Reviewerly/exHarmony huggingface dataset:
Cost-Efficient Anonymous Authentication Scheme Based on Set-Membership Zero-Knowledge Proof

In this paper, we propose zero-knowledge named proof, a replay attack prevention scheme that ensures the user's anonymity against malicious administrators. We begin with adopting the zero-knowledge set-membership proof into an authentication setting in which users would delegate their requests to an agent that obstructs the user's identity from the administrator. This anonymous agent carries the guarantee of authenticity, which the administrator through the set-membership proof can confirm. Next, we prevent replay attacks from other parties by binding the agent's identity to the delegation request verifiable by the administrators. By leveraging these properties, a blockchain-based authentication scheme is then built. We quantitatively evaluate the security, cost-efficiency, and performance of our scheme and provide a third-party authorization scheme from our authentication framework to demonstrate its real-world relevancy.
Decentralized Ethereum-Based Solution for Mitigating Oracle Single Point of Failure

While blockchain is a secure and tamper-proof registry, it is not well-suited for storing large amounts of data due to its limited storage capacity and high cost. Additionally, storing data on a blockchain can lead to scalability issues and slow down the network. Therefore, it is generally not recommended to store large amounts of data on a blockchain. This paper proposes a solution to transmit data to smart contracts without compromising security with a single point of failure, using a bounty hunting mechanism to ensure fair concurrency between oracles retrieving data on an IPFS database. The proposed solution has been implemented as a prototype and its feasibility has been demonstrated through experiments.
Selection of Research Approach for Development of Human-Centered Intrusion Detection System

The importance of the cybersecurity is growing in information technology field. Information and communication technologies are an integral part of everyday life and therefore are the usual targets of cybercrime. Intrusion detection systems and machine learning methods are able to detect known threats well, however, these solutions are not as adaptable as a human being which leads to lower performance of the new threats detection. Therefore, the human actions and/or choices should be integrated into Human-in-the-Loop intrusion detection system that would help to improve its ability to detect zero-day threats. The aim of this paper is to choose the most suitable research method for the creation of such a system. The literature analysis highlighted three research methods used in the literature: analytical research, experimental research, and design science research. Two research methods were chosen for the further selection process: experimental research and design science research. Based on the needs of the research problem, the design science research method was chosen.
Machine Learning Powered Code Smell Detection as a Business Improvement Tool

Code smell represents the level of human interpretability in a software project, which becomes increasingly challenging as modern-day software projects grow in complexity. Machine learning has promising signs of solving the problem of code smell detection but will ultimately be limited by the training dataset of the model. This paper investigates some machine learning approaches for code smell detection, the implications of using such a system, integration with business processes and how such a system would fit into IT governance using Latvia as an example.
AI Powered Multi-model Content Creation For Virtual Gallery Using Learning Machine

Online digital advertising is currently a crucial marketing tool. Presently, the production of marketing content, including images, copy, and music, is still carried out using traditional manual methods. The purpose of this paper is to utilize AI technology to automatically generate diverse and contextually relevant initial drafts of marketing content. Users can gain inspiration from these drafts, resulting in significant time and manpower cost savings. Users only need to input a product image, product name, and select a music style through the webpage. Subsequently, the webpage will showcase AI-generated initial drafts of product copy with various backgrounds and arrangements. Taking inspiration from a museum concept, when users hover their mouse over the displayed product, they can simply press a designated key to play the generated music. This offers users a preliminary idea of the product in terms of imagery, copy, and music – all three aspects combined.
Unlocking the Mysteries of Speech: A Hybrid RNN-CNN Model for Vocal Language Identification

Speech is the primary form of communication, and being able to identify the language used in an audio sample is crucial in numerous fields. Language identification serves a purpose in speech recognition, language translation, voice assistants, and many other domains. Traditional language identification systems rely on statistical or acoustic models, which frequently require extensive domain-specific knowledge and have limitations in accuracy and resilience. By employing a hybrid Recurrent neural network and Convolutional Neural Network, this paper aims to develop a novel method of language identification. The suggested approach entails spectrogram preprocessing of audio samples, Mel-Frequency cepstrum coefficient extraction, and Convolutional Neural Network and Recurrent neural network architecture model training on a sizable dataset. Recurrent neural networks are good at capturing temporal dependencies in data, while Convolutional Neural Networks are better at capturing spatial patterns. This methodology successfully identifies the languages with an overall accuracy of 93%, proving the efficacy of the proposed model.
Enhancing Research Results with Programmable Blockchain Network

