Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloud Computing – A market Perspective and Research Directions

Computational paradigm has been revolving round cloud computing and its offshoots for some time and till we see a breakout resulting in a breakthrough technology driven by advances in microelectronics and material technology. Till we experience a radically efficient technology for computation it is worth juxtaposing the virtues of cloud computing and market‟s longing for offering cost and quality arbitrage to the marketplace. Integration of cloud computing in enterprises has the potential to influence the way business gets carried out by them in the market place. Different reports show that demand for cloud computing products and processes is in an upward growth trajectory. This paper identified the characteristics, drivers and constraints of cloud computing which influence its adaptation and integration in enterprises. We are also examining India specific opportunities and threats of cloud computing tools and cloud driven practices in the context of fierce competition among enterprises to remain competitive in the marketplace by reducing software licensing fees, cost of capital to acquire digital systems and cost of maintenances. New directions in cloud computing are analyzed by using Gartner strategic technologies and trend in research publications. Paper focuses on exploring the research issues which are categorized into technical and business in nature for understanding the evolving fortunes of cloud computing. Number of papers published in IEEE is an indication of the popularity and relevance of the continued research initiatives happening in the area. It is also noticed that that very few researchers are attempting to understand the possibility of remodeling business processes leveraging the new found computational paradigm.
Multi-resolution composite array based radar with adaptive beamforming

This paper presents a novel multi-resolution composite array (MRCA) based Radar for high resolution digital beamforming (DBF) with much less elements than conventional uniform linear array. An adaptive beamforming based multi-targets distinguish scheme is proposed to improve multi-targets detection ability of MRCA based radar in a wide direction region. Adaptive MRCA examples were investigated with simulations to verify our radar system and show the effectiveness.
Arabic Words Stemming Approach Using Arabic Wordnet

The big growth of the Arabic internet content in the last years has raised up the need for an effective stemming techniques for Arabic language. Arabic stemming algorithms can be ranked, according to three category, as root-based approach (ex. Khoja); stem-based approach (ex. Larkey); and statistical approach (ex. N-Garm). However, no stemming of this language is perfect: The existing stemmers have a low efficiency. In this paper, we introduce a new stemming technique for Arabic words that also solve the problem of the plural form of irregular nouns in Arabic language, which called broken plural. The proposed stem extractor provides very accurate results in comparisons with other algorithms. Consequently the search effectiveness improved.
Automatic Generation of Process Models for Fed-Batch Fermentations Based on the Detection of Biological Phenomena

Using dynamic models to describe biotechnological processes leads to a better understanding of the complex process dynamics and helps to find optimal conditions that improve the process significantly. However, the development of adequate mathematical models is generally difficult, tedious, time-consuming, and requires extensive prior experimentation. This work presents an algorithm that automatically proposes process models from an automated processing and analysis of data from (fed-)batch experiments. For this purpose, the algorithm first uses different data smoothing and interpolation techniques to account for a typically noisy and poorly time-resolved data set. Then, the measurements are numerically compensated for the influence of feeding and sampling. To reveal the qualitative behavior of the measurements, a method is used that divides the compensated curves into several episodes in a probabilistic framework. Based on these episodes and transitions between them, crucial information about the underlying reaction network can be obtained. For this, different biological phenomena describing the relation between several measured variables are defined. Rules to (dis-)prove these phenomena are applied to the data. The uncertainty of the phenomena detection towards influences like the number of taken samples and considered experiments and the measurement noise is analyzed by a bootstrap method. The detected biological phenomena and the used measured variables then lead to an automated proposal of several model structures with different degrees of complexity. The best models are selected by Akaike’s Information Criterion (AIC) and modeldiscriminating experiments are planned. Furthermore, a procedure to detect model deficiencies is drafted. The phenomena detection is applied to simulations of a model and compared to the measurementinherent phenomena. This approach is initially tested on simple case studies. The presented algorithm is applied to fed-batch experiments of three different organisms (Paenibacillus polymyxa, Streptomyces tendae, and Streptomyces griseus). Smallsize and medium-size structured models are generated, identified, and validated. The results show that the models still need to be improved, but, in many cases, are able to describe the dynamics satisfactorily. Thus, the presented approach helps to speed up the modeling process significantly.
On learning from taxi-GPS traces

