Reviewerly/exHarmony huggingface dataset:
Prinet: A Prior Driven Spectral Super-Resolution Network

Spectral super-resolution aims to reconstruct hyperspectral images from RGB images directly. In recent years, convolutional networks have been successfully employed to this task. However, few of them take into account the specific properties of hyperspectral images. In this paper, we attempt to design a super-resolution network, named PriNET, based on two prior knowledge about hyperspectral images. The first one is spectral correlation. According to this property, we design a decomposition network to reconstruct hyperspectral images. In this network, the whole spectral bands of hyperspectral images are divided into several groups, and multiple residual networks are proposed to reconstruct them separately. The second knowledge is that the hyperspectral image should be able to generate its corresponding RGB image. Inspired from it, we design a self-supervised network to fine-tune the reconstruction results of the decomposition network. Finally, these two networks are combined together to constitute PriNET. Experimental results on two hyperspectral datasets demonstrate that the proposed PriNET can achieve better performance than several state-of-the-art networks.
Speaker Personality Recognition With Multimodal Explicit Many2many Interactions

Recently, speaker personality analysis has become an increasingly popular research task in human-computer interaction. Previous studies of user personality traits recognition normally focus on leveraging static information, i.e., tweets, images and social relationships in social platforms and websites. However, in this paper, we utilize three kinds of speaking dynamic information, i.e., textual, visual and acoustic temporal sequences, for a computer to interpret human personality traits from a face-to-face monologue. Specifically, we propose an explicit many2many (many-to-many) interactive approach to help AI efficiently recognize speaker personality traits. On the one hand, we encode the long feature sequence of human speaking for each modality with bidirectional LSTM network. On the other hand, we design a many2many attention mechanism explicitly to capture the interactions across multiple modalities for multiple interactive pairs. Empirical evaluation on 12 kinds of personality traits demonstrates the effectiveness of our proposed approach to multimodal speaker personality recognition.
Emotion analysis of microblog based on emotion dictionary and Bi-GRU

The colloquialism and conciseness of microblog text bring additional challenges to emotion classification. This paper proposes a new emotion classification model based on hybrid learning. In the first stage, the improved dictionary classification method is used to calculate emotion score in the whole data set, and the data with high or low scores are directly marked; in the second stage, the rest of the method is based on emotion dictionary and Bi-GRU fusion model to calculate emotion score, and the two stage hybrid frame makes the method effectively applied in microblog emotion classification. The microblog experiment with emoticons in COAE2014 (Chinese opinion analysis and evaluation 2014) dataset shows that the single model is not ideal for many kinds of complex contexts, and it is difficult and low accuracy. The multi model fusion method can effectively improve the error preference of the single model and improve the classification effect.
Hardening X.509 Certificate Issuance using Distributed Ledger Technology

The security of cryptographic communication protocols that use X.509 certificates depends on the correctness of those certificates. This paper proposes a system that helps to ensure the correct operation of an X.509 certification authority and its registration authorities. We achieve this goal by enforcing a policy-defined, multi-party validation and authorization workflow of certificate signing requests. Besides, our system offers full accountability for this workflow for forensic purposes. As a foundation for our implementation, we leverage the distributed ledger and smart contract framework Hyperledger Fabric. Our implementation inherits the strong tamper-resistance of Fabric which strengthens the integrity of the computer processes that enforce the validation and authorization of the certificate signing request, and of the metadata collected during certificate issuance.
UniOntBot: Semantic Natural Language Generation based API approach for Chatbot Communication

Natural Language Generation is a sub task of the natural language processing where the machine represents texts in human understandable language. Although there have been various researches carried out for Natural Language Generation since the 1970s, there are only few available work carried out with semantic technologies compared to linguistic surface oriented structures. This paper presents the available work which combine semantic technologies with Natural Language Generation, and it identifies opportunities and drawbacks of such systems. The research is carried out on how to use semantic natural language generation with chatbots with lower computational cost and on the ability to reuse it for similar domains with less coding on natural language generation component for small scale level domains. Additionally, a new architecture for chatbot using an ontology and a new domain ontology with natural language resource bind are contributed by the researchers.
Minimizing Discrete Total Curvature for Image Processing

