Reviewerly/exHarmony huggingface dataset:
Image encryption based on Kronecker inner product over finite fields and adversarial neural network

Abstract An image encryption algorithm based on Kronecker inner product matrix over the finite field and adversarial neural network (ANN) is designed. The Kronecker inner product matrix transform and the ANN fulfil the tasks of confusion and diffusion simultaneously. In addition, the look-up table method is used to complete the addition and multiplication operations over GF (2 B ) finite fields, which can effectively improve the finite field computation speed while retaining its performance of non rounding errors. At the same time, relying on the secure hash function SHA-256 of the plain image to control the Logistic-Sine map greatly improves the diffusion and security of the encryption system. The simulation results verify that the proposed algorithm not only has high security and sufficient sensitivity, but also has a good resistance to various common attacks.
A model of Cyber Threat Information Sharing with the Novel Network Topology

The digitized environments are particularly vulnerable to various attacks. In such a situation of a security attack, detecting and responding to attacks require effective actions. One of the most significant ways to improve resilience to security attacks is to obtain accurate and timely situational aspect of the security awareness. The efficient production and utilization of situation information is achieved by sharing information with other actors in the information sharing network quickly and reliably without compromising the confidential information of one's own organization. At the same time, it should also be possible to avoid a flood of irrelevant information in the sharing network, which wastes resources and slows down the implementation of security measures. In our study, we have investigated how security-related information can be shared online as efficiently as possible by building a security information sharing topology based on the two most widely used network optimization algorithms. In the article, we present a model of an information sharing network, in which three different parameters have been used to optimize the network topology: the activity level of organization, the similarity of information systems between different actors and the requirement for the level of information privacy generally in the organization.
A Primary Analysis of the Role of Intellectual Media Development in Promoting City Image Communication

Artificial intelligence, big data, VR and other technologies have promoted the intelligent development of media. In the context of intellectual media, intelligent algorithm distribution solves the correlation between audience and content; VR/AR and other technologies provide technical support for the “sense of presence” of city image communication. Intelligent software creates the conditions for content production to be convenient and low standard. These new changes will form the three-dimensional and multi-dimensional communication of city image between offline and online, between real and virtual, and open a new paradigm of city image communication.
Using Real-Pen Specific Features of Active Stylus to Cope with Input Latency

Despite the growing quality of touch screen mobile devices, most of them still suffer from input latency. This paper presents a new way to cope with this problem for users who use an active stylus to perform pointing tasks. It is based on the usage of real-pen specific features of an active stylus that can be obtained without any additional wearables. To assess latency compensation that uses orientation, tilt, and pressure values, two studies are conducted. The first study shows that using these features decreases prediction error by 2.5%, improves the distribution of deviation angle from the target direction, and has a good ability to reduce lateness and wrong orientation side-effect metrics by 9.4% and 3.3%, respectively. The second study reveals that users perceive fewer visual side-effects with latency compensation using real-pen specific features of the active stylus. The obtained results prove the effectiveness of utilizing orientation, tilt, and pressure to cope with the latency compensation problem.
Use of Zip Code Based Aggregate Indicators to Assess Race Disparities in COVID-19

In the first six months of the pandemic, information on race and ethnicity was missing for half of the US COVID-19 cases. Combining case ascertainment with census-based zip code indicators may identify COVID-19 race-ethnicity disparities in the absence of individual-level data.Ecological retrospective study for the period March-July 2020.Population-based investigation, Allegheny County, Pennsylvania.All COVID-19 cases, adjusted for zip code area population, in the early period of the pandemic.Monthly COVID-19 incidence and requests for human services by zip code level indicators of race-ethnicity and poverty.In the early period of the pandemic, COVID-19 incidence was higher in zip codes with a greater proportion of racial and ethnic minorities. Zip codes with the highest quartile of minority residents (>25.1% of population) had a COVID-19 incidence of 60.1 (95% CI: 51.7-68.5) per 10,000 in this period; zip codes with the lowest quartile of minority residents (<6.3%) had an incidence of 31.3 (95% CI: 14.4-48.2). Requests for human services during this period (volume of 211 calls and county services) confirm these disparities.Use of census-defined race-ethnicity proportions by zip code offers a way to identify disparities when individual race-ethnicity data are unavailable.
Intellectual property law: In the hands of artificial creator

Review of a book: Lee, J-.A., Hilty, R., &amp; Liu, K-.C. (Eds.). (2021). Artiﬁcial intelligence and intellectual property. Oxford University Press. https://doi.org/10.1093/oso/9780198870944.001.0001
Research on the Application of Maritime Combat Data Based on Big Data

