Reviewerly/exHarmony huggingface dataset:
In-room Voice-Based AI Digital Assistants Transforming On-Site Hotel Services and Guests’ Experiences

Abstract Voice-based artificial intelligence (AI) devices transform human-computer bidirectional interactions with new touchpoints. Despite the recent release of purpose-developed in-room voice assistants for hotels, they have not been widely deployed by hospitality companies. There is limited research on the phenomenon of voice-based digital assistants and a research gap in their adoption by hotels for automating workflows and enhancing guests’ experiences. This study analysed the role of voice devices for mediating interactions between hotels and guests from both the hospitality technology providers’ and guests’ perspectives. This was done by the means of inductive qualitative research using 28 semi-structured interviews. The findings revealed that benefits associated with the application of voice-based digital assistants in hospitality outweigh the drawbacks for both hotels and guests. The paper proposes a model which illustrates the essence of speech-based interactions between hotels and guests via voice assistants. This concept contributes to human-computer interactions in the hotel industry.
Optimal Design and Simulation for PID Controller Using Fractional-Order Fish Migration Optimization Algorithm

Proportional Integral Derivative (PID) controller is one of the most classical controllers, which has a good performance in industrial applications. The traditional PID parameter tuning relies on experience, however, the intelligent algorithm is used to optimize the controller, which makes it more convenient. Fish Migration Optimization (FMO) is an excellent algorithm that mimics the swim and migration behaviors of fish biology. Especially, the formulas for optimization were obtained from biologists. However, the optimization effect of FMO for PID control is not prominent, since it is easy to skip the optimal solution with integer-order velocity. In order to improve the optimization performance of FMO, Fractional-Order Fish Migration Optimization (FOFMO) is proposed based on fractional calculus (FC) theory. In FOFMO, the velocity and position are updated in fractional-order forms. In addition, the fishes should migration back to a position which is more conducive to survival. Therefore, a new strategy based on the global best solution to generate new positions of offsprings is proposed. The experiments are performed on benchmark functions and PID controller. The results show that FOFMO is superior to the original FMO, and the PID controller tuned by FOFMO is more robust and has better performance than other contrast algorithms.
Joint Visual-Temporal Embedding for Unsupervised Learning of Actions in Untrimmed Sequences

Understanding the structure of complex activities in untrimmed videos is a challenging task in the area of action recognition. One problem here is that this task usually requires a large amount of hand-annotated minute- or even hour-long video data, but annotating such data is very time consuming and can not easily be automated or scaled. To address this problem, this paper proposes an approach for the unsupervised learning of actions in untrimmed video sequences based on a joint visual-temporal embedding space. To this end, we combine a visual embedding based on a predictive U-Net architecture with a temporal continuous function. The resulting representation space allows detecting relevant action clusters based on their visual as well as their temporal appearance. The proposed method is evaluated on three standard benchmark datasets, Breakfast Actions, INRIA YouTube Instructional Videos, and 50 Salads. We show that the proposed approach is able to provide a meaningful visual and temporal embedding out of the visual cues present in contiguous video frames and is suitable for the task of unsupervised temporal segmentation of actions.
Study of the Performance of Machine Learning Algorithms for Face Mask Detection

Nowadays, the situation of the Covid-19 virus still intensifying throughout the world. The number of populations of each country is severely infected and deaths. One solution to prevent is to wearing a masked face. Many businesses and organization need to adapt and protect an infected person by detecting whoever does not wear masked face; however, the number of users or customers are more than staffs result in difficult checking. This paper studies the performance of the three algorithms: KNN, SVM and MobileNet to find the best algorithm which is suitable for checking who wearing masked face in a real-time situation. The results show that MobileNet is the best accuracy both from input images and input video from a camera (real-time).
Spatiotemporal Dilated Convolution With Uncertain Matching for Video-Based Crowd Estimation

In this paper, we propose a novel SpatioTemporal convolutional Dense Network (STDNet) to address the video-based crowd counting problem, which contains the decomposition of 3D convolution and the 3D spatiotemporal dilated dense convolution to alleviate the rapid growth of the model size caused by the Conv3D layer. Moreover, since the dilated convolution extracts the multiscale features, we combine the dilated convolution with the channel attention block to enhance the feature representations. Due to the error that occurs from the difficulty of labeling crowds, especially for videos, imprecise or standard-inconsistent labels may lead to poor convergence for the model. To address this issue, we further propose a new patch-wise regression loss (PRL) to improve the original pixel-wise loss. Experimental results on three video-based benchmarks, i.e., the UCSD, Mall and WorldExpo'10 datasets, show that STDNet outperforms both image- and video-based state-of-the-art methods. The source codes are released at \url{https://github.com/STDNet/STDNet}.
FACEGAN: Facial Attribute Controllable rEenactment GAN

