Reviewerly/exHarmony huggingface dataset:
How Images Defects in Street Scenes Affect the Performance of Semantic Segmentation Algorithms

Semantic segmentation methods are used in autonomous car development to label pixels of road images (e.g.street, building, pedestrian, car, and so on).DeepLabv3+ and PSPNet are two of the best performance semantic segmentation methods according to Cityscapes benchmark.Although these methods achieved a very high performance with clear road images, yet these two methods are not tested under severe imaging conditions.In this work, we provided new Cityscapes datasets with severe imaging conditions: foggy, rainy, blurred, and noisy datasets.We evaluated the performance of DeepLabv3+ and PSPNet using our datasets.Our work demonstrated that although these models have high performance with clear images, they show very weak performance among the different imaging challenges.We proved that the road semantic segmentation methods must be evaluated using different kinds of severe imaging conditions to ensure the robustness of these methods in autonomous driving.
Efficient task optimization algorithm for green computing in cloud

Cloud infrastructure assets are accessed by all hooked heterogeneous network servers and applications to maintain entail reliability towards global subscribers with high performance and low cost is a tedious challenging task. Most of the extant techniques are considered limited constraints like task deadline, which leads Service Level Agreement (SLA) violation. In this manuscript, we develop Hadoop based Task Scheduling (HTS) algorithm which considers a task deadline time, completion time, migration time and future resource availability of each virtual machine. The Intelligent System (IS) enabled with adaptive neural computation method to assess all above attributes. Specifically, the result of Prophecy Resource Availability (PRA) method has been used to assess the status of each Virtual Machine (VM), which helps to streamline the resource wastage and increases the response time with low SLA violation rate.
The Neural Representation of Room Locations in A Virtual Building

In order to navigate through a building, people need to keep track of their real-time location in the space. In this study, we aim to find the neural representation of room locations in the human brain using functional magnetic resonance imaging (fMRI) and multivariate pattern analysis (MVPA). Specifically, we asked participants to navigate through a learned virtual building. Their real-time brain activity in each room was recorded. It was found that the location of the current room could be decoded from the hippocampus. Consistent with previous studies in outdoor spaces, the hippocampus also tracks one's real-time location in an indoor space during navigation.
A new business model for digital governance of public records using blockchain

Digital India is a flagship program of the Government of India with a vision to transform India into a digitally empowered society and knowledge economy. Under this broad vision, transforming the functioning of the government to provide governance and services on demand using digital technologies is a key focus area for the government. This transformation requires not only the restructuring and refocusing of existing programs to align them with the principles of Digital India but also the launching of new programs, all to be implemented in a synchronized manner. Digital transformation focuses on the use of digital technologies to improve access to government information previously held in physical formats. The scope of work includes enabling new partnerships and business models to deliver public value using emerging technology alternatives and new development approaches to redefine what digital government can achieve and how new technologies can be deployed. This paper takes up the application of blockchain, one of the emerging technologies, for digital governance. The paper recommends through a sample use case on digital governance of Land Records as to how the government can navigate complexity and, while setting apart buzz from value, use this new technology strategically to develop a new business model that maximizes public value.
Unidirectional Ensemble Recognition and Translation of Phrasal Sign Language from ASL to ISL

Several countries around the world enable their hearing and speech impaired citizens to streamline communication among themselves and with the able through standardization of sign language. This standardization is typically present in two dialects; alphabetical and phrasal. Alphabetical sign recognition has been extensively researched, however, the phrasal variant, lesser so. We propose a novel approach to recognizing phrasal American Sign Language sequences through an ensemble of the SIFT feature recognition framework with a custom probabilistic prediction measurement and a convolutional neural network, preprocessed through optical flow. We further work on generating the intermediate English language representation and its translation to corresponding sequences in the Indian Sign Language.
From Transient Information to Persistent Documentation: Enhancing Software Documentation

Developers usually need to use different kinds of tools in the software development process, e.g., version control systems, code review tools, and bug or issue tracking systems. Some of those tools provide an enhanced approach for developers to communicate, i.e., discussions are recorded in the communication feature of those tools. There is important information, i.e., discussions and decisions, registered by those tools which are not propagated to the project documentation: the transient information. Thus, this work investigates how to improve the software documentation by making such transient important information into persistent documentation as source code comments. The results are relevant because they will aid the comprehension of the source code not only during the development process, but also in code reviews.
Design of Component Fault Recovery of Integrated Control System Based on Log Information