Since its inception in 2008, Blockchain has been proposed in different fields of study, and the research results have shown promising prospects in these areas. Despite these study results, blockchain technology has suffered some setbacks in adoption for real-life implementations. The unwillingness to adopt it stems from industries and organizations not being convinced about the proposed solutions’ results. The reason is that many of the presented solution results come from simulation. While simulation results are acceptable for research purposes, industries might be skeptical about adopting a new system based only on simulation results. Researchers must present results from real-life implementations to fully convince stakeholders of the usefulness of adopting blockchain technology. However, presenting blockchain results from real-life performance is challenging because of the following significant problems: 1. Blockchain networks are customized to implement a single approach, i.e., no blockchain network can test multiple proposed implementations concurrently, and 2. There is a lack of testbeds (with enough blockchain nodes) to test proposed solutions. This ongoing work presents a Programmable Blockchain Network (PBN), which can implement multiple approaches simultaneously and a global testbed to evaluate proposed solutions in real-life scenarios. The PBN, implemented on Generic Routing Encapsulation (GRE) global testbed, uses a master-slave model for smart contracts calling to implement concurrent blockchain solutions. The preliminary result shows that the proposed solution enhances research results, convincing more industries to adopt blockchain technology.
Aspect Based Sentiment Analysis System using Machine Learning Techniques

The evolution of e-commerce accompanied with enormous amount of sentiment reviews drive practical difficulty for consumers to decide upon a purchase choice. Sentiment analysis assists in analysing the reviews for a consumer to decide a purchase choice and a manufacturer to follow up on their clients and manage their requirements. Aspect based sentiment analysis is a subtask of sentiment research that aims to identify and analyse sentiment towards specific aspects or features of a target entity within a given text. Aspect extraction from the text input reviews is another important function in sentiment analysis at aspect level. Identifying those aspects and selecting required aspects is very essential task in opinion analysis based on features. In the proposed analysis, we initiate a supervised machine learning technique-decision tree classifier for selecting the required aspects and sentiment research is conducted by utilizing various machine learning techniques. The proposed work gives an accuracy of 85 - 93%.
Embedded Virtualization on RISC-V with seL4

Embedded virtualization is a key enabler for system designers to meet SWaP-C requirements for mission critical systems that execute various military, aerospace, medical, and commercial applications. The demand is now on the software designer to implement high assurance software that can reliably satisfy safety and time critical requirements. This article presents the first public implementation of the formally proven microkernel, seL4, on the RISC-V hypervisor using H-extension v0.6.1. This implementation uses the Rocketchip softcore executing in the FPGA fabric of a Zynq UltraScale+MPSoC ZCU102.
Forecasting Solar Irradiance Using Machine Learning Methods

Solar generation must be integrated into the power system to meet rising energy demand and reduce dependency on fossil fuels. The intermittent nature of solar energy output threatens the grid's stability and efficient functioning. Accurate solar power forecasting is essential to ensure the proper operation of the grid and the suitable placement of storage resources. In this work, we thoroughly investigate machine learning-based short-term solar irradiance forecasting. We assess how well several models-including gradient boosting regression, random forest, and KNN-perform using data from Izmir, Turkey. Our findings demonstrate that machine learning approaches outperform conventional time series models, with the KNN and random forest models generally demonstrating the highest performance. The suggested model may be applied practically in solar power plants and expanded to anticipate power production by incorporating pertinent input variables. Our research sheds light on the potential of machine learning methods for forecasting solar irradiance. It emphasizes the significance of precise forecasting for the reliability and efficient functioning of the electricity grid. Future studies can examine various feature engineering techniques and determine how machine learning models perform over extended periods.
Informative Path Planning Algorithm for the Exploration of Unknown 2D Environments

This paper addresses the Informative Path Planning (IPP) algorithm for autonomous robots to explore unknown 2D environments for mapping purposes. IPP can be beneficial to many applications such as search and rescue and cave exploration, where mapping an unknown environment is necessary. Autonomous robots’ limited operation time due to their finite battery necessitates an efficient IPP algorithm, however, it is challenging because autonomous robots may not have any information about the environment. In this paper, we formulate a mathematical structure of the IPP problem along with the derivation of the optimal control input. Then, a discretized model for the IPP algorithm is presented as a solution for exploring an unknown environment. The proposed approach provides relatively fast computation time while being applicable to broad robot and sensor platforms. Various simulation results are provided to show the performance of the proposed IPP algorithm.
Enhanced Nuclei Segmentation in Histopathology Image Leveraging RGB Channels through Triple-Encoder and Single-Decoder Architectures

