Reviewerly/exHarmony huggingface dataset:
Results

This chapter collects the results of tests performed with the aim of validating and evaluating the performance of the developed navigation algorithms. These tests, executed throughout the whole Ph.D. period, involved all the AUVs of the MDM Lab, and consist in simulations using experimental data acquired at sea (to, e.g., test the correctness of the algorithms or to investigate the causes of unexpected or undesired behavior), preliminary experimental tests executed in controlled environments, and even complete online validation tests, where the developed algorithms were actively used within the GNC loop of the vehicles. All the unknown coefficients, tunable parameters, and gains introduced in previous chapters were identified or set, for each vehicle, according to the outcome of preliminary experimental tests in order to achieve the best possible behavior for the algorithms under test.
Japan Association of Mineralogical Sciences

He learned how to use the transmission electron microscope (TEM) from Dr. Fujino, and the TEM subsequently became his preferred analytical tool for his research.His professional career started at Ibaraki University (Japan) where he developed a technique by which to prepare meteorite samples for TEM analysis using an ultramicrotome.From this, he could investigate, using electrons, the petrography of meteorites.Since 1997, he has been investigating Antarctic micrometeorites (AMMs).He has found that the major hydrated AMMs have a similar mineralogy to that of the matrix of the Tagish Lake meteorite (British Columbia, Canada), a result that contradicted the belief of the day.More recently, he has found that highly porous anhydrous AMMs are indistinguishable from chondritic porous interplanetary dust particles (CP IDPs), the latter having been regarded as cometary dust.These AMMs, thus, give a new opportunity to investigate cometary material in the laboratory.Dr. Noguchi is currently working on the early stages of aqueous alteration of comet-like materials by using these CP IDP-like AMMs, which will help us to understand the early evolution of very primitive volatile-rich solar system bodies.By making use of these experiences, he has been investigating fine-grained samples returned from a comet, an asteroid, the Moon, and the international space station.In the Hayabusa 1 mission, he worked as a member of the JAXA/Institute of Space and Astronautical Science curation team and found that the surface of the returned fine grains from asteroid Itokawa had been damaged by the irradiation of solar wind (charged particles).In the Hayabusa 2 mission, he is going to investigate samples of asteroid Ryugu as a sub-team leader of the initial analysis team.
Social Network Sites and Innovation Capabilities in the UAE Hotel Industry. Reliability and Normality Test

The integration of information technology through social network sites in business operating and management has been recognized as one of the most resource for the development of innovation capabilities. It plays a significant role in knowledge sharing and transform which are the seeds of innovation development. The service sector is now the main domain where IT plays is extensively integrated in operation and management functions. However, the literature still lacks of clear understanding about the concept of SNSs and the innovation capabilities in hotel industry which affect the effectively use IT in their businesses. Therefore, this study aims to model SNSs and innovation capabilities. The reliability test through Cronbach alpha as well as normality tests were used.
Disruptions in local and regional development and the way forward for a more humanitarian world

This chapter provides a synthesis of the disruptive factors and relevant concepts within the local and regional development context to promote the change process. Here, implications of sustainability concept, information and technological revolutions and social conflict for local and regional communities are discussed. A humanist approach is proposed to address problems and difficulties in the local and regional development process a way forward for a more humanitarian world.
A survey on Sentimental Analysis algorithms and techniques

Abstract-Social web has been the lead of data in recent years.The data on social web simulates the customer reviews, comments, and opinions of people about products, opinion, events, etc.The automatic mining technique that is used to gather and analyze text and classify it into positive, negative and neutral is called Sentimental analysis using Natural language processing.The main objective of this paper is to give explain insights on SA algorithms and techniques.
Opinion Mining for Travel Route Recommendation using Social Media Networks (Twitter)

Most of the organizations use text analytics to uncover purposeful information from an unstructured text as a result of considering the linguistic communication process techniques area unit extremely difficult. They typically cause several issues because of the inconsistency in syntax and linguistics. Sentiment analysis based on the opinion of the users. On twitter, many people post about their experience on the traffic routes. This project discusses the prediction of text mining analysis. On that post collecting from the data set and we find out which path is the best path for the travellers and waiting for commuters. In this project we discuss the traffic mining tweets using the keywords predicting the positive and negative comment on the Twitter. Experimentation involves discussion and comparison of ensemble classifiers over tagged tweets. Finally, it will be finding the best accuracy.
TIME ESTIMATION IMPROVEMENT IN AGILE DEVELOPMENT

