Accepted Papers

Improving the Digital Security of Smart Energy Systems with Smart Contracts

Pekka Koskela, Jarno Salonen and Juha Pärssinen, VTT Technical Research Centre of Finland, P.O. Box 1000, FI-02044 VTT, Finland

ABSTRACT

This paper explores how the digital security of smart energy systems could be improved through smart contracts and what new cybersecurity threats and risks will the introduction of smart contracts produce and how prevent them.

KEYWORDS

Blockchain, Smart contracts, Smart energy systems.

A Technological Approach to Address Deficiencies in UID (Aadhaar)

Rishabh Garg, Department of Electrical & Electronics Engineering,Birla Institute of Technology & Science, K.K. Birla Goa Campus, Sancoale, Goa - 403726, India

ABSTRACT

In order to provide an official identity to every citizen of India, the Department of Information Technology, had proposed an idea of biometric enabled unique identification number (UID). The system preserves the personally identifiable information (PII) of millions of users on a centralized government database, supported by some legacy software, with numerous SPOF (single points of failures). Such a centralized system, containing PII, acts like a honey pot to hackers. In a country where 2/3rd of the vulnerable citizens, do not have a bank account, but own a smartphone, echoes the possibility of a mobile based digital identity solution. The blockchain technology, by virtue of its key features like decentralization, persistency, anonymity and auditability, emerges as the most promising one amongst all.

KEYWORDS

Asynchronous Byzantine Fault Tolerance, Authentication, Blockchain, Channel, Cryptography, DApps,Data Portability, Decentralized Public Key Infrastructure (DPKI), DID, Ethereum, Hash function, Hashgraph, IAM framework, Identity Management System (IMS), Private Key, Public Key, Proof of Work algorithm, Revocation, SSI, Validation, Zero Knowledge Proof.

Security Concerns for Blockchain Based Sharing of Mobile Student Credential

Timothy Arndt, Department of Information Systems, Cleveland State University, Cleveland, OH, USA

ABSTRACT

Blockchain has recently taken off as a disruptive technology, from its initial use in cryptocurrencies to wider applications in areas such as property registration and insurance due to its characteristic as a distributed ledger which can remove the need for a trusted third party to facilitate transaction. This spread of the technology to new application areas has been driven by the development of smart contracts – blockchain-based protocols which can automatically enforce a contract by executing code based on the logic expressed in the contract. One exciting area for blockchain is higher education. Students in higher education are ever more mobile, and in an ever more agile world, the friction and delays caused by multiple levels of administration in higher education can cause many anxieties and hardships for students as well as potential employers who need to examine and evaluate student credentials. Distance learning as a primary platform for higher education promises to open up higher education to a wider range of learners than ever before. Blockchain-based storage of academic credentials is being widely studied due to the advantages it can bring. As with any network-based system, blockchain comes with a number of security and privacy concerns. Blockchain needs to meet several security-related requirements in order to be widely accepted: decentralization; confidentiality; integrity; transparency; and immutability. Researchers have been busy devising schemes to ensure that such requirements can be met in blockchain-based systems. Several types of blockchain-specific attacks have been identified: 51% attacks; malicious contracts; spam attacks; mining pools; targeted DDoS attacks; and others. Real-world attacks on blockchain-based systems have been seen on cryptocurrency sites. In this paper, we will at look at the specific privacy and security concerns for blockchain-based systems used for academic credentials as well as suggested solutions. We also examine the issues for academic credentials which are stored “off-chain” in such systems (as is often the case).

KEYWORDS

Blockchain, Mobile Education, Higher Education, Privacy, Security.

Blockchain-based Price Priority Matching Energy Trading Algorithm

Qingyu Meng and Zhaogong Zhang, Department of Computer Science and Technology, Heilongjiang University, Harbin, China

ABSTRACT

With the rapid development of energy Internet, the implementation of smart meters and distributed energy resources (DER) provide new opportunities for consumers. Distributed energy needs a new settlement mechanism. Blockchain technology can provide a means for the electricity trading market. This paper proposes a decentralized, two-stage double auction energy trading algorithm. The proposed market is a distributed and decentralized application and its rules may be specified through smart contracts. Market participants conduct energy transactions by interacting with smart contracts. The smart contract first collects the quotations and performs the same price priority matching, and then performs a unified price clearing on the remaining quotations to obtain the market clearing price. Compared with the traditional double auction, our method improves the consumption capacity and security, improves the economic income of the overall market, and makes the profits of producers and consumers higher.

