Reviewerly/exHarmony huggingface dataset:
Economical Quaternion Extraction from a Human Skeletal Pose Estimate using 2-D Cameras

In this paper, we present a novel algorithm to extract a quaternion from a two dimensional camera frame for estimating a contained human skeletal pose. The problem of pose estimation is usually tackled through the usage of stereo cameras and inertial measurement units for obtaining depth and euclidean distance for measurement of points in 3D space. However, the usage of these devices comes with a high signal processing latency as well as a significant monetary cost. By making use of MediaPipe, a framework for building perception pipelines for human pose estimation, the proposed algorithm extracts a quaternion from a 2-D frame capturing an image of a human object at a sub-fifty millisecond latency while also being capable of deployment at edges with a single camera frame and a generally low computational resource availability, especially for use cases involving last-minute detection and reaction by autonomous robots and similarly modelled intelligent transport systems. The algorithm seeks to bypass the funding barrier and improve accessibility for robotics researchers involved in designing control systems.
An Extensive Review on Recognition of Antique Tamil characters for Information Repossession from Epigraphic Inscriptions

The ancient script recognition is one of the most demanding areas for the researches due to numerous variations in writing styles. All-embracing research work is reported on recognition of various ancient scripts such as Brahmi, Grantha. However the work accounted on Tamil scripts is still in its infancy so momentous research is required in this field. The Tamil scripts which contain loop, curves, circle, dots, etc are the complex issues for identifying the characters present in the image or the input data. The Tamil script has many characters with many features in which those features should be extracted in an efficient way to envisage the character with higher accuracy. There is no proper technology to convert the ancient inscriptions to human readable form in digitised format. Several scripts and inscriptions are examined, which indicates some future research prospects. Based on study demeanour it has been found that there is a need of an automated system for ancient character recognition with hybrid feature extraction and classification techniques for attained the accurate results. This paper aims to provide the insights of types of scripts, inscriptions, various methods, techniques of feature extraction, classification reported over the last few years in the field of epigraphy. So, a novel framework has been proposed to recognize the ancient Tamil character with higher classification accuracy with an aim to surpass the workflow of existing systems.
JetVision: A Low-Cost Real-Time Depth Estimation System using Jetson Computing Platform

Stereo vision is an important technique in computer vision that enables depth information to be extracted from a pair of cameras. Depth estimation has numerous applications across various fields. Some of the notable applications include autonomous vehicles, augmented reality (AR) and virtual reality (VR), robotics, 3D reconstruction, surveillance and security and medical imaging. In this research, the authors develop and test a low-cost real-time stereo vision system, JetVision, that uses two Raspberry Pi cameras, a Jetson computing platform and CUDA programming. The algorithm developed for JetVision is designed to efficiently analyze the differences between the images captured by each camera to estimate the depth of objects in the scene using Semi-Global Matching (SGM) method. To achieve real-time performance, the algorithm is implemented using CUDA programming, which enables efficient parallel processing on Jetson’s GPU. The algorithm is tested on a dataset of real-world images captured by JetVision and is compared its result with high-grade Intel® RealSense <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> depth camera and ground truth depth information. The results demonstrate that JetVision is capable of accurately estimating depth information in real-time with an FPS ranging between 15 to 28. This research contributes to the field of computer vision by demonstrating the potential of stereo vision algorithms in real-world applications using cost-effective hardware and efficient parallel processing.
MNSIT Handwritten Digit Recognition Using Machine Learning Classification Algorithms

The paper discusses the use of machine learning in recognizing handwritten digits and text, which has wide applications in areas such as surveillance, healthcare, and document analysis. The study focuses on evaluating the accuracy and variability of classifying handwritten digits with different numbers of hidden layers using the Modified National Institute of Standards and Technology (MNIST) dataset, and compares the performance of common machine learning algorithms such as SVM, KNN, and RFC. The study notes that recognizing handwritten digits and text is challenging due to their dissimilarities in size, thickness, position, and orientation. The ability to accurately recognize handwritten digits is essential in various fields, including banking, post offices, and tax files. The paper demonstrates handwritten digit recognition (HDR) using the MNIST dataset and selected classification algorithms. Overall, handwriting recognition is a major area of development with many possibilities for applications.
Implementing Cross-Organizational FDA Medical Device Design Controls Using Blockchain