In order to better promote the application of big data technology in the sea battlefield command information system, this paper first analyzes the concept and characteristics of big data, analyzes the technical system of big data, then deeply analyzes the characteristics of big data in the sea battlefield, combs the key technologies needed for the application of big data in the sea battlefield command information system, and finally puts forward the sea data application processing based on big data The process framework can improve the decision-making efficiency of commanders.
Redundancy Removal Method for Multi-Document Query-Based Text Summarization

We present RedunWSD, a Word Sense Disambiguation (WSD) based redundancy removal method for multiple text documents query-based text summarization. Recognizing and identifying the redundancy from the text documents is a challenging task that has not been fully resolved yet. In the case of multiple text document summarization processes, redundancy removal helps in getting maximum content coverage by minimizing any redundant or repetitive sentences. Another novel contribution of our work is the disambiguating a word's sense, since it helps us in getting accurate semantic similarity score between two sentences. Along with the semantic similarity score, we also consider the order of words between two sentences. Our model has the additional advantage of detecting the redundant sentences based on its meaning. Our experimental results show that the proposed WSD method outperforms many existing and current WSD methods. We have evaluated the proposed RedunWSD method on Document Understanding Conference (DUC) benchmark datasets and show that it helps in getting better recall values than many top existing query-based text summarization methods.
A Study of Resource Management and Security-Based Techniques in Autonomic Cloud Computing

The predominant goal of autonomic computing is to manage the computer networks and software systems using some adaptive technologies to implement the services efficiently. Such a computing model becomes very useful in the networking scenarios, where a variety of services are offered to a wide range of audience with different user specifications such as cloud. Autonomic cloud computing (ACC) allows the cloud provider to manage the cloud services intelligently in an autonomous way with little or no human intervention. But, from implementation point of view, ACC faces numerous challenges such as availability, reliability, integration, security, interoperability, and resource management. Based on the literature study, it has been found that management and availability of cloud computing resources are the two major challenges. This chapter firstly offers a review of the resource management (provisioning and scheduling) techniques in an ACC environment. Secondly, the security-based methods for resource management to achieve the availability are discussed. In addition to this, an analysis of the relevant solutions in each category is also provided.
Stereoscopic image quality assessment considering visual mechanism and multi-loss constraints

In this paper, a convolutional neural network (CNN) with multi-loss constraints is designed for stereoscopic image quality assessment (SIQA). A stereoscopic image not only contains monocular information, but also provides binocular information which is as identically crucial as the former. So we take the image patches of left-view images, right-view images and the difference images as the inputs of the network to utilize monocular information and binocular information. Moreover, we propose a method to obtain proxy label of each image patch. It preserves the quality difference between different regions and views. In addition, the multiple loss functions with adaptive loss weights are introduced in the network, which consider both local features and global features and constrain the feature learning from multiple perspectives. And the adaptive loss weights also make the multi-loss CNN more flexible. The experimental results on four public SIQA databases show that the proposed method is superior to other existing SIQA methods with state-of-the-art performance.
UAS Flight Path Planning using Numerical Potential Fields in Dense Non-segregated Airspace

This paper addresses the problem of Urban aerial mobility (UAM), which refers to an air transportation system wherein unmanned aircraft systems (UAS's) are utilized to deliver small payloads on-demand by safely maneuvering in the known urban infrastructure occupied by dynamic air traffic. A multilevel framework is proposed that provides a solution for UAM in the non-segregated airspace. The proposed method takes advantage of a prediction-based zone partitioning algorithm that minimizes high-density traffic encounters. Numerical potential fields are used for global path planning as they provide optimum standoff clearance from obstacles and high-density traffic present in the environment. In the event of low-density traffic, a simple but effective octant-based local collision avoidance strategy is used.
DVQShare: An Analytics System for DNN-based Video Queries