The face reenactment is a popular facial animation method where the person's identity is taken from the source image and the facial motion from the driving image. Recent works have demonstrated high quality results by combining the facial landmark based motion representations with the generative adversarial networks. These models perform best if the source and driving images depict the same person or if the facial structures are otherwise very similar. However, if the identity differs, the driving facial structures leak to the output distorting the reenactment result. We propose a novel Facial Attribute Controllable rEenactment GAN (FACEGAN), which transfers the facial motion from the driving face via the Action Unit (AU) representation. Unlike facial landmarks, the AUs are independent of the facial structure preventing the identity leak. Moreover, AUs provide a human interpretable way to control the reenactment. FACEGAN processes background and face regions separately for optimized output quality. The extensive quantitative and qualitative comparisons show a clear improvement over the state-of-the-art in a single source reenactment task. The results are best illustrated in the reenactment video provided in the supplementary material. The source code will be made available upon publication of the paper.
Multiple UAVs trajectory generation and waypoint assignment in urban environment based on DOP maps

This paper tackles strategic path planning for a multi-UAV routing problem in low altitude urban environment, where GNSS coverage challenges typically affect navigation performance and thus autonomous flight capabilities. These issues are addressed with a multi-step strategy that includes automated definition of GNSS-challenging volumes based on a georeferenced three-dimensional environment model, derivation of candidate obstacle-free paths between waypoints, waypoint assignment and definition of time-tagged trajectories for all UAVs. In all the steps, attention is paid to limiting the computational burden, thus ensuring applicability in real mission scenarios. Going beyond binary logics which consider GNSS-challenging volumes as obstacles, a discrete number of dilution of precision levels is considered, leading to different three-dimensional maps of the environment defined as “DOP layers”. Then, the basic idea to ensure that the designed trajectories are “flyable” with given positioning accuracy requirements is to take navigation performance into account at waypoint assignment level, using propagated covariance as a metric. The approach thus combines “navigation aware” planning with multi-vehicle task assignment, generating a solution that depends on the sensors embarked onboard the UAVs, and can be naturally extended to account for multi-sensor-based navigation architectures and ground-infrastructure support. The algorithm is tested in simulations based on a real world scenario, considering a wide combination of input parameters in terms of positioning error threshold, maximum UAV velocity, number of UAVs, navigation sensors performance, and mission epoch.
Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

Abstract This paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.
Deep learning-based computer-aided diagnosis in screening breast ultrasound to reduce false-positive diagnoses

Abstract A major limitation of screening breast ultrasound (US) is a substantial number of false-positive biopsy. This study aimed to develop a deep learning-based computer-aided diagnosis (DL-CAD)-based diagnostic model to improve the differential diagnosis of screening US-detected breast masses and reduce false-positive diagnoses. In this multicenter retrospective study, a diagnostic model was developed based on US images combined with information obtained from the DL-CAD software for patients with breast masses detected using screening US; the data were obtained from two hospitals (development set: 299 imaging studies in 2015). Quantitative morphologic features were obtained from the DL-CAD software, and the clinical findings were collected. Multivariable logistic regression analysis was performed to establish a DL-CAD-based nomogram, and the model was externally validated using data collected from 164 imaging studies conducted between 2018 and 2019 at another hospital. Among the quantitative morphologic features extracted from DL-CAD, a higher irregular shape score ( P = .018) and lower parallel orientation score ( P = .007) were associated with malignancy. The nomogram incorporating the DL-CAD-based quantitative features, radiologists’ Breast Imaging Reporting and Data Systems (BI-RADS) final assessment ( P = .014), and patient age ( P &lt; .001) exhibited good discrimination in both the development and validation cohorts (area under the receiver operating characteristic curve, 0.89 and 0.87). Compared with the radiologists’ BI-RADS final assessment, the DL-CAD-based nomogram lowered the false-positive rate (68% vs. 31%, P &lt; .001 in the development cohort; 97% vs. 45% P &lt; .001 in the validation cohort) without affecting the sensitivity (98% vs. 93%, P = .317 in the development cohort; each 100% in the validation cohort). In conclusion, the proposed model showed good performance for differentiating screening US-detected breast masses, thus demonstrating a potential to reduce unnecessary biopsies.
Design and construction of a solar tracking system for small-scale linear Fresnel reflector with three movements

