Reviewerly/exHarmony huggingface dataset:
Emergence of Power-Law Behavior in Defect Distribution of IoT Software

Rapid growth of IoT in various areas is not possible without software. However, IoT software are inherently different from traditional software because of the requirement of supporting multiple types of devices, heterogeneity, distributed and dynamic environment, always-on nature of the system. This has led to altogether new software engineering approach, called IoT-oriented software engineering. Besides, the search for new paradigm of software development, one question worth examining is the distribution of defects in IoT software. The paper investigates the defect distribution in some IoT software to ascertain if they show similar behavior as observed in traditional software or not. To meet this objective, a detailed statistical analysis of defects in IoT software is performed using non-linear regression method. Along with lognormal, Weibull, Pareto and generalized two-parameter Pareto as candidate probability distributions, a new software defect model based on maximum Tsallis entropy framework, called Tsallis distribution, is also included in the study. Tsallis distribution depicts power-law asymptotically. The IoT software are selected from four different categories - in memory database, middleware, data stream processing engine and operating system. Thorough examination of the results of statistical analysis reveal that generalized two-parameter Pareto and Tsallis distributions outperform others leading to the confirmation of emergence of power-law behavior in defect distribution of IoT software a kin to traditional ones. One exception is a relatively new operating system software where lognormal distribution turns out to be best fit. These results augment the IoT-oriented software engineering literature and also help the practitioners in predicting defects in evolving field of IoT software.
An Android-Based Multifactor Authentication for Securing Passive Keyless Access System

Passive Keyless Entry System is a security system that allows a user to lock or unlock the vehicle without having to press any buttons on the keyfob. This PKE system is derived from existing remote keyless entry systems that utilise a remote key to lock or unlock the vehicle. Intrusion analysis and securing of this system are in line with defence monitoring in modern days. In this vein, securing the system against relay and replay attacks is addressed by this paper. Also, the keyfob used by the current system is computationally constrained to use stronger keys to effectively secure the system. To address this issue, an Android-based multi-factor authentication system is proposed that considers user activity, GPS based distancing and timestamp methods to validate the user over a secure Bluetooth channel before unlocking. Through a series of experiments, it is shown that the proposed system with the above-mentioned combination of factors is proven to be effective against relay and replay attacks.
Hand Gesture Interpretation Model for Indian Sign Language using Neural Networks

Exchange of words among the community is one of the essential mediums of survival. Besides that, as claimed by the World Health Organization in 2021 over 5% of the world population suffers from speech impairments, they are lacking the basic prerequisite that is needed for the survival of the human race. These people communicate using "Sign Language" among their communities which has its meaning, grammar, and lexicons, and it may not be comprehensible for every other individual. Our proposed methodology focuses on creating a vision-based prototype that interprets the Indian sign language into understandable speech or text on an embedded device and this is done using deep learning technique which is an advanced extension of machine learning technology. our model makes use of an integrated webcam for capturing the real-time hand gestures and a Raspberry Pi to process the collected samples. The model uses a dataset that has 1200 images for every gesture for better results. Training data and test data have been partitioned into 9:1 ratio. This work involves CNN, IoT, and Python Language.
New Approach of Steganographic Design of Speech Signals &amp; its Application to Voice Recognitions

The arts of encoding a secret messages in a nond-secret object is known as steganography. Steganography, on the other hand, is used by cyber criminals for more than merely conveying messagess. Steganography is a data encryption technology that protects data as it travels from sender to destination. It’s all about embedding data/information in seemingly innocuous covered speech modules in a secure and safe manner. This system secures the files by employing the fundamentals of steganography. A computer-based audio Steganography method is used to encode secret communications in digital sound. The simulation results shows the effectiveness of the proposed methods
RC-sized Autonomous vehicle with Prediction Model

