Reviewerly/exHarmony huggingface dataset:
Reference Implementations for Machine Learning Application Benchmark

This paper presents reference implementations for a multitude of space applications from the Machine Learning Application Benchmark. Reference implementations include the respective model, its on-board hardware implementation, test scripts and final benchmarking results. In publishing these reference implementations, we make a significant contribution to the benchmark and provide more insight into the viability of on-board machine learning applications.
AUTOPILOT: An Advanced Perception, Localization and Path Planning Techniques for Autonomous Vehicles using YOLOv7 and MiDaS

Self driving vehicles have emerged as a reliable technology that has the capability to transform transportation and mobility. The development of self-driving cars requires significant advances in a number of areas, including perception, localization, decision-making, and control. This research paper is based on the project implementation of the combination of object detection using YOLO (You Only Look Once), depth sensing using MiDaS for the localization and perception of obstacles, perspective transform and decision making for path-planning in self-driving cars. The contemporary state of the technology for object detection, depth sensing, localization and path planning evaluates the performance of the combined system through simulations and experiments. The results show that the combination of YOLO and MiDaS provides a new robust system for object detection and depth sensing. This research paper contributes to the advancement of self-driving car technology and provides new and innovative approaches to the perception and localization of obstacles in the environment.
Creating Traceability in Spice Production and Providing Crop Insurance to Farmers on Crop Failure using Blockchain: A Comprehensive Survey

In terms of production, consumption, and exports of spices, India leads the globe. Almost all of India’s states and union territories cultivate some type of spice. India exports spices to all over the world. Due to urbanization, individuals are now searching for information about the sources of their food. Meanwhile, the farmers who cultivate spice crops face a lot of crisis due to crop failure. This study presents utilization of block-chain technology to enhance traceability in spice production and provide crop insurance to farmers in the event of crop failure. There is a need to increase product quality, supply chain transparency, and lower the risk for farmers by offering a safety net in the form of a financial system.The system should make an unchangeable record of every stage of production from planting to harvesting, processing, and distribution—using blockchain.All supply chain participants will have access to this record, promoting accountability and openness. A crop insurance policy based on smart contracts will also be incorporated into the system, automatically compensating farmers if their crops fail. We intend to remove the middlemen from the system by using IoT technology and Machine Learning methods to identify the soil quality and crop diseases.
Development of a Real Time Vision-Based Hand Gesture Recognition System for Human-Computer Interaction

Speech impairment, a debility that affects an individual’s capacity to communicate via talking and hearing. Therefore, sign language is essential for facilitating communication with individuals who are deaf and mute. American Sign Language (ASL) is widespread in the application of sign language worldwide, with regional variations. Our proposed system is a vision-based hand gesture recognition software, where the camera and the software will observe the set of sign language or gestures and will convert it into normal text. So, with the help of this software, we can easily communicate with a mute person. In the proposed system we used ASL as the language dataset and YOLO-v5 as the main algorithm model. We also included a ‘BOT’ control with our gesture recognition system of hand for a better understanding of the power of the model and different application abilities of hand gesture recognition. Service bots have one-on-one conversations with users and discover "natural" and intuitive interfaces. The main highlighting point is we can use a custom dataset as per our flexibility, for example, we can move the bot forward by giving a sign like ‘1’ instead of sign language like ‘A’ or ‘B’. We captured nearly 700 pictures of sign language with different backgrounds and trained it where we got an overall 93% accuracy in the experiment.
Transfer Learning-Based Model for Oral Cancer Detection

This electronic Only 29% of oral cancer cases are currently diagnosed in the early stages, even though that oral cancer has an 83% survival probability if found early. Deep learning methods can recognize patterns in the cells that make up oral cancer and help in early detection. In this paper, we provide preliminary findings from neural networks for the identification of oral cancer in microscopic pictures. We gave a thorough analysis of several models’ performance with this kind of data. In comparison to a baseline simple convolution neural network, we achieved an overall improvement of 10-15% by utilizing transfer learning approach. The enhanced value of data preparation methods with a transfer learning model for this purpose is demonstrated by ablation studies.
A Hybrid Transfer Learning Architecture Based Image Captioning Model for Assisting Visually Impaired

