Miracle-dz/UniFAR-FTUs huggingface dataset:
An end to end security framework for service oriented architecture

Service Oriented Architecture has emerged as effective model and widely acceptable for distributed application development. To meet enterprises dynamics, SOA provides better functionalities but along with better functionality, SOA introduces security challenges which was not an issue with single hop client server architecture. Using SOA standards of distributed deployment, it is difficult for user to ensure that the deployment of each services is appropriately secure. Services are building blocks of SOA which provides better flexibility, reusability, composability making application building activity easier. Consumers are not interested only in SOA functionality but also in quality, reliability, security, performance and so on. SOA enables the design of flexible and modular software applications that can be used in a cross-organization context but these quality requirements may have a negative impact on the security of the software application. Several security standards like web service security standards and WS security policy have been adopted in recent years but these are not sufficient to provide end to end security to the SOA based project. After the survey, the authors found many security techniques have been suggested for SOA in past years but almost all are limited to higher level of service oriented architecture only. In this paper, the authors propose a framework which has enhanced security concepts by security implementation both at higher and lower level. This will increase security at service level. Also, it will be helpful in enhancing the security for service deployment. The authors have also suggested a technique to keep service level agreement time same as existing SLAs, so that secured applications can be used with same response time as provided to the customer for existing services. The proposed implementation will increase the software reliability by securing the application end to end.
Some Principles of Network Calculus Revisited

—Network calculus is a mathematical theory dealing with queueing problems in packet-switched computer networks. It provides algorithms to determine resource requirements of trafﬁc ﬂows using arrival and service curves and describes delays and backlogs in network systems. Network calculus framework is based on a min-plus algebra which allows to transform complex network optimization problems into analytically tractable ones. Recently, a fundamental book on principles, tools, techniques, and applications of network calculus, entitled: Network Calculus. A Theory of Deterministic Queuing Systems for the Internet, has been published by J. Y. Le Boudec and P. Thiran. Here, we refer to it in our reﬁnements of proof of one important theorem and its extension. The objective of this paper is twofold. First, we complete one of basic results regarding a network element that is called in network calculus a greedy shaper. Second, we present also the results of some illustrative calculations and measurements of network service curve. They aim in better understanding of its properties.
Vystadial 2013 – English data

Vystadial 2013 is a dataset of telephone conversations in English and Czech, developed for training acoustic models for automatic speech recognition in spoken dialogue systems. It ships in three parts: Czech data, English data, and scripts. The data comprise over 41 hours of speech in English and over 15 hours in Czech, plus orthographic transcriptions. The scripts implement data pre-processing and building acoustic models using the HTK and Kaldi toolkits. This is the English data part of the dataset.
WIRELESS OFDM SYSTEMS: CHANNEL PREDICTION AND SYSTEM CAPACITY

The general theme of this thesis is orthogonal frequency division multiplexing (OFDM) communications over time and frequency selective fading channels. We propose and study linear prediction techniques for acquiring channel state information (CSI) in OFDM receivers, and we perform an information-theoretic analysis of the performance of OFDM systems. After a review of the generic discrete-time pulse-shaping OFDM system (which comprises conventional cyclic-prefix OFDM systems as a special case), we consider the transmission over a time and frequency selective fading channel. We arrive at an approximate multiplicative system input-output relation in which intersymbol and interchannel interference is neglected. Based on this approximate input-output relation, we propose decision-directed channel predictors that are capable of yielding up-to-date CSI without regular transmission of pilot symbols. We derive the minimum mean-square error (MMSE) predictor and a reducedcomplexity version that allows for an efficient DFT implementation. We also develop adaptive predictors that do not need statistical prior knowledge and can track nonstationary channels. Several applications of channel prediction are discussed, and the excellent performance of the proposed techniques is demonstrated by computer simulations. The second major contribution of this thesis is an information-theoretic analysis of the performance of OFDM systems transmitting over time and frequency selective channels. We study the system capacity of wideband OFDM communications in the absence of CSI at the transmitter and the receiver. Using a codebook that is “peaky” in time and frequency, we show that OFDM can approach the infinite-bandwidth channel capacity. On the other hand, using a “nonpeaky” constant-modulus signaling scheme, we show that the information rate is reduced by a penalty term that is related to the predictability of the fading channel. We quantify the impact of the spread and shape of the scattering function on this penalty term. Finally, we formulate an upper and a lower bound on system capacity and demonstrate by simulations that both bounds are close to the AWGN channel capacity for large ranges of bandwidth and for practically relevant system parameters.
An Optimized Hash Map Algorithm for Each Individual Object and an Efficient Addressing Scheme

