Reviewerly/exHarmony huggingface dataset:
Enhanced Saliency Prediction via Orientation Selectivity

Saliency prediction can be treated as the activity of the human visual system (HVS). The most effective method should highly approximate the response of HVS to the perceived information. Motivated by that orientation selectivity (OS) mechanism occuring in primary visual cortex (PVC) tells us how the HVS extracts visual information for scene understanding, we propose a novel saliency model by combining an orientation selectivity based local feature called "excitement" map and a visual acuity based global feature called "acuity" map. Further, a saliency augmented operator based on visual error sensitivity is designed to enhance the saliency map. Experimental results on three benchmark databases demonstrate the superior performance of the proposed method compared to ten classical/ state-of-the-art algorithms.
A Framework for the Analysis of Deep Neural Networks in Autonomous Aerospace Applications using Bayesian Statistics

Deep Neural Networks (DNNs) are considered to be key components in many autonomous systems. Applications range from vision-based obstacle avoidance to intelligent/learning control and planning. Safety-critical applications as found in the aerospace domain require that the behavior of the DNN is validated and tested rigorously for safety of the autonomous system (AUS). In this paper, we present a framework to support testing of DNNs and the analysis of the network structure. Our framework employs techniques from statistical modeling and active learning to effectively generate test cases for DNN safety testing and performance analysis. We will present results of a case study on a physics-based Deep recurrent residual neural network (DR-RNN), which has been trained to emulate the aerodynamics behavior of a fixed-wing aircraft.
DEN: Disentanglement and Enhancement Networks for Low Illumination Images

Though learning-based low-light enhancement methods have achieved significant success, existing methods are still sensitive to noise and unnatural appearance. The problems may come from the lack of structural awareness and the confusion between noise and texture. Thus, we present a low-light image enhancement method that consists of an image disentanglement network and an illumination boosting network. The disentanglement network is first used to decompose the input image into image details and image illumination. The extracted illumination part then goes through a multi-branch enhancement network designed to improve the dynamic range of the image. The multi-branch network extracts multi-level image features and enhances them via numerous subnets. These enhanced features are then fused to generate the enhanced illumination part. Finally, the denoised image details and the enhanced illumination are entangled to produce the normal-light image. Experimental results show that our method can produce visually pleasing images in many public datasets.
DESIGN AND SYMBOLIC CONSUMPTION

This paper addresses the mechanism of symbolic consumption in modern culture as interaction of communicative and industrial design. Product design means creating a form of functionality that meets market requirements. Packaging, labeling and advertising immerse a produced object into the cultural context with imparts the associations with progress and the myths about commodity-based happiness to it. Brands are a semiotic system that connects the values of objects to market segments. A certain class of produced objects, machines and constructions receive the role of status markers for the owner who identifies himself through such object‐signs. Industrial and communicative design stimulates sales, stratifies and segments the consumer market. Marketing dictates the design objectives, and the emerging exchange value depends on the use value of objects and the saturation of the market with both low-end and high-end products.
An Organizational Structure for the Thing-User Community Formation

The special feature of the thing-user centric communication is that thing-users can form a society autonomously and collaborate to solve problems. To share experiences and knowledge, thing-users form, join, and leave communities. The thing-user, who needs a help from other thing-users to accomplish a mission, searches thing-user communities and nominates thing-users of the discovered communities to organize a collaborative work group. Thing-user community should perform autonomously the social construction process and need principles and procedures for the community formation and collaboration within the thing-user communities. This paper defines thing-user communities and proposes an organizational structure for the thing-user community formation.
Camera Based Presentation Support System for Multiscreen Venue Using Laser Pointer

During a presentation, a presenter usually uses a laser pointer to point to a particular location on a screen. However, in a multiscreen venue displaying presentation slides, the presenter points to only one screen, and the pointed spot is not reflected to other screens that audiences looking at. We propose a simple method that reflects a highlighting operation with a laser pointer to other screens without specific devices. It is expected that all audiences can understand the presentation even in a large venue.
Elixir: A 128-bit Stream Cipher Protocol for Lightweight IoT Devices

Over time, the Internet of Things (loT) has turned out to be most prevalent in the field of research and its applications. However, IoT devices are resource constraints and thus, require lightweight and secure ciphering protocols. This paper proposes a new lightweight stream cipher algorithm named as `Elixer', based upon classical 128-bit non-linear feedback shift register technique. This stream cipher is based on static and dynamic substitution-boxes with shift operations and results in an output keystream. Confusion and diffusion properties are added with the help of substitution and transposition techniques respectively. In the core design of Elixir, there are eight static 6 x 8 s-boxes and two dynamic s-boxes. The correctness of the proposed stream cipher is tested according to NIST randomness standards and Stream tests. Elixir is designed to be implemented easily and efficiently for both Hardware and Software implementations.
Explaining probabilistic Artificial Intelligence (AI) models by discretizing Deep Neural Networks

