This work provides a comprehensive analysis on naturalistic driving behavior for highways based on the highD data set. Two thematic fields are considered. First, some macroscopic and microscopic traffic statistics are provided. These include the traffic flow rate and the traffic density, as well as velocity, acceleration and distance distributions. Additionally, the dependencies to each other are examined and compared to related work. The second part investigates the distributions of criticality measures. The Time-To-Collision, Time-Headway and a third measure, which couples both, are analyzed. These measures are also combined with other indicators. Scenarios, in which these measures reach a critical level, are separately discussed. The results are compared to related work as well. The two main contributions of this work can be stated as follows. First, the analysis on the criticality measures can be used to find suitable thresholds for rare traffic scenarios. Second, the statistics provided in this work can also be utilized for traffic modeling, for example in simulation environments.
These two indicators fail for crossing scenarios. In such cases the indicators have to be adapted or replaced as discussed for example in @cite_4 .
@cite_4
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Abstract This paper addresses the problem of how city indicators and their supporting data are to be published on the Semantic Web so that automated analysis can be performed. With the publishing of ISO 37120, cities have a standard set of indicators that can be used to compare their performance. The problem is that no standards exist for publishing indicators on the Semantic Web. In this paper we introduce the Global City Indicator Ontology (GCIO). The GCIO addresses five issues: 1) how is meta-data associated with a single indicator value represented? 2) how are indicator definitions represented? 3) how is the data used to derive an indicator value represented? 4) how is indicator theme specific knowledge represented, and 5) how is city specific knowledge represented? The GCIO has been implemented and validated using the City of Toronto ISO 37120 indicators reported for 2013. Research continues in developing ontologies specifically for each of the indicator themes such as: Education, Shelter, Health, Transportation and Innovation. This paper discusses how preference information of the decision maker can in general be integrated into multiobjective search. The main idea is to first define the optimization goal in terms of a binary performance measure (indicator) and then to directly use this measure in the selection process. To this end, we propose a general indicator-based evolutionary algorithm (IBEA) that can be combined with arbitrary indicators. In contrast to existing algorithms, IBEA can be adapted to the preferences of the user and moreover does not require any additional diversity preservation mechanism such as fitness sharing to be used. It is shown on several continuous and discrete benchmark problems that IBEA can substantially improve on the results generated by two popular algorithms, namely NSGA-II and SPEA2, with respect to different performance measures. 1. Introduction, 355. — 2. Hiring as investment under uncertainty, 356. — 3. Applicant signaling, 358. — 4. Informational feedback and the definition of equilibrium, 359. — 5. Properties of informational equilibria: an example, 361. — 6. The informational impact of indices, 368. — Conclusions, 374. A new data structure for efficient similarity search in very large datasets of high-dimensional vectors is introduced. This structure called the inverted multi-index generalizes the inverted index idea by replacing the standard quantization within inverted indices with product quantization. For very similar retrieval complexity and pre-processing time, inverted multi-indices achieve a much denser subdivision of the search space compared to inverted indices, while retaining their memory efficiency. Our experiments with large datasets of SIFT and GIST vectors demonstrate that because of the denser subdivision, inverted multi-indices are able to return much shorter candidate lists with higher recall. Augmented with a suitable reranking procedure, multi-indices were able to significantly improve the speed of approximate nearest neighbor search on the dataset of 1 billion SIFT vectors compared to the best previously published systems, while achieving better recall and incurring only few percent of memory overhead. Data collected in run II of the Fermilab Tevatron are searched for indications of new electroweak-scale physics. Rather than focusing on particular new physics scenarios, CDF data are analyzed for discrepancies with the standard model prediction. A model-independent approach (VISTA) considers gross features of the data, and is sensitive to new large cross-section physics. Further sensitivity to new physics is provided by two additional algorithms: a Bump Hunter searches invariant mass distributions for "bumps'' that could indicate resonant production of new particles, and the SLEUTH procedure scans for data excesses at large summed transverse momentum. This combined global search for new physics in 2.0 fb(-1) of p (p) over bar collisions at root s = 1.96 TeV reveals no indication of physics beyond the standard model. The overall performance of content distribution networks as well as recently proposed information-centric networks rely on both memory and bandwidth capacities. The hit ratio is the key performance indicator which captures the bandwidth memory tradeoff for a given global performance. This paper focuses on the estimation of the hit ratio in a network of caches that employ the Random replacement policy (RND). Assuming that requests are independent and identically distributed, general expressions of miss probabilities for a single RND cache are provided