Miracle-dz/UniFAR-FTUs huggingface dataset:
A workflow for reproducing mean benthic gas fluxes

Long‐term data sets provide unique opportunities to examine temporal variability of key ecosystem processes. The need for such data sets is becoming increasingly important as we try to quantify the impact of human activities across various scales and in some cases, as we try to determine the success of management interventions. Unfortunately, long‐term benthic flux data sets for coastal ecosystems are rare and curating them is a challenge. If we wish to make our data available to others now and into the future, however, then we need to provide mechanisms that allow others to understand our methods, access the data, reproduce the results, and see updates as they become available. Here we use techniques, learned through the EarthCube Ontosoft Geoscience Paper of the Future project, to develop best practices to allow us to share a long‐term data set of directly measured net sediment N2 fluxes and sediment oxygen demand at two sites in Narragansett Bay, Rhode Island (USA). This technical report describes the process we used, the challenges we faced, and the steps we will take in the future to ensure transparency and reproducibility. By developing these data and software sharing tools we hope to help disseminate well‐curated data with provenance as well as products from these data, so that the community can better assess how this temperate estuary has changed over time. We also hope to provide a data sharing model for others to follow so that long‐term estuarine data are more easily shared and not lost over time.
AIS Electronic Library (AISeL) AIS Electronic Library (AISeL)

The shift from on-premise to cloud software has fundamentally changed the interactions between enterprise software vendors and their users. Where user involvement has traditionally been a challenge, increasingly large amounts of user input now allow for data-driven requirements engineering (RE). Research has paid little attention so far to the changes entailed by data-driven RE and addressed neither technical nor empirical perspectives of data-driven RE in enterprise software development. We aim to understand how the increasing availability of large amounts of user input impact RE in enterprise cloud software development. We provide a conceptualization of the newly available user input and how it changes traditional RE. We collect and analyze rich data from multiple product units at a leading enterprise software company and examine the integration of user input into RE; specifically requirements discovery, prioritization, experimentation, and specification. We thereby aim to contribute to non-normative and empirical work on RE.
Information Technology and Electrical Engineering-Devices and Systems, Materials and Technologies for the Future

A strictly mass conserving VOF method based on the advection of a local height function, which was used e.g. for water impact problems, is applied also for the simulation of MFD free surface flows. Specific stress and pressure boundary conditions are employed at the melt free surface. An experimental validating of the method was obtained by the good agreement of the calculated free surface form with the measurements performed in a laboratory induction coreless furnace. Index Terms – MFD free surface flow, finite difference method, VOF method, local height function, pressure at melt surface in electromagnetic field
Deep Learning Inference with Intel PAC on Dell EMC Infrastructure – Part II

Deep learning is a class of machine learning that learns a neural network model from sample data sets over a series of training iterations and loss function [1]. The output of this phase, the learned model, is then used to make predictions on new data. While this model-learning process lends itself well to singleinstruction multiple data (SIMD) type computing with coarsegrain architectures like many-core CPUs and GPUs, the inferencing process is much more amenable to irregular, finegrain architectures like FPGAs, which allow for greater architectural flexibility to meet specific application requirements: latency, throughput, power, etc. Inferencing is the stage where most enterprises realize the business value of their AI investments.
On scalability of software-defined networking

In this article, we deconstruct scalability concerns in software-defined networking and argue that they are not unique to SDN. We explore the often voiced concerns in different settings, discuss scalability trade-offs in the SDN design space, and present some recent research on SDN scalability. Moreover, we enumerate overlooked yet important opportunities and challenges in scalability beyond the commonly used performance metrics.
The USTC System for Blizzard Challenge 2016

This paper introduces the details of the speech synthesis entry developed by the USTC team for Blizzard Challenge 2016. A 5-hour corpus of highly expressive children’s audiobook was released this year to the participants. An hidden Markov model (HMM)-based unit selection system was built for the task. In addition, we utilized deep neural networks to improve the performance of our system, in both the front-end text processing and back-end acoustic modeling for unit selection. Firstly, an long short term memory (LSTM)-based recurrent neural networks (RNN) were adopted for tone and breaking indices (ToBI) prediction. Secondly, another LSTM-RNN was adopted to extract distributional representation of contextual features. The context embeddings can be used for evaluating contextual similarities between candidate and target units at the unit selection time. The evaluation results show the effectiveness of the submitted system. Our system achieved the highest scores in all metrics.
Learning Latent Features With Pairwise Penalties in Low-Rank Matrix Completion

