Reviewerly/exHarmony huggingface dataset:
Semantic integration of product model data in fire protection engineering

Planning processes in civil engineering are characterized by a great number of participants and a high amount of planning information stored in distributed and heterogeneous partial models. A main goal of the presented project is to provide the engineers with an integrated view on the relevant technical planning information. To realise this integration two aspects have to be considered: access level integration and semantic level integration. For the integration of partial models on the access level, different approaches like Web Services and mobile agents were developed and have been used in the last several years. However, in this context the core problem is the implicit and frequently inconsistent semantics of the information. Thus, this paper focuses on the interoperability on the semantic level. To provide the fire protection engineer with an integrated transparent view on the model data, a fire protection ontology has to be developed and the mappings between the ontology and the different database schemas have to be implemented. Following this approach the integration effort can be reduced effectively and resources can be integrated flexibly into the network based project organization.
Example-Based Objective Quality Estimation for Compressed Images

A method to estimate the peak signal-to-noise ratio of an image performs more accurately and has a smaller estimation bias and variance compared to the existing methods.
Delete Relaxations for Planning with State-Dependent Action Costs

Supporting state-dependent action costs in planning admits a more compact representation of many tasks. We generalize the additive heuristic and compute it by embedding decision-diagram representations of action cost functions into the RPG. We give a theoretical evaluation and present an implementation of the generalized additive heuristic. This allows us to handle even the hardest instances of the combinatorial Academic Advising domain from the IPPC 2014.
Computação e Linguística: importante diálogo para pesquisas e preservação da memória nos novos meios das antigas fontes (Computación y Lingüística: importante diálogo para investigaciones y preservación de la memoria en los nuevos medios de ...)

Resumo Neste artigo, exploramos alguns desafios atuais da pesquisa em Linguistica de Corpus, na sua vertente dedicada a Historia das Linguas - focalizando, particularmente, a experiencia do trabalho com textos historicos da Lingua Portuguesa em meio eletronico dando noticias do trabalho que vem sendo desenvolvido na Universidade Estadual do Sudoeste da Bahia, no âmbito dos projetos: (i) Corpora Digitais Para a Historia do Portugues Brasileiro - regiao Sudoeste da Bahia: Alianca PHPB - Tycho Brahe (FAPESB); (ii) Novos meios para antigas fontes: sintaxe diacronica em corpus eletronico (UESB/FAPESB); e (iii) O Portugues no tempo e no espaco (FAPESP). Sustentaremos alguns caminhos que ja se mostram promissores na exploracao da fronteira da pesquisa representada pela uniao da Linguistica e da Computacao. Palavras-chave: Documentos Antigos. Fotografia. Computacao. Linguistica. Resumen En este articulo, exploramos algunos de los retos actuales de la investigacion en Linguistica de Corpus en su capitulo sobre la Historia de las Lenguas - centrandose, en particular, la experiencia de trabajar con textos historicos de la lengua portuguesa en formato electronico, dando noticia de la labor que se esta desarrollado en la Universidade Estadual do Sudoeste de Bahia, en el marco de los proyectos: (i) Corpora Digitales para la Historia del Portugues Brasileno - region Suroeste de Bahia: Alianza PHPB - Tycho Brahe (FAPESB); (ii) Nuevos medios para antiguas fuentes: sintaxis diacronica en corpus electronico (UESB / FAPESB); y (iii) El portugues en el tiempo y en el espacio (FAPESP). Mantendremos algunos caminos que ya parecen ser prometedores en la exploracion de la frontera de la investigacion, representada por la union de la Linguistica y de la Computacion. Palabras-clave : Documentos antiguos. Fotografia. Computacion. Linguistica.
Device-independent tests of quantum states

We construct a correspondence between quantum states and the observable input-output correlations they are compatible with. The problem is framed as a game involving an experimenter, claiming to be able to prepare some family of states, and a theoretician, whose aim is to falsify such a claim based on observed correlations only. For any such claim, the optimal strategy consists of providing (i) to the experimenter, all the measurements that generate extremal input-output correlations, and (ii) to the theoretician, the full characterization of such correlations. Comparing the correlations observed in (i) with those predicted by (ii) corresponds to device-independently testing the states. While no assumption is made about the actual states and measurements, we derive the optimal strategy in closed form for the case when the claim consists of qubit states and the performed measurements are tests, and as applications we specify our results to the case of any pair of pure states and to the case of pure states uniformly distributed on the Bloch equatorial plane.
Detecting Image Brush Editing Using the Discarded Coefficients and Intentions

