Reviewerly/exHarmony huggingface dataset:
Decomposition of Invariant and Variant Features by Using Convolutional Autoencoder

In our brain, the visual information captured by the retina is processed by the two different visual pathways known as ventral stream and the dorsal stream. The ventral stream known as the “what pathway” is involved with object and visual identification and recognition and the dorsal stream known as “where pathway” is involved with processing the object’s spatial location relative to the viewer. This means that the human brain decomposes the visual information captured by the retina into the invariant information and the variant information. In this paper, we propose two network architectures based on the convolutional autoencoder in order to decompose the input information into invariant information and variant information. The decomposition of variant and invariant information is realized by decomposing the feature vectors obtained in the hidden layer of the convolutional autoencoder. To extract invariant information, the classifier or the estimator of the invariant image are combined with the convolutional autoencoder. Effectiveness of the proposed architectures are experimentally confirmed by visualizing the extracted feature vectors.
Optimization of the 3p keys kernel parameters by minimizing the ripple of the spectral characteristic

The ideal interpolation kernel is described by the sinc function, and its spectral characteristic is the box function. Due to the infinite length of the ideal kernel, it is not achievable. Therefore, convolutional interpolation kernels of finite length, which should better approximate the ideal kernel in a specified interval, are formed. The approximation function should have a small numerical complexity, so as to reduce the interpolation execution time. In the scientific literature, great attention is paid to the polynomial kernel of the third order. However, the time and spectral characteristic of the third-order polynomial kernels differs significantly from the shape of the ideal kernel. Therefore, the accuracy of cubic interpolation is lower. By optimizing the kernel parameters, it is possible to better approximate the ideal kernel. This will increase the accuracy of the interpolation. The first part of the paper describes a three-parameter (3P) Keys interpolation kernel, r. After that, the algorithm for optimizing the parameters of the 3P Keys kernel, is shown. First, the kernel is disassembled into components, and then, over each kernel component, Fourier transform is applied. In this way the spectral characteristic of the 3P Keys kernel, H, was determined. Then the spectral characteristic was developed in the Taylor series, HT. With the condition for the elimination of the members of the Taylor series, which greatly affect the ripple of the spectral characteristic, the optimal kernel parameters (?opt, ?opt, gopt) were determined. The second part of the paper describes an experiment, in which the interpolation accuracy of the 3P Keys kernel, was tested. Parametric cubic convolution (PCC) interpolation, with the 3P kernel, was performed over the images from the Test database. The Test database is created with standard Test images, which are intensively used in Digital Image Processing. By analyzing the interpolation error, which is represented by the Mean Square Error, MSE, the accuracy of the interpolation was determined. The results (?opt, ?opt, gopt, MSEmin) are presented on tables and graphs. Detailed comparative analysis showed higher interpolation accuracy with the proposed 3P Keys interpolation kernel, compared to the interpolation accuracy with, 1P Keys and 2P Keys interpolation kernels. Finally, the numerical values of the optimal kernel parameters, which are determined by the optimization algorithm proposed in this paper, were experimentally verified.
Echo State Networks in Data Marketplaces for Digital Content Creation

Data marketplaces are the digital platform for data buyers and data sellers to trade information as valuable products or items. The expectation taken for granted from the users of a data marketplace is the truth of the exchanged information. However, the trade of factual data also means the marketable product is no longer unique but a series of replicas. If every user within the data marketplace owns the same information, this data eventually becomes valueless. There are specific instances where the traded products are sought to be always unique, such as predictions or digital art. This paper presents Echo State Networks (ESNs) in data marketplaces that map tradeable data into higher dimensional spaces via the dynamics of a fixed and non-linear reservoir. The reservoir generates unique tradeable data products that can not be replicated, therefore ensuring its exclusivity and commercial value. The validation results show that ESNs can also be applied to generate random tradeable products on different dimensional spaces. Specifically, the reservoir with its associated neural perturbation emulates a digital artist that generates unique and exclusive content based on 2D functions and 3D images.
Model Building for better Transfer of AI Systems using Reinforcement Learning from Simulation to the Physical World