Vision begins in eyes, but truly takes place in the brain. So, in today’s world with the best high-definition cameras, high-speed computers, and artificial intelligence, computer vision was introduced. Computer vision is one of the latest advancements in technology that helps on giving the abilities of vision and understanding of the environment to computers so that they can extract high-level understanding from digital images and videos.Autonomous vehicles are the application of computer vision with main motive of eliminating human errors and drive the vehicles with high precision and remove the efforts taken by a person to drive.This paper provides a proof of concept for implementing an autonomous vehicle with a vehicle behaviour prediction model. It will be implemented over a small scale on a RC-sized car. The car would traverse autonomously in a demo map where we can simulate dynamic conditions and assess the model’s performance accordingly.This is the main motivation behind the project. To create a completely autonomous system capable of navigating around on its own. Detect, identify and follow traffic signs, signals and rules. Take appropriate actions for dynamic conditions and avoid any sort of loss. Provide user with an application interface to experience a safe journey to selected destination.The proposed system will be using LiDAR for mapping the telemetry and detecting surroundings of the vehicle, a camera module to identify the objects detected, a development board with the deployed autonomous driving model, being the brains of all the operations, and actuators for moving the car around. The autonomous driving model to be deployed, will be trained using YOLO for object detection and identification, along with a vehicle behaviour prediction subsystem assisting the dynamic decision-making subsystem to take some decisions based on the prediction to reduce severity of certain accidents or even avoid them, before they happen.
Recovering 3D from 2D Image Points (Calibrated Camera Approach for Surveillance)

Video cameras are being installed, both indoor and outdoor, for surveillance of critical installations to detect intrusions and suspicious actions. The 2D pixel location of the intrusions (e.g. humans, car etc.) in the scene can be detected using foreground extraction techniques or using deep learning based object detection models in the form of bounding-boxes. Since, these optical video cameras acquires only a 2D view of the scene (perspective projection), the physical dimensions of these intrusions in the scene and their location in 3D space are lost. Recovering this 3D information of the intrusions(objects) will certainly enhance the surveillance. Many computer vision techniques are available to obtain the 3D information from 2D images under Photogrammetry like calibrated stereo imaging, structure from motion etc. Currently, few deep learning based models are also being researched to extract 3D from single 2D images. In this paper, we propose a new methodology to obtain 3D information of the objects in the scene, which is suitable for surveillance applications. This methodology uses a calibrated single camera under an assumption that object-of-interest is standing on (known)ground plane. These recovered 3D points can be used readily in existing surveillance systems. We apply this methodology to three surveillance scenarios in this paper. The recovered 3D points are used 1) to estimate the intrusion location and dimensions 2) to detect abandoned baggage and identify possible owner 3) to detect physical-distance-norm violation in a crowded area.
PROBEXPERT: An Enhanced Q&amp;A Platform for Reducing Time Spent on Learning and Finding Answers

The World Wide Web contains a wide range of material from a variety of fields. However, when concerns towards the computer science domain, information users find on the internet may not be up-to-date due to the rapid pace of change and having to spend less time on the internet for researching and debugging tasks is an added luxury. Having an expertise level while providing answers through a platform is convenient for users, yet when a user signs into a platform, the user must start from the beginning, regardless of the level of competence in the field. Moreover, not having a proper way to evaluate the existing programming knowledge is another obstacle. To address mentioned complications, researchers of this paper have introduced a new e-learning platform- ‘ProbExpert’. The platform has been constructed with machine learning and deep learning approaches such as NLP, keyword extraction, semantic information analysis, cosine similarity, and information summarization. With aforesaid technologies, ProbExpert provides systems in automated answering, optimized answer generation, structured question-based quiz evaluation together with a fully automated portfolio generation with a novel user ranking algorithm based on the bell curve.
Ensemble Federated Learning for Classifying IoMT Data Streams

Recent advances in Internet-of-Medical-Things have transformed smart healthcare enabled by artificial intelligence (AI). However, to train classical AI-based machine learning (ML) and deep learning (DL) models, patients' data from individual devices, sensors, and wearables must be uploaded to central servers. This is infeasible in realistic healthcare scenarios as it may result in significant security and privacy issues because of the sensitive nature of healthcare data. Federated Learning (FL) is an emerging distributed collaborative AI paradigm that is particularly attractive for smart healthcare. FL coordinates multiple clients (e.g., hospitals, wearable devices) to train the model without sharing raw data. This paper proposed an ensemble federated learning approach for training a deep learning model for classifying decentralized data streams in IoMT environments. The individual federated models are trained locally, and the ensemble learning is carried out on a server. The results demonstrate that the ensemble federated model outperforms the base federated models.
draw2code: AI based Auto Web Page Generation from Hand-drawn Page Mock-up