We intend to propose in this paper a new data mining technique which uses the concept of hash maps and a static reference array, and will eliminate the need of frequently scanning the dataset. It creates the frequent item sets dynamically at run time. The subsets will be generated as the transactions are read and the counts will be updated. It eliminates the need to compare each transaction with the other transactions because each item will have its own hash map. This method can be used in real time systems as well.
Motion Control of an Underactuated Wheeled Mobile Robot: a Kinematic Input-Output Linearization Approach

This work addresses a trajectory tracking controller for the kinematics of an underactuated wheeled mobile robot, in environments without obstacles, using input-output linearization and the Follow the Carrot algorithm. The use of input-output linearization allows the reduction of error dynamics into a linear order system. In this case, the controller is expressed as a 2× 2 diagonal gain matrix. Different types of trajectories are devised in order to analyse the feasibility of the method. A specific trajectory consisting of a mix of smooth and sharp curvature sections is utilized in order to investigate the sensitivity of the control system in relation to gain variations. Some novelty is claimed by authors in relation to the use of kinematic input-output linearization combined to the Follow the Carrot algorithm.
A Game Theoretic Approach to Autonomous Two-Player Drone Racing

To be successful in multi-player drone racing, a player must not only follow the race track in an optimal way, but also avoid collisions with the opponents. Since unveiling one's own strategy to the adversaries in not desirable, this requires each player to independently predict the other players future actions. Nash equilibria are a powerful tool to model this and similar multi-agent coordination problems in which the absence of communication impedes full coordination between the agents. In this paper, we propose a novel receding horizon planning algorithm that, exploiting sensitivity analysis within an iterated best response computational scheme, can approximate Nash equilibria in real time. The planner only requires that each player knows its own position (e.g. with GPS or SLAM), and can sense the other player's relative position (e.g. with on board vision). Our solution is demonstrated to effectively compete against alternative strategies in a large number of drone racing simulations. Hardware experiments with onboard vision sensing prove the practicality of our strategy.
A Low-Complexity, Low-Phase-Noise, Low-Voltage Phase-Aligned Ring Oscillator in 90 nm Digital CMOS

An 8-phase phase-aligned ring oscillator in 90 nm digital CMOS is presented that operates up to 2 GHz. The low-complexity circuit consumes 13 mW at 2 GHz and 1.2 mW at 400 MHz, while a flat in-band phase noise below -120 dBc/Hz is achieved, in close agreement with the presented theory. The circuit occupies an area of 0.008 mm2 .
Improvement of control method over the environment of cognitive radio system using a neural network

In the course of present research, we examined a method to control the environment of a cognitive radio using a PNN neural network as a decision-making system. As a result of research into the WRAN environment control architecture using a neural network, a flow chart of the environment control algorithm has been developed. Its special feature is that a neural network is located at each base station and interacts with other WRANs according to the IEEE 802.22 standard. The cognitive radio environment control architecture has been improved using a PNN network. This is achieved by applying a special case of radial basis networks ‒ a probabilistic neural network and a hybrid learning system, as well as a hybrid form of error correction and accumulating the experience of past iterations. To simulate a PNN neural network, the MATLAB software package was selected using standard functions of "Neural" and "Simulink" sections. To determine the two measurable vectors of the input set, four domains of input vectors with a normal distribution law with arbitrary values have been created. As a result of the network simulation, a connectivity matrix corresponding to the input vector has been generated. A PNN neural network simulation showed statistically confirmed results. The network has one competing layer and a layer for receiving and splitting the attributes of the input vector. This ensures the use of a small number of network neurons and, accordingly, the fast learning ability of the network – 1200 ms, which is 1.67 times faster than the required value, which is achieved by employing parallel processing of information. Moreover, the improved method provides the ability to work in the presence of a large number of uninformative, noise input signals, as well as the adaptation to environmental changes
Analyze the Future: Cloud Computing, a New Phase in Information Technology Infrastructure Management

The use of cloud computing is becoming widespread, but systematic study of its managerial implications is lacking. This paper examines cloud computing in the context of other major changes in Information Technology (IT) and explores the revolutionary transformations and challenges which bring to IT management. Cloud computing has a high reliability, low cost and good availability. With the increasing use of technology in modern era, there is a growing requirement of information technology infrastructure management. Over some decades in the past, organizations have put their efforts in the development and application of information technology to collect, analyze and process the data. Various computing paradigms have been employed for the purpose and needs have emerged for enormous infrastructure. Cloud Computing is evolving as a key computing platform for sharing resources that include infrastructure, software application and business processes. Virtualization is a core technology for enabling cloud resources sharing (Liang et al. 2009). Virtualization refers to the abstracting of a form of technology away from its original environment. The benefit derives from being able to redeliver it in a virtual (or “logical”) form capable of the same functionality as the original, but with greater flexibility (Tata Communication White Paper 2012). This paper presents how Cloud Computing service model – Infrastructure as a Service (IaaS) can be used to meet the increasing demands of the Information Technology Infrastructure Management and how Cloud Computing paradigm can prove to be future solution for such systems. With IaaS, IT services can be delivered as a subscription service, eliminating up-front costs and driving down ongoing support costs.
Calculation Algorithm for Diffraction Losses of Multiple Obstacles Based on Epstein–Peterson Approach

