networks are a fitting classifier to predict student performance from Learning Management System data in the context of Educational Data Mining. The dataset used for this study is a Moodle log file containing log information about 4601 students over 17 undergraduate courses. To assess the applicability of Neural Networks, we compare their predictive performance against six other classifiers on this dataset. These classifiers are Naive Bayes, k-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression and will be trained on data obtained during each course. The features used for training originate from LMS data obtained during the length of each course, and range from usage data like time spent on each course page, to grades obtained for course assignments and quizes. After training, the Neural Network outperforms all six classifiers in terms of accuracy and is on par with the best classifiers in terms of recall. We also assessed the effect course predictors have on predictive performance by leaving out the course identifiers in the data. This does not affect predictive performance of the classifiers. Furthermore, the Neural Network is trained on individual course data to assess difference in classification performance between courses. Results show that half of these course classifiers outperform generally trained classifiers. The importance of individual predictors used for classification was also investigated, with previously obtained grades contributing most to succesful predictions. We can conclude that Neural Networks outperform the six other algorithms tested on this dataset and could be succesfully used to predict student performance.
A Random Walking Recommendation Algorithm Based on Conditional Restricted Boltzmann Machine in Trust Network

In order to improve the accuracy of the recommendation system and solve the data sparsity problem, this paper proposes a random walking recommendation algorithm based on conditional restricted Boltzmann machine in trust network, namely CRBM_PrTW. The algorithm fills the missing data in training data by utilizing the conditional restricted Boltzmann machine to improve the accuracy of similarity calculation, which effectively solves data sparsity problem. And on this basis, the comprehensive weight of the credibility, similarity and the trust value utilized as trust level has been implemented to random walking algorithm. The experimental result on the Epinions dataset demonstrates that our method can provide better recommendation result in terms of evaluation metrics when compared with the existing methods.
Continuous Dynamic Optimization

In this chapter we introduce a new ant colony algorithm aimed at continuous and dynamic problems. To deal with the changes in the dynamic problems, the diversification in the ant population is maintained by attributing to every ant a repulsive electrostatic charge, that allows to keep ants at some distance from each other. The algorithm is based on a continuous ant colony algorithm that uses a weighted continuous Gaussian distribution, instead of the discrete distribution, used to solve discrete problems. Experimental results and comparisons with two competing methods available in the literature show best performances of our new algorithm called CANDO on a set of multimodal dynamic continuous test functions. To find the shortest way between the colony and a source of food, ants adopt a particular collective organization technique (see figure 1). The first algorithm inspired from ant colonies, called ACO (refer to Dorigo & Gambardella (1997), Dorigo & Gambardella (2002)), was proposed as a multi-agent approach to solve hard combinatorial optimization problems. It was applied to discrete problems like the traveling salesman, routing and communication problems.
A comparative study of laravel and symfony PHP frameworks

With the current explosion of Information Systems, the market offers a wide range of interesting technological solutions. Yet, this does not mean adopting a technology without considering its impact on the existing information system and user expectations. It is recommended to identify and implement the technological solutions most suited to the Information Systems strategy. Therefore, new methods are emerging and design tools are still evolving; the PHP Frameworks are part of it, which open up new perspectives in terms of information system enrichment. In this context, this paper focuses on the elaboration of a comparative study between Laravel, symfony framworks, which are the most popular PHP frameworks. Thus, it provides an effective comparison model that merges seven dimensions: Features, Multilingual, System requirements, Technical architecture, Code Organization, Continuous Integration (CI) and finally Documentation and learning curve dimension. Results show that our model can be beneficial for IT project developers to select the suitable PHP Framework.
Reliability performance index probability distribution analysis of bulk electricity systems

System reliability performance is usually based on average customer interruption indices. The average values are valuable information, but provide only a single customer risk dimension without the underlying probability distributions. The average annual indices give no insight on how reliability may vary from year to year as a result of the random behavior of a bulk electric system. Reliability index probability distributions, therefore, provide additional valuable information and a more complete understanding of composite power system behavior. A significant advantage when utilizing sequential Monte Carlo simulation in bulk electric system reliability analysis is the ability to provide reliability index probability distributions in addition to the expected values of their indices. This paper illustrates the development of probability distributions for bulk electric system reliability performance indices using sequential simulation. The results obtained using the developed software show that the system performance index probability distributions have unique characteristics that are basically dependent on the system topology, operating philosophy and conditions. System conditions such as the peak load level and system reinforcement options have significant impacts on the performance index probability distribution characteristics. The basic concepts and their application in composite power system reliability evaluation are illustrated by application to a small practical test system
Research on Computer Aided Index System Establishment of SWOT