Manufacturing medical devices is a costly and com-plex operation. The Original Equipment Manufacturer (OEM) must enforce United States Food and Drug Administration (FDA) design control regulations during the entire medical device manufacturing life cycle. To lower the costs, the OEMs often out-sources device manufacturing to a Contract Manufacturer (CM). Usually, the OEMs and CMs use incompatible software tools to manage requirements, design documents, and manufacturing data, leading to information silos that makes business process orchestration, and maintaining traceability across documents mandated by FDA challenging. To our best knowledge, existing commercial software vendors still need to address these issues adequately. This paper presents a blockchain-based framework for implementing FDA design control regulations by achieving cross-organizational reviews, document traceability, and tamper-resistant storage and verification of records while allowing the OEM and CM to continue using incompatible software tools. The framework provides an FDA design control ontology, smart contract data model, architecture and a prototype to validate the framework and its applicability using Hyperledger Fabric. Our experimentation shows the framework achieved it's goals with satisfactory performance.
QoSEraser: A Data Erasable Framework for Web Service QoS Prediction

To select appropriate web services for users, the Quality-of-Service (QoS) based collaborative prediction models are widely used. Despite the success of collaborative prediction models in selecting appropriate web services for users, existing models do not take into account the users' authority to manage their own generated data as stipulated by many privacy-preserving regulations, such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA). Moreover, unlearning is urgently needed due to the security concerns such as data poisoning attacks. Existing QoS prediction methods are not optimized for unlearning, suffering from low model availability when handling unlearning requests by full re-training. To address this problem, we propose QoSEraser: a novel efficient machine unlearning framework for QoS prediction tasks. The central concepts of the QoSEraser involve: (1) dividing the training data into multiple shards to train submodels according to the cluster results on graph embeddings induced by random walk on contextual information graph. Such division ensures the preservation of collaborative signals in collected QoS records; (2) a concatenate aggregation method and a stacking & attention-based aggregation method are used to condense information in fragmented embeddings to a uniform one adaptively. Experiments on large-scale datasets show that QoSEraser achieves efficient forgetting learning and outperforms state-of-the-art unlearning approaches in terms of performance. Source codes are available at https://github.com/ZengYuXiang7/QoSEraser.
Chat2Code: A Chatbot for Model Specification and Code Generation, The Case of Smart Contracts

The potential of automatic code generation through Model-Driven Engineering (MDE) frameworks has yet to be realized. Beyond their ability to help software professionals write more accurate, reusable code, MDE frameworks could make programming accessible for a new class of domain experts. However, domain experts have been slow to embrace these tools, as they still need to learn how to specify their applications' requirements using the concrete syntax (i.e., textual or graphical) of the new and unified domain-specific language. Conversational interfaces (chatbots) could smooth the learning process and offer a more interactive way for domain experts to specify their application requirements and generate the desired code. If integrated with MDE frameworks, chatbots may offer domain experts with richer domain vocabulary without sacrificing the power of agnosticism that unified modelling frameworks provide. In this paper, we discuss the challenges of integrating chatbots within MDE frameworks and then examine a specific application: the auto-generation of smart contract code based on conversational syntax. We demonstrate how this can be done and evaluate our approach by conducting a user experience survey to assess the usability and functionality of the chatbot framework. The paper concludes by drawing attention to the potential benefits of leveraging Language Models (LLMs) in this context.
Building Smart Systems to Satisfice User Intention in Situ: A Look Back to the Future*

The world is evolving in a pace faster than ever, in terms of the ways in which human can interact with the rest of the world virtually or physically. Our exploration together with Carl along the road involves many models and methods that help better understand users' needs, desires and intentions, specify clearer system requirements and know-how, describe simpler service interface to find a match between requests and capabilities. There are implicit relations between these research topics, namely, building smarter software systems that can adapt to the needs and intentions of users, as well as the concrete operational context and environment. This paper may look retro-futurism in a sense, as we are looking forward to fulfillment the early AI and SE visions, by reviewing viable fundamental software requirements engineering principles and service system thinking methods, and at the same time by making sense of the on-going development of smart applications with the latest intelligent computing technologies.
Towards Better ML-Based Software Services: An Investigation of Source Code Engineering Impact

