Reviewerly/exHarmony huggingface dataset:
Quantum Deep Hedging

Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance. In our work we look at the problem of hedging where deep reinforcement learning offers a powerful framework for real markets. We develop quantum reinforcement learning methods based on policy-search and distributional actor-critic algorithms that use quantum neural network architectures with orthogonal and compound layers for the policy and value functions. We prove that the quantum neural networks we use are trainable, and we perform extensive simulations that show that quantum models can reduce the number of trainable parameters while achieving comparable performance and that the distributional approach obtains better performance than other standard approaches, both classical and quantum. We successfully implement the proposed models on a trapped-ion quantum processor, utilizing circuits with up to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>16</mml:mn></mml:math> qubits, and observe performance that agrees well with noiseless simulation. Our quantum techniques are general and can be applied to other reinforcement learning problems beyond hedging.
Blockchain technology and application: an overview

In recent years, with the rise of digital currency, its underlying technology, blockchain, has become increasingly well-known. This technology has several key characteristics, including decentralization, time-stamped data, consensus mechanism, traceability, programmability, security, and credibility, and block data is essentially tamper-proof. Due to these characteristics, blockchain can address the shortcomings of traditional financial institutions. As a result, this emerging technology has garnered significant attention from financial intermediaries, technology-based companies, and government agencies. This article offers an overview of the fundamentals of blockchain technology and its various applications. The introduction defines blockchain and explains its fundamental working principles, emphasizing features such as decentralization, immutability, and transparency. The article then traces the evolution of blockchain, from its inception in cryptocurrency to its development as a versatile tool with diverse potential applications. The main body of the article explores fundamentals of block chain systems, its limitations, various applications, applicability etc . Finally, the study concludes by discussing the present state of blockchain technology and its future potential, as well as the challenges that must be surmounted to unlock its full potential.
Tangling Schedules Eases Hardware Connectivity Requirements for Quantum Error Correction

Quantum computers have the potential to change the way we solve computational problems. Due to the noisy nature of qubits, the need arises to correct physical errors occurring during computation. The surface code is a promising candidate for such error correction that shows high threshold and which can store a logical quantum state on hardware with square-grid connectivity, a type of device that already exists. However, for logical quantum computation, the measurement of some irregular, non-local stabilisers is required, and it is not currently known how to do this efficiently without modifying the connectivity of the hardware. Here, we present a method to achieve this, closing this gap on the path to fault-tolerant quantum computation. We introduce a method of tangled syndrome extraction, which enables measurement of observables between distant qubits. As an application of the tangling technique, we show how to measure the aforementioned irregular non-local stabilisers, without physically modifying the hardware itself. We present a concrete scheme that enables general lattice surgery with the planar code. Therefore, tangling enables fault-tolerant logical quantum computation using the surface code on square-grid connectivity architectures.
Implementing a Hybrid Quantum-Classical Neural Network by Utilizing a Variational Quantum Circuit for Detection of Dementia

Magnetic resonance imaging (MRI) is a common technique to scan brains for strokes, tumors, and other abnormalities that cause forms of dementia. However, correctly diagnosing forms of dementia from MRIs is difficult, as nearly 1 in 3 patients with Alzheimer's were misdiagnosed in 2019, an issue neural networks can rectify. The performance of these neural networks have been shown to be improved by applying quantum algorithms. This proposed novel neural network architecture uses a fully-connected (FC) layer, which reduces the number of features to obtain an expectation value by implementing a variational quantum circuit (VQC). This study found that the proposed hybrid quantum-classical convolutional neural network (QCCNN) detected normal and demented images correctly 95 and 98 percent of the time, compared to the CNN accuracies of 89 and 91 percent. With more hospitals beginning to adopt machine learning applications for biomedical image detection, this proposed architecture would approve accuracies and potentially save more lives. Furthermore, the proposed architecture is generally flexible, and can be used for transfer-learning tasks, saving time and resources.
Privacy in Digital Payments—Escaping the Panopticon

Abstract: Digital payment innovations increase the tension between the right to privacy and combating illicit activity. Democratic societies must rise to the challenge of striking the proper balance. This will not be easy. Whether with bank accounts, cryptocurrencies, or central bank digital currencies (CBDCs), society needs an open dialogue on how to ensure the privacy of payments data. For CBDCs, technological and institutional safeguards must be used to help.
Designing a Concept for the Content of Journalism Training

