Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance optimization of Brayton heat engine at maximum efficient power using temperature dependent specific heat of working fluid

Efficient power optimization of Brayton heat engine with variable specific heat of the working fluid is analyzed from the view of finite time thermodynamics. The efficient power is defined as the multiplication of engine power and engine efficiency. Hence, the proposed method considers not only the power output but also the engine efficiency. Optimizing the efficient power gives a compromise between power and engine efficiency. Results obtained are compared with those obtained by using the maximum power (MP) and maximum power density (MPD) conditions. The results show that the engine designed at maximum efficient power (MEP) criterion is more efficient as compared with those designed at maximum power and maximum power density conditions. The system analysis is done with variable specific heat parameter due to which its performance is comparable to the real systems. Moreover, engine designed at maximum efficient power criterion requires lesser pressure ratio over those designed at maximum power density conditions. Brayton heat engine with variable specific heat of the working fluid gives realistic prediction of engine efficiency and engine power than does the isentropic Brayton heat engine with constant specific heat
Adaptive Hopfield neural networks for economic load dispatch

A large number of iterations and oscillations are those of the major concern in solving the economic load dispatch problem using the Hopfield neural network. This paper develops two different methods, the slope adjustment and bias adjustment methods, in order to speed up the convergence of the Hopfield neural network system. Algorithms of economic load dispatch for piecewise quadratic cost functions using the Hopfield neural network have been developed for the two approaches. The results are compared with those of a numerical approach and the traditional Hopfield neural network approach. To guarantee and for faster convergence, adaptive learning rates are also developed by using energy functions and applied to the slope and bias adjustment methods. The results of the traditional, fixed learning rate and adaptive learning rate methods are compared in economic load dispatch problems.
Interpretation of polarimetric radar covariance matrix for meteorological scatterers

Modern dual-polarization radars provide the unique capability to measure the fields of all components of the covariance backscattering matrix including co-cross-polar correlation coefficients that haven't been explored much for linearly polarized radars. The authors present full polarimetric examples of such data obtained with the CSU CHILL and NCAR S-Pol research polarimetric radars. Both radars operate at the wavelengths of about 10 cm. The radars have two receivers that allow to measure simultaneously co-polar and cross-polar components of the backscattered electromagnetic wave. At least ten radar variables can be obtained simultaneously: radar reflectivity factor Z, differential reflectivity Z/sub DR/, linear depolarization ratio LDR total differential phase /spl Phi//sub DP/, specific differential phase K/sub DP/, cross-correlation coefficient /spl rho//sub hv/, and the magnitudes and the phases of the two co-cross-polar correlation coefficients |/spl rho//sub xh/| (|/spl rho//sub xv/|) and arg(/spl rho//sub xh/) (arg(/spl rho//sub xv/)) respectively. First six variables have been studied quite well in the literature, whereas the information content of the co-cross-polar correlation coefficients /spl rho//sub xh/ and /spl rho//sub xv/ needs to be examined. This is a major focus of this study.
Conversation-based Learning in the Social Semantic Web

The collaborative approaches based on concepts of Knowledge construction and knowledge creation consider the learning as a direct function of processes of social participation and dynamic argumentative talk between peers. These approaches justify the Social Semantic Web’ success as a theoretical and technological model which establishes a synergy between an Ontology-based approach to knowledge (suitable for defining, structuring and sharing knowledge) and collaborative software environments (utilized to create and share knowledge socially). The above mentioned approaches make reference to Semantic Web and Social Web respectively. If the Ontologies are an effective mechanism for the formal representation and sharing of knowledge, the social/collaborative activities adopt pedagogical theories of Social Constructivism. Recently, the scientific community has promoted the Educational Social Semantic Web, where activities and educational content are easily created, shared and used by teachers and students even without possessing brilliant engineering knowledge skills or technological know-how. The purpose of this work is to model, according to Social Semantic Web’s principles, an adaptive environment able to support Instruction-based Conversation learning processes in a work environment. In such an environment, the conversational process is guided by a collaborative script automatically generated basing on the explicit representation of a disciplinary domain, a knowledge goal to be achieved, as well as learner’s characteristics (cognitive state) and corporate resources available at a given time. The environment maintains the learner's cognitive balance, minimizes the extraneous processing, appropriately manages the essential processing and maximizes the generative processing.
Design of sparse FIR filters based on branch-and-bound algorithm

