Coronavirus and Smart Hospitals Attacks: Proposed Model

Yassine Chahid and Mohammed Benabdellah, Faculty of Sciences, Mohamed first University, ACSA Laboratory, Morocco

ABSTRACT

The Covid-19 pandemic was infected more than 4,136,291 people around the world in 212 countries and territories, killed 291,181 people since its appearance in December in Wuhan, of which more than 76,942 people in the United States, 30,615 people in the UK and 29,958 people in Italy. This large number of infected people caused saturation in hospitals and put a big pressure on the intensive care units and hospital beds. While the medical staff are fighting to save the lives of these patients, others are playing with these lives by making cyber-attacks on research centers and health institutions. Smart hospitals are one of the most affected because they use its technology (Smart hospitals) that generates a lot of personal and critical data, these hospitals are equipped with “things” that are used for patient monitoring, maintenance, remote operation and control like wearables, smart pills, smart beds, remote monitoring systems, RTHS (Real-time Health Systems), biosensors, glucose measurement devices, robots, equipment monitoring devices, and more. In this paper, we will propose a new secured system model to keep these hospital systems safe against cyberattacks by using verification and classification of treatment requests and controlling the patients’ health using "sensors" to ensure the safety of the requested operations.

KEYWORDS

Coronavirus, IoT, Security, Cyber-attacks, Smart Hospitals.

Several Typical Paradigms of Industrial Big Data Application

Hu Shaolin, Zhang Qinghua, Su Naiquan and Li Xiwu, Guangdong University of Petrochemical Technology, Maoming, Guangdong, China

ABSTRACT

Industrial big data is an important part of big data family, which has important application value for industrial production scheduling, risk perception, state identification, safety monitoring and quality control, etc. Due to the particularity of the industrial field, some concepts in the existing big data research field are unable to reflect accurately the characteristics of industrial big data, such as what is industrial big data, how to measure industrial big data, how to apply industrial big data, and so on. In order to overcome the limitation that the existing definition of big data is not suitable for industrial big data, this paper intuitively proposes the concept of big data cloud and the 3M (Multi-source, Multi-dimension, Multi-span in time) definition of cloud-based big data. Based on big data cloud and 3M definition, three typical paradigms of industrial big data applications are built, including the fusion calculation paradigm, the model correction paradigm and the information compensation paradigm. These results are helpful for grasping systematically the methods and approaches of industrial big data applications.

KEYWORDS

Industrial Big Data, Paradigms, Big Data Fusion, Model Correction, Information Compensation.

An Intelligent System to Enhance Visually-impaired Navigation and Disaster Assistance using Geo-based Positioning and Machine Learning

Wenhua Liang¹, Ishmael Rico², Yu Sun³, ¹St.Margaret’s Episcopal School, San Juan Capistrano, CA 92675, ²University of California, Berkeley, CA, 94720, ³California State Polytechnic University, Pomona, CA, 91768

ABSTRACT

Technological advancement has brought many the convenience that the society used to lack, but unnoticed by many, a population neglected through the age of technology has been the visually-impaired population. The visually-impaired population has grown through ages with as much desire as everyone else to adventure but lack the confidence and support to do so. Time has transported society to a new phase condensed in big data, but to the visually-impaired population, this quick-pace living lifestyle, along with the unpredictable natural disaster and COVID-19 pandemic, has dropped them deeper into a feeling of disconnection from the society. Our application uses the global positioning system to support the visually-impaired in independent navigation, alerts them in face of natural disasters, and reminds them to sanitize the device during the COVID-19 pandemic.

KEYWORDS

Geo-based navigation assistance, machine learning, mobile computing.

Face Detection using Selfie Face Image on Instagram

Deepak Mane, Senior Data Scientist, Analytics and Insights, Tata consultancy Services, Australia

ABSTRACT

Instagram has been one of the fastest-growing social networks in recent years. Instagram is a common social network used for the posting of photos. A unique keyword or hashtag may be used for an image search on Instagram. When users upload videos, there are no guidelines for having a hashtag. This makes the given hashtag often not linked to the uploaded image. There are pictures occupied by a selfie face whose contents. It allows an image's background image or location not to be completely communicated.The aim of this research is to use the Haar Cascade approach to combine site data extraction and human face recognition techniques to processselfie face images on search results based on hashtags on Instagram. The experiment involved deciding which hashtags to be used as the basis for image searches on Instagram. The applied procedure detects human faces with a precision of 78 percent, according to experimental data. The Haar Cascade system will process cat, petlover, and selfie face images with a 69 percent accuracy score, according to the results of human face detection.