This study examined the issue of professional training of future journalists in terms of promoting the formation of their professional identity. The relevance of the research topic is due to trends in the profession represented in the content of professional training. The purpose of the study is to present a draft concept for the professional training of future journalists in terms of the formation of their professional identity. The research findings are presented by the Federal State Educational Standard, professional standards in «Journalism» major at Northern (Arctic) Federal University named after M.V. Lomonosov. The research methodology employed a systematic and constructivist approach, which makes it possible to analyze the system of professional training of future journalists from professional identity aspect. The concept of professional training of future journalists includes: goals (strategic and tactical); conceptual apparatus (professional identity, image of I-professional, image of the profession); characterization of objective factors of the macrosocial context and subjective factors of personality development that influence the formation of the professional identity of future journalists; the content of professional training (courses aimed at mastering the competencies of analytical and narrative journalism); organizational and pedagogical conditions for the formation of the professional identity of future journalists; stages of formation of professional identity of future journalists. As part of the study, we developed a learning methodology in a production studio based on a project-based, reflective and task-based approach and CDIO engineering education technology. The presented concept reflects the formation of a person as a professional, his professional identification through the stages of self-determination (understanding of his place and role in the profession), immersion in professional activities and self-actualization. The results of the study may be useful and interesting for the developers of educational programs in “Journalism” major.
Communication Audit of the Presence (Web Presence) of an Educational Organization Online

In this study the authors consider targeted information and communication management as a means of a unique entity of public capital of an educational organization, its web presence (“presence” in the information and telecommunications network “Internet”). The object of the study are state and non-state educational organizations (universities). The purpose of the study is to systematize the promotional communication of the universities and targeted activities in the university to manage them as contributing to the formation of competencies aimed at the effective use of information and communication technologies in the professional activities of specialists. The general scientific methodology of scientific research was developed according to the specifics of the subject and object of the research. In general, in all universities, the management of the information field (web presence) is as a rule systemic, in each university the technological subject of managing information and communication activities of the university is distributed, though there are no traces of coordination efforts within the framework of a distributed subject in order to achieve a synergistic effect. The analysis of the effectiveness of publications in social networks showed that subscribers of accounts in social networks, in general, actively respond to publications, willingly comment on posts (engagement rate), actively share the social values of the university (growth rate), and react emotionally to broadcast messages to a much lesser extent (sympathy coefficient). The results of the study allow optimizing the system of information and communication support for promoting communication in a modern educational organization. The findings can be used in the practice of promoting educational services and allow clarifying the elements of managing the media sphere of a university and the publicity capital of an educational organization.
YOLOv8-CGRNet: A Lightweight Object Detection Network Leveraging Context Guidance and Deep Residual Learning

The growing need for effective object detection models on mobile devices makes it essential to design models that are both accurate and have fewer parameters. In this paper, we introduce a YOLOv8 Res2Net Extended Network (YOLOv8-CGRNet) approach that achieves enhanced precision under standards suitable for lightweight mobile devices. Firstly, we merge YOLOv8 with the Context GuidedNet (CGNet) and Residual Network with multiple branches (Res2Net) structures, augmenting the model’s ability to learn deep Res2Net features without adding to its complexity or computational demands. CGNet effectively captures local features and contextual surroundings, utilizing spatial dependencies and context information to improve accuracy. By reducing the number of parameters and saving on memory usage, it adheres to a ‘deep yet slim’ principle, lessening channel numbers between stages. Secondly, we explore an improved pyramid network (FPN) combination and employ the Stage Partial Spatial Pyramid Pooling Fast (SimPPFCSPC) structure to further strengthen the network’s capability in processing the FPN. Using a dynamic non-monotonic focusing mechanism (FM) gradient gain distribution strategy based on Wise-IoU (WIoU) in an anchor-free context, this method effectively manages low-quality examples. It enhances the overall performance of the detector. Thirdly, we introduce Unifying Object Detection Heads with Attention, adapting to various input scenarios and increasing the model’s flexibility. Experimental datasets include the commonly used detection datasets: VOC2007, VOC2012, and VisDrone. The experimental results demonstrate a 4.3% improvement in detection performance by the proposed framework, affirming superior performance over the original YOLOv8 model in terms of accuracy and robustness and providing insights for future practical applications.
Robust Kalman Filters With Unknown Covariance of Multiplicative Noise

