Reviewerly/exHarmony huggingface dataset:
Integrating a spoken dialogue system, nursing records, and activity data collection based on smartphones

Background and Objective: This study describes the integration of a spoken dialogue system and nursing records on an Android smartphone application intending to help nurses reduce documentation time and improve the overall experience of a healthcare setting. The application also incorporates with collecting personal sensor data and activity labels for activity recognition. Methods: We developed a joint model based on a bidirectional long-short term memory and conditional random fields (Bi-LSTM-CRF) to identify user intention and extract record details from user utterances. Then, we transformed unstructured data into record inputs on the smartphone application. Results: The joint model achieved the highest F1-score at 96.79%. Moreover, we conducted an experiment to demonstrate the proposed model's capability and feasibility in recording in realistic settings. Our preliminary evaluation results indicate that when using the dialogue-based, we could increase the percentage of documentation speed to 58.13% compared to the traditional keyboard-based. Conclusions: Based on our findings, we highlight critical and promising future research directions regarding the design of the efficient spoken dialogue system and nursing records.
Fast depth estimation with cost minimization for structured light field

Depth estimation is a fundamental task in light field (LF) related applications. However, conventional light field suffers from the lack of features, which introduces depth ambiguity and heavy computation load to depth estimation. In this paper, we introduce phase light field (PLF), which uses sinusoidal fringes as patterns and the latent phases as the codes. With PLF and the re-formatted phase-epipolar-plane-images (phase EPIs), a global cost minimization framework is proposed to estimate the depth. In general, EPI-based depth estimation tests a set of candidate lines to find the optimal one with most similar intensities, and the slope of the optimal line is converted to disparity and depth. Based on this principle, for phase-EPI, we propose a cost with weighted phase variance in the candidate line, and we prove that the cost is a convex function. After that, the beetle antennae search (BAS) optimization algorithm is utilized to find the optimal line and thus depth can be obtained. Finally, a bilateral filter is incorporated to further improve the depth quality. Simulation and real experimental results demonstrate that, the proposed method can produce accurate depth maps, especially at boundary regions. Moreover, the proposed method achieves an acceleration of about 5.9 times over the state-of-the-art refocus method with comparable depth quality, and thus can facilitate practical applications.
Reverse Pass-Through VR

We introduce reverse pass-through VR, wherein a three-dimensional view of the wearer's eyes is presented to multiple outside viewers in a perspective-correct manner, with a prototype headset containing a world-facing light field display. This approach, in conjunction with existing video (forward) pass-through technology, enables more seamless interactions between people with and without headsets in social or professional contexts. Reverse pass-through VR ties together research in social telepresence and copresence, autostereoscopic displays, and facial capture to enable natural eye contact and other important non-verbal cues in a wider range of interaction scenarios.
Thai Food Recommendation System using Hybrid of Particle Swarm Optimization and K-Means Algorithm

A food recommendation system is an information filtering tool that helps suggest appropriate food menus to users based on their dietary behavior, nutrition, health, or activity. In this paper, a hybrid method of Particle Swarm Optimization (PSO) and K-Means algorithm is proposed to improve the user's dietary behavior clustering and using Principal Component Analysis (PCA) to reduce the data dimension. Moreover, the User-Based Collaborative Filtering technique is used to predict the rating of relevant Thai food menus and recommendation. The experimental result shows the hybrid method improves the clustering performance from 3 models: Hierarchical Clustering, K-Means, and K-Means with PCA, in terms of silhouette coefficient score. In addition, the hybrid method improves the Davies-Bouldin index score by 44%, 19%, and 17% compared to those models, respectively. The rating prediction result shows the hybrid method outperforms the other methods.
An Enhanced Deep Learning Model for Automatic Face Mask Detection

The recent COVID-19 pandemic has had lasting and severe impacts on social gatherings and interaction among people. Local administrative bodies enforce standard operating procedures (SOPs) to combat the spread of COVID-19, with mandatory precautionary measures including use of face masks at social assembly points. In addition, the World Health Organization (WHO) strongly recommends people wear a face mask as a shield against the virus. The manual inspection of a large number of people for face mask enforcement is a challenge for law enforcement agencies. This work proposes an automatic face mask detection solution using an enhanced lightweight deep learning model. A surveillance camera is installed in a public place to detect the faces of people. We use MobileNetV2 as a lightweight feature extraction module since the current convolution neural network (CNN) architecture contains almost 62,378,344 parameters with 729 million floating operations (FLOPs) in the classification of a single object, and thus is computationally complex and unable to process a large number of face images in real time. The proposed model outperforms existing models on larger datasets of face images for automatic detection of face masks. This research implements a variety of classifiers for face mask detection: the random forest, logistic regression, K-nearest neighbor, neural network, support vector machine, and AdaBoost. Since MobileNetV2 is the lightest model, it is a realistic choice for real-time applications requiring inexpensive computation when processing large amounts of data.
PEN4Rec: Preference Evolution Networks for Session-Based Recommendation