Web pages are conventionally created by first generating rough sketch outlines, then designing those individual components, following which software engineers write code in markup languages like HyperText Markup Language (HTML) and a finished product is rendered. This paper aims at automating the design process and the subsequent coding involved, by using deep learning techniques to create mock-ups from low fidelity prototypes that are hand drawn by the users. This auto code generation helps save time and reduce costs that are associated with the conventional methods of creating web pages. We propose a Document Object Model (DOM) approach which identifies the hierarchy of HTML elements in the sketches and generates its corresponding code. For this approach, we make use of Object Detector and Long Short Term Memory (LSTM) network. An implementation of the approach is also presented along with its results.
Improved Handwritten Character Recognition for Sinhala Language based on Convolutional Neural Networks

Automating handwritten character recognition (HCR) for Sinhalese is still an emerging field, as Sri Lanka is the only country in the world that uses Sinhala as the national language. The Sinhala alphabet includes 60 characters that appear to be fairly complex when compared to many other languages. Although approximately 25 research studies have been conducted since 1990s to recognize handwritten Sinhala characters, there is still a large room for improvement. We have investigated two different methods for handwritten Sinhala character recognition: (i) total pixel length-based method which was newly derived by assuming the differences among characters in pixel length and (ii) Convolution Neural Networks (CNN) based classification, to overcome the limitations in the primitive methods. Multiple preprocessing techniques were used in the pixel length-based method while only a resizing technique was used in the CNN based method. The total pixel length and CNN based methods were tested using a dataset of 55 characters, each with 33 samples. The result of the pixel length method is imperfect as distinct characters may hold the same length. Therefore, we abode this method for further processing in future with more improvements. But the proposed CNN classification produces an accuracy of 85.71% and when compared to current studies, this can be considered as the best result achieved for 55 characters among total 60 characters, with nearly 10% of accuracy improvement.
Designing Secure Clusters using Blockchain for IoT

Internet of Things(IoT) has become a major component of our lives, where numerous independent devices connect, communicate and interact with one another. It is an evolving technology where devices are intelligent, independent and exchange information with one another without the need for human intervention. Hence these devices must identify, authenticate each other and ensure data integrity and security to avoid being targeted by various malicious attacks. To overcome these limits, a secure decentralized system has been proposed in this work. Here devices can create secure clusters along with security features provided by blockchains, where devices can identify, authenticate and trust each other for data integrity and availability. The implementation of proposed work is done using java and python language and also using ethereum blockchain. Initially, there was only one mode of communication where devices belonging to the same cluster could communicate with each other which are termed as Intra cluster communication. In the proposed system, major focus is on the Inter cluster communication where devices belonging to different clusters can communicate with each other. By using inter cluster communication, the performance and availability of data for the application are enhanced. The results obtained in the proposed work will satisfy the security, efficiency, and low cost needs of the Internet of Things.
Video-based Anomalous Activity Detection Using 3D-CNN and Transfer Learning

Video surveillance is used to observe various behaviour. It is used for security purpose. CCTV cameras are used for detecting the anomaly. The transfer learning is used to detect any unusual behaviour by human. The collected by the CCTV can also be used for detecting anomalous activity from the recorded video. By extracting the features of a target object’s movement and status, and the use of transfer learning helps to recognize activities or unusual action by the human and describe them in natural language.Such a surveillance system can detect, track, and analyse any unusual behaviour of the person in high security needed areas or from the crowded scenes by using video data. Anomaly detection by the visual appearance and motion dynamics of the involved humans and objects in video sequences is explained here with the help of transfer learning.
Hybrid Recommendation Algorithm for Personalization of Customer Experience

With the widespread use of the internet, a massive volume of data is created on the network every second, while the user needs information that is relevant to his particular search. The processing of such large amounts of data is a difficult task. An information retrieval system that can analyze this enormous data is necessary to provide the user with the exact information he or she requires. A recommendation system is a type of needed technology that obtains information in order to enhance users' access to information and, as a result, recommends items that are relevant to his clearly stated behavior and preferences. The recommendation system examines a large data collection and focuses on providing the user with reliable content recommendations. There are a variety of recommendation systems in use today, including Netflix, YouTube, Tinder, and Amazon, to name a few. This article examines the numerous types of recommendation systems, as well as their varied difficulties and solutions, as well as application cases of correctly employed recommendation engines and their possible advantages.
Object Detection in Aerial Images with Orientation Awareness

