Reviewerly/exHarmony huggingface dataset:
Drone for Object Tracking

This work shows the building of an autonomous object tracking drone. Object tracking drones are not available in the market. A drone which can detect the object with the inbuilt camera into it and can track the device’s movement. The drone assembly is assembled in house by integrating various parts which are available in the market. The assembly is programmed based on an object tracking algorithm. The cost of this inhouse drone is less compared to the drones available in the market and provides similar performance as others.
SmartExecutor: Coverage-Driven Symbolic Execution Guided by a Function Dependency Graph

Symbolic execution of smart contracts suffers from sequence explosion. Some existing tools limit the sequence length, thus being unable to adequately evaluate some functions. In this paper, we propose a symbolic execution approach without limiting the sequence length. In our approach, the symbolic execution process is guided by a function dependency graph aiming to maximize code coverage while reducing the number of sequences to be executed. Our approach consists of two major phases. The first phase executes all sequences up to a length limit while the second attempts to cover the not-fully covered functions. We have developed a tool called SmartExecutor and conducted an experimental evaluation on the SGUARD dataset. The experimental results indicate that compared with a state-of-the-art symbolic execution tool Mythril, SmartExecutor achieves higher code coverage and detects more vulnerabilities with less time.
On the Blockchain Selection in Agri-Food Tracking Systems: Student Paper

In recent years, blockchain technology has spread widely in the agri-food sector. In particular, several blockchain-based tracking systems have been proposed. Most of these projects use mature technologies, such as Ethereum and Hyper-ledger. However, other blockchains were proposed recently (some particularly suited to IoT, commonly used in agri-food tracking systems). In the literature, several methodologies are proposed to guide the choice of the type of blockchain. These methodologies are generic, and a gap remains between the criteria used to guide the selection of the type of blockchain and how concrete use cases influence them. Building upon existing methodologies, this paper presents a specific approach for agri-food tracking. Once the parameters of the use case that impact the blockchain's performance have been identified, the methodology allows the association of these parameters with the criteria for identifying the types of blockchain to use (e.g., public permissionless) and the most suitable consensus algorithm. The output tuple \ [types], [algorithms], [functions]), therefore, allows the choice of the blockchain that best suits the usage scenario.
Fairness Notions in DAG-Based DLTs

This paper investigates the issue of fairness in Distributed Ledger Technology (DLT), specifically focusing on the shortcomings observed in current blockchain systems due to Miner Extractable Value (MEV) phenomena and systemic centralization. We explore the potential of Directed Acyclic Graphs (DAGs) as a solution to address or mitigate these fairness concerns. Our objective is to gain a comprehensive understanding of fairness in DAG-based DLTs by examining its different aspects and measurement metrics. We aim to establish a shared knowledge base that facilitates accurate fairness assessment and allows for an evaluation of whether DAG-based DLTs offer a more equitable design. We describe the various dimensions of fairness and conduct a comparative analysis to examine how they relate to different components of DLTs. This analysis serves as a catalyst for further research, encouraging the development of cryptographic systems that promote fairness.
Gas Optimization Patterns in Move Smart Contracts on the Aptos Blockchain

The Move language provides superior security and verifiability compared to existing smart contract languages such as Solidity. As the language becomes more popular, gas optimization will become an important field of research. This paper presents the first work on gas optimization in the Move language. We chose Aptos as the underlying platform for our analysis since it is the leading Move-enabled blockchain platform, and it was the first to develop a gas meter. In this paper, we describe Aptos' gas meter in detail. Then, we analyze the vast research on gas optimization in Solidity, and explore how it can be implemented in the Move language. Finally, this paper proposes 11 gas optimization patterns and principles for the Move language, presents 5 patterns that decrease the time complexity of the smart contract but have no effect on gas consumption, and implements a sample smart contract for each proposed gas optimization pattern. Our results show that the proposed patterns reduce gas consumption in a typical smart contract by 7 – 56%.
A Transpilation-Based Approach to Writing Secure Access Control Smart Contracts

In blockchain space, access control is a crucial aspect of smart contract development, as it guarantees that only authorized users can execute specific functions within a contract. The growing interest in employing smart contracts for access control mechanisms stems from their ability to provide reliable, secure and efficient enforcement of access control policies. However, Solidity, the most popular smart contract language, was not designed explicitly for writing access control policies, unlike specialized languages such as Alfa or XACML. The differences between these languages leads to a difficulty for those who wish to use smart contracts as access control mechanisms but lack the knowledge of Solidity or the ability to evaluate their code's security. To bridge this gap, we introduce ASAC, an Alfa to Solidity transpiler that translates Alfa policies into secure Solidity contracts. Our transpiler leverages the ANTLR (ANother Tool for Language Recognition) parser generator and translate complex Alfa policies into smart contracts. We showcase the effectiveness of our transpiler through a set of case studies and offer an evaluation of its performance.
Mining in Logarithmic Space with Variable Difficulty