In biomedical imaging, nucleus segmentation is critical for understanding cellular structure and function. This work proposes a novel approach to nucleus segmentation that exploits an image’s red, green, and blue channels as input. We use three Encoders for each channel and a single Decoder to reconstruct the original input image. Associating an Encoder with each of the three-color channels in our design allows us to combine them in a particular way that fully utilizes the RGB color space’s information. Flowing up-sampling for each color channel image decodes and reconstructs the original idea through the skip connection. Our model was successfully trained and evaluated using numerous datasets from Kaggle. To perform the segmentation task, we used a small publicly accessible prostate cancer dataset with more than 17000 labeled nuclei. Our model performed remarkably well on both datasets, obtaining a 99.98% accuracy, a 97% Dice coefficient, and a 95% Jaccard coefficient. These outstanding measures demonstrate the model’s capacity to provide cutting-edge nucleus segmentation. Our method gives fresh perspectives and establishes a standard for nucleus segmentation tasks by exploiting the rich information within the RGB channels. The success of our approach implies that incorporating data from many channels can produce more precise and dependable segmentation, creating new opportunities for biomedical image analysis.
A Monocular Depth Estimation Method for Indoor-Outdoor Scenes Based on Vision Transformer

In the field of computer vision, monocular depth estimation has garnered significant attention as a research direction. However, current depth estimation methods often overlook the impact of depth range variations in indoor and outdoor scenes, consequently limiting the model’s generalization ability. To achieve high-precision depth estimation across different depth ranges, we propose a new method. We employ the pretrained model Dinov2 as encoder, combined with decoder based on CNN architecture, to enhance the network’s capacity for extracting global information from indoor and outdoor scenes. Also, we design a mapping module to transform diverse depth ranges into a unified 0-1 range, which can effectively adapt to indoor and outdoor scenes. We validate our method on the DIODE dataset, which comprises mixed indoor and outdoor scenes. Experimental results demonstrate that our method achieves higher depth estimation accuracy and stronger generalization performance when dealing with scenes of diverse depth ranges.
Assistive Reading Device for Blind Individuals

Completely blind individuals face significant challenges when it comes to reading text. Braille is the most used method for reading and writing, but braille may not always be available. Therefore, many blind individuals rely on assistive technologies to help them navigate the world around them. One such technology is smart glasses that can read text aloud to the wearer. However, the high cost of these devices is a significant barrier to access for many individuals. Our solution aims to provide a low-cost alternative for blind individuals who need a reliable text-reading device. By using open-source libraries and commercial hardware, we have been able to reduce the cost for our system. Our prototype is made of a camera, microcontroller, speaker, amplifier, battery, and power supply. Utilizing Pytesseract for Optical Character Recognition (OCR), OpenCV libraries for image preprocessing, and Google’s text-to-speech synthesis technology, the program captures an image, extracts the English text as a string, and plays the audio back to the wearer. The OCR obtained an accuracy of 99% during our testing. With a high accuracy rate, the listener can be confident that the text is accurately transcribed, making it easy to comprehend.
Intentional Biases in LLM Responses

In this study we intentionally introduce biases into large language model responses in an attempt to create specific personas for interactive media purposes. We explore the differences between open source models such as Falcon-7b and the GPT-4 model from Open AI, and we quantity some differences in responses afforded by the two systems. We find that the guardrails in the GPT-4 mixture of experts models with a supervisor, while useful in assuring AI alignment in general, are detrimental in trying to construct personas with a variety of uncommon viewpoints. This study aims to set the groundwork for future exploration in intentional biases of large language models such that these practices can be applied in the creative field, and new forms of media.
Enhancing Scalability and Reliability in Semi-Decentralized Federated Learning With Blockchain: Trust Penalization and Asynchronous Functionality

The paper presents an innovative approach to address the challenges of scalability and reliability in Distributed Federated Learning by leveraging the integration of blockchain technology. The paper focuses on enhancing the trustworthiness of participating nodes through a trust penalization mechanism while also enabling asynchronous functionality for efficient and robust model updates. By combining Semi-Decentralized Federated Learning with Blockchain (SDFL-B), the proposed system aims to create a fair, secure and transparent environment for collaborative machine learning without compromising data privacy. The research presents a comprehensive system architecture, methodologies, experimental results, and discussions that demonstrate the advantages of this novel approach in fostering scalable and reliable SDFL-B systems.
Artificial Intelligence Applied to Digital Marketing