KEYWORDS

Haar, Face Recognition, Insta, Social.

Adoption Factors of Enabling I4.0 Technologies and Benefits in the Supply Chain

José Carlos Franceli and Silvia Novaes Zilber Turri, Universidade Federal do ABC, São Paulo, Brazil

ABSTRACT

The Industry 4.0 (I4.0) technologies represents a new paradigm of integration of cyber physical, information and communication solutions, and finds applications in many different domains. This topic has had little exploration in the social sciences and this study helps bridge that gap by investigating the challenge of adopting innovations based on the I 4.0 technologies , more specifically on factors that affect the adoption decision. This work based on studies that seek to integrate adoption variables also aims to identify the benefits generated in the Supply Chain. Given the nature and novelty of the technology whose adoption is the object of this study, a systematic literature review was developed. The results present an adoption framework integrating factors of adoption, technologies and benefits for the Supply Chain. The model can be easily adapted to serve as a tool in the evaluation and eventual selection of technological innovations.

KEYWORDS

IoT, Supply Chain, Digital Transformation, I 4.0, Adoption.

Using Voice Assistants with AI for Learning Academic English

Vladimir Tregubov, Department of Business and Logistics, Yuri Gagarin State Technical University of Saratov, Saratov, Russia

ABSTRACT

The article describes the current state and prospects of the use of artificial intelligence (AI) and the technology of voice recognition in education. There are some common approaches to using AI in education: adaptive assessment of knowledge, spaced repetition, virtual assistants, adaptive feedback, and automated verification of works. We described the basic functionality of chatbots, which were developed specially for studying foreign language. We also made a review of special applications for Google Assistant which use the voice interface and help to improve language skills, study grammatic, build up a vocabulary. In the conclusion there is a description of the project " Academic phrase bank Trivia" developed by authors. This application is expanding opportunities for learning of academic English. There were created some actions for Google assistant with functions: increasing academic vocabulary, training of creating grammatically correct academic expressions, memorizing some templates of academic phrases, etc. In the active mode, this application helps to create correct phrases of academic English and improve the abilities of understanding English speech by hearing.

KEYWORDS

Academic English, Google home, personal assistant, academic publications, applications for learning English.

Technique for Removing Unnecessary Superimposed Patterns from Image using Generative Network

Kazutake Uehira and Hiroshi Unno, Department of Network Engineering, Kanagawa Institute of Technology, Kanagawa, Japan

ABSTRACT

A technique for removing unnecessary patterns from captured images by using a generative network is studied. The patterns, composed of lines and spaces, are superimposed onto a blue component image of RGB color image when the image is captured for the purpose of acquiring a depth map. The superimposed patterns become unnecessary after the depth map is acquired. We tried to remove these unnecessary patterns by using a generative adversarial network (GAN) and an auto encoder (AE). The experimental results show that the patterns can be removed by using a GAN and AE to the point of being invisible. They also show that the performance of GAN is much higher than that of AE and that its PSNR and SSIM were over 45 and about 0.99, respectively. From the results, we demonstrate the effectiveness of the technique with a GAN.

KEYWORDS

GAN, Auto encoder, Depth map, Pattern removing.

Adaptive Filtering Remote Sensing Image Segmentation Network Based on Attention Mechanism

Cong zhong Wu, Hao Dong, Xuan jie Lin, Han tong Jiang, Li quan Wang and Xin zhi Liu, Department of Computer Engineering and Information , Hefei University of Technology, Anhui, P.R.China

ABSTRACT

It is difficult to segment small object and the edge of object in the remote sensing imagery because of larger-scale variation , larger intra-class variance of background , and foreground-background imbalance. In convolutional neural networks, high frequency signals may degenerate into completely different ones after downsampling. We define this phenomenon as aliasing. Meanwhile, although dilated convolution can expand receptive field of feature map, much more complex background can cause serious alarms. To tackle these ploblems, we propose an attention-based mechanism adaptive filtered segmentation network . The experimental results obtained using DeepGlobe Road Extraction dataset and Inria Aerial Image Labeling dataset suggest that the proposed method can achieve better performance.