In recent years, the development of machine learning-based solutions for software services, particularly for source code, has grown rapidly. It is witnessed that many machine learning models for software services require the input of source code snippets in a desired form of abstract syntax tree (AST), which is mostly generated from an external tool. However, such data pre-processing tasks could be done by different engineering tools, and the impact of these tools towards final models is often neglected. In this work, we aim to investigate the source code engineering impacts towards machine learning-based software services. Three different types of parsing tools are identified, which are parser generator, parsing library and parser developed for a certain purpose. They are thoroughly evaluated towards the impacts on the prediction model of Code2Vec for the prediction task of the method name in Java language. The collective result on the Java-small dataset shows that the generated ASTs differ a lot in terms of source code structures and contents when using different parsing tools. The difference could influence the performance of the trained model significantly. Our result suggests that when machine learning models are implemented for software services, especially for code-related tasks, the selection of parsing tools should be thoroughly considered during the data pre-processing stage. While there are some interesting findings on Java-med and Java-small, we anticipate this work could provide some insights for better ML-based software service solutions from the perspective of source code engineering.
A Workflow for the Continuous Deployment of Quantum Services

The rapid advancements in quantum computing have opened new possibilities for solving complex problems in various fields, including cryptography, optimization, and simulation. However, the current approaches to quantum computing often require a deep understanding of quantum hardware and low-level programming languages, making it difficult for software developers to create and deploy quantum services. This paper argues for the adaptation of Service-Oriented Computing principles to quantum computing, enabling a level of abstraction from hardware that allows developers to focus on application development. This will allow developers to create and deploy quantum services, similarly to how Service-Oriented Computing has made it easier to develop traditional software services. This paper presents a Continuous Deployment approach for quantum service development, which involves the generation and deployment of quantum services. To do that, we propose an extension of the OpenAPI specification to generate services implementing a quantum algorithm. To validate the feasibility of the proposed process, we use a variety of quantum algorithm implementations to test their generation and deployment of quantum services.
Assessment of Higher Education Information Security Risk Management Practices in Tanzania

This study assessed the information security risk management practices in in Tanzanian Higher Education Institutions (HEIs). It employed the sequential explanatory research design. Out of 51 HLIs in Tanzania, the study selected 10 HEIs from Dar es Salaam. The researchers computed the sample estimation through the Cochran’s formula for large population with a precision level of ±10 percentage and confidence level of 95%. The actual sample size was 96 ICT professionals in terms of ICT directors, network administrators, system administrators, ICT support staff and lecturers of ICT. The study used a closed-ended questionnaire, which had Yes/No questions and a structured interview, which collect qualitative data. Quantitative data analysis from the questionnaire was done through descriptive statistics using the SPSS whereas qualitative data from interviews was analyzed using the thematic analysis approach. The study uncovered a notable absence of risk management frameworks and inadequate integration of procedures within institutional strategies. While some HEIs demonstrated effective safeguarding of sensitive information, others required enhancements. The study recommend that HEIs should establish formal risk management frameworks and integrate them strategically into institutional plans. To bridge the implementation gap, HEIs should prioritize comprehensive training, require management support and tailor practices according to their specific contexts.
III-V Nanowire Based Neuromorphic Nanophotonic Circuits

Combining highly efficient nanophotonic structures into artificial neural networks hold significant promise for superior hardware solutions [1]. Well-defined circuit architectures and concepts, with limited size and clear functionality are useful to develop and verify such novel concepts. Insects are capable of amazing autonomous feats well beyond current computers, such as navigating across hundreds of kilometres of unfamiliar terrain, with only a few drops of nectar as energy supply. One important module of the insect brain, conserved across species with vastly different lifestyles, is the central complex navigation circuit. This has been distilled to its fundamental neuroarchitecture and the function of a number of its components into a biologically constrained computational model [2].
Delay-based reservoir computing with spin-VCSELs: Interplay between internal dynamics and performance

Machine learning setups that are able to process data in the optical domain are ideal for on -chip hardware implementations [1]. Due to the fact that the training of hardware based solutions is complicated, a delay-based reservoir computing (RC) realization, where only the output weights need to be trained via linear regression, is very promising [2]. In this paper we investigate vertical cavity surface emitting laser with two mode emission (spin-VCSEL) as the nonlinear node for a delay-based RC setup. These lasers have the ability to exibit reprodicible and high speed dynamics [3] and are thus ideal candidates to increase the data injection rates which are limited by the clocktime [4], [5]. The focus of our numerical investigations is on the interplay between the internal charge carrier dynamics of the spin-VCSEL and its performance when operated in a delay-based RC setup with optically-injected phase-modulated data injection.
Tailoring Photon Pair Emission Characteristics Via Bi-Chromatic Excitation of an Integrated Nonlinear Cavity