Applying deep neural networks (DNNs) to video analytics tasks has drawn attention from both academic and industry communities. However, due to the high computational complexity of DNN models and the explosion of video data, it is challenging to process massive concurrent video queries efficiently and effectively. In this paper, we propose a video analytics system named DVQShare to process DNN-based video queries in a batch mode. The key idea is sharing, including time sharing, spatial sharing, and logical sharing. In principle, sharing across queries can help us reduce the overall amount of frames to be analyzed, which can help us improve the overall performance and reduce the monetary cost. Two modules are designed to process video queries by exploiting the above three sharing opportunities. First, an analysis module is integrated to guide the generation of query processing plans. Within this module, temporal sharing is considered to reuse historical results produced by other queries to remove pending frames that have been analyzed, and spatial sharing is adopted to avoid redundant processing over overlapping video clips. Additionally, we utilize logical sharing to further improve system's overall performance by considering the logical relationship between queries. Second, a query processing engine is devised to execute the query pipeline generated by the analysis module and return the final results. In experiments, we implement a prototype of the DVQShare system based on MXNet, and results show that it can achieve up to 2X performance speedup.
Image Classification Between Two Animals

We are going to examine the fine-grained object categorization problem of identifying the breed of animal from a picture. To this end we introduce a replacement annotated dataset of pets covering 37 different breeds of cats and dogs. The visual problem is extremely challenging for the cat and dog, particularly cats, are very deformable and there are often exactly subtle differences between their breeds. We make variety of contributions: we first introduce a model to classify cat and dog breed automatically from a picture. The model adding the shape of the pet animals, captured by a deformable part model detecting the cat and dog face, and appearance, captured by a bag-of-words model that describes the pet fur. Fitting the model involves automatically segmenting the cat and dog within the image. Second, we compare two classification approaches: a hierarchical one, during which a pet animal is first assigned to the cat or dog family then to a breed, and a flat one, during which the breed is obtained directly.
Research on Multi-sensor Information Fusion Algorithm Based on SCI-AUKF

For multi-sensor network systems with uncertain noise variances, traditional fusion estimation algorithms need to optimize multi-dimensional nonlinear cost functions, resulting in greater computational complexity. This paper proposes a fast sequential covariance cross-fusion adaptive unscented Kalman filter algorithm (SCI-AUKF), which mainly solves the optimization problem of multiple one-dimensional nonlinear cost functions. It is a recursive two-sensor filter. Its accuracy is higher than that of local estimators, and it will effectively solve the problem of state estimation under uncertain noise variance. Through simulation, the filtering method is compared with the other filtering methods, and its accuracy is significantly improved. An application example of the algorithm in radar nets is given, which shows the superiority and effectiveness of the SCI-AUKF filtering method.
Multi-Scale Semantic Transfer for Person Image Generation

Pose transfer is a task of transferring the given person pose image to the target pose image. There are mainly two problems of inaccurate generated pose and the texture dissimilarity between generated pose and target pose in the previous work. We propose the multi-scale semantic transfer network(MSTN) to generate the target pose image. It contains the semantic parsing generation module(SPGM) and multi-scale semantic transfer module(MSTM). The semantic parsing generation module(SPGM) generates target human mask with semantic information to improve structural similarity. The multi-scale semantic transfer module(MSTM) transfers pose both in high dimension and low dimension. Then it merges the information of different scales to improve texture similarity. Compared with the previous work, the posture of person and the texture such as face, clothes and hair in the images generated by our networks are more similar with the target pose image
Resource clustering algorithm for cloud data centers

Recently, the virtual machine deployment algorithm uses physical machine less or consumes higher energy in data centers, resulting in declined service quality of cloud data centers or rising operational costs, which leads to a decrease in cloud service provider’s earnings finally. According to this situation, a resource clustering algorithm for cloud data centers is proposed. This algorithm systematically analyzes the cloud data center model and physical machine’s use ratio, establishes the dynamic resource clustering rules through k-means clustering algorithm, and deploys the virtual machines based on clustering results, so as to promote the use ratio of physical machine and bring down energy consumption in cloud data centers. The experimental results indicate that, regarding the compute-intensive virtual machines in cloud data centers, compared to contrast algorithm, the physical machine’s use ratio of this algorithm is improved by 12% on average, and its energy consumption in cloud data center is lowered by 15% on average. Regarding the general-purpose virtual machines in cloud data center, compared to contrast algorithm, the physical machine’s use ratio is improved by 14% on average, and its energy consumption in cloud data centers is lowered by 12% on average. Above results demonstrate that this method shows a good effect in the resource management of cloud data centers, which may provide reference to some extent.
A modified expectation‐maximization algorithm for latent Gaussian graphical model

