Miracle-dz/UniFAR-FTUs huggingface dataset:
Minding the Gaps: Exploring the Space Between Vision and Assessment in Information Literacy Work

The current “ACRL Information Literacy Competency Standard Review Task Force” presents information literacy practitioners with an engaging intellectual endeavor: how might these standards be revised, rethought, re-envisioned? Regardless of what the review yields, the process is an excellent opportunity for us to think broadly and creatively about the Standards and to remember that they are not a fixed set of rules but a malleable and evolving document. Asking questions about the practical, pedagogical, and theoretical implications of the Standards and considering alternative approaches will yield engaging, fruitful, and necessary conversations not only about the teaching of information literacy but about our role as librarians within the educational mandates of our institutions.
Big Data Analytics Beyond Hadoop: Real-Time Applications with Storm, Spark, and More Hadoop Alternatives

Master alternative Big Data technologies that can do what Hadoop can't: real-time analytics and iterative machine learning. When most technical professionals think of Big Data analytics today, they think of Hadoop. But there are many cutting-edge applications that Hadoop isn't well suited for, especially real-time analytics and contexts requiring the use of iterative machine learning algorithms. Fortunately, several powerful new technologies have been developed specifically for use cases such as these. Big Data Analytics Beyond Hadoop is the first guide specifically designed to help you take the next steps beyond Hadoop. Dr. Vijay Srinivas Agneeswaran introduces the breakthrough Berkeley Data Analysis Stack (BDAS) in detail, including its motivation, design, architecture, Mesos cluster management, performance, and more. He presents realistic use cases and up-to-date example code for: Spark, the next generation in-memory computing technology from UC Berkeley Storm, the parallel real-time Big Data analytics technology from Twitter GraphLab, the next-generation graph processing paradigm from CMU and the University of Washington (with comparisons to alternatives such as Pregel and Piccolo) Halo also offers architectural and design guidance and code sketches for scaling machine learning algorithms to Big Data, and then realizing them in real-time. He concludes by previewing emerging trends, including real-time video analytics, SDNs, and even Big Data governance, security, and privacy issues. He identifies intriguing startups and new research possibilities, including BDAS extensions and cutting-edge model-driven analytics. Big Data Analytics Beyond Hadoop is an indispensable resource for everyone who wants to reach the cutting edge of Big Data analytics, and stay there: practitioners, architects, programmers, data scientists, researchers, startup entrepreneurs, and advanced students.
INFORMATION EXTRACTION USING DISCOURSE ANALYSIS FROM NEWSWIRES

This paper proposes Natural language based Discourse Analysis method used for extracting information from the news article of different domain. The Discourse analysis used the Rhetorical Structure theory which is used to find coherent group of text which are most prominent for extracting information from text. RST theory used the Nucleus- Satellite concept for finding most prominent text from the text document. After Discourse analysis the text analysis has been done for extracting domain related object and relates this object. For extracting the information knowledge based system has been used which consist of domain dictionary . The domain dictionary has a bag of words for domain. The system is evaluated according gold-of-art analysis and human decision for extracted information.
Integrating Algorithm-Level Design and System-Level Design through specification synthesis

INTEGRATING ALGORITHM-LEVEL DESIGN AND SYSTEM-LEVEL DESIGN THROUGH SPECIFICATION SYNTHESIS by Jiaxing Zhang Doctor of Philosophy in Computer Engineering Northeastern University, August 2014 Prof. Gunar Schirner, Adviser Modern Multiprocessor System-On-Chip (MPSoC) designs face challenges in tremendous design complexity imposed by the wide range of functional requirements and versatile architectural platforms. The Electronic Design Automation (EDA) research has been striving to accelerate the creation, deployment and validation cycles from initial design modeling to final system delivery. However, establishing a universal, automatic and rapid design flow from concept modeling to heterogeneous implementations with synthesis, optimization and exploration capability remains an open challenge due to the overwhelming complexity, vastly different abstractions, and market adaptability and usability. This thesis introduces a specification synthesis approach that joins two design methodologies, Algorithm-Level Design (ALD) and System-Level Design (SLD), to establish a new AlgorithmArchitecture Co-design flow. We designed and implemented an algorithm-to-specification synthesizer: Algo2Spec, which out of an algorithm model captured in ALD, such as Simulink, synthesizes into an SLD languages (SLDL) specification (e.g. SpecC/SystemC) enabling SLD exploration. Expanding the rich sets of SLD facilities into higher abstraction levels in ALD forms a new joint co-design methodology. The new flow seamlessly spans from the Simulink environment down to heterogeneous implementations crossing multiple abstractions. Our tools empower designers to construct, simulate, validate, explore, and deploy models in rapid feedback cycles. Our results illustrated the opportunities and benefits of our approach on a set of real-world applications and showed a significantly shortened design time. In addition, we explore new optimization opportunities emerged from the specification synthesis with respect to Design Space Exploration (DSE) and software synthesis. We have identified several DSE challenges, such as selecting a suitable model granularity to balance the model
Expanding Co-author Network for Author Disambiguation in Scholarly Data