Estimating the time needed to execute an IT project has turned out to be a major challenge in daily praxis.Despite high inaccuracies, these estimations are important as they can be used to determine the cost for offering an IT project and in deciding whether to execute it at all.In this study we focused on the estimations for user stories created by a European Software company delivering 100% of their software in an agile way of working.Over a time-span of 10 months, 1738 estimations were created on small user stories (around 5 hours of work on average).Three main conclusions could be drawn from the data.Firstly, it was found that smaller-than-two-hour user stories were over-estimated roughly as frequently as under-estimated.In contrast, estimations for larger-than-two-hour user stories were less balanced and on average too low (under-estimated).Secondly, and surprisingly, we did not see that estimations were better when the estimator was able to execute the task himself versus when he was not.The statistics suggest that estimators who are able to do the task themselves tend to underestimate the time needed to deliver the user story.Finally, we found that the accuracy of estimations does not improve over time.
A Statistical Measurement of Randomness Based on Pattern Vectors

Randomness of data or signals has been applied and studied in various theoretical and industrial fields. There are many ways to define and measure randomness. The most popular one probably is the statistical testing for randomness. Among the approaches adopted, Runs Test is a highly used technique in testing the randomness. In this article, we demonstrate the inefficient aspects of Runs Test and put forward a new approach, or pattern-vector-based statistic, based on pattern vectors that could effectively enhance the precision of testing randomness. A random binary sequence is supposedly to have less or no patterns. Based on this, we put forward our randomness-testing statistic. We also run an experiment to demonstrate how to apply this statistic and compare the efficiency or failure rate with Runs Test in dealing with a set of randomly generated input sequences. Moreover, we devise a statistically-justifiable measure of randomness for any given binary sequence. In the end, we demonstrate a way to combine this new device with Kalman filters to enhance the data assimilation.
Training Future Teachers for School Work: Linguistic and Cultural Direction

Modern society is characterized by multidirectional, complex interacting processes of unification, diversification, globalization and multiculturalism.These processes cause significant changes in the education system.One of them is multicultural education.The relevance of research on various aspects of multicultural education in modern Russia has led to the emergence of multidimensional works.The multicultural orientation is reflected in the Federal educational standards of general education, in the standards of vocational education for bachelors and masters of pedagogical and psychological specialties.The objective of this work is to identify and theoretically substantiate the pedagogical conditions for the effectiveness of the formation of cultural pluralism in young professionals working in schools of a new type.The solution of the stated goal and verification of the proposed assumptions were provided by a set of complementary methods: retrospective, systemic, interviewing, included observation, conversation, testing, questioning, modeling and statistical method.The source of empirical data for this study was the questionnaire of young teachers of new type schools during the International School Teachers Festival in Tatarstan, Russian Federation, as well as the questionnaire of Alabuga International School teachers.As a result of the survey, the questionnaires were analyzed and problems encountered by young teachers were diagnosed.The authors of the article offer a number of recommendations for a more effective crosscultural education for future bachelors, young English language specialists.In their opinion, nursery rhymes, clerihews, which are "linguo-culturems", recreating the historical and cultural context, can serve as a successful material for intercultural education of future teachers.Here are some recommendations of the practical implementation of the nursery rhymes, clerihews in the training of the future teachers which are accompanied with illustrations and necessary notes.Special emphasis should be laid on the role of the pedagogical university in modern society, in the formation of competencies of future teachers and young specialists in the polyethnic region.Polycultural education promotes harmonization of relations between representatives of different civilizations and cultures.The main directions of multicultural education are bilingual education, multicultural education, accompanied by measures against ethnocentrism.The presence of a multicultural environment allows stimulating the interest of learners in new knowledge and at the same time it offers different points of view on the world around us.
Hierarchical Adversarial Search Applied to Real-Time Strategy Games

Real-Time Strategy (RTS) video games have proven to be a very challenging application area for artificial intelligence research. Existing AI solutionsare limited by vast state and action spaces and real-time constraints. Most implementations efficiently tackle various tactical or strategic sub-problems, but there is no single algorithm fast enough to be successfully applied to big problem sets (such as a complete instance of the StarCraft RTS game). This paper presents a hierarchical adversarial search framework which more closely models the human way of thinking --- much like the chain of command employed by the military. Each level implements a different abstraction --- from deciding how to win the game at the top of the hierarchy to individual unit orders at the bottom. We apply a 3-layer version of our model to SparCraft ---a StarCraft combat simulator --- and show that it outperforms state of the art algorithms such as Alpha-Beta, UCT, and Portfolio Search in large combat scenarios featuring multiple bases and up to 72 mobile units per player under real-time constraints of 40 ms per search episode.
The essential histogram