Applying propagation models with good accuracy is an essential issue for increasing the capacity and improving the coverage of cellular communication systems. This work presents an algorithm to calculate total diffraction losses for multiple obstacles objects using Epstein–Peterson approach. The proposed algorithmic procedure to model the diffracting can be integrated with other propagation mechanisms in ray-tracing for the prediction of received signal level in non-line-of-sight environments. This algorithm can be interpreted into software application to scan large areas with a reasonable simulation time.
Low-power biomedical processors with embedded machine-learning accelerators for analytically-intractable physiological signals

Low-power sensing technologies have emerged for acquiring physiologically-indicative patient signals. However, to achieve high clinical value, it is critical to analyze the signals to extract specific medical information. Given the complexities of the underlying processes, high-order signal models are required for accurate signal analysis. Machine-learning offers distinct advantages, but the computations are not well supported by traditional DSP platforms; high-order models lead to energy and memory intensive computations. This thesis investigates these challenges from the levels of kernel functions, microprocessor architectures, and algorithms. To enable low-energy computations, a reformulation of a polynomial supportvector machine (SVM) kernel function is proposed that can substantially reduce the real-time computations involved. Using ECG-based arrhythmia-detection and EEGbased seizure-detection applications with clinical patient data, it is shown that the polynomial models yield performance accuracy comparable to the most powerful available transformation (i.e., the radial-basis function), and yet the proposed formulation reduces energy by over 2500× and 9.3 198× (depending on the patient), respectively. Next, an accelerator-based biomedical processor is proposed. It employs a lowpower SVM accelerator realizing various kernel functions and reformulations, spanning design points within an accuracy-versus-energy and -memory trade-off space. An active-learning accelerator enables patient-specific model customization while minimizing the modeling effort from human experts. The prototype is implemented in 130nm CMOS. Medical applications for EEG-based seizure detection and ECG-based cardiac-arrhythmia detection are demonstrated using clinical data which reduce energy by 62.4× (273μJ) and 144.7× (124μJ), respectively, compared to a CPU-alone implementation. A patient-adaptive cardiac-arrhythmia detector is also demonstrated which reduces the training data required by a factor of 20×.
Study on data compression algorithm and its implementation in portable electronic device for Internet of Things applications

An Internet of Things (IoT) device is usually powered by a small battery, which does not last long. As a result, saving energy in IoT devices has become an important issue when it comes to this subject. Since power consumption is the primary cause of radio communication, some researchers have proposed several compression algorithms with the purpose of overcoming this particular problem. Several data compression algorithms from previous reference papers are discussed in this paper. The description of the compression algorithm in the reference papers was collected and summarized in a table form. From the analysis, MAS compression algorithm was selected as a project prototype due to its high potential for meeting the project requirements. Besides that, it also produced better performance regarding energy-saving, better memory usage, and data transmission efficiency. This method is also suitable to be implemented in WSN. MAS compression algorithm will be prototyped and applied in portable electronic devices for Internet of Things applications.
Presencia de revistas españolas en las bases de datos internacionales

This chapter analyses the presence of Spanish scientific journals in the main international databases (Web of Science, Scopus, DOAJ and other multidisciplinary databases), with a special focus on distribution by subject matter. This was achieved using the records and tools of the MIAR database (Information Matrix for the Analysis of Journals), and the content was limited to a total of 3,382 Spanish journals published in January 2017. The presence of Spanish journals in the most selective WoS indices (SCI, SSCI and AHCI) is clearly strongest in the SCI index, in which more than 10% of journals in eight disciplines are indexed and mathematics plays a particularly significant role, along with chemistry, geology and industrial engineering, electronics and telecommunications. Spanish journals feature considerably more often in Scopus than in WoS, and those that are indexed in Scopus encompass a wider range of disciplines, notably veterinary medicine and mathematics, with over 50% of the journals in these fields, as well as areas such as civil engineering, medical sciences, chemistry and geology. The penetration of open access among Spanish journals revealed by the DOAJ directory demonstrates that some disciplines have seen a remarkable rise in the use of OA in their journals, as in the case of architecture and urban planning, industrial engineering, electronics and telecommunications, and computer science, while it has a very weak presence in other disciplines, including law and medical and health sciences.
Multiple Domain Basis Functions and Other Recent Advances in Mom