In view of the limitations of conventional fault recovery design technology applied to the fault recovery of integrated control system components, and for the purpose of improving the software fault tolerance, this paper proposes a design technology of integrated control system component fault recovery based on log information, which uses the log checkpoint information to recover system faulty component using the checkpoint backward recovery technology and the system log information. It relies on the log information to repeat the execution steps since the last checkpoint, so that the process of task execution is exactly the same as that before the fault, which keep the integrity of task execution. The experimental results show that the design technology of component fault recovery based on log information in an integrated control system in a certain field has better fault recovery, it improves system fault tolerance ability.
Continuous Fusion of IMU and Pose Data using Uniform B-Spline

In this work, we present an uniform B-spline based continuous fusion approach, which fuses the motion data from an inertial measurement unit and the pose data from a visual localization system accurately, efficiently and continu-ously. Currently, in the domain of robotics and autonomous driving, most of the ego motion fusion approaches are filter based or pose graph based. By using the filter based approaches like the Kalman Filter or the Particle Filter, usually, many parameters should be set carefully, which is a big overhead. Besides that, the filter based approaches can only fuse data in a time forwards direction, which is a big disadvantage in processing async data. Since the pose graph based approaches only fuse the pose data, the inertial measurement unit data should be integrated to estimate the corresponding pose data firstly, which can however bring accumulated error into the fusion system. Additionally, the filter based approaches and the pose graph based approaches only provide discrete fusion results, which may decrease the accuracy of the data processing steps afterwards. Since the fusion approach is generally needed for robots and automated driving vehicles, it is a major goal to make it more accurate, robust, efficient and continuous. Therefore, in this work, we address this problem and apply the axis-angle rotation representation method, the Rodrigues’ formula and the uniform B-spline implementation to solve the ego motion fusion problem continuously. Evaluation results performed on the real world data show that our approach provides accurate, robust and continuous fusion results.
Neurophysiological Reactions to Social Media Logos

Use of social media is pervasive, and research has identified many benefits that arise from the affordances of social media platforms. Yet, social media use can often interfere with other areas of life, a condition termed social media overuse. Prior literature suggest that social media use can satisfy basic hedonic needs, triggering activation of the brain’s reward processing network and inducing higher levels of use. In this paper we propose a study to examine the relationship between social media cues and the neurological reactions that underlie social media overuse. The study employs the affect misattribution procedure (AMP) to elicit spontaneous reactions to cues from three task conditions (social media logos, office images, non-social media company logos). Participants complete the study while situated in a magnetic resonance imaging (MRI) machine. We postulate an interaction between task condition and reported social media cravings/usage in rewards network regions of the brain.
Searching For Desired Person Doing Desired Action based on Visual and Audio Feature in Large Scale Video Database

How to find a person doing an action in a video database is a challenging problem because the result must be correct at an instance level with the specific person doing the appropriate action. Even though there have been many works about face recognition and action recognition, they often focus on only one separate task. In this paper, the problem could be formulated into an instance retrieval where input is a query consisting of examples of the target person and examples of the desired action, and the result is a list of ranked positive shots. In this work, we proposed a simple but efficient person-action retrieval system by combining multimodal features including visual feature and audio feature to deal with various types of instances by making use of available visual or audio cues. The evaluation results on a large-scale BBC Eastenders dataset with 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> rank in a total of 6 teams in TRECVID INS 2019 has proved the effectiveness of the proposed method.
Segmentation of Cardiac Chambers in 2D Echocardiographic Images by Using a New Atlas-Based Deformable Model

Cardiovascular diseases (CVDs) are considered as the main reason of mortality around the world. Echocardiography is the most common imaging modality for diagnosis or treatment follow-up of CVDs. However, because of speckle-noise corruption and low resolution, segmentation of heart chambers in echo-images is a challenging endeavor. We previously proposed a probabilistic atlas as a prior model for the heart chambers in echo-images. In this paper, we propose a new active contour for the segmentation of cardiac chambers by using that digital atlas. Our deformable model effectively combines the global and local (patch-based) region-based energy functionals. Also, to extract all the cardiac chambers, we determine four active contours in every echo-image (one contour for each chamber in the four-chamber view). For simultaneously evolving all the curves, a coupling term is also added to the energy functional. Finally, the evolution equation of each active contour is computed through the Euler-Lagrange equation. Experimental results demonstrate that our method provides accurate solutions compared to expert delineations.
A Spatio-Temporal Context Tracking Method Blended with LBP Texture Feature

