Miracle-dz/UniFAR-FTUs huggingface dataset:
A Practical Approach to Process Streaming Data using Graph Database

In today’s information scenario, processing of data for exact knowledge has become a very important but critical task for the researchers and organizations. Involvement of Big Data and real time streaming data makes the data processing more challenging in order to extract and to visualize the exact data. In the most popular social media portals that includes streaming data like Twitter, Facebook and LinkedIn the rapidly growing information is updated several thousand times within one second. In this research, the extraction of streaming data from most popular social media portal (Facebook) is done using different SDKs, Graph API Explorer and other APIs and data processing is done using Cypher Query Language (CQL) and Neo4jCLient (C# API for Neo4j) in Microsoft C#. Then Neo4j Graph Database along with Microsoft Visual Studio 2013 is used for the Visualization and knowledge extraction. CQL facilitate the extraction of streaming data in an efficient manner as its development intended the processing of data in a linked manner. The processing and visualization of rapidly growing streaming data is done as linked data which makes the data and knowledge extraction very easy as the relationship between data is present along with the link between them.
Argo: A Real-Time Network-on-Chip Architecture With an Efficient GALS Implementation

In this paper, we present an area-efficient, globally asynchronous, locally synchronous network-on-chip (NoC) architecture for a hard real-time multiprocessor platform. The NoC implements message-passing communication between processor cores. It uses statically scheduled time-division multiplexing (TDM) to control the communication over a structure of routers, links, and network interfaces (NIs) to offer real-time guarantees. The area-efficient design is a result of two contributions: 1) asynchronous routers combined with TDM scheduling and 2) a novel NI microarchitecture. Together they result in a design in which data are transferred in a pipelined fashion, from the local memory of the sending core to the local memory of the receiving core, without any dynamic arbitration, buffering, and clock synchronization. The routers use two-phase bundled-data handshake latches based on the Mousetrap latch controller and are extended with a clock gating mechanism to reduce the energy consumption. The NIs integrate the direct memory access functionality and the TDM schedule, and use dual-ported local memories to avoid buffering, flow-control, and synchronization. To verify the design, we have implemented a 4 × 4 bitorus NoC in 65-nm CMOS technology and we present results on area, speed, and energy consumption for the router, NI, NoC, and postlayout.
30 AMAC : A Versatile and Efficient Receiver-Initiated Link Layer for Low-Power Wireless PRABAL DUTTA

We present A-MAC, a receiver-initiated link layer for low-power wireless networks that supports several services under a unified architecture, and does so more efficiently and scalably than prior approaches. A-MAC’s versatility stems from layering unicast, broadcast, wakeup, pollcast, and discovery above a single, flexible synchronization primitive. A-MAC’s efficiency stems from optimizing this primitive and with it the most consequential decision that a low-power link makes: whether to stay awake or go to sleep after probing the channel. Today’s receiver-initiated protocols require more time and energy to make this decision, and they exhibit worse judgment as well, leading to many false positives and negatives, and lower packet delivery ratios. A-MAC begins to make this decision quickly, and decides more conclusively and correctly in both the negative and affirmative. A-MAC’s scalability comes from reserving one channel for the initial handshake and different channels for data transfer. Our results show that: (i) a unified implementation is possible; (ii) A-MAC’s idle listening power increases by just 1.12× under interference, compared to 17.3× for LPL and 54.7× for RI-MAC; (iii) A-MAC offers high single-hop delivery ratios; (iv) network wakeup is faster and more channel efficient than LPL; and (v) collection routing performance exceeds the state-of-the-art.
Scalable statistics counters

Naive statistics counters that are commonly used to monitor system events and performance become a scalability bottleneck as systems become larger and more NUMA; furthermore some are so inaccurate that they are not useful. We present a number of techniques to address these problems, evaluating solutions in terms of performance, scalability, space overhead, and accuracy.
Spatiotemporal Distribution of Microearthquakes and Implications Around the Seismic Gap Between the Wenchuan and Lushan Earthquakes

The mechanism of why the rupture terminated sharply on both sides of the seismic gap between the Wenchuan and Lushan earthquakes is not yet known along the southwestern Longmen Shan fault zone. The accommodation modes of compressional strain and stress are still not known well in the gap subregion. Using the Match and Locate method, we obtained a detailed catalog of background microearthquakes around the seismic gap from May to December 2015. The characteristics of spatiotemporal distribution of the events is highlighted with a complete magnitude of 0.2. These small events tend to concentrate at different depth ranges on two sides of the seismic gap, indicating that the gap serves as a transition zone between the Wenchuan and Lushan source regions. Strain release through microearthquakes is an unlikely candidate for accommodating modes in the gap, because of the sustained deficit of microseismicity. A strong anticorrelation between background seismicity and monthly precipitation variation rate is observed for different magnitude scales. Enhanced seismicity may be activated by the terrestrial water storage reduction next to a precipitation peak with a time lag of 1–2 months during the summer and autumn, indicating a seasonal modulation of seismicity by the water storage cycle. The background microseismicity exhibits prominent responses to precipitation variation due to a short nucleation duration of less than one quarter.
Quantum machine learning using atom-in-molecule-based fragments selected on-the-fly.