KEYWORDS

Convolutional Neural Network, Remote Sensing Imagery Segmentation, Adaptive Filter, Attention Mechanism, Feature Fusion.

Facial Recognition-based Students Attendance Management System

Abdulrahman Olalekan Yusuf¹, Onuh Victor¹, Kehinde Ridwan Kamil² and Shehu Lukman Ayinla¹, ¹Department of Computer Engineering, University of Ilorin, Ilorin, Nigeria, ²Nigerian Building and Road Research Institute (NBRRI), Abuja, Nigeria

ABSTRACT

Tracking of students’ attendance and the problem of attendance by proxy or impersonation is a challenge faced in marking attendance in the institution, this paper proposes an automated students attendance management system using facial recognition. The system employs both the Viola-Jones face Detection Algorithm for automatic image capturing and the Local Binary Pattern Histogram (LBPH) Face Recognition Algorithm. Students’ attendances are marked by their various course lecturers at the venue of the lecture after the student might have registered 21 samples of their faces, including their courses. The results obtained show a system capable of accurately marking students’ attendance in classrooms with considerably good lighting conditions and deployed over a Local or a Metropolitan Area Network to serve students and lecturers in the various higher institutions of learning. The attendance time per student was approximately 3 seconds with a good network connection.

KEYWORDS

Local Binary Pattern Histogram (LBPH), Face Recognition, Face Detection, Viola-Jones algorithm, Open Computer Vision (OpenCV).

Emotion Predictions of Sentiment During Covid Pandemic using Intelligent Chatbots

Venkata Duvvuri, Chetan Kulkarni, Sritha Gogineni and Sumair Sayani, Northeastern University, United States of America

ABSTRACT

The COVID-19 pandemic has had a major impact around the world. Governments and businesses around the world are facing unprecedented decisions to either close up or reopen or drive other policies based on the sentiment of people. While, understanding this sentiment and accompanying emotions has been researched (especially in social media channels like Twitter), we propose a novel way to capture sentiment and emotions using intelligent chatbots (EmoBot) that reduces the participants biases inherent in prior analysis. We devise Emotion Extraction Layers (EEL) based on the latest deep learning techniques like BERT (Bidirectional Encoder Representations from Transformers) and compare these models with traditional machine learning models. We show for a variety of emotions that the new deep learning models predict 1-5% (Sad, Fearful & Angry) better than traditional machine learning techniques. Further, we showcase that leveraging retail sentiment data using transfer learning techniques can help cross the cold start chasm of having no chatbot data initially, and this technique achieves -8% closer in performance when compared to having enough COVID sentiment data.

KEYWORDS

COVID, sentiment analysis, chatbots, BERT, deep learning, transfer learning.

FINDTHATQUOTE: A Question-Answering Web-based System to Locate Quotes using Deep Learning and Natural-language Processing

Nathan Ji¹ and Yu Sun², ¹Portola High School, Irvine, CA, 92618, ²California State Polytechnic University, Pomona, CA, 91768

ABSTRACT

The digital age gives us access to a multitude of both information and mediums in which we can interpret information. A majority of the time, many people find interpreting such information difficult as the medium may not be as user friendly as possible. This project has examined the inquiry of how one can identify specific information in a given text based on a question. This inquiry is intended to streamline one's ability to determine the relevance of a given text relative to his objective. The project has an overall 80% success rate given 10 articles with three questions asked per article. This success rate indicates that this project is likely applicable to those who are asking for content level questions within an article.

KEYWORDS

Deep learning, question-answer engine, natural-language processing.