Low-rank matrix completion has achieved great success in many real-world data applications. A matrix factorization model that learns latent features is usually employed and, to improve prediction performance, the similarities between latent variables can be exploited by pairwise learning using the graph regularized matrix factorization (GRMF) method. However, existing GRMF approaches often use the squared loss to measure the pairwise differences, which may be overly influenced by dissimilar pairs and lead to inferior prediction. To fully empower pairwise learning for matrix completion, we propose a general optimization framework that allows a rich class of (non-)convex pairwise penalty functions. A new and efficient algorithm is developed to solve the proposed optimization problem, with a theoretical convergence guarantee under mild assumptions. In an important situation where the latent variables form a small number of subgroups, its statistical guarantee is also developed. In particular, we theoretically characterize the performance of the complexity-regularized maximum likelihood estimator, as a special case of our framework, which is shown to have smaller errors when compared to the standard matrix completion framework without pairwise penalties. We conduct extensive experiments on both synthetic and real datasets to demonstrate the superior performance of this general framework.
EVALUATING A CONTINUOUS SONIC INTERACTION: COMPARING A PERFORMABLE ACOUSTIC AND DIGITAL EVERYDAY SOUND

This (cid:1) paper (cid:1) reports (cid:1) on (cid:1) the (cid:1) procedure (cid:1) and (cid:1) results (cid:1) of (cid:1) an (cid:1) experiment (cid:1) to (cid:1) evaluate (cid:1) a (cid:1) continuous (cid:1) sonic (cid:1) interaction (cid:1) with (cid:1) an (cid:1) everyday (cid:1) wind-like (cid:1) sound (cid:1) created (cid:1) by (cid:1) both (cid:1) acoustic (cid:1) and (cid:1) digital (cid:1) means. (cid:1) The (cid:1) interaction (cid:1) is (cid:1) facilitated (cid:1) by (cid:1) a (cid:1) mechanical (cid:1) theatre (cid:1) sound (cid:1) effect, (cid:1) an (cid:1) acoustic (cid:1) wind (cid:1) machine, (cid:1) which (cid:1) is (cid:1) performed (cid:1) by (cid:1) participants. (cid:1) This (cid:1) work (cid:1) is (cid:1) part (cid:1) of (cid:1) wider (cid:1) research (cid:1) into (cid:1) the (cid:1) potential (cid:1) of (cid:1) theatre (cid:1) sound (cid:1) effect (cid:1) designs (cid:1) as (cid:1) a (cid:1) means (cid:1) to (cid:1) study (cid:1) multisensory (cid:1) feedback (cid:1)
for short range

—Design details and characterisation results of an integrated digital to light converter (DLC) for short range visible light communication (VLC) is reported. The integrated DLC can generate 16 light intensity levels at fast switching speeds, up to 500 MHz, thus enabling fast intensity modulated VLC. Data rates up to 365 Mb/s are achieved with bit error rate (BER) 1 × 10 − 3 at a link distance of 5 cm and average electrical power efﬁciency of 70%. Optimisation in the micro light emitting diode ( µ LED) manufacturing process has resulted in approximately three fold increase in data rate of the system. Spectrally efﬁcient modulation schemes like orthogonal frequency division multiplexing (OFDM) and pulse amplitude modulation (PAM) are also demonstrated using this integrated system.
The Impact of Person-Organization Fit on Turnover in Open Source Software Projects

Participant turnover in open source software development is a critical problem. Using Schneider’s (1987) Attraction Selection and Attrition Framework and the notion of PersonOrganization fit, we hypothesize about the relationship between a participant's fit with an open source project and turnover. Specifically we predict that value fit, needs-supplies fit and demandsabilities fit between a participant and a particular project will have a negative association with participant turnover and that the role of the participant in the project acts a moderator. An empirical study is designed to examine the hypotheses using a combination of survey and archival data. Since for-profit companies are increasingly leveraging open source software development, implications of our findings will be useful for project managers seeking to retain talented contributors in the absence of financial compensation.
A Multi-Phase Protocol for Negotiation with Interdependent Issues

Multi-issue negotiations are a central component of many important coordination challenges. Almost all previous work in this area has assumed that negotiation issues are independent, making it relatively easy to find high-quality agreements. In many real-world problem domains, however, issues are interdependent, making hard to find good agreement due to the nonlinearity of the agent's utility functions. The key challenge, in this context, is finding high-quality agreements without making unrealistic demands concerning how much agents reveal about their utilities. In this paper, we propose a protocol wherein the negotiating agents, working with the mediator, progress through a multi-phase narrowing of the space of possible agreements. We show that our method outperforms existing methods in large nonlinear utility spaces.
Cross-Domain Recommender Systems