Autonomous product service systems (PSS) provide services by autonomously controlling products to deliver value. This requires powerful intelligences embedded in products. The development of powerful intelligences can be carried out by means of reinforcement learning in simulations, in which products act as agents and independently learn optimal behaviors via trial-and-error procedures. These simulations can refer to different system levels (e.g. car as an agent in a city, microcontroller as an agent in a car) and can vary in terms of visualization (e.g. 3D, 2D, block diagrams, no visualization). However, they always involve an agent, that can choose from a set of possible actions that cause changes to the environment, which can be evaluated against an objective based on a reward function, that needs to be defined by engineers before. Within this process, a neural network gradually learns an optimal behavioral model through a reinforcement learning algorithm that makes use of the data on selected actions by the agent, outcomes and yielded reward. However, the models required for the agent, environment, artificial intelligence (e.g. deep neural networks), reinforcement learning algorithm and reward function are highly interdependent and their design can have a significant influence on the training result. When training results are poor, rapid adaptation of these models is thereby desirable to improve the training process, but difficult to implement due to the interdependencies between models. In this paper, we discuss the challenges of model building for reinforcement learning in simulation and propose a general approach.
Using AI for Designing ID cards Embedded with Invisible QR code

Organizations strongly encourage the usage of Identification cards (ID cards) as a measure of security. ID cards are used to reliably identify a person and, as a result, serve to improve the safeness of an organization by preventing unauthorized individuals from gaining access to the premises. However, these ID cards contain the user’s personal information, which must be protected in today’s world. This paper proposes an approach for obtaining a data hiding mechanism for personal information on ID cards by using user data to develop a QR (Quick Response) code and embedding that QR code into the user image, making the QR code invisible to the naked eye. QR codes are wellknown for their error-correcting mechanism and are particularly good in hiding arbitrary information. Convolution neural networks (CNN) are a class of Deep neural networks that helps us analyze visual imagery and recognize patterns. The objective of this paper is to use the CNN mechanism to hide a QR code in a user image. The proposed model is comprised of two CNNs (encoder CNN and decoder CNN). The encoder CNN’s function will embed the QR code into the user image and generate an output image that appears just like the user image. The decoder CNN’s role will be to take the encoder CNN’s output as input and generate the embedded QR code image as output. By ensuring enhanced security and hiding critical information, our approach prohibits the duplication of the general QR code, preventing its misuse.
Network Security Issues in Universities and Exploration of Defense System

Campus network is an important infrastructure of colleges and universities, and it plays an important role in university teaching, research, management and external communication. As the demand for campus networks continues to grow and network users continue to grow, some security problems have emerged one by one, which sometimes seriously hinders the normal operation of campus networks. The main purpose of this article is to study the research and protection system of network security in colleges and universities. Based on the current situation of school network security, this paper analyzes the hidden dangers of network security, and establishes a security protection system that is compatible with the reality of the school campus network. Based on the actual situation of the school campus network, this article focuses on the current implementation of some technologies, such as secure password verification technology, firewall technology and intrusion detection technology, and conducts detailed research on the problems to be solved and verifies the results. This article focuses on firewall technology, intrusion detection, and encryption and decryption technologies to build a platform for managing network security system operations. Research experiments show that Sophos’s detection rate of Trojan horse viruses reached 77.4%, and Av’s detection rate of the virus reached 73.4%. After the implementation of the network security strategy, hacker attacks can be avoided scientifically and efficiently.
An Online Design Framework for Interactive Genetic Algorithms/IGAOD

In order to prompt the theoretical research and application of interactive genetic algorithm (IGA), the framework for IGAOD (Interactive Genetic Algorithm Online Design) is developed from the perspective of software reuse and the commonness and individuality of IGA. This framework encapsulates the basic commonality of IGA, integrates some classic evolution operators of IGA, and provides visual application scenarios and data analysis charts to help users quickly build their own IGA systems. In addition, the template design pattern can be used to construct the genetic algorithm library, which encapsulates the commonness of a series of selection,crossover and mutation operators into corresponding abstract base classes. These classes inherit the identical abstract base class, making it easier for the algorithm to maintain and expand. In this article, the use of the frame is introduced with the example of 3D vase shape design and plate pattern design. Therefore, the framework is more suitable for the theoretical and application research of IGA.
A Scalable Density Based Clustering Method for Large Datasets with Noise