KEYWORDS

Blockchain, Smart contract, Double Auction, Smart Grids.

Implementing Blockchain Technology in Supply Chain Management

Atul Anand¹, Dr A Seetharaman² and Dr K Maddulety³, ¹Researcher, SP Jain School of Global Management- Mumbai, ²Dean Research, SP Jain School of Global Management- Singapore, ³Deputy Dean, SP Jain School of Global Management- Mumbai

ABSTRACT

This paper is aimed at studying the factors influencing the implementation of blockchain in supply chain management to solve the current issues faced in the supply chain ecosystem.

KEYWORDS

Blockchain technology, Supply chain management, Technology, Intracompany synergies, Intercompany collaboration; Extrinsic Factors, Innovation.

Instantaneous Frequency and AOA Estimation of Multicomponent Signals based on Born-Jordan Distribution

Gan Quan, Tang Jie, Song Huan Huan, Wen Hong, Institute of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China

ABSTRACT

Based on the spatial sample of the multi-component signals by the array antenna, using Born-Jordan distribution which is a kind of typical Cohen type time-frequency distribution, the detection and estimation approach of the spatial multi-component signals angle-of-arrival and instantaneous frequency is proposed by comparison of the instantaneous frequency of the signals at the same time from the point of digital signal processing. The simulation results show that the proposed method not only has high noise performance, but also enhances the real-time performance of the spatial signal detection.

KEYWORDS

Angle-of-arrival Estimation, Time-frequency Distribution, BJD, Instantaneous Frequency.

Detecting Moving Target with Doppler Spread and Range Migration for FDA-MIMO Radar

Meihui Liu and Shunsheng Zhang, Research Institute of Electronic Science and Technology, University of Electronic Science and Technology of China，Chengdu, China

ABSTRACT

In this paper, we study the moving target detection problem for frequency diverse array multiple input multiple output (FDA-MIMO) radar under the assumption of Doppler spread and range migration. Doppler spread is caused by the frequency offset between the arrays being closely coupled with the target velocity. Due to the range migration in each receiving channel in a coherent processing interval and Doppler spread in the joint spatial-time domain, the degraded coherent integration gain will reduce the target detection performance. To address this problem, we first use the Keystone transform to compensate the range migration. Next, a matched function based on the Doppler ambiguity factors is proposed to correct the Doppler spread. The effectiveness of the proposed approach is validated by numerical results.

KEYWORDS

Frequency diverse array (FDA) radar, Keystone transform, range migration, Doppler spread.

Dealing with Distribution Shift in Acoustic Mosquito Datasets

Hermann Y. Nkouanga, Department of Computer Science, Portland State University, Oregon, USA

ABSTRACT

Mosquito sounds classification is a problem that has been studied extensively in recent years. Developing a system that can accurately detect and identify species of mosquitoes would greatly help save lives as it will allow identifying places that host dangerous species of mosquitoes so that people living there can take adequate precautions to avoid mosquito bites. However, researchers aiming to develop such a system are often confronted with an issue called distribution shift. Distribution shift alludes to a situation in which a classification model is trained and tested on data sampled from different distributions. When this situation arises, a model, even with good training statistics, is almost always guaranteed to perform poorly during testing. This issue has also been studied extensively over the last decade and many solutions have been proposed, which are often classified under different categories, including domain generalization, domain adaptation, and sub-population shift. In this paper, we propose a simple approach that leverages the domain generalization framework to tackle the issue of distribution shift in mosquito sound datasets. The first main phase of our system consists of identifying noise present in the dataset and getting rid of as much as possible of it in order to guarantee that all datasets passing through the system end up containing about the same amount of noise. The second main phase consists of performing a dimensionality reduction to get rid of all non-essential and redundant features in the data. We tested our system on a large and publicly available dataset of mosquito recordings (HumBugDB) and the results showed a maximum improvement of nearly 28% compared to a baseline classification scheme.

KEYWORDS

HumBugDB, Distribution Shift, Signal Processing, Deep Learning, Mosquito Acoustics.

