Reviewerly/exHarmony huggingface dataset:
Fine-tuned CNN-based Sri Lankan Currency Note Detection Method for the Visually Impaired People Using Smartphones

The currency has a significant meaning in everyday life. Researchers have suggested diverse approaches to improve currency recognition for visually impaired people by implementing portable devices and various image-processing techniques. However, only some of the nearly developed currency recognition systems cater to meet the Sri Lankan currency identification requirement. This paper presents a model that works on a mobile application based on a Convolutional Neural Network using a pre-trained model to identify currency notes in real time from a smartphone camera. Crucial factors in constructing this model are speed, accuracy, and size. A self-collected dataset of 14,000 Sri Lankan currency note images in numerous conditions was used for primary model training. Data augmentation, transfer learning through VGG16, VGG19, ResNet50, DenseNet, MobileNet, NASNet, and EfficientNet pre-trained models using bottleneck features and fine-tuning on top of bottleneck features are the main methods used in this model. This model reached above 98% in terms of testing accuracy using DenseNet121 with fine-tuning by testing 315 partially occulted, rotated Sri Lankan currency notes in different daylight conditions and cluttered backgrounds. The proposed model can recognize the currency note using TensorFlow Lite when it is pointed at the camera. The present research is limited to daylight image capturing to ensure the better performance of the model. The findings of this research would help visually impaired people to recognize Sri Lankan currency notes precisely and with less effort through the simple installation of a smartphone.
Analysis of Keylogging spyware for information theft

Although the continued advancement of technology has undoubtedly made our lives easier, it has also made it possible for thieves like hackers and intrusions to track our internet activities in sophisticated ways. Few cyber risks have developed to the point where they could be able to get past any software’s defense. Keyloggers is a best-of-best example of the "silent" cyber threats – they give easy access to your personal data like username, Password, Date-of-Birth, etc to hackers but as it may be nearly impossible to detect until. A keylogger is a tool that is used by the intruder/hackers to monitor all the activities or record the keystrokes that you make on your keyboard device. Whether they are run or installed on your operating system or embedded with the hardware, few keyloggers are very difficult to detect by Antivirus. Aside keylogger is utilized for revengeful purpose like gathering account information, numbers, names, passwords, and other personal data, it could be use in office on your company to spy on your employees, at home to observe your children’s activities and by legislation imposition to inspect and follow occurrences connected to the implementation of PCs. This project will solely use Python 3. (version 3.10.7). Multiple modules, such as pynput, smtplib, urllib, multiprocessing, date time, as well as email Message, will be implemented by me.. These modules are not standard Python modules and must be installed. I’m planning to write software to monitor keystrokes on the keyboard and save them to an output file. To expand the project, I will also include a feature that will periodically (every 60 seconds) check for an internet connection. If one is found, the logs will be transmitted immediately to an email; otherwise, keystrokes will be recorded.
Human Activity Recognition on Smartphone Using Transformer Network

Automatic behaviour inspection for sports players, older people, and applications for 1OT require sensor-based human activity identification. Conventional means rely on finger features to tend to fluctuate from raw data using preset mathematical concepts and therefore are incapable of domain adaptation. We proposed a Transformer network in this study to recognize six human actions from Smartphone data. Each participant did several activities while holding a Mobile device around their waist (moving, going up, and coming down, resting, constant, and set down). We first should define the Titans model using the Tensor Flow backend module the tests were recorded and labelled manually and partitioned randomly into two groups. Furthermore, the activity of the project has been examined.
Content-Based Filtering Using K-Nearest Neighbor

The purpose of recommendation system is to guide to user, drew on their preferences. This saves consumers to take moment to browse on web for films where millions of films accessible on web. Film based on one or more attribute or mixture of two combinations of categories of films (comedy, action) is proposed by system. In this article, recommendation process constructed the types of films categories, directors, IMDB rating or stars that consumer may decide to view. The methodology used it to accomplish this by filtering technique using KNN (K-Nearest Neighbor) Algorithm. It predicts user’s like or dislike about movie based on different parameters like genres categories, movie titles, imdb ratings. Proposed system using Movie_meta data from Kaggle and data analysis done using python.
OCR based Cheque Validation using Image Processing

Online transactions of money are so much comfortable and time saving. Instead, banks continue to use the manual handling of bank checks which is the normal practice. This procedure can take several days for the money to transfer. In this project, we proposed a system that validates the cheque by retrieving details like the Payee name, Amount (in words as well as in digits), date, and bank details using optical character recognition (OCR) it also does verification of the signature on cheque and cross checks it with the image stored in the database. The proposed system uses OpenCV for image processing and segmentation of bank cheque. This system uses EasyOCR for extracting text from the image whereas a custom dataset is used to test this system and get the result. The performance and efficiency are measured on the self-generated dataset of bank cheque.
Survey on Virtual Recruitment System

