Reviewerly/exHarmony huggingface dataset:
A Collaborative Framework With Artificial Intelligence for Long-Term Care

The trend of aging population among working families has made health care services for sub-healthy people more important. In Taiwan, caregivers are often hired by human resource agencies to provide long-term care, and they are the main supervisors responsible for the care of the sub-healthy people. However, most agencies only consider the cost of their caregivers and have insufficient staff to take care of the sub-healthy people, leading to the failure of the long-term care system. The lack of an effective collaborative framework for long-term care leads to sub-healthy people being at high risks. Existing frameworks for long-term care are still in the early stages of capturing suitability information dynamically. This paper proposes a new framework that includes all possible features suitable to support the needs of all sub-healthy people and provides a solution for the issue of determining suitable features for collaboration. This study applies association rules to long-term care to handle the mapping process and uses artificial intelligence technology to solve the issues of adjusting human variability dynamically based on the mapping result of sub-healthy people.
Color image encryption via compressive sensing and chaotic systems

In this paper, we design a color image encryption algorithm based on chaotic system and block compressive sensing. Firstly, the sparse representation of the plain-image is obtained by an adaptive learning dictionary. Secondly, the key streams are produced from two excellent low-dimensional chaotic maps, where updating the initial values and parameters rely on the SHA-384 and the input image. Thirdly, three measurement matrices of R, G, B components are constructed from the chaotic sequences, respectively. Finally, a random rows and columns diffusion method is performed on the encrypted image. Experimental results and safety analysis prove that the proposed scheme has excellent performance.
An Efficient Encrypted Floating-Point Representation Using HEAAN and TFHE

As a method of privacy-preserving data analysis (PPDA), a fully homomorphic encryption (FHE) has been in the spotlight recently. Unfortunately, because many data analysis methods assume that the type of data is of real type, the FHE-based PPDA methods could not support the enough level of accuracy due to the nature of FHE that fixed-point real-number representation is supported easily. In this paper, we propose a new method to represent encrypted floating-point real numbers on top of FHE. The proposed method is designed to have analogous range and accuracy to 32-bit floating-point number in IEEE 754 representation. We propose a method to perform arithmetic operations and size comparison operations. The proposed method is designed using two different FHEs, HEAAN and TFHE. As a result, HEAAN is proven to be very efficient for arithmetic operations and TFHE is efficient in size comparison. This study is expected to contribute to practical use of FHE-based PPDA.
From a posteriori to a priori solutions for a two-level system interacting with a single-photon wavepacket

We present the analytical formulas for the conditional and unconditional states of a two-level atom interacting with a single-photon wavepacket. We express the a priori state of the system by means of the quantum trajectories related to the process of detection of photons in the output field. We give the formulas for the mean number of photons detected up to the given time, and we derive the expressions for the mean time of detection of the photons.
Generalizing convolution neural networks on stain color heterogeneous data for computational pathology

Hematoxylin and Eosin (H&amp;E) are one of the main tissue stains used in histopathology to discriminate between nuclei and extracellular material while performing a visual analysis of the tissue. However, histopathology slides are often characterized by stain color heterogeneity, due to different tissue preparation settings at different pathology institutes. Stain color heterogeneity poses challenges for machine learning-based computational analysis, increasing the difficulty of producing consistent diagnostic results and systems that generalize well. In other words, it is challenging for a deep learning architecture to generalize on stain color heterogeneous data, when the data are acquired at several centers, and particularly if test data are from a center not present in the training data. In this paper, several methods that deal with stain color heterogeneity are compared regarding their capability to solve center-dependent heterogeneity. Systematic and extensive experimentation is performed on a normal versus tumor tissue classification problem. Stain color normalization and augmentation procedures are used while training a convolutional neural networks (CNN) to generalize on unseen data from several centers. The performance is compared on an internal test set (test data from the same pathology institutes as the training set) and an external test set (test data from institutes not included in the training set). This also allows to measure generalization performance. An improved performance is observed when the predictions of the two best-performed stain color normalization methods with augmentation are aggregated. An average AUC and F1-score on external test are observed as 0:892&plusmn;0:021 and 0:817&plusmn;0:032 compared to the baseline 0:860&plusmn;0:027 and 0:772 &plusmn; 0:024 respectively.
Service Robots and the Changing Roles of Employees in Restaurants: A Cross Cultural Study