In this paper, the joint estimation of state and noise covariance for linear systems with unknown covariance of multiplicative noise is considered. The measurement likelihood is modelled as a mixture of two Gaussian distributions and a Student's <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> distribution, respectively. The unknown covariance of multiplicative noise is modelled as an inverse Gamma/Wishart distribution and the initial condition is formulated as the nominal covariance. By using robust design and choosing hierarchical priors, two variational Bayesian (VB) based robust Kalman filters are proposed. The stability and convergence of the proposed filters and the covariance parameters are analyzed. The lower and upper bounds are also provided to guarantee the performance of the proposed filters. A target tracking simulation is provided to validate the effectiveness of the proposed filters.
Implementasi Artificial Intelligence (AI) di Bidang Pendidikan Kejuruan Pada Era Revolusi Industri 4.0

Artificial Intelligence (AI) is a concept closely related to technological advancements in the era of Industry 4.0 and Society 5.0. AI can be described as a computer program that involves integrated machine learning, hardware, and software components. This concept of AI is inspired by the neocognitron patterns in the human brain and utilizes knowledge from hardware and software to fulfill its functions. With the progress of Industry 4.0, AI has been widely employed in various fields, including vocational education. The aim of this research is to elucidate the role of AI in vocational education during the Industry 4.0 era, utilizing qualitative research methods. In the context of vocational education, AI is used to personalize learning for each learner. AI systems assist in creating customized learning profiles, such as practical exercises, for individual learners. This enables the adaptation of learning materials to align with the abilities, learning styles, and experiences of each learner. The implementation of AI technology in vocational education is considered to be helpful and facilitates the tasks of educators, particularly in administrative aspects such as assessment and determining final grades based on applicable weights and evaluations. Additionally, AI technology can also create more active and interactive learning experiences, thereby facilitating the teaching and learning process for both educators and learners to achieve their goals.
Balanced Fair K-Means Clustering

Fairness in clustering has recently received significant attention. The goal of fair clustering is to ensure that a clustering algorithm mitigates or even eliminates bias in the original dataset. Many existing fair clustering algorithms will sometimes generate numerous small clusters to satisfy the fairness constraint. In this article, we present a balanced fair K-means clustering algorithm that integrates a fairness constraint and a balance constraint into the K-means objective function. The proposed model is a tradeoff between the K-means objective and the fairness constraint and their relative importance can be controlled. The balance constraint prevents the generation of small clusters. Experimental results on both real-world and synthetic datasets demonstrate that the proposed method achieves a better fairness performance than some other fair clustering methods, with an acceptable loss of clustering quality in some cases and an improvement in others.
Statistical Clustering Approach: Mapping Population Indicators Through Probabilistic Analysis in Aceh Province, Indonesia

The clustering, one of statistical analysis, can be used for understanding population patterns and as a basis for more targeted policy making. In this ecological study, we explored the population dynamics across 23 districts/cities in Aceh Province. The study used the Aceh Population Development Profile Year 2022 data, focusing on the total population, in-migrants, out-migrants, fertility, and maternal mortality as variables. The study employed descriptive statistics to ascertain the data distribution, followed by the Shapiro-Wilk test to evaluate normality, which is crucial for selecting the appropriate statistical methods. The Spearman test was used to determine correlations between the total population and the variable as indicators. Probabilistic Fuzzy C-Means (PFCM) method is used for clustering. To optimize clustering, the silhouette coefficient was calculated using the Euclidean Distance and the elbow method, with the results analyzed using R-4.3.2 software. This study's design and methods aim to provide a nuanced understanding of demographic patterns for targeted policy-making and regional development in Aceh, Indonesia. Based on the data normality test results, only fertility (p-value = 0.45), while the other variables are not normally distributed. Spearman test was used, and the results showed that only in-migrants (p-value = 1.78 x 10-6) and out-migrants (p-value = 2.30 x 10-6) correlated to the Aceh Province population. Using the population variable and the two variables associated with it, it was found that 4 is the best optimum number of clusters, where clusters 1, 2, 3, and 4 consist of three districts/city, nine districts/city, four districts/city and seven districts/city respectively.
The Application of ChatGPT in Language Test Design – The What and How

ChatGPT, an artificial intelligence-based chatbot, has recently gone viral among users thanks to its ability to create human-like interactive experiences. It is now a trend to make use of this artificial intelligence (AI) tool in different fields of language education, including language testing and assessment. With a concern related to the potential and challenges of applying ChatGPT in designing language tests, this study has investigated the current use of the chatbot among language teachers at different schools and institutions. Participants are expected to share their experiences utilizing the chatbot to assess their learners. Data from a survey and in-depth interviews will reveal initial findings to answer questions related to the popularity of this chatbot among language teachers and its application in language test design. Based on the result discussion, the presentation concludes with some implications for language teachers to make the most of their future use of ChatGPT in the field of language testing.
WOA-AGA Algorithm Design for Robot Path Planning

