Miracle-dz/UniFAR-FTUs huggingface dataset:
Hadoop System With Speed Up Extension

Applications like log processors, search engines etc. using Hadoop MapReduce for computing and HDFS for storage. Hadoop is most popular for analysis, storage and processing very large data but there need to be lots of changes in Hadoop system. Here problem of data storage and data processing try to solve which helps Hadoop system to improve processing speed and reduce time to execute the task. During placement of data files default placement of Hadoop does not consider any data characteristics. If the related set of files is stored in the same set of nodes, the efficiency and access latency can be increased. Solution for above problem is to co- locate related files by considering content and using locality sensitive hashing algorithm which is a clustering based algorithm will try to co -locate related file streams to the same set of nodes without affecting the default scalability and fault tolerance properties of Hadoop By storing related files in same cluster which improve data locality mechanism and improves processing time, helps to speed up execution of Hadoop.
Efficient Instantiation of Disjunctive Databases

Most deductive database systems are endowed with an instantiation module. The instantiator generates a new program which is equivalent to the input program, but does not contain any variables (i.e., it is ground). The instantiation process may be computationally expensive in some cases, and the instantiator is crucial for the efficiency of the entire ASP system. In this report we describe the instantiation procedure of DLV system, which is one of its strong points. Using differential and other advanced database techniques together with suitable data structures, the DLV instantiator efficiently generates a ground instantiation of the input that has the same stable models as the full program instantiation, but is much smaller in general. Moreover, in case of normal stratified programs, it already computes the single stable model without producing any instantiation.
ORCHESTRA: an experimental agent-based service control architecture for broadband multimedia networks

This paper deals with the development of a prototype software architecture, named ORCHESTRA, aimed at the provision of advanced multimedia services in broadband networks, and supporting by both Internet and ATM signaling protocols. The focus is an experimental service control architecture capable of fulfilling the major requirements of emerging broadband multimedia services. The key points of the implementation are: (1) the use of software agents to enhance the architecture's flexibility, autonomy and distribution, and (2) the use of global service brokers for orchestrating services among terminals, servers and network elements, and for the control and management of network resources.
A Brief Overview of Functional Programming Languages

Functional programming is an important programming paradigm. It is based on a branch of mathematics known as lambda calculus. In this article, we provide a brief overview, aimed at those new to the field, and explain the progress of functional programming since its inception. A selection of functional languages are provided as examples. We also suggest some improvements and speculate on the potential future directions of this paradigm.
Distributed Systems, 2007/2008 Project Lab

1 Goal The goal of these projects is to develop epidemic protocols in the Cloudware framework. Cloudware (a.k.a. Cloudman) was implemented by Erik Dassi in Java. The general idea is to write simple protocols, in a clean way, to test them thouroghly, and then to make them available on the web. We did a similar experience last year and although some delay in publishing the code, it is proving now a good idea.
Mining call center dialog data

We consider the problem of mining conversations between customers and call center representatives for automatically classifying calls into predefined categories. We analyze the conversations for speaker dependent information content using several multi-class classification technologies. The data consists of 539 manually transcribed conversations belonging to 15 categories. Classifiers were built using Support Vector Machines, Naive Bayes, Latent Semantic Analysis, Vector Space and K-Nearest Neighbor technologies. SVM classifiers were found to perform consistently well giving an accuracy of about 74% on the entire data and about 92% when considering only the 4 largest classes. It is observed that very high weightage to either the customer part of the dialog or that of the agent results in poor accuracy. Nearly equal weightage to the customer and agent provides the best results consistently. This approach has potential to identify cross sell and up-sell opportunities in real-time.
CHORUS, a New Technology for Building UNIX Systems

The CHORUS ® technology has been designed for building ‘‘new generations’’ of open, distributed, and scalable Operating Systems. CHORUS has the following main characteristics: - a communication-based technology, relying on a minimum Nucleus integrating distributed processing and communication at the lowest level, and providing generic services used by a set of subsystem servers to provide extended standard operating system interfaces (a UNIX † interface has been developed, others such as OS/2 and Object Oriented systems are envisaged). - a modular architecture providing scalability, and allowing in particular dynamic configuration of the system and its applications over a wide range of hardware and network configurations, - real time services provided by a real-time executive, and accessible by ‘‘system programmers’’ at the different system levels.
Automatic Construction of Interactive Machine Improvisation Scenarios from Audio Recordings