The work discusses several extentions to the method of moments (MoM), considered in the context of electromagnetics. The topics include the interpolating multiple domain basis functions (MDBF), and improvement to the condition number of the impedance matrix. The main focus in the work is on a realization of the novel composite multiple domain basis functions (MDBF), enabling an efficient treatment for curved structures, which is backward compatible with most of the existing MoM codes. The MDBF is a profiled linear combination of common basis functions (BF), such as piecewise linear (PWL). The method also permits to extend the thin wire approximation. Several automatic algorithms were developed to implement the method. The method realizing MDBFs has been applied to several examples, demonstrating an order of magnitude reduction in the number of unknowns required and translating into a hundred-fold memory saving. Furthermore, a theoretical basis for applying higher order polynomial basis functions has been introduced. Most of the methods introduced in the work may be applied in other computational fields, e.g. structural analysis.
The epidemiology of perinatally HIV-infected adolescents: a CIPHER cohort collaboration global analysis

Methods: Through the Collaborative Initiative for Paediatric HIV Education and Research (CIPHER), individual retrospective data from 12 cohort networks were pooled. Included PHA entered care before age 10 years with no known non-vertical route of HIV infection, and followed beyond age 10 years. This initial analysis describes characteristics at first visit, start of antiretroviral therapy (ART), start of adolescence (age 10 years) and surviving patients at last follow-up.
Code Selection for Output Coding

Output Coding, in which a multiclass problem is decomposed into simpler binary sub-problems, has become a popular ensemble classifier approach. Originally the code matrix was chosen based on error-correcting principles, which led to Error-Correcting Output Coding (ECOC). However, various code matrices have been proposed for use with the Output Coding technique. In this paper we consider properties of the code matrix that affect performance, and with the help of synthetic and natural data, compare random, BCH and equidistant codes.
Things that Make Robots Go HMMM : Heterogeneous Multilevel Multimodal Mixing to Realise Fluent, Multiparty, Human-Robot Interaction

Fluent, multi-party, human-robot interaction calls for the mixing of deliberate conversational behaviour and re- active, semi-autonomous behaviour. In this project, we worked on a novel, state-of-the-art setup for realising such interactions. We approach this challenge from two sides. On the one hand, a dialogue manager requests deliberative behaviour and setting parameters on ongoing (semi)autonomous behaviour. On the other hand, robot control software needs to translate and mix these deliberative and bottom-up behaviours into consistent and coherent motion. The two need to collaborate to create behaviour that is fluent, naturally varied, and well-integrated. The resulting challenge is that, at the same time, this behaviour needs to conform to both high level requirements and to content and timing that are set by the dialogue manager. We tackled this challenge by designing a framework which can mix these two types of behaviour, using AsapRealizer, a Behaviour Markup Language realiser. We call this Heterogeneous Multilevel Mul- timodal Mixing (HMMM). Our framework is showcased in a scenario which revolves around a robot receptionist which is able to interact with multiple users.
Performance evaluation and analysis of space-time coding for high data rate wireless personal communications

The third generation wireless personal and mobile communication systems are required to support both high-quality and high data-rate services. However, high-speed transmission over wireless communication links is severely restricted by the wireless environment limitations such as intersymbol interference, propagation fading, additive noise and co-channel interference. This paper investigates the use of space-time coding for high-speed data transmission, as well as, study the effect of time delay spread on such scheme in multipath fading channels. Using a random variable decomposition technique, we also present an analytical model and obtain an approximate bound of the pair-wise error probability for space-time coded systems over multipath and time-dispersive fading channels. It is shown that the presence of multipath does not reduce the diversity gain provided by the original design criteria which is adopted to construct specific space-time codes in quasi-static flat fading, but the coding gain decreases due to the effect of multiple paths.
DIGITAL MULTI-SENSOR SYSTEMS - CALIBRATION AND PERFORMANCE ANALYSIS

Currently, the mapping industry is focusing on the implementation of multi-sensor systems for image acquisition and georeferencing. Small format digital cameras are of a particular focus nowadays, due to their numerous advantages and suitability for a number of low- to medium-class applications. Calibration is, however, a critical factor in such a multi-sensor environment. This paper is, therefore, dedicated to present the new developments in calibrating a multi-sensor digital system. The concept of boresight/camera calibration in airborne and terrestrial modes is presented. Data results and analysis of multiple data sets are also presented