Reviewerly/exHarmony huggingface dataset:
Variability of echo state network prediction horizon for partially observed dynamical systems

Study of dynamical systems using partial state observation is an important problem due to its applicability to many real-world systems. We address the problem by studying an echo state network (ESN) framework with partial state input with partial or full state output. Application to the Lorenz system and Chua's oscillator (both numerically simulated and experimental systems) demonstrate the effectiveness of our method. We show that the ESN, as an autonomous dynamical system, is capable of making short-term predictions up to a few Lyapunov times. However, the prediction horizon has high variability depending on the initial condition-an aspect that we explore in detail using the distribution of the prediction horizon. Further, using a variety of statistical metrics to compare the long-term dynamics of the ESN predictions with numerically simulated or experimental dynamics and observed similar results, we show that the ESN can effectively learn the system's dynamics even when trained with noisy numerical or experimental data sets. Thus, we demonstrate the potential of ESNs to serve as cheap surrogate models for simulating the dynamics of systems where complete observations are unavailable.
Bitcoin: Between A Bubble and the Future

This paper demonstrates that the Millennial generation exhibits unique personal traits that have implications for their portfolio choice and, hence, for the stock market. Specifically, Millennials display greater propensity to participate in the stock market, exhibit more confidence (as they trade more frequently), and more diversification (invest in greater number of stocks and foreign assets). At the macro level, we find that the Millennials influence the stock market to behave differently surrounding holidays, and the statistical significance of key financial anomalies is disrupted. Despite the Millennials’ proficiency in using internet, they utilize more social methods (friends/relatives) when they invest than previous generations. Collectively, we infer that the financial market is not only exposed to business cycles, but also to generation cycles.
Trust in service robot: the role of appearance anthropomorphism

An increasing number of hotels and restaurants are using service robots as front-line employees, yet the lack of consumer trust in service robots affects the service experience. To enhance trust, companies have added anthropomorphism designs for service robots. This study investigated the effects of service robot anthropomorphism on trust and psychological mechanisms of trust in service robot. This study conducted three scenario-based experiments to explore the mechanisms of the service robot appearance anthropomorphism effect on trust. The results show that consumers are more likely to trust service robots with a higher degree of appearance anthropomorphism. The anthropomorphism appearance of service robots affects consumers’ perceived interpersonal attraction, including physical attraction, task attraction and social attraction, which in turn affects trust. In addition, consumers’ technology readiness moderates the effect of service robot anthropomorphism on interpersonal attraction. This paper develops research on consumer trust in service robots, and provides guidance for marketing practices of service robots.
Efficient two-qutrit gates in superconducting circuits using parametric coupling

Recently, significant progress has been made in the demonstration of single-qutrit and coupled-qutrit gates with superconducting circuits. Coupled-qutrit gates have significantly lower fidelity than single-qutrit gates, owing to long implementation times. We present a protocol to implement the CZ universal gate for two qutrits based on a decomposition involving two partial-state swaps and local operations. The partial-state swaps can be implemented effectively using parametric coupling, which is fast and has the advantage of frequency selectivity. We perform a detailed analysis of this protocol in a system consisting of two fixed-frequency transmons coupled by a flux-tunable transmon. The application of an AC flux in the tunable transmon controls the parametric gates. This protocol has the potential to lead to fast and scalable two-qutrit gates in superconducting circuit architectures.
Tradurre, adattare e riscrivere in versi il <i>Chisciotte</i> in Italia ad inizio Ottocento (parte II) Nicola Limosino e Vincenzio Moreno

Cervantes’ masterpiece Don Quixote was translated into Italian in prose in the 16th century by Franciosini and then a second time only in 1818 by Bartolomeo Gamba. In between, Italian readers could amuse themselves by reading some adaptations of the Spanish novel in verse. The focus will be the poems written by Nicola Limosino and Vincenzio Moreno at the beginning of the 18th century. The analysis of these poems will focus both on meter and linguistic aspects to stress the art of translation, rewriting and adaptation of Don Quixote into Italian verse.
Graph-theoretical optimization of fusion-based graph state generation

Graph states are versatile resources for various quantum information processing tasks, including measurement-based quantum computing and quantum repeaters. Although the type-II fusion gate enables all-optical generation of graph states by combining small graph states, its non-deterministic nature hinders the efficient generation of large graph states. In this work, we present a graph-theoretical strategy to effectively optimize fusion-based generation of any given graph state, along with a Python package OptGraphState. Our strategy comprises three stages: simplifying the target graph state, building a fusion network, and determining the order of fusions. Utilizing this proposed method, we evaluate the resource overheads of random graphs and various well-known graphs. Additionally, we investigate the success probability of graph state generation given a restricted number of available resource states. We expect that our strategy and software will assist researchers in developing and assessing experimentally viable schemes that use photonic graph states.
Callsafe – the Vishing Barrier