First principles based exploration of chemical space deepens our understanding of chemistry, and might help with the design of new materials or experiments. Due to the computational cost of quantum chemistry methods and the immens number of theoretically possible stable compounds comprehensive in-silico screening remains prohibitive. To overcome this challenge, we combine atoms-in-molecules based fragments, dubbed "amons" (A), with active learning in transferable quantum machine learning (ML) models. The efficiency, accuracy, scalability, and transferability of resulting AML models is demonstrated for important molecular quantum properties, such as energies, forces, atomic charges NMR shifts, polarizabilities, and for systems ranging from organic molecules over 2D materials and water clusters to Watson-Crick DNA base-pairs and even ubiquitin. Conceptually, the AML approach extends Mendeleev's table to effectively account for chemical environments, which allows the systematic reconstruction of many chemistries from local building blocks.
TR 02-020 Supporting Innovation : A Behavior-Sketching Tool for Early Multimedia Design

To design innovative temporal and interactive behavior for a multimedia application, a multimedia designer needs to rapidly explore numerous behavioral design ideas early in the design process. Because current mechanisms do not adequately support this need, we have developed an electronic sketching tool called DEMAIS. DEMAIS enables a designer to rapidly sketch behavioral design ideas and then run that sketch to experience those ideas. Through its sketching language and layers, DEMAIS reduces the need to create multiple copies of similar storyboards when sketching behavior. In this paper, we discuss the use of layers for sketching, present usability lessons learned from an evaluation of the tool, and discuss how sketching toolkits can better support future applications.
ScholarWorks ScholarWorks

Children use popular web search tools, which are generally designed for adult users. Because children have different developmental needs than adults, these tools may not always adequately support their search for information. Moreover, even though search tools offer support to help in query formulation, these too are aimed at adults and may hinder children rather than help them. This calls for the examination of existing technologies in this area, to better understand what remains to be done when it comes to facilitating query-formulation tasks for young users. In this paper, we investigate interaction elements of query formulation–including query suggestion algorithms–for children. The primary goals of our research efforts are to: (i) examine existing plug-ins and interfaces that explicitly aid children’s query formulation; (ii) investigate children’s interactions with suggestions offered by a general-purpose query suggestion strategy vs. a counterpart designed with children in mind; and (iii) identify, via participatory design sessions, their preferences when it comes to tools / strategies that can help children find information and guide them through the query formulation process. Our analysis shows that existing tools do not meet children’s needs and expectations; the outcomes of our work can guide researchers and developers as they implement query formulation strategies for children.
SciPDFindexer : Distributed Information Retrieval system using MapReduce

Indexing allows the conversion of raw document collections into easily searchable formats. Bigger scale indexing poses some challenges in terms of efficiently distributing indexing computation on a cluster of nodes. MapReduce framework promises to be an effective tool for parallelizing such tasks as inverted index construction. We propose SciPDFindexer, a distributed information retrieval system for scientific articles in PDF. Given a large collection of scientific articles in PDF, our system parses and extracts metadata from the articles, and then indexes the extracted content using our proposed scheme.
Assessment of Risk Factors of a Completed Oil and Gas Project, with the Use of a Hybrid EVM-SAW Method

The motivation of current research study is the way of diagnosing and ranking risk factors of a completed real-world gas injection project. A Nominal Group Technique is used to establish a Risk Matrix Structure, and to identify risk factors. Next, a hybrid group Eigen Vector Method and Simple Additive Weighting is applied to assign the weighs to risk factors. In this procedure, it is assumed that risk factors influence on the project constraints which are scope, quality, cost and time. Moreover, at the whole assessment stages, the opinions of twelve experts are utilized through several expertise meetings. The results indicate that the top-ten risk factors are: Poor or nonexistent project management system in the project; Bad estimation of Scope of Work; Lack of frozen requirements; Lack of cooperation among consultants, suppliers or subcontractors; Overemphasis of technical specifications in design; Conduct planning without a view on the project priorities; Inadequate/Invalid project assumptions; Failure to receive requirements from the client; Lack of people skills in the project leadership; and Absence of precise/accurate information.
Regret in Online Combinatorial Optimization