In a time when the interest in artificial intelligence (AI) is increasingly gaining prominence, being considered by many as the beginning of the 4th Industrial Revolution, this chapter of this book explores, using qualitative research methodology, the impact resulting from AI applied to digital marketing. The analysis of interviews with ten experterts in AI and digital marketing, from different sectors of economic activity, showed that the impact of AI on marketing roles, skills, and capabilities is significant and will continue to develop in the future. This study reinforces that the impact of AI on digital marketing offers capabilities that allow more efficient and effective execution of marketing actions, allowing a global view of the audience, as well as the personalization and customization of the experience in real-time. The study proved that there is a need for investment in training by marketing professionals. The study also reveals that at the center of ethical concerns are concerns about the monopolization of data and its inappropriate, deliberate, or involuntary use.
Performance and Energy Evaluation of Lightweight Cryptography for Small IoT Devices

As the Internet of Things (IoT) continues to evolve, connecting physical objects to a shared network, the issue of security remains paramount. It is crucial to safeguard sensitive data to enable smooth data exchange and constant communication. Projections indicate that by 2030, the IoT will comprise approximately 25 billion connected devices. Security breaches occurring within the computing clusters supporting these devices can have far-reaching consequences. Cryptographic algorithms that were effective for traditional computing machines may not be suitable for devices with limited computing power, often referred to as lightweight devices. This limitation represents a significant obstacle to fully harnessing the potential of the IoT. This paper addresses two critical research questions aimed at evaluating the performance of cryptographic algorithms on resource-constrained IoT devices compared to conventional computing platforms. Our contributions in this work encompassed the proposal of a comprehensive evaluation method, incorporating novel metrics, to investigate six cryptographic algorithms, both heavyweight and lightweight, on resource-constrained IoT devices. Furthermore, I provide empirical evidence indicating the inferior performance of traditional heavyweight cryptographic algorithms on the Arduino platform, underscoring the significant gap in latency between observed and expected values. I believe this research presents invaluable insights into cryptographic algorithm performance within the IoT context, emphasizing the intricate challenges and considerations that influence the selection of suitable cryptographic solutions for resource-constrained environments.
Advancing Person Re-Identification through Pose-Estimated Features

This work presents a novel framework for person re-identification that leverages 2D pose estimation to enhance the accuracy and robustness of the existing method. By incorporating pose estimation, the proposed method captures the spatial relationships between object key points, enabling a more comprehensive understanding of object configurations. We employ a CNN-based model, using a bounding box as the backbone for the detector. The model’s ability to generate precise person class facilitates accurate pose estimation by establishing associations between key points and then using those features to identify the same person from different camera angles. As the technology of computer vision develops, employing pose estimation in identifying a person has tremendous potential for improving the capabilities of a wide range of real-world applications.
Image Augmentation with Convolutional Neural Networks

Data Augmentation is a data-space solution to the problem of limited data. Data augmentation uses a suite of techniques to enhance the size and quality of training datasets in order to build up better Deep Learning models. Convolutional Neural Networks (CNNs) have achieved significant improvements in computer vision due to the efficient computing power and large volumes of images. However, image collection is an expensive and challenging process. It is particularly challenging to obtain satisfactory performance with a limited dataset due to overfitting arising in the training process. To address this issue, a convolutional neural network model is built up with image augmentation to combat overfitting in this study. The available image data is augmented by implementing blending modes to expose the full tonal range contained in each training image. Additionally, a horizontal flip transformation to create mirror versions of training images is implemented. These data augmentations are shown to reduce overfitting in CNN models. Moreover, different combinations of blending mode layers are explored to maximize the validation accuracy of the network model.
A Study on Paraphrase Corpus Detection Using Various ML Models

Paraphrase detection, a crucial task in natural language processing, involves determining if two given sentences convey the same meaning. This research paper explores the advancements made by Microsoft Research in paraphrase detection using a range of machine learning models, ensemble techniques, and fine-tuned hyper parameters. The paper then presents the significant contributions of Microsoft Research, which involve employing a diverse set of machine learning models and leveraging ensemble techniques to enhance the accuracy and robustness of paraphrase detection. Additionally, the use of grid search cross-validation for hyper parameter optimization is explored to fine-tune the models' performance. The BERT (Bidirectional Encoder Representations from Transformers) model was also trained for paraphrase detection using the ktrain library. Experimental setups, including datasets, evaluation metrics, and preprocessing steps, are described in detail. Results and analysis showcase the effectiveness of the comparative analysis, demonstrating significant improvements over existing methods. Overall, this research paper provides valuable insights into the advancements achieved by Microsoft Research in paraphrase detection through the utilization of diverse machine learning models, ensemble techniques, and fine-tuned hyper parameters, thus driving the progress in natural language processing tasks.