Referring Expressions with Rational Speech Act Framework: A Probabilistic Approach

Hieu Le, Fabian Zhafransyah, Boston University, United States of America

ABSTRACT

This paper focuses on a referring expression generation (REG) task in which the aim is to pick out an object in a complex visual scene. One common theoretical approach to this problem is to model the task as a two-agent cooperative scheme in which a ‘speaker’ agent would generate the expression that best describes a targeted area and a ‘listener’ agent would identify the target. Several recent REG systems have used deep learning approaches to represent the speaker/listener agents. The Rational Speech Act framework (RSA), a Bayesian approach to pragmatics that can predict human linguistic behavior quite accurately, has been shown to generate high quality and explainable expressions on toy datasets involving simple visual scenes. Its application to large scale problems, however, remains largely unexplored. This paper applies a combination of the probabilistic RSA framework and deep learning approaches to larger datasets involving complex visual scenes in a multi-step process with the aim of generating better-explained expressions. We carry out experiments on the RefCOCO and RefCOCO+ datasets and compare our approach with other end-to-end deep learning approaches as well as a variation of RSA to highlight our key contribution. Experimental results show that while achieving lower accuracy than SOTA deep learning methods, our approach outperforms similar RSA approach in human comprehension and has an advantage over end-to-end deep learning under limited data scenario. Lastly, we provide a detailed analysis on the expression generation process with concrete examples, thus providing a systematic view on error types and deficiencies in the generation process and identifying possible areas for future improvements.

KEYWORDS

Referring expression generation, RSA, probabilistic RSA.

Sentiment Analysis of Cybersecurity Content in Twitter and Reddit

Bipun Thapa, College of Business, Innovation, Leadership, and Technology, Marymount University, USA

ABSTRACT

Sentiment Analysis provides an opportunity to understand the subject(s), especially in the digital age, due to an abundance of public data and ef ective algorithms. Cybersecurity is a subject where opinions are plentiful and dif ering in the public domain. This descriptive research analyzed cybersecurity content in Twitter and Reddit to measure its sentiment, which was positive or negative or neutral. The data from Twitter and Reddit was amassed via technology-specific APIs during a selected timeframe to create datasets with all the entries, which were then analyzed individually for its sentiment by VADER, an NLP (Natural Language Processing) algorithm. A random sample of cybersecurity content (10 tweets and 10 posts) was also classified for sentiments by human respondents to evaluate the performance of VADER. Cybersecurity content on Twitter was at least 48% positive and Reddit was at least 26.5% positive. The positive or neutral content far outweighed negative sentiments across both platforms. When compared to human classification, which was considered the standard or source of truth, VADER produced 60% accuracy for Twitter and 70% for Reddit in assessing the sentiment; in other words agreement between algorithm and human classifiers. Overall, the goal was to explore an uninhibited research topic about cybersecurity sentiment.

KEYWORDS

NTLK, NLP, VADER, Sentiment Analysis, API, Python, Polarity, Evaluation Metrics.

Multilingual Speech Recognition Methods Using Deep Learning And Cosine Similarity

P Deepak Reddy, Chirag Rudresh and Adithya A S, Department of Computer Science Engineering, PES University, Bengaluru, Karnataka

ABSTRACT

The paper includes research on discovering new methods for multilingual speech recognition and comparing the effectiveness of the existing solutions with the proposed novelty approaches. The audio and textual multilingual dataset contains multilingual sentences where each sentence contains words from two different languages - English and Kannada. Our proposed speech recognition process includes preprocessing and splitting each audio sentence based on words, which is then given as input to the DL translator (using MFCC features) along with next word predictions. The use of a Next Word Prediction model along with the DL translator to accurately identify the words and convert to text.Similarly the other approach proposed is the use of cosine similarity where the speech recognition is based on the similarity between word uttered and the generated training dataset. Our models were trained on an audio and textual dataset that were generated by the team members and the test accuracies were measured based on the same dataset. The accuracy of our speech recognition model, using the novelty method, is 71%. This is a considerably good result compared to the existing multilingual translation solutions. Communication gap has been a major issue for many natives and locals trying to learn or move ahead in this tech-savvy English-speaking world. To communicate effectively, it is not only essential to have a single language translator but also a tool that can help understand a mixture of different languages to bridge the gap of communication with the non-English speaking communities. Integrating a multilingual translator with the power of a smart phone voice ssistant can help aid this process.

KEYWORDS

Natural Language Processing, Deep Learning, Multilingual Speech Recognition, Machine Learning, Speech to Tex.

Treating Crowdsourcing as Examination: How to Score Tasks and Online Workers?

