Reviewerly/exHarmony huggingface dataset:
Dialogue Graph Modeling for Conversational Machine Reading

Conversational Machine Reading (CMR) aims at answering questions in complicated interactive scenarios.Machine needs to answer questions through interactions with users based on given rule document, user scenario and dialogue history, and even initiatively asks questions for clarification if necessary.Namely, the answer to the task needs a machine in the response of either Yes, No, Irrelevant or to raise a follow-up question for further clarification.To effectively capture multiple objects in such a challenging task, graph modeling is supposed to be adopted, though it is surprising that this does not happen until this work proposes a dialogue graph modeling framework by incorporating two complementary graph models, i.e., explicit discourse graph and implicit discourse graph, which respectively capture explicit and implicit interactions hidden in the rule documents.The proposed model is evaluated on the ShARC benchmark and achieves new state-of-the-art by first exceeding the milestone accuracy score of 80%.
Forensic Analysis of Social Networking Applications on an Android Smartphone

Smartphone users spend a substantial amount of time in browsing, emailing, and messaging through different social networking apps. The use of social networking apps on smartphones has become a dominating part of daily lives. This momentous usage has also resulted in a huge spike in cybercrimes such as social harassing, abusive messages, vicious threats, broadcasting of suicidal actions, and live coverage of violent attacks. Many of such crimes are carried out through social networking apps; therefore, the forensic analysis of allegedly involved digital devices in crime scenes and social apps installed on them can be helpful in resolving criminal investigations. This research is aimed at performing forensic investigation of five social networking apps, i.e., Instagram, LINE, Whisper, WeChat, and Wickr on Android smart phones. The essential motivation behind the examination and tests is to find whether the data resides within the internal storage of the device or not after using these social networking apps. Data extraction and analysis are carried out using three tools, i.e., Magnet AXIOM, XRY, and Autopsy. From the results of these experiments, a considerable amount of essential data was successfully extracted from the examined smartphone. This useful data can easily be recovered by forensic analysts for future examination of any crime situation. Finally, we analyzed the tools on the basis of their ability to extract digital evidences from the device and their performance are examined with respect to NIST standards.
Spherical DNNs and Their Applications in 360 Images and Videos

Spherical images or videos, as typical non-euclidean data, are usually stored in the form of 2D panoramas obtained through an equirectangular projection, which is neither equal area nor conformal. The distortion caused by the projection limits the performance of vanilla Deep Neural Networks (DNNs) designed for traditional euclidean data. In this paper, we design a novel Spherical Deep Neural Network (DNN) to deal with the distortion caused by the equirectangular projection. Specifically, we customize a set of components, including a spherical convolution, a spherical pooling, a spherical ConvLSTM cell and a spherical MSE loss, as the replacements of their counterparts in vanilla DNNs for spherical data. The core idea is to change the identical behavior of the conventional operations in vanilla DNNs across different feature patches so that they will be adjusted to the distortion caused by the variance of sampling rate among different feature patches. We demonstrate the effectiveness of our Spherical DNNs for saliency detection and gaze estimation in <inline-formula><tex-math notation="LaTeX">$360^\circ$</tex-math></inline-formula> videos. For saliency detection, we take the temporal coherence of an observer’s viewing process into consideration and propose to use a Spherical U-Net and a Spherical ConvLSTM to predict the saliency maps for each frame sequentially. As for gaze prediction, we propose to leverage a Spherical Encoder Module to extract spatial panoramic features, then we combine them with the gaze trajectory feature extracted by an LSTM for future gaze prediction. To facilitate the study of the <inline-formula><tex-math notation="LaTeX">$360^\circ$</tex-math></inline-formula> videos saliency detection, we further construct a large-scale <inline-formula><tex-math notation="LaTeX">$360^\circ$</tex-math></inline-formula> video saliency detection dataset that consists of 104 <inline-formula><tex-math notation="LaTeX">$360^\circ$</tex-math></inline-formula> videos viewed by 20+ human subjects. Comprehensive experiments validate the effectiveness of our proposed Spherical DNNs for 360 <inline-formula><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> handwritten digit classification and sport classification, saliency detection and gaze tracking in <inline-formula><tex-math notation="LaTeX">$360^\circ$</tex-math></inline-formula> videos. We also visualize the regions contributing to the classification decisions in our proposed Spherical DNNs via the Grad-CAM technique in the classification task, and the results show that our Spherical DNNs constantly leverage reasonable and important regions for decision making, regardless the large distortions. All codes and dataset are available on <uri>https://github.com/svip-lab/SphericalDNNs</uri> .
A Rigorous Runtime Analysis of the $$(1 + (\lambda , \lambda ))$$ GA on Jump Functions

