Reviewerly/exHarmony huggingface dataset:
Quantum metrology with generalized cat states

We show a general relationship between a superposition of macroscopically distinct states and sensitivity in quantum metrology. Generalized cat states are defined by using an index which extracts the coherence between macroscopically distinct states, and a wide variety of states, including a classical mixture of an exponentially large number of states, has been identified as the generalized cat state with this criterion. We find that if we use the generalized cat states for magnetic field sensing without noise, the sensitivity achieves the Heisenberg scaling. More importantly, we even show that sensitivity of generalized cat states achieves the ultimate scaling sensitivity beyond the standard quantum limit under the effect of dephasing. As an example, we investigate the sensitivity of a generalized cat state that is attainable through a single global manipulation on a thermal equilibrium state and find an improvement of a few orders of magnitude from the previous sensors. Clarifying a wide class that includes such a peculiar state as metrologically useful, our results significantly broaden the potential of quantum metrology.
Key Success Factors of Blockchain Platform for Micro-enterprises

The purposes of the study are threefold: (1) develop key successful factors of blockchain platform for micro-enterprises based on Balanced Scorecard (BSC), (2) find out the correlation between the key successful factors of blockchain platform for micro-enterprises, and (3) understand the perception of micro-enterprise blockchain and the difference between theoretical guidance and practical application.This study combines Interpretative Structural Modeling Method, (ISM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) to analyze the causal relationships and hierarchical structures of the 12 key successful factors of blockchain platform for micro-enterprises and understand the correlation between factors.The 12 key successful factors developed based on the four perspectives of the Balanced Scorecard, and the questionnaire designs based on the concept of DEMATEL, and then analyzed data by DEMATEL and ISM methods to understand the correlation between key factors.The research results show that the key successful factors of blockchain platform for microenterprises include "brand equity", "security and anti-counterfeiting", "sales growth", "transparency and clear", "trust", "consensus mechanism", "traceability", "consistency", "tracking", "innovation management", "international", "organizational adaptation", in which consumer "trust" plays an important role.Micro-enterprises can use blockchain to expand the market, provide customers with better service quality, and bring sales growth to micro-enterprises.
Effective Data Classification via Combining Neural Networks and SVM

In this paper, we propose an effective data classification method via combining neural networks and support vector machine (SVM). The neural networks are able to effectively extract data features. SVM is good at solving the small-sample classification problem. In order to combine the complementary advantages of neural networks and SVM, we first utilize neural works to extract data features, which are then classified with SVM. The proposed method is named as NN-SVM. We use the Iris dataset and the Lead isotope dataset to train and test NN-SVM. Experimental results show that NN-SVM outperforms neural networks based and SVM based classification algorithms.
Building English-to-Serbian Machine Translation System for IMDb Movie Reviews

This paper reports the results of the first experiment dealing with the challenges of building a machine translation system for usergenerated content involving a complex South Slavic language.We focus on translation of English IMDb user movie reviews into Serbian, in a low-resource scenario.We explore potentials and limits of (i) phrase-based and neural machine translation systems trained on out-of-domain clean parallel data from news articles (ii) creating additional synthetic indomain parallel corpus by machine-translating the English IMDb corpus into Serbian.Our main findings are that morphology and syntax are better handled by the neural approach than by the phrase-based approach even in this lowresource mismatched domain scenario, however the situation is different for the lexical aspect, especially for person names.This finding also indicates that in general, machine translation of person names into Slavic languages (especially those which require/allow transcription) should be investigated more systematically.
An Efficient and Low-Power Design of the SM3 Hash Algorithm for IoT

The Internet-of-Things (IoT) has a security problem that has become increasingly significant. New architecture of SM3 which can be implemented in loT devices is proposed in this paper. The software/hardware co-design approach is put forward to implement the new architecture to achieve high performance and low costs. To facilitate software/hardware co-design, an AHB-SM3 interface controller (AHB-SIC) is designed as an AHB slave interface IP to exchange data with the embedded CPU. Task scheduling and hardware resource optimization techniques are adopted in the design of expansion modules. The task scheduling and critical path optimization techniques are utilized in the compression module design. The proposed architecture is implemented with ASIC using SMIC 130 nm technology. For the purpose of comparison, the proposed architecture is also implemented on Virtex 7 FPGA with a 36 MHz system clock. Compared with the standard implementation of SM3, the proposed architecture saves the number of registers for approximately 3.11 times, and 263 Mbps throughput is achieved under the 36 MHz clock. This design signifies an excellent trade-off between performance and the hardware area. Thus, the design accommodates the resource-limited IoT security devices very well. The proposed architecture is applied to an intelligent security gateway device.
BEN: An Agent Architecture for Explainable and Expressive Behavior in Social Simulation