Guangyang Han, Sufang Li, Runmin Wang and Chunming Wu, College of Computer and Information Sciences, Southwest University, Chongqing, China

ABSTRACT

Crowdsourcing is an online outsourcing mode which can solve the current machine learning algorithms urge need for massive labeled data. Requester posts tasks on crowdsourcing platforms, which employ online workers over the Internet to complete tasks, then aggregate and return results to requester. How to model the interaction between different types of workers and tasks is a hot spot. In this paper, we try to model workers as four types based on their ability: expert, normal worker, sloppy worker and spammer, and divide tasks into hard, medium and easy task according to their difficulty. We believe that even experts struggle with difficult tasks while sloppy workers can get easy tasks right, and spammers always give out wrong answers deliberately. So, good examination tasks should have moderate degree of difficulty and discriminability to score workers more objectively. Thus, we first score workers ability mainly on the medium difficult tasks, then reducing the weight of answers from sloppy workers and modifying the answers from spammers when inferring the tasks ground truth. A probability graph model is adopted to simulate the task execution process, and an iterative method is adopted to calculate and update the ground truth, the ability of workers and the difficulty of the task successively. We verify the rightness and effectiveness of our algorithm both in simulated and real crowdsourcing scenes.

KEYWORDS

Crowdsourcing, Human-in-the-Loop, Worker model, Task difficulty, Quality control, Data mining.

Analysing and Highlighting auto Insurance Group Claim Fraud Connections using AI NLP ML and Knowledge Graph Techniques

Subhashini LakshmiNarayanan¹ and Shantanu S. Sahasrabudhe², ¹AI Capability, Accenture ATCi, Chennai, India, ²AI Capability, Accenture ATCi, Mumbai, India

ABSTRACT

Every major insurance company loses money to fraud daily. Frauds are known to be dynamic and have no patterns, hence they are not easy to identify. Fraudsters use recent technological advancements to their advantage. They somehow bypass security checks, leading to the loss of millions of dollars. Analysing and detecting unusual activities using data mining techniques is one way of tracing fraudulent transactions. The algorithms sometimes help to identify the suspects based on feature patterns. Our proposed solution not only detects suspects but also uncovers fraud ring using AI and analytics techniques which provides more robust and holistic framework examining all potential areas for fraud. Fraud ring is a way to represent the extracted knowledge of interest using a knowledge graph bringing out the collusion between people playing different roles.

KEYWORDS

Fraudulent claims, Insurance, fraud ring, prediction, Data mining, Natural Language Processing, Machine Learning, Predictive Analytics, knowledge graph, graph mining.

GDGRU-DTA: Predicting Drug-Target Binding Affinity based on GNN and Double GRU

Lyu Zhijian, Jiang Shaohua and Gao Min, College of Information Science and Engineering, Hunan Normal University, Chang Sha, China

ABSTRACT

Drug development is a standing challenge in medical community, since the process of drug development is time-consuming and laborious; therefore, the repurposing of drugs is a good option, studying the relationship between drug and target is an indispensable step in drug repurposing, and many works are based on this to narrow the search space. In this paper, we propose a novel method called GDGRU-DTA to predict the binding affinity between drugs and targets, which is based on deep learning. This work is based on GraphDTA, but we believe that protein sequences are long sequences, and there may be dependencies between contexts, so simple CNN cannot extract the sequence features of proteins very well. Therefore, we improve it by interpreting the protein sequences as time series and extracting their features using GRU (Gate Recurrent Unit) and BiGRU (Bidirectional Gate Recurrent Unit). For the drug, we consider that the graph representation of drugs can better reflect its structural features than the string representation of SMILES (Simplified Molecular Input Line Entry System), so drugs are represented as graphs and graph convolutional networks are applied to learn the features of drugs. In addition to the four graph convolution methods mentioned in GraphDTA, we also use two other better graph convolution methods, namely Transformer and GatedGraph to learn the features of drugs, on the one hand, they demonstrate the good performance of extracting drug features, on the other hand, they also a good interpretation of the generalization ability of the GRU model we proposed. Subsequently, the representation of drugs and proteins are concatenated and the final prediction is obtained through several fully connected layers. We evaluate the proposed model on two benchmark drug-target binding affinity datasets, Davis and KIBA. The model outperforms GraphDTA and some other state-of-the-art deep learning methods, the results demonstrate the feasibility of our improved approach to drug and protein feature extraction and the excellent feature capture ability of our model and it will contribute to the repurposing of drugs.