This paper addresses the problem of blockchain scalability, problem that might be detrimental to their widespread adoption. Blockchain data can be categorized into both an application part and a consensus part, the latter one growing linearly over time. The authors in [1] provably solves this problem in the context of work-based blockchains. Their solution achieves an exponential reduction both for storage and communication assuming a static mining difficulty, that is assuming that the blockchain is beeing built by a fixed number of miners. This paper improves upon this work by handling variable mining difficulty. Experimental results show that our solution still guarantees exponential reduction of both storage and communication.
Local (Per-Input) Robustness Based Guided Adversarial training of Deep Neural Networks

Adversarial attacks are inputs that are purposely perturbed to deceive machine learning models. Adversarial training enhances model robustness and the standard approach uses augmented datasets with both clean and adversarial dataset. However, generating adversarial examples for training instance can be computationally expensive and redundant in training models. In this study, we propose a novel approach to generate adversarial samples using only a subset of the given dataset. We identify weak robust samples, inputs with small perturbation added to them result in poor model performance, and generate adversarial examples from them. This approach is more efficient than traditional adversarial training since it eliminates the need to generate adversarial examples for every training sample. Since adversarial training is typically expressed as a min-max optimization problem, concentrating on the weak robust samples allows us to create samples that are more likely to lead to a strong inner maximization in the optimization formulation of adversarial training. We also propose combined adversarial training (CAT), which incorporates adversarial samples from different models using Fast Gradient Sign Method (FGSM) and Projected Gradient Decent (PGD) attacks generation techniques. Experimentation on the MNIST and CIFAR10 datasets validates our approaches. Our proposed adversarial training methods for both FGSM and PGD show comparable robustness to baseline adversarial models. Furthermore, our combined (CAT) approach consistently outperforms the baseline adversarial models. These findings highlight the potential of our novel approaches to enhance the robustness of deep neural networks against adversarial attacks while training on samples-of-interest.
Enhancing IoT Security and User Experience: Leveraging SGIoT-SURE for Effective Security Implementations

The rapid growth of Internet of Things (IoT) devices underscores the need for user-centric science gateways focused on IoT security. This study aims to leverage the insights gathered from the SGIoT-SURE (Science Gateway & Internet of Things -Security User Readiness Evaluation) scale to inform the future design and adaptation of a science gateway dedicated to IoT security. Key factors identified include self-efficacy, user trust and the importance of security measures. Our results highlight user preferences for an intuitive interface, customizable toolkits, and educational resources. Positive correlations with the SGIoT-SURE total score suggest that a more user-friendly and secure gateway is likely to encourage greater user engagement in IoT security. Based on these findings, we recommend an iterative, user-engaged approach for the future design of a science gateway that meets diverse user needs and expectations.
Knowledge-Based Scene Graph Generation in Medical Field

This work aims to combine object detection and knowledge graphs to understand medical scenes. The rapid growth of the medical field has introduced various equipment types which makes scene analysis more crucial. By representing object detection results as a knowledge graph, one can derive conclusions and gain a clear understanding of the scene through scene graphs. While object detection identifies objects in images, it lacks the ability to utilize contextual information and visual reason. Therefore, knowledge-aware object detection becomes essential in allowing the integration of external knowledge graphs into object detection algorithms. Therefore, the work focuses on the medical field that combines object detection and object relationships to form a correlated understanding of the scene. Construct relationships between objects, generate scene graphs, and derive inferences to predict the overall scene understanding is achieved through the knowledge graph. The proposed work incorporates knowledge graphs in the medical domain that addresses the need for improved scene understanding in medical contexts.
CUSC: Composable User-Centric Smart Contracts

Smart Contract (SC) Programming Languages (PL) are inspired by Non-SC PLs. Many, like Solidity, use an object-oriented approach with interfaces and inheritance-based sub-typing. However, the main focus of these concepts is on abstraction and extend-ability, whereas for SC Systems, robust, secure and composable SCs are of higher importance. Further, despite supporting inheritance, Solidity and other SC PLs fail to leverage the full benefits of the object-oriented paradigm when multiple SCs are involved. This work presents an approach to SC composability that enables highly composable and secure SCs by encapsulating logic in small traits that serve as interfaces.
Deferral: on the Feasibility of High-Volume Blockchain-based Referral Systems