Artificial Intelligence (AI) models can learn from data and make decisions without any human intervention. However, the deployment of such models is challenging and risky because we do not know how the internal decisionmaking is happening in these models. Especially, the high-risk decisions such as medical diagnosis or automated navigation demand explainability and verification of the decision making process in AI algorithms. This research paper aims to explain Artificial Intelligence (AI) models by discretizing the black-box process model of deep neural networks using partial differential equations. The PDEs based deterministic models would minimize the time and computational cost of the decision-making process and reduce the chances of uncertainty that make the prediction more trustworthy.
Clustering Alexa Internet Data using Auto Encoder Network and Affinity Propagation

Non-linear mapping is one of the most popular solutions for complex data structures and distinct patterns to cluster data. Auto encoder Networks (AENs) are widely used in clustering as they improve data representation. In this paper, we collect Alexa.com data by crawling popular websites profiles, where dataset has 84 columns with type number and array of words. Next, an AEN architecture is presented to identify specific websites with exceptional patterns and the encoded data expresses new feature space of our original data. (Our) Encoded data is clustered by Affinity Propagation which is a partitioning algorithm without the need for specifying the number of clusters. There are important results based on 194 clusters and exemplars which are filtered and analyzed. One remarkable fact about results is that the first 11 columns as raw data are not clustered by Affinity Propagation a which re considered all as outlier. The results are summarized by selecting the best option w.r.t. the statistics and charts. Some of the obtained results are useful for website owners and provide some suggestions and solutions for Search Engine Optimization (SEO). Finally, we propose our crawler application which crawls and records data over 4 days. It must be added that the proposed web crawler faces challenges and their solutions can be helpful in most commonly used web crawling algorithms and libraries.
Large families of permutations of Zn whose pairwise sums are permutations

Abstract We construct some large families of permutations of such that the sum of any two permutations from a family is again a permutation of (not necessarily in the same family). Our families are significantly larger than the largest such families known so far in the literature (depending on the value of , the improvement can be as large as exponential). We also show that our families are maximal in the sense that no permutations can be added to them while maintaining the required property.
Unsupervised Abnormality Detection Using Heterogenous Autonomous System

Due to the rise of autonomous vehicles like drones and cars anomaly detection for better and robust surveillance becomes prominent for real-time recognition of normal and abnormal states. But the whole system fails if the unmanned device is unable to detect its own device's anomaly in real-time. Considering the scenario, we can make use of various data of autonomous vehicles like images, video streams, and other digital or analog sensor data to detect device anomaly. In this paper, we have demonstrated a heterogeneous system that estimates the degree of an anomaly in unmanned surveillance drone by inspecting IMU (Inertial Measurement Unit) sensor data and real-time image in an unsupervised approach. We've used AngleNet for detecting images taken in an abnormal state. On top of that, an autoencoder fed by the IMU data has been ensembled with AngleNet for evaluating the final degree of the anomaly. This proposed method is based on the result of the IEEE SP Cup 2020 which achieved 97.3 percent accuracy on the provided dataset. Besides, this approach has been evaluated on an in-house setup for substantiating its robustness.
Web Query Reformulation Using FireFly Algorithm

Searching for information on the Web engages the user in a process of questioning for the choice of search engines. However, many Internet users suffer for the information choice which these search engines receive. On the other hand, if the queries do not express their needs or else their objectives, this implies that some information is not formulated, requiring the reformulation of these queries. In this paper, an approach of bio-inspired optimization based on the FireFly Algorithm is used to formulate the query by providing a new suggestion. This algorithm has been applied on the frequent item sets generated by FP-Growth (frequent-pattern Growth). Moreover, every user interaction with the search engine has been treated as a Firefly path. The algorithmic solution allows the user to select the best path (which contains key words) among all possible solutions for the initial query. Experimentally, we study the performance of the proposed method in comparison to different techniques (particle swarms optimization and genetic algorithms).
Recognition of Connection Relationship Based on the Spectrum Monitoring Data by Deep Convolutional Neural Networks

