OpenRaiser/Intern-Atlas huggingface dataset:
empathi: An ontology for Emergency Managing and Planning about Hazard Crisis
Venue: arXiv cs.SI 2018
Year: 2018

In the domain of emergency management during hazard crises, having sufficient situational awareness information is critical. It requires capturing and integrating information from sources such as satellite images, local sensors and social media content generated by local people. A bold obstacle to capturing, representing and integrating such heterogeneous and diverse information is lack of a proper ontology which properly conceptualizes this domain, aggregates and unifies datasets. Thus, in this paper, we introduce empathi ontology which conceptualizes the core concepts concerning with the domain of emergency managing and planning of hazard crises. Although empathi has a coarse-grained view, it considers the necessary concepts and relations being essential in this domain. This ontology is available at https://w3id.org/empathi/.
A Unified 3D Mapping Framework using a 3D or 2D LiDAR
Venue: arXiv cs.RO 2018
Year: 2018

Simultaneous Localization and Mapping (SLAM) has been considered as a solved problem thanks to the progress made in the past few years. However, the great majority of LiDAR-based SLAM algorithms are designed for a specific type of payload and therefore don't generalize across different platforms. In practice, this drawback causes the development, deployment and maintenance of an algorithm difficult. Consequently, our work focuses on improving the compatibility across different sensing payloads. Specifically, we extend the Cartographer SLAM library to handle different types of LiDAR including fixed or rotating, 2D or 3D LiDARs. By replacing the localization module of Cartographer and maintaining the sparse pose graph (SPG), the proposed framework can create high-quality 3D maps in real-time on different sensing payloads. Additionally, it brings the benefit of simplicity with only a few parameters need to be adjusted for each sensor type.
Random Temporal Skipping for Multirate Video Analysis
Venue: arXiv cs.CV 2018
Year: 2018

Current state-of-the-art approaches to video understanding adopt temporal jittering to simulate analyzing the video at varying frame rates. However, this does not work well for multirate videos, in which actions or subactions occur at different speeds. The frame sampling rate should vary in accordance with the different motion speeds. In this work, we propose a simple yet effective strategy, termed random temporal skipping, to address this situation. This strategy effectively handles multirate videos by randomizing the sampling rate during training. It is an exhaustive approach, which can potentially cover all motion speed variations. Furthermore, due to the large temporal skipping, our network can see video clips that originally cover over 100 frames. Such a time range is enough to analyze most actions/events. We also introduce an occlusion-aware optical flow learning method that generates improved motion maps for human action recognition. Our framework is end-to-end trainable, runs in real-time, and achieves state-of-the-art performance on six widely adopted video benchmarks.
Almost-unsupervised Speech Recognition with Close-to-zero Resource Based on Phonetic Structures Learned from Very Small Unpaired Speech and Text Data
Venue: arXiv cs.CL 2018
Year: 2018

Producing a large amount of annotated speech data for training ASR systems remains difficult for more than 95% of languages all over the world which are low-resourced. However, we note human babies start to learn the language by the sounds of a small number of exemplar words without hearing a large amount of data. We initiate some preliminary work in this direction in this paper. Audio Word2Vec is used to obtain embeddings of spoken words which carry phonetic information extracted from the signals. An autoencoder is used to generate embeddings of text words based on the articulatory features for the phoneme sequences. Both sets of embeddings for spoken and text words describe similar phonetic structures among words in their respective latent spaces. A mapping relation from the audio embeddings to text embeddings actually gives the word-level ASR. This can be learned by aligning a small number of spoken words and the corresponding text words in the embedding spaces. In the initial experiments only 200 annotated spoken words and one hour of speech data without annotation gave a word accuracy of 27.5%, which is low but a good starting point.
Key Stakeholders' Value Propositions for Feature Selection in Software-intensive Products: An Industrial Case Study
Venue: arXiv cs.SE 2018
Year: 2018

Numerous software companies are adopting value-based decision making. However, what does value mean for key stakeholders making decisions? How do different stakeholder groups understand value? Without an explicit understanding of what value means, decisions are subject to ambiguity and vagueness, which are likely to bias them. This case study provides an in-depth analysis of key stakeholders' value propositions when selecting features for a large telecommunications company's software-intensive product. Stakeholders' value propositions were elicited via interviews, which were analyzed using Grounded Theory coding techniques (open and selective coding). Thirty-six value propositions were identified and classified into six dimensions: customer value, market competitiveness, economic value/profitability, cost efficiency, technology & architecture, and company strategy. Our results show that although propositions in the customer value dimension were those mentioned the most, the concept of value for feature selection encompasses a wide range of value propositions. Moreover, stakeholder groups focused on different and complementary value dimensions, calling to the importance of involving all key stakeholders in the decision making process. Although our results are particularly relevant to companies similar to the one described herein, they aim to generate a learning process on value-based feature selection for practitioners and researchers in general.
Adaptive Transfer Learning in Deep Neural Networks: Wind Power Prediction using Knowledge Transfer from Region to Region and Between Different Task Domains
Venue: arXiv cs.LG 2018
Year: 2018