We address online linear optimization problems when the possible actions of the decision maker are represented by binary vectors. The regret of the decision maker is the difference between her realized loss and the minimal loss she would have achieved by picking, in hindsight, the best possible action. Our goal is to understand the magnitude of the best possible minimax regret. We study the problem under three different assumptions for the feedback the decision maker receives: full information, and the partial information models of the so-called “semi-bandit” and “bandit” problems. In the full information case we show that the standard exponentially weighted average forecaster is a provably suboptimal strategy. For the semi-bandit model, by combining the Mirror Descent algorithm and the INF Implicitely Normalized Forecaster strategy, we are able to prove the first optimal bounds. Finally, in the bandit case we discuss existing results in light of a new lower bound, and suggest a conjecture on the optimal regret in that case.
A Statistical Study for File System Meta Data On High Performance Computing Sites Submitted in partial fulfillment of the requirements for the degree of Master of Science in Information Networking

ii Acknowledgements I would like to express my gratitude to my advisor Prof. Garth Gibson for working closely with me through the entire study, offering precious advice and guidance and helping connect with industrial and academic HPC sites. I would like to thank my academic adviser Prof. Greg Ganger for offering valuable advice on the paper and defense. I wish to acknowledge Meghan W. Abstract High performance parallel file systems are critical to the performance of super computers, are specialized to provide different computing services and are changing rapidly in both hardware and software, whose unusual access pattern has drawn great research interest. Yet little knowledge of how file systems evolve and how the way people use file systems change is known, even though significant effort and money has been put into upgrading storage device and designing new file systems. In this paper, we report on the statistics of supercomputing file systems from Parallel Data Lab (PDL) and Los Alamos National Lab (LANL) and compare the current data against their statistics 4 years ago to discover changes in technology and usage pattern and to observe new interesting characters.
An Attribute-Centre Based Decision Tree Classification Algorithm

Decision tree algorithms have very important place at classification model of data mining. In literature, algorithms use entropy concept or gini index to form the tree. The shape of the classes and their closeness to each other some of the factors that affect the performance of the algorithm. In this paper we introduce a new decision tree algorithm which employs data (attribute) folding method and variation of the class variables over the branches to be created. A comparative performance analysis has been held between the proposed algorithm and C4.5. Keywords—Classification, decision tree, split, pruning, entropy, gini.
Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction

The idea that brain activity could be used as a communication channel has rapidly developed. Indeed, Electroencephalography (EEG) is the most common technique to measure the brain activity on the scalp and in real-time. In this study we examine the use of EEG signals in Brain Computer Interface (BCI). This approach consists of combining the Empirical Mode Decomposition (EMD) and band power (BP) for the extraction of EEG signals in order to classify motor imagery (MI). This new feature extraction approach is intended for non-stationary and non-linear characteristics MI EEG. The EMD method is proposed to decompose the EEG signal into a set of stationary time series called Intrinsic Mode Functions (IMF). These IMFs are analyzed with the bandpower (BP) to detect the characteristics of sensorimotor rhythms (mu and beta) when a subject imagines a left or right hand movement. Finally, the data were just reconstructed with the specific IMFs and the bandpower is applied on the new database. Once the new feature vector is rebuilt, the classification of MI is performed using two types of classifiers: generative and discriminant. The results obtained show that the EMD allows the most reliable features to be extracted from EEG and that the classification rate obtained is higher and better than using the direct BP approach only. Such a system is a promising communication channel for people suffering from severe paralysis, for instance, people with myopathic diseases or muscular dystrophy (MD) in order to help them move a joystick to a desired direction corresponding to the specific motor imagery.
DAT A DRIVEN INTELLIGENCE FOR A SMARTER WORLD

ing and Library Use: Abstracting is permitted with credit to the source. Libraries are permitted to photocopy for private use of patrons, provided the percopy fee indicated in the code at the bottom of the first page is paid through the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923. Unsubscribe: If you no longer wish to receive this ComputingEdge mailing, please email IEEE Computer Society Customer Service at help@computer.org and type “unsubscribe ComputingEdge” in your subject line. IEEE prohibits discrimination, harassment, and bullying. For more information, visit www.ieee. org/web/aboutus/whatis/policies/p9-26.html. IEEE COMPUTER SOCIETY computer.org • +1 714 821 8380 www.computer.org/computingedge 1 IEEE Computer Society Magazine Editors in Chief Computer David Alan Grier (Interim), Djaghe LLC IEEE Software Ipek Ozkaya, Software Engineering Institute IEEE Internet Computing George Pallis, University of Cyprus IT Professional Irena Bojanova, NIST IEEE Security & Privacy David Nicol, University of Illinois at Urbana-Champaign IEEE Micro Lizy Kurian John, University of Texas, Austin IEEE Computer Graphics and Applications Torsten Möller, University of Vienna IEEE Pervasive Computing Marc Langheinrich, University of Lugano Computing in Science & Engineering Jim X. Chen, George Mason University IEEE Intelligent Systems V.S. Subrahmanian, Dartmouth College IEEE MultiMedia Shu-Ching Chen, Florida International University IEEE Annals of the History of Computing Gerardo Con Diaz, University of California, Davis JUNE 2019 • VOLUME 5, NUMBER 6
Approximate Seeds of Strings

