International Conference on Natural Language Computing and AI (NLCAI)

July 25-26, 2020, London, United Kingdom

Accepted Papers


Investigation of Faster-RCNN Inception RESNET V2 on Off-line Kanji Handwriting Characters

Anthony A. Adole1, Dr. Chris Bearchell2 and Prof. Eran Edirisinghe1, 1Department of Computer Science, EPSRC Centre for Doctoral Training in Embedded Intelligence, Loughborough University, Leicestershire, UK, 2Surface Intelligence, Oxford, UK

ABSTRACT

In recent years detection and recognition of Off-line handwriting character has being a major task in the computer vision sector, researchers are looking on to developing deep learning models to avoid the traditional approaches which involves the tedious task of using the conventional methods for feature extraction and localization. However, state-of-the-art object detection modelsrely upon region proposal algorithms as a result, they settle for object locations principles, such network reduces thetime period of those detection network, exposing region proposal computation as a bottleneck. Faster-RCNN is a popular model used for recognition purpose in many recognition tasks, the goal of this paper is to serve as a guide for Multi-Classification on offline Handwriting Document using Pre-trained Faster-RCNN with inception resnet v2 feature Extractor. The result obtained from the experiments shows improved pre-trained models can be used insolving the research question concerning handwriting detection and recognition.

KEYWORDS

Offline Handwriting recognition and detection, faster-RCNN, inception resnet v2, Kanji handwriting, Japanese offline document, recognition and detection


An Inner/Outer Loop Ensemble-variational Data Assimilation Method

Yueqi Han1,2, Bo Yang1,2, Yun Zhang1, Bojiang Yang1 and Yapeng Fu1,2, 1College of Meteorology and Oceanography, National University of Defense Technology, Nanjing, China, 2National Key Laboratory on Electromagnetic Environmental Effects and Electro-optical Engineering, PLA Army Engineering University, Nanjing, China

ABSTRACT

Data assimilation (DA) for the non-differentiable parameterized moist physical processes is a complicated and difficult problem, which may result in the discontinuity of the cost function (CF) and the emergence of multiple extreme values. To solve the problem, this paper proposes an inner/outer loop ensemble-variational algorithm (I/OLEnVar) to DA. It uses several continuous sequences of local linear quadratic functions with single extreme values to approximate the actual nonlinear CF so as to have extreme point sequences of these functions converge to the global minimum of the nonlinear CF. This algorithm requires no adjoint model and no modification of the original nonlinear numerical model, so it is convenient and easy to design in assimilating the observational data during the non-differentiable process. Numerical experimental results of DA for the non-differentiable problem in moist physical processes indicate that the I/OLEnVar algorithm is feasible and effective. It can increase the assimilation accuracy and thus obtain satisfactory results. This algorithm lays the foundation for the application of I/OLEnVar method to the precipitation observational data assimilation in the numerical weather prediction (NWP) model.

KEYWORDS

Ensemble-variational Data Assimilation, Non-differentiable, Inner/Outer Loop


Using SDR Platform to Extract The RF Fingerprint of the Wireless Devices for Device Identification

Ting-Yu Lin, Chia-Min Lai and Chi-Wei Chen, Institute for Information Industry, Taipei, R.O.C

ABSTRACT

Due to the advent of the Internet of Things era, the number of related wireless devices is increasing, making the abundant and complex information networks formed by communication between devices. Therefore, security and trust between devices a huge challenge. In the traditional identification method, there are identifiers such as hash-based message authentication code, key, and so on, often used to mark a message that the receiving end can verify it. However, this kind of identifiers is easy to tamper. Therefore, recently researchers address the idea that using RF fingerprint, also called radio frequency fingerprint, for identification. Our paper demonstrates a method that extracts properties and identifies each device. We achieved a high identification rate, 99.9% accuracy in our experiments where the devices communicate with Wi-Fi protocol. The proposed method can be used as a stand-alone identification feature, or for two-factor authentication.