The histogram is widely used as a simple, exploratory display of data, but it is usually not clear how to choose the number and size of bins. We construct a confidence set of distribution functions that optimally address the two main tasks of the histogram: estimating probabilities and detecting features such as increases and modes in the distribution. We define the essential histogram as the histogram in the confidence set with the fewest bins. Thus the essential histogram is the simplest visualization of the data that optimally achieves the main tasks of the histogram. The only assumption we make is that the data are independent and identically distributed. We provide a fast algorithm for the essential histogram, and illustrate our methodology with examples. An R-package is available on CRAN.
Composable security against collective attacks of a modified BB84 QKD protocol with information only in one basis

Quantum Cryptography uses the counter-intuitive properties of Quantum Mechanics for performing cryptographic tasks in a secure and reliable way. The Quantum Key Distribution (QKD) protocol BB84 has been proven secure against several important types of attacks: collective attacks and joint attacks. Here we analyze the security of a modified BB84 protocol, for which information is sent only in the z basis while testing is done in both the z and the x bases, against collective attacks. The proof follows the framework of a previous paper [1], but it avoids a classical information-theoretical analysis and proves a fully composable security. We show that this modified BB84 protocol is as secure against collective attacks as the original BB84 protocol, and that it requires more bits for testing.
Reconstructing Point Sets From Distance Distributions

We address the problem of reconstructing a set of points on a line or a loop from their unassigned noisy pairwise distances. When the points lie on a line, the problem is known as the turnpike; when they are on a loop, it is known as the beltway. We approximate the problem by discretizing the domain and representing the $N$ points via an $N$-hot encoding, which is a density supported on the discretized domain. We show how the distance distribution is then simply a collection of quadratic functionals of this density and propose to recover the point locations so that the estimated distance distribution matches the measured distance distribution. This can be cast as a constrained nonconvex optimization problem which we solve using projected gradient descent with a suitable spectral initializer. We derive conditions under which the proposed distance distribution matching approach locally converges to a global optimizer at a linear rate. Compared to the conventional backtracking approach, our method jointly reconstructs all the point locations and is robust to noise in the measurements. We substantiate these claims with state-of-the-art performance across a number of numerical experiments. Our method is the first practical approach to solve the large-scale noisy beltway problem where the points lie on a loop.
Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation

Automating the navigation of unmanned aerial vehicles (UAVs) in diverse scenarios has gained much attention in recent years. However, teaching UAVs to fly in challenging environments remains an unsolved problem, mainly due to the lack of training data. In this paper, we train a deep neural network to predict UAV controls from raw image data for the task of autonomous UAV racing in a photo-realistic simulation. Training is done through imitation learning with data augmentation to allow for the correction of navigation mistakes. Extensive experiments demonstrate that our trained network (when sufficient data augmentation is used) outperforms state-of-the-art methods and flies more consistently than many human pilots. Additionally, we show that our optimized network architecture can run in real-time on embedded hardware, allowing for efficient on-board processing critical for real-world deployment. From a broader perspective, our results underline the importance of extensive data augmentation techniques to improve robustness in end-to-end learning setups.
Fully Nonparametric Bayesian Additive Regression Trees

Bayesian additive regression trees (BART) is a fully Bayesian approach to modeling with ensembles of trees. BART can uncover complex regression functions with high-dimensional regressors in a fairly automatic way and provide Bayesian quantification of the uncertainty through the posterior. However, BART assumes independent and identical distributed (i.i.d) normal errors. This strong parametric assumption can lead to misleading inference and uncertainty quantification. In this chapter we use the classic Dirichlet process mixture (DPM) mechanism to nonparametrically model the error distribution. A key strength of BART is that default prior settings work reasonably well in a variety of problems. The challenge in extending BART is to choose the parameters of the DPM so that the strengths of the standard BART approach is not lost when the errors are close to normal, but the DPM has the ability to adapt to non-normal errors.
Stochastic Approximations to the Pitman–Yor Process

In this paper we consider approximations to the popular Pitman-Yor process obtained by truncating the stick-breaking representation.The truncation is determined by a random stopping rule that achieves an almost sure control on the approximation error in total variation distance.We derive the asymptotic distribution of the random truncation point as the approximation error goes to zero in terms of a polynomially tilted positive stable random variable.The practical usefulness and effectiveness of this theoretical result is demonstrated by devising a sampling algorithm to approximate functionals of the -version of the Pitman-Yor process.
Accurate Modeling and Efficient Estimation of the Print-Capture Channel With Application in Barcoding