The task of automatically presenting the subject matter of a picture without human involvement is a highly intricate task. The issue at hand is commonly addressed through the utilization of computer vision and natural language processing techniques. Firstly, the image content must be analyzed and interpreted. Subsequently, this understanding needs to be transformed into grammatically accurate and contextually meaningful sentences. An issue that arises when employing deep learning (DL) based model that produces insufficient understanding of semantic context along with the encoder’s inability to transmit essential visual information to the decoding unit in an efficient manner. To mitigate this issue, a hybrid transfer learning scheme has been proposed that is capable of generating image captions with a high degree of accuracy and minimal occurrence of errors. In this article, Inception module has been fused with residual network for extracting significant features from the images for encoding. Gated recurrent unit has been used as the decoder by producing appropriate captions for the images. The novelty lies in the inclusion of an attention layer that facilitates the acquisition of a linguistically relevant and syntactically coherent representation by the model. The attention interface is situated between the encoder and decoder components. Experimental study reveals the effectiveness of the proposed hybrid DL framework over existing DL methods. The proposed image captioning model can further be implemented for assisting the visually impaired.
Design and Analysis of Space Objects Tracking Algorithm

Space object tracking plays a critical role in space situational awareness, collision avoidance, and the overall safety and efficiency of space operations. With the ever-increasing number of active satellites, debris fragments, and other celestial bodies in orbit around the Earth, accurate and reliable tracking systems are essential for monitoring their positions and trajectories. Conventional methods for space object tracking include radar, optical telescopes, and satellite-based tracking using GNSS. Extended Kalman Filter, unscented Kalman filter, batch least squares method, particle filter method and numerical integration techniques are generally employed for orbit determination. This research focuses on using the Keplerian approximation for orbit determination, a simple yet effective approach based on Kepler’s laws. It offers computational efficiency and is particularly valuable for initial orbit determination. This paper demonstrates comparisons of orbit plots and presents error analysis for the obtained results.
Blockchain-aided Decentralized Collaborative Automatic Modulation Classification for Next-Generation Wireless Networks

Automatic modulation classification (AMC) is essential to dynamic spectrum access in B5G and 6G networks for refarming the spectrum resources. However, the recent deep learning (DL)-based AMC framework has communication overhead, requires more computing resources, and poses security issues. Next-generation wireless networks allow distributed collaborative scenarios to provide ultra-reliable and low-latency communications (URLLC) services. This study proposes a blockchain-assisted decentralized collaborative AMC framework with a lightweight convolution neural network (CNN) model. The proposed system utilizes the federated learning (FL) technique to enable a privacy-preserving scheme by performing local training on edge and collaboratively producing a reliable model. A blockchain-based decentralized aggregation technique is implemented, using the Interplanetary File System (IPFS) as off-chain distributed storage for local and global models. This framework applied an improved accuracy-aware client selection mechanism (iACSM) to enhance the model performance and reduce communication overhead by selecting high-reputation authorized clients. Moreover, an ERC-20 token-based incentive mechanism has been developed to incentivize high-reputation selected clients. The proposed model measurement achieves high accuracy AMC with a low-complexity model structure in a trust-based decentralized learning scenario for constrained-resource wireless devices.
Node Degree Classification using Graph Modelling

Graphs serve as a potent tool for representing network data with nodes as objects and links as their relationships. The information within these graphs can be evaluated, retrieved, and visualized through Machine Learning (ML) techniques. However, using ML techniques with graphs is not so east because graphs are non-Euclidean structures and ML Methods are only suitable with Euclidean structures. To reduce this complication, the graph is encoded into a low dimension Euclidean vector representation, known as embedding vector, via the process known as node embedding. Recently, various Machine learning techniques had been developed that will automatically learns to encode the graphs in very fast and highly efficient way. Some of these techniques are, known as (GCNs). The aim of the project is to unfold: firstly, the objective of this study is to address the node degree classification and develop a model that can be utilized for node prediction on a high-precision dataset. to predict subject of a paper based on its words and citations network in an academic network that comprises citation information from top ML research papers. Initially, a homogeneous model is constructed utilizing GCN, GAT, and Graph SAGE techniques. To achieve node degree classification, a straightforward but efficient self-designed framework is introduced by implementing and evaluating three different architectures (GCN, GAT, and Graph SAGE) in PyTorch Geometric. For node prediction, the most precise framework with GNN is employed, and ensemble learning is utilized to improve performance. Detailed experiments conducted to establish robustness and effectiveness of the proposed solution
CloneBAS: A Code Clone Detection Method Based on Abstract Syntax Tree and Simhash