Object detection plays a vital role for aerial images because of the ease of access to images in any geographical region. Object detection on aerial images can automate many tasks from traffic management to surveillance. The major challenge faced during object detection in areal images is that objects are not associated with vertical height whereas objects are usually height dominant in natural images. Moreover, the orientation in areal images is arbitrary with huge variations in the scale of an object. Existing work performs detection with a bounding box aligned with the axis which limits the algorithm to have an oriented or a rotated bounding box which captures a lot of redundant regions and leads to misclassification especially in the case of densely populated regions. Therefore, in this paper, an object detection approach, that can be resilient to the random orientation of the object, is presented. The proposed mechanism will have the exact spatial information of the object and will increase the detection accuracy. The deTection in Aerial Images (DOTA) dataset is employed for evaluating the performance of the proposed algorithm.
AI-based Screening System for COVID-19

The outbreak of the COVID-19 pandemic caused by the novel coronavirus has disrupted global health systems and changed the way of life. Leveraging the potential of technology has become indispensable to ensure safety as new strains emerge. In this paper, we propose a low-cost AI-based screening system that can be installed at various locations. The objective of our solution is to automate the task of face mask detection, checking for social distancing, and body temperature scanning. The dataset used for our AI model consisted of 2314 images combining those without a mask and those with an artificial mask attached using computer vision techniques. These three functionalities were carried out by combining AI and IoT technologies. Specifically, we employed the SingleShot-Multibox detector (SSD) and ResNet-10 architecture as the first part of our model for face detection. MobileNetV2 architecture was used as the second part of the model for binary classification (with or without mask). Various IoT components were integrated to achieve a real-time screening process and subsequently simulate entry access or denial. Our proposed model outperformed other existing solutions by achieving an accuracy of 99% and an F1 score of 0.99.
STONE: Secret-data Transmission On Novelty Encryption

With the increasing growth of the data innovation area, information security has become a critical concern and significant feature. In several confidential applications, it is possible to break the security message. As a result, secret message communication technology is required to avoid message breaches. Steganography is the method of embedding any hidden data or information into a cover file, such as a picture, text, audio, or video. The majority of hidden information concealment methods in the current decade make use of multimedia artefacts. With an in-depth literature review, this paper elaborates on two-stage multimedia steganography. In the first stage, the secret message is implanted in the original sound from a multimedia file, resulting in stego-audio. The second stage embeds stego audio with the video frames once more. To incorporate the hidden audio, the audio steganography employs the 3-bit LSB encryption technique. To the recipient side, the processed video with stego-audio is sent. The audio is derived from the video stream in the receiver part. A concealed message is separated from stego audio using the 3-bit LSB decryption technique. PSNR, SSIM, and MSE metrics are used to evaluate the suggested algorithm's performance. This well-thought-out method has yielded encouraging results.
Outlier Sensitive Online Change Detection using Convex Combination of Adaptive Filters in Sensor Data Streams

Detecting changes in multivariate streaming sensor data by estimating the probability distribution of the log-likelihood over different time windows is difficult due to presence of outliers. To deal with such situations this paper proposes a modified-adaptive online change detection algorithm. This approach utilizes a convex combination a slow and a fast LMS-based adaptive filters making it robust against outliers. The robustness is ensured by finding the probability of occurrence of the outliers determined based on the change in the mean and variance of the moving averages within a threshold. The versatility of the proposed approach is tested out in two different scenarios; a synthetically generated dataset and a real-world dataset featuring sensor streams from a reefer container. The algorithm’s effectiveness is also evaluated against the state-of-the-art change detection methods with the help of appropriate metrics like False Positive Rate, False Negative Rate, and Latency which are crucial in assessing the model’s responsiveness in real-time. In particular high false positive rate in case of the proposed algorithm indicates, it is insensitive to the presence of outliers in both univariate and multi variate data streams.
Signed English Recognition with Spatio-Temporal Graphs and Language Modelling

Sign Languages are a unique family of languages that use visual cues rather than verbal means for conveying meaning and a mode for communication. They are extensively used by the deaf and dumb community. Sign language interpretation is a skill with a steep learning curve and is quite essential to understand for accessibility concerns. But with the current advancements in computer vision, recognizing human action and translating sign language into text is possible. This paper proposes a system that uses a combination of MediaPipe holistic (for human pose estimation), graph neural networks, and the Electra transformer to interpret and classify the 3D skeletal data of the signed word. The proposed model predicts the word signed using Graph Convolutional Network (GCN) with Natural Language Processing (NLP) to utilize the context for the word being gestured. Using this approach the model achieves around 97% accuracy on words signed based on its context which is comparable to State-of-the-art models.