Enhancement of Consistent Depth Estimation for Monocular Videos Approach

Nikolay Tolstokulakov and Mohamed Sweilam, Department of Information technology, Novosibirsk state University, Russia

ABSTRACT

Depth estimation is a very attractive point of research recently due to its’ applications such as in Robotics field it can be used to provide the distance information needed for different applications, in addition to make the estimation of depth geometric consistency and without flickers is a challenge for a lot of researchers specially for the video applications work ,as it still has multiple challenges like the complexity of the neural network which effects on the run time and online processing.Moreover to use such input like monocular devices for depth estimation considered as a challenge too,specially for hand-held devices such as nowadays mobile phones are are very popular to use for capturing a video. Here in that work we focused on enhancing the existing consistent depth estimation for monocular videos to be with less usage of memory than the existing one and with using less number of parameters without having a significant reduction in the quality of the depth estimation during the video time.

KEYWORDS

Single image, monocular depth estimation, deep learning, geometric consistency, lightweight network.

Detection of Depression in Individuals using Video, Audio and Text Features

Anirudh Khatry, Shubham Pakhare, Nishit Salot, Sarang Ghode, Vijay Sambhe, Department of Computer Engineering and Information Technology, Veermata Jijabai Technological Institute, Mumbai, India

ABSTRACT

Automatic detection of depression has attracted increasing attention from researchers in psychology, computer science, linguistics, and related disciplines. As a result, promising depression detection systems have been reported. Depression is a major mental health disorder that is rapidly affecting lives worldwide. With the increase in demand to detect depression automatically with the help of machine learning algorithms, we decided to find a solution to detect depression in the early stages.In this paper, we address the Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ)[1] database, comprising clinical interviews and questionnaire assessments of over a hundred individuals and propose a method to detect depression using a multimodal approach.

KEYWORDS

Depression, Machine Learning, Ensemble Learning, Natural Language Processing, Multimodal approach.

An Analysis of a Bert Deep Learning Strategy on a Technology Assisted Review Task

Alexandros Ioannidis, Leif Azzopardi and Martin Halvey, Computer and Information Sciences Department, University of Strathclyde, Glasgow, UK

ABSTRACT

Document screening is a central task within EBM (Evidence-based Medicine), which is a clinical discipline that supplements scientific proof to back medical decisions. Given the recent advances in DL (Deep Learning) methods applied to IR (Information Retrieval) tasks, we propose a DL document classification approach with BERT (Bidirectional Encoder Representations from Transformers) or PubMedBERT embeddings and a DL similarity search path using SBERT (Sentence-BERT) embeddings to reduce physicians’ tasks of screening and classifying immense amounts of documents to answer clinical queries. We test and evaluate the retrieval effectiveness of our DL strategy on the 2017 and 2018 CLEF eHealth collections. We find that the proposed DL strategy works, we compare it to the recently successful BM25+RM3 (IR) model, and conclude that the suggested method accomplishes advanced retrieval performance in the initial ranking of the articles with the aforementioned datasets, for the CLEF eHealth Technologically Assisted Reviews in Empirical Medicine Task.

KEYWORDS

Systematic Reviews, Document Screening, Document Classification, Similarity Search, Evaluation.

Rational Mobile Application to Detect Language and Compose Annotations: Notespeak App

Yingzhi Ma¹ and Yu Sun², ¹Crean Lutheran High School, Irvine, CA 92618, ²California State Polytechnic University, Pomona, CA, 91768

ABSTRACT

Students in international classroom settings face difficulties comprehending and writing down data shared with them, which causes unnecessary frustration and misunderstanding. However, utilizing digital aids to record and store data can alleviate these issues and ensure comprehension by providing other means of studying/reinforcement. This paper presents an application to actively listen and write down notes for students as teachers instruct class. We applied our application to multiple class settings and company meetings, and conducted a qualitative evaluation of the approach.

KEYWORDS

Digital learning aids, digital note-taking, note-taking mobile applications.