as well as exact results for specific popularity distributions (such results also hold for the FIFO replacement policy). Moreover, for any Zipf popularity distribution with exponent @a>1, we obtain asymptotic equivalents for the miss probability in the case of large cache size. We extend the analysis to networks of RND caches, when the topology is either a line or a homogeneous tree. In that case, approximations for miss probabilities across the network are derived by neglecting time correlations between miss events at any node; the obtained results are compared to the same network using the Least-Recently-Used discipline, already addressed in the literature. We further analyze the case of a mixed tandem cache network where the two nodes employ either Random or Least-Recently-Used policies. In all scenarios, asymptotic formulas and approximations are extensively compared to simulation results and shown to be very accurate. Finally, our results enable us to propose recommendations for cache replacement disciplines in a network dedicated to content distribution. Mapping of science and technology can be done at different levels of aggregation, using a variety of methods. In this paper, we propose a method in which title words are used as indicators for the content of a research topic, and cited references are used as the context in which words get their meaning. Research topics are represented by sets of papers that are similar in terms of these word-reference combinations. In this way we use words without neglecting differences and changes in their meanings. The method has several advantages, such as high coverage of publications. As an illustration we apply the method to produce knowledge maps of information science. Sports analytics in general, and football (soccer in USA) analytics in particular, have evolved in recent years in an amazing way, thanks to automated or semi-automated sensing technologies that provide high-fidelity data streams extracted from every game. In this paper we propose a data-driven approach and show that there is a large potential to boost the understanding of football team performance. From observational data of football games we extract a set of pass-based performance indicators and summarize them in the H indicator. We observe a strong correlation among the proposed indicator and the success of a team, and therefore perform a simulation on the four major European championships (78 teams, almost 1500 games). The outcome of each game in the championship was replaced by a synthetic outcome (win, loss or draw) based on the performance indicators computed for each team. We found that the final rankings in the simulated championships are very close to the actual rankings in the real championships, and show that teams with high ranking error show extreme values of a defense attack efficiency measure, the Pezzali score. Our results are surprising given the simplicity of the proposed indicators, suggesting that a complex systems' view on football data has the potential of revealing hidden patterns and behavior of superior quality. In the field of evolutionary multi-criterion optimization, the hypervolume indicator is the only single set quality measure that is known to be strictly monotonic with regard to Pareto dominance: whenever a Pareto set approximation entirely dominates another one, then the indicator value of the dominant set will also be better. This property is of high interest and relevance for problems involving a large number of objective functions. However, the high computational effort required for hypervolume calculation has so far prevented the full exploitation of this indicator's potential; current hypervolume-based search algorithms are limited to problems with only a few objectives. This paper addresses this issue and proposes a fast search algorithm that uses Monte Carlo simulation to approximate the exact hypervolume values. The main idea is not that the actual indicator values are important, but rather that the rankings of solutions induced by the hypervolume indicator. In detail, we present HypE, a hypervolume estimation algorithm for multi-objective optimization, by which the accuracy of the estimates and the available computing resources can be traded off; thereby, not only do many-objective problems become feasible with hypervolume-based search, but also the runtime can be flexibly adapted. Moreover, we show how the same principle can be used to statistically compare the outcomes of different multi-objective optimizers with respect to the hypervolume----so far, statistical testing has been restricted to scenarios with few objectives. The experimental results indicate that HypE is highly effective for many-objective problems in comparison to existing multi-objective evolutionary algorithms. HypE is available for download at http: www.tik.ee.ethz.ch sop download supplementary hype . We present insights from a bibliometric analysis and scientific paper publication mining of 551 papers in Requirements Engineering (RE) series of conference (11 years from 2005 to 2015). We study cross-disciplinary and interdisciplinary nature of RE re- search by analyzing the cited disciplines in the reference section of each paper. We apply topic modeling on a corpus consisting of 551 abstracts and extract topics as frequently co-occurring and connected terms. We use topic modeling to study the structure and composition of RE research and analyze popular topics in industry as well as research track. Co-authorship in papers is an indicator of collaboration and interaction between scientists as well as institutions and we analyze co-authorship data to in- vestigate university-industry collaboration, internal and external collaborations. We present results on the distribution of the num- ber of co-authors in each paper as well as distribution of authors across world regions. We present our