Transfer Learning (TL) in Deep Neural Networks is gaining importance because in most of the applications, the labeling of data is costly and time-consuming. Additionally, TL also provides an effective weight initialization strategy for Deep Neural Networks . This paper introduces the idea of Adaptive Transfer Learning in Deep Neural Networks (ATL-DNN) for wind power prediction. Specifically, we show in case of wind power prediction that adaptive TL of Deep Neural Networks system can be adaptively modified as regards training on a different wind farm is concerned. The proposed ATL-DNN technique is tested for short-term wind power prediction, where continuously arriving information has to be exploited. Adaptive TL not only helps in providing good weight initialization, but is also helpful to utilize the incoming data for effective learning. Additionally, the proposed ATL-DNN technique is shown to transfer knowledge between different task domains (wind power to wind speed prediction) and from one region to another region. The simulation results show that the proposed ATL-DNN technique achieves average values of 0.0637,0.0986, and 0.0984 for the Mean-Absolute-Error, Root-Mean-Squared-Error, and Standard-Deviation-Error, respectively.
Do interdisciplinary research teams deliver higher gains to science?
Venue: arXiv cs.DL 2018
Year: 2018

The present paper takes its place in the stream of studies that analyze the effect of interdisciplinarity on the impact of research output. Unlike previous studies, in this study the interdisciplinarity of the publications is not inferred through their citing or cited references, but rather by identifying the authors' designated fields of research. For this we draw on the scientific classification of Italian academics, and their publications as indexed in the WoS over a five-year period (2004-2008). We divide the publications in three subsets on the basis the nature of co-authorship: those papers coauthored with academics from different fields, which show high intensity of inter-field collaboration ("specific" collaboration, occurring in 110 pairings of fields); those papers coauthored with academics who are simply from different "non-specific" fields; and finally co-authorships within a single field. We then compare the citations of the papers and the impact factor of the publishing journals between the three subsets. The results show significant differences, generally in favor of the interdisciplinary authorships, in only one third (or slightly more) of the cases. The analysis provides the value of the median differences for each pair of publication subsets.
Neuromorphic hardware as a self-organizing computing system
Venue: arXiv cs.NE 2018
Year: 2018

This paper presents the self-organized neuromorphic architecture named SOMA. The objective is to study neural-based self-organization in computing systems and to prove the feasibility of a self-organizing hardware structure. Considering that these properties emerge from large scale and fully connected neural maps, we will focus on the definition of a self-organizing hardware architecture based on digital spiking neurons that offer hardware efficiency. From a biological point of view, this corresponds to a combination of the so-called synaptic and structural plasticities. We intend to define computational models able to simultaneously self-organize at both computation and communication levels, and we want these models to be hardware-compliant, fault tolerant and scalable by means of a neuro-cellular structure.
How long do top scientists maintain their stardom? An analysis by region, gender and discipline: evidence from Italy
Venue: arXiv cs.DL 2018
Year: 2018

We investigate the question of how long top scientists retain their stardom. We observe the research performance of all Italian professors in the sciences over three consecutive four-year periods, between 2001 and 2012. The top scientists of the first period are identified on the basis of research productivity, and their performance is then tracked through time. The analyses demonstrate that more than a third of the nation's top scientists maintain this status over the three consecutive periods, with higher shares occurring in the life sciences and lower ones in engineering. Compared to males, females are less likely to maintain top status. There are also regional differences, among which top status is less likely to survive in southern Italy than in the north. Finally we investigate the longevity of unproductive professors, and then check whether the career progress of the top and unproductive scientists is aligned with their respective performances. The results appear to have implications for national policies on academic recruitment and advancement.
A comparison of university performance scores and ranks by MNCS and FSS
Venue: arXiv cs.DL 2018
Year: 2018