Digital marketing has transformed referral marketing, revealing limitations in traditional centralized systems such as trust, transparency, and efficiency, however, the potential advantages of decentralized systems remain underexplored.This paper investigates the feasibility of a high-volume, decentralized referral system.The approach assesses smart contract prototypes for cost-effectiveness and performance in high-user engagement scenarios in different EVM-compatible blockchains and referral strategies, such as multilevel referrals.Findings confirm the technical viability as a blueprint for designing and implementing similar systems, highlighting challenges in real-world deployments, such as Sybil attacks, and the interplay between technical and economical design factors.
The Role of Chatbot Technology in the Cultural Value Proposition

In recent years, the cultural value proposition has acquired an innovative technological component. The daily overexposure to multimedia platforms and the pervasiveness of social networks requires cultural organizations to develop strategic trajectories that can stimulate interest and involvement of current audiences on a par with the attraction of potential audiences. This chapter explores, from a managerial perspective, emerging experimentations regarding the use of artificial intelligence for the enhancement of the cultural-based experience through chatbot technology. The research's findings highlight that this technology can take on different characteristics depending on the implementation used and the purpose to be achieved. The innovativeness of the approach lies in the components of interactivity and customization of human-like interaction, through which museums attract and involve more effectively current and potential audiences.Request access from your librarian to read this chapter's full text.
Assessing the Intersection of Artificial Intelligence and Digital Advertising

The advancements in artificial intelligence are among the factors influencing the components of various topics within the marketing and advertising industry. The usability of artificial intelligence and related technologies in sub-contexts within the scope of digital advertising generates the opportunity for various research questions. This study aims to conclude the future of the subjects based on assessing intersections between digital advertising and artificial intelligence. Following the purpose of the study, a literature review has been used, and three main intersection points in the field of AI and digital advertising are included. Three main section points concluded in the study refer to AI's contribution to analytics and decision making, advertising content production with the help of AI, and implementation of digital advertising with the help of AI, advertising measurement and optimization. Solutions and recommendations, and future research directions are also presented in the study.
Sentiment Mining

With millions of users active on social media, businesses have the opportunity to reach a vast audience and gain valuable insights into customer preferences and behavior. However, with the increase in social media usage, the challenge for businesses is to effectively analyze and interpret the vast amount of data generated by social media and other digital channels. This is where sentiment mining comes into play. Sentiment mining involves using machine learning algorithms to analyze and classify online content, such as social media posts and reviews, to determine the overall sentiment or tone of the content. The purpose of this chapter is to explore the concept of sentiment mining and its application in optimizing digital marketing strategies. The concept of sentiment mining has gained significant attention in recent years, with businesses recognizing its potential to gain insights into customer sentiment and preferences. This chapter aims to bridge this gap in literature and explore the potential of sentiment mining in optimizing digital marketing strategies.
How Artificial Intelligence (AI) is Transforming the User Experience in Digital Marketing

In this part of the book, the authors are diving into how artificial intelligence (AI) is completely changing the game in digital marketing, especially when it comes to the user experience. You see, AI gives companies these super cool abilities to deliver personalized content, analyze data like a pro, and create marketing strategies that really focus on the customer. Plus, AI has given birth to chatbots that have seriously shaken up customer service, making it way more personal and immediate. But, as cool as all this AI stuff is, it's not all smooth sailing. This chapter tackles things like data privacy, security, and making sure there are people who know how to handle it all. What really shows off the power of AI is how companies like Amazon and Spotify are using it to make the customer experience better. The more AI becomes a part of digital marketing, the more necessary it is to understand the good, the bad, and the sometimes ugly, to make sure AI is being used in the best and most ethical way possible.Request access from your librarian to read this chapter's full text.
Improving the efficiency of a solar PV panel's power generation by using a Dual-Axis tracker with a Cooling system