analysis on the public or proprietary dataset as well as the domain of the dataset used in studies published in Requirements Engineering (RE) series of conferences. The proliferation of misinformation in online news and its amplification by platforms are a growing concern, leading to numerous efforts to improve the detection of and response to misinformation. Given the variety of approaches, collective agreement on the indicators that signify credible content could allow for greater collaboration and data-sharing across initiatives. In this paper, we present an initial set of indicators for article credibility defined by a diverse coalition of experts. These indicators originate from both within an article's text as well as from external sources or article metadata. As a proof-of-concept, we present a dataset of 40 articles of varying credibility annotated with our indicators by 6 trained annotators using specialized platforms. We discuss future steps including expanding annotation, broadening the set of indicators, and considering their use by platforms and the public, towards the development of interoperable standards for content credibility. Due to the widespread use of encryption in Over-The-Top video streaming traffic, network operators generally lack insight into application-level quality indicators (e.g., video quality levels, buffer underruns, stalling duration). They are thus faced with the challenge of finding solutions for monitoring service performance and estimating customer Quality of Experience (QoE) degradations based solely on passive monitoring solutions deployed within their network. We address this challenge by considering the concrete case of YouTube, whereby we present a methodology for the classification of end users' QoE when watching YouTube videos, based only on statistical properties of encrypted network traffic. We have developed a system called YouQ which includes tools for monitoring and analysis of application-level quality indicators and corresponding traffic traces. Collected data is then used for the development of machine learning models for QoE classification based on computed traffic features per video session. To test the YouQ system and methodology, we collected a dataset corresponding to 1060 different YouTube videos streamed across 39 different bandwidth scenarios, and tested various classification models. Classification accuracy was found to be up to 84 when using three QoE classes ("low", "medium" or "high") and up to 91 when using binary classification (classes "low" and "high"). To improve the models in the future, we discuss why and when prediction errors occur. Moreover, we have analysed YouTube's adaptation algorithm, thus providing valuable insight into the logic behind the quality level selection strategy, which may also be of interest in improving future QoE estimation algorithms. Pupil dilation and blinks provide complementary, mutually exclusive indices of information processing. Though each index is associated with cognitive load, the occurrence of a blink precludes the measurement of pupil diameter. These indices have generally been assessed in independent literatures. We examine the extent to which these measures are related on two cognitive tasks using a novel method that quantifies the proportion of trials on which blinks occur at each sample acquired during the trial. This measure allows cross-correlation of continuous pupil-dilation and blink waveforms. Results indicate that blinks occur during early sensory processing and following sustained information processing. Pupil dilation better reflects sustained information processing. Together these indices provide a rich picture of the time course of information processing, from early reactivity through sustained cognition, and after stimulusrelated cognition ends. Descriptors: Blinks, Pupil dilation, Cognitive load, Attention, Stroop Bibliometric measures of individual scientific achievement are of particular interest if they can be used to predict future achievement. Here we report results of an empirical study of the predictive power of the h index compared with other indicators. Our findings indicate that the h index is better than other indicators considered (total citation count, citations per paper, and total paper count) in predicting future scientific achievement. We discuss reasons for the superiority of the h index. Relying on Gudykunst's cultural variability in communication (CVC) framework and culture-specific facial expressions of emotion, we examined how people's use of emoticons varies cross-culturally. By merging emoticon usage patterns on Twitter with Hofstede's national culture scores and national indicators across 78 countries, this study found that people within individualistic cultures favor horizontal and mouth-oriented emoticons like :), while those within collectivistic cultures favor vertical and eye-oriented emoticons like ^_^. Our study serves to demonstrate how recent big data-driven approaches can be used to test research hypotheses in cross-cultural communication effectively from the methodological triangulation perspective. Implications and limitations regarding the findings of this study are also discussed. Component failure in large-scale IT installations is becoming an ever larger problem as the number of components in a single cluster approaches a million. In this paper, we present and analyze field-gathered disk replacement data from a number of large production systems, including high-performance computing sites and internet services sites. About 100,000 disks are covered by this data, some for an entire lifetime of five years. The data include drives with SCSI and FC, as well as SATA interfaces. The mean time to failure (MTTF) of those drives, as specified in their datasheets, ranges from 1,000,000 to 1,500,000 hours, suggesting a nominal annual failure rate of at most 0.88 . We find that in the field, annual disk replacement rates typically exceed 1 , with 2-4 common and up to 13 observed on some systems. This suggests that field replacement is a fairly different process than one might predict based on datasheet MTTF. We also find evidence, based on records of disk replacements in the field, that failure rate is not constant with age, and that, rather than a significant infant mortality effect, we see a significant early onset of wearout degradation. That is, replacement rates in our data grew constantly with age, an effect often assumed not to set in until after a nominal lifetime of 5 years. Interestingly, we observe little difference in replacement rates between SCSI, FC and SATA drives, potentially an indication that disk-independent factors, such as operating conditions, affect replacement rates more than component specific factors. On the other hand, we see only one instance of a customer rejecting an entire population of disks as a bad batch, in this case because of media error rates, and this instance involved SATA disks. Time between replacement, a proxy for time between failure, is not well modeled by an exponential distribution and exhibits significant levels of correlation, including autocorrelation and long-range dependence. For arbitrary graphs G and H, a G-factor of H is a spanning subgraph of H composed of disjoint copies of G. G-factors are natural generalizations of l-factors (or perfect matchings), in which G replaces the complete graph on two vertices. Our results show that the perfect matching problem is essentially the only instance of the G-factor problem that is likely to admit a polynomial time bounded solution. Specifically, if G has any component with three or more vertices then the existence question for G-factors is NP-complete. (In all other cases the question can be resolved in polynomial time.) .br The notion of a G-factor is further generalized by replacing G by an arbitrary family of graphs. This generalization forms the foundation for an extension of the traditional theory of matching. This theory, whose details will be developed elsewhere, includes, in addition to further NP-completeness results, new polynomial algorithms and simple duality results. Some indication of the nature and scope of this theory are presented here. A binomial approximation theorem for dependent indicators using Stein's method and coupling is proved. The approximating binomial distribution B(n � ,p � ) is chosen in such a way that its first moment is equal to that of W and its variance is asymptotically equal to that of W as ntends to infinity where W is the sum of independent indicators and pis bounded away from 1. Three examples, one of which concerns two different approximations for the hypergeometric distribution, are given to illustrate applications of the theorem obtained. Abstract With the aid of a Bayesian statistical model of the natural course of relapsing remitting Multiple Sclerosis (MS), we identify short-term clinical predictors of long-term evolution of the disease, with particular focus on predicting onset of secondary progressive course (failure event) on the basis of patient information available at an early stage of disease. The model specifies the full joint probability distribution for a set of variables including early indicator variables (observed during the early stage of disease), intermediate indicator variables (observed throughout the course of disease, prefailure) and the time to failure. Our model treats the intermediate indicators as a surrogate response event, so that in right-censored patients, these indicators provide supplementary information pointing towards the unobserved failure times. Moreover, the full probability modelling approach allows the considerable uncertainty which affects certain early indicators, such as the early relapse rates, to be incorporated in the analysis. With such a model, the ability of early indicators to predict failure can be assessed more accurately and reliably, and explained in terms of the relationship between early and intermediate indicators. Moreover, a model with the aforementioned features allows us to characterize the pattern of disease course in high-risk patients, and to identify short-term manifestations which are strongly related to long-term evolution of disease, as potential surrogate responses in clinical trials. Our analysis is based on longitudinal data from 186 MS patients with a relapsing–remitting initial course. The following important early predictors of the time to progression emerged: age; number of neurological functional systems (FSs) involved; sphincter, or motor, or motor-sensory symptoms; presence of sequelae after onset. During the first 3 years of follow up, to reach EDSS≥4 outside relapse, to have sphincter or motor relapses and to reach moderate pyramidal involvement were also found to be unfavourable prognostic factors. Research evaluation is based on a representation of the research. Improving the quality of the representations cannot prevent the indicators from being provided with meaning by a receiving discourse different from the research system(s) under study. Since policy decisions affect the systems under study, this configuration generates a tension that has been driving the further development of science indicators since World War II. The article discusses historically the emergence of science indicators and some of the methodological problems involved. More recent developments have been induced by the emergence of the European Union as a supra-national level of policy coordination and by the Internet as a global medium of communication. As science, technology, and innovation policies develop increasingly at various levels and with different objectives, the evaluative discourses can be expected to differentiate with reference to the discourses in which they are enrolled.