As many researchers work hard in various academic fields, they pro- duce more scholarly data. In the course, it happens that some of the academic papers are published by different researchers but with same name. The problem 'different researchers with the same name' results in lowering the accuracy and increasing time and effort to screen out necessary academic information. It is 'author identification' that solves the problem. Author identification is to classi- fy different researchers with the same name into individual entity. In this paper, we propose a method to achieve author identification by expanding co-author network with co-author network information and basic bibliographic infor- mation, which is used as feature for machine learning SVM. To see the effec- tiveness of the proposed method, experiments are conducted on 92,100 scholar- ly data related to IT, which have been published in Korea. The results of author identification test using expanded co-author network showed F1 measure was 94.79%. It verified that the proposed method for author identification using co- author network is effective.
Generating clusters' explanations in just one data scan

Knowledge discovery ilom unlabeled data comprises two main tasks: identification of “natural groups” and analysis of these groups in order to interpret their meaning. These tasks are accomplished by unsupervised and supervised learning, respectively, and correspond to the phases of the discovery process described by Langley. The efforts of Knowledge Discovery fi-om Databases (KDD) research field have addressed these two processes into two main dimensions: (1) scaling up the learning algorithms to very large databases and (2) improving the efficiency of the KDD process. In this paper we argue that the advances achieved in scaling up supervised and unsupervised learning algorithms allow us to combine these two processes in just one stream, providing extensional and intensional descriptions of unlabeled data. We propose a framework, called Orpheo, which enables the integration of any two unsupervised and supervised algorithms to compose the stream. A particular advantage of our approach is that, if the two algorithms are incremental, the system will build the model in just one data scan. This characteristic satisfies important desiderata for clustering in data mining posed by Bradley et al. The framework is instantiated using as building blocks two incremental neural
A Method for Load Balancing based on Software- Defined Network

The control plane is decoupled from data plane in the Software- Defined Networking (SDN) architecture, under which network intelligence and state are logically centralized and the underlying network infrastructure is abstracted from the applications. Load balancing is a significant technology and it can help save power and improve resource utilization in network. Since SDN makes it possible to manage the whole network with a centralized controller, with which we can distribute traffic more efficiently and easily. In this paper, the research background of OpenFlow-based SDN technology is summarized firstly. Then we will analysis current methods to achieve load balancing, and propose taking advantage of SDN to solve the problem.
An algorithm for joint identification and control

This paper proposes a unified algorithm for identification and control. Frequency domain data of the plant is weighted to satisfy the given performance specifications. A model is then identified from this weighted frequency domain data and a controller is synthesised using the /spl Hscr//sub /spl infin// loopshaping design procedure. The cost function used in the identification stage essentially minimises a tight upper bound on the difference between the achieved and the designed performance in the sense of the /spl Hscr//sub /spl infin// loopshaping design paradigm.
Transforms and Applications Primer for Engineers with Examples and MATLAB®

Considered an essential handbook for engineering professionals, Transformations and Applications Primer for Engineers with MATLAB contains the most important transforms that undergraduate students and practicing engineers need to know. The text provides a large number of examples to explain their use in different areas including circuit analysis, differential equations, signals and systems, and mechanical vibrations. Offering numerous MATLAB functions created by the author, this comprehensive primer also includes extensive tables of transforms to supplement learning. A valuable teaching tool, this comprehensive text presents advanced material in a format that makes it easier to understand.
Implementasi Algoritma K-Nearest Neighbour untuk Prediksi Waktu Kelulusan Mahasiswa

Koleksi data pada database sistem informasi akademik Perguruan Tinggi sering tidak dimanfaatkan secara maksimal, padahal dari data tersebut dengan teknik data mining dapat memberikan pengetahuan yang belum diketahui sebelumnya. Prediksi waktu kelulusan mahasiswa dapat dilakukan dengan menggunakan metode k-Nearest Neighbor yang merupakan sebuah metode untuk melakukan klasifikasi terhadap objek berdasarkan data training yang jaraknya paling dekat dengan objek tersebut. Prediksi waktu kelulusan mahasiswa menggunakan algoritma k-NN dilakukan dengan tahapan data selection , transformasi data, data mining dan interpretation . Penelitian ini menggunakan 154 data latih dan 10 data uji. Ketepatan prediksi berdasarkan data uji yang diambil secara acak adalah 90%.
An Efficient Monte Carlo Approach to Compute PageRank for Large Graphs on a Single PC