The dual-axis solar tracking device with cooling system tracks the sun's movement and keeps solar panels cool, enhancing their effectiveness and energy output. This device follows the direction of the sun using a microcontroller-based tracking system, and it employs a cooling mechanism to keep the panel at the right temperature. In order to disperse heat, the cooling system pumps water through the panels using a water pump and radiator. This system is a beneficial addition to any solar power generating system because of the way it is constructed, which increases the energy output of solar panels. This underlines the value of the dual-axis solar tracking device with a cooling system and demonstrates its significance in the solar energy industry. A few benefits of the dual-axis solar tracking device with cooling system include improved energy output, lower maintenance costs, and longer panel lifespan. This technique enables solar power generation systems to function at their highest efficiency, improving their viability for large-scale energy production. Due to its reliance on renewable energy sources and decreased use of fossil fuels, the system is also ecologically benign. The dual-axis solar tracking device with cooling system, in conclusion, is a creative way to increase the production and effectiveness of solar panels. It is a promising technology that has the potential to significantly aid in the shift to an era of sustainable energy.
Recognition of Indian sign language alphanumeric gestures based on global features

This manuscript presents a novel approach for accurately recognizing Indian sign language alphabets and numbers (1-9), which play a vital role in communication for individuals with hearing impairments in India. We conducted extensive experiments on our self-created dataset named "ISL 2022: A novel dataset creation on Indian sign language". This dataset contains images with non-uniform and complex backgrounds, reflecting the real-world scenarios where sign language gestures are performed. After identifying the region of interest i.e. the hand region from the images, we extract discriminative features using global image descriptors and Hu moments. Global image descriptors capture the global spatial layout of the gesture, providing valuable information about its overall structure and appearance. On the other hand, Hu moments capture shape characteristics essential for differentiating between different gestures. We employ the Random Forest and Naive Bayes algorithms to classify the gestures. The experimental results demonstrate the effectiveness and recognition rate of 98%. By combining the features of both the feature extractors, our approach showcases promising advancements in recognizing ISL gestures, especially considering the challenges posed by diverse backgrounds. The proposed methodology has the potential to contribute significantly to the development of practical and efficient sign language recognition systems.
Intelligent Framework for Auditory and Speech Impaired Individuals by Accelerating Sign Image Analysis using Graph Data Science

The prevalence of auditory and speech impairment in India is a significant public health concern, affecting a huge number of individuals across the country. The existing body of research believes that sign language is directly equivalent to spoken language, ignoring the linguistic aspects of sign language. Therefore, an inventive end-to-end system for translating and recognising sign language is required in order to facilitate real-time communication between users of sign language and those who do not. By integrating Sign Language Recognition (SLR) and Sign Language Translation (SLT) techniques, the system incorporates a deep learning network that analyses images for detection after being trained on a sizable dataset of hand gestures, and landmark estimation using the MediaPipe library, thus ensuring accurate real-time recognition of sign gestures. The SLT module utilizes graph data science and NetworkX to construct a directed weighted graph of Indian Sign Language (ISL) gestures and their corresponding English translations, facilitating the translation of gesture sequences into coherent English sentences. The study highlights the effectiveness and potential of combining SLR and SLT approaches using CNN is a dependable communication aid that enables people with speech and hearing problems to converse fluently with non-sign language users. Also, an extensive evaluation of a comprehensive dataset consisting of 56,200 images demonstrates outstanding performance and an impressive accuracy of 98.64% in recognizing and translating gestures.
Comparison of different Transformer models for Meeting Recapitulation Tool

Meeting recapitulation tools are designed to automatically extract key points from meeting transcripts or audio recordings. Hence, we present a new meeting recapitulation tool based on Natural Language Processing (NLP) techniques and machine learning algorithms. Our tool is able to accurately identify and extract crucial in- formation from audio meetings. The Transformer model learns context by tracking relationships in continuous data, such as the words in this sentence. We compare the performance of various Transformer models on a dataset of real-world meetings and find the model that outperformed other approaches in terms of both precision and recall. Overall, our meeting summarization tool represents a significant advancement in the field and has the potential to greatly improve the efficiency and productivity of meetings.
Assessment of Visual Patient Re-Identification in a Live Emergency Department Waiting Room

The re-identification of individuals through computer vision has been extensively studied. As “smart hospitals” come online, the use of computer vision to identify and monitor patients could become a core element of healthcare. Investigators therefore studied an experimental patient re-identification system in an Emergency Department (ED) ambulatory waiting room setting. The study investigated the feasibility of deploying a vision-based patient deterioration monitoring system with a patient re-identification subsystem in an ED waiting room. After implementing appropriate privacy, confidentiality, and security measures, investigators studied 440 consented patients and companions who were enrolled over a 12-month period to determine the performance characteristics of the experimental system’s video-based person re-identification solution. Several commonly used convolutional neural network algorithms, from lightweight MobileNet to larger ResNet models, were investigated in the study. We report on the system’s exhibited state-of-the-art accuracy and remaining challenges.