Reviewerly/exHarmony huggingface dataset:
Measurements of Entropic Uncertainty Relations in Neutron Optics

The emergence of the uncertainty principle has celebrated its 90th anniversary recently. For this occasion, the latest experimental results of uncertainty relations quantified in terms of Shannon entropies are presented, concentrating only on outcomes in neutron optics. The focus is on the type of measurement uncertainties that describe the inability to obtain the respective individual results from joint measurement statistics. For this purpose, the neutron spin of two non-commuting directions is analyzed. Two sub-categories of measurement uncertainty relations are considered: noise–noise and noise–disturbance uncertainty relations. In the first case, it will be shown that the lowest boundary can be obtained and the uncertainty relations be saturated by implementing a simple positive operator-valued measure (POVM). For the second category, an analysis for projective measurements is made and error correction procedures are presented.
Proposal of a Sustainable Agile Model for Software Development

Sustainability is even more important now than ever if we speak in the context of organizational growth, it is necessary that technological products, such as software developments, are certified as green-environmental friendly technology that would mean a competitive advantage for an organization that implements an agile methodology for software development that takes sustainability into account, giving the organization new ways to market their software products as environmentally friendly.This study proposes a model for agile software development, it has taken into account that software development must be based upon reusing old hardware, free nonprivative software and code (open source), as well as virtualization of servers and machines, to create software that can be useful for over a decade, as a result, we expect a reduction of planned obsolescence in hardware, which means taking one step ahead to help solve the problem that the big amount of electronic waste (e-waste) means nowadays worldwide.
A General Fine-tuned Transfer Learning Model for Predicting Clinical Task Acrossing Diverse EHRs Datasets

Data analysis of electronic health record (EHRs) system using machine learning, statistical methods can predict relevant clinical tasks. However, there is no uniform standard for current electronic health record systems, and the clinical outcome prediction models trained on one EHR dataset cannot be applied well on other EHR datasets from different medical institutions. Data differences between different medical institutions pose a huge challenge to the study of electronic health records. In this study, we proposed a general transfer learning strategy which can enable models to make clinical prediction acrossing diverse EHRs datasets and validated its strong versatility on three deep learning models. Two different intensive care units (ICU) databases (MIMIC-III and eICU) and one clinical task (in-hospital mortality) are used to evaluate our method. At first, we trained the deep learning models on the source dataset and saved the model states after each epoch. Then, we selected the best performing model as the pre-training model, transferred it to the target dataset and fine-tuned the whole network on target dataset. Finally, we use the fine-tuned models to make predictions on the target dataset. Experiment results show that AUROC score increased by 3%-20% with transfer strategy, which indicated that the general strategy can provide more reliable predictions acrossing EHRs databases to predict clinical tasks.
Analysis of the synchronous phase-shifting method in a white-light spectral interferometer

For white-light spectral interferometry, the phase information is usually retrieved via the Fourier transform method and the temporal phase-shifting method. In comparison, the synchronous phase-shifting method can be used to synchronously acquire interferometric signals with good accuracy and reduced noise. Therefore, it has potential for online measurement and is suitable for application in precision industries and for ultrahigh-speed measurement. In this work, a white-light spectral interferometer for synchronous phase shifting based on polarization interference was built, and the two-step phase-shifting algorithm was used to retrieve phase information. A variety of spectral interferometric signals were simulated based on the mathematical model of the two-step phase-shifting algorithm to illustrate the effects of differences in intensity and envelope shape, random noise, and phase-shift error on measurement of the absolute distance. Measurements of the absolute distance were conducted, and they indicated that the system had high accuracy.
Hybrid Cloud Data Integration Critical Success Factors: A Case Study at PT Pos Indonesia

When an organization stores some of its data and services in a public cloud, data integration is often required to transfer data between its on-premise infrastructure or private cloud to its public cloud, and vice versa. This hybrid cloud architecture provides some benefits for the organization but also poses several challenges and risks such as security, cost, network latency, and legal compliance that can hinder its successful implementation. Thus, this exploratory study was conducted to identify critical success factors for data integration in a hybrid cloud, advancing state-of-the art of the knowledge in cloud computing and data integration. This research identified twelve factors that can be categorized using TOE framework into technology, organization, and environment criteria. Those success factors are then analyzed and ranked using Analytical Hierarchy Process (AHP) method to obtain critical success factors (CSF). The result shows that critical factor in organization, technology, and environment criteria are top management support, security, and government regulation respectively.
Structured Light Based High Precision 3D Measurement and Workpiece Pose Estimation