A spatio-temporal context (STC) tracking method blended with LBP texture feature is proposed with regard to the problem that it's hard to track a target effectively by a conventional STC tracking method where target background mutation, shading or shape change takes place during tracking. First, the similarity in the texture histogram of the target area between the first image frame and each image frame behind it is calculated to solve the problem of central position shift as a result of the background mutation of the tracked target. The similarity in the texture histogram of the target area between two adjacent frames is then calculated to solve the problem of shading that happens to the tracked target. Finally, a judgment is made as to whether or not the STC update coefficient is changed and the central position coordinates of the tracked target offset, based on the relation between the worked-out similarities in texture histogram and the threshold. Shape change does not alter local texture information about the tracked target and the STC tracking method blended with LBP textural feature has thus good robustness to shape change. Experimental results show that the method in this paper sees a 73.06% increase in average rate of successful tracking and an 89.94-pixel decrease in average error in target's central position, compared with the original STC tracking method. The method in this paper allows more stable tracking of a target where target background mutation, shading or shape change takes place.
Intelligent Crowd Counting System with Gender Classification

Evaluating the quantity of individuals in exceptionally bunched swarm scenes is an amazingly testing task because of genuine impediment and non-consistency dispersion in one group picture. Human Counting innovation can be summed up into two sorts of writing: identification strategies and tallying techniques. Conventional methodologies for swarm tallying from pictures depended available made portrayals to remove low-level highlights. These highlights were then mapped for checking or creating thickness maps by means of different tallying procedures. The identification-based model commonly utilizes sliding window-based recognition calculations to include individuals in a picture. This Project likewise correlation of various sex grouping strategies and utilization of various racial highlights, for example, eyes, nose, and mouth, and so on for Gender orientation characterization its applications in numerous regions like observing, reconnaissance, and business profiling, and human-PC cooperation video order assignments.
Writer verification based on simple graphemes and extreme learning machine approaches

Abstract mTraditional literature presents complex biometric sources, descriptors, and classifiers to solve the writer’s verification problem. The simple graphemes have been studied recently considering classifiers such as multilayer perceptron, support vector machine and convolutional neural network, which allow a high level of performance but with high computational cost in the training. In this paper, we propose the use of extreme learning neural networks to verify the writer identity based on simple graphemes with the aim of achieve a better descriptor performance in a less training time. The proposal allows verify peoples identity through the analysis of handwritten text in order to fakes detect, authorship identification, fakes, threats and thefts in documents. The experimental results show that this type of classifiers achieve a rate of success greater to the 95% for all five characters in the problem addressed, but with significantly less training times than traditionally used techniques.
Traffic Surveillance Using Smart Drone

Day by day the number of vehicles is increasing very fast as the demand is increasing. So, the details of the vehicles are very important to maintain for the government of a country. Information like ownership, insurance, emission, road tax etc., need to be maintained and accessed very efficiently and easily. Even for crime purpose the vehicles are used. So, depending on the demand of the requirements we have proposed this model for real time vehicle monitoring and intimating for violation of traffic rules using drone. In our proposed system, the drone is fitted with cameras and Raspberry Pi. The drone will keep on monitoring the non-parking areas from above the level of ground. The drone will capture the image and detect the vehicle and if the vehicle is not moving after two minutes, transmit the vehicle image to the server along with the road signal code.
Secure Web Browsing Using Trusted Platform Module (TPM)

According to the Internet usage statistics published on June 2019, around 59% of the world’s human population uses the Internet. The web browser is one of the most used applications to access the Internet. How many of these 59% population trust the browser and the environment they are using? Thus, there is a necessity to configure the browser for secure browsing as well as the configuration and the browsing system needs to be protected from malicious intent and process. The paper proposes an architecture based on a trusted platform module (TPM) to configure the browser for secure browsing and to provide trust in the browsing environment. The proposed system will help general web users and system administrators to maintain and control a secure browsing environment in the enterprise.
Design of a smart controller for the self-learning of Differently Abled