Density based clustering methods are widely used for clustering spatial data to identify arbitrary cluster shapes and isolate noise in the dataset. DBSCAN (Density Based Spatial Clustering of Applications with Noise) is a popular density clustering method which finds clusters by computing nearest neighbor points located in a threshold distance from a given point. Sampling based heuristics are proposed in the literature to improve the scalability of DBSCAN by producing approximate cluster results. Hierarchical index structures are utilized to improve the performance of DBSCAN by accelerating the neighbor search operations. However, the hierarchical index structures tend to perform poor with data dimension and also the performance depends on user supplied parameters. This paper presents a scalable density clustering method that minimize the distance computations involved during cluster expansion and accelerate the neighbor search operation using a graph index structure. Experiments are performed on thirteen popular datasets from UCI and LIBSVM repository to establish the efficacy of proposed method with state-of-the-art sampling and index based density clustering methods. The proposed method outperforms sampling based density clustering methods in terms of both running time and accuracy of cluster results.
An Early Warning System for Corporate Information Disclosure Violations Based on Association Rule Algorithm

In recent years, the rapid development of my country’s financial enterprises has attracted the attention of many investors and financiers. However, due to the opacity of information among enterprises, such enterprises have encountered problems one after another in many aspects, and negative public opinion has impacted the entire industry. Although the regulatory authorities have issued many relevant policies to regulate, the problem of corporate information disclosure violations is still very serious. Therefore, designing and developing a set of enterprise information disclosure violation warning (Information Disclosure Violation Warning, IDVW) system is of great significance to the development of the industry. This article first uses association rule algorithm to mine and analyze the enterprise IDVW data, and then designs the enterprise IDVW system, which mainly explains the design and realization of key functions in the information disclosure system, including the information disclosure module and the violation warning module. In order to verify the effectiveness of the system, this paper collects information from companies with normal information disclosure and companies with violations of information disclosure to bring it into the early warning system test, and the results are obtained. In the training sample and the test sample, the overall identification success rate of the enterprise IDVW system is 85.99%, 85.72%. This shows that the prediction effect of the enterprise IDVW system constructed in this paper is better.
Transforming Cultural Heritage—A Digital Humanity Perspective with Virtual Reality

The study aims to explore a virtual reality (VR) approach representing a local history in the sense of preserving cultural heritage. Two 400-year fort cities, Tamsui and Keelung, located in northern Taiwan, were chosen and transformed into digital fort cities by the researchers from the fields of history, information sciences, and computer science. The researchers invited participants to the VR lab and experience a VR tour. Research tools used for collecting user data include before and after attitude scales and a post-task interview for each participants. The findings of the research revealed that the senses of joy, presence, and control could critically impact VR user experience. The design of the content needed to be more informative and interactive. Affordances in the immersive environment were key for the user to experience a smooth and positive user journey with less pain points. For future work, the next round of data collection will be carried out. Data will be compared and contrasted further to see the differences.
Fine-Grained I/O Traffic Control Middleware for I/O Fairness in Virtualized System

The development of IT technology in the 21st century has created a new paradigm for real-time, data-intensive user services, such as connected cars, smart factories, and remote health care services. The considerable computational resources required by these services are rendering the cloud increasingly more important. In the cloud server, user services are forced to share physical resources because of the emerging resource competition, thus introducing various types of unpredictable workloads. The core technology of the cloud is a virtualized system, which isolates and shares the powerful physical resources of the server in the form of a virtual machine (VM) to increase resource efficiency. However, the scheduling policy of a virtual CPU (vCPU), which is a logical CPU of a VM, generally schedules the vCPU based on the degree of occupation of the physical CPU (pCPU) without regarding I/O strength; so it brings the unfair I/O performance among VMs in the virtualized systems. The user services performing on the VM are not aware of the user-contention architectures, which sharing of I/O devices, in the virtualized systems; Furthermore, the current virtualized system simply adopts the Linux-based I/O processing process which optimized for user-contention-free architectures. Therefore, the architecture that brings the unfair usage of I/O devices among user services is hardly regarded and has low awareness in current virtualized systems. To overcome this problem, in this study, I-Balancer is presented to provide fair I/O performance among I/O-intensive user services by applying an asynchronous inter-communication control technique for the virtualized system with a high VM density. The main design goal of I-Balancer is to increase the awareness of user-contention architectures in the hypervisor. I-Balancer derives the fine-grained workload and I/O strength for each vCPU during the scheduler and event channel areas. Subsequently, to strengthen fair I/O performance, an I/O traffic control mechanism is implemented to control the inter-domain communication traffic according to the I/O strength of the VMs. The experimental results prove that I-Balancer provides a fairer network and disk I/O performance than the existing virtualized systems with negligible performance interference and overhead.
Ilmdnacnn: Intertwining Logistic Map and DNA Encoding Based Image Encryption Using Cnn