Electronic taxi dispatch systems are in wide use today. These systems have replaced the traditional VHF-radio dispatch by installing mobile data terminals in taxis, which typically provide GPS localization information and taximeter state. In the last couple of years, the broadcast-based radio messages for service dispatching were replaced by unicast-based messages between the taxi central and the selected vehicle. In most cases, taxi drivers operating through an electronic dispatch system do not indicate the final destination of the ride. In some cases, particularly when the demand for taxis is higher than the taxi availability, the closest taxi to a particular location is exactly the taxi that will end its current ride at that location. While in broadcast-based radio dispatching this was not a problem, in unicast-based electronic dispatching it becomes a problem, given that most drivers do not indicate the final destination of their current ride. To improve the efficiency of electronic taxi dispatching systems it becomes important to be able to predict the final destination of busy taxis. The spatial trajectory of a busy taxi could provide some hints on where it is going. Similarly, given the taxi id, it might be possible to guess its final destination based on the regularity of prehired services. In a significant number of taxi rides (approximately 25%), the taxi has been called through the taxi call-center, and thus the passenger’s telephone id can be used to narrow the destination prediction based on the historical ride data of such telephone id. In this challenge, the goal was to build a predictive framework able to infer the final destination of each taxi ride based on their (initial) partial trajectories. This challenge was divisible in two different outputs: (a) the destination coordinates (WGS84) and (b) the total trip’s travel time (counting from the service’s starting point, in seconds). The entries were evaluated using the Mean Haversine Distance and the Root Mean Squared Logarithmic Error, regarding the destination and the travel time prediction problems, respectively.
Assessing the quality of multilingual open knowledge resources

– Acquiring of new knowledge is the core process not only in academic, but in entrepreneurial organizations as well. Information technology (IT) offers tools and resources for providing a knowledge acquisition continuum between them. Academic knowledge can be improved with best practice examples from enterprises, while entrepreneurial staff can keep pace with new academic knowledge within the lifelong learning paradigm. Open knowledge resources publicly available on the web play a pivotal role in this approach. In addition to that, Human language technology (HLT) has developed electronic tools and resources for supporting multilingualism in knowledge acquisition. The purpose of this paper is to offer an approach to quality assurance within a multilingual IT supported learning environment.
A Self-updating Template Approach for Human Action Recognition Using Improved DTW ?

The existing recognition algorithms always need large number of samples to train models or templates. Nevertheless, they aren’t view-invariant and afiected by performing speed and scale variations of difierent actors. In this paper, we capture the skeleton information by Kinect and present a novel approach based on self-updating template for human action recognition using 4D Quaternions as a compact representation of postures. We adopt the new template generation method and update the template if it satisfles speciflc conditions. This method solves the above problems. On the other hand, we improve the decision function of DTW that the number of alignment paths may have a great in∞uence on recognition. We tested our approach on our dataset containing 960 action samples and the public MSR Action3D dataset. Compared with Li et al.’s [10], our approach produced better results on the most of cases, especially with the increased number of recognition.
High performance imaging using arrays of inexpensive cameras

Digital cameras are becoming increasingly cheap and ubiquitous, leading researchers to exploit multiple cameras and plentiful processing to create richer and more accurate representations of real settings. This thesis addresses issues of scale in large camera arrays. I present a scalable architecture that continuously streams color video from over 100 inexpensive cameras to disk using four PCs, creating a one gigasample-per-second photometer. It extends prior work in camera arrays by providing as much control over those samples as possible. For example, this system not only ensures that the cameras are frequency-locked, but also allows arbitrary, constant temporal phase shifts between cameras, allowing the application to control the temporal sampling. The flexible mounting system also supports many different configurations, from tightly packed to widely spaced cameras, so applications can specify camera placement. Even greater flexibility is provided by processing power at each camera, including an MPEG2 encoder for video compression, and FPGAs and embedded microcontrollers to perform low-level image processing for real-time applications. I present three novel applications for the camera array that highlight strengths of the architecture and the advantages and feasibility of working with many inexpensive cameras: synthetic aperture videography, high speed videography, and spatiotemporal view interpolation. Synthetic aperture videography uses numerous moderately spaced cameras to emulate a single large-aperture one. Such a camera can see through partially occluding objects like foliage or crowds. I show the first synthetic aperture images and videos of dynamic events, including live video accelerated by image warps performed at each camera. High-speed videography uses densely packed cameras with staggered trigger times to increase the effective frame rate of the system. I show how to compensate for artifacts induced by the electronic rolling shutter commonly used in inexpensive CMOS image sensors and present results streaming 1560 fps video using 52 cameras. Spatiotemporal view interpolation processes images from multiple video cameras to synthesize new views from times and positions not in the captured data. We simultaneously extend imaging performance along two axes by properly staggering the trigger times of many moderately spaced cameras, enabling a novel multiple-camera optical flow variant for spatiotemporal view interpolation. * *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation). The CD requires the following system requirements: Windows MediaPlayer or RealPlayer.
Impacts of computer use upon primary classroom routines