After the recent wave of covid, many companies are yet to shift their focus on recruiting candidates directly through in-campus recruitment. Many companies still do prefer the online way of conducting interviews on platforms like meet, zoom, etc. Even forms are shared to apply to a particular company and many more variable methods. Due to this rising uncertainty, the online recruitment system has started gaining more popularity. However, this procedure presents difficulties for recruiters in managing the flood of applications and maintaining contact with the applicants. Previously, such recruitment systems were inefficient and lacked many parameters like accuracy, not upgraded and not frequently managed. Therefore, there was a need to build a website which is capable of handling all the recruitment related activities. With the help of AI-ML techniques it has become possible to rank students according to the requirements of the companies. We discuss the features and research gaps for each method in applying Machine learning and other techniques for enhancing the recruitment process.
Real-time Surveillance system using Face Recognition and AR

In the age of information, surveillance systems are needed to be accurate and robust in order to provide exact outcomes. This paper discusses the idea to upgrade the surveillance system of high-profile building complexes by providing a real time access of information of any visitor/guest, using face detection and recognition and also providing the real time information of visitors with the help of augmented reality. The proposed system works on the principle of identification and data retrieval from the database. At first, it registers every visitor and then captures visitor’s face data, followed by this wherever visitor comes in camera range inside campus , it recognizes the visitor’s face based on data retrieval from dataset , further it projects the visitor data at real time as an augmented reality. It has significant applications in the field of security and surveillance especially for high-profile buildings like government offices, confidential complexes and corporate offices etc.
Performance Analysis of Machine Learning and Deep Learning Techniques for Answer Type extraction of Marathi Questions

Question answering systems involve extraction of correct answers for natural language questions provided as input. Question classification is an important part of the question processing phase where natural language questions are categorized into predefined classes which specifies expected type of answer for the question. Extraction of answer type can be performed using machine learning and deep learning classification techniques which helps to reduce the list of possible correct answers for a question. In this paper we have compared various classification techniques which can be used for building a Marathi question classification system. Additionally, we have created a Marathi question classification dataset by translating existing TREC dataset available in English language. We observed that fine tuning the RoBERTa based monolingual language model for question classification was the best classification technique with accuracy of 91% in the coarse grained category of question classification and accuracy of 85% in the fine grained category of question classification.
AlgoViz : Algorithm Visualization

Visualizations many times make things easier to understand. Understanding the working of an algorithm and identifying its complexity becomes easier if it can be visualized. The AlgoViz system illustrates the use of graphics to show how algorithms operate. It primarily seeks to deepen and simplify comprehension of how the algorithms work. It will allow us to see how actually the algorithm proceeds step by step and how different algorithms take different times to complete. In this work, we have proposed and implemented the visualization of various algorithms using the PyGame and Tkinter modules of python. The main focus of the work is on searching, sorting and basic route-finding algorithms. Identifying the number of iterations and time complexity of the searching or sorting algorithms becomes easy with AlgoViz: ‘Algorithm Visualization’. AlgoViz shows how the algorithm proceeds step by step and how different algorithms take different times to complete various tasks. This represents a new and interesting way of learning and making use of technology to understand technology better.
Camera Detection for Blind People Using OCR

According to the World Health Organization, there are millions of visually impaired people in the world. This paper is based on the development of a system that provides aid to such people. A pi camera is used to implement the system which is based on the Tesseract OCR (Optical Character Recognition) library of OpenCV in Raspberry pi. OCR library is used to recognize text from the captured image by the pi camera module. This helps to convert the picture of the desired page taken by the camera into respective text. Finally, this text is converted into speech through headphones using the TTS (Text-to-Speech) Engine. A headset is used for hearing the converted speech. The text-to-speech application assists them to detect text from printed documents and avoiding obstructions while traveling hence a blind person can feel not visually impaired.
AI-based Desktop Voice Assistant

From the past few years, Artificial Intelligence (AI) has shown remarkable progress, and its future is growing day by day. Natural Language Processing (NLP) is an application of AI. A voice assistant utilizes cloud computing to integrate AI and communicate with users in natural language. In households today, millions of devices use voice assistants due to their ease of use. Universities and schools are just starting to use smart speakers with voice assistants. Voice assistants are the greatest innovation in AI that can change people's lives in a significant way. Initially, voice assistants were introduced on smartphones, then they became popular. The idea was widely accepted by all. A voice assistant was initially used in smartphones and laptops, but now it is also found in home automation systems and smart speakers. As devices become smarter, they are interfacing with humans in a language that is easier to understand. Programs based upon desktop voice assistants recognize and respond to human voices via an integrated voice system. We will discuss the main problems and limitations of voice assistants in this paper.
IoT Device Authentication Using Self-Organizing Feature Map Data Sets