The connection relationship can reflect the hidden information in the network to a certain extent. Through the recognition of the connection relationship, it can infer its hierarchical position in the communication network, and then find the key nodes in the network and analyze the hidden information in the network. To overcome the obstacles of analyzing communication information from frequency hopping signal cracking, a method for recognition of connection relationships based on deep neural networks is proposed by this paper. Automatically learn features directly from simple spectrum monitoring data without the need to manually extract expert features. This paper implements the solution to the problem of imbalanced data categories of self-built data sets; proposes two design schemes based on different design goals and analyzes the performance of these two design schemes; attempts to use Convolutional Neural Networks (CNN) with different input lengths to realize the recognition, it is found that the input length affects the recognition result; the output of the neural network is corrected by the physical meaning so that the recognition result is better. The experimental results show that the CNN models are suitable for connection relationship recognition.
Backbone Based Feature Enhancement for Object Detection

FPN (Feature Pyramid Networks) and many of its variants have been widely used in state of the art object detectors and made remarkable progress in detection performance. However, almost all the architectures of feature pyramid are manually designed, which requires ad hoc design and prior knowledge. Meanwhile, existing methods focus on exploring more appropriate connections to generate features with strong semantics features from inherent pyramidal hierarchy of deep ConvNets (Convolutional Networks). In this paper, we propose a simple but effective approach, named BBFE (Backbone Based Feature Enhancement), to directly enhance the semantics of shallow features from backbone ConvNets. The proposed BBFE consists of two components: reusing backbone weight and personalized feature enhancement. We also proposed a fast version of BBFE, named Fast-BBFE, to achieve better trade-off between efficiency and accuracy. Without bells and whistles, our BBFE improves different baseline methods (both anchor-based and anchor-free) by a large margin (\(\sim \)2.0 points higher AP) on COCO, surpassing common feature pyramid networks including FPN and PANet.
Convergence of AI, ML, and DL for Enabling Smart Intelligence

The evolution of digital era and improvements in technology have enabled the growth of a number of devices and web applications leading to the unprecedented generation of huge data on a day-to-day basis from many applications such as industrial automation, social networking cites, healthcare units, smart grids, etc. Artificial intelligence acts as a viable solution for the efficient collection and analyses of the heterogeneous data in large volumes with reduced human effort at low time. Machine learning and deep learning subspaces of artificial intelligence are used for the achievement of smart intelligence in machines to make them intelligent based on learning from experience automatically. Machine learning and deep learning have become two of the most trending, groundbreaking technologies that enable autonomous operations and provide decision making support for data processing systems. The chapter investigates the importance of machine learning and deep learning algorithms in instilling intelligence and providing an overview of machine learning, deep learning platforms.
Image Segmentation Based on Finite IBL Mixture Model with a Dirichlet Compound Multinomial Prior

In this paper, we propose a novel image segmentation approach based on finite inverted Beta-Liouville (IBL) mixture model with a Dirichlet Compound Multinomial prior. The merits of this work can be summarized as follows: 1) Our image segmentation approach is based on a finite mixture model in which each mixture component can be responsible for interpreting a particular segment within a given image; 2) We adopt IBL distribution as the basic distribution in our mixture model, which has demonstrated better modeling capabilities than Gaussian distribution for non-Gaussian data in recent research works; 3) The contextual mixing proportions (i.e., the probabilities of class labels) of our model are assumed to have a Dirichlet Compound Multinomial prior, which makes our model more robust against noise; 4) We develop a variational Bayes (VB) method that can effectively learn model parameters in closed form. The performance of the proposed image segmentation approach is compared with other related segmentation approaches to demonstrate its advantages.
Image Classification Based On Deep Convolutional Network And Gaussian Aggregate Encoding

With the rapid rise of deep learning, the deep convolutional neural network(CNN) has become a basic tool in image classification tasks. The depth and width of CNNs keep increasing, which leads to higher risk of overfitting and puts more requirements to the hardware. Therefore, how to improve the classification accuracy and reduce the parameters of the model is still a challenge. In this paper, we propose a novel image classification network based on deep CNNs and gaussian aggregate encoding, denoted as DCGAE. It combines existing CNNs and traditional encoding techniques to accomplish parameter reduction while retaining outstanding accuracy. In terms of the architecture, DCGAE first extracts image features by a deep CNN, which allows images of different sizes to be taken as input, and then append a gaussian aggregate encoding layer before classification. Extensive experiments conducted on multiple classification datasets demonstrate the effectiveness of our algorithm. Our model achieves state-of-the-art 98.30% accuary on STL-10 dataset.
A Random Fourier Feature Method for Emulating Computer Models With Gradient Information