This paper proposes a high precision, robust 3D measurement method and a fast, accurate pose estimation method. The RGB-based Gray code Shifting and Positive/Negative coding method (RGB-GSPN) is introduced to improve the structured light measurement system. This coding method is easy to be decoded. It can eliminate the influence of scattered light and reduce the influence of the objects surface color on the measurement precision. Then, we combine 2D and 3D information to perform pose estimation. SIFT features of 2D images are used to roughly match the position. Subsequently, we send this pose to the Iterative Closest Point (ICP) algorithm as its initial matching position and use ICP for accurate estimation. This can avoid getting into a local optimum and speed up the convergence of the algorithm. Experiments show that the system has a high precision and can estimate the pose of the workpiece accurately.
Integration of optimal spatial distributed tie-points in RANSAC-based image registration

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Neural Architecture Search over Decentralized Data

To preserve user privacy while enabling mobile intelligence, techniques have been proposed to train deep neural networks on decentralized data. However, training over decentralized data makes the design of neural architecture quite difficult as it already was. Such difficulty is further amplified when designing and deploying different neural architectures for heterogeneous mobile platforms. In this work, we propose an automatic neural architecture search into the decentralized training, as a new DNN training paradigm called Federated Neural Architecture Search, namely federated NAS. To deal with the primary challenge of limited on-client computational and communication resources, we present FedNAS, a highly optimized framework for efficient federated NAS. FedNAS fully exploits the key opportunity of insufficient model candidate re-training during the architecture search process, and incorporates three key optimizations: parallel candidates training on partial clients, early dropping candidates with inferior performance, and dynamic round numbers. Tested on large-scale datasets and typical CNN architectures, FedNAS achieves comparable model accuracy as state-of-the-art NAS algorithm that trains models with centralized data, and also reduces the client cost by up to two orders of magnitude compared to a straightforward design of federated NAS.
An Application of Physics Experiments of High School by using Augmented Reality

There has been done little research to validate the utility and usability of virtual and augmented reality environments.The evaluation of usability of these new technologies is very important to design systems that are more intuitive than a traditional method.Such an evaluation is also important for future development of applications that can gain from this new technology.The augmented reality (AR) is a technology that embedded virtual object (video, picture and 3D object) to the user view the real world.The combination of AR technology with the educational content creates new type of automated applications and acts to enhance the effectiveness and attractiveness of teaching and learning for students in real life scenarios.The study aims to improve the teaching methods used in secondary school by employing modern educational technology and thus assess the effectiveness of AR apps in teaching students the physics experiments.Therefore, in this study we took the challenge of adapting this technology to facilitate physics subject in secondary school.
Pyramidal Part-Based Model for Partial Occlusion Handling in Pedestrian Classification

Pedestrian detection and classification are of increased interest in the intelligent transportation system (ITS), and among the challenging issues, we can find limitations of tiny and occluded appearances, large variation of human pose, cluttered background, and complex environment. In fact, a partial occlusion handling is important in the case of detecting pedestrians, in order to avoid accidents between pedestrians and vehicles, since it is difficult to detect when pedestrians are suddenly crossing the road. To solve the partial occlusion problem, a pyramidal part-based model (PPM) is proposed to obtain a more accurate prediction based on the majority vote of the confidence score of the visible parts by cascading the pyramidal structure. The experimental results on the proposed scheme achieved 96.25% accuracy on the INRIA dataset and 81% accuracy on the PSU (Prince of Songkla University) dataset. Our proposed model can be applied in the real-world environment to classify the occluded part of pedestrians with the various information of part representation at each pyramid layer.
Facial Expression Recognition by Regional Weighting with Approximated Q-Learning

Several facial expression recognition methods cluster facial elements according to similarity and weight them considering the importance of each element in classification. However, these methods are limited by the pre-definitions of units restricting modification of the structure during optimization. This study proposes a modified support vector machine classifier called Grid Map, which is combined with reinforcement learning to improve the classification accuracy. To optimize training, the input image size is normalized according to the cascade rules of a pre-processing detector, and the regional weights are assigned by an adaptive cell size that divides each region of the image using bounding grids. Reducing the size of the bounding grid reduces the area used for feature extraction, allowing more detailed weighted features to be extracted. Error-correcting output codes with a histogram of gradient is selected as the classification method via an experiment to determine the optimal feature and classifier selection. The proposed method is formulated into a decision process and solved via Q-learning. To classify seven emotions, the proposed method exhibits accuracies of 96.36% and 98.47% for four databases and Extended Cohn-–Kanade Dataset (CK+), respectively. Compared to the basic method exhibiting a similar accuracy, the proposed method requires 68.81% fewer features and only 66.33% of the processing time.
Experiments with Cross-Language Speech Retrieval for Lower-Resource Languages