In a previous article of ours, we explained the reasons why the MNCS and all similar per-publication citation indicators should not be used to measure research performance, whereas efficiency indicators (output to input) such as the FSS are valid indicators of performance. The problem frequently indicated in measuring efficiency indicators lies in the availability of input data. If we accept that such data are inaccessible, and instead resort to per-publication citation indicators, the question arises as to what extent institution performance rankings by MNCS are different from those by FSS (and so what effects such results could have on policy-makers, managers and other users of the rankings). Contrasting the 2008-2012 performance by MNCS and FSS of Italian universities in the Sciences, we try to answer that question at field, discipline, and overall university level. We present the descriptive statistics of the shifts in rank, and the correlations of both scores and ranks. The analysis reveals strong correlations in many fields but weak correlations in others. The extent of rank shifts is never negligible: a number of universities shift from top to non-top quartile ranks.
From rankings to funnel plots: the question of accounting for uncertainty when measuring university research performance
Venue: arXiv cs.DL 2018
Year: 2018

The work applies the funnel plot methodology to measure and visualize uncertainty in the research performance of Italian universities in the science disciplines. The performance assessment is carried out at both discipline and overall university level. The findings reveal that for most universities the citation-based indicator used gives insufficient statistical evidence to infer that their research productivity is inferior or superior to the average. This general observation is one that we could indeed expect in a higher education system that is essentially non-competitive. The question is whether the introduction of uncertainty in performance reporting, while technically sound, could weaken institutional motivation to work towards continuous improvement.
Research Issues in Mining User Behavioral Rules for Context-Aware Intelligent Mobile Applications
Venue: arXiv cs.LG 2018
Year: 2018

Context-awareness in smart mobile applications is a growing area of study, because of it's intelligence in the applications. In order to build context-aware intelligent applications, mining contextual behavioral rules of individual smartphone users utilizing their phone log data is the key. However, to mine these rules, a number of issues, such as the quality of smartphone data, understanding the relevancy of contexts, discretization of continuous contextual data, discovery of useful behavioral rules of individuals and their ordering, knowledge-based interactive post-mining for semantic understanding, and dynamic updating and management of rules according to their present behavior, are investigated. In this paper, we briefly discuss these issues and their potential solution directions for mining individuals' behavioral rules, for the purpose of building various context-aware intelligent mobile applications. We also summarize a number of real-life rule-based applications that intelligently assist individual smartphone users according to their behavioral rules in their daily activities.
Compositional Attention Networks for Interpretability in Natural Language Question Answering
Venue: arXiv cs.LG 2018
Year: 2018

MAC Net is a compositional attention network designed for Visual Question Answering. We propose a modified MAC net architecture for Natural Language Question Answering. Question Answering typically requires Language Understanding and multi-step Reasoning. MAC net's unique architecture - the separation between memory and control, facilitates data-driven iterative reasoning. This makes it an ideal candidate for solving tasks that involve logical reasoning. Our experiments with 20 bAbI tasks demonstrate the value of MAC net as a data-efficient and interpretable architecture for Natural Language Question Answering. The transparent nature of MAC net provides a highly granular view of the reasoning steps taken by the network in answering a query.
Should the research performance of scientists be distinguished by gender?
Venue: arXiv cs.DL 2018
Year: 2018

The literature on gender differences in research performance seems to suggest a gap between men and women, where the former outperform the latter. Whether one agrees with the different factors proposed to explain the phenomenon, it is worthwhile to verify if comparing the performance within each gender, rather than without distinction, gives significantly different ranking lists. If there were some structural factor that determined a penalty in performance of female researchers compared to their male peers, then under conditions of equal capacities of men and women, any comparative evaluations of individual performance that fail to account for gender differences would lead to distortion of the judgments in favor of men. In this work we measure the extent of differences in rank between the two methods of comparing performance in each field of the hard sciences: for professors in the Italian university system, we compare the distributions of research performance for men and women and subsequently the ranking lists with and without distinction by gender. The results are of interest for the optimization of efficient selection in formulation of recruitment, career advancement and incentive schemes.
Statistical robustness analysis of fractional and integer order PID controllers for the control of a nonlinear system
Venue: arXiv cs.SY 2018
Year: 2018

This paper presents the design and robustness analysis of fractional and integer order PID controllers for the control of a non-linear industrial process in the presence of parametric uncertainness and external disturbances. The nonlinear system is linearized using an input-output linearization technique. Three controllers were designed for the linearized system, an integer order PID controller, a fractional PID controller, and a SIMC PID controller. The robustness analysis of the proposed controllers is based on a factorial experimental design. The input factors for the experiment are the uncertainty in gains of the plant, the presence of random noise in the feedback loop, and the existence of external perturbations. The outputs of the experiment measure the performance of each controller through the time step response and the control action of each controller using the mean value and the standard deviation. The obtained results show that the fractional order PID controller has better performance in the presence of the analyzed experimental factors, especially in the control action indicating greater robustness and lower energy consumption.
Boost Blockchain Broadcast Propagation with Tree Routing
Venue: arXiv cs.DC 2018
Year: 2018