The study investigated some of the difficulties of integrating a single computer into the curriculum of the primary . classroom. It described, from the point of view of primary teachers, the nature and range of unanticipated impacts of computer use on classroom routines (both management and instructional routines) and on instructional purposes. These impacts were examined in the light of an hypothesis that they are barriers to successful implementation. Four primary teachers (Kindergarten through grade three) and one computer helping teacher in one elementary school were interviewed five times each over a four month period to gather their perceptions on the impacts of computer use on their routines and purposes. The school was located in a large metropolitan school district in British Columbia. The interview transcripts were analyzed for similarities and differences in the teachers' reported experiences. It was found that, although unanticipated impacts on routines and purposes occurred, they were not described by the interviewees as the only deterrents to implementation. A range of circumstantial, institutional, and attitudinal factors were also influential in shaping a particular level of computer integration for each teacher. Those teachers who held simple goals for the computer (such as drill and practice, and student motivation) reported fewer unexpected impacts upon their classroom routines and instructional purposes, whereas those teachers with more complex goals (such as word processing) experienced more pervasive impacts. The study has implications for a school-based computer helping teacher whose role it is to facilitate computer implementation with colleagues. 111
Design and Implementation of the Face Tracking Algorithm Based on Spatio-temporal Context Learning ?

Visual tracking via dense Spatio-temporal Context learning algorithm (STC) is a rapid, accurate and e‐cient visual tracking algorithm, but it’s flrst frame for tracking target is predetermined, so it can not satisfy the actual application well. In this paper, flrstly, fast and accurately detecting and locating the face which should be tracked in the flrst video frame based on face detection method via skin color detection and visual attention theory, and then, using it as the flrst input frame for STC tracking algorithm for a scale-adaptive tracking the face target. Experiments show that the algorithm is fast, accurate, e‐cient, and quite robustness of light. It can meet the actual application.
Multichannel adaptive noise reduction in digital hearing aids

We have developed a digital binaural hearing aid using Texas Instrument's floating point TMS320C3X digital signal processing (DSP) chip. The device is referred to as the Digital Programmable Hearing Aid (DIPHA). It has the capability of being programmed to perform a variety of speech processing tasks and is in essence a digital hearing lens. It permits matching the processing strategy to an individual's specific hearing loss. This paper deals with the adaptive noise reduction algorithm employed by DIPHA. Typically the audiologist/therapist selects a number of bandpass filters to design (in effect) a custom binaural equalizer for the hearing impaired subject and cascades the noise reduction algorithm with the equalizer. This paper explores the concept of performing the adaptive noise reduction in individual bands of the equalizer, a multichannel adaptive noise reduction strategy for the hearing impaired.
Uncertainty Analysis for Software Service Evolution in the Heterogeneous Cloud Environment

To solve the problem of resource heterogeneity and the dynamic structure, loose coupling of integrated applications has brought a lot of benefits in clouds environment. Thus, the development of highly robust service-oriented applications has many challenges, especially for the autonomy of service resources over the system components to the end-user portal. In this paper, a proposed method for the business users can satisfy the service availability changes in the early warning and application for service relationship adjustment. Then, the designed mechanism can deal with exception not available for service in a real-time development application for a business user. Based on the heterogeneous model of service-oriented applications, an availability process with lifecycle analysis is proposed to ensure that service resources are available to integrate components at different levels.
On the Use of the Card Catalog

