Reviewerly/exHarmony huggingface dataset:
Copyright protection method for 3D model of geological body based on digital watermarking technology

In the continuous development of Internet technology and geological information technology, problems such as illegal possession, copying, modification and dissemination of unauthorized digital products have occurred in the field of geological information technology. In the field of geological information technology, not only traditional digital products such as text, image, audio and video are produced, but also 3D model digital products unique to geological information technology, which are geological bodies, geological phenomena, geological structures, geological processes and geology. Regular 3D visualization analysis and the foundation and platform for comprehensive decision making of 3D visualization, the 3D model of geological body plays an increasingly important role in the field of geological information technology. Of course, it also faces the problems faced by traditional digital products and copyright protection. In this paper, the advantages and disadvantages of digital watermarking technology in copyright protection and the characteristics of geological body 3D model itself, and the current mature 3D model digital watermarking algorithm are introduced. The research idea of 3D geological body digital watermarking algorithm based on point cloud model is proposed. According to the spatial characteristics of the point cloud model of geological body, the spatial characteristic variables of the point cloud model are obtained by using the spatial characteristic analysis algorithm. The spatial domain information represented by the spatial characteristic variables is transformed into the frequency domain information, and then the frequency domain information is analyzed by means of mathematical statistics to extract the digital watermarking information of the whole geological body point cloud model. The feasibility of the algorithm is analyzed and verified by the experimental analysis. The digital watermark information before and after the attack is extracted by geometric attacks such as affine transformation and shearing. The extracted digital watermark information is correlated with the coefficient analysis and the robustness of the algorithm is obtained. A better robustness effect can effectively protect the copyright of the owner of the 3D model of the geological body.
Feature Selection for Histopathological Image Classification using levy Flight Salp Swarm Optimizer

Background: An efficient feature selection method for Histopathological image classification plays an important role to eliminate irrelevant and redundant features. Therefore, this paper proposes a new levy flight salp swarm optimizer based feature selection method. Methods: The proposed levy flight salp swarm optimizer based feature selection method uses the levy flight steps for each follower salp to deviate them from local optima. The best solution returns the relevant and non-redundant features, which are fed to different classifiers for efficient and robust image classification. Results: The efficiency of the proposed levy flight salp swarm optimizer has been verified on 20 benchmark functions. The anticipated scheme beats the other considered meta-heuristic approaches. Furthermore, the anticipated feature selection method has shown better reduction in SURF features than other considered methods and performed well for histopathological image classification. Conclusion: This paper proposes an efficient levy flight salp Swarm Optimizer by modifying the step size of follower salp. The proposed modification reduces the chances of sticking into local optima. Furthermore, levy flight salp Swarm Optimizer has been utilized in the selection of optimum features from SURF features for the histopathological image classification. The simulation results validate that proposed method provides optimal values and high classification performance in comparison to other methods.
Enabling Semantic Discovery in the Swarm

Given the large amount of devices in the Internet of Things (IoT), a major challenge is to efficiently find services that meet specific requirements. The proliferation of standards and protocols precludes the existence of a common nomenclature to describe IoT devices, and hence hinders device interoperability. In the previous work, we had proposed the Swarm, a framework to create a common interface for IoT devices. The Swarm provides a software agent to facilitate the decentralized communication and interaction of devices, constituting a step toward interoperability in the IoT. Semantic technologies have been successfully used for service discovery in business applications, and more recently are being used to solve interoperability problems in the IoT. This paper aims to propose a framework to enable semantic discovery in the Swarm. We provide an architecture and describe a reference implementation, based on open Web technologies. We also provide a method to validate our results and propose future directions of this paper.
Pre-treatment for preventing degradation of measurement accuracy from speckle noise in speckle interferometry

Speckle interferometers, which can measure three-dimensional deformations with the same sensitivity in each direction of a three-dimensional axis, have been employed in the industry. However, the measurement results of a complex shape deformation using this method do not always produce a smooth phase map due to the influence of speckle noise. In this paper, this problem that occurs with speckle noise, which is included in the speckle pattern, is discussed. In addition, the solution to the problem concerning speckle noise is discussed. An idea for noise reduction is proposed by using a simple speckle pattern intensity distribution model based on the interferometry. It is confirmed that the proposed method can reduce the influence of speckle noise to 1/1000 of the light source wavelength.
A generic construction of tightly secure signatures in the multi-user setting