Most recommender systems work on single domains, i.e., they recommend items related to the same domain where users have expressed ratings. However, the integration of different domains into one recommender system could allow users to span over different types of items. For instance, users that have watched live TV programs could like to be recommended with on-demand movies, music, mobile applications, friends to connect to, etc. This paper focuses on cross-domain collaborative recommender systems, whose aim is to suggest items related to multiple domains. We first formalize the cross-domain problem in order to provide a common framework for the classification and the evaluation of state-of-the-art algorithms. We later define a new class of cross-domain algorithms based on neighborhood collaborative filtering, either item-based or user-based. The main idea is to first model the classical similarity relationships (e.g., Pearson, cosine) as a direct graph and to later explore all possible paths connecting users or items in order to find new, cross-domain, relationships. The algorithms have been tested on three cross-domain scenarios artificially reproduced by partitioning the Netflix dataset.
Stay off the lawn - Creating smooth paths based on region preferences

Modern virtual environments can contain a variety of characters and traversable regions. Each character may have different preferences for the traversable region types. Pedestrians may prefer to walk on sidewalks, but they may occasionally need to traverse roads and dirt paths. By contrast, wild animals might try to stay in forest areas, but they are able to leave their protective environment when necessary. This paper presents a novel path planning method named MIRAN (Modified Indicative Routes and Navigation) that takes a character’s region preferences into account. Given an indicative route as a rough estimation of a character’s preferred route, MIRAN efficiently computes a visually convincing path that is smooth, keeps clearance from obstacles, avoids unnecessary detours, and allows local changes to avoid other characters. To the best of our knowledge, MIRAN is the first path planning method that supports the above features while using an exact representation of the navigable space. Experiments show that with our approach a wide range of different character behaviors can be simulated. It also overcomes problems that occur in previous path planning methods such as the Indicative Route Method. The resulting paths are well-suited for real-time simulations and gaming applications.
Clustering the wireless Ad Hoc networks : A distributed learning automata approach

In Ad Hoc networks, the performance is significantly degraded as the size of the network grows. The network clustering by which the nodes are hierarchically organized on the basis of the proximity relieves this performance degradation. Finding the weakly connected dominating set (WCDS) is a promising approach for clustering the wireless Ad Hoc networks. Finding theminimumWCDS in the unit disk graph is an NP-Hard problem, and a host of approximation algorithms has been proposed. In this article, we first proposed a centralized approximation algorithm called DLA-CC based on distributed learning automata (DLA) for finding a near optimal solution to the minimumWCDS problem. Then, we propose a DLA-based clustering algorithm called DLA-DC for clustering the wireless Ad Hoc networks. The proposed cluster formation algorithm is a distributed implementation of DLA-CC, in which the dominator nodes and their closed neighbors assume the role of the cluster-heads and cluster members, respectively. In this article, we compute the worst case running time and message complexity of the clustering algorithm for finding a near optimal cluster-head set. We argue that by a proper choice of the learning rate of the clustering algorithm, a trade-off between the running time and message complexity of algorithm with the clusterhead set size (clustering optimality) can be made. The simulation results show the superiority of the proposed algorithms over the existing methods. © 2009 Elsevier Inc. All rights reserved.
A Concise Taxonomy for Describing Data as an Art Material

Abstract:How can we describe data when used as an art material? As the number of artists using data in their work increases, so too must our ability to describe the material in a way that is understood by both specialist and general audiences alike. Based on a review of existing vocabularies, glossaries and taxonomies of data, we propose our own concise taxonomy. To conclude, we propose the adoption of this concise taxonomy by artists, critics and curators, and suggest that ongoing refinement of the taxonomy takes place through crowdsourced knowledge sharing on the Web.
Title : Systematic approaches for increasing soundness and precision of static analyzers

Building static analyzers for modern programming languages is difficult. Often soundness is a requirement, perhaps with some well-defined exceptions, and precision must be adequate for producing useful results on realistic input programs. Formally proving such properties of a complex static analysis implementation is rarely an option in practice, which raises the challenge of how to identify causes and importance of soundness and precision problems. Through a series of examples, we present our experience with semi-automated methods based on delta debugging and dynamic analysis for increasing soundness and precision of a static analyzer for JavaScript. The individual methods are well known, but to our knowledge rarely used systematically and in combination. CCS Concepts •Theory of computation → Program analysis
Streaming Algorithms for Constrained Submodular Maximization