The curvature regularities have received growing attention with the advantage of providing strong priors in the continuity of edges in image processing applications. However, owing to the non-convex and non-smooth properties of the high-order regularizer, the numerical solution becomes challenging in real-time tasks. In this paper, we propose a novel curvature regularity, the total curvature (TC), by minimizing the normal curvatures along different directions. We estimate the normal curvatures discretely in the local neighborhood according to differential geometry theory. The resulting curvature regularity can be regarded as a re-weighted total variation (TV) minimization problem, which can be efficiently solved by the alternating direction method of multipliers (ADMM) based algorithm. By comparing with TV and Euler's elastica energy, we demonstrate the effectiveness and superiority of the total curvature regularity for various image processing applications.
Application of Personalized Recommendation Technology in MOOC System

In order to solve the problem that it is difficult for users to retrieve information and the retrieval results are inaccurate, this paper proposes a personalized recommendation algorithm based on user collaborative filtering applied to MOOC system. First of all, the system forms the user interest model vector by collecting the user's behavior log and analyzing it. Secondly, the label is created for each resource of the system, and the user's score of the resource is collected in the process of user use. Finally, on the basis of the user interest model and resource characteristics, combined with the personalized recommendation algorithm, the resources that meet the user's requirements are recommended to the target user. The system test results show that the users are highly satisfied with the recommendation results of the system, and the application of personalized recommendation algorithm in MOOC system can provide users with more effective information and resources.
Dynamics of quantum coherence and entanglement in an intrinsic noise model of a V-type qutrit system interacting with a coherent field

An analytical solution of the dynamical evolution of an intrinsic noise model of a V-type three-level atom confined in a cavity having one-mode coherent field via multi-photon transitions is obtained. We investigate the effect of the physical parameters of the qutrit-cavity unitary interaction, the intrinsic noise and the photon multiplicity, on important quantum effects such as: the atomic inversion, the quantum coherence, the entanglement and the total correlation. These physical parameters control the phenomenon of the collapses and revivals, and the regularity of the oscillatory behavior of the atomic-inversion. When the intrinsic noise is considered, the dynamics of the entropy, the negativity and the mutual information are different, while the entropy grows, the entanglement negativity and the mutual information degrades to their stationary values. It is found that the quantum coherence, the entanglement, the total correlation and their stationary values are very sensitive to any change in the physical parameters.
Detection of Phishing Website Using Machine Learning Approach

Phishing is one kind of cyber-attack and at once, it is a most dangerous and common attack to acquire personal information, account details, credit card details, organizational details or password of a user to conduct transactions. Phishing websites seem to like the appropriate ones and it is difficult to differentiate among those websites. The motive from that study is to perform ELM derived from different 30 main components which are categorized using the ML approach. Most of the phishing URLs use HTTPS to avoid getting detected. There are three ways for the detection of website phishing. The primitive approach evaluates different items of URL, the second approach analyzing the authority of a website and calculating whether the website is introduced or not and it also analyzing who is supervising it, the third approach checking the genuineness of the website.
Throughput optimization for Storm-based processing of stream data on clouds

There is a rapidly growing need for processing large volumes of streaming data in real time in various big data applications. As one of the most commonly used systems for streaming data processing, Apache Storm provides a workflow-based mechanism to execute directed acyclic graph (DAG)-structured topologies. With the expansion of cloud infrastructures around the globe and the economic benefits of cloud-based computing and storage services, many such Storm workflows have been shifted or are in active transition to clouds. However, modeling the behavior of streaming data processing and improving its performance in clouds still remain largely unexplored. We construct rigorous cost models to analyze the throughput dynamics of Storm workflows and formulate a budget-constrained topology mapping problem to maximize Storm workflow throughput in clouds. We show this problem to be NP-complete and design a heuristic solution that takes into consideration not only the selection of virtual machine type but also the degree of parallelism for each task (spout/bolt) in the topology. The performance superiority of the proposed mapping solution is illustrated through extensive simulations and further verified by real-life workflow experiments deployed in public clouds in comparison with the default Storm and other existing methods.
Constraint-Aware Importance Estimation for Global Filter Pruning under Multiple Resource Constraints

