Reviewerly/exHarmony huggingface dataset:
Multi-keywords carrier-free text steganography method based on Chinese Pinyin

By combining big data with the characteristics of steganography, carrier-free steganography was proposed to resist all the steganalysis attacks. A novel method named multi-keywords carrier-free text steganography method based on Chinese Pinyin is introduced in this paper. In the proposed method, the hidden tags are selected from the Pinyin combinations of two words. In the process of information hiding, the part of speech (POS) is used for hiding the number of keywords. Also, the redundancy of hidden tags in extraction process is eliminated by ensuring the uniqueness of each hidden tag in each stego-text. Meanwhile, the way of joint retrieval is used for hiding multi-keywords. Experimental results show that the proposed method has good performance in the hiding capacity, the success rate of hiding, the extraction accuracy and the time efficiency with appropriate hidden tags and large-scale of the big text data.
La tutela della salute nella procreazione medicalmente assistita fra progresso scientifico e interpretazione della Corte costituzionale

The following essay focuses on the role of the Constitutional Court in the matter of medically assisted procreation, highlighting the role of constitutional principles in the light of scientific progress.
Evaluation the Potential Performance of the DCT-Transformants Non-Equilibrium Positional Encoding Method

The article explores the possibilities of a non-equilibrium positional code description of a transformant bit representation. The research is conducted from the standpoint of the possibilities of providing a non-equilibrium positional code to reduce transformant structural redundancy. We consider the worst case when the number of binary transitions between the elements of the transformant bit representation is maximum.
Extension and adaptation of an aspect oriented programming language for real-time systems

Aspect oriented programming (AOP) supports the separation of concerns cutting across the implementation of a software application to avoid scattering and tangling. We focus on the integration of this paradigm in the development of real-time systems. AOP is based on the weaving process to automatically integrate separately created segments of code, into the application. In the context of real-time systems, this can compromise determinism by including constructs that may violate real-time constraints. In this paper, we both study and test the AspectAda language, an existing aspect extension for Ada, in order to adapt its implementation to respect real-time constraints. We study this language's grammar, runtime and especially weaving operations. Then, we extract its limits and provide solutions to adapt it to support real-time constraints. Finally, we propose a new architecture of the compiler/weaver to overcome the problems of the existing one and we detail its different parts.
Motion Detector Training with Virtual Data for Semi-Automatic Motion Analysis-Elimination of Real Training Data Collection using 3DCG Synthesis

A visitor's interest in the exhibits at a museum can be understood by analyzing his or her behavior. Conventionally, behavior analysis is performed manually, but it is troublesome to do this in practice. Therefore, our aim is to reduce this trouble by the automation and semi-automation of behavior analysis. We focus on motion detection by machine learning as the first step in this process. To detect motion using machine learning, it is necessary to collect training motions in advance and create a detector. In this study, human models and motions were generated using three-dimensional computer graphics. Then, we synthesized them and collected training motions using a virtual environment. We used the collected training motions to create a detector and detected motions collected from people in a real environment. The results of the experiments indicate that this training data collection method is effective.
JavaServer Faces Fundamentals

In 2004 Sun Microsystems introduced a Java web framework called JavaServer Faces (JSF) in an effort to help simplify web application development. It is an evolution of the JavaServer Pages (JSP) framework, adding a more organized development life cycle and the ability to more easily utilize modern web technologies.
The Effect of Teacher's Construal Level on the Evaluation of Student's Creative Product Moderated by Openness to Experience

