Multifunctional neurosynaptic devices for human perception systems

The traditional Von Neumann architecture for processing information is difficult to meet the needs of the big data era, while low-power, small-sized neurosynaptic devices can operate and store information, so that they have received extensive attention. Due to the development of artificial intelligence and robotics, neurosynaptic devices have been given high expectations and requirements. The trend of functionalization, intelligence, and integration of computing and storage is obvious. In this review, the basic principles and types of neurosynaptic devices are summarized, the achievements of neurosynaptic devices for human perception systems are discussed and a prospect on the development trend is also given.

Human Perception of Group Synchronization Error in Remote Learning: Dependencies of Voice and Video Contents in One-Way Communication

This paper examines dependencies of voice and video contents on human perception of group (or inter-destination) synchronization error in remote learning by Quality of Experience (QoE) assessment. In our assessment, we use two videos and three voices (two voices for one video and one voice for the other video). We also investigate influences of silence periods in the voices and temporal relations between the voices and videos (called the tightly-coupled and loosely-coupled contents here). The voices are spoken by a teacher according to the videos. Each subject as a student assesses the group synchronization quality by watching each lecture video and the corresponding explanation voice, and then the subject answers whether he/she perceives the group synchronization error or not. As a result, assessment results illustrate that silence periods mitigate the perception rate of the error, and we can also find that we can more easily perceive the error for tightly-coupled contents than loosely-coupled ones.

The Impact of Central Environmental Inspections on the Perception of Respect for Human Rights and Its Empirical Analysis

To develop China’s human rights cause with a people-centered approach,we should pay close attention to the concrete experiences of the general public regarding the protection of human rights.Deepening the research on the perception of respect for human rights can contribute to a more comprehensive understanding of the practical achievements of the hu man rights cause.Public environmental rights,as a new type of human rights,have become an important aspect of the development of the human rights cause in the new era.The central envi ronmental inspection,as an authoritative and interventionist vertical governance mechanism,promotes the implementation of environmental policies by local Party committees and govern ments and strengthens environmental information disclosure and public participation in environ mental matters.As a result,it contributes to the realization of public environmental rights and stimulates public perception of respect for human rights.Among them,the“look-back inspec tion is an important component of the central environmental inspection,and its implemen tation consolidates and enhances the previous inspection work.An empirical analysis based on the World Values Survey’s data for China indicates that residents in the provinces that have underg one“look-back inspections are more inclined to believe that human rights are adequately re spected compared to residents in the provinces that have not underwent such inspections.It suggests that the advancement and improvement of the central environmental inspection system promote improvements in ecological environment quality and contribute to enhancing the public percep tion of respect for human rights.

Bridging the semantic gap with human perception based features for scene categorization

Purpose–The purpose of this paper is to build a classification system which mimics the perceptual ability of human vision,in gathering knowledge about the structure,content and the surrounding environment of a real-world natural scene,at a quick glance accurately.This paper proposes a set of novel features to determine the gist of a given scene based on dominant color,dominant direction,openness and roughness features.Design/methodology/approach–The classification system is designed at two different levels.At the first level,a set of low level features are extracted for each semantic feature.At the second level the extracted features are subjected to the process of feature evaluation,based on inter-class and intra-class distances.The most discriminating features are retained and used for training the support vector machine(SVM)classifier for two different data sets.Findings–Accuracy of the proposed system has been evaluated on two data sets:the well-known Oliva-Torralba data set and the customized image data set comprising of high-resolution images of natural landscapes.The experimentation on these two data sets with the proposed novel feature set and SVM classifier has provided 92.68 percent average classification accuracy,using ten-fold cross validation approach.The set of proposed features efficiently represent visual information and are therefore capable of narrowing the semantic gap between low-level image representation and high-level human perception.Originality/value–The method presented in this paper represents a new approach for extracting low-level features of reduced dimensionality that is able to model human perception for the task of scene classification.The methods of mapping primitive features to high-level features are intuitive to the user and are capable of reducing the semantic gap.The proposed feature evaluation technique is general and can be applied across any domain.