KEYWORDS

Internet-of-Things (IoT), Authentication, RF fingerprint, Machine Learning (ML), Device Identification


Change Detection using Synthetic Aperture Radar Videos

Hasara Maithree, Dilan Dinushka and Adeesha Wijayasiri, Department of Computer Science and Engineering, University of Moratuwa, Moratuwa, Sri Lanka

ABSTRACT

Many researches have been carried out for change detection using temporal SAR images. In this paper an algorithm for change detection using SAR videos has been proposed. There are various challenges related to SAR videos such as high level of speckle noise, rotation of SAR image frames of the video around a particular axis due to the circular movement of airborne vehicle, non-uniform back scattering of SAR pulses. Hence conventional change detection algorithms used for optical videos and SAR temporal images cannot be directly utilized for SAR videos. We propose an algorithm which is a combination of optical flow calculation using Lucas Kanade (LK) method and blob detection. The developed method follows a four steps approach: image filtering and enhancement, applying LK method, blob analysis and combining LK method with blob analysis. The performance of the developed approach was tested on SAR videos available on Sandia National Laborataries website and SAR videos generated by a SAR simulator.

KEYWORDS

Remote Sensing, SAR videos, Change Detection


Derivation of Loop Gain And Stability Test for Low-pass Tow-Thomas Biquad Filter

MinhTri Tran, Anna Kuwana and Haruo Kobayashi, Division of Electronics and Informatics, Gunma University, Kiryu 376-8515, Japan

ABSTRACT

Proposed derivation and measurement of self-loop function for a low-pass Tow Thomas biquadratic filter are introduced. The self-loop function of this filter is derived and analyzed based on the widened superposition principle. The alternating current conservation technique is proposed to measure the selfloop function. Research results show that the selected passive components (resistors, capacitors) of the frequency compensation of Miller’s capacitors in the operational amplifier and the Tow Thomas filter can cause a damped oscillation noise when the stable conditions for the transfer functions of these networks are not satisfied.

KEYWORDS

Superposition, Self-loop Function, Stability Test, Tow-Thomas Biquadratic Filter, Voltage Injection


Design of Active Inductor and Stability Test for Passive RLC Low-Pass Filter

MinhTri Tran, Anna Kuwana, and Haruo Kobayashi, Division of Electronics and Informatics, Gunma University, Kiryu 376-8515, Japan

ABSTRACT

Proposed stability test for RLC low-pass filters is presented. The self-loop functions of these filters are derived and analyzed based on the widened superposition principle. The alternating current conservation technique is proposed to measure the self-loop function. An active inductor is replaced with a general impedance converter. Our research results show that the values of the selected passive components (resistors, capacitors, and inductors) in these filters can cause a damped oscillation noise when the stable conditions for the transfer functions of these networks are not satisfied.

KEYWORDS

Widened Superposition, RLC Low-Pass Filter, Stability Test, Self-loop Function, Voltage Injection


A Spectrally Efficient MU-MIMO Turbo Receiver

Ye-Shun Shen, Fang-Biau Ueng* and Hung-Sheng Wang, Department of Electrical Engineering,National Chung-Hsing University, Taichung, Taiwan

ABSTRACT

Single carrier-frequency division multiple access (SC-FDMA) has been adopted as the uplink transmission standard in fourth generation cellular network to enable the power efficiency transmission in mobile station. Since multiuser multiple input multiple output (MU-MIMO) is a promising technology to fully exploit the channel capacity in mobile radio network, this paper investigates the uplink transmission of MU-MIMO SC-FDMA system with orthogonal space frequency block codes (SFBC). It is preferable to minimize the length of the cyclic prefix (CP) to improve the transmission energy and spectrum efficiency. Several techniques for block transmission without CP have been investigated. CP removal at the transmitter is compensated by a CP reconstruction at the receiver where only the past interference components are considered. In this paper, the chained turbo equalization with chained turbo estimation is employed in the designed receiver. The chained turbo estimation employs short training sequence (TR) that can improve the spectrum efficiency without sacrificing the estimation accuracy. In this paper, we propose a novel spectrally efficient iterative joint channel estimation, multiuser detection and turbo equalization for MU-MIMO SC-FDMA system without CP and with short TR. Some simulation examples for uplink scenario are given to demonstrate the effectiveness of the proposed scheme.