The advent of increasingly pervasive automation of front-of-house restaurant service processes calls for a cross-cultural examination of employee roles in robotised service encounters. Through an ethnographic approach this study explores robotised service encounters in two culturally distinct contexts: the US and Japan. Five roles service employees may assume are observed to varying degrees of importance depending on cultural context: enabler, coordinator, differentiator, educator, and innovator. The roles of enabler and coordinator seem the most dominant in Japan, while in the US the future of work in restaurants seems more skewed towards the roles of educator and innovator. Implications for hospitality management are discussed, and an agenda for future research is presented.
Stereoscopic construction of textual information in presentations of research results on an autostereoscopic monitor

This work is devoted to the general problem of constructing stereo presentations of scientific research in stereo modes.The construction of texts and formulas is considered when creating stereo presentations representing the results of solving mathematical modeling problems.The construction of texts and formulas, which are an integral part of a scientific presentation, is considered as a structural subproblem when constructing a stereo presentation as a whole.In this paper, the construction of stereo images on an autostereoscopic monitor is considered.An autostereoscopic monitor allows you to watch stereo images without glasses, while ensuring the quality of the stereo image, which is not inferior to the quality of the stereo image presented using the classic 3D projection stereo system.The results of building text labels using a multi-view representation of the object are considered.Multi-view presentation is one of the ways to build stereo images on modern autostereoscopic systems.The problem of combining text labels and the main image on one stereo frame is considered.
A Principled Approach to Learning Stochastic Representations for Privacy in Deep Neural Inference

INFerence-as-a-Service (INFaaS) in the cloud has enabled the prevalent use of Deep Neural Networks (DNNs) in home automation, targeted advertising, machine vision, etc. The cloud receives the inference request as a raw input, containing a rich set of private information, that can be misused or leaked, possibly inadvertently. This prevalent setting can compromise the privacy of users during the inference phase. This paper sets out to provide a principled approach, dubbed Cloak, that finds optimal stochastic perturbations to obfuscate the private data before it is sent to the cloud. To this end, Cloak reduces the information content of the transmitted data while conserving the essential pieces that enable the request to be serviced accurately. The key idea is formulating the discovery of this stochasticity as an offline gradient-based optimization problem that reformulates a pre-trained DNN (with optimized known weights) as an analytical function of the stochastic perturbations. Using Laplace distribution as a parametric model for the stochastic perturbations, Cloak learns the optimal parameters using gradient descent and Monte Carlo sampling. This set of optimized Laplace distributions further guarantee that the injected stochasticity satisfies the -differential privacy criterion. Experimental evaluations with real-world datasets show that, on average, the injected stochasticity can reduce the information content in the input data by 80.07%, while incurring 7.12% accuracy loss.
Coordination-Free Byzantine Replication with Minimal Communication Costs

State-of-the-art fault-tolerant and federated data management systems rely on fully-replicated designs in which all participants have equivalent roles. Consequently, these systems have only limited scalability and are ill-suited for high-performance data management. As an alternative, we propose a hierarchical design in which a Byzantine cluster manages data, while an arbitrary number of learners can reliable learn these updates and use the corresponding data.To realize our design, we propose the delayed-replication algorithm, an efficient solution to the Byzantine learner problem that is central to our design. The delayed-replication algorithm is coordination-free, scalable, and has minimal communication cost for all participants involved. In doing so, the delayed-broadcast algorithm opens the door to new high-performance fault-tolerant and federated data management systems. To illustrate this, we show that the delayed-replication algorithm is not only useful to support specialized learners, but can also be used to reduce the overall communication cost of permissioned blockchains and to improve their storage scalability.
MusQ: A Multi-Store Query System for IoT Data Using a Datalog-Like Language