This paper describes a quick and simple method to detect brush editing in JPEG images.The novelty of the proposed method is based on detecting the discarded coefficients during the quantization of the image.Another novelty of this paper is the development of a subjective metric named intentions.The method directly analyzes the allegedly tampered image and generates a forgery mask indicating forgery evidence for each image block.The experiments show that our method works especially well in detecting brush strokes, and it works reasonably well with added captions and image splicing.However, the method is less effective detecting copy-moved and blurred regions.This means that our method can effectively contribute to implementing a complete imagetampering detection tool.The editing operations for which our method is less effective can be complemented with methods more adequate to detect them.
Stationary Phase Method in Discrete Wigner Functions and Classical Simulation of Quantum Circuits

One of the lowest-order corrections to Gaussian quantum mechanics in infinite-dimensional Hilbert spaces are Airy functions: a uniformization of the stationary phase method applied in the path integral perspective. We introduce a "periodized stationary phase method" to discrete Wigner functions of systems with odd prime dimension and show that the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mfrac><mml:mi>π</mml:mi><mml:mn>8</mml:mn></mml:mfrac></mml:math> gate is the discrete analog of the Airy function. We then establish a relationship between the stabilizer rank of states and the number of quadratic Gauss sums necessary in the periodized stationary phase method. This allows us to develop a classical strong simulation of a single qutrit marginal on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>t</mml:mi></mml:math> qutrit <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mfrac><mml:mi>π</mml:mi><mml:mn>8</mml:mn></mml:mfrac></mml:math> gates that are followed by Clifford evolution, and show that this only requires <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mn>3</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mfrac><mml:mi>t</mml:mi><mml:mn>2</mml:mn></mml:mfrac><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:math> quadratic Gauss sums. This outperforms the best alternative qutrit algorithm (based on Wigner negativity and scaling as <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>∼</mml:mo><mml:mspace width="-3pt" /><mml:msup><mml:mn>3</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mn>0.8</mml:mn><mml:mi>t</mml:mi></mml:mrow></mml:msup></mml:math> for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mn>10</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>−</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math> precision) for any number of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mfrac><mml:mi>π</mml:mi><mml:mn>8</mml:mn></mml:mfrac></mml:math> gates to full precision.
Improved niche genetic algorithm based parameter identification of excitation system considering parameter identifiability

In order to obtain the parameters of excitation system accurately, and improve the accuracy and efficiency of parameter identification much further, an improved niche genetic algorithm was adopted, which can overcome local convergence of genetic algorithm by the fitness sharing mechanism that could increase population diversity. Moreover, the proposed algorithm could avoid the disadvantage of usual niche genetic algorithm that is difficult in determining the niches. By using the method of fuzzy clustering, niche groups can be created dynamically. In addition, regarding the problem that the partial parameters of excitation system cannot identify stably because of the relevance among these parameters, this study analysed the identifiability of excitation system model primarily, separated associated parameters into several parameter sets then. This type of relevance would be removed by assigning typical values to representative parameters, which belong to different sets. Identifiability analysis can not only avoid identifying parameters blindly, but also increase the credibility of the results. The test study shows that the proposed method can acquire accurate and reliable parameter values.
Nonparametric Estimation of Probabilistic Membership for Subspace Clustering

Recent advances of subspace clustering have provided a new way of constructing affinity matrices for clustering. Unlike the kernel-based subspace clustering, which needs tedious tuning among infinitely many kernel candidates, the self-expressive models derived from linear subspace assumptions in modern subspace clustering methods are rigorously combined with sparse or low-rank optimization theory to yield an affinity matrix as a solution of an optimization problem. Despite this nice theoretical aspect, the affinity matrices of modern subspace clustering have quite different meanings from the traditional ones, and even though the affinity matrices are expected to have a rough block-diagonal structure, it is unclear whether these are good enough to apply spectral clustering. In fact, most of the subspace clustering methods perform some sort of affinity value rearrangement to apply spectral clustering, but its adequacy for the spectral clustering is not clear; even though the spectral clustering step can also have a critical impact on the overall performance. To resolve this issue, in this paper, we provide a nonparametric method to estimate the probabilistic cluster membership from these affinity matrices, which we can directly find the clusters from. The likelihood for an affinity matrix is defined nonparametrically based on histograms given the probabilistic membership, which is defined as a combination of probability simplices, and an additional prior probability is defined to regularize the membership as a Bernoulli distribution. Solving this maximum a posteriori problem replaces the spectral clustering procedure, and the final discrete cluster membership can be calculated by selecting the clusters with maximum probabilities. The proposed method provides state-of-the-art performance for well-known subspace clustering methods on popular benchmark databases.
A new method of geochemical allocation and monitoring of commingled crude oil production using trace and ultra-trace multi-element analyses