Session-based recommendation aims to predict user the next action based on historical behaviors in an anonymous session. For better recommendations, it is vital to capture user preferences as well as their dynamics. Besides, user preferences evolve over time dynamically and each preference has its own evolving track. However, most previous works neglect the evolving trend of preferences and can be easily disturbed by the effect of preference drifting. In this paper, we propose a novel Preference Evolution Networks for session-based Recommendation (PEN4Rec) to model preference evolving process by a two-stage retrieval from historical contexts. Specifically, the first-stage process integrates relevant behaviors according to recent items. Then, the second-stage process models the preference evolving trajectory over time dynamically and infer rich preferences. The process can strengthen the effect of relevant sequential behaviors during the preference evolution and weaken the disturbance from preference drifting. Extensive experiments on three public datasets demonstrate the effectiveness and superiority of the proposed model.
Exploring multi-tasking learning in document attribute classification

In this work, we adhere to explore a Multi-Tasking learning (MTL) based network to perform document attribute classification such as the font type, font size, font emphasis and scanning resolution classification of a document image. To accomplish these tasks, we operate on either segmented word level or on uniformed size patches randomly cropped out of the document. Furthermore, a hybrid convolution neural network (CNN) architecture ”MTL+MI”, which is based on the combination of MTL and Multi-Instance (MI) of patch and word is used to accomplish joint learning for the classification of the same document attributes. The contribution of this paper are three fold: firstly, based on segmented word images and patches, we present a MTL based network for the classification of a full document image. Secondly, we propose a MTL and MI (using segmented words and patches) based combined CNN architecture (“MTL+MI”) for the classification of same document attributes. Thirdly, based on the multi-tasking classifications of the words and/or patches, we propose an intelligent voting system which is based on the posterior probabilities of each words and/or patches to perform the classification of document’s attributes of complete document image.
4G-VOS: Video Object Segmentation using guided context embedding

Video Object Segmentation (VOS) is a fundamental task required in many high-level real-world computer vision applications. VOS becomes challenging due to the presence of background distractors as well as to object appearance variations. Many existing VOS approaches use online model updates to capture the appearance variations which incurs high computational cost. Template matching and propagation-based VOS methods, although cost-effective, suffer from performance degradation under challenging scenarios such as occlusion and background clutter. In order to tackle these challenges, we propose a network architecture dubbed 4G-VOS to encode video context for improved VOS performance to tackle these challenges. To preserve long term semantic information, we propose a guided transfer embedding module. We employ a global instance matching module to generate similarity maps from the initial image and the mask. Besides, we use a generative directional appearance module to estimate and dynamically update the foreground/background class probabilities in a spherical embedding space. Moreover, during feature refinement, existing approaches may lose contextual information. Therefore, we propose a guided pooled decoder to exploit the global and local contextual information during feature refinement. The proposed framework is an end-to-end learning architecture that is trained in an offline fashion. Evaluations over three VOS benchmark datasets including DAVIS2016, DAVIS2017, and YouTube-VOS have demonstrated outstanding performance of the proposed algorithm compared to 40 existing state-of-the-art methods.
Exploration vectoring from the white mica spectral footprint in the atypical auriferous Lavra Velha deposit, San Francisco Craton, Brazil

The Lavra Velha deposit is a poorly understood gold deposit hosted by the 2.1 Ga Ibitiara Granitoid, within the São Francisco Craton (Brazil). Although not consensually agreed, Lavra Velha has been inserted into the Iron Oxide-Copper-Gold (IOCG) class. The major alteration footprint of the deposit involves an upward mineralogical zoning from widespread chlorite-epidote-calcite (propylitic) to white mica-hematite-quartz association with an intervening magnetite-tourmaline-quartz-white mica realm. The ore occurs mostly as massive lenses composed of magnetite, hematite, pyrite, tennantite, bismuthinite, digenite and native gold. Gold is present as inclusion within hematite, pyrite, quartz and cassiterite, in association with white mica, but also with supergene jarosite-goethite. The proximal footwall and hanging walls of the mineralization are commonly anomalous in Cr-Ni-Co and, where weathered, a talc-nontronite association is documented. Such features are considered to represent ultramafic rocks closely associated with mineralization. Reflectance spectroscopy results show that white mica, hematite and Fe-tourmaline are closely and spatially associated with gold mineralization, thus representing a proximal alteration mineralogy. Notably, white mica becomes progressively more phengitic (>2210 nm) as higher Au grades are laterally approached, whereas it has muscovitic composition (<2198 nm) where gold grades are waning. This chemical gradient can be used as an exploratory guide to find zones of higher-grade mineralization. The crystallinity of white mica reveals a strong zonation from the top to the bottom of the deposit defined by the downward variation from muscovite to illite. The passage zone from muscovite to illite represents potential sites for gold deposition. In weathered profiles, the spectral mapping of talc-nontronite would be key in detecting gold-related ultramafic rocks. This work found that SWIR hyperspectral measurement of white mica features provides an efficient means for vectoring to gold mineralization, at least to district scale. The unusual mineralogical signature of Lavra Velha is not diagnostic of any specific style of known deposit and further in-depth studies are required to decipher what kind of deposit it belongs.
Scene-aware Learning Network for Radar Object Detection