A new open-loop solar tracking system for a small-scale linear Fresnel reflector with three movements has been designed, fabricated, and simulated. The control system of the solar tracker is governed by a Raspberry Pi together with other auxiliary devices which include a Global Positioning System. The electronic control system consists of a master controller (Raspberry pi 3), 4 slave microcontrollers (Arduino), Global Positioning System module, thermocouples, laser sensors, transversal positioning sensors, and longitudinal positioning sensors. It also allows the communication between these microcontrollers based on long range wireless solutions (XBee). All the electronic circuits have been designed and constructed. The solar tracking system uses offline data. The software has been designed and developed to track the sun path using astronomical equations. In this way, the solar tracking system is able to position itself automatically using the solar position algorithm and the Global Positioning System with an accuracy of ±0.006°. The solar tracking system can be deployed automatically at any location on the Earth. The total cost of the implemented solar tracking system has been calculated. The system performance, in terms of the tracking error, annual energy, energy-to-area ratio and levelized cost of energy has been evaluated. Tracking errors smaller than 0.06 (°) are acceptable (they cause power losses smaller than 1%), whereas errors larger than 0.36 (°) start being noticeable (power losses greater than 3%). The proposed new tracking system gives 16.64% more energy, a 78.46% higher energy-to-area ratio, and a 4.62% less levelized cost of energy that the classic tracking system with one movement used in large-scale linear Fresnel reflectors.
A human-centered artificial intelligence approach for privacy protection of elderly App users in smart cities

Artificial Intelligence and Machine Learning based Ambient Assisted Living systems play an important role in smart cities by improving the quality of life of the elderly population. Many Ambient Assisted Living systems are coupled with Android Apps for command-and-control purposes. Consequently, the privacy and security of Ambient Assisted Living systems depend on the privacy and security of the corresponding Android Apps, which follow a privacy self-management model. Unfortunately, the privacy self-management model ignores the decision-making abilities of the elderly and increases their cognitive loads, which put their privacy protection and wellbeing at stake. In this paper, we follow a Human-Centered Artificial Intelligence inspired approach for addressing these issues. This approach uses privacy as a shared responsibility model instead of the privacy self-management model. We have proposed two algorithms, the participatory privacy protection algorithm-I, and participatory privacy protection algorithm-II, for determining optimal privacy settings of an Ambient Assisted Living App and handling its runtime Permission requests, respectively. We demonstrated the working of these algorithms using a case study. We have also compared the proposed approach with state-of-the-art privacy management schemes for Android Apps. The proposed algorithms can improve the privacy protection of Ambient Assisted Living App users in smart cities and relieve them through cognitive offloading.
A Design Space Exploration of Worlds in Miniature

Worlds-in-Miniature (WiMs) are interactive worlds within a world and combine the advantages of an input space, a cartographic map, and an overview+detail interface. They have been used across the extended virtuality spectrum for a variety of applications. Building on an analysis of examples of WiMs from the research literature we contribute a design space for WiMs based on seven design dimensions. Further, we expand upon existing definitions of WiMs to provide a definition that applies across the extended reality spectrum. We identify the design dimensions of size-scope-scale, abstraction, geometry, reference frame, links, multiples, and virtuality. Using our framework we describe existing Worlds-in-Miniature from the research literature and reveal unexplored research areas. Finally, we generate new examples of WiMs using our framework to fill some of these gaps. With our findings, we identify opportunities that can guide future research into WiMs.
Development and Validation of a Deep Learning Model to Quantify Glomerulosclerosis in Kidney Biopsy Specimens