Social Simulations are used to study complex systems featuring human actors. This means reproducing real-life situations involving people in order to explain an observed behavior. However, there are actually no agent architectures among the most popular platforms for agent-based simulation enabling to easily model human actors. This situation leads modelers to implement simple reactive behaviors while the EROS principle (Enhancing Realism Of Simulation) fosters the use of psychological and social theory to improve the credibility of such agents. This paper presents the BEN architecture (Behavior with Emotions and Norms) that uses cognitive, affective and social dimensions for the behavior of social agents. This agent architecture has been implemented in the GAMA platform so it may be used by a large audience to model agents with a high level explainable behavior. This architecture is used on an evacuation case, showing how it creates believable behaviors in a real case scenario.
Multi-task Layout Analysis of Handwritten Musical Scores

Document Layout Analysis (DLA) is a process that must be performed before attempting to recognize the content of handwritten musical scores by a modern automatic or semiautomatic system. DLA should provide the segmentation of the document image into semantically useful region types such as staff, lyrics, etc. In this paper we extend our previous work for DLA of handwritten text documents to also address complex handwritten music scores. This system is able to perform region segmentation, region classification and baseline detection in an integrated manner. Several experiments were performed in two different datasets in order to validate this approach and assess it in different scenarios. Results show high accuracy in such complex manuscripts and very competent computational time, which is a good indicator of the scalability of the method for very large collections.
Towards Designing Diegetic Gaze in Games: The Use of Gaze Roles and Metaphors

Gaze-based interactions have found their way into the games domain and are frequently employed as a means to support players in their activities. Instead of implementing gaze as an additional game feature via a game-centred approach, we propose a diegetic perspective by introducing gaze interaction roles and gaze metaphors. Gaze interaction roles represent ambiguous mechanics in gaze, whereas gaze metaphors serve as narrative figures that symbolise, illustrate, and are applied to the interaction dynamics. Within this work, the current literature in the field is analysed to seek examples that design around gaze mechanics and follow a diegetic approach that takes roles and metaphors into account. A list of surveyed gaze metaphors related to each gaze role is presented and described in detail. Furthermore, a case study shows the potentials of the proposed approach. Our work aims at contributing to existing frameworks, such as EyePlay, by reflecting on the ambiguous meaning of gaze in games. Through this integrative approach, players are anticipated to develop a deeper connection to the game narrative via gaze, resulting in a stronger experience concerning presence (i.e., being in the game world).
Development of "Child Friendly ICT" Textbooks to Improve Professional Competence of Teacher Candidates : A Case Study of Early Childhood Education Program Students

The purpose of this study is to develop textbooks on child-friendly of Information, Communication, &amp;amp; Technology (ICT) that are valid, practical and effective in improving the professional competence of teacher education students (PG) PAUD Sriwijaya University. This study uses the Research and Development model with the type of Analysis, Design, Development, Implementation, and Evaluation (ADDIE) which consists of five stages, namely analysis, design, develop, implementation, and evaluation. The sample in this study was Sriwijaya University PAUD teacher education study program students. In the implementation phase, field trials were carried out as a result of the development of child-friendly ICT-based teaching books with a sample of 42 people consisting of 12 samples for the small group test and 30 samples for the large group test. The data obtained in this study were analyzed using qualitative analysis and quantitative analysis. Qualitative data analysis was obtained from questionnaires and quantitative data obtained from the results of the pre-test and post-test at the implementation stage. Based on the results of the calculation of the data, it was obtained that the data were normally distributed and homogeneous, so that the inferior statistical test used the Independent Sample T-Test with a significance value of 0.05. Based on the test results of data analysis, it was found that the "child-friendly ICT" textbook that had been developed was valid, practical and effective. The results obtained prove that child-friendly ICT textbooks contribute to improving the professional competence of students. Child-friendly ICT textbooks not only provide new understanding and knowledge of ICT use in child-friendly learning, but also enhance the skills of prospective teachers in using ICT-based teaching materials.
Цифровизация процессов обслуживания в библиотеках – это уже реальность