Electromagnetic Analysis of an Ultralightweight Cipher: Present

Nilupulee A. Gunathilake¹, Ahmed Al-Dubai², William J. Buchanan¹, Owen Lo¹, ¹Blockpass ID Lab, School of Computing, Edinburgh Napier University, UK, ²School of Computing, Edinburgh Napier University, UK

ABSTRACT

Side-channel attacks are an unpredictable risk factor in cryptography. Therefore, continuous observations of physical leakages are essential to minimise vulnerabilities associated with cryptographic functions. Lightweight cryptography is a novel approach in progress towards internet-of-things (IoT) security.Thus, it would provide sufficient data and privacy protection in such a constrained ecosystem. IoT devices are resource-limited in terms of data rates (in Kbps), power maintainability (battery) as well as hardware and software footprints (physical size, internal memory, RAM/ROM). Due to the difficulty in handling conventional cryptographic algorithms, lightweight ciphers consist of small key sizes, block sizes and few operational rounds. Unlike in the past, affordability to perform side-channel attacks using inexpensive electronic circuitries is becoming a reality. Hence, cryptanalysis of physical leakage in these emerging ciphers is crucial. Among existing studies, power analysis seems to have enough attention in research, whereas other aspects such as electromagnetic, timing, cache and optical attacks continue to be appropriately evaluated to play a role in forensic analysis. As a result, we started analysing electromagnetic emission leakage of an ultra-lightweight block cipher, PRESENT. According to the literature, PRESENT promisesto be adequate for IoT devices, and there still seems not to exist any work regarding correlation electromagnetic analysis (CEMA) of it. Firstly, we conducted simple electromagnetic analysis in both time and frequency domains and then proceeded towards CEMA attack modelling. This paper provides a summary of the related literature (IoT, lightweight cryptography, side-channel attacks and EMA), our methodology, current outcomes and future plans for the optimised results.

KEYWORDS

Side-channel attacks, electromagnetic analysis, lightweight cryptography, PRESENT and IoT.

Applications of SKREM-like symmetric key ciphers

Mircea-Adrian Digulescu, Individual Researcher, Worldwide, Formerly: Department of Computer Science, Faculty of Mathematics and Computer Science, University of Bucharest, Romania

ABSTRACT

In a prior paper we introduced a new symmetric key encryption scheme called Short Key Random Encryption Machine (SKREM), for which we claimed excellent security guarantees. In this paper we present and briefy discuss some of its applications outside conventional data encryption. These are Secure Coin Flipping, Cryptographic Hashing, Zero-Leaked-Knowledge Authentication and Authorization and a Digital Signature scheme which can be employed on a block-chain. We also briefy recap SKREM-like ciphers and the assumptions on which their security are based. The above applications are novel because they do not involve public key cryptography. Furthermore, the security of SKREM-like ciphers is not based on hardness of some algebraic operations, thus not opening them up to specific quantum computing attacks.

KEYWORDS

Symmetric Key Encryption, Provable Security, One Time Pad, Zero Knowledge, Cryptographic Commit Protocol, Secure Coin Flipping, Authentication, Authorization, Cryptographic Hash, Digital Signature, Chaos Machine.

Hiding Data in Plain Sight: Towards Provably Unbreakable Encryption with Short Secret Keys and One-Way Functions

Mircea-Adrian Digulescu, Individual Researcher, Russian Federation, Formerly: Department of Computer Science, Faculty of Mathematics and Computer Science, University of Bucharest, Bucharest, Romania, Romania

ABSTRACT

It has long been known that cryptographic schemes offering provably unbreakable security exist - namely the One Time Pad (OTP). The OTP, however, comes at the cost of a very long secret key - as long as the plain-text itself. In this paper we propose an encryption scheme which we (boldly) claim offers the same level of security as the OTP, while allowing for much shorter keys, of size polylogarithmic in the computing power available to the adversary. The Scheme requires a large sequence of truly random words, of length polynomial in the both plain-text size and the logarithm of the computing power the adversary has. We claim that it ensures such an attacker cannot discern the cipher output from random data, except with small probability. We also show how it can be adapted to allow for several plain-texts to be encrypted in the same cipher output, with almost independent keys. Also, we describe how it can be used in lieu of a One Way Function.

KEYWORDS

Encryption, Provable Security, Chaos Machine, Truly Random, One Time Pad, One Way Function.