Sensors and actuators connected via the Internet of Things (loT) have now become embedded within our critical infrastructure offering improved observation and control as well as reduced costs. Given that software defined radios (SDRs) can be readily programmed to imitate loT devices, there is now a greater risk that assets can be spoofed or compromised. This necessitates an urgent need for loT device authentication, avoiding the need to upgrade the many thousands of individual devices. However, the lack of publicly available data sets severely hampers the development of effective authentication algorithms and mechanisms. In this regard, this article introduces a technique for facilitating loT device authentication when the radio frequency (RF) characteristics are highly correlated using self-organizing feature maps (SOFMs), thus aiming to promote state-of-the-art research in this field. The associated techniques demonstrated in this article exploit a novel data set of RF fingerprints and are, in particular, suitable for low-cost and long-range wireless application scenarios of the loT, for example, LoRa. Here, a well trained convolutional neural network (CNN) based on the SOFM data set can rapidly profile apparently correlated RF fingerprint patterns and thereby ascertain the nature of a specific device (friend or foe). In this way, a reliable and efficient loT device authentication strategy for LoRa devices can be established. The experimental results presented in this article substantiate the effectiveness and efficiency of the SOFM based approach, and the data sets are introduced in detail and shared with the research community.
MetaCube: A Crypto-Based Unique User-Generated Content Editor for Web3 Metaverse

Web3 (also known as Web 3.0) metaverse is a blockchain-driven networked, decentralized, and open virtual world. The key feature of the Web3 metaverse is that the ownership of digital assets is recorded by non-fungible token (NFT) protocol on the blockchain. Thus, users are better encouraged to construct Web3 metaverse due to the ownership of their user-generated content (UGC). However, the existing UGC editors mainly face two challenges: they cannot guarantee the uniqueness of UGC; and they are hard-pressed to find a trade-off between model granularity and 3D modeling difficulty. In this article, we design a novel UGC editor for the Web3 metaverse, named MetaCube, to address these challenges. MetaCube applies an artificial intelligence (AI) method to assist the UGC creation for decreasing the 3D modeling difficulty while maintaining the model granularity. To guarantee the uniqueness of UGC, this article proposes 3D Crypto-dropout, a specially designed dropout that can utilize user information to control the UGC creation process and generate unique fine-grained 3D models. Our experimental results demonstrate that the proposed 3D Crypto-dropout can effectively guarantee the uniqueness of UGC from both numerical and human-centered evaluation. Moreover, the existing challenges and open research topics for the uniqueness of UGC are also profoundly discussed.
A Connectivity Aware Path Planning for a Fleet of UAVs in an Urban Environment

Unmanned Aerial Vehicles (UAVs) are known for their highly dynamic nature, as a result of which their applications demand high design consideration in urban areas. It is imperative to have trajectories that avoid UAV-to-UAV and UAV-to-obstacle collision to ensure the safety of a fleet and people on the ground. Moreover, many applications, like temporary network provision, require continuous backhaul fleet connectivity. This work simultaneously addresses UAVs’ path planning and routing issues to propose connectivity-aware path planning for a fleet of UAVs in an urban environment. The proposed scheme is a graph-based offline path planning with fleet line formation that ensures continuous backhaul connectivity. This feature allows any UAV to play the role of leader and guide the entire fleet according to a desired speed. Thanks to the continuous backhaul connectivity, the Base Station (BS) can disseminate commands to the connected fleet as required. Fleet line formation acts as a backbone network and allows additional UAVs or ground users to become a part of this network. The proposed approach is implemented in MATLAB’s UAV Toolbox and evaluated in a network simulator. The simulation results demonstrate that the proposed scheme provides collision-free trajectories while ensuring continuous BS connectivity.
Incentives in Adopting Cloud Computing for Shaikh Zayed University, Khost Afghanistan

Teaching in these days is almost regarded impossible without the Information Technology resources which needs a great amount of investments in this sector, there are a huge number of educational institutions which cannot afford these IT investments. Many institutions are in search of a cheap and economic way of delivering and managing these resources mainly in the Asian countries. Among such solutions, it is debated that Cloud computing is the right choice to reduce the expenses of the institutions. This paper describes the common benefits of cloud computing for Shaikh Zayed University. To provide imported and updated information and to deliver daily lectures on time, a physical structure such as (hardware and software) is needed. To attain this objective, the researcher conducted qualitative data to collect the data. Data from different experts were collected using the questionnaires in this field. The results of these survey showed that cloud computing had great role in reducing the Costs, achieved a Collaborating environment and all time Availability.
Opponent Exploitation Based on Bayesian Strategy Inference and Policy Tracking