The article considers theoretical issues of digitalization in libraries, characterizes modern forms and methods of providing library and information services, ways of communication between libraries and users. It reveals qualitatively new opportunities of library and information service due to spreading digital technologies and robotics in libraries. Cases of successful implementation of library and information services digitization projects demonstrate the library readiness to move to a new level of development. The author concludes that despite the fact that digitalization significantly increases services efficiency, effects positively on their quality and, consequently, attracts new users to libraries, library information services also include processes that are impossible without human participation with qualities such as the ability to anticipation, emotional experience, intuitive forecasting. Therefore, it is necessary to be aware of the boundaries and professional needs when using digital technologies in library and information services.
A Review of Use Case-Based Development Effort Estimation Methods in the System Development Context

Software Effort Estimation – (further only SEE), is a critical factor in the early phase of the software development life-cycle and hence - the success or failure of a software project depends on the accuracy of the estimated effort. In recent years, Use Cases for Software Effort Estimation has gained wide-ranging popularity. It is suitable for Effort Estimation in the early stages of software development since it helps project managers to bid on projects, and to efficiently allocate resources. It has attracted many researchers’ interest in Use Case-based approaches due to the promising results obtained - including their early applicability. In this article, we look into a systematic review of previously published materials in order to summarise various Software Effort Estimation – (further only SEE), models and developments, based on Use Case Point. The study also provides insights into the effects of all factors that contributed to the Use Case size as an estimation for effort. Apart from this, the paper also provides standard criteria to evaluate the models’ accuracy and effectiveness.
Denoising for variable density ESPI fringes in nondestructive testing by an adaptive multiscale morphological filter based on local mean

Analysis of speckle images with variable density fringes is a challenging task when electronic speckle pattern interferometry (ESPI) is used for nondestructive testing of defects. In this paper, an adaptive multiscale morphological filter based on local mean is proposed. First, the image is segmented, and the regions are divided into different density levels using local mean. Then, the structural elements that adapt to different density levels are designed, and the proper size of the structural elements is determined by an iterative procedure. Finally, the morphological open&#x2013;closing filtering is conducted, and the block edges are smoothed by averaging. The proposed method was applied to computer-simulation fringes and fringes experimentally obtained from a prefabricated defect specimen under thermal loading and then compared with the commonly used methods, i.e., discrete cosine filter, wavelet filter, Lee filter, and nonlocal mean filter. The experimental results showed that the proposed method had the best performance in terms of noise reduction and edge preservation. With the capability of noise reduction for ESPI images of variable density fringes, the proposed method will be helpful to build a quantitative relationship between fringes and defects in the cases of nonuniform deformation of speckle interferometry, such as nondestructive defect detecting, thermal structural analysis, and heterogeneous materials mechanical analysis.
Fuzzifier Selection in Fuzzy C-Means from Cluster Size Distribution Perspective

Fuzzy c-means (FCM) is a well-known and widely applied fuzzy clustering method.Although there have been considerable studies which focused on the selection of better fuzzifier values in FCM, there is still not one widely accepted criterion.Also, in practical applications, the distributions of many data sets are not uniform.Hence, it is necessary to understand the impact of cluster size distribution on the selection of fuzzifier value.In this paper, the coefficient of variation (CV) is used to measure the variation of cluster sizes in a data set, and the difference of coefficient of variation (DCV) is the change of variation in cluster sizes after FCM clustering.Then, considering that the fuzzifier value with which FCM clustering produces minor change in cluster variation is better, a criterion for fuzzifier selection in FCM is presented from cluster size distribution perspective, followed by a fuzzifier selection algorithm called CSD-m (cluster size distribution for fuzzifier selection) algorithm.Also, we developed an indicator called Influence Coefficient of Fuzzifier (ICF) to measure the influence of fuzzifier values on FCM clustering results.Finally, experimental results on 8 synthetic data sets and 4 real-world data sets illustrate the effectiveness of the proposed criterion and CSD-m algorithm.The results also demonstrate that the widely used fuzzifier value m = 2 is not optimal for many data sets with large variation in cluster sizes.Based on the relationship between CV 0 and ICF, we further found that there is a linear correlation between the extent of fuzzifier value influence and the original cluster size distributions.
Analyze and predict the perceptibility of UHD video contents