Computer models with gradient information are increasingly used in engineering and science. The gradient-enhanced Gaussian process emulator can be used for emulating such models. Because the size of the covariance matrix increases proportionally with the dimension of inputs and the sample size, it is computationally challenging to fit such an emulator for large datasets. We propose a random Fourier feature method to mitigate this difficulty. The key idea of the proposed method is to employ random Fourier features to obtain an easily computable, low-dimensional feature representation for shift-invariant kernels involving gradients. The effectiveness of the proposed method is illustrated by several examples.
Correlation between the immersion and presence: focused on virtual reality contents

We studied how the user's usual immersion and the content's presence while using the content correlate to the factors that affect virtual reality content quality. Users experienced three virtual reality contents, and correlation analysis was conducted based on this data. As a result, only the correlation between the GAME-SOP survey scale set as a variable in the three virtual reality content was found to be of ordinary significance. This study is valuable as a prior study to determine the correlation between the user's usual immersion and virtual reality content. The analysis results can be used as survey data suitable for evaluating virtual reality content's existentialism.
Effect of Noise Adaptive Wavelet Filter on Diagnostic Performance in Stroke Perfusion

Background : Computed tomography perfusion (CTP) images include more noise than routine clinic computed tomography (CT) images. Singular value decomposition based deconvolution algorithms are widely used for obtaining several functional perfusion maps. Recently block circulant singular value decomposition algorithms become popular for its superior property of immunity to contrast bolus lag. It is well known from literature that these algorithms are very sensitive to noise. There are a lot of examples of noise reduction filters in the literature as well as commercial ones. Functional maps which help physicians in the diagnostic process can be obtained with better image quality by de-noising CTP images with adaptive noise reduction filters. Objective : In this study, the effect of a noise adaptive wavelet filtering method on diagnostic performance on CTP stroke patient images is investigated. Method : Images of acute stroke patients were de-noised by this method and their diagnostic value were evaluated by visual means, peak signal-to-noise ratio and time intensity profile metrics. An observer evaluation study was carried out in order to validate quantitative image quality metrics. The results are compared with Gaussian and a bilateral filter based filtering method called TIPS (Time Intensity Profile Similarity) on same images sets to benchmark proposed method. Results : The diagnostic value of the images obtained from noise adaptive wavelet filtering method were better than Gaussian filter method and were compatible with a wellknown time intensity profile similarity bilateral filter method. Diagnostic performance of the both observers were improved compared to both Gaussian and TIPS methods. Conclusion : The noise adaptive wavelet filter method succeeded to reduce noise while preserving details contained in the contrast bolus. Its final effect on the timeintensity profiles and generated perfusion maps are compatible with the literature and showed improvements on diagnostic performance on specificity and overall accuracy when compared to other methods.
Multi-person Pose Estimation with Object Occlusion Information

Multi-person pose estimation is a fundamental task with many huge challenges in computer vision, such as close-interaction scenarios, limb occlusion. In this paper, we present two novel modules to group invisible keypoints occluded by various objects into the right body part. First, the Attention Residual Bottleneck is designed to generate high-resolution representations with enhanced channel-wise and spatial contextual information, which is integrated into the original residual unit with an attention mechanism. Second, the Object De-Occlusion Module is proposed to inference the order of object occlusion via occlusion relationship recovery strategy, predict and complete content for the invisible region of each person instance. Our proposed modules are evaluated on the COCO2017 keypoint benchmark, and experimental results show that our model has greater performance and faster inference speed compared to most of previous methods. It achieves the balance between accuracy and speed.
Healthcare Security Based on Blockchain within Multi-parameter Chaotic Map

Healthcare-related costs still pose problems despite the availability of large quantities of healthcare information. In this paper we propose reducing health-related costs while not violating the privacy of information related to the patients, and we also address the authentication of the blockchain. This study makes use of raw data by processing it in a reasonable way to provide effective and less costly healthcare services along with the needed privacy and ease of accessibility of information to the relevant providers. The current paper utilizes the properties of the logistic map as SHA-256 calculates the hash value for the plain text, with the results being applied to change the initial keys for the logistic map. For expansion of the chaotic region of the logistic map and for making it better suited for the generation key, a mixture of multiple parameters of the logistic map key generation is proposed. Algorithm 2 shows how to generate a new key for the hash function for the new block. A key generator based on logistic map theory can be applied. The key generator is not meant to regenerate, which means that it is not possible to regenerate the same key. Therefore, the authentication is strengthened.