KEYWORDS

Drug &Target, GRU, BiGRU, GNN, Deep Learning.

The Impact of using a Contract-driven, Test interceptor based Software Development Approach

Justus Posthuma and Fritz Solms and Bruce W. Watson, Department of Information Science, Stellenbosch University, Stellenbosch, South Africa

ABSTRACT

Contract Driven Development formalizes functional requirements within component contracts. The process aims to produce higher quality software, reduce quality assurance costs and improve reusability. However, the perceived complexity and cost of requirements formalization has limited the adoption of this approach in industry. In this article, we consider the extent to which the overheads of requirements formalization can be netted off against the reduced quality assurance costs arising from being able to auto-generate functional test interceptors from component contracts. Test-interceptors are used during testing to verify that component contracts are satisfied. In particular, we investigate the impact of contract-driven development on both the quality attributes of the software development process and the quality of the software produced by the process. Empirical data obtained from an actual software project using contract-driven development with test interceptor generation is compared to that obtained from similar projects that used a traditional software development process with informal requirements and manually written functional tests.

KEYWORDS

Software and its engineering, Software development methods, Software implementation.

A Data-Driven Real-time Analytical Framework with Improved Granularity using Machine Learning and Big Data Analysis

Yubo Zhang¹, Yu Sun², ¹Shenzhen College of International Education, 3 Antuoshan 6th Rd, Futian District, Shenzhen, China, 518043, ²California State Polytechnic University, Pomona, CA, 91768, Irvine, CA92620

ABSTRACT

During daily studying and working, people have to research massive amounts of information on the internet anddownload numerous files. Some of these files can be easily categorized to relevant files. However, there are alwayssome files left unorganized due to their dif iculty in categorization [1]. Such files pile up in the download folder astime passes, making the folder extremely messy. Many people do not have the motive to clean up the folder as it requires a lot of energy and time. Based on this common problem, my group developed an app that can cleanthemessy folder up. After applying our program which is based on machine learning, the files will firstly be dividedintofive general parts – document, video, music, photos and package. Then the files will be further categorized basedonthe contents they present. For example, photos are divided into animals, families and so on. In order to achieve thecontent-categorizing function, several powerful apis were introduced in our program, and they helped us to achievethe optimal results.

KEYWORDS

Data Processing, Deep Learning, Machine learning.

A Pose-based Image Searching using Computer Vision and Post-Estimate

Hang Wang¹ and Yu Sun², ¹University of California—Berkeley, Berkeley, USA, ²Cal Poly Pomona, USA

ABSTRACT

As the cost of human forces increases, people in some careers, like the artists, may find some difficulties when models are needed in the processes of making art. Obviously, one alternative solution is to find pictures online, however, when some specific poses are needed, they may also find some difficulties to describe them. This paper develops an application to search for pictures with the target pose described by the users graphical input. In this project, one hundred images describing distinct actions and activities with various poses and view-angles are collected. Mediapipe is then used to analyze those images in a quantitative way. We also embedded a User Interface that allows the user to imitate the intended pose as well as the viewing angles by simply dragging around body joints of the figure. Different sets of feature points and matching algorithms are also tested to find out the best solution.

KEYWORDS

Computer vision, Pose detection, Image searching.

Semantic Inferences Towards Smart IoT-based Systems Actuation Conflicts Management

Gérald Rocher, Jean-Yves Tigli and Stéphane Lavirotte, I3S laboratory, Université Côte dAzur, CNRS, Sophia-Antipolis, France

ABSTRACT

IoT-based systems have long been limited to collecting field information via sensors distributed at the edge of their infrastructure. However, in many areas such as smart home, smart factory, etc. these systems include devices that interact with the physical environment via common actuators. Throughout the lifecycle of these systems, from design, to deployment to operation, the ability to avoid actuation conflicts, both in terms of the commands that actuators receive (direct conflicts) and the effects that they produce (indirect conflicts), is a new challenge in the realm of trustworthy Smart IoT-based Systems (SIS). As part of the European project ENACT, which aims to provide full DevOps support for trustworthy SIS, we present a lightweight ontology that provides SIS designers with (1) a semantic metamodel to formally describe SIS subsystems and the actuators they interact with, and (2) a set of SWRL inference rules to automatically identify and semi-automatically resolve actuation conflicts. Consistent with the best practices of the DevOps approach, a particular emphasis is placed on facilitating the use and interpretation of inference results. To provide insight into the appropriateness of the proposed approach in the context of SIS, rule processing times for different actuation conflicts configurations are provided.