In this paper we study approximate seeds of strings, that is, substrings of a given string x that cover (by concatenations or overlaps) a superstring of x, under a variety of distance rules (the Hamming distance, the edit distance, and the weighted edit distance). We solve the smallest distance approximate seed problem and the restricted smallest approximate seed problem in polynomial time and we prove that the general smallest approximate seed problem is NP-complete.
La marge africaine du canal du Mozambique, le système turbiditique du Zambèze : une approche « Source to Sink » au Méso – Cénozoïque

Le systeme Zambeze localise sur le versant est de l'Afrique est caracterise par un systeme turbiditique de tres grande dimension (x 100 km) et est associe a terre au troisieme plus grand bassin versant d'Afrique (1 320 000 km2) apres ceux du Nil et du Congo. A ce jour, peu d'etudes ont porte sur l'evolution du systeme terre - mer depuis le bassin versant en erosion jusqu'au bassin en sedimentation. Ce travail s'attachera a : [1] caracteriser et a discuter l'origine des deformations enregistrees dans le systeme sedimentaire Zambeze depuis 155 Ma, [2] quantifier les bilans erosion - sedimentation du systeme du Zambeze, pour une meilleure comprehension du routage sedimentaire.
Persistence and Corresponding Time Scales of Soil Moisture Dynamics During Summer in the Babao River Basin, Northwest China

Soil moisture (SM) is an essential Earth surface and climate system variable. Insights into its persistence and corresponding time scales can improve numerical modeling and climate system prediction. In this study, SM from 17 observation stations within the Babao River Basin, Northwest China, between 1 June and 31 August 2014 were used to investigate persistence and corresponding time scales via adaptive fractal analysis. By conducting an adaptive fractal analysis of net radiation and by estimating the complementary cumulative distribution function of precipitation intervals, the relation between meteorological factors and the persistence and time scales of SM are determined and discussed. Results show that persistence and corresponding time scales of SM could be described using a three‐phase concept diagram. (1) At short time scales (approximately 0–14 hr [4 cm], 0–15 hr [10 cm], or 0–19 hr [20 cm]), the persistence of SM at most observation stations shows a strong long‐range correlation or nonstationarity. This phenomenon is essentially due to evaporation influenced by net radiation processes and the effects of rain. (2) At moderate time scales (approximately 14–159 hr [4 cm], 15–161 hr [10 cm], or 19–143 hr [20 cm]), the persistence of SM mostly exhibits a weak long‐range correlation or antipersistence due to net radiation process uncertainty and the probability of precipitation. (3) At long time scales (approximately greater than 159 hr [4 cm], 161 hr [10 cm], and 143 hr [20 cm]), the persistence of SM dynamics exhibits antipersistence because a high probability of precipitation reverses changes in the SM persistence.
Automated annotation system for natural images

Automated annotation of digital images remains a highly challenging task. This process can be used for indexing, retrieving, and understanding of large collections of image data. This paper presents an image annotation system used for annotating natural images. The proposed system is using an efficient annotation model called Cross Media Relevance Model for the annotation process. Image's regions are described using a vocabulary of blobs generated from image features using the K-means clustering algorithm. Using SAIAPR TC-12 Dataset of annotated images it is estimated the joint probability of generating a word given the blobs in an image. The annotation process of each new image starts with a segmentation phase. An original and efficient segmentation algorithm based on a hexagonal structure is applied to obtain the list of regions. Each meaningful word assigned to the annotated image is retrieved from an ontology derived in an original manner starting from the hierarchical vocabulary associated with SAIAPR TC-12 and from the spatial relationships between regions.
Long Range Planning

An initial public offering (IPO) represents a unique milestone in the lifecycle of a firm. Yet, our understanding of the IPO phenomenon remains incomplete and marked by mixed findings. Using meta-analytical techniques on a sample of 123 empirical studies, we examine the factors that influence the short-term outcomes (i.e., underpricing and proceeds) and the long-term outcomes (i.e., financial performance and risk) of IPO firms. Our efforts yield few significant results for antecedents that prior research had proposed as effective signals of IPO firm quality, and we thus conclude that signaling theory is limited in its ability to capture the IPO process. Our findings, however, provide preliminary support for our contention that stakeholders influence short-term IPO outcomes depending on their temporal orientation. We also find strong support for our prediction that the IPO has consequences for long-term firm development. While high underpricing positively affects long-term firm performance, it also heightens long-term firm risk. Large proceeds result in higher long-term firm performance and lower firm risk, but they come at the expense of a higher percentage of equity being traded during the IPO.