The $$(1 + (\lambda ,\lambda ))$$ genetic algorithm is a younger evolutionary algorithm trying to profit also from inferior solutions. Rigorous runtime analyses on unimodal fitness functions showed that it can indeed be faster than classical evolutionary algorithms, though on these simple problems the gains were only moderate. In this work, we conduct the first runtime analysis of this algorithm on a multimodal problem class, the jump functions benchmark. We show that with the right parameters, the $${(1 + (\lambda , \lambda ))}$$ GA optimizes any jump function with jump size $$2 \le k \le n/4$$ in expected time $$O(n^{(k+1)/2} e^{O(k)} k^{-k/2})$$ , which significantly and already for constant k outperforms standard mutation-based algorithms with their $$\Theta (n^k)$$ runtime and standard crossover-based algorithms with their $${\tilde{O}}(n^{k-1})$$ runtime guarantee. For the isolated problem of leaving the local optimum of jump functions, we determine provably optimal parameters that lead to a runtime of $$(n/k)^{k/2} e^{\Theta (k)}$$ . This suggests some general advice on how to set the parameters of the $${(1 + (\lambda , \lambda ))}$$ GA, which might ease the further use of this algorithm.
An Improved Bees Algorithm for Training Deep Recurrent Networks for Sentiment Classification

Recurrent neural networks (RNNs) are powerful tools for learning information from temporal sequences. Designing an optimum deep RNN is difficult due to configuration and training issues, such as vanishing and exploding gradients. In this paper, a novel metaheuristic optimisation approach is proposed for training deep RNNs for the sentiment classification task. The approach employs an enhanced Ternary Bees Algorithm (BA-3+), which operates for large dataset classification problems by considering only three individual solutions in each iteration. BA-3+ combines the collaborative search of three bees to find the optimal set of trainable parameters of the proposed deep recurrent learning architecture. Local learning with exploitative search utilises the greedy selection strategy. Stochastic gradient descent (SGD) learning with singular value decomposition (SVD) aims to handle vanishing and exploding gradients of the decision parameters with the stabilisation strategy of SVD. Global learning with explorative search achieves faster convergence without getting trapped at local optima to find the optimal set of trainable parameters of the proposed deep recurrent learning architecture. BA-3+ has been tested on the sentiment classification task to classify symmetric and asymmetric distribution of the datasets from different domains, including Twitter, product reviews, and movie reviews. Comparative results have been obtained for advanced deep language models and Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. BA-3+ converged to the global minimum faster than the DE and PSO algorithms, and it outperformed the SGD, DE, and PSO algorithms for the Turkish and English datasets. The accuracy value and F1 measure have improved at least with a 30–40% improvement than the standard SGD algorithm for all classification datasets. Accuracy rates in the RNN model trained with BA-3+ ranged from 80% to 90%, while the RNN trained with SGD was able to achieve between 50% and 60% for most datasets. The performance of the RNN model with BA-3+ has as good as for Tree-LSTMs and Recursive Neural Tensor Networks (RNTNs) language models, which achieved accuracy results of up to 90% for some datasets. The improved accuracy and convergence results show that BA-3+ is an efficient, stable algorithm for the complex classification task, and it can handle the vanishing and exploding gradients problem of deep RNNs.
Dicke model

The Dicke model is a fundamental model of quantum optics, which describes the interaction between light and matter. In the Dicke model, the light component is described as a single quantum mode, while the matter is described as a set of two-level systems. When the coupling between the light and matter crosses a critical value, the Dicke model shows a mean-field phase transition to a superradiant phase. This transition belongs to the Ising universality class and was realized experimentally in cavity quantum electrodynamics experiments. Although the superradiant transition bears some analogy with the lasing instability, these two transitions belong to different universality classes.
Enhanced (t, n) threshold d-level quantum secret sharing

Abstract The quantum secret sharing is an essential and fundamental technique for sharing a secret with the all participants in quantum cryptography. It can be used to design many complex protocols such as secure multiparty summation, multiplication, sorting, voting, etc. Recently, Song et al. have discussed a quantum protocol for secret sharing, which has ( t , n ) threshold approach and modulo d , where t and n denote the threshold number of participants and total number of participants, respectively. Kao et al. point out that the secret in the Song et al.’s protocol cannot be reconstructed without other participants’ information. In this paper, we discuss a protocol that overcomes this problem.
Vehicle Detection in Very-High-Resolution Remote Sensing Images Based on an Anchor-Free Detection Model with a More Precise Foveal Area