KEYWORDS

MU-MIMO SC-FDMA, chained turbo equalization, chained turbo estimation


Automated Classification of EEG Signals using Bagging and Boosting

Abdulhamit Subasi, Saeed Mian Qaisar, Effat University, College of Engineering, Jeddah, 21478, Saudi Arabia

ABSTRACT

In the cerebral surgery the positioning of epileptic foci is an elementary step. It is carried out by detecting the seizure in the electroencephalographic (EEG) recordings. In this framework, EEG Signals are composed of two classes, focal and non-focal. The focal signals are captured from brain areas in which the initial modifications to ictal EEG are sensed. The non-focal signals are recorded from brain areas that are not included at the seizure onset. A new focus area localization method is introduced based on various ensemble machine learning strategies and signal processing approach. The efficiency of the proposed method is assessed using classification accuracy, the area under the receiver operating characteristic (ROC) curve (AUC), and the F-measure. The system attains 98.8% accuracy. It confirms the potential of using the proposed solution in modern EEG analysis systems.

KEYWORDS

Electroencephalogram (EEG), Auto regressive (AR) method, Ensemble Machine Learning Methods.


Fast Prototyping Executable Model for Distributed Embedded Systems

Tubonimi Jenewari and David Mulvaney, Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, UK

ABSTRACT

The aim of the research is to provide a framework for prototyping executable model for distributed embedded system, it includes both hardware and software, in this way the modelling and implementation process will allow seamless execution of distributed system at functional, hardware simulation and hardware realization levels. An example of the setup will be an ABS (anti-lock braking system) that comprises of system components; sensor, actuator, ECU’s which are interconnected through the vehicle communication bus. Software automobile vehicle model will provide the vehicle dynamics and the model of the ABS that will mimic the functionality of ABS. Multiple processors (ECU) will be interconnected in a distributed format to reduce the time of execution.
All this is captured in a Unified Modelling Language (UML) a standard for the idea of the design to be captured, expressive enough to be understood. The UML notation will be converted to Extensible Markup Language (XML), and a parser code written to extract all the necessary class information (to get the classes where the simulation code will come in) from the overwhelming lines of XML to be transferred to the ECU for execution of the simulation. The simulation is carried out using GDB.

KEYWORDS

Prototyping, distributed embedded system, executable model, UML


Ready to Use Virtual Machine Pool Cache usingWarm Cache

Sudeep Kumar, Deepak Kumar Vasthimal, and Musen Wen, eBay Inc., 2025 Hamilton Ave, San Jose, CA 95125, USA

ABSTRACT

Today, a plethora of distributed applications are managed on internally hosted cloud platforms. Such managed platforms are often multi tenant by nature and not specifically tied to a single use-case. Smaller footprint of infrastructure on a managed cloud platform has its own set of challenges especially when applications are required to be infrastructure aware for quicker deployments and response times. There are often times and challenges to quickly spawn ready to use instances or hosts on such infrastructure. As part of this paper we outline mechanisms to quickly spawn ready to use instances for application while also being infrastructure aware. In addition, paper proposes architecture that provides high availability to deployed distributed applications.

KEYWORDS

cloud computing, virtual machine, elastic, elastic search, consul, cache, java, kibana, mongoDB, high performance computing, architecture.


