Reviewerly/exHarmony huggingface dataset:
Gaka-Chu: A Self-Employed Autonomous Robot Artist

The physical autonomy of robots is well understood both theoretically and practically. By contrast, there is almost no research exploring their potential economic autonomy. In this paper, we present the first economically autonomous robot-a robot able to produce marketable goods while having full control over the use of its generated income. Gaka-chu (“painter” in Japanese) is a 6-axis robot arm that creates paintings of Japanese characters from an autoselected keyword. By using a blockchain-based smart contract, Gaka-chu can autonomously list a painting it made for sale in an online auction. In this transaction, the robot interacts with the human bidders as a peer not as a tool. Using the blockchain-based smart contract, Gaka-chu can then use its income from selling paintings to replenish its resources by autonomously ordering materials from an online art shop. We built the Gaka-chu prototype with an Ethereum-based smart contract and ran a 6-month long experiment, during which the robot created and sold four paintings, simultaneously using its income to purchase supplies and repay initial investors. In this work, we present the results of the experiments conducted and discuss the implications of economically autonomous robots.
Bibliometric analysis of cybercrime and cybersecurity risks literature

Purpose This study aims to assess the research developments and works pertaining to cybersecurity risks. Design/methodology/approach A bibliometric analysis of 749 studies on cybersecurity risks published between 1999 and 2021 was conducted using Scopus and the VOSviewer software. Findings This study reveals various findings, including the most influential authors and the top countries, journals, papers, funding institutions and affiliations publishing research on cybersecurity risks. The bibliometric analysis shows that the existing studies have affected the knowledge of the consequences of cybersecurity risks. However, some research gaps still exist in this field. Originality/value This study’s contribution is that it presents a comprehensive evaluation of the research on cybercrime and cybersecurity risks. Moreover, to the best of the authors’ knowledge, bibliometric analysis has not been conducted on cybersecurity risks. This study’s findings are likely to prove useful to practitioners and academics in mitigating the consequences of cybercrime and cybersecurity risks.
Demonstration of a Bosonic Quantum Classifier with Data Reuploading

In a single qubit system, a universal quantum classifier can be realized using the data reuploading technique. In this study, we propose a new quantum classifier applying this technique to bosonic systems and successfully demonstrate it using a silicon-based photonic integrated circuit. We established a theory of quantum machine learning algorithm applicable to bosonic systems and implemented a programmable optical circuit combined with an interferometer. Learning and classification using part of the implemented optical quantum circuit with uncorrelated two photons resulted in a classification with a success probability of 94±0.8% in the proof of principle experiment. As this method can be applied to an arbitrary two-mode N-photon system, further development of optical quantum classifiers, such as extensions to quantum entangled and multiphoton states, is expected in the future.
Multilabel Image Classification using the CNN and DC-CNN Model on Pascal VOC 2012 Dataset

The recent widespread usage of image resources has led to the availability of a vast amount of data in for of images. Classifying or labelling images in an automated way is an ongoing research problem and categorizing data with several labels is still a challenging ask. Due to the intricacy of the data, it could be hard to infer information about classes that don’t mutually exclude one another. Many studies have concentrated on developing deep learning models for image classification, but the results do suffer due to poor low-frequency labelling accuracy and lack of fair distribution of the semantic images with annotations. This research study develops a dual-channel convolutional neural network (DC-CNN) model to increase the efficiency of automatic labelling. Two distinct CNN channels are included in the model of convolutional neural networks (CNN). To enhance the ratio of low-frequency samples, the first channel is used for training on low-frequency sampling, while the second channel is utilized for training on all training sets. While performing labeling, the results from the two channels are unifies to create a labelling decision. The suggested approach was validated using the Pascal VOC 2012 standard dataset. On just the Pascal VOC 2012 data sets, the proposed DC-CNN approach obtains an average maximum accuracy (AP) of above 95% for train data class. Additionally, for the train data class, the suggested CNN approach achieves 100% accuracy. Additionally, in terms of performance, our suggested model exceeds the conventional approach.
Artificial Intelligence in Finance and Industry: Opportunities and Challenges