Vehicle detection in aerial images is a challenging task. The complexity of the background information and the redundancy of the detection area are the main obstacles that limit the successful operation of vehicle detection based on anchors in very-high-resolution (VHR) remote sensing images. In this paper, an anchor-free target detection method is proposed to solve the problems above. First, a multi-attention feature pyramid network (MA-FPN) was designed to address the influence of noise and background information on vehicle target detection by fusing attention information in the feature pyramid network (FPN) structure. Second, a more precise foveal area (MPFA) is proposed to provide better ground truth for the anchor-free method by determining a more accurate positive sample selection area. The proposed anchor-free model with MA-FPN and MPFA can predict vehicles accurately and quickly in VHR remote sensing images through direct regression and predict the pixels in the feature map. A detailed evaluation based on remote sensing image (RSI) and vehicle detection in aerial imagery (VEDAI) data sets for vehicle detection shows that our detection method performs well, the network is simple, and the detection is fast.
Senti-COVID19: An Interactive Visual Analytics System for Detecting Public Sentiment and Insights Regarding COVID-19 From Social Media

As governments take measures against COVID-19, the epidemic situation is expected to improve, but public sentiment is likely to fluctuate during this process, potentially influencing the best course of action.Social media has become a prevalent way for the public to express emotions and opinions in recent times.So that, the sentiment analysis on top of it may detect and provide valuable evidence of public attitude and help governments subsequent formulation of measures and policies.We present Senti-COVID19, an interactive visual analytic system for reflecting and analyzing public sentiment and detecting sentiment fluctuation triggers on social media.Senti-COVID19 adopts lexicon-based sentiment analysis to divulge the public opinion to COVID-19 events, employing libraries to extract keywords and statistics for providing detailed information.In addition, it offers visualizations for presenting the analysis, allowing users to quickly discover relevant information.Our results show that Senti-COVID19 can be used effectively to analyze sentiment from social media text, allowing users to explore derived data and identify insights from the collected tweets.
Provably Secure Data Sharing Approach for Personal Health Records in Cloud Storage Using Session Password, Data Access Key, and Circular Interpolation

Personal health record (PHR) system has become the most important platform to exchange health information, in which the patients can share and manage personal health information more effectively in cloud storage. However, the cloud server is unreliable, and the secure data of users may be disclosed. Therefore, a secure data sharing mechanism is developed in this research using the proposed session password, data access key, and circular interpolation (SKC)-based data-sharing approach for the secure sharing of PHR in the cloud. The proposed SKC-based data sharing approach provides high efficiency and high-security guarantee. It effectively satisfies various security properties, such as tamper resistance, openness, and decentralization. The proposed SKC-based data sharing approach is the reliable mechanism created for the doctors to share the PHR and to access the patient historical data while meeting the privacy preservation.
Superradiance and subradiance in inverted atomic arrays

Superradiance and subradiance are collective effects that emerge from coherent interactions between quantum emitters. Due to their many-body nature, theoretical studies of extended samples with length larger than the atomic transition wavelength are usually restricted to their early-time behavior or to the few-excitation limit. We use herein a mean-field approach to reduce the complex many-body system to an effective two-atom master equation that includes all correlations up to second order and that can be numerically propagated in time. We find that three-dimensional and two-dimensional inverted atomic arrays sustain superradiance below a critical lattice spacing and quantify the scaling of the superradiant peak for both dimensionalities. Finally, we study the late-time dynamics of the system and demonstrate that a subradiant phase appears before the system finally relaxes.
A real-time AI-assisted seismic monitoring system based on new nodal stations with 4G telemetry and its application in the Yangbi MS 6.4 aftershock monitoring in southwest China

A rapidly deployable dense seismic monitoring system which is capable of transmitting acquired data in real time and analyzing data automatically is crucial in seismic hazard mitigation after a major earthquake. However, it is rather difficult for current seismic nodal stations to transmit data in real time for an extended period of time, and it usually takes a great amount of time to process the acquired data manually. To monitor earthquakes in real time flexibly, we develop a mobile integrated seismic monitoring system consisting of newly developed nodal units with 4G telemetry and a real-time AI-assisted automatic data processing workflow. The integrated system is convenient for deployment and has been successfully applied in monitoring the aftershocks of the Yangbi MS 6.4 earthquake occurred on May 21, 2021 in Yangbi County, Dali City, Yunnan in southwest China. The acquired seismic data are transmitted almost in real time through the 4G cellular network, and then processed automatically for event detection, positioning, magnitude calculation and source mechanism inversion. From tens of seconds to a couple of minutes at most, the final seismic attributes can be presented remotely to the end users through the integrated system. From May 27 to June 17, the real-time system has detected and located 7 905 aftershocks in the Yangbi area before the internal batteries exhausted, far more than the catalog provided by China Earthquake Networks Center using the regional permanent stations. The initial application of this integrated real-time monitoring system is promising, and we anticipate the advent of a new era for Real-time Intelligent Array Seismology (RIAS), for better monitoring and understanding the subsurface dynamic processes caused by Earth's internal forces as well as anthropogenic activities.
A real-time dynamic concept adaptive learning algorithm for exploitability prediction