Synchronization Aspects in 5G

Mridula Korde, Department of Electronics and Communication Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, India

ABSTRACT

Increasing Internet data traffic has driven the capacity demands for currently deployed 3G and 4G wireless technologies. Now, intensive research toward 5th generation wireless communication networks is progressing in many fronts. 5G technologies are expected to be in use around 2020. Moving toward 5G, network synchronization is expected to play a key role in the successful deployment of the new mobile communication networks. Synchronization is an essential prerequisite for all mobile networks to operate. It’s fundamental to data integrity, and without it data will suffer errors and networks can suffer outages. ‘Loss of synchronization’ problems can be difficult to diagnose and resolve quickly and add to operational costs. Poor synchronization affects customer satisfaction and is therefore revenue affecting too. This paper presents synchronization requirement and related aspects in upcoming 5G technologies.

KEYWORDS

5G, MIMO, Synchronization


Experience in the Implementation of Wi-Fi Community Solution for Rural In Kiulu, Sabah

Rosli Uzairi and Ir. Badrul Zaman Adnan, Department of Research and Development, MIMOS Berhad, Malaysia

ABSTRACT

The implementation of communication technology and infrastructure has it challenges especially in the rural area when there are issues that needs exploration such as the basic infrastructure, size of coverage and the right application to suit the local flavour in order to bridge the digital divide plus mitigating the gap between urban and rural in terms of internet literacy. The project in Kiulu, Sabah presents our involvement in planning, wireless infrastructure design, site survey, interaction with local authorities and communities, site preparation and implementation, operations and management of community based communications solution. There was no internet access in this area prior to this project therefore this effort receive overwhelming support from the public. The contribution of this exercise includes the sharing of deployment experience together with the successful execution of a locally developed high tech radio in mesh and point to point topology.

KEYWORDS

Community Wi-Fi, Wi-Fi Mesh rural area, Solution Wi-Fi rural area


Apply Modern Statistical Clustering Analysis on Detecting Altitude Sickness and Sports Fatigue Behavior

Mason Chen, OHS, Stanford University, Palo Alto, USA

ABSTRACT

This paper will address Altitude Sickness risk when hiking on the high Mountains. It’s very risky if the people are not aware of their altitude sickness symptom such as Fatigue, Headache, Dizziness, Insomnia, Shortness of breath during exertion, Nausea, Decreased appetite. The consequence of altitude sickness could be dangerous on the inconvenient high mountains. Pulse Oximeter was used to monitor the Oxygen% and Heart Beat at different altitude levels from near-sea level in San Jose, Denver (5,000 Feet), Estes Park (8,000 Feet), Rocky Mountains Alpine Center (12,000 Feet). 2.5-mins Jumping Rope exercise was conducted to analyze the fatigue behavior associated with Altitude Sickness. Statistical analysis was conducted to verify several hypotheses to predict the Altitude Sickness Risk as well as the Exercise Fatigue Behavior. This paper has demonstrated how to assess their body strength and readiness before they may take a strenuous hiking on the high mountains.

KEYWORDS

JMP, Statistics, Altitude Sickness, Data Mining, AI


A Volume-adaptive Big Data Model in Relational Databases

Emeka Ogbuju, Federal University Lokoja, Nigeria

ABSTRACT

Big data has been defined in terms of the V-dimensions, namely volume, variety and velocity to mention a few. It is within the context of this definition of big data that some database models have been faulted and departure from their usage contemplated by the database community. The drive towards a one-size-fits-all the dimensions of data as proposed by several researchers may end up as a mirage given that the application area determines the priority each dimension gets in a software development project. A transaction-laden application may demand more of the volume dimension of big data and a guarantee of the ACID properties of its transaction than a variety of data types. Given that it is not always the case that all the dimensions are required on every application, this paper is of the view that it may yield more results if database models are rated and used on the basis of their inherent strengths augmented by the extent to which they can be made adaptive to some or all the V-dimensions of data. Based on this submission, a volume-adaptive big data model of the relational database model is proposed. The model partitions a relation such that the sum of all partitions makes up the original relation. The query times of equivalent queries on the original and any of the partitions show that the query time of the partitions are well optimised relative to the query time of the original relation. The partitions are scalable across several servers and in this way, the model adapts to the volume dimension of data and at the same time, takes advantage of the ACID properties of the relational database model.