Filter pruning is an efficient way to structurally remove the redundant parameters in convolutional neural network, where at the same time reduces the computation, memory storage and transfer cost. Recent state-of-the-art methods globally estimate the importance of each filter based on its impact to the loss and iteratively remove those with smaller values until the pruned network meets some resource constraints, such as the commonly used number (or ratio) of filter left. However, when there is a more practical constraint like the total number of FLOPs, they ignore its relation to the estimation of filter importance. We propose a novel method called Constraint-Aware Importance Estimation (CAIE) that integrates information of the impact on the given resource into the original importance estimation only based on loss when pruning each filter. Moreover, our CAIE can be generalized to the pruning problem under multiple resource constraints simultaneously. Extensive experiments show that under the same multiple resource constraints, the model pruned with our CAIE method can not only accurately meet the constraints but also achieve the optimal performance results when comparing to existing state-of-the-art methods.
A Survey on Blood Image Diseases Detection Using Deep Learning

Blood disease detection and diagnosis using blood cells images is an interesting and active research area in both the computer and medical fields. There are many techniques developed to examine blood samples to detect leukemia disease, these techniques are the traditional techniques and the deep learning (DL) technique. This article presents a survey on the different traditional techniques and DL approaches that have been employed in blood disease diagnosis based on blood cells images and to compare between the two approaches in quality of assessment, accuracy, cost and speed. This article covers 19 studies, 11 of these studies were in traditional techniques which used image processing and machine learning (ML) algorithms such as K-means, K-nearest neighbor (KNN), Naïve Bayes, Support Vector Machine (SVM), and 8 studies in advanced techniques which used DL, particularly Convolutional Neural Networks (CNNs) which is the most widely used in the field of blood image diseases detection since it is highly accurate, fast, and has the least cost. In addition, it analyzes a number of recent works that have been introduced in the field including the size of the dataset, the used methodologies, the obtained results, etc. Finally, based on the conducted study, it can be concluded that the proposed system CNN was achieving huge successes in the field whether regarding features extraction or classification task, time, accuracy, and had a lower cost in the detection of leukemia diseases.
Reseach and Implementation of Face Detection, Tracking and Recognition Based on Video

With the continuous development of computer, many advanced technologies are mostly based on computer vision, face recognition technology plays an important role. The camera captures the image or video stream containing the human face, and detects and tracks the face in the image. This series of related technologies are also called face detection and recognition. The method of face recognition is more natural, more intuitive, and has the characteristics of indirect and concurrency. It has been widely used in many fields. Now, computer vision is an active research field. In this paper, we study the methods of face detection and recognition in the field of computer vision. The user interface is designed with PyQt, and the function of face model training, reading the image to be recognized, extracting the image to be recognized and the recognition of human face are realized. The experimental results show that the proposed method has high recognition efficiency, and a complete algorithm system based on face tracking detection and face recognition is realized, which can lay a good foundation for future research on the aspect of vision.
Dual Focus Attention Network For Video Emotion Recognition

Video emotion recognition is a challenging task due to complex scenes and various forms of emotion expression. Most existing works focus on fusing multiple features over the whole video clips. According to our observations, given a long video clip, the emotion is usually presented by only several actions/objects in a few short snippets, and the meaningful cues are buried in the noisy background. When human judging the emotion in videos, we first find the informative clips and then closely look for emotional cues in the frames. In this paper, we propose Dual Focus Attention Network to mimic this process. First, three kinds of features including action, object, and scene are extracted from videos. Second, Two attention modules are used to focus on the visual features of the videos from temporal and spatial dimensions respectively. With our dual focus attention network, we can effectively discover the most emotional frames along the time dimension and the most emotional visual cues in each frame. Our experiments conducted on two widely used datasets Ekman and VideoEmotion show that our proposed approach outperforms the existing approaches.
Visual odometry particle filter for improving accuracy of visual object trackers

This Letter proposes a novel state estimator called the visual odometry particle filter (VOPF) for improving accuracy of visual object trackers. For the VOPF, a novel visual odometry motion model that is better than the conventional constant velocity motion model is proposed. In addition, a new particle injection method to prevent sample impoverishment and the incorrect measurement detection method are proposed. Visual object tracking experiments using 30 visual object trackers demonstrate that the VOPF improves accuracy of the trackers and outperforms the conventional particle and Kalman filters using the CV motion model.
Boosting Memory with a Persistent Memory Mechanism for Remote Sensing Image Captioning