In recent years, with the rapid development and popularization of BitCoin, the research of blockchain technology has also shown growth. It has gradually become a new generation of distributed, non-centralized and trust-based technology solution. However, the blockchain operation is expensive and transaction is delayed. Take BitCoin as an example. On the one hand, a block is produced every ten minute. On the other hand, once the new block is generated, it takes a certain time to propagate world wide. The slow speed of propagation determines that BitCoin can not use too small block interval time. Ethereum also faces similar problems, so the concept of uncle block was introduced to reduce blockchain forks. This paper introduces a new tree structure based broadcast propagation routing model, providing a novel method to organize network nodes and message propagation mechanism. In oder to avoid the single node failure problem, the tree cluster routing is proposed. The research shows that the tree based routing can accelerate broadcast convergence time and reduce redundant traffic.
A multivariate stochastic model to assess research performance
Venue: arXiv cs.DL 2018
Year: 2018

There is a worldwide trend towards application of bibliometric research evaluation, in support of the needs of policy makers and research administrators. However the assumptions and limitations of bibliometric measurements suggest a probabilistic rather than the traditional deterministic approach to the assessment of research performance. The aim of this work is to propose a multivariate stochastic model for measuring the performance of individual scientists and to compare the results of its application with those arising from a deterministic approach. The dataset of the analysis covers the scientific production indexed in Web of Science for the 2006-2010 period, of over 900 Italian academic scientists working in two distinct fields of the life sciences.
Are the authors of highly cited articles also the most productive ones?
Venue: arXiv cs.DL 2018
Year: 2018

Ever more frequently, governments have decided to implement policy measures intended to foster and reward excellence in scientific research. This is in fact the intended purpose of national research assessment exercises. These are typically based on the analysis of the quality of the best research products; however a different approach to analysis and intervention is based on the measure of productivity of the individual scientists, meaning the overall impact of their entire scientific production over the period under observation. This work analyzes the convergence of the two approaches, asking if and to what measure the most productive scientists achieve highly-cited articles; or vice versa, what share of highly-cited articles is achieved by scientists that are "non-top" for productivity. To do this we use bibliometric indicators, applied to the 2004-2008 publications authored by academics of Italian universities and indexed in the Web of Science.
Measuring institutional research productivity for the life sciences: the importance of accounting for the order of authors in the byline
Venue: arXiv cs.DL 2018
Year: 2018

Accurate measurement of institutional research productivity should account for the real contribution of the research staff to the output produced in collaboration with other organizations. In the framework of bibliometric measurement, this implies accounting for both the number of co-authors and each individual's real contribution to scientific publications. Common practice in the life sciences is to indicate such contribution through the order of author names in the byline. In this work, we measure the distortion introduced to university-level bibliometric productivity rankings when the number of co-authors or their position in the byline is ignored. The field of observation consists of all Italian universities active in the life sciences (Biology and Medicine). The analysis is based on the research output of the university staff over the period 2004-2008. Based on the results, we recommend against the use of bibliometric indicators that ignore co-authorship and real contribution of each author to research outputs.
Improved Bounds for Randomly Sampling Colorings via Linear Programming
Venue: arXiv cs.DS 2018
Year: 2018

A well-known conjecture in computer science and statistical physics is that Glauber dynamics on the set of $k$-colorings of a graph $G$ on $n$ vertices with maximum degree $Δ$ is rapidly mixing for $k\geΔ+2$. In FOCS 1999, Vigoda showed that the flip dynamics (and therefore also Glauber dynamics) is rapidly mixing for any $k>\frac{11}{6}Δ$. It turns out that there is a natural barrier at $\frac{11}{6}$, below which there is no one-step coupling that is contractive with respect to the Hamming metric, even for the flip dynamics. We use linear programming and duality arguments to fully characterize the obstructions to going beyond $\frac{11}{6}$. These extremal configurations turn out to be quite brittle, and in this paper we use this to give two proofs that the Glauber dynamics is rapidly mixing for any $k\ge\left(\frac{11}{6} - ε_0\right)Δ$ for some absolute constant $ε_0>0$. This is the first improvement to Vigoda's result that holds for general graphs. Our first approach analyzes a variable-length coupling in which these configurations break apart with high probability before the coupling terminates, and our other approach analyzes a one-step path coupling with a new metric that counts the extremal configurations. Additionally, our results extend to list coloring, a widely studied generalization of coloring, where the previously best known results required $k > 2 Δ$.