Cross-language speech retrieval systems face a cascade of errors due to transcription and translation ambiguity. Using 1-best speech recognition and 1-best translation in such a scenario could adversely affect recall if those 1-best system guesses are not correct. Accurately representing transcription and translation probabilities could therefore improve recall, although possibly at some cost in precision. The difficulty of the task is exacerbated when working with languages for which limited resources are available, since both recognition and translation probabilities may be less accurate in such cases. This paper explores the combination of expected term counts from recognition with expected term counts from translation to perform cross-language speech retrieval in which the queries are in English and the spoken content to be retrieved is in Tagalog or Swahili. Experiments were conducted using two query types, one focused on term presence and the other focused on topical retrieval. Overall, the results show that significant improvements in ranking quality result from modeling transcription and recognition ambiguity, even in lower-resource settings, and that adapting the ranking model to specific query types can yield further improvements.
A Dynamic Neural Network Model for Click-Through Rate Prediction in Real-Time Bidding

Real-time bidding (RTB) that features perimpression-level real-time ad auctions has become a popular practice in today's digital advertising industry. In RTB, click-through rate (CTR) prediction is a fundamental problem to ensure the success of an ad campaign and boost revenue. In this paper, we present a dynamic CTR prediction model designed for the Samsung demand-side platform (DSP). From our production data, we identify two key technical challenges that have not been fully addressed by the existing solutions: the dynamic nature of RTB and user information scarcity. To address both challenges, we develop a Dynamic Neural Network model. Our model effectively captures the dynamic evolutions of both users and ads and integrates auxiliary data sources (e.g., installed apps) to better model users' preferences. We put forward a novel interaction layer that fuses both explicit user responses (e.g., clicks on ads) and auxiliary data sources to generate consolidated user preference representations. We evaluate our model using a large amount of data collected from the Samsung advertising platform and compare our method against several state-of-the-art methods that are likely suitable for real-world deployment. The evaluation results demonstrate the effectiveness of our method and the potential for production. In addition, we discuss how to address a few practical engineering challenges caused by big data toward making our model in readiness for deployment.
Impoliteness in Indonesian Language Hate Speech on Social Media Contained in the Instagram Account

The phenomenon of hate speech is very interesting to draw. This is related to various recent events that have caused unrest in the community. Hate speech is an expression that stimulates people from certain social groups that are oriented toward differences, race, national origin, religion, and gender. The hate test in this study is seen from the perspective of the impoliteness theory. The research uses forensic theory, while the methodology uses qualitative. The data used, as many as 40 comments were used as research objects. Realization of hate speech based on impoliteness, there are 3 strategies, namely positive impoliteness strategies, negative impoliteness strategies, and satire or scoffing strategies. In other words, linguistic hate speech can be sent in the form of words, phrases, clauses, sentences, and in the form of discourse.
Virtual Reality: General Issues of Legal Regulation

The article focuses on the general issues of legal regulation of relations that emerge in the field of application of VR technologies and presents issues associated with the regulation of development of such technologies. It looks at the features of this technology that create challenges for the development of a system of legal regulation of its application. The article also gives a perspective at major factors that make application of the existing law difficult and offers analysis of the emerging issues of its regulation. The author arrives at a conclusion that this technology is fundamentally different from the other existing technologies as it combines the properties of both physical reality and cyberspace. Among the challenges of the legal regulation of VR are a high realism, complete immersion user experience, and low cyber protection of both hardware and software components. The author evaluates several regulatory approaches, which could be used in the case of virtual reality and finds that all of them have major deficiencies. Contemporary research findings in secure application of VR in the fields of teaching and entertainment get rapidly outdated as they cannot catch up with the technology development, therefore they can only serve as a ground for the development of a system of VR regulation with consideration of this factor.
Differentially Private Mean Embeddings with Random Features (DP-MERF) for Simple & Practical Synthetic Data Generation.