T NHE processes of cataloging have been the subject of much investigation, individual and co-operative. Perhaps no professional group has been so alert to questions of method, technique, and improvement as catalogers. Yet a fundamental question has not been answered. It has seldom been approached. Indeed, it has not often been asked. In 1930, Randall suggested to the Catalog Section at the Los Angeles Conference that it was essential to know "what the patron wants to find out from the catalog. "' Again, in 1940, he stated "the most significant statement of the objective of cataloging is not the production of catalog cards or the description of individual books by means of such cards but rather the production of a catalog which will serve the identified needs of the users of a particular library. "2 Wright framed the same question as "What information should be given in the catalog? " and suggested a need for a study on the usefulness of information provided on the catalog card.3 Of the many approaches that might be made to the problem of ascertaining patron satisfaction witlh the catalog or the usefulness of cataloged information, there is one that can be undertaken simply. Let us ask the patron what information he actually used in finding or selecting a book from the card catalog. XWhile his response will give a practical criterion of usefulness, it cannot be a complete index to his satisfaction with the catalog itself. There may be items of information not now normally recorded on the catalog card which would prove very useful to him. He may prefer a printed catalog to the card catalog. The color of the card, the paragraph form, or the arrangement of the cards in the drawer may or may not contribute to his satisfaction. When these and other possibilities are considered, it is apparent that the measurement of patron satisfaction with catalogs (and cataloging)
OVERLAPPING OPTIMIZATION WITH PARSING THROUGH METAGRAMMARS

This paper describes techniques for improving the performance of meta framework developed by combining C++ and Java language segments through reducing the number of bytecodes generated. Augmented versions of existing languages can be developed by combining good properties of those languages. It increases the flexibility of programmers in using language constructs of those languages. The framework identifies and parses source code with C++ and Java language statements using metagrammar developed and create a unified AST for the hybrid source code. Bytecodes are generated for AST and interpreted. The performance of Bytecodes can be improved through optimization techniques associated with metagrammars, like constant propagation which identifies constant values for variables and propagate it to the place where the variable occurs and replace it with corresponding value. Function inlining and exception optimization greatly improves the execution time performance of Bytecodes. Optimization through metagrammars eliminates rigorous analysis of bytecodes to identify hot spots and optimize them.
Goal-Based Scenarios: A Radical Look at Education

Every aspect of human behavior involves the pursuit of goals. Sometimes these goals are rather simple, like brushing your teeth to prevent decay. Sometimes they are quite unconscious, like having your mind search for similar experiences when you encounter some new experience. And, sometimes they are quite complex, like trying to build high-quality software to affect change in the school system. When goals are simple, we really don't think about them much. When they are unconscious, we do not think about them at all. And, when they are complex, we may think about them, but find the going so rough that we hone in on the simplest ones and lose the forest for the trees. But understanding how people pursue goals is a critical aspect of understanding cognition. For computers to really understand human stories, they would need a complete model of the goals that people pursue, the plans, the use, and the complexities that arise. The issue is this: If goals underlie human behavior to the extent that we cannot understand a story, what someone says, or what someone wants without a clear assessment of the underlying goals and their interaction, then it follows that goals are at the root of human learning. Why would anyone learn anything if not to help in the pursuit of a goal? Why would people try to understand anything if not because they had the goal of learning new information from what they were trying to understand? The desire to change one's knowledge base, to comprehend what is going on in the environment, and to learn from experience, are all pretty much different ways of saying the same thing. And, all of these are goal-directed.
Media Access Control and Time Synchronization in Delay-Sensitive Multi-Channel Wireless Sensor Networks

Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto. fi Author Jari Nieminen Name of the doctoral dissertation Media Access Control and Time Synchronization in Delay-Sensitive Multi-Channel Wireless Sensor Networks Publisher School of Electrical Engineering Unit Department of Communications and Networking Series Aalto University publication series DOCTORAL DISSERTATIONS 22/2012 Field of research Communications Engineering Manuscript submitted 24 October 2011 Manuscript revised 23 January 2012 Date of the defence 30 March 2012 Language English Monograph Article dissertation (summary + original articles)
Completeness Integrity Protection for Outsourced Databases Using Semantic Fake Data

As cloud storage and computing gains popularity, data entrusted to the cloud has the potential to be exposed to more people and thus more vulnerable to attacks. It is important to develop mechanisms to protect data privacy and integrity so that clients can safely outsource their data to the cloud. We present a method for ensuring data completeness which is one facet of the data integrity problem. Our approach converts a standard database to a Completeness Protected Database (CPDB) by inserting some semantic fake data before outsourcing it to the cloud. These fake data are initially produced using our generating function which uses Order Preserving Encryption, which allows the user to be able to regenerate these fake data and match them to fake data returned from a range query to check for completeness. The CPDB is innovative in the following ways: (1) fake data is deterministically generated but is semantically indistinguishable from other existing data; (2) since fake data is generated by deterministic functions, data owners do not need to locally store the fake data that have been inserted, instead they can re-generate fake data using the functions; (3) no costly data encryption/signature is used in our scheme compared to previous work which encrypt/sign the entire database