Object detection is essential to safe autonomous or assisted driving. Previous works usually utilize RGB images or LiDAR point clouds to identify and localize multiple objects in self-driving. However, cameras tend to fail in bad driving conditions, e.g. bad weather or weak lighting, while LiDAR scanners are too expensive to get widely deployed in commercial applications. Radar has been drawing more and more attention due to its robustness and low cost. In this paper, we propose a scene-aware radar learning framework for accurate and robust object detection. First, the learning framework contains branches conditioning on the scene category of the radar sequence; with each branch optimized for a specific type of scene. Second, three different 3D autoencoder-based architectures are proposed for radar object detection and ensemble learning is performed over the different architectures to further boost the final performance. Third, we propose novel scene-aware sequence mix augmentation (SceneMix) and scene-specific post-processing to generate more robust detection results. In the ROD2021 Challenge, we achieved a final result of average precision of 75.0% and an average recall of 81.0%. Moreover, in the parking lot scene, our framework ranks first with an average precision of 97.8% and an average recall of 98.6%, which demonstrates the effectiveness of our framework.
Hourly clear-sky solar irradiance estimation in China: Model review and validations

• 61 commonly used Clear-sky irradiance models and 27 Machine Learning models are reviewed. • Those models have been evaluated in various climate zones across China. • The best performing model for hourly clear-sky irradiance estimation is obtained from this study. • Some advisory advice can be obtained for areas with similar climatic types in China. Accurate hourly clear-sky irradiance (CSI) estimation is a crucial factor for most solar technologies in improving cost competitiveness and ensuring supply–demand balance. Numerous models have been developed to estimate CSI, including Clear-sky Irradiance models (CSIM) and Machine Learning (ML) models. In this study, 61 CSIM and 23 ML models for estimating hourly CSI are evaluated across the land of China, using hourly solar irradiance measurements observed at 35 stations of the China Ecological Research Network (CERN). The results reveal that the ML models generally obtain outperformed accuracy than other CSIM models. Among all selected models, the GPR3 model, Integretion3 model and Integretion2 model attain generally better ranks in China and each climatic zone, with global performance indicators (GPI) values of 31.66, 30.27 and 23.01, respectively. The GPR3 model is the top-ranked model in MPZ, SMZ, TCZ and TMZ, while the best model for TPMZ is the Integration3 model. In terms of the CSIM models, the Iqabal_C model is the most worth recommending model for hourly CSI estimation in China. The Kasten_I model is the top-ranked model in MPZ, SMZ and TCZ with GPI values of −1.23, 5.94 and −0.20. The Bird model and Iqbal_C model are the best models for hourly CSI estimation in TMZ and TPMZ with GPI values of 5.69 and 1.92, respectively. This study can offer guidance for the hourly CSI estimation models selection for different climatic zones in China.
Clearness index forecasting: A comparative study between a stochastic realization method and a machine learning algorithm

With the increase in solar energy penetration in electric power systems, it is necessary to predict future solar radiation. With this, the solar power generation capacity can be predicted, allowing accurate scheduling for other sources. The solar radiation or the clearness index (CI) prediction was already presented using classical time series methods, artificial intelligence algorithms, among other approaches. In this paper, two methods are proposed to predict the CI. The first one solves a stochastic realization problem to find a mathematical model for the stochastic process that describes the CI. Then, a state observer is implemented to predict its future values. The second one performs the k-Nearest Neighbors (k-NN) regression algorithm for time series forecasting using two different approaches: the traditional k-NN and a recursive version proposed by the authors. As far as the authors know, the statistical and the recursive k-NN methods are different from the ones found in the literature to predict the CI. The methods were implemented and compared for short and long-term predictions. The results illustrate that one approach is more accurate than the other, depending on the prediction horizon. The comparison between the methods for different horizons is another contribution of this paper.
IDV: Internet Domain Name Verification Based on Blockchain