We describe a system that allows improvisers and composers to construct an interactive musical environment directly from a musical recording. Currently, interactive music pieces require separate phases of constructing generative models and structuring them into a larger compositional plan. In the proposed system we combine machine improvisation tools based on Variable Markov Oracle (VMO) with an interactive score (i-score) to control the improvisation according to larger structures found in that recording. This allows construction of improvisation scenarios in ways that are organic with the musical materials used for generating the music. The method uses new results of audio segmentation based on VMO and translates it into a Petri Net (PN) model with transition rules left open to be defined by a musician. The PN structure is finally translated into a timed representation for a live i-score control.
Intensity-based 3-D Reconstruction with Non-linear Optimi zation

Image-based rendering methods such as light fields are used in computer graphics for the computation of new views of a scene out of a set of recorded images with known camera positions. The quality of the rendered images depends on the accuracy of information about the structure of the scene, consisting of a geometric 3-D model. In order to accurately reconstruct and optimize such a 3-D model, we define a residual function which represents the difference between an image rendered with the light field from a known viewpoint, and the original image at the same position. In order to get optimal results, we minimize this particular function by defining a non-linear least-squares problem which is solved by an appropriate optimization method. Here, we use a largely unknown, non-monotone variant of the LevenbergMarquardt method. Thus, starting from a flat 3-D model in our experiments the non-monotone variant decreases the error by a factor of up to 3 in comparison to the monotone variant.
Objective Image Quality Assessment Based Tone Mapping Optimization

xi List of Abbreviations and Symbols Used . xii Acknowledgements . xiv Chapter
Texture descriptions and classification for pathological analysis of cancerous colonic mucosa

Image analysis for the classification of normal and cancerous colonic mucosa is reported. Pathology samples were taken from human colon mucosa, and 44 normal and 58 cancer images captured to computer via an optical microscope with a CCD camera. Texture analysis was performed using fractal dimension, entropy and correlation. Using non-parametric classification, fractal dimension improved the classification accuracy from 88% to 94% in comparison with the combined entropy and correlation analysis.
The hepatitis called

This paper reports electrophysiological correlates of enriched composition, i.e. when certain aspects of the interpretation of an utterance must be constructed on the basis of information outside the syntactic and lexical representations associated with the utterance itself. It investigates the processes underlying reference transfer, a process by which a salient property of an individual may be used to refer to this individual. On the basis of the experimental data from reference transfer, but also from previous research on thematically-driven event structure updating triggered by certain inferential relations, it is proposed that enriched composition is discernible on the basis of a particular event-related brain potential (ERP) signature: a late positivity.
Incorporating topic information into semantic analysis models

This paper reports experiments in classifying texts based upon their favorability towards the subject of the text using a feature set enriched with topic information on a small dataset of music reviews hand-annotated for topic. The results of these experiments suggest ways in which incorporating topic information into such models may yield improvement over models which do not use topic information.
“Audacious, brilliant!! What a strike!”

Live text commentaries, published on the Internet, are written narratives that give a minute-by-minute account of a sports event while it is actually unfolding. Alternative terms that have been suggested for them are “event tracker”, “live ticker”, “minute-by-minute report”, or “matchcast”. They come in very different shapes and forms, and while they share some properties with unscripted radio sports commentaries, they have their own specific features. The textual account of the events on the field is very often accompanied by background information on the contestants, lists, statistics, pictures and diagrams. But live text commentaries also share many features with personal narratives. However, in contrast to personal narratives and to unscripted radio commentaries, live text commentaries are not ephemeral. Each new update, reporting the most recent state of the events on the field, also provides a step-by-step overview of what has already happened.
Building the Earth System Science Community

The authors consider the benefits that ensued as a result of the creation of the old scientific societies such as the Royal Society. These institutions only cater for the postgraduate community. The authors therefore consider the position of both undergraduate and school science and they outline their efforts to create a high school curriculum for Earth System Science (ESS). ESS is a holistic approach to the study of the Earth that stresses investigations of the interactions among the Earth's components in order to explain Earth dynamics, Earth evolution and global change. The challenge to ESS is to develop the capability to understand those changes that will occur in the next decade to century, both naturally and in response to human activity. ESS, by design, intensifies the significance of scientific community-building. In May of 1993, ECOlogic was awarded a contract by NASA Space Data and Computing Division through the High Performance Computing and Communications program. Under that two-year contract, the authors worked closely with NASA scientists and staff to develop ECOlogica, a multimedia Earth System Science curriculum using NASA computational resources delivered over the Internet using the World Wide Web (WWW). In support of this effort, NASA Science Internet installed a DS 1 high-speed connection to the Internet at their host institution, Gonzaga College High School. ECOlogica set down the foundation for an Earth System Science curriculum. The instructional and curricular design approaches and methods of getting advice and support from researchers and data providers were tested and refined in pilot classes.
Architecture and Design for Regression Test Framework to Fuse Big Data Stack