In this paper, we present a generic construction of tightly secure signature schemes in the multi-user setting, which is in turn used to construct tightly secure identity-based signature schemes. Both of the securities of the constructions are based on the hardness of some subset membership problems (SMP). By instantiating SMP with the Decisional Composite Residuosity (DCR) and Matrix Decisional Diffie–Hellman (MDDH) problems, we obtain tightly secure signature schemes and tightly secure identity-based signatures based on the DCR and MDDH assumptions, respectively.
An educational game to teach numbers in Brazilian Sign Language while having fun

This work presents an educational, cross-platform, turn-based, heavily visual-based and touch-ready digital game that targets the learning of signs in BSL for some Natural Numbers by children and adults, called MatLIBRAS Racing. This solution differs from others by having its gameplay based on a cognitive perspective, by promoting the deaf culture in its features as game requirements, by targeting multiple age groups with a single software and by offering a modular software architecture that enables the use of other sign languages besides Brazilian Sign Language (BSL) in the game. An evaluation about educational and gaming features of the game was conducted, focusing its learning potential, gaming components, user experience and social engagement, using as tools surveys, quizzes and observation. The results demonstrated the potential MatLIBRAS Racing as an educational tool, since the majority of its features were rated positively by students, it engaged social relationship among players and helped the learning of signs in BSL, suggesting a promising future for this game in academic environments.
Big Data in Cybersecurity for Smart City Applications

With the advancement of information and communication technologies, traditional cities are transforming to Smart Cities, where different components of cities are interconnected through smart devices. The smart devices communicate with each other and gather information for different Smart City applications. Smart Cities are going to have a large number of smart devices connected for different applications. Those devices are expected to produce a large amount of data. Terabytes of message data will be exchanged among these devices in this connected environment. These connected devices bring many security vulnerabilities and privacy issues, as some of these data may contain sensitive private information. Recently, a great deal of research has been done to ensure the protection of data in Smart City applications. This chapter presents the Big Data-enabled Smart City and its applications, including recent research that has been done in this area, and how data can be protected to ensure its integrity and confidentiality for efficient and accurate Smart City applications.
Wisdom extraction in knowledge-based information systems

Purpose This paper aims to provide a number of distinct approaches towards this goal, i.e. to translate the information contained in the repositories into knowledge. For centuries, humans have gathered and generated data to study the different phenomena around them. Consequently, there are a variety of information repositories available in many different fields of study. However, the ability to access, integrate and properly interpret the relevant data sets in these repositories has mainly been limited by their ever expanding volumes. The goal of translating the available data to knowledge, eventually leading to wisdom, requires an understanding of the relations, ordering and associations among the data sets. Design/methodology/approach While the existing information repositories are rich in content, there are no easy means of understanding the relevance or influence of the different facts contained therein. Therefore, the interest of the general populace in terms of prioritizing some data items (or facts) over others is usually lost. In this paper, the goal is to provide approaches for transforming the available facts in the information repositories to wisdom. The authors target the lack of order in the facts presented in the repositories to create a hierarchical distribution based on the common understanding, expectations, opinions and judgments of the different users. Findings The authors present multiple approaches to extract and order the facts related to each concept, using both automatic and semi-automatic methods. The experiments show that the results of these approaches are similar and very close to the instinctive ordering of facts by users. Originality/value The authors believe that the work presented in this paper, with some additions, can be a feasible step to convert the available knowledge to wisdom and a step towards the future of online information systems.
Halo concentrations from extended Press–Schechter merger histories

We apply the model relating halo concentration to formation history proposed by Ludlow et al. to merger trees generated using an algorithm based on excursion set theory. We find that while the model correctly predicts the median relation between halo concentration and mass, it underpredicts the scatter in concentration at fixed mass. Since the same model applied to N-body merger trees predicts the correct scatter, we postulate that the missing scatter is due to the lack of any environmental dependence in merger trees derived from excursion set theory. We show that a simple modification to the merger tree construction algorithm, which makes merger rates dependent on environment, can increase the scatter by the required amount, and simultaneously provide a qualitatively correct correlation between environment and formation epoch in the excursion set merger trees.
Formalization of Metatheory of the Quipper Quantum Programming Language in a Linear Logic

We develop a linear logical framework within the Hybrid system and use it to reason about the type system of a quantum lambda calculus. In particular, we consider a practical version of the calculus called Proto-Quipper, which contains the core of Quipper. Quipper is a quantum programming language under active development and recently has gained much popularity among the quantum computing communities. Hybrid is a system that is designed to support the use of higher-order abstract syntax for representing and reasoning about formal systems implemented in the Coq Proof Assistant. In this work, we extend the system with a linear specification logic (SL) in order to reason about the linear type system of Quipper. To this end, we formalize the semantics of Proto-Quipper by encoding the typing and evaluation rules in the SL, and prove type soundness.
A Parallel Social Spider Optimization Algorithm Based on Emotional Learning