Abstract This paper describes a novel Monte Carlo based random walk to compute PageRanks of nodes in a large graph on a single PC. The target graphs of this paper are ones whose size is larger than the physical memory. In such an environment, memory management is a difficult task for simulating the random walk among the nodes. We propose a novel method that partitions the graph into subgraphs in order to make them fit into the physical memory, and conducts the random walk for each subgraph. By evaluating the walks lazily, we can conduct the walks only in a subgraph and approximate the random walk by rotating the subgraphs. In computational experiments, the proposed method exhibits good performance for existing large graphs with several passes of the graph data.
Electrical Time Recovery in OTDM

We explicate the necessity and feasibility of directly recovering an electrical frame clock in OTDM systems and present a concrete scheme. One bit frame header with a different amplitude is inserted into every frame in order to make the OTDM signal contain the frame frequency spectral line in its signal spectrum. The performance is analyzed, including the time jitter and the bit-error caused by it.
A NOVEL REFACTORING APPRAOCH TO REMOVE THE SMELS PRESENT IN PROGRAMMING LANGUAGES

Mash ups are the one which is created as a new source by using the knowledge of already existing sources. The Mash ups are used to create and develop new innovative ideas from the already existing knowledge’s. However the mash up creation by users may tend to some deficiencies which need to be concentrated to avoid the complexity. There may be a possibility of creation of smells during the mash up creation process which need to be addressed well in order to avoid the software failure. This is avoided by introducing the methodology called refactoring approach which tends to find and eliminate the possible smells which may occur at the time of mash up creation. However in programming cannot support the programming languages with different object behavior in different places. The refactoring cannot be applicable for the smells identified in the new type of programming language. It is overcome in our work by analyzing the object behavior when it is used in different places. Based on this knowledge, refactoring has been applied with the consideration of the moving the objects across different classes and modules and predict the behavior changes occurred in the programming languages. By doing so, efficient refactoring can be done which can be used for any type of programming languages in order to avoid the smells at the time of mash up creation. The experimental conducted were proves that the proposed methodology lead to better performance than the existing approach in terms of mash up creation.
Model-Based Test Case Prioritization Using Neural Network Classification

Model-based testing for real-life software systems often require a large number of tests, all of which cannot exhaustively be run due to time and cost constraints. Thus, it is necessary to prioritize the test cases in accordance with their importance the tester perceives. In this paper, this problem is solved by improving our given previous study, namely, applying classification approachto the results of our previous study functional relationship between the test case prioritization groupmembership and the two attributes: important index and frequency for all events belonging togiven group are established. A for classification purpose, neural network (NN)that is the most advances is preferred and a data set obtainedfrom our study for all test cases is classified using multilayer perceptron (MLP) NN. The classification results for commercial test prioritization application show the high classification accuracies about 96 % and the acceptable test prioritizationperformances areachieved.
A modeling language and its supporting tools for avionics systems

Practical experience with the object-oriented development of complex reusable avionics application software has led to the recognition that domain-specific model-based approaches to software component and system configuration are not only viable, but also essential to success in terms of productivity, assurance, and quality. This technology requires a domain-specific modeling approach as it is closely related to the model of computation utilized in the underlying integration platform, and must be designed to support analysis and synthesis activities as well for fullest benefit. The modeling approach goes beyond the current capabilities of standard tools, like UML, and is specialized for the needs of the application domain. In this paper, we present a modeling language, the Embedded System Modeling Language (ESML), which supports the architectural modeling of avionics systems built using the Boeing Bold Stroke architecture. ESML utilizes integrated modeling of software components, component interactions, hardware configurations, scheduling policies, and other aspects relevant for the system developer. The modeling language is supported by a multi-aspect visual editor, which allows the diagrammatic specification of complex models.
Could olfactory displays improve data visualization?

Smell (olfaction) can be critical to our daily living - for example, smelling smoke from a fire in time to leave a burning building - but it is seldom used in data visualizations or virtual reality (VR) systems. Could olfaction displays (devices that output scented air) augment data visualization, that is, communicate information relevant to many fields? Perhaps incorporating underutilized modalities such as haptics (touch), olfaction, and gustation (taste) as data visualization aids is the next logical step to optimizing human information processing. It seems reasonable that adding the sense of smell to a virtual environment (VE) would enhance the environment's presence or "realness". Attempts have been made to include olfactory displays in VEs (for example, John Cater's Deep Immersion Virtual Environment Laboratory at the Southwest Research Institute), but most have been unsuccessful. One reason is the lack of a standard to represent and playback smells. Olfactory effects could play a crucial role in certain training environments, such as those for fire fighters and medical personnel. Current VEs include advanced visual and audio outputs, but smell is either very limited or absent. Very few studies focus on this subject, with most discussing ambient (whole-room and long-duration) rather than specific (localized and short-duration) odors, which are relevant to data visualization and VR.
When standards become business models: reinterpreting “failure” in the standardization paradigm