We present a differentially private data generation paradigm using randomfeature representations of kernel mean embeddings when comparing thedistribution of true data with that of synthetic data. We exploit the randomfeature representations for two important benefits. First, we require a verylow privacy cost for training deep generative models. This is because unlikekernel-based distance metrics that require computing the kernel matrix on allpairs of true and synthetic data points, we can detach the data-dependent termfrom the term solely dependent on synthetic data. Hence, we need to perturb thedata-dependent term once-for-all and then use it until the end of the generatortraining. Second, we can obtain an analytic sensitivity of the kernel meanembedding as the random features are norm bounded by construction. This removesthe necessity of hyperparameter search for a clipping norm to handle theunknown sensitivity of an encoder network when dealing with high-dimensionaldata. We provide several variants of our algorithm, differentially private meanembeddings with random features (DP-MERF) to generate (a) heterogeneous tabulardata, (b) input features and corresponding labels jointly; and (c)high-dimensional data. Our algorithm achieves better privacy-utility trade-offsthan existing methods tested on several datasets.
A New Object Tracking Framework for Interest Point Based Feature Extraction Algorithms

This paper presents a novel object tracking framework for interest point based feature extracting algorithms. The proposed framework uses the feature extracting algorithm without making any changes and it relies on outlier detection, object modelling, and object tracking. At first, the keypoints are extracted by using a feature extraction algorithm. Then, incorrect keypoint matches are detected by the DBScan algorithm. The second step of our tracking framework is object modelling. The object model is defined as a bounding box. The box model has six points and each of these points has its own Gaussian model. Finally, the Gaussian model is performed for object tracking. In object tracking, the old five values are retained to detect incorrect position information. Thus, while the object movements are softened, the instant deviations are eliminated also. Our interest point based object tracking framework (IPBOT) works with any interest point based feature extracting algorithm. Thus, a new algorithm can be added to the object tracking framework with a short integration process. The experiment results show that the proposed tracker significantly improves the success rate of the object tracking.
Efficient monitoring of hyperproperties using prefix trees

Abstract Hyperproperties, such as non-interference and observational determinism, relate multiple computation traces with each other and are thus not monitorable by tools that consider computations in isolation. We present the monitoring approach implemented in the latest version of $$\text {RVHyper}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mtext>RVHyper</mml:mtext> </mml:math> , a runtime verification tool for hyperproperties. The input to the tool are specifications given in the temporal logic $$\text {HyperLTL}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mtext>HyperLTL</mml:mtext> </mml:math> , which extends linear-time temporal logic (LTL) with trace quantifiers and trace variables. $$\text {RVHyper}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mtext>RVHyper</mml:mtext> </mml:math> processes execution traces sequentially until a violation of the specification is detected. In this case, a counterexample, in the form of a set of traces, is returned. $$\text {RVHyper}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mtext>RVHyper</mml:mtext> </mml:math> employs a range of optimizations: a preprocessing analysis of the specification and a procedure that minimizes the traces that need to be stored during the monitoring process. In this article, we introduce a novel trace storage technique that arranges the traces in a tree-like structure to exploit partially equal traces. We evaluate $$\text {RVHyper}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mtext>RVHyper</mml:mtext> </mml:math> on existing benchmarks on secure information flow control, error correcting codes, and symmetry in hardware designs. As an example application outside of security, we show how $$\text {RVHyper}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mtext>RVHyper</mml:mtext> </mml:math> can be used to detect spurious dependencies in hardware designs.
The effect of employee happiness on performance of employees in public organization in United Arab Emirates

This study aims to examine the effect of employee happiness on employee performance in the public sector in the United Arab Emirates (UAE).The study operationalized employee happiness into performance appraisal, lexible working hours, promotion, rewards and recognition, income, peer and supervisor support, workload, and workplace environment.The data was collected from 319 employees working in Abu Dhabi and Dubai using a questionnaire "The effect of employee spirituality and emotional wellness toward the relationship between employee happiness and employee performance 2019".The indings were derived using Smart Partial Least Squares (PLS).The indings showed that performance appraisal, income, workplace environment, promotion, reward and recognition, and peer and supervisor support are signiicant predictors of employee performance.Flexible Working Hours (FWH) and workload have an insigniicant effect on employee performance.Findings were discussed, and decision-makers are advised to increase employee happiness among employees in UAE.