Code clone, which is one of the common “code smells”, is the duplicate code that exists in the source code. In this paper, we present CloneBAS, a code clone detection method based on Abstract Syntax Tree (AST) and Simhash. First, code block is considered as the detection granularity. Second, a fixed-length fingerprint with the AST of each code block is generated. As a result, multi-type clone detection capabilities are achieved. Third, based on the Hamming distance restriction, clone fingerprint groups and their corresponding clone code block groups are attained. Source codes from four projects are selected as subjects and the clone detectors of CCFinderSW and Deckard are compared with CloneBAS in our experiments. In terms of clone quality, the lowest accuracy of over 86% is achieved, which is better than the highest accuracy of 74% of the two comparators. In terms of clone quantity, Deckard is outperformed by CloneBAS in three projects.
VATMAN : Video-Audio-Text Multimodal Abstractive Summarization with Trimodal Hierarchical Multi-head Attention

Multimodal Abstractive Summarization is a challenging task that aims to generate concise and informative summaries from diverse modalities, such as video, audio, and text. In this study, we propose VATMAN, a novel approach for multimodal abstractive summarization. To effectively capture the hierarchical relationships and dependencies between modalities, we introduce Trimodal Hierarchical Multi-head Attention (THMA). THMA hierarchically attends to the video, audio, and textual representations, enabling the model to distill salient information and generate cohesive and coherent summaries. VATMAN leverages state-of-the-art generative pretrained language models (GPLMs), specifically Transformer-based models, and applies hierarchical attention at the modality level, which enhances the utilization of contextual information. The proposed VATMAN model on the How2 dataset demonstrates the ability to create more fluent summaries than those generated by human authors, showcasing its potential for utilization in various industrial environments.
From learned to new relations through generative models combined with relations clustering and few-shot learning

From the release of BERT in 2018 to the newest large language model of OpenAI, GPT-4, the corpus size of the training data has increased to the order of trillions of tokens. However, the models’ capabilities to expand and generalize their acquired knowledge remain a challenge. This research aims to analyze the ability of models to generalize relation types for the task of relation extraction, by clustering them in categories and training the model on half of the relations in a cluster. The generalization capabilities will be tested against zero-shot, one-shot, and few-shot learning, introducing relations from the other half of the cluster, to analyze how much data a model needs to achieve satisfactory results on generating the unseen relation types.
Case study: using AI-assisted code generation in mobile teams

We evaluate the performance of AI-assisted programming in mobile development teams that are focused on native mobile languages like Kotlin and Swift. The case study involves 16 participants and 2 technical reviewers, from a software development department and it is designed to understand the impact of using Large Language Models trained for code generation in particular phases of the team, more specifically, technical onboarding and technical stack switch. The study uses technical problems dedicated to each phase and requests solutions from the participants with and without using AI-Code generators. It measures time, correctness, and ‘technical integration’ using a new proposed metric ReviewerScore, extracted from industry standards, the code reviewer of merge/pull requests.The output is converted and analyzed together with feedback from the participants in an attempt to determine if using AI-assisted programming tools will have an impact on getting developers onboard in a project or helping them with a smooth transition between the two native development environments of mobile development, Android and iOS. The study was performed between May and June 2023 with members of the mobile department of a software development company based in Cluj-Napoca, with Romanian ownership and management.
X-HDNN: Explainable Hybrid DNN for Industrial Internet of Things Backdoor Attack Detection

This study proposes a hybrid deep neural network (HDNN) framework, called X-HDNN, for detecting backdoor attacks in Industrial Internet of Things (IIoT) data. The X-HDNN combines LeakyReLU and focal loss functions to reduce false positives and losses. A comparative analysis of the performance of the primary deep neural network and the proposed X-HDNN had remarkable improvement in f-score value from 57% to 78% and loss of 0.0044 to 0.0014. It also incorporates the SHAP explainability technique to provide interpretable and reliable detection. Evaluating the X-HDNN model using backdoor impact ratio, feature importance score, and decision confidence helps understand the model’s outcomes and the significance of each feature. The findings enhance trust in the model and facilitate better decision-making based on the provided explanations.
Design of STEM Teaching Network Publishing Framework Based on Web Interactive Programming Environment

