Reviewerly/exHarmony huggingface dataset:
The application framework of big data technology in the COVID-19 epidemic emergency management in local government—a case study of Hainan Province, China

As COVID-19 continues to spread globally, traditional emergency management measures are facing many practical limitations. The application of big data analysis technology provides an opportunity for local governments to conduct the COVID-19 epidemic emergency management more scientifically. The present study, based on emergency management lifecycle theory, includes a comprehensive analysis of the application framework of China's SARS epidemic emergency management lacked the support of big data technology in 2003. In contrast, this study first proposes a more agile and efficient application framework, supported by big data technology, for the COVID-19 epidemic emergency management and then analyses the differences between the two frameworks.This study takes Hainan Province, China as its case study by using a file content analysis and semistructured interviews to systematically comprehend the strategy and mechanism of Hainan's application of big data technology in its COVID-19 epidemic emergency management.Hainan Province adopted big data technology during the four stages, i.e., migration, preparedness, response, and recovery, of its COVID-19 epidemic emergency management. Hainan Province developed advanced big data management mechanisms and technologies for practical epidemic emergency management, thereby verifying the feasibility and value of the big data technology application framework we propose.This study provides empirical evidence for certain aspects of the theory, mechanism, and technology for local governments in different countries and regions to apply, in a precise, agile, and evidence-based manner, big data technology in their formulations of comprehensive COVID-19 epidemic emergency management strategies.
Improving Query Graph Generation for Complex Question Answering over Knowledge Base

Most of the existing Knowledge-based Question Answering (KBQA) methods first learn to map the given question to a query graph, and then convert the graph to an executable query to find the answer.The query graph is typically expanded progressively from the topic entity based on a sequence prediction model.In this paper, we propose a new solution to query graph generation that works in the opposite manner: we start with the entire knowledge base and gradually shrink it to the desired query graph.This approach improves both the efficiency and the accuracy of query graph generation, especially for complex multi-hop questions.Experimental results show that our method achieves state-of-the-art performance on ComplexWebQuestion (CWQ) dataset.
Treasures Outside Contexts: Improving Event Detection via Global Statistics

Event detection (ED) aims at identifying event instances of specified types in given texts, which has been formalized as a sequence labeling task.As far as we know, existing neural-based ED models make decisions relying on the contextual semantic features of each word in the input text, which we find is easy to get confused by varied contexts in the test stage.To this end, we come up with the idea of introducing a set of statistical features from word-event co-occurrence frequencies in the entire training set to cooperate with the contextual features.Specifically, we propose a Semantic and Statistic-Joint Discriminative Network (S 2 -JDN) consisting of a semantic feature extractor, a statistical feature extractor, and a joint event discriminator.In experiments, S 2 -JDN effectively exceeds ten recent state-ofthe-art (SOTA) baseline methods on ACE2005 and KBP2015 benchmark datasets.Further, we perform extensive experiments to investigate the effectiveness of S 2 -JDN.
On the Use of Augmented Reality to Reinforce the Learning of Power Electronics for Beginners

This study aims to develop and evaluate an Augmented Reality (AR) application to teach power electronics to beginners. For this purpose, two topics were presented: The first was the design of a series-connected Resistance–Inductor–Capacitor (RLC) circuit in AR, the space-state equations of which were analyzed in an interactive way, and its assembly in a virtual protoboard to analyze the voltage and currents as measured by an oscilloscope. The second presented topic in AR was about Bidirectional Direct Current (DC)–DC converters, known as Buck–Boost; the aim was to study their behavior when energy is exchanged between two systems, usually photovoltaic panels, electric vehicles, and storage systems. The attitudes of the students towards the AR application was significantly better than those towards traditional teaching. The measurements of the developed skills indicated better cognitive performance when using AR technology. The designed AR tool was used in an industry course to explore the students’ opinions, who provided valuable feedback.
Multimedia Cryptosystem for IoT Applications Based on a Novel Chaotic System around a Predefined Manifold

Multimedia data play an important role in our daily lives. The evolution of internet technologies means that multimedia data can easily participate amongst various users for specific purposes, in which multimedia data confidentiality and integrity have serious security issues. Chaos models play an important role in designing robust multimedia data cryptosystems. In this paper, a novel chaotic oscillator is presented. The oscillator has a particular property in which the chaotic dynamics are around pre-located manifolds. Various dynamics of the oscillator are studied. After analyzing the complex dynamics of the oscillator, it is applied to designing a new image cryptosystem, in which the results of the presented cryptosystem are tested from various viewpoints such as randomness, time encryption, correlation, plain image sensitivity, key-space, key sensitivity, histogram, entropy, resistance to classical types of attacks, and data loss analyses. The goal of the paper is proposing an applicable encryption method based on a novel chaotic oscillator with an attractor around a pre-located manifold. All the investigations confirm the reliability of using the presented cryptosystem for various IoT applications from image capture to use it.
Polarity-based graph neural network for sign prediction in signed bipartite graphs