Purpose - This paper aims to explore the question: 'What is the relationship between standards and business models? ' and illustrate the conceptual linkage with reference to developments in the mobile communications industry. Design/methodology/approach - A succinct overview of literature on standardization, business models and platform markets in the paper leads to a hypothesis on the relationship between present-day standardization processes and business model design. This is then explored by means of three short case studies. Findings - The case studies of Mobile-ICT illustrate that regardless of institutional orientation or process, the most important standardization strategy for equipment and service providers is to create platforms that are open to the development of complementary products and services while at the same time preserving the proprietary edge necessary to ensure lock-in effects. All three cases yielded strong reasons to doubt whether many of the traditional advantages of standardization (interoperability, economies of scale, positive externalities, etc.) will be achieved equitably for all of the stakeholders. Originality/value - This is an exploratory paper that aims to shed light on present-day concerns about "failure" in the standardisation paradigm.
A Functional Semantics for the Awkward Squad

It can be very difficult to debug impure code, let alone prove i ts correctness. To address these problems, we provide a functional specification of three central components of Peyton Jones’s awkw ard squad: teletype IO, mutable state, and concurrency. By constructing an internal model of such concepts within our programming language, we can test, debug, and reason about programs that perform IO as if they were pure. In particular, we demonstrate how our specifications may be used in tandem with QuickCheck to autom atically test complex pointer algorithms and concurrent programs.
The Effectiveness of Browsing

SEVERAL PREVIOUS STUDIES have attempted to determine the comparative importance of the different sources employed by scientists in locating information. Voigt reviewed some of these studies and found considerable agreement in the ranking of the various sources.1 Browsing was found to be the method most often used to learn about printed information sources. The second most important method was the recommendations of colleagues. Ot4er sources, listed in order of their importance, were: the scientist's own memory, citations found in books and periodicals, personal indexes, and library catalogs. The use studies reviewed by Voigt compared the different methods of locating information from a quantitative point of view. That is, the various studies ranked the methods according to how often they were used or how much information they produced. Except for a few indexing studies, such as the Cranfield investigations, which have included library card catalogs, very little research is available about the quality of the information produced by each of the methods. Therefore, little is known about the value or usefulness of information discovered in different ways in libraries. One study which briefly touched on the usefulness of books and serials found through browsing was made by Fussier and Simon.2 They found that
PEMBELAJARAN FISIKA DENGAN INKUIRI TERBIMBING MENGGUNAKAN HYPERMEDIA DAN MEDIA RIIL DITINJAU GAYA BELAJAR DAN KEMAMPUAN AWAL

Penelitian ini bertujuan untuk mengetahui perbedaan pembelajaran dengan inkuiri terbimbing menggunakan hypermedia dan media riil ditinjau dari kemampuan awal dan gaya belajar, serta mengetahui interaksi antara variabel terhadap prestasi belajar siswa. Penelitian ini menggunakan metode eksperimen. Sampel penelitian ditentukan dengan teknik cluster random sampling yang terdiri 4 kelas. Kelas eksperimen I diberi pembelajaran dengan hypermedia dan Kelas eksperimen II dengan media riil. Pengumpulan data menggunakan teknik tes utuk prestasi belajar dan kemampuan awal, angket untuk gaya belajar. Uji hipotesis penelitian menggunakan anava dengan desain 2x3x2. Hasil penelitian menunjukkan bahwa (1) terdapat perbedaan prestasi belajar siswa dengan metode pembelajaran inkuiri terbimbing menggunakan hypermedia dan media riil, siswa yang menggunakan hypermedia memberikan rataan prestasi belajar ranah kognitif yang lebih baik daripada media riil, (2) terdapat perbedaan prestasi belajar fisika, antara siswa yang mempunyai gaya belajar visual, auditorial, dan kinestetik (3) terdapat perbedaan prestasi belajar yang signifikan antara siswa yang mempunyai kemampuan awal tinggi dan rendah, siswa yang mempunyai kemampuan awal tinggi mencapai prestasi belajar yang lebih tinggi daripada siswa yang memiliki kemampuan awal rendah, (4) terdapat interaksi antara media pembelajaran yang digunakan dengan gaya belajar siswa terhadap prestasi belajar fisika, (5) terdapat interaksi antara media pembelajaran dengan kemampuan awal terhadap prestasi belajar fisika, (6) tidak terdapat interaksi antara gaya belajar dan kemampuan awal terhadap prestasi belajar fisika, (7) tidak terdapat interaksi antara media pembelajaran dengan gaya belajar dan kemampuan awal terhadap prestasi belajar fisika. Kata kunci: pembelajaran fisika, inkuiri terbimbing, hypermedia, media riil, gaya belajar, Kemampuan awal