KEYWORDS

Big Data, V-dimensions of data, adaptive model of relational DBMS, application prototypes, NoSQL, ACID properties


A Review of Behavior Analysis of College Students

Wei-hong WANG, Hong-yan LV, Yu-hui CAO, Lei SUN, Qian FENG, School of Information Technology, Hebei University of Economics and Business, Shijiazhuang, China

ABSTRACT

Student behavior analysis plays an increasingly important role in education data mining research, but it lacks systematic analysis and summary. Based on reading a large amount of literature, this paper has carried out the overall framework, methods and applications of its research. Comprehensive combing and elaboration. Firstly, statistical analysis and knowledge map analysis of the relevant literature on student behavior analysis in the CNKI database are carried out, and then the research trends and research hot spots are obtained. Then, from the different perspectives of the overall process and technical support of student behavior analysis, the overall framework of the research is constructed, and the student behavior evaluation indicators, student portraits and used tools and methods are highlighted. Finally, it summarizes the principal applications of student behavior analysis and points out the future research direction..

KEYWORDS

Student Behavior, Knowledge Graph, Behavior Analysis, Student Portraits, Data Mining


Employee Scheduling As A Machine Learning Problem

Fred N. Kiwanuka, Louay Karadsheh, Ja’far alqatawna, and Anang Hudaya Muhamad Amin, Higher Colleges of Technology, Dubai Men’s College, Dubai

ABSTRACT

The problem of developing a high quality employee schedule has been studied by many researchers and is now widely used by many organization in an attempt to automate and achieve high quality scheduling. The challenge is how to develop a proper quality schedule that e?ectively caters for employee needs and satisfaction. During process of employee scheduling many constraints have to be considered and may require negotiating a large dimension of hard and soft constraints. These constraints make scheduling a complex task. The problem with current scheduling models is that, they are rigid and always sacri?ce the soft constraints. Current scheduling algorithms are mostly modeled as NP problems or constraint optimization problems and this comes with massive computational complexity. In this research, we propose a machine learning approach that takes advantage of mining user-de?ned constraints or soft constraints and transforms these constraints into a classi?cation problem. We propose automatically extracting employee personal schedules like calendars in order to extract their availability. We then show how to use the extracted knowledge to formulate a machine learning problem in order to generate a schedule for faculty sta? in a University that supports ?exible working. We show that the results of this approach are comparable to that of a constraint satisfaction and optimization method that is commonly used in literature. Results show although our approach achieved a performance of 86.4% of satisfying all constraints as compared to 92.7% of a common SAT Solver approach, it was simpler and faster to implement.

KEYWORDS

Schedulin, Constraint Programming, Data mining, Machine Learning, Deep Learning


Coding with Logistic Softmax Sparse Units

Gustavo A. Lado and Enrique C. Segura, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires

ABSTRACT

This paper presents a new technique for efficient coding of highly dimensional vectors, overcoming the typical drawbacks of classical approaches, both of the type of local representations and those of distributed codifications. The main advantages and disadvantages of those classical approaches are revised and a novel, fully parameterizaed strategy is introduced to obtain representations of intermediate levels of locality and sparsity, according to the neccesities of the particular problem to deal with. The proposed method, called COLOSSUS (COding with LOgistic Softmax Sparse UnitS) is based on an algorithm that permits a smooth transition between both extreme behaviors -local, distributed- via a parameter that regulates the sparsity of the representation. The activation function is of the logistic type. We propose an appropiate cost function and derive a learning rule that happens to be similar to the Oja's Hebbian learning rule. Experiments are reported that show the efficiency of the proposed technique.