The encoder–decoder framework has been widely used in the remote sensing image captioning task. When we need to extract remote sensing images containing specific characteristics from the described sentences for research, rich sentences can improve the final extraction results. However, the Long Short-Term Memory (LSTM) network used in decoders still loses some information in the picture over time when the generated caption is long. In this paper, we present a new model component named the Persistent Memory Mechanism (PMM), which can expand the information storage capacity of LSTM with an external memory. The external memory is a memory matrix with a predetermined size. It can store all the hidden layer vectors of LSTM before the current time step. Thus, our method can effectively solve the above problem. At each time step, the PMM searches previous information related to the input information at the current time from the external memory. Then the PMM will process the captured long-term information and predict the next word with the current information. In addition, it updates its memory with the input information. This method can pick up the long-term information missed from the LSTM but useful to the caption generation. By applying this method to image captioning, our CIDEr scores on datasets UCM-Captions, Sydney-Captions, and RSICD increased by 3%, 5%, and 7%, respectively.
Amplification Requirements For Quantum Radar Signals

Quantum illumination radars rely on quantum sources of two-mode squeezing vacuum to produce signals with quantum-enhanced correlations. The recent demonstration of quantum radar transmitters in the laboratory has shown promising progress in the technology. Amplification of the faint quantum signals is however necessary in order to make quantum radars practical. Here we discuss the possibility of amplifying quantum signals while preserving a quantum advantage. We find that amplifying both signals equally cannot beat an ideal source of classically correlated signals, but does provide a quantum advantage over noisy classical sources. We also briefly discuss other amplifications scheme that may provide further enhancements.
Efficient Strategies of VMs Scheduling Based on Physicals Resources and Temperature Thresholds

Cloud computing offers a variety of services, including the dynamic availability of computing resources. Its infrastructure is designed to support the accessibility and availability of various consumer services via the Internet. The number of data centers allow the allocation of the applications, and the process of data in the cloud is increasing over time. This implies high energy consumption, thus contributing to large emissions of CO2 gas. For this reason, solutions are needed to minimize this power consumption, such as virtualization, migration, consolidation, and efficient traffic-aware virtual machine scheduling. In this article, the authors propose two efficient strategies for VM scheduling. SchedCT approach is based on dynamic CPU utilization and temperature thresholds. SchedCR approach takes into consideration dynamic CPU utilization, RAM capacity, and temperature thresholds. These approaches have efficiently decreased the energy consumption of the data centers, the number of VM migrations, and SLA violations, and this reduces, therefore, the emission of CO2 gas.
Security and trust in cloud application life-cycle management

Cloud technologies are currently experiencing a remarkable degree of pervasiveness in most of the applications and services that are consumed daily by both individual users and companies. The sheer volume of sensitive data and operations that are regularly outsourced to the cloud calls for the adoption of adequate measures to fulfill the existing security requirements and to increase the trust into cloud providers. The papers included in this special issue address a variety of concerns related to the development of secure, reliable, and trusted cloud applications, including: enforcement of specific security mechanisms (e.g., to protect shared data), integrity verification of virtualized environments, and selection of the most appropriate cloud services/resources for application deployment.
Application of Acupuncture Acupoint Visual Teaching System by Neural Regulation

This thesis uses the traditional Chinese medicine theory as its background. This article analyzes in detail the visual teaching system for acupuncture on the human body to solve the problems of insufficient teaching resources and waste of corpses used in teaching. The dissertation is based on the mobile platform development technology of the Android operating system. Based on this, researchers have developed a visual teaching system for human acupuncture points based on digital platforms and mobile devices. This system presents acupuncture knowledge with mobile learning method. It can be displayed systematically on the selected human acupoint anatomy teaching model for 14 common clinical conditions. This article provides an intuitive and dynamic teaching method by presenting a system should realize modern digital teaching and application of traditional acupuncture theory.
Multi-Step Reinforcement Learning for Single Image Super-Resolution

Deep Learning (DL) has become prevalent in today's image processing research due to its power and versatility. It has dominated the Single Image Super-Resolution (SISR) field with its ability to obtain High-Resolution (HR) images from their Low-Resolution (LR) counterparts, particularly using Generative Adversarial Networks (GANs). Interest in SISR comes from its potential to increase the performance of supplementary image processing tasks such as object detection, localization, and classification. This research applies a multi-agent Reinforcement Learning (RL) algorithm to SISR, creating an advanced ensemble approach for combining powerful GANs. In our implementation each agent chooses a particular action from a fixed action set comprised of results from existing GAN SISR algorithms to update its pixel values. The pixel-wise or patch-wise arrangement of agents and rewards encourages the algorithm to learn a strategy to increase the resolution of an image by choosing the best pixel values from each option.