Social spider optimization (SSO) is a swarm algorithm designed for solving complex optimization problems. It is an effective approach for searching a global optimum by simulating the cooperative behavior of social-spiders. However, SSO takes too much computation time and shows premature convergence on some problems. In order to accelerate the computation speed and further enhance the search ability, a parallel SSO (PSSO) algorithm with emotional learning is proposed in this paper. First, we develop a parallel structure for the female and male individuals to update their positions, and each individual can be computed in parallel during the search process. Second, an emotional learning mechanism is used to increase swarm diversity which is helpful to improve the search performance. Furthermore, the convergence property and computational complexity of PSSO are discussed in detail. To test the effectiveness of the proposed algorithm, it is applied to solve data clustering problem. The experimental results demonstrate that the overall performance of PSSO is superior to six other clustering algorithms on several standard data sets. In the aspect of search performance, the results obtained by PSSO are better than the comparison algorithms in most used data sets. In the aspect of time performance, the computation time of PSSO is greatly reduced in the parallel computing environment. It is comparable with K-means which is the fastest among the comparison algorithms when the number of processors larger than and equals to 16.
MulTed: a multilingual aligned and tagged parallel corpus

Recently, more data-driven approaches are demanding multilingual parallel resources primarily in the cross-language studies. To meet these demands, building multilingual parallel corpora are becoming the focus of many Natural Language Processing (NLP) scientific groups. Unlike monolingual corpora, the number of available multilingual parallel corpora is limited. In this paper, the MulTed, a corpus of subtitles extracted from TEDx talks is introduced. It is multilingual, Part of Speech (PoS) tagged, and bilingually sentence-aligned with English as a pivot language. This corpus is designed for many NLP applications, where the sentence-alignment, the PoS tagging, and the size of corpora are influential such as statistical machine translation, language recognition, and bilingual dictionary generation. Currently, the corpus has subtitles that cover 1100 talks available in over 100 languages. The subtitles are classified based on a variety of topics such as Business, Education, and Sport. Regarding the PoS tagging, the Treetagger, a language-independent PoS tagger, is used; then, to make the PoS tagging maximally useful, a mapping process to a universal common tagset is performed. Finally, we believe that making the MulTed corpus available for a public use can be a significant contribution to the literature of NLP and corpus linguistics, especially for under-resourced languages.
Efficient two-stage processing for joint sequence model-based Thai grapheme-to-phoneme conversion

Thai grapheme-to-phoneme conversion (G2P) is a challenging task due to its difficulties found by many previous studies. This paper introduces a novel two-stage processing for Thai G2P. The first stage uses Conditional Random Fields (CRF) to segment input text into pseudo-syllable (PS) units, the smallest unit with pronunciation inconfusable. This first CRF simultaneously segments input text into PS units and predicts the function of each character. Outputs from the first stage are used to efficiently align graphemes and phonemes, forming graphone joint sequences as input for the next stage. The second stage uses another CRF to model the graphone joint sequences. The character function predicted by the first stage is the cue to explicitly solve some critical Thai G2P difficulties such as hidden syllables often appeared in loan words and complicated character ordering. An evaluation is done using a large pronunciation dictionary that covers over 70% of Thai word usage. Experimental results show that 6.55% and 8.43% word error rates (WER) are obtained at the first and the second prediction states, while the overall G2P achieves a 9.94% WER. This is as much as 14.49% absolute improvement from a baseline model using Context Free Grammar (CFG) syllabification and syllable n-gram modeling.
AnswerNet: Learning to Answer Questions

Multi-modal tasks like visual question answering (VQA) are an important step towards human-level artificial intelligence. In general, the input of the VQA task consists of an image and a related question. In order to correctly answer the question, a model needs to extract and integrate useful information from both the image and the question. In this paper, we propose a model named AnswerNet to tackle this task. In the proposed model, discriminative features are extracted from both the image and the question. Specifically, high-level image features are extracted by the state-of-the-art convolutional neural network, i.e., Deep Residual Net. For question features, the semantic representations of the question and the term frequencies of the distinct words are captured by long short-term memory network and bag-of-words model, respectively. Then, a hierarchical fusion network is proposed to effectively fuse the image features with the question features. Experimental results on three large-scale datasets, VQA, COCO-QA, and VQA2, demonstrate the effectiveness of the proposed AnswerNet.
A robust methodology for time picking and error analysis of ultrasonic waveforms and rock densities in the laboratory