Branch-and-bound algorithm is applied to the design of sparse FIR filters having intentionally zeroed tap positions. It is shown that this algorithm coupled with a suitable optimization technique for filter design can lead to an optimal sparse FIR filter satisfying given specifications. Design examples demonstrate that the proposed method requires less computation than the conventional optimization such as the subset selection method.
AMHRP: Adaptive Multi-Hop Routing Protocol to Improve Network Lifetime for Multi-Hop Wireless Body Area Network

A Wireless Body Area Sensor Network (WBASN) is combination of numerous sense nodes, positioned onto/close or inside a person body. Wireless Body Area Sensor Networks (WBASN) is a developing automation trend that exploits wireless sensor nodes to put instantaneous wearable well-being of ill person to improve individual’s existence. The sensor nodes might be used outwardly to observe abundant health parameters (like heart activity, blood pressure and cholesterol) of an ill person at a vital site within hospital. Hence the goal of WBASN is much crucial, enhancing the lifetime of nodes is compulsory to sustain many issues such as utility and efficiency. It is essential to evaluate time that when the first node will die it we want to refresh or change the battery reason is that loss of crucial information is not tolerable. The lifetime is termed as the time interval when a first node dies out due to battery exhaustion. In our proposed protocol life time of a network is the main concern as well other protocol related issues such as throughput, path loss, and residual energy. Bio-sensors are used for deployment on human body. Poisson distribution and equilibrium model techniques have been used for attaining the required results. Multi-hop network topology and random network node deployment used to achieve minimum energy consumption and longer network lifetime.
A New Method of Constrained Optimization and a Comparison With Other Methods

A new method for finding the maximum of a general non-linear function of several variables within a constrained region is described, and shown to be efficient compared with existing methods when the required optimum lies on one or more constraints. The efficacy of using effective constraints to eliminate variables is demonstrated, and a program to achieve this easily and automatically is described. Finally, the performance of the new method (the "Complex" method) with unconstrained problems, is compared with those of the Simplex method, from which it was evolved, and Rosenbrock's method.
Automated call routing in a telecommunications network

Considers the task of automated call routing in a telecommunications network. When a customer desires some service, they should proceed by dialing a single universal number, which prompts them with "Hello, how may I help you. " The person then responds to this prompt via unconstrained fluent speech, on which basis the call is automatically routed to an appropriate destination. We report on an on-line experimental system which explores the feasibility of this concept, comprising an information-theoretic connectionist network. Embedded in a feedback control system. Experimental results are reported for a database of recorded customer/operator dialogs. <<ETX>>
Using Learning Analytics to evaluate the quality of multiple-choice questions

Purpose The purpose of this paper is to find appropriate forms of analysis of multiple-choice questions (MCQ) to obtain an assessment method, as fair as possible, for the students. The authors intend to ascertain if it is possible to control the quality of the MCQ contained in a bank of questions, implemented in Moodle, presenting some evidence with Item Response Theory (IRT) and Classical Test Theory (CTT). The used techniques can be considered a type of Descriptive Learning Analytics since they allow the measurement, collection, analysis and reporting of data generated from students’ assessment. Design/methodology/approach A representative data set of students’ grades from tests, randomly generated with a bank of questions implemented in Moodle, was used for analysis. The data were extracted from a Moodle database using MySQL with an ODBC connector, and collected in MS ExcelTM worksheets, and appropriate macros programmed with VBA were used. The analysis with the CTT was done through appropriate MS ExcelTM formulas, and the analysis with the IRT was approached with an MS ExcelTM add-in. Findings The Difficulty and Discrimination Indexes were calculated for all the questions having enough answers. It was found that the majority of the questions presented values for these indexes, which leads to a conclusion that they have quality. The analysis also showed that the bank of questions presents some internal consistency and, consequently, some reliability. Groups of questions with similar features were obtained, which is very important for the teacher to develop tests as fair as possible. Originality/value The main contribution and originality that can be found in this research is the definition of groups of questions with similar features, regarding their difficulty and discrimination properties. These groups allow the identification of difficulty levels in the questions on the bank of questions, thus allowing teachers to build tests, randomly generated with Moodle, that include questions with several difficulty levels in the tests, as it should be done. As far as the authors’ knowledge, there are no similar results in the literature.
Maintaining stability during new learning in neural networks