본 연구의 목적은 교사가 학생의 창의적 산출물을 평가하는데 상황적, 개인적 특성이 어떠한 영향을 미치는지 탐색하는 것이다. 이를 위하여 상황적 변인으로는 해석 수준을 조작하여 해석 수준에 따라 창의성 평가가 어떻게 달라지는지 탐색하였다. 개인적 특성으로는 경험에 대한 개방성을 설정하여 해석 수준이 창의성 평가에 미치는 영향에 조절 효과를 보이는지 탐색하였다. 실험에는 대전, 충남, 서울 지역의 초등교사 126명이 참여하였고, 참여자들은 창의적 산출물에 대해 독창성과 유용성을 각각 평가하였다. 연구 결과, 상위 해석 수준이 유발된 참여자들은 하위 해석 수준이 유발된 집단에 비해 독창성과 유용성에서 모두 높은 점수를 주었다. 한편, 경험에 대한 개방성은 해석 수준과 창의성 평가 사이에서 조절효과를 보이지 않았다. 이 같은 결과는 창의적 수준을 평가하는데 해석 수준과 같은 상황적 변인이 영향을 미칠 수 있음을 뜻하는 것으로 교육 상황에 따라 통제하거나 조작할 필요가 있음을 시사한다.This study was conducted to explore the effects of construal level on the evaluation of creative products and examine whether the evaluator s openness to experience moderates this relationship. Construal level was set as a contextual variable and openness to experience was set as a personal trait. The evaluation of creative products was separately measured by originality and usefulness. A total of 136 elementary school teachers participated in this study, and the data from 126 people was used(27 males, 99 females) for final analysis. As a result of the repeated measures ANOVA, participants in the high level construal group rated the originality and usefulness of the creative products higher than those in the low level construal group. But openness to experience did not significantly affect both the evaluations of originality and usefulness of creative products, nor did it moderate the relationship between construal level and creativity evaluation. The results of this study were meaningful in that it empirically verified the teacher s construal level as a situational variable, which can influence the evaluation of the student s creative products.
Designing Non-additive Distortions for JPEG Steganography Based on Blocking Artifacts Reduction

The JPEG image is the most commonly used image format, and the content-adaptive embedding mechanism is widely adopted for JPEG steganography. The embedding distortions for existing adaptive JPEG steganography are mostly additive distortions, while the non-additive distortions in JPEG steganography have not been sufficiently explored. In this paper, we propose a non-additive distortion design method to measure the embedding effects of DCT coefficients on the spatial domain by using blocking artifacts reduction (BAR). The main idea is to reduce the spatial domain blocking artifacts, from which to guide the selection of the polarity of embedding changes for DCT coefficients in the JPEG domain. Because the changes of DCT coefficients will increase the blocking artifacts, the BAR principle can maintain the spatial continuity in both inter-blocks and intra-blocks. The proposed BAR principle can be applied to current additive JPEG steganography. Experimental results show that our method significantly improves the security of additive JPEG steganography, especially for high embedding payloads.
A Study on the Quality Improvement of Practical-Based Education for Creative Products: Focusing on the Lecture Evaluation of Contents Creation Major

이 연구의 목적은 콘텐츠 창작전공의 강의평가 결과를 바탕으로 창의적 산출물을 위한 실습중심 교육의 질제고 방안을 탐색하고자 하는 것이다. A대학의 콘텐츠 창작전공과 이론중심 전공의 실습수업과 이론수업 강의평가 355개를 분석한 결과는 다음과 같다. 먼저, 콘텐츠 창작전공의 강의평가 평균이 이론중심전공보다 예복습(t=2.837, p<.01), 강의계획(t=2.644, p<.01), 출결관리(t=2.863, p<.01), 능동적참여유도(t=3.194, p<.01), 교수법(t=2.900, p<.01)에서 통계적으로 유의미하게 높다. 둘째, 콘텐츠 창작전공 실습수업의 강의평가 결과 중 과제(t=-2.162, p<.05), 예복습(t=-4.557, p<.000), 학습목표(t=-3.431, p<.001), 강의계획운영(t=-2.227, p<.05), (t=-3.076, p<.01), 교수법(t=-2.362, p<.05)항목의 평균이 이론중심수업보다 통계적으로 유의미하게 높았다. 셋째, 콘텐츠 창작전공 실습수업에서 학습자의 지각된 학습목표 달성에 영향을 미친 교수자 변인을 확인한 결과, 상집단은 교수자의 강의계획 운영(β=.643, t=8.173, p=.000), 하집단은 능동적 참여유도(β=.469, t=4.047, p=.000)가 유의한 영향을 미치는 것으로 확인되었다. 마지막으로 이러한 결과를 바탕으로 콘텐츠 창작 실습수업을 운영하는 교수자가 고려해야 할 시사점을 제시하였다.The purpose of this study is to explore about how to improve the quality of the practice-oriented education for the creative output based on the results of lecture evaluation in the content creation major. The results of the analysis of 355 practical and theoretical class lectures in content creation and theory-based majors are as follows; firstly, the lecture evaluation averages of contents creative major were statistically significantly higher than those of theoretical majors. Secondly, the average of lecture evaluation in the practical class of contents creation major was statistically higher than that of the theory-based class. hirdly, the instructor variables that influenced the achievement of perceived learning goals were identified. It shows that the upper group was influenced by the teacher s lecture plan operation meaningfully, but the lower group was affected by an active induction of participation. Lastly, based on these results, this study suggested implications for instructors who run the content creation practical classes.
Probabilistic bisection with spatial metamodels