Vishing, a form of phone scam where fraudsters impersonate legitimate organizations to deceive victims into divulging personal information or making financial transactions, poses a significant threat in today’s digital landscape. As vishing attacks continue to rise, effective measures are required to protect individuals from falling prey to these scams. This paper introduces Callsafe, a user-friendly software application developed to detect and prevent vishing attacks as it equips everyday users with the means to safeguard their personal information.
PhishTransformer: A Novel Approach to Detect Phishing Attacks Using URL Collection and Transformer

Phishing attacks are a major threat to online security, resulting in millions of dollars in losses. These attacks constantly evolve, forcing the cyber security community to improve detection systems. One major problem with current detection systems is that they cannot detect new phishing attacks, such as Browser in the Browser (BiTB) and malvertising attacks. These attacks hide behind legitimate Uniform Resource Locators (URLs) and can evade detection systems that only analyze a web page URL without exploring the page content. To address this problem, we propose PhishTransformer, a deep-learning model that can detect phishing attacks by analyzing URLs and page content. We propose only using URLs embedded within a webpage, such as hyperlinks and JFrames, to train PhishTransformer. This helps reduce the number of features that need to be extracted from the page content, which makes training the model more efficient. PhishTransformer combines convolutional neural networks and transformer encoders to extract features from website URLs and page content. These features are then used to train a classifier that can distinguish between phishing attacks and legitimate websites. We tested PhishTransformer on a dataset of 10,000 URLs. Our results show that PhishTransformer can achieve an F1-score of 99%, precision of 99%, and recall of 99%. This result suggests that PhishTransformer is a promising new approach to phishing detection.
Integrating immersive experience into hybrid education: a case study in fintech experimental education

The fintech (financial technology) employment market presents significant barriers to entry, including rigorous selection criteria based on factors such as profitability, risk, security, and confidentiality, which limit students' opportunities to enter the industry. The negative impact of the COVID-19 pandemic has further exacerbated the challenges faced by the fintech employment market in China. Consequently, it is crucial to assist fintech students in enhancing their professional abilities to navigate the job market successfully. Virtual reality is gaining increasing importance in educational fields due to its immersive virtual simulation capabilities. In this study, a hybrid fintech course was designed based on the principles of outcome-based education theory and the flipped classroom model. The project objective was to provide students with virtual training and project-based assessments that facilitate a seamless transition from theoretical knowledge to practical application in the industry. To evaluate the effectiveness of hybrid education, a comparative user experiment was conducted involving 60 participants (students) from Guangdong University of Finance. The study used median data, average data, and the entropy weight method for analysis. The results indicated that hybrid education has a positive impact on individual-level critical thinking, communication, and teamwork skills. We believe that our study can provide critical data references for global online/hybrid education researchers and contribute to the education field.
The Influence of Personality Structure on the Level of Professional Burnout

The influence of the level of professional (emotional) burnout on the quality of work of educational psychologists of municipal educational organizations has determined the relevance of this article, which analyzes the dependence of the personality structure presented in Transactional Analysis as the combination of three ego states (Parent, Child and Adult) on the coefficient of professional burnout. This coefficient is interpreted as an integral indicator of three factors: psycho-emotional exhaustion, depersonalization/dehumanization and reduction of professional achievements. Identification of the personality structure of the study participants, calculation of the coefficient of their professional (emotional) burnout, structuring the levels of severity of factors of professional (emotional) burnout, implemented as research objectives, created the possibility of achieving the research goal. The hypothesis was tested by means of mathematical statistics methods. The visibility and accessibility of conclusions about the correlations between the integral indicator of professional (emotional) burnout and the level of severity of ego states in the egogram are provided by their graphical and textual expression. The presented data on the absence of a critical level of professional (emotional) burnout of educational psychologists of municipal educational organizations and the presence of a correlation between the reduction of professional achievements and the Adult ego state substantiate the vector of further work to ensure conditions for maintaining the desired psycho-emotional state of specialists.
An Optimized Hardware Implementation of a Non-Adjacent Form Algorithm Using Radix-4 Multiplier for Binary Edwards Curves

Binary Edwards Curves (BEC) play a pivotal role in modern cryptographic processes and applications, offering a combination of robust security as well as computational efficiency. For robust security, this article harnesses the inherent strengths of BEC for the cryptographic point multiplication process by utilizing the Non-Adjacent Form (NAF) algorithm. For computational efficiency, a hardware architecture for the NAF algorithm is proposed. Central to this architecture is an Arithmetic Logic Unit (ALU) designed for streamlined execution of essential operations, including addition, squaring, and multiplication. One notable innovation in our ALU design is the integration of multiplexers, which maximize ALU efficiency with minimal additional hardware requirements. Complementing the optimized ALU, the proposed architecture incorporates a radix-4 multiplier, renowned for its efficiency in both multiplication and reduction. It eliminates resource-intensive divisions, resulting in a substantial boost to overall computational speed. The architecture is implemented on Xilinx Virtex series Field-Programmable Gate Arrays (FPGAs). It achieves throughput-to-area ratios of 14.819 (Virtex-4), 25.5 (Virtex-5), 34.58 (Virtex-6), and 37.07 (Virtex-7). These outcomes underscore the efficacy of our optimizations, emphasizing an equilibrium between computational performance and area utilization.
OPTIMIZING ULTRASOUND IMAGE CLASSIFICATION THROUGH TRANSFER LEARNING: FINE-TUNING STRATEGIES AND CLASSIFIER IMPACT ON PRE-TRAINED INNER-LAYERS