A fundamental problem of neural networks and similar distributed systems is that the learning of new information potentially interferes with information already stored in the network. The author reviews the pseudorehearsal solution to this problem proposed by Robins (1995). He describes and evaluates a variant of pseudorehearsal that provides an alternative to the methods current use of significant amounts of temporary storage space. Pseudorehearsal methods allow networks to "self stabilize" in the face of new learning, providing a theoretical framework for modelling-and a practical method for implementing-continuous/ongoing learning with neural networks.
Text Categorization and Information Retrieval Using WordNet Senses

In this paper we study the inuence of semantics in the Text Categorization (TC) and Information Retrieval (IR) tasks. The K Nearest Neighbours (K-NN) method was used to perform the text categorization. The experimental results were obtained taking into account for a relevant term of a document its corresponding WordNet synset. For the IR task, three techniques were investigated: the direct use of a weighted matrix, the Singular Value Decomposition (SVD) technique in the Latent Semantic Indexing (LSI) model, and the bisecting spherical k-means clustering technique. The experimental results we obtained taking into account the semantics of the documents, allowed for an improvement of the performance for the text categorization whereas they were not so promising for the IR task.
Efficient fast ME predictions and early-termination strategy based on H.264 statistical characters

This paper analyzes the statistical characters on MV (motion vector) and cost function (called SAD) in ME (motion estimation) of H.264, and proposes four efficient predictions and early-termination strategy in ME, which keeps video quality while decreasing greatly computation complexity. Simulation results from QCIF format to HD (high definition) format show that the proposed method has good performance for H.264, and it is also easily be suitable to other standard platforms. This method has been adopted as part of the fast motion estimation in JVT test model.
AUTOMATIC IN-SITU SELF-CALIBRATION OF A PANORAMIC TLS FROM A SINGLE STATION USING 2D KEYPOINTS

Abstract. Terrestrial laser scanner (TLS) measurements are unavoidably affected by systematic influences due to internal misalignments. The magnitude of the resulting errors can exceed the magnitude of random errors significantly deteriorating the quality of the obtained point clouds. Hence, the task of calibrating TLSs is important for applications with high demands regarding accuracy. In recent years, multiple in-situ self-calibration approaches were derived allowing the successful estimation of up-to-date calibration parameters. These approaches rely either on using manually placed targets or on using man-made geometric objects found in surroundings. Herein, we widen the existing toolbox with an alternative approach for panoramic TLSs, for the cases where such prerequisites cannot be met. We build upon the existing target-based two-face calibration method by substituting targets with precisely localized 2D keypoints, i.e. local features, detected in panoramic intensity images using the Förstner operator. To overcome the detriment of the perspective change on the feature localization accuracy, we estimate the majority of the relevant calibration parameters from a single station. The approach is verified on real data obtained with the Leica ScanStation P20. The obtained results were tested against the affirmed target-based two-face self-calibration. Analysis proved that the estimated calibration parameters are directly comparable both in the terms of parameter precision and correlation. In the end, we employ an effective evaluation procedure for testing the impact of the calibration results on the point cloud quality.
On growing better decision trees from data

This thesis investigates the problem of growing decision trees from data, for the purposes of classification and prediction. After a comprehensive, multi-disciplinary survey of work on decision trees, some algorithmic extensions to existing tree growing methods are considered. The implications of using (1) less greedy search and (2) less restricted splits at tree nodes are systematically studied. Extending the traditional axis-parallel splits to oblique splits is shown to be practical and beneficial for a variety of problems. However, the use of more extensive search heuristics than the traditional greedy heuristic is argued to be unnecessary, and often harmful. Any effort to build good decision trees from real-world data involves "massaging" the data into a suitable form. Two forms of data massaging, domain-independent and domain-specific, are distinguished in this work. A new framework is outlined for the former, and the importance of the latter is illustrated in the context of two new, complex classification problems in astronomy. Highly accurate and small decision tree classifiers are built for both these problems through a collaborative effort with astronomers.
Digital platform innovation in European SMEs. An analysis of SME Instrument Business Proposals and Case Studies

The study explores how European SMEs applying to the SME Instrument (SMEi) funding scheme under Horizon 2020 innovate use the digital platform business model. The study demonstrates a widespread awareness of the digital platform concept as a tool to be applied to gain momentum and growth, taking advantage of the digital affordances. The main challenges to scale-up include how to manage external communities and orchestrate them in order to build innovation ecosystems; how to find a profitable business model; and secure funding for growth. Firms located in peripheral regions face additional difficulties in finding complementary resources.
Robust facial feature point detection under nonlinear illuminations