720p, Full-HD, 4K, 8K, &#x2026;, display resolutions are increasing heavily over the past time. However, many video streaming providers are currently streaming videos with a maximum of 4K/UHD-1 resolution. Considering that normal video viewers are enjoying their videos in typical living rooms, where viewing distances are quite large, the question arises if more resolution is even recognizable. In the following paper we will analyze the problem of UHD perceptibility in comparison with lower resolutions. As a first step, we conducted a subjective video test, that focuses on short uncompressed video sequences and compares two different testing methods for pairwise discrimination of two representations of the same source video in different resolutions. We selected an extended stripe method and a temporal switching method. We found that the temporal switching is more suitable to recognize UHD video content. Furthermore, we developed features, that can be used in a machine learning system to predict whether there is a benefit in showing a given video in UHD or not. Evaluating different models based on these features for predicting perceivable differences shows good performance on the available test data. Our implemented system can be used to verify UHD source video material or to optimize streaming applications.
The European Metrology Cloud: Impact of European Regulations on Data Protection and the Free Flow of Non-Personal Data

New digital technologies, such as cloud computing, big data, artificial intelligence and the Internet of Things (IoT) are designed to maximize efficiency, enable economies of scale and develop new services. They offer benefits to users, such as agility, productivity, speed of deployment and autonomy. In the sector of Legal Metrology, it must be ensured that digital system architectures, digital services, and digital infrastructures are legally compatible. To benefit the stakeholders in this sector, the industry, the notified bodies and the market surveillance/verification authorities alike, the digital transformation of Legal Metrology shall remove barriers to innovation within the legal processes and reduce costs and time to market for new digital products. To this end a European consortium has formed to establish a digital quality infrastructure; the “European Metrology Cloud”, designed to support the processes of conformity assessment and market surveillance/verification and the development of reference architectures and new technology- and data-driven services for this infrastructure. With this approach, the digital single market that the European Commission envisions will be fostered. This article analyzes how recent regulations within the digital single market strategy of the commission - the Data Protection Police Directive (2016/679/EU) and the Regulation on a framework for the free flow of non-personal data in the European Union (Regulation (EU) 2018/1807) – may be integrated into the European Metrology Cloud initiative to, e.g. guaranty that its underlying Blockchain approach complies to these norms and exploit their benefits.
Distortion measurement of optical system using phase diffractive beam splitter

Traditional methods for distortion measurement of large-aperture optical systems are time-consuming and ineffective because they require each field of view to be individually measured using a high-precision rotating platform. In this study, a new method that uses a phase diffractive beam splitter (DBS) is proposed to measure the distortion of optical systems, which has great potential application for the large-aperture optical system. The proposed method has a very high degree of accuracy and is extremely economical. A high-precision calibration method is proposed to measure the angular distribution of the DBS. The uncertainty analysis of the factors involved in the measurement process has been performed to highlight the low level of errors in the measurement methodology. Results show that high-precision measurements of the focal length and distortion were successfully achieved with high efficiency. The proposed method can be used for large-aperture wide-angle optical systems such as those used for aerial mapping applications.
Implicit Objective Network for Emotion Detection

Emotion detection has been extensively researched in recent years. However, existing work mainly focuses on recognizing explicit emotion expressions in a piece of text. Little work is proposed for detecting implicit emotions, which are ubiquitous in people’s expression. In this paper, we propose an Implicit Objective Network to improve the performance of implicit emotion detection. We first capture the implicit sentiment objective as a latent variable by using a variational autoencoder. Then we leverage the latent objective into the classifier as prior information for better make prediction. Experimental results on two benchmark datasets show that the proposed model outperforms strong baselines, achieving the state-of-the-art performance.
A new backpropagation neural network classification model for prediction of incidence of malaria

Malaria is an infectious disease caused by parasitic protozoans of the Plasmodium family. These parasites are transmitted by mosquitos which are common in certain parts of the world. Based on their specific climates, these regions have been classified as low and high risk regions using a backpropagation neural network (BPNN). However, this approach yielded low performance and stability necessitating development of a more robust model. We hypothesized that by spiking neuron models in simulating the characteristics of a neuron, which when embedded with a BPNN, could improve the performance for the assessment of malaria prone regions. To this end, we created an inter-spike interval (ISI)-based BPNN (ISI-BPNN) architecture that uses a single-pass spiking learning strategy and has a parallel structure that is useful for non-linear regression tasks. Existing malaria dataset comprised of 1296 records, that met these attributes, were used. ISI-BPNN showed superior performance, and a high accuracy. The benchmarking results showed reliability and stability and an improvement of 11.9% against a multilayer perceptron and 9.19% against integrate-and-fire neuron models. The ISI-BPNN model is well suited for deciphering the risk of acquiring malaria as well as other diseases in prone regions of the world.