: Regression is a statistical measure that attempts to determine the strength of the relationship between one dependent variable and a series of other changing variables (known as independent variables). Big Data bring to real world with paradigm to automatically parallelized tasks and executed them on a large cluster of commodity machines. Regression is part of integrity to bring test automation faster and become need to achieve target of reinvesting resources at lower cost for huge clusters. Our Architecture and design of regression framework to support big data stack on one location. Provide flexibility to compile big data ecosystem components together to allow processing proof and data accuracy. Programmers will find it smooth to implement and ease to integration with big data stack.
ANONYMOUS AUTHENTICATION AND SECURE DATA STORAGE USING DECENTRALIZED ACCESS CONTROL SCHEME

The new suburbanised access management theme for secure knowledge storage in clouds that supports anonymous authentication. Within the planned theme, the cloud verifies the genuineness while not knowing the user’s identity before storing knowledge. The theme conjointly has the accessorial feature of access management within which solely valid users can measure and able to decipher the hold on info. The theme prevents replay attacks and supports creation, modification, and reading knowledge hold on within the cloud. we have a tendency to conjointly address user revocation. Moreover, our authentication and access management theme is suburbanised and strong, not like different access management schemes designed for clouds that square measure centralized. Cloud storage provides a extremely out there, simply accessible and cheap remote knowledge repository to purchasers who cannot afford to take care of their own storage infrastructure. Whereas several applications of cloud storage need security guarantees against the cloud supplier (e.g., storage of high-impact business knowledge or medical records), most services cannot guarantee that the supplier won't see or modify shopper knowledge. this can be mostly as a result of this approaches for providing security (e.g., encoding and digital signatures) diminish the utility and/or performance of cloud storage. User privacy is additionally needed so the cloud or different users don't grasp the identity of the user. The validity of the user who stores the info is additionally verified.
Determining pose in a multiple-view based 3-D object recognition system

The authors address the pose determination problem in constructing multiple-view-based three-dimensional (3-D) object recognition systems. They propose a procedure which performs a sequence of 3-D transformations on the model object so that the pose of the unknown object can be determined. A series of 3-D transformation matrices is derived. The composition of these matrices relates the unknown object to its corresponding model object. A procedure to determine the pose of the unknown object by combining the derived transformation matrices is described. The experimental results that corroborate the proposed method are reported. <<ETX>>
A direction-finding algorithm for lens-based antenna systems

In this paper, we describe a high-resolution direction-of-arrival (DOA) estimation algorithm for the enhancement of a lens-based antenna system. In such a system, the antenna array is followed by an electromagnetic lens and a bank of power detectors (crystal video receivers). This system can handle sources over a large bandwidth and can detect pulsed sources that exist for short periods of time. The proposed algorithm operates directly on the power estimates provided by a bank of crystal video receivers (CVRs), yielding accurate DOA estimates for multiple narrow-band sources. We present computer simulations to demonstrate the improved performance achieved by the algorithm. <<ETX>>
Outfit Recommendation System Based on Deep Learning

In this paper, we propose an outfit recommendation system based on deep learning. Our goal is to use the system not only to judge an outfit if it is good or not but also to recommend good outfit to users when it is given a pool of cloth items. Our proposed model includes two parts: one is feature extractor based on ResNet-50, and the other is a binary classifier which is to classify the outfits into good ones and bad ones. Since our model is based on deep learning, it is necessary to use huge data to train the model. We collected a dataset which consists of 409,776 outfits with 644,192 items from the famous fashion website called Polyvore.com. With this dataset, we trained our model and the performance of it is over 84%. And our model can also recommend daily outfit to users.
An Acoustic Analysis of Azerbaijani Vowels in Tabrizi Dialect