The growing number of connected Internet of Things (IoT) devices has increased the necessity for processing IoT data from multiple heterogeneous data stores. IoT data integration is a challenging problem owing to the heterogeneity of data stores in terms of their query language, data models, and schemas. In this paper, we propose a multi-store query system for IoT data called MusQ, where users can formulate join operation queries for heterogeneous data sources. To reconcile the heterogeneity between source schemas of IoT data stores, we extract a global schema from local source schemas semi-automatically by applying schema-matching and schema-mapping steps. In order to minimize the burden on the user to understand the finer details of various query languages, we define a unified query language called the multi-store query language (MQL), which follows a subset of the Datalog grammar. Thus, users can easily retrieve IoT data from multiple heterogeneous sources with MQL queries. As the three MQL query-processing join algorithms are based on a mediator-wrapper approach, MusQ performs efficient data integration over significant volumes of IoT data from multiple stores. We conduct extensive experiments to evaluate the performance of the MusQ system using a synthetic and large real IoT data set for three different types of data stores (RDBMS, NoSQL, and HDFS). The experimental results demonstrate that MusQ is suitable, scalable, and efficient query processing for multiple heterogeneous IoT data stores. Those advantages of MusQ are important in several areas that involve complex IoT systems, such as smart city, healthcare, and energy management.
Open Set SAR Target Recognition Using Class Boundary Extracting

Typically, synthetic aperture radar (SAR) target recognition uses supervised learning techniques to identify target types. A problem is when identifying the new untrained classes and the false targets, they will be misjudged as known classes. In this paper, an open set recognition method is proposed to provide a rejection option for classifier so that the new untrained target type is identified. The main idea is to model closed category boundaries for the known classes and detect the new classes beyond the reasonable scope of known data. The edge exemplar selection method based on local statistical information is proposed to extract the class boundaries. And the boundaries are used to train the open set recognition/outlier detection model. Experimental results demonstrate that the proposed O_SAR outperforms the other state of the arts on the accuracy of false target rejection and muti-class open set recognition.
Entanglement swapping with autonomous polarization-entangled photon pairs from a warm atomic ensemble

Entanglement swapping forms a key concept in the realization of scalable quantum networks and large-scale quantum communication. For the practical implementation of entanglement swapping, completely autonomous entanglement sources and a joint Bell-state measurement (BSM) between two independent photons are essential. Here, we experimentally demonstrate entanglement swapping between two independent polarization-entangled photon-pair sources obtained via spontaneous four-wave mixing (SFWM) in a Doppler-broadened atomic ensemble of ${^{87}}{\rm Rb}$87Rb atoms. From the joint BSM, we estimate the ${\rm S}$S parameter in the Clauser-Horne-Shimony-Holt (CHSH) form of Bell's inequality and confirm the violation of the Bell-CHSH inequality by ${\rm S}={2.32} \pm {0.07}$S=2.32±0.07 with 4.5 standard deviations. We believe that this work is an important step toward realizing practical scalable quantum networks.
Deep Adversarial Canonical Correlation Analysis

Canonical Correlation Analysis (CCA) aims to learn the linear projections of two sets of variables where they are correlated maximally, which is not optimal for variables with non-linear relations. Recent years have witnessed great efforts in developing deep neural networks based CCA models, which are able to learn flexible non-linear and highly correlated representations between two variables. In addition to learning representations, generating realistic multi-view samples is also becoming highly desired in many real-world applications. However, the majority of existing CCA models do not provide mechanisms for realistic samples generation. Meanwhile, adversarial learning techniques such as generative adversarial networks have been proven to be effective in generating realistic samples similar to real data distribution. Thus, incorporating adversarial learning techniques has a great potential to advance Canonical Correlation Analysis. In this paper, we harness the power of adversarial learning techniques to equip Canonical Correlation Analysis with the ability of realistic data generation. In particular, we propose a Deep Adversarial Canonical Correlation Analysis model (DACCA), which can simultaneously learn representation of multi-view data but also generate realistic multi-view samples. Comprehensive experiments have been conducted on three real-world datasets and the results demonstrate the effectiveness of the proposed model. Our code is available at https://github.com/wenqifan03/DACCA.MSC codesCanonical Correlation Analysis (CCA)Representation LearningGenerative Adversarial Network (GAN).
Using the system "Virtual Psychologist" in determining the psychological and pedagogical readiness of students for professional education