This paper considers the latent Gaussian graphical model, which extends the Gaussian graphical model to handle discrete data as well as mixed data with both continuous and discrete variables by assuming that discrete variables are generated by discretizing latent Gaussian variables. We propose a modified expectation‐maximization (EM) algorithm to estimate parameters in the latent Gaussian model for binary data. We also extend the proposed modified EM algorithm to the latent Gaussian model for mixed data. The conditional dependence structure can be consequently constructed by exploring the sparsity pattern of the precision matrix of the latent variables. We illustrate the performance of our proposed estimator through comprehensive numerical studies and an application to voting data of the United Nations General Assembly.
A comparison of attentional neural network architectures for modeling with electronic medical records

Attention networks learn an intelligent weighted averaging mechanism over a series of entities, providing increases to both performance and interpretability. In this article, we propose a novel time-aware transformer-based network and compare it to another leading model with similar characteristics. We also decompose model performance along several critical axes and examine which features contribute most to our model's performance.Using data sets representing patient records obtained between 2017 and 2019 by the Kaiser Permanente Mid-Atlantic States medical system, we construct four attentional models with varying levels of complexity on two targets (patient mortality and hospitalization). We examine how incorporating transfer learning and demographic features contribute to model success. We also test the performance of a model proposed in recent medical modeling literature. We compare these models with out-of-sample data using the area under the receiver-operator characteristic (AUROC) curve and average precision as measures of performance. We also analyze the attentional weights assigned by these models to patient diagnoses.We found that our model significantly outperformed the alternative on a mortality prediction task (91.96% AUROC against 73.82% AUROC). Our model also outperformed on the hospitalization task, although the models were significantly more competitive in that space (82.41% AUROC against 80.33% AUROC). Furthermore, we found that demographic features and transfer learning features which are frequently omitted from new models proposed in the EMR modeling space contributed significantly to the success of our model.We proposed an original construction of deep learning electronic medical record models which achieved very strong performance. We found that our unique model construction outperformed on several tasks in comparison to a leading literature alternative, even when input data was held constant between them. We obtained further improvements by incorporating several methods that are frequently overlooked in new model proposals, suggesting that it will be useful to explore these options further in the future.
Ensemble Maximum Likelihood Estimation Based Logistic MinMaxScaler Binary PSO for Feature Selection

High-dimensional data is a situation when there is a large number of dimensions compared to the number of samples. It becomes difficult to analyze the data and may give the wrong result of the prediction. Dimension reduction in used to solve this problem. Feature selection is one of the ways to apply dimension reduction. Feature selection is the method of selecting those features which help in prediction most or gives the desired output. Its performance is lacking in high-dimensional data and complex problems. It is found that most of the time, authors have given inertia weight as a solution and tried to set its value using various methods. But the answer still lacks the optimal value of parameters, hence learning-induced procedures are required to update them. Therefore, a method is proposed for the optimization of parameters of PSO for optimum feature selection. The proposed solution is given to solve these two objectives. First, to handle feature selection using PSO and get the PSO’s optimal parameters’ optimal values. The three main parameters are inertia weight, cognitive, and social constants. Ensemble maximum likelihood estimation based MinMaxScaler binary PSO is proposed to optimize the parameters of PSO for feature selection. In this, the Sigmoid function of binary PSO is replaced with MinMaxScaler function (Shaheen et al. in First international conference on sustainable technologies for computational intelligence. Springer, Singapore, pp 705–716, 2020 []), and parameter optimization is done using maximum likelihood estimation. The results of experiments performed on the dataset showed the comparable classification accuracy.
Training Utilizing Adobe After Effect In Advertising

Along with the development of the times and technology requires everyone to have creativity and skills in him. Creativity and skills in the field of technology are a unity that go hand in hand and are indispensable in the world of work. For this reason, it needs to be instilled from an early age in directing and developing the skills and creativity of a student. By providing direction and guidance to students from an early age, it will help students in terms of learning and competition in the world of work. Adobe After Effects is a skillful skill that is rarely owned by everyone and needs to be introduced to students to help them do a creativity that will later be appreciated by companies or universities. Where every company needs a creative person to help work in the fields of design, animation, and multimedia, as well as universities. Universities really need teachers who are experts in the multimedia field.
A Selection and Comparison of Several Algorithms Implemented in the Pymoo Library

In this paper, a selection and comparison of several algorithms implemented in the “pymoo” library have been utilized in the selection process of optimal Fuzzy Logic Controller (FLC) controlling a Differential Drive Wheeled Mobile Robot (DDWMR). The optimization of this FLC based on the minimum energy consumption criterion by using these algorithms has been implemented in the Google Colab® and Jupyter platforms using the Python language. The simulation results have shown that the NSGA-III_FLC has significantly better control performance than the others.