Access to and management of financial resources are being transformed by artificial intelligence (AI). Everything from chatbot assistants to automated task processing and fraud detection are all within the scope of artificial intelligence (AI) and machine learning in the financial sector. According to Insider Intelligence’s analysis on artificial intelligence in banking, the vast majority of financial institutions (80%) see the possibilities afforded by AI. Technological progress, rising user acceptability, and altering regulatory frameworks will all hasten financial institutions’ (FIs) choice to employ AI. By automating routine tasks and providing customers with access to their accounts and financial advisory services around the clock, banks utilising AI may significantly enhance the customer care they provide.
Unique Parameters Selection Strategy of Linear Canonical Wigner Distribution via Multiobjective Optimization Modeling

There are many kinds of linear canonical transform (LCT)-based Wigner distributions (WDs), which are very effective in detecting noisy linear frequency-modulated (LFM) signals.Among WDs in LCT domains, the instantaneous cross-correlation function type of Wigner distribution (ICFWD) attracts much attention from scholars, because it achieves not only low computational complexity but also good detection performance.However, the existing LCT free parameters selection strategy, namely a solution of the expectation-based output signal-to-noise ratio (SNR) optimization model, is not unique.In this paper, by introducing the variance-based output SNR optimization model, a multiobjective optimization model is established.Then the existence and uniqueness of the optimal parameters of ICFWD are investigated.The solution of the multiobjective optimization model with respect to one-component LFM signal added with zero-mean stationary circular Gaussian noise is derived.A comparison of the unique parameters selection strategy and the previous one is carried out.The theoretical results are also verified by numerical simulations.
Benchmarking and Analysing the NIST PQC Lattice-Based Signature Schemes Standards on the ARM Cortex M7

This paper presents an analysis of the two lattice-based digital signature schemes, Dilithium and Falcon, which have been chosen by NIST for standardisation, on the ARM Cortex M7 using the STM32F767ZI NUCLEO-144 development board. This research is motivated by the ARM Cortex M7 device being the only processor in the Cortex-M family to offer a double precision (i.e., 64-bit) floating-point unit, making Falcon’s implementations, requiring 53 bits of double precision, able to fully run native floating-point operations without any emulation. When benchmarking natively, Falcon shows significant speed-ups between 6.2–8.3x in clock cycles, 6.2-11.8x in runtime, and Dilithium does not show much improvement other than those gained by the slightly faster processor. We then present profiling results of the two schemes on the ARM Cortex M7 to show their respective bottlenecks and operations where the improvements are and can be made. This demonstrates, for example, that some operations in Falcon’s procedures observe speed-ups by an order of magnitude. Finally, since Falcon’s use of floating points is so rare in cryptography, we test the native FPU instructions on 4 different STM32 development boards with the ARM Cortex M7 and also a Raspberry Pi 3 which is used in some of Falcon’s official benchmarking results. We find constant-time irregularities in all of these devices, which makes Falcon insecure on these devices for applications where signature generation can be timed by an attacker.
Face Recognition Based Attendance Management System using DLIB

Real time face recognition technology has become a prominent tool for addressing solutions to many complex problems. Such as identification and the verification of identity. Face recognition technology also addresses the time consumption issue that arises in other biometric systems. Taking Attendances manually is always a monotonous job and it additionally consumes heap of our time. The prevailing biometric attendances wastes a great deal of our time and these systems can be cheated easily. In our proposed system the attendance is recorded by using a camera that is attached in front of classroom which is continuously recording but the system will never store any recorded files. And the features obtained from the detected images are compared with the features stored in the database and the system mark’s the attendance. This paper aims at automating the whole process and implementing a system that can’t be cheated. The entire system is built by using a machine learning tool called DLIB.
Wavelength-stepping algorithm for testing the thickness and front and back surfaces of optical plates with high signal-to-noise ratio

We propose a least-squares phase-stepping algorithm (LS-PSA) consisting of only 14 steps for high-quality optical plate testing. Optical plate testing produces an infinite number of simultaneous fringe patterns due to multiple reflections. However, because of the small reflection of common optical materials, only a few simultaneous fringes have amplitudes above the measuring noise. From these fringes, only the variations of the plate's surfaces and thickness are of interest. To measure these plates, one must use wavelength-stepping, which corresponds to phase-stepping in standard digital interferometry. The designed PSA must phase-demodulate a single fringe sequence and filter out the remaining temporal fringes. In the available literature, researchers have adapted PSAs to the dimensions of particular plates. As a consequence, there are as many PSAs published as different testing plate conditions. Moreover, these PSAs are designed with too many phase-steps to provide detuning robustness well above the required level. Instead, we mathematically prove that a single 14-step LS-PSA can adapt to several testing setups. As is well known, this 14-step LS-PSA has a maximum signal-to-noise ratio (SNR) and the highest harmonics rejection among any other 14-step PSA. Due to optical dispersion and experimental length measuring errors, the fringes may have a slight phase detuning. Using propagation error theory, we demonstrate that measuring distances with around 1% uncertainty produces a small and acceptable detuning error for the proposed 14-step LS-PSA.
Augmented reality applications for mathematical creativity: a systematic review