Human Rights-Reflections of the East and Perceptions of the West

Introduction There is a time and tide tor ev- erything. This is a season in which the global quest for human rights is at its peak. Freedom is on the march. Throughout Asia and Africa, the ardor for liberty is spreading. The quest for the inalienable fights of human beings has gained universal appeal. It is now recognized that state sovereignty is a shield against external aggression. It cannot be used as a sword against one＇s own nationals. Human rights abuses in any land deserve world-wide condem- nation because ＂injustice anywhere is a threat to iustice everywhere.＂

Effective search with saliency-based matching and cluster-based browsing

Similarity matching and this paper, a saliency-based information presentation are two matching algorithm is proposed key factors in information retrieval. In for user-oriented search based on the psychological studies on human perception, and major emphasis on the saliently similar aspect of objects to be compared is placed and thus the search result is more agreeable for users. After relevant results are obtained, the cluster-based browsing algorithm is adopted for search result presentation based on social network analysis. By organizing the results in clustered lists, the user can have a general understanding of the whole collection by viewing only a small part of results and locate those of major interest rapidly. Experimental results demonstrate the advantages of the proposed algorithm over the traditional work.

Subjective Evaluation of Clothing Comfort and Their Correlation with Laboratory Test

A series of polyester, cotton, and polyester/cotton blend fabrics were evaluated in a subjective wear. Subjective trial data were correlated with the results of in-house laboratory tests with the regression methods. Experimental results indicate that clothing-comfort assessment by human perception provides a sound basis for comparison of apparel garments under combined microclimates and human-activity levels of

Recent Advances and Challenges of Visual Signal Quality Assessment

While quality assessment is essential for testing, optimizing, benchmarking, monitoring, and inspecting related systems and services, it also plays an essential role in the design of virtually all visual signal processing and communication algorithms, as well as various related decision-making processes. In this paper, we first provide an overview of recently derived quality assessment approaches for traditional visual signals (i.e., 2D images/videos), with highlights for new trends (such as machine learning approaches). On the other hand, with the ongoing development of devices and multimedia services, newly emerged visual signals (e.g., mobile/3D videos) are becoming more and more popular. This work focuses on recent progresses of quality metrics, which have been reviewed for the newly emerged forms of visual signals, which include scalable and mobile videos, High Dynamic Range (HDR) images, image segmentation results, 3D images/videos, and retargeted images.

Fundamentals of Visualizing Communication Networks

The human brain is built to process complex visual impressions within milliseconds. In comparison with sequentially coded spoken language and written texts, we are capable of consuming graphical information at a high bandwidth in a parallel fashion, producing a picture worth more than a thousand words. Effective information visualization can be a powerful tool to capture people's attention and quickly communicate large amounts of data and complex information. This is particularly important in the context of communication data, which often describes entities (people, organizations) and their connections through communication. Visual analytics approaches can optimize the user-computer interaction to gain insights into communication networks and learn about their structures. Network visualization is a perfect instrument to better communicate the results of analysis. The precondition for effective information visualization and successful visual reasoning is the capability to draw "good" pictures. Even though communication networks are often large, including thousands or even millions of people, underlying visualization principles are identical to those used for visualizing smaller networks. In this article, you will learn about these principles, giving you the ability to assess the quality of network visualizations and to draw better network pictures by yourself.

Application of fuzzy logic in content-based image retrieval

We propose a fuzzy logic-based image retrieval system, in which the image similarity can be inferred in a nonlinear manner as human thinking. In the fuzzy inference process, weight assignments of multi-image features were resolved impliedly. Each fuzzy rule was embedded into the subjectivity of human perception of image contents. A color histogram called the average area histogram is proposed to represent the color features. Experimental results show the efficiency and feasibility of the proposed algorithms.

Objective measurement for image defogging algorithms

Since there is lack of methodology to assess the performance of defogging algorithm and the existing assessment methods have some limitations,three new methods for assessing the defogging algorithm were proposed.One was using synthetic foggy image simulated by image degradation model to assess the defogging algorithm in full-reference way.In this method,the absolute difference was computed between the synthetic image with and without fog.The other two were computing the fog density of gray level image or constructing assessment system of color image from human visual perception to assess the defogging algorithm in no-reference way.For these methods,an assessment function was defined to evaluate algorithm performance from the function value.Using the defogging algorithm comparison,the experimental results demonstrate the effectiveness and reliability of the proposed methods.