The photon pair generation (PPG) via spontaneous four-wave mixing (SFWM) in an optically excited third-order nonlinear cavity forms large-scale high-dimensional quantum states, called quantum frequency combs (QFC). The scalable practical generation and control of these photonic states via integrated devices enable quantum information processing for applications in secured communications and quantum computing [1]. Modifying the excitation field properties in different degrees of freedom, e.g., polarization [2], frequency or spatial modes [3], allows controlling the structure of the generated quantum state by favoring different types of SFWM processes. In this work, by using two excitation fields and varying their intensity, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P_{1}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P_{2}$</tex> , respectively, we demonstrate the occurrence of two different SFWM processes (degenerately and non-degenerately excited SFWM) and a control of the pair generation rate: <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu(r,P)=\mu_{1}(P_{1}(r,P))+\mu_{2}(P_{2}(r,P))+\mu_{ND}(r,P)$</tex> , where P is the total excitation power, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r=P_{1}/P$</tex> is the power ratio, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu_{1},\mu_{2}$</tex> describe the degenerate process and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu_{ND}$</tex> the non-degenerate process.
Analog-Digital Hybrid Computations with Trapped Ions

Universal digital quantum computers are able to approximate any Hamiltonian evolution as a digital quantum circuit consisting of discrete time steps. Strongly interacting physics models, for example in high-energy physics or materials science, make this a challenging task for near-term devices as they place a high demand on the quantum resources required [1]. One alternative is analog quantum simulation where the native evolution of a quantum system can be tailored to mimic that of the model of interest. While more efficient at specific problems, there are limits on the types of evolution available and on the observables that can be measured. In this conference paper we present first results towards unifying these two concepts in a trapped-ion experiment. Trapped atomic ions are one of the most advanced experimental platforms for both analog quantum simulation and digital quantum computing. Typically, the internal spin degrees of freedom are used qubits or for the simulation of spin models. The harmonic oscillator modes given by the motion of the ions in the trap are only employed transiently to create quantum gates or spin-spin interactions. However, these motional modes constitute a large Hilbert space themselves and present a bosonic degree of freedom, which is costly to model with qubits. Many systems of interest, for example Lattice gauge theories, require both spin and boson degrees of freedom. It was recently shown that they can be encoded more efficiently using the harmonic oscillator modes directly [2].
Experimental Anonymous Conference Key Agreement

Quantum conference key agreement (QCKA) generalises the traditional two-party quantum key distribution (QKD) paradigm to multiple users, enabling them to share a common quantum-safe key. Deriving quantum conference keys from multi-partite entanglement can have significant resource advantages over two-party schemes [1]. Anonymous quantum key agreement (AQCKA) is a related protocol which has been theoretically shown to obtain an even more substantial reduction in network resources when multi-partite entanglement is available and incorporated into the protocol. The addition of anonymity can be useful in circumstances where users require guaranteed secrecy, e.g. for electronic voting, or for whistle blowers.
Photonic Neuromorphic Accelerator Combined with an Event-Based Neuromorphic Camera for High-Speed Object Classification

In this work, we demonstrate the impact of an unconventional convolutional photonic accelerator, based on an optical spectrum slicing (OSS) [1], on the classification accuracy of objects, generated through a high-frame rate neuromorphic event-based camera [2]. The experimental setup is depicted in Fig. 1a. It consists of a 5 mW-632 nm-LED source, two objective lenses with NA=0.65 that concentrate the light beam into a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$100\ \mu \mathrm{m}\times 100\ \mu \mathrm{m}$</tex> channel. In our experiments, the targeted objects consist of test spheres, of different diameters (12, 16, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$20 \mu{m}$</tex> ), used in aqua solutions. A pump regulated the sphere's speed to 0.8 m/s. The objects were recorded by a 10 kframe/s capable neuromorphic camera, with a temporal resolution of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1\ \mu \mathrm{s}$</tex> . The camera detects pixel's contrast changes (events), similar to biological systems [2]. The recorded events were exported into 1 kframes/sec videos through a synthetic frame generator software, resulting to 2988 images per particle [2]. Images were post-processed using only noise reduction by frame subtraction and image cropping so as to reduce data volume to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$100\times 100$</tex> pixels.