<h3>Importance</h3> A chronic shortage of donor kidneys is compounded by a high discard rate, and this rate is directly associated with biopsy specimen evaluation, which shows poor reproducibility among pathologists. A deep learning algorithm for measuring percent global glomerulosclerosis (an important predictor of outcome) on images of kidney biopsy specimens could enable pathologists to more reproducibly and accurately quantify percent global glomerulosclerosis, potentially saving organs that would have been discarded. <h3>Objective</h3> To compare the performances of pathologists with a deep learning model on quantification of percent global glomerulosclerosis in whole-slide images of donor kidney biopsy specimens, and to determine the potential benefit of a deep learning model on organ discard rates. <h3>Design, Setting, and Participants</h3> This prognostic study used whole-slide images acquired from 98 hematoxylin-eosin–stained frozen and 51 permanent donor biopsy specimen sections retrieved from 83 kidneys. Serial annotation by 3 board-certified pathologists served as ground truth for model training and for evaluation. Images of kidney biopsy specimens were obtained from the Washington University database (retrieved between June 2015 and June 2017). Cases were selected randomly from a database of more than 1000 cases to include biopsy specimens representing an equitable distribution within 0% to 5%, 6% to 10%, 11% to 15%, 16% to 20%, and more than 20% global glomerulosclerosis. <h3>Main Outcomes and Measures</h3> Correlation coefficient (<i>r</i>) and root-mean-square error (RMSE) with respect to annotations were computed for cross-validated model predictions and on-call pathologists' estimates of percent global glomerulosclerosis when using individual and pooled slide results. Data were analyzed from March 2018 to August 2020. <h3>Results</h3> The cross-validated model results of section images retrieved from 83 donor kidneys showed higher correlation with annotations (<i>r</i> = 0.916; 95% CI, 0.886-0.939) than on-call pathologists (<i>r</i> = 0.884; 95% CI, 0.825-0.923) that was enhanced when pooling glomeruli counts from multiple levels (<i>r</i> = 0.933; 95% CI, 0.898-0.956). Model prediction error for single levels (RMSE, 5.631; 95% CI, 4.735-6.517) was 14% lower than on-call pathologists (RMSE, 6.523; 95% CI, 5.191-7.783), improving to 22% with multiple levels (RMSE, 5.094; 95% CI, 3.972-6.301). The model decreased the likelihood of unnecessary organ discard by 37% compared with pathologists. <h3>Conclusions and Relevance</h3> The findings of this prognostic study suggest that this deep learning model provided a scalable and robust method to quantify percent global glomerulosclerosis in whole-slide images of donor kidneys. The model performance improved by analyzing multiple levels of a section, surpassing the capacity of pathologists in the time-sensitive setting of examining donor biopsy specimens. The results indicate the potential of a deep learning model to prevent erroneous donor organ discard.
Validation of the 5-min irradiance from the National Solar Radiation Database (NSRDB)

The National Solar Radiation Database (NSRDB) offers satellite-derived irradiance over most of the Americas. The latest version of the NSRDB hosts broadband irradiance estimates at 5-min–2-km resolution, which have improved from the previous 30-min–4-km resolution. Whereas this new dataset is expected to continue providing invaluable solar resource information to researchers and practitioners worldwide, its accuracy needs to be validated. Validating high-resolution data must go beyond reporting the overall error metrics. To that end, time series features are used here to study the correspondence between the satellite-derived and measured irradiance transients. This paper also provides an in-depth discussion on the data quality issues observed during the validation.
Effective use of blockchain technology for facilities management procurement process

Purpose Facilities management (FM) has become a revolutionary profession, which adds value to the built environment. Compared to other industries, the development of the FM industry is at a preliminary stage owing to poor adaptation of digitalisation. Although FM procurement process has improved over time, it is still complex, inefficient and challenging with the lack of digital innovations. Blockchain technology has emerged as a revolutionary digital technology under “Procurement 4.0”. The purpose of this paper is to develop a framework that enables the effective use of blockchain technology for FM procurement process. Design/methodology/approach Data collection using interviews was performed in two stages adopting a mono method qualitative methodological choice using case studies and survey strategies. Template analysis and content analysis techniques were used to analyse the collected data. Findings Through the findings, main steps in the current FM process were identified. It was revealed, manual procurement has issues related to, efficiency, data security, communication and transparency. Similarly, e-procurement systems also have issues related to data security, poor integration and transparency. FM industry has identified the requirement of transforming into an advanced digitalised technology-based procurement system. This study revealed, blockchain features to overcome these issues and the application of blockchain technology in different steps of the procurement process through the developed framework. Originality/value The developed framework offers a solution for the future technological transformation of FM procurement using blockchain technology.
Optical selective encryption based on the FRFCM algorithm and face biometric for the medical image

In this paper, we propose an optical selective encryption scheme for the medical image based on the fast and robust Fuzzy C-Means Clustering (FRFCM) algorithm and face biometric. In the proposed scheme, we propose a novel selective method for obtaining the region of interest (ROI) based on the FRFCM algorithm. Due to the good performance of the FRFCM, the selective method proposed in this paper can successfully extract the ROI. In addition, we propose an encryption method for generating the random phase mask based on the face biometric for the first time. The security level of the proposed scheme can be improved as the non-contact nature and the uniqueness of the human face. The face biometric and the chaotic Standard map are modulated to generate the chaotic face phase mask (CFaPM). Then the selected ROI image can be encrypted in the Gyrator transform domain based on the CFaPM. We evaluate the performance of the proposed scheme via application to twenty medical test images. Numerical simulation results are shown to verify the security and robustness of the proposed cryptosystem.