In this study, a novel, more stable & secured and more reliable image encryption model has been introduced. It uses both a Convolutional Neural Network (ConvNet/CNN) model and intertwining logistic map to generate secret keys. Further, initial conditions, control parameters and secret keys are used by intertwining logistic map to generate diverse chaotic sequences. Processes like permutation, DNA encoding, diffusion and bit reversion operations are used for scrambling and manipulation of image pixels. The proposed encryption model was examined precisely using various analysis methods like cropping attack, histogram, key space, noise attack, information entropy, differential attack, key sensitivity and correlation coefficient. Further, numerical and perceptible results were compared with the available state of art outcomes to validate the model. Finally, one can notice from the derived results that the proposed intertwining logistic map based image encryption models using Convolutional Neural
A Polarization Identification Method of Full Polarization Phased Array Radar and Active Decoy

Aiming at the problem of too few identification methods for phased array radar and active decoys and the low recognition rate, this paper proposes a polarization identification algorithm combining polarization descriptor and Multivariate Long Short Term Memory Fully Convolutional Network (MLSTM-FCN) neural network. Firstly, this paper realizes the accurate modeling of fully polarized phased array radar and active decoys in detail, and analyzes the polarization characteristics in spatial domain. The polarization characteristics of full polarization phased array radar and active decoy are distributed differently in the same spatial domain. The sample datasets of polarization descriptors of polarized phased array radar and active decoys in spatial domain is obtained. Finally, we train the recognition model with MLSTM-FCN neural network. The simulation results show that the recognition rate is above 92% at a low signal-to-noise ratio (SNR) of 7 dB.
Improved Particle Swarm Optimization With Spiral Updating Mechanism for Multimodal Image Registration

A modified particle swarm optimization (PSO) algorithm is proposed with a spiral updating mechanism (Spiral-PSO) to optimize the balance between exploration and exploitation phases of the standard PSO. The Spiral-PSO approach reduces the possibility of the local optima trapping issue during the optimization process. The proposed algorithm has been tested with 23 standard benchmark functions to compare the Spiral-PSO with the standard PSO algorithm. We report the application of the Spiral-PSO for registration of multimodal remote sensing images and compared the registration results with that of the standard PSO. The proposed approach performs successful image registration and provide better optimization of objective functions as compared to conventional PSO.
A Power Grid Equipment Fault Prediction Model Based on Faster RCNN and Video Streaming

With the increasing proportion of power equipment in human life and production, the safe and stable operation of power equipment is related to the people's livelihood and national economy, and it is also very important to the sustainable development of the power industry. Grid equipment in long-term operation condition, the equipment of continuous monitoring and forecasting can guarantee the health of the whole grid system is running, the traditional way of artificial detection disadvantages such as low efficiency, high cost and difficult, gradually are replaced by the method of artificial intelligence. At present, power equipment detections mainly include two categories: image detection and video detection. Considering the real-time and image integrity of video stream detection, this article is based on the Faster RCNN model, using VGG16 network and ResNet50 network to detect video streams respectively, and comparing the efficiency and accuracy between them. According to the test results of the collected power grid video dataset, the performance of Resnet50 network is better than VGG16 network in terms of accuracy and recall rate, but VGG16 has more advantages in terms of processing speed.
On-Device Language Detection and Classification of Extreme Short Text from Calendar Titles Across Languages

Smartphones have become indispensable part of day-to-day human life. These devices provide rapid access to digital calendars enabling users to schedule their personal and professional activities with short titles referred as event titles. Event titles provide valuable information for personalization of various services. However, very nature of the event titles to be short with only few words, pose a challenge to identify language and exact event the user is scheduling. Deployment of robust machine learning pipelines that can continuously learn from data on the server side is not feasible as the event titles represent private user data and raise significant concerns. To tackle this challenge, we propose a privacy preserving on-device solution namely Calendar Event Classifier (CEC) to classify calendar titles into a set of 22 event types grouped into 3 categories using the fastText library. Our language detection models with accuracies of 96%, outperform existing language detection tools by 20% and our event classifiers achieved 92%, 94%, 87% and 90% accuracies across, English, Korean and German, French respectively. Currently tested CEC module architecture delivers the fastest (4 ms/event) predictions with <8 MB memory footprint and cater multiple personalization services. Taken together, we present the need for customization of machine learning models for language detection and information extraction from extremely short text documents such as calendar titles.