Addresses the problem of facial feature point detection under different lighting conditions. Our goal is to develop an efficient detection algorithm, which is suitable for practical applications. The problems that we need to overcome include (1) high detection accuracy, (2) low computational time and (3) nonlinear illumination. An algorithm is developed and reported in the paper. One of the key factors affecting the performance of feature point detection is the accuracy in locating face boundary. To solve this problem, we propose to make use of skin color, lip color and also the face boundary information. The basic idea to overcome the nonlinear illumination is that, each person shares the same/similar facial primitives, such as two eyes, one nose and one mouth. So the binary images of each person should be similar. Again, if a binary image (with appropriate thresholding) is obtained from the gray scale image, the facial feature points can also be detection easily. To achieve this, we propose to use the integral optical density (IOD) on face region. We propose to use the average IOD to detect feature windows. As all the above-mentioned techniques are simple and efficient, the proposed method is computationally effective and suitable for practical applications. 743 images from the Omron database with different facial expressions, different glasses and different hairstyle captured indoor and outdoor have been used to evaluate the proposed method and the detection accuracy is around 86%. The computational time in Pentium III 750 MHz using matlab for implementation is less than 7 seconds.
Study of Regional Medical Collaboration Based on Cloud Computing

In order to solve information resources sharing and real-time monitoring of the patient, a new design of network system based on cloud computing has been accomplished aimed to mine the hospital health information. The paper introduces the overall architecture of Regional Medical Collaboration, which can promote standardized construction of the regional medical information system. Regional Medical Collaboration is better for information interconnection and intercommunication than an ordinary hospital. The system integrates the Internet of things, mobile Internet, cloud computing, big data and other technologies to achieve intelligent, visual and continuous collection of health information. By mining the big data through the cloud platform, the patient can achieve earlier intervention and treatment, improving the success rate of diagnosis.
Opacity in Šmartno Slovenian

Šmartno is a critically endangered dialect of Slovenian that exhibits three interacting processes: final devoicing, unstressed high vowel deletion and vowel–glide coalescence. Their interaction is opaque: final obstruents devoice, unless they become final due to vowel deletion; high vowels delete, but not when created by coalescence. These patterns constitute a synchronic chain shift that leads to two emergent contrasts: final obstruent voicing and vowel length (due to compensatory lengthening). The paper examines all nominal paradigms, and complements them with an acoustic analysis of vowel duration and obstruent voicing. This work presents one of the most thoroughly documented instances of counterfeeding opacity on environment.
Hoare logic for higher order store with simple foundations

We revisit the problem of providing a Hoare logic for a simple language for higher order store programs, considered by Reus and Streicher (ICALP, 2005). In a higher order store program, the procedures/commands of the program are not xed, but can be manipulated at runtime by the program itself; such programs provide a foundation to study language features such as reection, dynamic loading and runtime code generation. We present progress in three areas. Firstly, we present a new semantic model of the programming language, using at states rather than domains. This model is much simpler and leads to a more powerful logic: unintuitive restrictions on proof rules are eliminated, nondeterministic programs are handled, programs which perform syntactic equality tests on commands can be reasoned about, and some convenient new proof rules are validated. Secondly we explain and demonstrate with an example that, contrary to what has been stated in the literature, such a proof system does support proofs which are (in a specic sense) modular. Thirdly we extend the programming language with an operator for runtime specialisation of code, which is a simple form of runtime code generation. We provide new proof rules for reasoning about this operator, including a new recursion rule. We demonstrate these rules with an example.
Robust speech recognition techniques using a radial basis function neural network for mobile applications

We present the development of robust speech recognition techniques for voice activated dialing (VAD) for a cellular phone in a car. Noise reduction techniques are implemented before modeling the speech parameters in the homomorphic domain. The FFT derived cepstral coefficients are liftered and then Mel-scale warped to generate the feature vector. A radial basis function neural network is used as the final classifier and variation in its structure is studied to evaluate its real-time performance. Results on speaker-dependent and speaker-independent recognition across -5 dB to 25 dB SNR range are presented and a performance evaluation is carried out for different front-end techniques. The system is evaluated using the NOISEX-92 database.
Information Retrieval System Assigning Context to Documents by Relevance Feedback

In this paper we have proposed user feedback driven Information retrieval model. The proposed model assigns weights to the retrieved documents based on its context. The documents are re-ranked based on the user profile and his feedback. Proposed Information retrieval system uses vector space model and expert system. Need for user profile and relevance of information while searching and extracting information, from information retrieval system is highlighted.