Secure Cloud Key Management based on Robust Secret Sharing

Ahmed Bentajer¹, Mustapha Hedabou², Sara Ennaama¹, and Abderrahim Tahiri¹, ¹SIGL LAB., ENSA of Tetouan, University Abdelmalek Essaadi Tetouan, Morocco, ²UM6P Benguerir, Morocco

ABSTRACT

Managing encryption keys on-premise infrastructure in not straightforward matter for IT teams. In cloud computing environments, where the model is shared or entirely controlled by the providers, it becomes a very challenging task. In addition to critical issue related to properly manage and secure the keys on providers infrastructures, cloud users have to deal with concerns specific to multi-cloud key management. Hardware Security Module (HSM) solution that offers a secure on-premise encryption key management turned out be impracticable for widespread cloud deployment. HSM as a Service seems to be the best approach for key management in multicloud but the service is wholly owned and managed by another cloud provider. In This paper we present an efficient and secure cloud key management that fulfills the requirements of multi-cloud deployment. The proposed design splits the master key into a blinded version of n shares that will be stored in encrypted format at the cloud provider side. Well established trusted computation and execution facilities will be leveraged to share, store and securely compute the key shares and reconstruction.

KEYWORDS

Key Management Security, Secret sharing, MultiCloud , Cryptography, Security and Privacy.

Ship identity authentication security model based on Blockchain

Qing Hu, Wenshuo Han and Hao Zhang, College of Information Science and Technology, Maritime University, Dalian, China

ABSTRACT

To meet the needs of shipowners, shipping authorities and third-party users, this paper investigates the ship information authentication system, which is based on blockchain smart contracts and security mechanisms, and proposes a framework for storing and sharing ship information based on blockchain in multichannel mode. In the framework, token-based authentication, attribute-based fine-grained permission control, and a combined security model, which is based on the transmission encryption of double asymmetric certificates, are developed to authenticate the identity of each agent and control the access rights of ship information in the blockchain data storage mode. We employ the automated verification tool Automated Validation of Internet Security Protocols and Applications to verify the security of the model. Additionally, the performance of the system is tested and analyzed, and the optimal parameter range is obtained. The proposed system can solve the long-standing trust and security problems of ship communication while guaranteeing performance.

KEYWORDS

Security, Blockchain Identity, authenticationl, Hyperledger Fabric, Shipping.

A review of daylighting optimization and design systems in buildings

Mona Rashidi Ghane^1*, and Hamed Reza H. Mohajer², ¹Iran University of Science and Technology, Tehran, Iran, ²University of New South Wales, New South Wales, Australia

ABSTRACT

Over the past years, designers and researchers developed different tools and various methods to understand daylighting performance inside buildings and create a more sustainable built environment. Despite a wide range of developed and commercially simulation tools that provide the user with valuable information, their application to help the architectural design procedure is limited, due to the complexity, cost, time, etc. This study critically reviews the conducted research on daylighting for building performance and design methods for daylighting optimization and compares simulation tools which analyse daylight in buildings, such as, full-scale models for field measurement, scale models, and computer simulation software. Such information can inform specialists and designers about the effectiveness of the developed systems and the potential of daylighting control systems and thermal comfort as well as lighting energy consumption. This research also presents a review that reveals the knowledge gaps and highlights the future perspectives to improve the daylighting performance through totally interactive designs.

KEYWORDS

daylighting, design process, energy, interactive, simulation, visualization; Building Design.

Framework for Enterprise Local Area Network Design: An Object-Connectivity Approach

Sunil Seneviratne¹ and Rohan de Silva², ¹School of Engineering and Technology Central Queensland University Melbourne, Australia, ²School of Engineering and Technology Central Queensland University Sydney, Australia

ABSTRACT

Local Area Networks (LANs) provide necessary infrastructure and services required for organizations to conduct their businesses efficiently and securely. While a small LAN could be designed and deployed in ad-hoc fashion, an enterprise LAN should be designed systematically starting from analyzing the business and technical requirements and constraints. The Top-down network design methodology has been an excellent way to design a new network, however it has disadvantages considering its time-consuming process of designing networks. The organizational structures change in a fast pace in these days. These changes require their networks also to be realigned at the same pace. In this climate, a time-consuming design approach such as the Top-down design approach may not be the best option. In this paper, we propose a framework of a network design methodology that could be used for quickly designing a LAN for an organization in this landscape while satisfying their business and technical requirements.

KEYWORDS

LAN, Network design, Object-connectivity, Top- down network design.