Exploitability prediction has become increasingly important in cybersecurity, as the number of disclosed software vulnerabilities and exploits are soaring. Recently, machine learning and deep learning algorithms, including Support Vector Machine (SVM), Decision Tree, deep Neural Networks and their ensemble models, have achieved great success in vulnerability evaluation and exploitability prediction. However, they make a strong assumption that the data distribution is static over time and therefore fail to consider the concept drift problems due to the evolving system behaviours. In this work, we propose a novel consecutive batch learning algorithm, called Real-time Dynamic Concept Adaptive Learning (RDCAL), to deal with the concept drift and dynamic class imbalance problems existing in exploitability prediction. Specifically, we develop a Class Rectification Strategy (CRS) to handle the ‘actual drift’ in sample labels and a Balanced Window Strategy (BWS) to boost the minority class during real-time learning. Experimental results conducted on the real-world vulnerabilities collected between 1988 to 2020 show that the overall performance of classifiers, including Neural Networks, SVM, HoeffdingTree and Logistic Regression (LR), improves over 3% by adopting our proposed RDCAL algorithm. Furthermore, RDCAL achieves state-of-the-art performance on exploitability prediction compared with other concept drift algorithms.
Transferability of Spectral Graph Convolutional Neural Networks

This paper focuses on spectral graph convolutional neural networks (ConvNets), where filters are defined as elementwise multiplication in the frequency domain of a graph. In machine learning settings where the dataset consists of signals defined on many different graphs, the trained ConvNet should generalize to signals on graphs unseen in the training set. It is thus important to transfer ConvNets between graphs. Transferability, which is a certain type of generalization capability, can be loosely defined as follows: if two graphs describe the same phenomenon, then a single filter or ConvNet should have similar repercussions on both graphs. This paper aims at debunking the common misconception that spectral filters are not transferable. We show that if two graphs discretize the same continuous space, then a spectral filter or ConvNet has approximately the same repercussion on both graphs. Our analysis is more permissive than the standard analysis. Transferability is typically described as the robustness of the filter to small graph perturbations and re-indexing of the vertices. Our analysis accounts also for large graph perturbations. We prove transferability between graphs that can have completely different dimensions and topologies, only requiring that both graphs discretize the same underlying space in some generic sense.
Virtual Reality and Augmented Reality for Education

Virtual reality (VR) and augmented reality (AR) will help students understand and interact more effectively. VR and AR are examples of emerging technologies that are characterized by their novelty, rapid development and effects on human existence. New capabilities are easily embraced by product makers and turned into useful goods. Designers must reconsider the foundations of human-computer relationships because of emerging technology. Like too many modern innovations, architecture practices must rapidly change their processes to accommodate these new developments. Students would be able to comprehend and communicate more easily through VR and AR. VR curriculum can transform the way educational materials are delivered; it works by creating a simulated reality – actual or imaginary – and empowering users to not only see but also interact with it. This chapter will discuss the subject and see how items are placed together with VR instead of relying on word definitions or book diagrams. VR education encourages students to learn for themselves. Instead of passively consuming information, students may learn by conducting. In this chapter, we will discuss different dimensions of AR and VR, which are used in education and how students and teachers will interact with each other through AR and VR.
Deep Learning Capabilities for the Categorization of Microcalcification

Breast cancer is the most common cancer in women worldwide. It is the most frequently diagnosed cancer among women in 140 countries out of 184 reporting countries. Lesions of breast cancer are abnormal areas in the breast tissues. Various types of breast cancer lesions include (1) microcalcifications, (2) masses, (3) architectural distortion, and (4) bilateral asymmetry. Microcalcification can be classified as benign, malignant, and benign without a callback. In the present manuscript, we propose an automatic pipeline for the detection of various categories of microcalcification. We performed deep learning using convolution neural networks (CNNs) for the automatic detection and classification of all three categories of microcalcification. CNN was applied using four different optimizers (ADAM, ADAGrad, ADADelta, and RMSProp). The input images of a size of 299 × 299 × 3, with fully connected RELU and SoftMax output activation functions, were utilized in this study. The feature map was obtained using the pretrained InceptionResNetV2 model. The performance evaluation of our classification scheme was tested on a curated breast imaging subset of the DDSM mammogram dataset (CBIS–DDSM), and the results were expressed in terms of sensitivity, specificity, accuracy, and area under the curve (AUC). Our proposed classification scheme outperforms the ability of previously used deep learning approaches and classical machine learning schemes.