It is of great importance to design streaming algorithms for submodular maximization, as many applications (e.g., crowdsourcing) have large volume of data satisfying the well-known ''diminishing returns'' property, which cannot be handled by offline algorithms requiring full access to the whole dataset. However, streaming submodular maximization has been less studied than the offline algorithms due to the hardness brought by more stringent requirements on memory consumption. In this paper, we consider the fundamental problem of Submodular Maximization under k-System and d-Knapsack constraints (SMSK), which has only been successfully addressed by offline algorithms in previous studies, and we propose the first streaming algorithm for it with provable performance bounds. Our approach adopts a novel algorithmic framework dubbed MultiplexGreedy, making it also perform well under a single k-system constraint. For the special case of SMSK with only d-knapsack constraints, we further propose a streaming algorithm with better performance ratios than the state-of-the-art algorithms. As the SMSK problem generalizes most of the major problems studied in submodular maximization, our algorithms have wide applications in big data processing.
Reasoning on aspectual-temporal information in French within conceptual graphs

This paper presents a modeling of time, aspect and verbal meanings in natural language processing within Simple Conceptual Graphs (SCG) by way of Semantico-Cognitive Schemes (SCS) and the aspectual-temporal theory. Our system translates a semantico-cognitive representation in terms of SCGs. The SCS allows us to build a representation of a text taking into account fine subtleties of natural language as the information about time and aspect. The Conceptual Graphs formalism provides a powerful inferential mechanism which makes it possible to reason from texts. Our work bears on French texts. A text is represented by two different structures both represented within the SCG model. The first structure models the semantico-cognitive representation while the second one is the temporal diagram representing the temporal constraints between the situations described in the text. Linking these structures entails an expansion of the original SCG model.
Linux Data Integrity Extensions

Many databases and filesystems feature checksums on their logical blocks, enabling detection of corrupted data. The scenario most people are familiar with involves bad sectors which develop while data is stored on disk. However, many corruptions are actually a result of errors that occurred when the data was originally written. While a database or filesystem can detect the corruption when data is eventually read back, the good data may have been lost forever. A recent addition to SCSI allows extra protection information to be exchanged between controller and disk. We have extended this capability up into Linux, allowing filesystems (and eventually applications) to be able to attach integrity metadata to I/O requests. Controllers and disks can then verify the integrity of an I/O before committing it to stable storage. This paper will describe the changes needed to make Linux a data-integrity-aware OS.
Change-point detection in a sequence of bags-of-data

In this paper, the limitation that is prominent in most existing works of change-point detection methods is addressed by proposing a nonparametric, computationally efficient method. The limitation is that most works assume that each data point observed at each time step is a single multi-dimensional vector. However, there are many situations where this does not hold. Therefore, a setting where each observation is a collection of random variables, which we call a bag of data, is considered.
USING SIMULATION AS A TOOL TO IMPROVE HEALTH PRACTITIONER KNOWLEDGE, SKILLS AND CONFIDENCE IN DELIVERING SAFER CARE TO ACUTELY UNWELL PATIENTS

tilation rate was set for a period of 4 hours and measured against the devices performance. This was repeated 3 separate times, for 4 hour periods, of 10, 20 and 30 breaths/min. The device allows for historical and real-time plotted graphs of RR, which were measured against the set CMV rate over the periods. The device provided accurate and stable measurement of RR, including RR extremes in a controlled environment, using a human patient simulator under mandatory controlled ventilation. Connection problems were identified between the sensor and hardware device during the trial, which were communicated to the product engineers. Discussion The utilisation of simulation centre expertise and technologies by engineers, clinicians and others in the medical device field may expedite product design, identify design errors, increase end-user satisfaction and reduce patient harm.2 Conclusion Evaluating medical devices utilising simulation offers the opportunity to discover and correct design errors which may not become apparent until later, during clinical trials or post-market surveillance.
PENGARUH KEMISKINAN DAN PERTUMBUHAN EKONOMI TERHADAP INDEKS PEMBANGUNAN MANUSIA DI PROVINSI RIAU

This study aims to determine the effect of economic growth on poverty and human development index in Riau Province. The data used is secondary data with cross section including data one year in every district in the province of Riau on each variable used.This secondary data sourced from the Central Statistics Agency Pekanbaru. Methods of data analysis used in this research is quantitative method with data analysis and linear regression analysis using SPSS. In this model the independent variable used is the Poverty and Economic Growth. While the dependent variable is the Human Development Index. The result showed that first, Poverty and significant effect on the human development index in the province of Riau. From equation known poverty variables showed a coefficient of -0.079 means that if there is an increase poverty by 1% will decrease the HDI of 0.079% means that every increase in poverty would decrease the percentage of HDI in Riau Province. For poverty variables obtained t is greater than t table so Ho rejected Ha accepted. Second, Economic growth showed a coefficient of 0.024 means that if there is a change of economic growth of 1% there will be changes to the HDI of 0.024% means that any changes in the economic growth will affect the percentage of HDI in Riau Province.