Abstract As a fundamental data structure, graphs are ubiquitous in various applications. Among all types of graphs, signed bipartite graphs contain complex structures with positive and negative links as well as bipartite settings, on which conventional graph analysis algorithms are no longer applicable. Previous works mainly focus on unipartite signed graphs or unsigned bipartite graphs separately. Several models are proposed for applications on the signed bipartite graphs by utilizing the heuristic structural information. However, these methods have limited capability to fully capture the information hidden in such graphs. In this paper, we propose the first graph neural network on signed bipartite graphs, namely Polarity-based Graph Convolutional Network (PbGCN), for sign prediction task with the help of balance theory. We introduce the novel polarity attribute to signed bipartite graphs, based on which we construct one-mode projection graphs to allow the GNNs to aggregate information between the same type nodes. Extensive experiments on five datasets demonstrate the effectiveness of our proposed techniques.
Cube attacks on round-reduced TinyJAMBU

Lightweight cryptography has recently gained importance as the number of Internet of things (IoT) devices connected to Internet grows. Its main goal is to provide cryptographic algorithms that can be run efficiently in resource-limited environments such as IoT. To meet the challenge, the National Institute of Standards and Technology (NIST) announced the Lightweight Cryptography (LWC) project. One of the finalists of the project is the TinyJAMBU cipher. This work evaluates the security of the cipher. The tool used for the evaluation is the cube attack. We present five distinguishing attacks DA1-DA5 and two key recovery attacks KRA1-KRA2. The first two distinguishing attacks (DA1 and DA2) are launched against the initialisation phase of the cipher. The best result achieved for the attacks is a distinguisher for an 18-bit cube, where the cipher variant consists of the full initialisation phase together with 438 rounds of the encryption phase. The key recovery attacks (KRA1 and KRA2) are also launched against the initialisation phase of the cipher. The best key recovery attack can be applied for a cipher variant that consists of the full initialisation phase together with 428 rounds of the encryption phase. The attacks DA3-DA5 present a collection of distinguishers up to 437 encryption rounds, whose 32-bit cubes are chosen from the plaintext, nonce, or associated data bits. The results are confirmed experimentally. A conclusion from the work is that TinyJAMBU has a better security margin against cube attacks than claimed by the designers.
Emotion Intensity and its Control for Emotional Voice Conversion

Emotional voice conversion (EVC) seeks to convert the emotional state of an utterance while preserving the linguistic content and speaker identity. In EVC, emotions are usually treated as discrete categories overlooking the fact that speech also conveys emotions with various intensity levels that the listener can perceive. In this paper, we aim to explicitly characterize and control the intensity of emotion. We propose to disentangle the speaker style from linguistic content and encode the speaker style into a style embedding in a continuous space that forms the prototype of emotion embedding. We further learn the actual emotion encoder from an emotion-labelled database and study the use of relative attributes to represent fine-grained emotion intensity. To ensure emotional intelligibility, we incorporate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">emotion classification loss</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">emotion embedding similarity loss</i> into the training of the EVC network. As desired, the proposed network controls the fine-grained emotion intensity in the output speech. Through both objective and subjective evaluations, we validate the effectiveness of the proposed network for emotional expressiveness and emotion intensity control.
M-SENA: An Integrated Platform for Multimodal Sentiment Analysis

M-SENA is an open-sourced platform for Multimodal Sentiment Analysis.It aims to facilitate advanced research by providing flexible toolkits, reliable benchmarks, and intuitive demonstrations.The platform features a fully modular video sentiment analysis framework consisting of data management, feature extraction, model training, and result analysis modules.In this paper, we first illustrate the overall architecture of the M-SENA platform and then introduce features of the core modules.Reliable baseline results of different modality features and MSA benchmarks are also reported.Moreover, we use model evaluation and analysis tools provided by M-SENA to present intermediate representation visualization, on-the-fly instance test, and generalization ability test results.
A generalized framework for lung Cancer classification based on deep generative models