Currently, mobile robot has great application value in industrial production. It can play a unique advantage in improving industrial production efficiency and saving industrial production costs. Path planning plays an important role in the performance of mobile robots. Therefore, to improve the path planning efficiency of mobile robots in complex environments, a path planning model combining genetic algorithm (GA) and whale optimization algorithm (WOA), namely WOAAGA model, is proposed. In the model, the traditional GA model is introduced into the difference degree function. WOA makes up for the local optimization problem and the low proficiency of AGA algorithm. WOA-AGA effectively solves the problems of local optimization, long convergence time and unstable optimization results. The experiment is simulated in dynamic and static environment: AGA algorithm has 1.87% higher efficiency than GA algorithm; Compared with AGA algorithm, the overall operation efficiency of WOA-AGA algorithm is increased by 3.87%. Finally, two types of complex scenes are selected for path planning in the experiment. The results indicate that WOAAGA algorithm can obtain shorter and more reasonable optimal path than other similar algorithms. From the perspective of improving the path planning effect of mobile robots, this study aims to obtain the best path through the reasonable application of WOA-AGA model to improve industrial production efficiency.
Automatic Segmentation and Tagging of Hanzi Text Using a Hybrid Algorithm

In this paper we present a simple but effective approach to segment and to perform word tagging on Hanzi-based languages. The essence of the approach is based on a hybrid algorithm, which combines the learning capability with the flexibility of a digital processing system. Our ideas was implemented by a system with 72 tags and a corpus with 111, 068 Chinese words (173, 497 Chinese characters). Our closed test results reached 99.5%segmentation accuracy and 95.3% tagging accuracy. The opened test results for the specified domain is 91.6% segmentation accuracy and 89.6% tagging accuracy. Various applications of this system are also discussed in this paper.
Economic Development: What Everyone Needs to Know

The practice of economic development has undergone significant transformation over the past decade, due to globalization and democratization. While beneficiaries previously held little sway in the way international economic institutions delegated funds for projects, today it would be difficult-if not impossible-for the average government or for the average multilateral organization to build a road, reform an education curriculum, or sign a mining concession without, at a minimum, a process of consultation with those affected. This change has created a greater demand for development knowledge. Economic Development: What Everyone Needs to Know provides a clear and concise introduction to the development problems that policy-makers, professionals, development agencies, NGOs, charities, and private citizens face. Beginning with the basic concepts that inform the practice of development, this wide-ranging book addresses the major challenges that shape the field, highlighting the doubts, trade-offs, and dilemmas. Drawing on his more than twenty-five years of experience working in development in the Middle East, Eastern Europe, Central Asia, Latin America and Africa, Marcelo Giugale illustrates his chapters with real-life examples from all over the globe. He looks at a host of topics including the reasons why seemingly obvious reforms never happen, power dynamics between governments and beneficiaries, government corruption, state violence, natural resources, globalization and trade, anticipating global crises, measuring equity, ending poverty, the new poor, gender, indigenous peoples, children, health care, food prices, and technology. While development is a complex area in which there are no definitive answers, Giugale highlights the very real challenges that face the profession now and will continue to face development practitioners in the years to come.
Object Shape in Basic Level Categorisation

Categorisation and Shape One of the central questions in object categorisation is how the shape variability of different category members can be accounted for. It was proposed recently that the shape variability of objects from a basic level category usually can be described well with continuous deforming shape transformations (topological or morphing transformations). Previous experiments with line drawings showed that categorisation performance depends systematically on the amount of shape transformation, both in speeded categorisation tasks and rating tasks (Graf, 2002). We investigated whether this systematic dependency holds for grey-level images, and for rotated and shifted objects.
Interactive Graphics for Analysing Quality of Survey Data

Despite the availability of software for interactive graphics, current survey processing systems make limited use of this modern tool.Interactive graphics offer insights, which are difficult to obtain with traditional statistical tools.This paper shows the use of interactive graphics for analysing survey data.Using Labour Force Survey data from Pakistan, we describe how plotting data in different ways and using interactive tools enables analysts to obtain information from the dataset that would normally not be possible using standard statistical methods.It is also shown that interactive graphics can help the analyst to improve data quality by identifying erroneous cases.