This paper describes the design of controller which is helpful for differently abled persons for self-learning. This controller design helps to learn the respective features efficiently irrespective of the disability. The prototype model has the ability to convert text to brail, visual to speech, eye tracking and hands sign language to speech. The main objective of the paper is to describe the idea to develop a handheld portable device to meet different kinds of learning required for the persons with different disabilities. The handheld device can be connected to smart phone or tablet and these devices can be connected to internet through Wi-Fi or LTE technologies.
SDAE-LFM: A Latent Factor Model for Recommendation Based on Stack Denoising AutoEncoder

Abstract Recommendation methods usually associated with data sparsity. The traditional recommendation methods take the users’ rating information as the recommendation basis, which ignore the latent features that can be taking into consideration to model for better recommendations. In order to deal with these problems, we proposed a latent factor model recommendation algorithm based on stack denoising autoencoder (SDAE-LFM), applying Deep Learning technology for latent feature representation learning. A stack denoising autoencoder is applied to extracting feature about item from the label information. Then we factorize the item feature information to perform matrix decomposition training. Finally, we predict the result by the user-item preference matrix. Experimental results on these datasets demonstrate that the proposed recommendation method has better performance.
The LSTM Neural Network Based on Memristor

Abstract The recurrent neural network adds the concept of time series on the basis of the traditional multi-layer feedforward neural networks, provides the memory function, and makes the network show good modeling ability on time-series data. Therefore, this paper proposes a LSTM (Long Short-Term Memory) neural network based on memristor. It establishes a discrete weighted LSTM network model by simplifying the traditional recurrent neural network, and uses memristor arrays on the premise of ensuring recognition performance. We realize the function of weight matrix to improve the structure of LSTM neural network, and finally carry out simulation research on the proposed neural network. And due to the volatility and yield of memristors, this paper also demonstrates and analyzes the impact of these two characteristics on network performance, and the performance level of the LSTM neural network based on memristor is verified under the existing preparation level. Experiments on the TIMIT speech database show that the proposed neural network in this paper has good accuracy and its speech recognition performance is superior.
Sketch drawing on infinite paper sheets

The process of drawing theoretically infinite drawings on infinite paper sheets is normal for many industries. Ordinary today used techniques exploited for relatively short drawings (size A0 or similar) are useless for this problem. The proposed paper presents a solution of a flatbed plotter for drawing long (theoretically infinite) drawings. The problems with paper shifting and rotation as well paper corrugation are solved using specific optical sensor and the appropriate software.
A Graph Embedding Based Real-Time Social Event Matching Model for EBSNs Recommendation

Event-based social networks (EBSNs), are platforms that provide users with event scheduling and publishing. In recent years, the number of users and events on such platforms has increased dramatically, and interactions have become more complicated, which has made modeling heterogeneous networks more difficult. Moreover, the requirement of real-time matching between users and events becomes urgent because of the significant dynamics brought by the widespread use of mobile devices on such platforms. Therefore, we proposed a graph embedding based real-time social event matching model called GERM. We first model heterogeneous EBSNs into heterogeneous graphs, and use graph embedding technology to represent the nodes and their relationships in the graph which can more effectively reflect the hidden features of nodes and mine user preferences. Then a real-time social event matching algorithm is proposed, which matches users and events on the premise of fully considering user preferences and spatio-temporal characteristics, and recommends suitable events to users in real-time efficiently. We conducted experiments on the Meetup dataset to verify the effectiveness of our method by comparison with the mainstream algorithms. The results show that the proposed algorithm has a good improvement on the matching success rate, user satisfaction, and user waiting time.
Adversarial Transform Networks for Unsupervised Transfer Learning

Transfer learning, especially unsupervised domain adaptation, is a crucial technology for sample-efficient learning. Recently, deep adversarial domain adaptation methods perform remarkably well in various tasks, which introduce a domain classifier to promote domain-invariant representation. However, previous methods either constrain the representative ability with an identical feature extractor for both domains or ignore the relationship between domains with separate extractors. In this paper, we propose a novel adversarial domain adaptation method named Adversarial Transform Network (ATN) to both enhance the representative ability and transfer general information between domains. Residual connections are used to share features in the bottom layers, which deliver transferrable features to boost generalization performance. Moreover, a regularizer is proposed to alleviate a vanishing gradient problem, thus stabilizing the optimization procedure. Extensive experiments are conducted to show that the proposed ATN is comparable with the methods of the state-of-the-art and effectively deals with the vanishing gradient problem.