KEYWORDS

Neural Networks, Sparse Coding, Autoencoders


Asynchronous Advantage Actor-critic Based Autonomous Obstacle of Four-axis UAV

Haixin Wang1,2 and Jianxin Shen1, 1College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China and 2School of Mechanical & Aerospace Engineering, Queen's University Belfast, Belfast, UK

ABSTRACT

One of the challenges for autonomous aircraft is safe and reliable navigation in urban or indoor environments. The path planning of aerial robot is a complicated task due to the factors such as the decreasing accuracy of global positioning system (GPS), the narrow space and the dynamic movement of obstacles. To navigate effectively in such an environment, one of the skills an agent needs is to develop the ability to avoid collisions. In this study, we investigated a possible approximation (called a partially observable Markov decision process) to improve the performance of autonomous UAVS in GPS-free environments by combing the newly developed A3C reinforcement learning approach. Developing and testing algorithms in the real world is expensive and time consuming for drones. In addition, taking advantage of current research and advances in machine intelligence requires the collection of extensive training and testing to determine changes in conditions and environment. This article leverages open source tools such as Microsoft’s state-of-the-art drone simulator Airsim, a machine learning framework that leverages TensorFlow, a tensor library of Google. The main method of the paper is Asynchronous Advantage Actor-Critic network.

KEYWORDS

Unmanned aerial vehicle, Asynchronous advantage actor-critic algorithm, Simulation, Path planning


Lessons Learned with Developing A Web Application with Reactjs

Hung Lay2, Peiqi Gu1 and Yu Sun2, 1University High School, 2California State Polytechnic University, Pomona, CA 91768

ABSTRACT

The Internet has been rapidly increasing and people are using it in almost everything for their daily life. Thus, a good web application or mobile application will offer many advantages for users. Web technology innovations in the last few years allow software designers with the help of feature-rich frameworks such as e.g. jQuery, AngularJS, Bootstrap, ReactJS and Node.js to quickly develop responsive mobile-friendly applications [3, 4] The main purpose of this project is to develop a web application by using JavaScript as the base frontend library. JavaScript is a client-side scripting language which is used for providing dynamic attributes and features to the HTML webpages. JavaScript was developed for supporting the browser with feature of asynchronous communication, controlling the browser and for user interaction with the web page components [2]. One of the most challenges in developing web-applications that developers must be tackled is simplicity. This project will detail the development of using ReactJS technologies for building a front-end web application with LoopBack for back-end. React is a library for building composable user interfaces and React is one of the best solutions for front-end programming. It’s fast, scalable, flexible, powerful, and it has been growing in the developer’s community. Therefore, it will be a good time to start to learn and use React to develop a web application.

KEYWORDS

Web Application, ReactJS


Big Data and Astronomy: A Statistical Approach

F. Barbato and M. Giacalone, University of Naples Federico II, Italy

ABSTRACT

This paper aims to show how Big Data analysis can direct scientific investigations in the field of astronomy, of which a considerable amount of data is available. The goal is to use Big Data and statistical approach to make decisions about the events to be investigated regarding a specific problem, in particular the analysis of celestial bodies that could meet the conditions of planetary habitability. In particular, starting from an initial number of 120000 celestial bodies, the statistical approach will allow to consider only 2367, reducing scientific analyses, and therefore times and costs, by 98%.