Production allocation refers to the practice of quantifying proportions of extracted commingled hydrocarbons across various contributing sources. In this paper we tested a new geochemical technique of trace element production allocation by analyzing the mass fractions of specific target elements in five end-member natural crude oils and the manually mixed crude oil in precisely controlled proportions. We analyzed target elements by ICP-OES and Triple Quadrupole (QQQ)-ICP-MS techniques in tandem on each sample. In our test, the contributing fractions of the five end-member oils were measured by weight and mixed in proportions of ∼30%, 25%, 20%, 15%, and 10% in the commingled oil. The obtained mass fractions for specific target elements in both the five end-member oils and the commingled oil are input into a program developed called “ALLO-TRACE”. ALLO-TRACE calculates the contributing fractions of all the end-member oils to the commingled oil using multiple analyte-based linear equations. Our repeated tests have shown that the calculated contribution fractions based on the mass fractions of multiple trace elements agree well with their known contribution fractions in the commingled oil. Accuracies of most calculations for all the five end-member oil target proportions are within 4%, and the best can be less than 0.6% for all end members (average 0.17% and median 0.1%). Most calculation uncertainties in terms of relative standard deviations of the five end-member oils are within 3%, and the best can be less than 2.3% for all end members (average 1.2% and median 0.9%).
Sentence-Wise Smooth Regularization for Sequence to Sequence Learning

Maximum-likelihood estimation (MLE) is widely used in sequence to sequence tasks for model training. It uniformly treats the generation/prediction of each target token as multiclass classification, and yields non-smooth prediction probabilities: in a target sequence, some tokens are predicted with small probabilities while other tokens are with large probabilities. According to our empirical study, we find that the non-smoothness of the probabilities results in low quality of generated sequences. In this paper, we propose a sentence-wise regularization method which aims to output smooth prediction probabilities for all the tokens in the target sequence. Our proposed method can automatically adjust the weights and gradients of each token in one sentence to ensure the predictions in a sequence uniformly well. Experiments on three neural machine translation tasks and one text summarization task show that our method outperforms conventional MLE loss on all these tasks and achieves promising BLEU scores on WMT14 English-German and WMT17 Chinese-English translation task.
Lexicon Based Twitter Sentiment Analysis for Vote Share Prediction Using Emoji and N-gram Features

Recently, Twitter sentiment analysis (TSA) has been successfully employed to monitor and forecast elections in many studies. However, most of the existing studies rely on extracting sentiments from explicit textual features. Moreover, only few studies have included non-textual features such as emojis for election forecasts. In this study, we incorporated N-gram features to predict vote shares of 2017 Uttar Pradesh (UP) legislative elections. Also, sentiment distribution of tweets containing emojis was significantly different from tweets without emojis. Therefore, emoji sentiments were detected and incorporated to predict the vote shares. We collected more than 0.3 million tweets, wherein geo-tagging was applied on search keywords that were not exclusive to elections. We employed seven lexicons for labelling tweets and compared two methods to reduce prediction error: sentiment magnitude-based criteria and polarity of tweets. Results show that proposed method of incorporating N-gram features and emoji sentiments significantly decreases prediction error.
Fully-Deformable 3D Image Registration in Two Seconds

We present a highly parallel method for accurate and efficient variational deformable 3D image registration on a consumer-grade graphics processing unit (GPU). We build on recent matrix-free variational approaches and specialize the concepts to the massively-parallel manycore architecture provided by the GPU. Compared to a parallel and optimized CPU implementation, this allows us to achieve an average speedup of 32:53 on 986 real-world CT thorax-abdomen follow-up scans. At a resolution of approximately 2563 voxels, the average runtime is 1:99 seconds for the full registration. On the publicly available DIR-lab benchmark, our method ranks third with respect to average landmark error at an average runtime of 0:32 seconds.
Block Argumentation

We advance the point of view: an argument as an argumentation and vice versa, to formulate Block (Bipolar) Argumentation (BBA), a bipolar argumentation theory that recursively instantiates an abstract argument with a bipolar argumentation. Multiple occurrence of the same argument(ation) can become issues in such a self-similar argumentation theory, for which we consider a graphical (syntactic) constraint, in relation to which we define its acceptability semantics. For some highlight, acceptability of unattacked arguments is not always warranted once this kind of a constraint must be taken into account.
Special Issue on “Smart Homes”: Editors’ Notes

In this editorial, we provide an overview of the content of the Special Issue on "Smart Homes". The aim of this Special Issue is to provide a comprehensive collection of some of the current state-of-the-art technologies in the context of smart homes, together with new advanced theoretical and technological solutions that enable smart technology diffusion into homes.
RED: Redundancy-Driven Data Extraction from Result Pages?