Image Dehazing Based on Pixel Guided CNN with PAM via Graph Cut

Image dehazing is still an open research topic that has been undergoing a lot of development,especially with the renewed interest in machine learning-based methods.A major challenge of the existing dehazing methods is the estimation of transmittance,which is the key element of haze-affected imaging models.Conventional methods are based on a set of assumptions that reduce the solution search space.However,the multiplication of these assumptions tends to restrict the solutions to particular cases that cannot account for the reality of the observed image.In this paper we reduce the number of simplified hypotheses in order to attain a more plausible and realistic solution by exploiting a priori knowledge of the ground truth in the proposed method.The proposed method relies on pixel information between the ground truth and haze image to reduce these assumptions.This is achieved by using ground truth and haze image to find the geometric-pixel information through a guided Convolution Neural Networks(CNNs)with a Parallax Attention Mechanism(PAM).It uses the differential pixel-based variance in order to estimate transmittance.The pixel variance uses local and global patches between the assumed ground truth and haze image to refine the transmission map.The transmission map is also improved based on improved Markov random field(MRF)energy functions.We used different images to test the proposed algorithm.The entropy value of the proposed method was 7.43 and 7.39,a percent increase of4.35%and5.42%,respectively,compared to the best existing results.The increment is similar in other performance quality metrics and this validate its superiority compared to other existing methods in terms of key image quality evaluation metrics.The proposed approach’s drawback,an over-reliance on real ground truth images,is also investigated.The proposed method show more details hence yields better images than those from the existing state-of-the-art-methods.

An interactive approach to investigate brightness perception of daylighting in Immersive Virtual Environments:Comparing subjective responses and quantitative metrics

Daylighting plays an eminent role in the performance of indoor environments and their occupants,thus necessitating the need to investigate daylight perception of potential occupants at early design stages.The present study introduces an interactive approach to collect and visualize brightness perception of daylighting in a large-scale immersive virtual environment using a game engine as a daylight simulation tool.The developed system allows users to explore building models freely at different day times set in virtual reality and report their perceptions in real time.Following a validation study(N=36)to investigate the consistency of brightness perceptions in a real environment and its virtual replica,a set of 24 participants were recruited to use the system to report their brightness perception in a virtual model of a daylit art museum,through snapshotting the scenes where they perceive as one of the following in terms of daylighting:(very dark,dark,bright,or very bright).Using an output of 419 snapshots,a"Perceptual Light Map"(PLM)was developed to visualize the collective brightness perception of participants as a heat map.Subjective responses were found to be positively correlated with four daylight metrics,with the highest correlation to mean luminance and the lowest to luminance ratio.The findings of this exploratory study represent a step towards a user-oriented supplement tool to the existing quantitative daylight metrics,validating game engines’adequacy as a daylight simulation tool,and illustrating the potentials of immersion and interaction principles for the perception of daylit spaces in virtual reality.

Visual perception driven collage synthesis

A collage is a composite artwork made from the spatial layout of multiple pictures on a canvas,collected from the Internet or user photographs.Collages,usually made by skilled artists,involve a complex manual process,especially when searching for component pictures and adjusting their spatial layout to meet artistic requirements.In this paper,we present a visual perception driven method for automatically synthesizing visually pleasing collages.Unlike previous works,we focus on how to design a collage layout which not only provides easy access to the theme of the overall image,but also conforms to human visual perception.To achieve this goal,we formulate the generation of collages as a mapping problem:given a canvas image,first,compute a saliency map for it and a vector field for each sub-region of it.Second,using a divide-and-conquer strategy,generate a series of patch sets from the canvas image,where the salient map and the vector field are used to determine each patch’s size and direction respectively.Third,construct a Gestalt-based energy function to choose the most visually pleasing and orderly patch set as the final layout.Finally,using a semantic-color metric,map the picture set to the patch set to generate the final collage.Extensive experimental and user study results show that this method can generate visual pleasing collages.