KEYWORDS

Astronomy, Stars, Big Data, planetary habitability


Analysis and Defense Mechanism for Blockchain Network Security Issues

Sheikh Mohamad Arsalan and Farooq Hussain, Department of Computer Engineering, University of Technology Sydney, Sydney, Nsw, Australia

ABSTRACT

The blockchain innovation is accepted by numerous individuals to be a distinct advantage in numerous application areas, particularly monetary applications. While the origin of blockchain innovation (i.e., Blockchain 1.0) is used exclusivel y for digital currency purposes, the later era (i.e., Blockchain 2.0), as described by Ethereum, is a transparent and decentralised stage that empowers another authentication paradigm — Decentralized Applications (DApps) running through blockchains. DApps ' rich applications and semantics inevitably pose numerous vulnerabilities to protection, which have no partners in unadulterated digital money systems like Bitcoin. Since Ethereum is another, yet intricate, framework, it is basic to have an orderly and far reaching understanding on its security from an all-encompassing point of view, which is inaccessible. As far as we could possibly know, the present review, which can likewise be utilized as an instructional exercise, fills this void. In particular, we systematise three parts of Ethereum's security frameworks: vulnerabilities, assaults, and resistance. We bring bits of knowledge into, in addition to other things, powerlessness underlying drivers, assault outcomes, and barrier abilities, which shed light on future research.

KEYWORDS

Blockchain, Ethereum, Security, Smart Con-tract, Network


Agreements between Enterprises Digitized by Smart Contracts in the Domain of Industry 4.0

Kevin Wallis, Jan Stodt, Eugen Jastremskoj, and Christoph Reich, Furtwangen University of Applied Science, Germany

ABSTRACT

The digital transformation of companies is expected to increase the digital interconnection between different companies to develop optimized, customized, hybrid business models. These cross-company business models require secure, reliable and traceable logging and monitoring of contractually agreed information sharing between machine tools, operators and service providers. This paper discusses how the major requirements for building hybrid business models can be tackled by the blockchain for building a chain of trust and smart contracts for digitized contracts. A machine maintenance use case is used to discuss the readiness of smart contracts for the automation of workflows defined in contracts.

KEYWORDS

Blockchain, Smart Contracts, Industry 4.0, Digitized Agreements, Maintenance


Data Confidentiality in P2P Communication and Smart Contracts of Blockchain in Industry 4.0

Jan Stodt and Christoph Reich, Institute for Data Science, Cloud Computing, and IT Security at the University of Applied Sciences Furtwangen, Furtwangen, Baden-W¨urttemberg, Germany

ABSTRACT

Increased collaborative production and dynamic selection of production partners within industry 4.0 manufacturing leads to everincreasing automatic data exchange between companies. Automatic and unsupervised data exchange creates new attack vectors, which could be used by a malicious insider to leak secrets via an otherwise considered secure channel without anyone noticing. In this paper we reflect upon approaches to prevent the exposure of secret data via blockchain technology. We show that previous blockchain based privacy protection approaches offer protection, but give the control of the data to (potentially not trustworthy) third parties, which also can be considered as a privacy violation. The approach taken in this paper is not utilize centralized data storage for data. It realizes data confidentiality of P2P communication and data processing in smart contracts of blockchains.

KEYWORDS

blockchain, privacy protection, P2P communication, smart contracts, industry 4.0


Arabot: A Chatbot for Covid-19 Concerns

Heba Almorad, Sara Helal, and Abdulhamit Subasi, College of Engineering, Effat University, Jeddah, Saudi Arabia

ABSTRACT

ABSTRACT A chatbot is an intelligent conversation simulator that interacts with users utilizing natural languages. This technology has the power to give the illusion of a true human-to-human interaction in a wide application, i.e., health consultation. Due to the exclusively explosive demand for Coronavirus disease (COVID-19) awareness guidelines, this paper proposes an Arabic chatbot (Arabot) that answers any question regarding this disease to take the burden off consultation centres. Many websites and organizations have provided tips and advice to prevent spreading the virus to others. However, none were in an Arabic, interactive, and intelligent approach. Thus, Arabot is a retrieval based self-learning program that is trained based on the World Health Organization (WHO) database to provide reliable assistance.

KEYWORDS

Artificial intelligence (AI), chatterbot, Arabic chatbot, Python, chatterbot corpus, neural network (NN), heuristics, coronavirus disease (COVID-19), World Health Organization (WHO).