Data-driven websites are mostly accessed through search interfaces. Such sites follow a common publishing pattern that, surprisingly, has not been fully exploited for unsupervised data extraction yet: the result of a search is presented as a paginated list of result records. Each result record contains the main attributes about one single object, and links to a page dedicated to the details of that object.
Light-field image acquisition from a conventional camera: design of a four minilens ring device

In the past few years, a new type of camera has been emerging on the market: a digital camera capable of capturing both the intensity of the light emanating from a scene and the direction of the light rays. This camera technology called a light-field camera uses an array of lenses placed in front of a single image sensor, or simply, an array of cameras attached together. An optical device is proposed: a four minilens ring that is inserted between the lens and the image sensor of a digital camera. This device prototype is able to convert a regular digital camera into a light-field camera as it makes it possible to record four subaperture images of the scene. It is a compact and cost-effective solution to perform both postcapture refocusing and depth estimation. The minilens ring makes also the plenoptic camera versatile; it is possible to adjust the parameters of the ring so as to reduce or increase the size of the projected image. Together with the proof of concept of this device, we propose a method to estimate the positions of each optical component depending on the observed scene (object size and distance) and the optics parameters. Real-world results are presented to validate our device prototype.
A New Group Location Privacy-Preserving Method Based on Distributed Architecture in LBS

Nowadays, the location privacy problem has become an important problem for the users who enjoy the location-based services (LBSs). Researchers have focused on the problem of how to protect the location privacy of user efficiently for a long time. On one hand, many achievements adopt the centralized structure in which there is an additional center server. Additionally, some other researchers adopt the distributed structure to overcome the disadvantages brought by the center server in the centralized anonymous system structure. On the other hand, the existing methods of solving the problem are always to protect the individual user’s location privacy in LBSs, without considering the user group’s location privacy. This kind of methods is not very applicable to the status of a number of users who formed a group to complete a LBS task together by collaborative computing. In order to solve the problem of location privacy protection for a user group in the untrusted mobile social networks, a location privacy protection method based on the distributed structure is discussed in this paper. In the scheme, the special homomorphic features of BGN cryptosystem are cleverly used so that it can solve the group’s three classical location service applications simultaneously, namely, group nearest neighbor query, optimal group collection point determination, and group friend’s distance query, by only one security policy. If there are k users who formed the group, it could achieve k-anonymity without exposing the coordinate of each individual user or using any anonymous areas. Furthermore, theoretical and experimental analysis proves that the proposal can efficiently protect each user’s location privacy in the group through taking full advantage of the collaborative computing and communication capabilities of the mobile terminals. It can resist the existing distance interaction attack and collusion attack and can realize the secure and efficient fine-grained controllable location privacy protection for the user group.
Distributional reinforcement learning with linear function approximation

Despite many algorithmic advances, our theoretical understanding of practical distributional reinforcement learning methods remains limited. One exception is Rowland et al. (2018)'s analysis of the C51 algorithm in terms of the Cramer distance, but their results only apply to the tabular setting and ignore C51's use of a softmax to produce normalized distributions. In this paper we adapt the Cramer distance to deal with arbitrary vectors. From it we derive a new distributional algorithm which is fully Cramer-based and can be combined to linear function approximation, with formal guarantees in the context of policy evaluation.In allowing the model's prediction to be any real vector, we lose the probabilistic interpretation behind the method, but otherwise maintain the appealing properties of distributional approaches. To the best of our knowledge, ours is the first proof of convergence of a distributional algorithm combined with function approximation. Perhaps surprisingly, our results provide evidence that Cramer-based distributional methods may perform worse than directly approximating the value function.
Depth-Based Fall Detection: Outcomes from a Real Life Pilot

With the increasing ageing population representing a challenge for society and health care systems, solutions based on ICT to prolong the independent living of older adults become critical. Among them, systems able to automatically detect falls are being investigated since several years, because many solutions that appear promising when tested in lab settings, fail when faced with the constraints and unforeseen circumstances of real deployments. In this paper, we present the outcomes resulting from the pilot installation of a fall detection system based on the use of depth sensors located on the ceiling of the monitored apartment, where a 75 years old woman lives alone. We highlight the system design process, moving from the research leading to an original algorithm working offline, preliminarily tested in a lab setting, to the real-time engineering of the software, and the physical deployment of the system. Testing the system in a real-life scenario allowed us to identify a number of tricks and conditions that should to be taken into account since the initial steps, but the lab experimentation alone can barely help to focus on.