With the rapid advancement of sensing capability and computational power in consumer electronic devices, the use of mobile cameras for communications has attracted significant attention from both academia and industry. The design of a reliable communication system over the print-capture channel is a key challenge in some important applications, such as barcoding and document authentication. However, the real-world printcapture channel is spatially non-linear and non-stationary, and lacks a standard parametric model. As a consequence, advanced channel coding techniques developed for common channel models are not applicable in the existing systems without costly and device-dependent pre-training. In this work, an accurate parametric print-capture channel model and an efficient channel estimation scheme requiring low training overhead are proposed. With the estimated parameters, the proposed print-capture channel model can be linearized to the (possibly input-dependent) additive white Gaussian noise (AWGN) channel model. This allows the use of state-of-the-art channel coding schemes, such as low-density parity-check (LDPC) codes with iterative soft decoding, to improve the reliability of the communication system under challenging conditions, e.g., at a low signal-to-noise ratio. As an example application of the proposed print-capture channel model, a demonstrative multilevel 2D barcode using an LDPC code with iterative soft decoding, is designed to enhance the reliability over the conventional QR code which is based on the Reed-Solomon code with hard decoding. At a printing resolution of 600dpi, the 8-level 2D barcode achieves data capacity gains of about 10% and 56% over the QR code in the in-focus and blurlimited scenarios, respectively. It is noteworthy that the enhanced data capacity of the multilevel barcode is made possible by the proposed channel estimation scheme, which incurs an additional training overhead of about 3.3% only.
Context-Aware Deep Spatiotemporal Network for Hand Pose Estimation From Depth Images

As a fundamental and challenging problem in computer vision, hand pose estimation aims to estimate the hand joint locations from depth images. Typically, the problems are modeled as learning a mapping function from images to hand joint coordinates in a data-driven manner. In this paper, we propose a context-aware deep spatiotemporal network, a novel method to jointly model the spatiotemporal properties for hand pose estimation. Our proposed network is able to learn the representations of the spatial information and the temporal structure from the image sequences. Moreover, by adopting the adaptive fusion method, the model is capable of dynamically weighting different predictions to lay emphasis on sufficient context. Our method is examined on two common benchmarks, the experimental results demonstrate that our proposed approach achieves the best or the second-best performance with the state-of-the-art methods and runs in 60 fps.
&lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$\tau$ &lt;/tex-math&gt; &lt;/inline-formula&gt;-Safe (&lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$l,k$ &lt;/tex-math&gt; &lt;/inline-formula&gt;)-Diversity Privacy Model for Sequential Publication With High Utility

Preserving privacy while maintaining high utility during sequential publication for data providers and data users in mathematical statistics, scientific researching, and organizations making decisions play an important role recently.The τ -safety model is the state-of-the-art model in sequential publication.However, it is based on the generalization technique, which has some drawbacks such as heavy information loss and difficulty of supporting marginal publication.Besides, the privacy of individuals is the major aspect that needs to be protected in privacy preserving data publishing.In this paper, to protect the privacy of individuals in sequential publication, we develop a new τ -safe (l, k)-diversity privacy model based on generalization and segmentation by record anonymity satisfying l-diversity and individual anonymity satisfying k-anonymity.This privacy model ensures that each record's signatures keep consistency or have no intersection in all releases.It can get high data utility while resisting the linking attacks due to arbitrary updates.In addition, it can also be applied to a dataset where individual has multiple records and arbitrary marginal publication.The results of our experiments show that the proposed privacy model achieves better anonymization quality and query accuracy in comparison with the m-invariance and τ -safety model in the sequential publication with arbitrary updates.
Learning How to Ground a Plan – Partial Grounding in Classical Planning

Current classical planners are very successful in finding (nonoptimal) plans, even for large planning instances. To do so, most planners rely on a preprocessing stage that computes a grounded representation of the task. Whenever the grounded task is too big to be generated (i.e., whenever this preprocess fails) the instance cannot even be tackled by the actual planner. To address this issue, we introduce a partial grounding approach that grounds only a projection of the task, when complete grounding is not feasible. We propose a guiding mechanism that, for a given domain, identifies the parts of a task that are relevant to find a plan by using off-the-shelf machine learning methods. Our empirical evaluation attests that the approach is capable of solving planning instances that are too big to be fully grounded.
Quantum regression theorem for out-of-time-ordered correlation functions

We derive an extension of the quantum regression theorem to calculate out-of-time-order correlation functions in Markovian open quantum systems. While so far mostly being applied in the analysis of many-body physics, we demonstrate that out-of-time-order correlation functions appear naturally in optical detection schemes with interferometric delay lines, and we apply our extended quantum regression theorem to calculate the non-trivial photon counting fluctuations in split and recombined signals from a quantum light source.