Probabilistic Bisection Algorithms perform root finding based on knowledge acquired from noisy oracle responses. We consider the generalized PBA setting (G-PBA) where the statistical distribution of the oracle is unknown and location-dependent, so that model inference and Bayesian knowledge updating must be performed simultaneously. To this end, we propose to leverage the spatial structure of a typical oracle by constructing a statistical surrogate for the underlying logistic regression step. We investigate several surrogates, including Binomial Gaussian Processes (B-GP), Polynomial, Kernel, and Spline Logistic Regression. In parallel, we develop sampling policies that adaptively balance learning the oracle distribution and learning the root. One of our proposals mimics active learning with B-GPs and provides a novel look-ahead predictive variance formula. The resulting gains of our Spatial PBA algorithm relative to earlier G-PBA models are illustrated with synthetic examples and a challenging stochastic root finding problem from Bermudan option pricing.
The theory of entanglement-assisted metrology for quantum channels

The quantum Fisher information (QFI) measures the amount of information that a quantum state carries about an unknown parameter. The (entanglement-assisted) QFI of a quantum channel is defined to be the maximum QFI of the output state assuming an entangled input state over a single probe and an ancilla. Both the channel QFI and the optimal input state could be solved via a semidefinite program (SDP). In quantum metrology, people are interested in calculating the QFI of $N$ identical copies of a quantum channel when $N \rightarrow \infty$, which we call the asymptotic QFI. It was known that the asymptotic QFI grows either linearly or quadratically with $N$. Here we obtain a simple criterion that determines whether the scaling is linear or quadratic. In both cases, we found a quantum error correction protocol achieving the asymptotic QFI and an SDP to solve the optimal code. When the asymptotic QFI is quadratic, the Heisenberg limit, a feature once thought unique to unitary quantum channels, is recovered. When the asymptotic QFI is linear, we show it is still in general larger than $N$ times the channel QFI, showing the non-additivity of the channel QFI of general quantum channels.
DeepLPF: Deep Local Parametric Filters for Image Enhancement

Digital artists often improve the aesthetic quality of digital photographs through manual retouching. Beyond global adjustments, professional image editing programs provide local adjustment tools operating on specific parts of an image. Options include parametric (graduated, radial filters) and unconstrained brush tools. These highly expressive tools enable a diverse set of local image enhancements. However, their use can be time consuming, and requires artistic capability. State-of-the-art automated image enhancement approaches typically focus on learning pixel-level or global enhancements. The former can be noisy and lack interpretability, while the latter can fail to capture fine-grained adjustments. In this paper, we introduce a novel approach to automatically enhance images using learned spatially local filters of three different types (Elliptical Filter, Graduated Filter, Polynomial Filter). We introduce a deep neural network, dubbed Deep Local Parametric Filters (DeepLPF), which regresses the parameters of these spatially localized filters that are then automatically applied to enhance the image. DeepLPF provides a natural form of model regularization and enables interpretable, intuitive adjustments that lead to visually pleasing results. We report on multiple benchmarks and show that DeepLPF produces state-of-the-art performance on two variants of the MIT-Adobe-5K dataset, often using a fraction of the parameters required for competing methods.
Affine structured light sensor for measurements through inspection windows: basic concept and direct calibration approach