Determining the acoustic properties of the sounds of any language and its related dialects is assumed to be the initial step in pedagogical or linguistic studies and comparing languages. No attempt to study Azeri vowels in Tabrizi Dialect has been reported yet according to our knowledge. The reported works are on North Azerbaijani variety (spoken in Azerbaijan). The present paper tried to do a computer-based acoustic analysis of Tabrizi dialect with the help of spectrographic and F1 and F2 comparison of Azeri vowels in Tabrizi dialect, and provided a digitized and authentic chart of its vowels. As a result, based on the formant values, a vowel space plot was developed. The participants in the study were 30 male and 30 female with an age range of 20 to 30. To have a more homogenous data we tried to have the subjects with the same or adjacent accents. The findings of the present study could be utilized in linguistic studies or in language pedagogy to help the foreign language learners to improve their pronunciation.
Model of Bilingual Electronic Glossary of Scientific Terminology (on the Example of Fire Science vocabulary)

The paper focuses on conception and technology of a scientific glossary creation which could contribute to coordination of terms within the language and harmonization of terms between the languages. There are examples of logical-conceptual schemes for systematization of Fire Science terminology, detailed description of the glossary structure, and a comparative analysis of Russian and English terminological fields in Fire Science.
The light propagation in biological tissue for cancer treatment

Studies the accessible depth by photons within biological tissue for photodynamic therapy of cancer. For measurements of light propagation within tissue, we applied a diode laser of 660 nm wavelength and by assuming the medium to be homogeneous, we neglect the effects of any refractive index mismatches between the tissue layers. The result have yielded the penetration depth of light within biological tissue.
Web Mining - a predictive approach

• Introduction, Motivation • Web Data nature (graph, time evolving) • Examples of data sets (snap – our data set) • Aspects: content, structure and behavior mining. • Data Preprocessing – Content preprocessing – Dimensionality reduction algorithms – Feature selection fundamentals • Ranking Web data – Graph based ranking – Pagerank, HITS – Text based ranking • Unsupervised mining algorithms and their foundations: – Graph based Clustering • Predictive learning – Markov Models, – regression models, variants of regression with probabilistic – clustering and Principal Components Analysis (PCA) – EM based approach with Bayesian learning. • Top-k similarity measures – K-Sim, Osim, NDCG, Spearman correlation. • Predictive learning case studies
Methods and systems for vision-based proactive applications

Human-computer interaction (HCI) is an integral part of modern society. Since the number of technical devices around us is increasing, the way of interacting is changing as well. The systems of the future should be proactive, so that they can adapt and adjust to people’s movements and actions without requiring any conscious control. Visual information plays a vital role in this kind of implicit human-computer interaction due to its expressiveness. It is therefore obvious that cameras equipped with computing power and computer vision techniques provide an unobtrusive way of analyzing human intentions. Despite its many advantages, use of computer vision is not always straightforward. Typically, every application sets specific requirements for the methods that can be applied. Given these motivations, this thesis aims to develop new vision-based methods and systems that can be utilized in proactive applications. As a case study, the thesis covers two different proactive computer vision applications. Firstly, an automated system that takes care of both the selection and switching of the video source in a distance education situation is presented. The system is further extended with a pan-tilt-zoom camera system that is designed to track the teacher when s/he walks at the front of the classroom. The second proactive application is targeted at mobile devices. The system presented recognizes landscape scenes which can be utilized in automatic shooting mode selection. Distributed smart cameras have been an active area of research in recent years, and they play an important role in many applications. Most of the research has focused on either the computer vision algorithms or on a specific implementation. There has been less activity on building generic frameworks which allow different algorithms, sensors and distribution methods to be used. In this field, the thesis presents an open and expendable framework for development of distributed sensor networks with an emphasis on peer-to-peer networking. From the methodological point of view, the thesis makes its contribution to the field of multiobject tracking. The method presented utilizes soft assignment to associate the measurements to the objects tracked. In addition, the thesis also presents two different ways of extracting location measurements from images. As a result, the method proposed provides location and trajectories of multiple objects which can be utilized in proactive applications.
Legal call interception in next generation networks

Absfruct In the new environment of different fraudulent threat, challenge, and at ti'nes erode, the te~rcommunieation =hility to legal call ability of law enforcement agencies to fully implement lawful interception has become more important than ever. The telecom orders to intercept communications. At the Sanle time these service provider should be capable of providing law enforcement advances also challenge the ability of telecommunications authorities with loeeed information reeardine all calls originated cairiers to meet their assistance remonsibilities. Thus. law and