KEYWORDS

Actuation, Conflict, Identification, Resolution, Ontology, Internet of Things, DevOps.

How to enhance the sharing of cyber incident information via fine-grained access control

Jarno Salonen¹, Tatu Niskanen² and Pia Raitio³, ¹VTT Technical Research Centre of Finland, Tampere, Finland, ²VTT Technical Research Centre of Finland, Espoo, Finland, ³Finnish Transport Infrastructure Agency, Helsinki, Finland

ABSTRACT

Industry 4.0 and the ongoing digital transformation along with a large number interconnected machines and devices increase the role of cybersecurity, cyber incident handling and incident response in the factories of the future (FoF). Cyber incident information sharing plays a major role when we need to formulate situational pictures about FoF operations and environment, and respond to cybersecurity threats related to e.g. the implementation of novel technologies. Sharing of incident information has a major drawback since it may reveal too much about the attack target, e.g. in the case of legacy systems and therefore restrictions may apply. We have developed a proof-of-concept service that combines access control and encryption of data at high granularity and a mechanism for requesting access to restricted cyber incident information. The objective was to demonstrate how access to restricted incident data fields could be managed in a fine-grained manner to enhance information sharing.

KEYWORDS

Incident Management, Visualisation, Cybersecurity, Information Sharing.

Image Encryption Algorithm of Chaos System Adding Cosine Excitation Function

Zhenzhou GUO¹ and Xintong LI², ¹School of Artificial Intelligence,Shenyang Aerospace University, Shenyang, China, ²School of Computer, Shenyang Aerospace University, Shenyang, China

ABSTRACT

In order to increase the chaotic performance of the chaotic system, the chaotic system A-S proposed by Sprott is improved by adding a cosine excitation function to a controller, and a series of new chaotic systems are obtained, and the chaotic performance of the improved system is verified. The image is encrypted by the chaotic sequence generated by the improved A chaotic system. In the scrambling part of the image encryption algorithm, the zigzag transformation is improved, and different directions are selected to start the traversal, so that the scrambling process is not easy to be restored. The diffusion part draws on the traditional IDEA algorithm to perform diffusion operation on the image. Finally, the encryption algorithm is analyzed and tested, and the results show that the algorithm has fast encryption and decryption speed, sufficient key space, and can resist statistical analysis attacks well. The algorithm can provide better guarantee for the security of images.

KEYWORDS

Cosine Excitation Function, Three-dimensional Chaotic System, Digital Image Encryption.

Assessing the Effect of Virtual Learning in Bridging the Gap of Science Teaching and Learning Caused by Covid 19

OJO Folorunso Fidelis, Computer Science Department, Federal College of Education, Abeokuta, AMUDA Tajudeen Gbenga, General Studies in Education Department Federal College of Education, Abeokuta

ABSTRACT

The Pandemic of Coronavirus which is popularly known as COVID 19 is a health crisis issue. Countries around the world have rightly decided to close schools and colleges to curtail the spread of the pandemic thus reducing social and physical contact and saving lives. The best public policy tool available to raise scientific skills and social awareness is attending school. Missing school because of COVID 19 has interruptedly affected teaching and learning of sciences and have consequences for science and technology growth. The study is aimed to assess the extent this has actually bridged the gap of science teaching and learning caused by COVID 19, Questionnaire was used for data collection from both the teachers and learners that has partake in the e-classes (Online classes/digital classes). The data collected were analyzed using Independent T-test and Regression analysis. Results from the findings reveals that Virtual classroom has indeed bridge gaps and thus improved academic performance of Basic Science students. In recommendation, despite that virtual learning is not relatively new but have recently emerged as an important option to teaching and learning amidst COVID 19 helping the learners to study from home. This, increasingly improving dynamic teaching and learning, moving Education from traditional classroom of face - to - face learning environment to more interactive and collaborative environment.

KEYWORDS

Virtual Learning, Pandemic, Science, Technology, Digital classes.