The realization of 3D triangulation measurements in inhomogeneous media is challenging, as the sensor light path is not necessarily rectilinear anymore and the triangulation principle is violated. An exemplary measurement scenario under inhomogeneous optical conditions is the geometry characterization through an inspection window. The discrete refractive index variation from air to inspection window and back to air (or even water) can require complicated light path modeling approaches in order to triangulate 3D surface data correctly. As commonly used entocentric lenses “fan out” the projected light rays, the rays’ incidence angles onto the refractive index interface are not constant, and the rays are individually deflected. In consequence, the typically used camera pinhole model does not apply anymore, or can only approximate the actual light path under refraction. In this paper, we present a structured light sensor concept for measurements through inspection windows, which does not require a special adaption of the light path model. The key is the application of a telecentric stereo camera pair and high-quality optical inspection windows. It can be shown theoretically, that no additional parameters are necessary to model light refraction induced by a plane-parallel plate – such as a window –, when used in combination with a telecentric lens. Next to the affine stereo camera pair, the sensor comprises an entocentric projector unit as feature generator. In previous work, the projector was calibrated with refraction model in order to provide a 3D point cloud basis for the affine camera calibration.<sup>1</sup> In the new approach, the projector is merely used as feature generator to solve the correspondence problem between the affine stereo camera pair. Besides the developed sensor hardware concept, we present an overview on the calibration strategy based on an affine self-calibration approach, the solving of the correspondence problem between the cameras, and first calibration and measurement results. In forthcoming work, the sensor is also meant to be used in three-media refraction scenarios.
Fibre optic angle rate gyroscope performance evaluation in terms of Allan variance

Based on the analysis of the error-sources of the fibre optic angle rate gyroscope (FOARG), the Allan parameters are focused on calculation the Allan variances. The relationship between the Allan variance and the accuracy of FOARG is given. For the existences in the output of some-type FOARG, such as high noise, large volatilities in value and existing notable errors, a data-process algorithm is proposed with meaning and smoothing one. A lot of MATLAB blocks, such as data-sampling, meaning and smoothing, are designed to process some-type FOARG's dynamic data and static data and to evaluate its performance with Allan variance.
A Hierarchical Fine-Tuning Approach Based on Joint Embedding of Words and Parent Categories for Hierarchical Multi-label Text Classification

Text classification has become increasingly challenging due to the continuous refinement of classification label granularity and the expansion of classification label scale. To address that, some research has been applied onto strategies that exploit the hierarchical structure in problems with a large number of categories. At present, hierarchical text classification (HTC) has received extensive attention and has broad application prospects. Making full use of the relationship between parent category and child category in text classification task can greatly improve the performance of classification. In this paper, We propose a joint embedding of text and parent category based on hierarchical fine-tuning ordered neurons LSTM (HFT-ONLSTM) for HTC. Our method makes full use of the connection between the upper-level and lower-level labels. Experiments show that our model outperforms the state-of-the-art hierarchical model at a lower computation cost.
Speckle denoising by the family of nonlocal means methods