SWOT analysis is a classical analysis tool of competitive intelligence. The affect depends on whether all internal and external factors were analyzed. The construction of index system of SWOT analysis requires a lot of human resource. To resolve this issue, a method for computer aided index system establishment of SWOT analysis is proposed. This method can be automated extract and classify the key words. And the index system of SWOT analysis is generated by words classified information contained in words categories. In addition, the method can generate suggestions of indexes weight according to the amount, frequency and timing of key words contained in their classes.
Non-linear Function Optimization Using a Genetic Local Search Suitable for Parallelization

In this paper, we propose a new genetic local search named GLSDC (a Genetic Local Search with distance independent Diversity Control) by extending the basic idea of DIDC (a genetic algorithm with Distance Independent Diversity Control) to coarse grained parallelization. GLSDC employs a local search method as a search operator. GLSDC also uses genetic operators, i.e., a crossover operator and a generation alternation model. However, in GLSDC, the crossover operator is not used as a search operator, but is used only for converging the population. GLSDC has an ability to find multiple optima simultaneously by stacking good individuals that have been found by the local search. Finding multiple optima is often required when we try to solve real world problems. The effectiveness of the proposed method is verified through numerical experiments on several benchmark problems.
Robust stabilization of uncertain dynamic time-delay systems with unknown bounds of uncertainties

The problem of robust stabilization for a class of uncertain dynamic systems with state and multiple delayed state perturbations is considered. It is assumed that each perturbation of state and time-delayed state is bounded by a linear function with unknown gains. For such uncertain systems, two classes of continuous state feedback controllers are proposed in this paper. Based on Lyapunov stability theory and Lyapunov-Krasovskii function, a robust adaptive feedback controller is proposed. By employing Razumikhin lemma, we also develop a novel nonlinear feedback controller. Both the controllers are completely independent of the time delays and can render the closed-loop systems globally stable in the sense of uniform ultimate boundedness. Finally, a numerical example is given to demonstrate the validity of the results.
A vibrotactile transducer and its applications in the study of perception

Vibrotactile signals are ubiquitous in everyday life, occurring both when manipulating objects and operating power tools. To bring haptics to ambient systems (defined as being embedded in everyday objects), the vibrotactile channel presents itself as a perceptually and energetically efficient method of conveying haptic information. Mobile phones are by far the most popular haptic-enabled devices. Yet, they are often equipped with common vibration motors of narrow-bandwidth capability. A voice-coil vibrotactile transducer design has been demonstrated to be high-bandwidth and capable of functioning under the same enclosure-vibration paradigm. The transducer was modeled by converting its mechanical free-body diagram into equivalent electrical circuits. The experimentally obtained transfer function was combined with the function established theoretically to obtain the impedance expression for each parameter. Using the aforementioned actuator, mock cellphones were made for vibrotactile perception experiments. Employing pulsed vibration signals to combat adaptation effects, experiments were performed to study the effect of weight and underlying vibration frequency on perceived strength. Results show that for the same measured acceleration on the device, a heavier box is perceived to vibrate with greater strength. Furthermore, signals with higher underlying frequency are perceived to be weaker for the same measured acceleration. The results obtained from ungrounded, vibrating objects are consistent with previous studies using grounded devices. The findings suggest the need for a systematic correction rule that assists cellphone designers in how to modify the device’s vibratory characteristics according to its weight and the operating frequency. An ambient haptic device is implemented to synthesize haptic cues resulting from an object rolling down and impacting the inside wall of a tubular cavity. When an object rolls or slides, a variety of cues become available for the estimation of its location inside the cavity. These cues are related to the dynamics of an object subjected to the laws of physics such as gravity and friction. Perception experiments were conducted, in which participants attempted to discriminate among three virtual tubes of different lengths after making the virtual ball roll down. The results support the hypothesis that the subjects mastered the laws related to the dynamics of objects under the influence of gravity and used them to perceive the length of the invisible cavities.
CONTROL OF BACK-TO-BACK VOLTAGE SOURCE CONVERTER

Voltage Source Converter High Voltage Direct Current (VSC-HVDC) systems have the ability to control rapidly the transmitted active power and independently exchange a reactive power with transmissions systems. The present work investigates the modeling and control design of a high-voltage direct current (HVDC) transmission system based on three-level NPC multilevel converters with Subharmonic PWM modulation technique (SH-PWM), and a feed-forward decoupled current control strategy. By using this control strategy, not only the active and reactive power of HVDC can be controlled independently but also the dynamic responded time can be shortened. Simulation studies of a 200 MW/±100 kV back to back VSC-HVDC system connecting two asynchronous ac networks are presented to confirm the satisfactory performance of the proposed system under active and reactive power variations from single phase to ground and three phases to ground fault conditions.