Abstract A new generalized framework for lung cancer detection and classification are introduced in this paper. Specifically, two types of deep models are presented. The first model is a generative model to capture the distribution of the important features in a set of small class-unbalanced collected CXR images. This generative model can be utilized to synthesize any number of CXR images for each class. For example, our generative model can generate images with tumors with different sizes and positions in the lung. Hence, the system can automatically convert the small unbalanced collected dataset to a larger balanced one. The second model is the ResNet50 that is trained using the large balanced dataset for cancer classification into benign and malignant. The proposed framework acquires 98.91% overall detection accuracy, 98.85% area under curve (AUC), 98.46% sensitivity, 97.72% precision, 97.89% F1 score. The classifier takes 1.2334 s on average to classify a single image using a machine with 13GB RAM.
ClusterSCL: Cluster-Aware Supervised Contrastive Learning on Graphs

We study the problem of supervised contrastive (SupCon) learning on graphs. The SupCon loss has been recently proposed for classification tasks by pulling data points in the same class closer than those of different classes. However, it could be difficult for SupCon to handle datasets with large intra-class variances and high inter-class similarities. This issue is also challenging when it couples with graph structures. To address this, we present the cluster-aware supervised contrastive learning loss (ClusterSCL1) for graph learning tasks. The main idea of ClusterSCL is to retain the structural and attribute properties of a graph in the form of nodes’ cluster distributions during supervised contrastive learning. Specifically, ClusterSCL introduces the strategy of cluster-aware data augmentation and integrates it with the SupCon loss. Extensive experiments on several widely adopted graph benchmarks demonstrate the superiority of ClusterSCL over the cross-entropy, SupCon, and other graph contrastive objectives.
Heterogeneous Face Recognition via Face Synthesis With Identity-Attribute Disentanglement

Heterogeneous Face Recognition (HFR) aims to match faces across different domains (<i>e.g.</i>, visible to near-infrared images), which has been widely applied in authentication and forensics scenarios. However, HFR is a challenging problem because of the large cross-domain discrepancy, limited heterogeneous data pairs, and large variation of facial attributes. To address these challenges, we propose a new HFR method from the perspective of heterogeneous data augmentation, named Face Synthesis with Identity-Attribute Disentanglement (FSIAD). Firstly, the identity-attribute disentanglement (IAD) decouples face images into identity-related representations and identity-unrelated representations (called attributes), and then decreases the correlation between identities and attributes. Secondly, we devise a face synthesis module (FSM) to generate a large number of images with stochastic combinations of disentangled identities and attributes for enriching the attribute diversity of synthetic images. Both the original images and the synthetic ones are utilized to train the HFR network for tackling the challenges and improving the performance of HFR. Extensive experiments on five HFR databases validate that FSIAD obtains superior performance than previous HFR approaches. Particularly, FSIAD obtains 4.8&#x0025; improvement over state of the art in terms of VR&#x0040;FAR=0.01&#x0025; on LAMP-HQ, the largest HFR database so far.
High-speed imaging of spatiotemporal correlations in Hong-Ou-Mandel interference

The Hong-Ou-Mandel interference effect lies at the heart of many emerging quantum technologies whose performance can be significantly enhanced with increasing numbers of entangled modes one could measure and thus utilize. Photon pairs generated through the process of spontaneous parametric down conversion are known to be entangled in a vast number of modes in the various degrees of freedom (DOF) the photons possess such as time, energy, and momentum, etc. Due to limitations in detection technology and techniques, often only one such DOFs can be effectively measured at a time, resulting in much lost potential. Here, we experimentally demonstrate, with the aid of a time tagging camera, high speed measurement and characterization of two-photon interference. With a data acquisition time of only a few seconds, we observe a bi-photon interference and coalescence visibility of ∼64% with potentially up to ∼2 × 103 spatial modes. These results open up a route for practical applications of using the high dimensionality of spatiotemporal DOF in two-photon interference, and in particular, for quantum sensing and communication.
AGSDNet: Attention and Gradient-Based SAR Denoising Network

Synthetic aperture radar (SAR) images are mainly corrupted by speckle noise, which needs to be removed for further processing. In this letter, we propose an attention and gradient-based SAR denoising network (AGSDNet) to remove speckle noise from SAR images while preserving finer details. In the proposed network, gradient information of the noisy image is first concatenated with its features in order to increase the feature information content (map). An intermediate feature denoising block (FDB) is then employed to reduce noise from this feature map. Finally, two attention blocks, designed and deployed, in the network focus on preserving the more informative features in the image thereby generating a feature preserved denoised image. The proposed network is compared with several classical and deep learning-based SAR denoising methods to demonstrate its superiority in terms of qualitative as well as quantitative measures. We have provided the training and testing code of AGSDNet at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/RTSIR/AGSDNet</uri> .
Estimation of Rooftop Solar Power Potential by Comparing Solar Radiation Data and Remote Sensing Data—A Case Study in Aichi, Japan