With the rapid development of e-learning, collaborative learning platforms such as Web-Based Education Domain are gradually being promoted in subject education. However, with the deepening of the application of platform environments, the problem of time lag in learning interaction in virtual classrooms has gradually been exposed. The traditional one-way collaborative education platform has been difficult to meet the demand for real-time interaction in smart classroom teaching. Based on this, the research was inspired by programming education and introduced a Web-based interactive programming environment. Through segmented programming technology, real-time interaction between teachers and students in the virtual classroom was realized, and animation media resources were finally generated and published to the learning network. middle. In the environment design, the study introduced four dimensions of environmental learning goals (entry skills, primitive skills, emotional skills and wisdom skills) to analyze learners, and based on the analysis results, a corresponding teaching case for the interactive programming environment system framework was designed, discuss how to use this framework to implement subject teaching ideas and practical methods to promote real-time interaction in virtual classrooms.
Privacy Preserving Neural Network Models based Homomorphic Encryption : A Case Study for Diabetes Prediction

This paper addresses the problem of data privacy in distributed applications using machine learning models in the context of modern privacy laws, such as GDPR, by proposing a machine learning model hybridized to use homomorphic operators to perform model evaluations on predicted data. The paper presents the general approach of building such a model using frameworks such as TesSeal, Keras, Microsoft SEAL, and their integration in a web-distributed application. Due to the complexity of the homomorphic operations, only the prediction is proposed to be executed on encrypted data. An experimental platform is developed where a machine learning algorithm is trained to predict diabetes based on laboratory tests. The proposed models that use homomorphic operation to run on encrypted data are evaluated and compared to the same models that operate on plain-text data to determine the cost of encrypting the data from the point of view of model accuracy and execution time. Experiments show that both the model that runs on encrypted data and the model that runs on plain data have similar accuracy. However, the execution time of the model that runs on encrypted data is 10 to 20 times higher than of the model using plain text data.
Benchmarking Anomaly Detection Algorithms using Hyperparameter Optimization

Anomaly detection is the technique of finding out-of-the-ordinary occurrences inside datasets. A multitude of anomaly detection methods was developed for this purpose in a wide variety of domains (e.g., economy, industrial processes, health, environmental monitoring, etc.), using as support more generic artificial intelligence and signal processing algorithms. The performance of these algorithms is strongly influenced by the initial dataset characteristics and by the hyperparameter settings that users choose for each algorithm. There is no a-priory best solution for a given domain or type of dataset; therefore, the researchers must test a multitude of methods with multiple hyperparameter settings. The research community does not yet have a multi-perspective assessment of the performance of these algorithms. Most of the work is focused on very specific problems or domains, and the algorithms are evaluated only from one or a few perspectives, using the ”default” settings, without hyperparameter optimization. In this article, we will discuss the findings obtained from an extensive anomaly detection benchmarking that covers 34 anomaly detection algorithms, tested on several relevant datasets from a wide area of domains. The results are presented from multiple perspectives. For each algorithm and dataset, a hyperparameter optimization approach is used in order to establish the optimal settings for each algorithm in each test case. This study discloses both the merits and limitations of the various techniques and proposes a taxonomy of anomaly detection algorithms based on the nature of the problem and the characteristics of the input data.
Spread Spectrum Digital Watermarking Using Orthogonal Ramanujan Wavelet Transform

This paper aims to explore the embedded watermarking scheme using a spread spectrum based algorithm employing Discrete Wavelet Transform (DWT) and Orthogonal Ramanujan Wavelet Transform (ORWT). We further carry out detailed comparison between the results obtained from the two algorithms for grayscale as well as color images in RGB format. We have also tested the robustness of the watermarking techniques against various channel attacks. We obtain an improvement of upto 12.634% in correlation and upto 5.247% in normalized correlation value for original and recovered watermark in case of grayscale images, using ORWT compared to DWT.
Chambolle’s Projection-based Total Variation Deepnet Layer for Image Denoising

This research paper presents the implementation and evaluation of a Total Variation (TV) layer within a deep learning framework for image denoising tasks. The TV layer is based on Chambolle’s projection method and aims to enhance the performance of a baseline denoising network. The study investigates the effectiveness of incorporating a single TV denoising layer, which adds only six parameters to an arbitrary baseline autoencoder. The resulting models are evaluated across three datasets with different Gaussian noise. By comparing an arbitrary network architecture with an identical counterpart model that incorporates the TV denoising layer, the results indicate significant improvements in performance. The magnitude of these performance gains diminishes as the variance of noise increases. Notably, for lower Gaussian noise variance such as for a standard deviation of 25, the gain in terms of the mean peak signal-to-noise ratio (PSNR) metric is estimated to be approximately +0.89 dB, a discernible improvement that is visually perceptible. Although the resulting model has no significant increase in terms of model size, it does present significant increases in complexity.