Abstract Augmented reality (AR) applications are often studied in education. However, little literature has systematically reviewed the applications of AR for mathematical creativity. This paper aims to analyze the research trends comprehensively and systematically on 66 articles from 2015 to 2023. Based on the NCTM Principles and Standards guidelines, trends in the application of AR in mathematics education can be analyzed into six themes: equity, mathematics curriculum, mathematics teaching, learning, assessment, and mathematics technology. The results of the study show that (1) AR for mathematical creativity has implications for improving students' cognitive performance; problem-solving process; self-potential; social skills, and self-ability of students; (2) the most dominant features in developing AR applications for mathematical creativity are the Unity3D tool and Vuforia; (3) AR has a positive impact on equity for improving the quality of teaching and learning, supporting the educational curriculum; improve the teaching and learning of mathematics; effective evaluation and technology development in the learning process; (4) AR as a creative learning media; AR helps creative collaboration between students; and able to improve students' creative thinking skills. The results of a systematic review of AR applications for mathematical creativity can help educators and the development of future educational research.
Sentiment Analysis Meets Explainable Artificial Intelligence: A Survey on Explainable Sentiment Analysis

Sentiment analysis can be used to derive knowledge that is connected to emotions and opinions from textual data generated by people. As computer power has grown, and the availability of benchmark datasets has increased, deep learning models based on deep neural networks have emerged as the dominant approach for sentiment analysis. While these models offer significant advantages, their lack of interpretability poses a major challenge in comprehending the rationale behind their reasoning and prediction processes, leading to complications in the models' explainability. Further, only limited research has been carried out into developing deep learning models that describe their internal functionality and behaviors. In this timely study, we carry out a first of its kind overview of key sentiment analysis techniques and eXplainable artificial intelligence (XAI) methodologies that are currently in use. Furthermore, we provide a comprehensive review of sentiment analysis explainability.
HyLECA: A Framework for Developing Hybrid Long-term Engaging Controlled Conversational Agents

We present HyLECA, an open-source framework designed for the development of long-term engaging controlled conversational agents. HyLECA’s dialogue manager employs a hybrid architecture, combining rule-based methods for controlled dialogue flows with retrieval-based and generation-based approaches to enhance the utterance variability and flexibility. The motivation behind HyLECA lies in enhancing user engagement and enjoyment in task-oriented chatbots by leveraging the natural language generation capabilities of open-domain large language models within the confines of predetermined dialogue flows. Moreover, we discuss the technical capabilities, potential applications, relevance, and adaptability of the system. Lastly, we report preliminary findings from integrating state-of-the-art large language models in simulating a conversation centred on smoking cessation.
Underwater image enhancement via red channel maximum attenuation prior and multi-scale detail fusion

The underwater environment poses great challenges, which have a negative impact on the capture and processing of underwater images. However, currently underwater imaging systems cannot adapt to various underwater environments to guarantee image quality. To address this problem, this paper designs an efficient underwater image enhancement approach that gradually adjusts colors, increases contrast, and enhances details. Based on the red channel maximum attenuation prior, we initially adjust the blue and green channels and correct the red channel from the blue and green channels. Subsequently, the maximum and minimum brightness blocks are estimated in multiple channels to globally stretch the image, which also includes our improved guided noise reduction filtering. Finally, in order to amplify local details without affecting the naturalness of the results, we use a pyramid fusion model to fuse local details extracted from two methods, taking into account the detail restoration effect of the optical model. The enhanced underwater image through our method has rich colors without distortion, effectively improved contrast and details. The objective and subjective evaluations indicate that our approach surpasses the state-of-the-art methods currently. Furthermore, our approach is versatile and can be applied to diverse underwater scenes, which facilitates subsequent applications.
The plague years in Australian higher education