In a multi-agent competitive environment, it is important for an agent to detect the opponent's policy and adopt a suitable policy to exploit the opponent. Conventionally, most methods, e.g., Bayesian Policy Reuse (BPR) variants, assume the opponent adopts a fixed policy or a randomly changing policy. In this paper, we make a more realistic and reasonable assumption that the opponent may select its policy based on the previous observation. Here, we define the term “strategy” as the mapping from the previous observation to the opponent's selected policy, and we propose the Bayesian Strategy Inference (BSI) framework to infer the opponent's strategy. Furthermore, to deal with opponents who may randomly select their policies, the BSI framework is combined with an intra-episode policy tracking mechanism to construct the Bayesian Strategy Inference plus Policy Tracking (BSI-PT) algorithm. In our experiments, we design an extended batter vs. pitcher game (EBvPG) for the evaluation of the proposed BSI-PT framework. The experimental results demonstrate that BSI-PT obtains higher policy prediction accuracy and winning percentage than three other BPR variants against the opponents with a specific policy selection strategy, with a random selection strategy, or with a partially random strategy.
Fast Rolling Shutter Correction in the Wild

This paper addresses the problem of rolling shutter correction (RSC) in uncalibrated videos. Existing works remove rolling shutter (RS) distortion by explicitly computing the camera motion and depth as intermediate products, followed by motion compensation. In contrast, we first show that each distorted pixel can be implicitly rectified back to the corresponding global shutter (GS) projection by rescaling its optical flow. Such a point-wise RSC is feasible with both perspective and non-perspective cases without the pre-knowledge of the camera used. Besides, it allows a pixel-wise varying direct RS correction (DRSC) framework that handles locally varying distortion caused by various sources, such as camera motion, moving objects, and even highly varying depth scenes. More importantly, our approach is an efficient CPU-based solution that enables undistorting RS videos in real-time (40fps for 480p). We evaluate our approach across a broad range of cameras and video sequences, including fast motion, dynamic scenes, and non-perspective lenses, demonstrating the superiority of our proposed approach over state-of-the-art methods in both effectiveness and efficiency. We also evaluated the ability of the RSC results to serve for downstream 3D analysis, such as visual odometry and structure-from-motion, which verifies preference for the output of our algorithm over other existing RSC methods.
Tensor-Enabled Communication-Efficient and Trustworthy Federated Learning for Heterogeneous Intelligent Space–Air–Ground-Integrated IoT

Federated Learning (FL) could provide a promising privacy-preserving intelligent learning paradigm for Space-Air-Ground Integrated Internet of Things (SAGI-IoT) by breaking down data islands and solving the dilemma between data privacy and data sharing. Currently, adaptivity, communication efficiency and model security are the three main challenges faced by FL, and they are rarely considered by existing works simultaneously. Concretely, most existing FL works assume that local models share the same architecture with global model, which is less adaptive and cannot meet the heterogeneous requirements of SAGI-IoT. Exchanging numerous model parameters not only generates massive communication overhead but also poses the risk of privacy leakage. The security of FL based on homomorphic encryption with single private key is weak as well. Given this, this paper proposes a tensor-empowered communication-efficient and trustworthy heterogeneous FL, where various participants could choose suitable heterogeneous local models according to their actual computing and communication environment, so that clients with different capabilities could do what they are good at. Additionally, tensor train decomposition is leveraged to reduce communication parameters while maintaining model performance. The storage requirements and communication overhead for heterogeneous clients are reduced further. Finally, the homomorphic encryption with double trapdoor property is utilized to provide a robust and trustworthy environment, which can defend against the inference attacks from malicious external attackers, honest-but-curious server and internal participating clients. Extensive experimental results show that the proposed approach is more adaptive and can improve communication efficiency as well as protect model security compared with the state-of-the-art.
Consensus Control Based on Privacy-Preserving Two-Party Relationship Test Protocol

Preservation of privacy is a challenging and significant constraint in multi-agent systems. This paper aims to introduce a framework that enables the states of a multi-agent system to reach a consensus while preserving the confidentiality of each agent’s initial states from others. First, a protocol for a privacy-preserving two-party relationship test is proposed. Subsequently, the protocol is employed to devise the average consensus controller for the first-order system, and the rendezvous controller for the second-order system. In contrast to prior research that relies on stochastic coupling weights, our approach circumvents the random chattering problem of the control input, resulting in improved convergence performance. Finally, numerical verification is conducted to demonstrate the effectiveness of the proposed controllers in both first-and second-order systems.