There have been significant advances in the shift from fossil-based energy systems to renewable energies in recent years. Decentralized solar photovoltaic (PV) is one of the most promising energy sources because of the availability of rooftop areas, ease of installation, and reduced cost of PV panels. The current modeling method using remote sensing data based on a geographic information system (GIS) is objective and accurate, but the analysis processes are complicated and time-consuming. In this study, we developed a method to estimate the rooftop solar power potential over a wide area using globally available solar radiation data from Solargis combined with a building polygon. Our study also utilized light detection and ranging (LiDAR) data and AW3D to estimate rooftop solar power potential in western Aichi, Japan, and the solar radiation was calculated using GIS. The estimation using LiDAR data took into account the slope and azimuth of rooftops. A regression analysis of the estimated solar power potential for each roof between the three methods was conducted, and the conversion factor 0.837 was obtained to improve the accuracy of the results from the Solargis data. The annual rooftop solar power potential of 3,351,960 buildings in Aichi Prefecture under Scenario A, B, and C was 6.92 × 107, 3.58 × 107, and 1.27 × 107 MWh/year, estimated using Solargis data after the adjustment. The estimated solar power potential under Scenario A could satisfy the total residential power demand in Aichi, revealing the crucial role of rooftop solar power in alleviating the energy crisis. This approach of combining Solargis data with building polygons can be easily applied in other parts of the world. These findings can provide useful information for policymakers and contribute to local planning for cleaner energy.
An Intelligent Motion Detection Using OpenCV

A computer vision system's basic goal is to detect moving things. For many applications, the performance of these systems is insufficient. One of the key reasons is that dealing with numerous restrictions such as environmental fluctuations makes the moving object detection process harder. Motion detection is a well-known computer technology associated with computer vision and image processing that focuses on detecting objects or instances of a specific class in digital photos and videos (for example, humans, flowers, and animals). Face detection, character recognition, and vehicle calculation are just a few of the well-studied applications of object motion detection. Object detection has a wide range of applications, including retrieval and surveillance. Object counting is a step after object detection that gets more exact and robust with the help of OpenCV. For object detection and counting, OpenCV includes a number of useful techniques. Object counting has a variety of applications in the fields of transportation, medicine, and environmental science, among others. Computer vision and image processing research is progressing rapidly and is being used to improve human lives. To avoid the drawbacks of current and newly established techniques, the suggested algorithm was tested on many open source images by imposing a single set of variables. The motion detection software system proposed in this paper allows us to see movement around an item or a visual area.
Does Vision-and-Language Pretraining Improve Lexical Grounding?

Linguistic representations derived from text alone have been criticized for their lack of grounding, i.e., connecting words to their meanings in the physical world.Vision-and-Language (VL) models, trained jointly on text and image or video data, have been offered as a response to such criticisms.However, while VL pretraining has shown success on multimodal tasks such as visual question answering, it is not yet known how the internal linguistic representations themselves compare to their text-only counterparts.This paper compares the semantic representations learned via VL vs. text-only pretraining for two recent VL models using a suite of analyses (clustering, probing, and performance on a commonsense question answering task) in a language-only setting.We find that the multimodal models fail to significantly outperform the text-only variants, suggesting that future work is required if multimodal pretraining is to be pursued as a means of improving NLP in general.
(Nothing But) Futures

The year after the journal ACCESS originally launched, Octavia Butler (1983) published a short story titled “Speech Sounds” in the science fiction monthly Asimov’s Science Fiction. The story takes place in the aftermath of a pandemic that seemingly has one of multiple effects: one either loses the ability to speak coherently, or one loses the ability to read and write (but not both). In this paper, I will discuss how this story has utility as an example of future studies. Futures studies is an attempt to game out multiple futures by using our present-day anxieties, institutions, and value systems to consider what is probable, what is possible, and what is preferable. Through future studies, I am looking for a new way for thinking about theory so that we can engage in imagining any number of educational futures, one that takes the scaffolding of futures studies and both looks to science fiction as its object of inquiry and reads educational research and policy as science fiction writing.