This chapter explores the plague years of Australian Higher education ushered in by the Covid-19 global pandemic. In so doing we are interested in temporal fissures that are illustrative of a foregrounding of compliance to bureaucracy and the elevation of the clock over equity and relationality. At a time when the creep of neoliberalism hollows out both humanity and community, inclusive ways of knowing and our obligations to communities of difference are negated. The chapter highlights an indecorous struggle for a moral compass, unrequited in the material foundations of the post-pandemic university. The pandemic has exposed our collective ability for selfishness at the expense of inclusive educational ideals. In so doing, criticality becomes a hindrance, and populist thought a solution, when those outside of mainstream discourse are degraded and humbled. The chapter concludes with practical orientations: here we draw on notions of temporality, resourced with ideas from Arendt and Bergson to contest exclusion from higher education in the face of centralised power, expanded surveillance and democratic participation contorted by Covid-19.
Blockchain Adoption in the Financial Sector

Blockchain technology can be easily considered one of the most revolutionary innovations of our times. The open ledger has proved enormously beneficial for the financial sector, and thanks to its characteristics like decentralized structure, high safety, and immutable traceability, its adoption has witnessed unprecedented growth in the financial sector. That said, there are many challenges that blockchain adoption in the financial domain faces, and this research takes a close look at all these issues that can potentially hamper its adoption in the sector. The research found that primary inhibitors in the growth of blockchain include cost factors, lack of regulations, rigid work culture, and inadequate infrastructure and offer solutions to tackle these issues. Further, the study also develops a conceptual model involving important stakeholders and interaction variables between them to facilitate the adoption of an open ledger in the sector.
Machine economies

Abstract This fundamentals article discusses efficient machine economies in which non-human agents can autonomously exchange information and value. We first identify criteria for achieving Pareto efficiency in such economies by drawing on the Coase Theorem. We then translate these economic criteria to technical requirements before developing a framework that characterizes four types of machine economies. We discuss real-life examples for each type to highlight key challenges in achieving Pareto efficiency. In particular, we highlight that machine economies with human involvement in economic interactions and governance face significant challenges regarding perfect information, rationality, and transaction costs. Machine economies without human involvement, in turn, promise a high degree of Pareto efficiency, but there are still many open questions, particularly regarding machine-enforced governance. We conclude with opportunities for future research on the interactions and governance in machine economies.
A Novel Approach for Fair Principal Component Analysis based on Eigendecomposition

Principal component analysis (PCA), a ubiquitous dimensionality reduction technique in signal processing, searches for a projection matrix that minimizes the mean squared error between the reduced dataset and the original one. As the classical PCA is not tailored to address concerns related to fairness, its application to actual problems may lead to disparity in the reconstruction errors of different groups (e.g., men and women, whites and blacks, etc.), with potentially harmful consequences. For instance, in terms of quality of representation in the projected space, one may retain more information from a specific group (e.g., men) instead of another one (e.g., women), which may introduce bias towards sensitive groups. Although several fair versions of PCA have been proposed recently, there still remains a fundamental gap in the search for algorithms that are simple enough to be deployed in real systems. Moreover, the considered fairness measure does not minimize, necessarily, the reconstruction errors of different groups. To address this, we propose a novel PCA algorithm which tackles fairness issues by means of a simple strategy comprising a one-dimensional search which exploits the closed-form solution of PCA. As attested by numerical experiments, the proposal can significantly improve fairness, by reducing disparities in reconstruction errors, with a very small loss in the overall reconstruction error and without resorting to complex optimization schemes. Moreover, our findings are consistent in several real situations as well as in scenarios with both unbalanced and balanced datasets.
Track Merging for Effective Video Query Processing

Video analysis frameworks supporting declarative queries are actively researched in recent years. A major prerequisite in executing such queries is the ability to accurately extract metadata at the frame level utilizing various computer vision algorithms, including object tracking models. Tracking models are of profound importance as they establish unique identifiers for the objects across frames.Despite the maturity of tracking algorithms, they still face challenges (such as occlusions, object glaze etc.) which diminish their quality and accuracy. This gives rise to the track fragmentation problem in which a single track is fragmented into multiple smaller tracks. This impacts downstream temporal querying applications degrading query accuracy.In this paper, we propose an algorithm, TMerge for identifying and merging fragmented tracks that constitutes a pre-processing step during data ingestion for video query processing. The algorithm exploits the properties of the problem and utilizes a sampling methodology that significantly reduces the time required to pre-process and ingest the video sequence.We comprehensively describe and analyze our proposals utilizing real data sets and also present the results of a detailed experimental evaluation varying parameters of interest. We demonstrate performance savings of up to two orders of magnitude without loss in accuracy.