We demonstrate the performance of nonlocal means (NLM) and its related adaptive kernel-based methods for speckle denoising for the intensity and phase images acquired from the digital speckle pattern interferometric (DSPI) and digital holographic interferometric (DHI) techniques, respectively. The speckle denoise capabilities of NLM and its variant denoising methods such as NLM-average (NLM-av), NLM-local polynomial regression (NLM-PLR), and NLM-shape adaptive patches (NLM-SAP), and various NLM-reprojection schemes are implemented on simulated. Their performances are quantified on the basis of two metric criteria – peak signal-to-noise ratio (PSNR) and the image quality index (Q). The effectiveness of these denoising methods is compared with other existing speckle denoising methods. The obtained results suggest that these denoising methods have the ability to denoise speckles from the DSPI/DHI fringes and provide better visual and quantitative results.
Performance evaluation for the hash generation phase of a democratic blockchain

Distributed ledger technologies (DLTs) have the potential to transform different areas of research and industry. Initially created to support a peer-to-peer electronic cash system – more commonly known as bitcoin – blockchains provide a decentralised transactions and data management technology. This decentralised technology is secure, anonymous, and transparent. Nonetheless, the blockchain protocol has a number of technical weaknesses such as power computing dependency and high power consumption. An alternative protocol proposes a democratic approach in which the computing power is not a determinant factor in user participation. The democratic protocol has three phases: hash generation, hash broadcast, and hash validation. We evaluate the performance of the hash generation phase on CPU, GPU, and cluster platforms. A considerable speedup is achieved with GPUs when using final nonce values similar to a real blockchain application. Also, it provides the best power efficiency.
Implement a Proactive Event Briefing Process

Special Events GaloreVolume 20, Issue 5 p. 4-4 Moves Management Strategies Implement a Proactive Event Briefing Process Megan Venzin, Megan VenzinSearch for more papers by this author Megan Venzin, Megan VenzinSearch for more papers by this author First published: 09 April 2020 https://doi.org/10.1002/speg.31347Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume20, Issue5May 2020Pages 4-4 RelatedInformation
Timed CTL checking of time critical cloud applications using timed bigraphs

Controlling cloud resources provisioning for time critical applications is crucial to meet required quality of service (QoS) of modern applications. Both computational resources capabilities and network bandwidth should be considered to accurately serve practical needs, while complying relevant constraints. The objective of this work is to show how to add time constructs to the bigraphical reactive systems (BRSs) formalism, in order to describe and analyse the execution of cloud applications having temporal constraints. In particular, we are interested in the cloud resource allocation problem to meet end user requests while considering network deadline, as well as the service processing time. The proposed time-aware BRS model is then integrated in the Real-Time Maude language and its TCTL model checker in order to obtain an executable and analysable specification of cloud applications having temporal constraints.
Design of information management system for structural monitoring based on network fragmentation

Aiming at the shortcomings of current information management system, such as low security, poor economy and long response time, a design method of structure monitoring information management system based on network fragments is proposed. Firstly, the overall architecture of the network debris structure monitoring information management system is designed and the requirements of the monitoring information management system are analysed. Then, based on the analysis of the main characteristics of the network debris monitoring information management system, the modules and subsystems of the system are designed separately, and the system data management scheme is given. In this way, the design of structured monitoring information management system based on network fragments is completed. The experimental results show that the information management system can manage the network information safely and economically. The response time is short, and the overall performance of the structured monitoring information management system is improved.
Perceptual Image Hashing Based on Weighted LLE and FMT

This paper presents a new image hashing technique that exploits the invariant properties of Fourier Mellin Transform (FMT) and Local Linear Embedding (LLE). First, Fourier-Mellin transform is integrated into the domain pixel blocks of the normalized image due to its invariance property in order to improve performance under geometric operations, hence generating a stable feature matrix. Then, data reduction with LLE is applied to the generated feature matrix, and the final hash is constructed after encrypting the invariances of LLE. Conducted experimental evaluation shows that our proposed hashing is robust against digital manipulations, such as rotation, scaling, cropping, JPEG compression, brightness/contrast adjustment, etc, and also reaches good discrimination. Comparisons with other state-of-the-art algorithms also reveals that our hashing outperforms its counterparts in classification performances between robustness and discriminative capability.