A Generic Framework of Three Factor Authentication with Optional Bio-metric or Graphical Password

Mohammad Naveed Hossain, Sheikh Fahim Uz Zaman and Tazria Zerin Khan

ABSTRACT

We live in a technological era where technology controls our lives in a good way or in a bad way. In today’s digital world we cannot imagine a single day without technology and for security for purposes we mostly rely on single-factor authentication or two factor authentication. While using two factor authentication (2FA) our data can still be hacked by several ways. 2FA has some weaknesses and for that reason our password can be easily cracked or hacked by hackers, even if hackers do not have our OTP. To solve this weakness and make our data more secure and reliable we use three factor authentication so that any unauthorized person cannot easily access our data. We use 5 steps with 3 authentications. First one is username and password the most common system after verification of the first one, second one is OTP and if password and OTP are verified, the last and third one is bio-metric such as fingerprint, voice recognition etc., but not every device supports the bio-metric system for those devices graphical password can be used. By these three authentications we can protect our data, make it secure and trustworthy for every user. [1] [2]

KEYWORDS

OTP, Authentication, 2FA, 3FA, Hacked, Bio-Metric, alphanumeric password,data protection, network security, three-factor authentication.

Cost-Efficient Data Privacy Protection in Multi Cloud Storage

Artem Matveev, Buryat Institute of Infocommunication (branch of) Siberian State University of Telecommunication and Information Science, Ulan-Ude, Republic of Buryatia, Russia

ABSTRACT

Data privacy in the cloud is a big concern for all of its users, especially for public clouds. Modern trends in studies utilise multiple clouds to achieve data privacy protection. Most of the present studies focus on business-oriented solutions, but our study aims to create a solution for individual users which would not increase the cost of ownership and providing enough flexibility and privacy protection. New scheme design involves key-distribution across multiple clouds and performs multi-layered encryption. As a result, the proposed scheme is easy to use, password protected and fully relies on the cloud. This provides high Data Availability. Meanwhile the proposed scheme is tolerant to brute force attacks and data-leakage incidents.

KEYWORDS

Multi Cloud Storage, Privacy Protection, Password, Key Distribution, Cloud Data Security.

Fraud Detection System based on Artifical Immune System

Vitaly Krokhalev, Siberian State University of Telecommunication and Information Science, Novosibirsk, Novosibirsk Oblast, Russia

ABSTRACT

Nowadays, one of the most important problems for financial companies is fraud related to online transactions. It is becoming increasingly sophisticated and advanced, leading to financial losses on the part of both customers and companies. Based on this, my company was tasked with creating a fraud detection system that is scalable and adaptable to change. This research aims to create a solution that can be used to identify differences in customer behavior patterns and detect fraud. The artificial immune system model proposed in this article, combined with certain informative features, is simple to implement and can describe customer behavior patterns.

KEYWORDS

Fraud Detection, Artificial Immune System, Informative Features, Machine Learning, Information Security.

An Intelligent Social-based Assistant Application for Study Time Management using Artificial Intelligence and Natural Language Processing

Haoyu Li¹, Ryan Yan², Ang Li³, ¹Whittier Christian High School, 501 N Beach Blvd, La Habra, CA 90631, ²Cal Poly Pomona, 3801 West Temple Avenue, Pomona, California 91768, ³California State University, Long Beach, 1250 Bellflower Blvd, Long Beach, CA 90840

ABSTRACT

The question that we aim to solve is “How will elderly people be able to increase their productivity and remember their tasks?” There are many ways to go about answering this question, but we have devised a simple solution to this question that can directly and quickly have a positive impact on these individuals. Our method to solving this is creating a to-do list in Flutter, which will allow elderly people to have easy access to a list of tasks that they have to complete [5]. Some predicted results of this to-do list is that it can raise productivity for its users. Our to-do list features a ChatBot, which talks to the user through a message-like system in order to prompt user input for specific details such as the time, date, and main description of the task [6]. Then, the ChatBot will take in all this information to produce a clean and concise final task description that takes keywords from the user-inputted description. This provides users of our to-do list with an alternative method of adding tasks, which may be greatly appreciated by those who are less able-bodied or struggle to type. By offering the elderly a way of adding tasks that can take less typing, these individuals may rely on this to-do list as a great convenience to their lives.