This article is devoted to the development of testing methods that allow to diagnose the psychological predisposition of students to religious and scientific views. The aim of the work is to develop a convenient and effective software tool that allows you to determine and psycho-diagnose the predisposition of young people, especially students, to follow various misguided religious movements.
Building Smart City Drone for Graffiti Detection and Clean-up

Graffiti on buildings and bridges are oftentimes an eyesore. Those on road symbol signs can even pose safety risks to motorists. Not only is graffiti cleaning costly, it also disrupts normal traffic. Graffiti is a widespread problem in many cities in the U.S. This paper proposes a machine learning approach to unmanned aerial vehicle (UAV) graffiti detection and removal. Our solution builds on the smart city framework. The proposed solution is expected to lower graffiti cleaning cost and minimize impact on city traffic.
The Institutionalisation of Digital Public Health: Lessons Learned from the COVID-19 App

Amid the outbreak of the SARS-CoV-2 pandemic, there has been a call to use innovative digital tools for the purpose of protecting public health. There are a number of proposals to embed digital solutions into the regulatory strategies adopted by public authorities to control the spread of the coronavirus more effectively. They range from algorithms to detect population movements by using telecommunications data to the use of artificial intelligence and high-performance computing power to detect patterns in the spread of the virus. However, the use of a mobile phone application for contact tracing is certainly the most popular.
Energy-Based Models for Text

Current large-scale auto-regressive language models display impressive fluency and can generate convincing text. In this work we start by asking the question: Can the generations of these models be reliably distinguished from real text by statistical discriminators? We find experimentally that the answer is affirmative when we have access to the training data for the model, and guardedly affirmative even if we do not. This suggests that the auto-regressive models can be improved by incorporating the (globally normalized) discriminators into the generative process. We give a formalism for this using the Energy-Based Model framework, and show that it indeed improves the results of the generative models, measured both in terms of perplexity and in terms of human evaluation.
Multimodal Biometrics Recognition from Facial Video with Missing Modalities Using Deep Learning

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, nonplanar movement, and pose variations present in the video clips even in the situation of missing modalities.
Deep learning from label proportions with labeled samples

Learning from label proportions (LLP), where the training data is in form of bags, and only the proportions of classes in each bag are available, has attracted wide interest in machine learning community. In general, most LLP algorithms adopt random sampling to obtain the proportional information of different categories, which correspondingly obtains some labeled samples in each bag. However, LLP training process always fails to leverage these labeled samples, which may contain essential data distribution information. To address this issue, in this paper, we propose end-to-end LLP solver based on convolutional neural networks (ConvNets), called LLP with labeled samples (LLP-LS). First, we reshape the cross entropy loss in ConvNets, so that it can combine the proportional information and labeled samples in each bag. Second, in order to comply with the training data in a bag manner, ADAM based on batch is employed to train LLP-LS. Hence, the batch size in training process is in accordance with the bag size. Compared with up-to-date methods on multi-class problem, our algorithm can obtain the state-of-the-art on several image datasets.
Learning Weighted Automata over Principal Ideal Domains

Abstract In this paper, we study active learning algorithms for weighted automata over a semiring. We show that a variant of Angluin’s seminal $$\mathtt {L}^{\!\star }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mrow> <mml:mspace /> <mml:mo>⋆</mml:mo> </mml:mrow> </mml:msup> </mml:math> algorithm works when the semiring is a principal ideal domain, but not for general semirings such as the natural numbers.
Class Balanced Loss for Image Classification

In the study of image classification, neural network learning relies heavily on datasets. Due to variability in the difficulty of collecting images in reality, datasets tend to have class imbalance problems, which undoubtedly increases the difficulty of classification. During the training of a neural network, classes with a large number of images are naturally trained more often than classes with a small number of images. Because of imbalanced training, the classification ability of neural networks on test and validation sets differs greatly in different categories. The test results of more training classes are better, and the test results of classes with less training are poor. In this paper, we propose two kinds of balanced loss functions, namely, CEFL loss and CEFL2 loss, by rebalancing the cross-entropy loss function and focal loss function. The experimental results show that the proposed loss functions are significantly able to improve classification accuracy on class-imbalanced datasets.