The relationships between elastic wave speeds and rock physical properties are commonly studied through laboratory experiments. However, the estimation of elastic wave arrival times and the associated errors are not reported consistently. Changes in experimental conditions such as pressures or fluid substitution vary the arrival time and shape of the recorded ultrasonic signals. Crosscorrelation techniques are a simple way of estimating time differences between waveforms for a range of experimental conditions. However, in the presence of elastic wave attenuation, which results in the change of waveform shape (frequency content), this technique can be unstable. We design a methodology based on dynamic time warping functions for consistently picking arrival times between different ultrasonic waveforms. This methodology is robust to small changes in ultrasonic waveform shape due to variation in the experimental conditions. Here, we estimate and correlate P- and S-wave time picks for variations in (1) fluid type or saturation and (2) confining or fluid pressures. We also develop a semiautomated error analysis that uses Monte Carlo simulations to propagate uncertainty in time picks into wave speed and elastic moduli errors. We test the methodology on a set of volcaniclastic and intrusive rocks from a geothermal field in New Zealand at atmospheric conditions and confining stresses. The Python code for this analysis is freely available to the community.
Stable and efficient cubature rules by metaheuristic optimization with application to Kalman filtering

The numerical approximation of Gaussian integrals is of great relevance for a wide variety of scientific applications. In recent decades, numerous efficient cubature rules of high polynomial exactness for the case of high-dimensional Gaussian integrals have been proposed. In addition to the question of efficiency, however, the question of stability, which depends on the influence of negative weights, is also of crucial importance. For certain degrees of polynomial exactness no stable rules are known. Therefore, a new approach based on metaheuristic optimization is presented which allows the computation of cubature rules which exceed comparable rules known from the literature with respect to stability and efficiency. Especially with regard to iterative numerical calculations, the use of unstable cubature rules over time can lead to a distortion and thus a significant deterioration of the results. To illustrate this fact, the cubature-based Kalman filter is utilized. Based on the results of two simulation studies, the necessity for the use of stable cubature rules is motivated.
Multi-channel acoustic analysis of phoneme /s/ mispronunciation for lateral sigmatism detection

The paper presents a method for computer-aided detection of lateral sigmatism. The aim of the study is to design an automated sigmatism diagnosis tool. For that purpose, a reference speech corpus has been collected. It contains 438 recordings of a phoneme /s/ surrounded by certain vowels with normative and simulated pathological pronunciation. The acoustic signal is recorded with an acoustic mask, which is a set of microphones organised in a semi-cylindrical surface around the subject's face. Frames containing /s/ phoneme are subjected to beamforming and feature extraction. Two different feature vectors containing, e.g., Mel-frequency cepstral coefficients and fricative formants, are defined and evaluated in terms of binary classification involving support vector machines. A single-channel analysis is confronted with multi-channel processing. The experimental results show that the multi-channel speech signal processing supported by beamforming is able to increase the pathology detection capabilities in general.
An efficient image processing methodology based on fuzzy decision for dental shade matching

For dentists, it is very important to determine the color of the denture. Shade selection in dental practice is an important and difficult task. In the dental shade matching process, the shade selection will be affected by the observer’s physiological conditions such as age, mood, fatigue, and so on. These will make a difference on the judgement between the matching shade and the actual teeth color. In the past, dentists use shade tabs as a reference basis to match the teeth in the intra-oral environment. In this paper, an efficient color analysis methodology based on image processing and fuzzy decision techniques is proposed for dental shade matching. Since the color information is a very important index for the shade matching, the proposed methodology used the chrominance values Cb and Cr to increase the accuracy of color analysis. In order to improve the performance of the proposed methodology, three formulas, such as PSNR value of Cb, PSNR value of Cr, and S-CIELAB value, were selected by a fuzzy decision model. As shown in the results, the proposed efficient methodology based on fuzzy decision techniques improved at least 1.92 % in average accuracy and 0.59 in average score from the PSNR (Cb) and PSNR (Cr) in this work. In addition, the average values of the accuracies and scores in this work are 92.31% and 98.74, respectively, which are much better than the previous studies. To summarized, this work is the first study that applied fuzzy decision with the PSNR (Cb), PSNR (Cr) and S-CLIELAB information for dental shade matching. The results showed that the proposed methodology performs better than the previous work and other methods.