KEYWORDS

Flutter, To-Do List, Tasks, Productivity.

rop Suitability Prediction Systems using different Parameters and Algorithms: A Comparative Study

Joey Fernando, Jonh Ray Medina and Dr. Lilibeth Timbol-Cuison, Angeles University Foundation, Angeles City, Pampanga, Philippines

ABSTRACT

This paper assesses several published studies pertaining to crop prediction systems. Each study developed a predictive model based on different parameters like soil conditions, agrometeorological forecasts, weather parameters, crops composition, geographical survey, microclimate data, meteorological conditions, and historical data. Techniques utilized in the development were also diverse, several studies used the Machine Learning algorithms specifically Decision Tree, Simple Moving Average, and Classification Technique, while some studies applied the approaches from the Geographic Information System, Microclimate Modeling, Data Mining for geographical and historical data, and intelligent computing technique like Intuitionistic Fuzzy Logic and Neural Network to analyze the researchs undefined and inaccurate data. This evaluation found that the forecasting system had successfully achieved its goal of assisting the agriculture industry, particularly farmers, in selecting the appropriate crops for their land. Considering the positive achievement of these papers, the accurateness of the data is still subject for deep investigation and protocol -based study because several researches only used limited criteria in the prediction model. None of the studies reviewed uses the complete land characteristics and land suitability ratings for each crop as parameters. The findings of this work will be used as basis for future studies regarding this topic.

KEYWORDS

crop prediction, crop suitability, land characteristics, machine learning, data mining, geographic information system.

Microbial Load, Prevalence and Antibiotic Resistance of Microflora Isolated from the Ghanaian Paper Currency Note: A Potential Health Threat

Simon Nyarko¹ and Emmanuel Atiatorme², ¹Department of Pharmaceutics, Kwame Nkrumah University of Science and Technology, Kumasi, ²Department of Biotechnology, College of Advanced Sciences and Technology, Andhra University, Visakhapatnam-530003, India

ABSTRACT

This study examined the microbial flora and antibiotic activities of Ghana paper currency notes in circulation in Mampong Municipal, Ashanti Region, Ghana. A cross-sectional design was used to assess bacterial contaminants and their antibiotic activities. A total of 70 GH¢ notes, 15 each of GH ¢1, GH ¢2, and GH ¢5, 10 each of GH ¢10 and GH ¢20, and 5 of GH ¢50, were randomly sampled from people. The surfaces of each paper note were gently swabbed, and tenfold serial dilutions were inoculated on PCA, MSA, DCA and MSA agar. For bacterial identification, the study used appropriate laboratory and biochemical tests. It was found out that 95.2 % of the 70 GH¢ notes tested positive for one or more bacterial isolates. Mean counts on PCA ranged from 3.0x105 cfu/ml to 4.8x105 cfu/m5. Escherichia coli (25.81%) was the highest isolate with Proteus species (3.23%) being the least.

KEYWORDS

Microflora, Staphylococcus aureus, Antibiotic resistance, Culture media, Isolates and Multi-drug resistance.

An Energy Efficient Clustering Technique for WSN

Dr. S.Diwakaran¹, Atla Praghna², Elluru Thanusha Reddy², Akula Sravani², ¹Associate Professor, Electronic and Communication Engineering, Kalasalingam Academy of Research And Education, ²UG Student, Electronic and Communication Engineering , Kalasalingam Academy of Research And Education

ABSTRACT

The utilization of battery sources has been used in distant sensor associations. Every sensor center point in the structure looks out for the physical and biological components in its close by natural components. Hubs handle information handling and capacity, while the identifier is a gadget that distinguishes their presence. A message was shipped off BS. This errand includes the use of more energy than expected. The items lifetime is impacted by information handling. Grouping is a decent method for saving energy. In any case, the energy assets accessible to sensor hubs in a WSN-based IoT network are confined. By orchestrating hubs into groups to limit the transmission distance between sensor hubs and base stations, a bunching method guarantees hub energy reserve funds and organization life expectancy (BS). On the opposite side, current grouping conventions dislike the bunching component, decreasing their effectiveness. To work on the life expectancy of WSN-based IoT gadgets, we propose an improved energy-proficient grouping convention (EECP) in this study.Three segments make up the proposed EECP. For the covering changed bunches, an appropriate number of groups is first surveyed. The decent static bunches are then built by joining a tailor made fluffy C-implies calculation with a method for decreasing and adjusting the sensor hubs energy use. At last, by pivoting the CH work among group individuals with another CH choice revolution calculation that joins a back-off planning system for CH determination with a turn instrument for CH revolution, bunch heads (CHs) are chosen at ideal spots. The proposed method, specifically, eliminates and balances energy use.

KEYWORDS

Wireless sensor network, clustering protocol, energy consumption, network lifetime, energy-efficient clustering protocol.