Pedestrian Detection with Wearable Cameras for the Blind: A Two-way Perspective

Blind people have limited access to information about their surroundings, which is important for ensuring one's safety, managing social interactions, and identifying approaching pedestrians. With advances in computer vision, wearable cameras can provide equitable access to such information. However, the always-on nature of these assistive technologies poses privacy concerns for parties that may get recorded. We explore this tension from both perspectives, those of sighted passersby and blind users, taking into account camera visibility, in-person versus remote experience, and extracted visual information. We conduct two studies: an online survey with MTurkers (N=206) and an in-person experience study between pairs of blind (N=10) and sighted (N=40) participants, where blind participants wear a working prototype for pedestrian detection and pass by sighted participants. Our results suggest that both of the perspectives of users and bystanders and the several factors mentioned above need to be carefully considered to mitigate potential social tensions.Comment: The 2020 ACM CHI Conference on Human Factors in Computing Systems (CHI 2020

Deep Learning-based Service Distribution Model for Wireless Network Assisted Internet of Everything

Internet of Everything (IoE) provides scalable service support for a heterogeneous class of users and applications. Concorde service delivery and quality are the focused problems retaining the user\u27s satisfaction in the next-generation Wireless Networks (WNs). However, the flexible support for applications and users remains biased due to WN connections and unstable network architectures. This article introduces a Mutable Service Distribution Model (MSDM) for providing unified IoE application response. The proposed model handles the bias in IoE supporting WN connections and service drops between successive intervals. In this process, deep recurrent learning identifies the continuity between different intervals, preventing service overlapping. The service overlapping that degrades the Quality of Service (QoS) is distributed for different resource providers and WN architectures for delay-less responses. The bias is monitored by the learning output, ensuring re-assignment of application/ user service requests. Therefore, the service response drops are reduced with controlled time, aiding flexibility. Besides, the proposed model\u27s performance is verified using response ratio, overhead, and assigning time

Sentiment Analysis on Twitter Data by Using Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM)

Twitter sentiment analysis is an automated process of analyzing the text data which determining the opinion or feeling of public tweets from the various fields. For example, in marketing field, political field huge number of tweets is posting with hash tags every moment via internet from one user to another user. This sentiment analysis is a challenging task for the researchers mainly to correct interpretation of context in which certain tweet words are difficult to evaluate what truly is negative and positive statement from the huge corpus of tweet data. This problem violates the integrity of the system and the user reliability can be significantly reduced. In this paper, we identify the each tweet word and we are assigning a meaning into it. The feature work is combined with tweet words, word2vec, stop words and integrated into the deep learning techniques of Convolution neural network model and Long short Term Memory, these algorithms can identify the pattern of stop word counts with its own strategy. Those two models are well trained and applied for IMDB dataset which contains 50,000 movie reviews. With huge amount of twitter data is processed for predicting the sentimental tweets for classification. With the proposed methodology, the samples are experimentally collected from the real-time environment can be discriminated well and the efficacy of the system is improved. The result of Deep Learning algorithms aims to rate the review tweets and also able to identify movie review with testing accuracy as 87.74% and 88.02%

Traffic scheduling, network slicing and virtualization based on deep reinforcement learning

The revolutionary paradigm of the 5 G network slicing introduces promising market possibilities through multi-tenancy support. Customized slices might be provided to other tenants at a different price as an emerging company to operators. Network slicing is difficult to deliver higher performance and cost-effective facilities through render resources utilisation in alignment with customer activity. Therefore, this paper, Deep Reinforcement Learning-based Traffic Scheduling Model (DRLTSM), has been proposed to interact with the environment by searching for new alternative actions and reinforcement patterns believed to encourage outcomes. The DRL for network slicing situations addresses power control and core network slicing and priority-based sizing involves radio resource. This paper aims to develop three main network slicing blocks i) traffic analysis and network slice forecasting, (ii) network slice admission management decisions, and (iii) adaptive load prediction corrections based on calculated deviations; Our findings suggest very significant possible improvements show that DRLTSM is dramatically improving its efficiency rate to 97.32%, scalability and compatibility in comparison with its baseline

A digital twin framework for Industry 4.0 enabling next-gen manufacturing

Digital twins offer a framework to support the ever-rising demands in the fast-paced industrial evolution. This technology not only adds to the reliability of industrial processes but also offers an insight in to long-term behaviors and pattern during the aging of the industrial equipment. In this paper, a digital twin framework is presented to replicate the processes of a real production line for product assembly. The proposed work implements a digital/graphical replica of Festo Cyber Physical Factory (CPF) for Industry 4.0 (I4.0). The implemented system allows to schedule orders and specify product configuration which embodies the actions of CPF in digital world. In addition, the paper also presents a viable framework to interlink the physical system with the digital instance to offer extended services and a pathway towards realization of fully functional digital twins