The rapid development of blockchain technology has provided new ideas for network security research. Blockchain-based network security enhancement solutions are attracting widespread attention. This paper proposes an Internet... | Find, read and cite all the research you need on Tech Science Press
FeatureVista

Comprehending and characterizing the spread and interaction of features in a software system is know to be difficult and error-prone. This paper presents FeatureVista, a lightweight tool providing interactive, glyph-based, and iconic visualization concepts designed to visually characterize the feature locations in software assets (source code). FeatureVista supports navigating between software components and features in an equal fashion. Our pilot study indicates that FeatureVista is intuitive and supports comprehending features. It helps to precisely characterize relations among features in large software systems and to contrast explicit software component definitions (e.g., package, class, method) with annotated feature portions---which so far was a largely manual and error-prone activity, albeit essential to get an adequate understanding of a software system. We suggest research directions for true, feature-oriented interfaces that can be used to manage software assets.
Privacy-aware supervised classification: An informative subspace based multi-objective approach

Sharing the raw or an abstract representation of a labelled dataset on cloud platforms can potentially expose sensitive information of the data to an adversary, e.g., in the case of an emotion classification task from text, an adversary-agnostic abstract representation of the text data may eventually lead an adversary to identify the demographics of the authors, such as their gender and age. In this paper, we propose a universal defense mechanism against such malicious attempts of stealing sensitive information from data shared on cloud platforms. More specifically, our proposed method employs an informative subspace based multi-objective approach to obtain a sensitive information aware encoding of the data representation. A number of experiments conducted on both standard text and image datasets demonstrate that our proposed approach is able to reduce the effectiveness of the adversarial task (i.e., in other words is able to better protect the sensitive information of the data) without significantly reducing the effectiveness of the primary task itself.
Embedded YOLO: A Real-Time Object Detector for Small Intelligent Trajectory Cars

YOLO-Tiny is a lightweight version of the object detection model based on the original “You only look once” (YOLO) model for simplifying network structure and reducing parameters, which makes it suitable for real-time applications. Although the YOLO-Tiny series, which includes YOLOv3-Tiny and YOLOv4-Tiny, can achieve real-time performance on a powerful GPU, it remains challenging to leverage this approach for real-time object detection on embedded computing devices, such as those in small intelligent trajectory cars. To obtain real-time and high-accuracy performance on these embedded devices, a novel object detection lightweight network called embedded YOLO is proposed in this paper. First, a new backbone network structure, ASU-SPP network, is proposed to enhance the effectiveness of low-level features. Then, we designed a simplified version of the neck network module PANet-Tiny that reduces computation complexity. Finally, in the detection head module, we use depthwise separable convolution to reduce the number of convolution stacks. In addition, the number of channels is reduced to 96 dimensions so that the module can attain the parallel acceleration of most inference frameworks. With its lightweight design, the proposed embedded YOLO model has only 3.53M parameters, and the average processing time can reach 155.1 frames per second, as verified by Baidu smart car target detection. At the same time, compared with YOLOv3-Tiny and YOLOv4-Tiny, the detection accuracy is 6% higher.
Moire profilometry through simultaneous dual fringe projection for accurate phase demodulation: a comparative study

In optical metrology, fringe projection and moire techniques have been widely used to measure the topography of objects. We can combine the advantages of the two techniques by applying a configuration of simultaneous dual projection in the fringe projection technique, which generates a superimposed fringe pattern containing a moire pattern that is phase modulated according to the topography. In this work, we present an analytic and comparative study of three methods to demodulate the phase of the moire pattern: the spatial, spatial-temporal, and temporal methods. Those methods consist of two steps: first, the moire pattern is extracted from the superimposed fringe pattern; next, the phase of the moire pattern is demodulated. The analytical results show that the resulting phase map has double phase sensitivity compared to that of the classical fringe projection technique. Experimental and numeric results prove the feasibility of this technique.
A process calculus approach to detection and mitigation of PLC malware

We define a simple process calculus, based on Hennessy and Regan's Timed Process Language, for specifying networks of communicating programmable logic controllers (PLCs) enriched with monitors enforcing specification compliance at runtime. We define a synthesis algorithm that given an uncorrupted PLC returns a monitor that enforces the correctness of the PLC, even when injected with malware that may forge/drop actuator commands and inter-controller communications. Then, we strengthen the capabilities of our monitors by allowing the insertion of actions to mitigate malware activities. This gives us deadlock-freedom monitoring: malware may not drag monitored controllers into deadlock states. Our enforcing monitors represent a formal mechanism for prompt detection of malicious activities within PLCs. Finally, we illustrate our results by implementing in Simulink a non-trivial Water Transmission Network (WTN) system, and testing the effectiveness of our monitors in detecting and mitigating three different attacks targeting the PLCs of our WTN.