Global Systems Performance Analysis for Mobile Communications (GSM) using Cellular Network Codecs

Maphuthego Etu Maditsi, Thulani Phakathi, Francis Lugayizi and Michael Esiefarienrhe, Department of Computer Science, North West University, Mahikeng, South Africa

ABSTRACT

Global System for Mobile Communications (GSM) is a cellular network that is popular and has been growing in recent years. It was developed to solve fragmentation issues of the first cellular system and it addresses digital modulation methods, level of the network structure, and services. It is fundamental for organizations to become learning organizations to keep up with the technology changes for network services to be at a competitive level. A simulation analysis using the NetSim tool is presented for comparing different cellular network codecs for GSM network performance. Performance parameters such as throughput, delay, and jitter are analyzed for the quality of service provided by each network codec. Unicast application for the cellular network is modeled for different network scenarios. Depending on the evaluation and simulation, we discovered that G.711, GSM_FR, and GSM-EFR performed better than the other codecs and they are considered to be the best codecs for cellular networks.

KEYWORDS

GSM, CODECS, Cellular Network & Performance Analysis.

Block-Chain and Smart Contract based Medical Health Data Privacy Preservation

Alpesh Vaghela¹ and Anilkumar Suthar², ¹Department of Computer Science, Gujarat Technological University, Gujarat, India, ²Director, New LJIET, Gujarat, India

ABSTRACT

This paper describes a block chain-based privacy preservation method for medical health records. IoT, e-health, and cloud-based hospital management systems have all become important components of our health-care system. Hospital patient data is extremely sensitive, and preserving patient data privacy appears to be difficult. The current method for data storage is to use a cloud storage system. All of this is done through a centralised system, and some management people control everything, which increases the risk of data breach. We propose a block-chain architecture for the privacy of medical health data. We take advantage of the unique characteristics of Block-chain (BC), such as immutability and user anonymity, while modifying the traditional blockchain structure to overcome its limitations in IoT applications (i.e., low throughput, high overhead and latency). To that end, we're clustering the BC miners and sending data to the nearest patient cluster with various keywords and multi-functionality.

KEYWORDS

Block-Chain(BC), IPFS (The Inter Planetary File System), Smart Contract, Bigdata.

Web Scraper Utilizes Google Street View Images to Power a University Tour

Peiyuan Sun¹ and Yu Sun², ¹Webb School of California, Claremont, CA 91711, ²California State Polytechnic University, Pomona, CA, 91768

ABSTRACT

Due to the outbreak of the Covid-19 pandemic, college tours are no longer available, so many students have lost the opportunity to see their dream school’s campus. To solve this problem, we developed a product called “Virtourgo,” a university virtual tour website that uses Google Street View images gathered from a web scraper allowing students to see what college campuses are like even when tours are unavailable during the pandemic. The project consists of 3/4 parts: the web scraper script, the GitHub server, the Google Domains DNS Server, and the HTML files. Some challenges we met include scraping repeated pictures and letting the HTML dropdown menu jump to the correct location. We solved these by implementing Python and Javascript functions that specifically target such challenges. Finally, after experimenting with all the functions of the web scraper and website, we confirmed that it works as expected and can scrape and deliver tours of any university campus or public buildings we want.

KEYWORDS

Web Scraping, Virtual Tour, Cloud Computing.

A Video Note Taking System to Make Online Video Learning Easier

Haochen Han¹ and Yu Sun², ¹Haochen Han, Troy High School, Fullerton, CA 92831, ²California State Polytechnic University, Pomona, CA, 91768

ABSTRACT

Recent coronavirus lockdowns have had a significant impact on how students study. As states shut down schools, millions of students are now required to study at home with pre-recorded videos. This, however, proves challenging, as teachers have no way of knowing whether or not students are paying attention to the videos, and students may be easily distracted from important parts of the videos. Currently, there is virtually no research and development of applications revolving specifically around the subject of effectively taking digital notes from videos. This paper introduces the web application we developed for streamlined, video-focused auto-schematic note-taking. We applied our application to school-related video lectures and conducted a qualitative evaluation of the approach. The results show that the tools increase productivity when taking notes from a video, and are more effective and informational than conventional paper notes.

KEYWORDS

Web Service, Note Taking, React JS.