Transfer Learning (TL) is a popular deep learning technique used in medical image analysis, especially when data is limited. It leverages pre-trained knowledge from State-Of-The-Art (SOTA) models and applies it to specific applications through Fine-Tuning (FT). However, fine-tuning large models can be time-consuming, and determining which layers to use can be challenging. This study explores different fine-tuning strategies for five SOTA models (VGG16, VGG19, ResNet50, ResNet101, and InceptionV3) pre-trained on ImageNet. It also investigates the impact of the classifier by using a linear SVM for classification. The experiments are performed on four open-access ultrasound datasets related to breast cancer, thyroid nodules cancer, and salivary glands cancer. Results are evaluated using a five-fold stratified cross-validation technique, and metrics like accuracy, precision, and recall are computed. The findings show that fine-tuning 15% of the last layers in ResNet50 and InceptionV3 achieves good results. Using SVM for classification further improves overall performance by 6% for the two best-performing models. This research provides insights into fine-tuning strategies and the importance of the classifier in transfer learning for ultrasound image classification.
Spatial feature embedding for robust visual object tracking

Abstract Recently, the offline‐trained Siamese pipeline has drawn wide attention due to its outstanding tracking performance. However, the existing Siamese trackers utilise offline training to extract ‘universal’ features, which is insufficient to effectively distinguish between the target and fluctuating interference in embedding the information of the two branches, leading to inaccurate classification and localisation. In addition, the Siamese trackers employ a pre‐defined scale for cropping the search candidate region based on the previous frame's result, which might easily introduce redundant background noise (clutter, similar objects etc.), affecting the tracker's robustness. To solve these problems, the authors propose two novel sub‐network spatial employed to spatial feature embedding for robust object tracking. Specifically, the proposed spatial remapping (SRM) network enhances the feature discrepancy between target and distractor categories by online remapping, and improves the discriminant ability of the tracker on the embedding space. The MAML is used to optimise the SRM network to ensure its adaptability to complex tracking scenarios. Moreover, a temporal information proposal‐guided (TPG) network that utilises a GRU model to dynamically predict the search scale based on temporal motion states to reduce potential background interference is introduced. The proposed two network is integrated into two popular trackers, namely SiamFC++ and TransT, which achieve superior performance on six challenging benchmarks, including OTB100, VOT2019, UAV123, GOT10K, TrackingNet and LaSOT, TrackingNet and LaSOT denoting them as SiamSRMC and SiamSRMT, respectively. Moreover, the proposed trackers obtain competitive tracking performance compared with the state‐of‐the‐art trackers in the attribute of background clutter and similar object, validating the effectiveness of our method.
BROWSERSPOT – A MULTIFUNCTIONAL TOOL FOR TESTING THE FRONT-END OF WEBSITES AND WEB APPLICATIONS

The article presents the multifunctional BrowserSpot tool, which serves as an automated environment for testing websites and web applications for Android and iOS systems. It highlights and describes the individual stages of research and development work, the issues with solutions currently available on the market, as well as the project's results. The article also discusses the reasons for undertaking work on the tool, its functionalities, and the methods of its usage.
Hybrid/Advanced Session-Based Recommender Systems

The deep learning models in SBRS which have been discussed in the previous chapters have their own strengths and weaknesses. Due to the high flexibility of deep neural networks, many neural network blocks can be integrated to construct more robust and accurate models. Many session-based recommender system utilize hybrid deep neural network models. There are also several advanced deep learning approaches that are very popular in SBRS, including graph neural networks (GNNs) and deep reinforcement learning (DRL). To this end, advanced and hybrid deep neural network models in SBRS are discussed in this chapter.
Deep Generative Session-Based Recommender System

The inherent structural sequences in sessions and the mutual influence of complex variables in different time steps make deep generative models effective solutions for a session-based recommender system (SBRS). In addition, in real-world scenarios, users usually only select a limited number of items, and their interactions in response to items are very sparse. Deep generative models that produce more training samples can help reduce the data sparsity problem. To this end, we discuss different deep generative models in SBRS in this chapter, such as autoencoders (AE), generative adversarial networks (GAN), and flow-based models (FBM).
Learning to Rank in Session-Based Recommender Systems

Today, our daily activities are increasingly dependent on data-oriented systems. A new trend emerged based on machine learning techniques to rank the results in information retrieval and recommender systems automatically called learning to rank (LtR). Two main important subsets of LtR systems include ranking creation and ranking aggregation. This chapter of the book discussed different models of LtR in information retrieval, recommender systems, and session-based recommender systems.
Deep Discriminative Session-Based Recommender System

Due to the sequential nature and time-ordered session data, much research in a session-based recommender system (SBRS) focuses on recurrent neural networks (RNNs), including GRU and LSTM. On the other hand, convolutional neural networks (CNNs) provide very effective solutions for modeling sequential data when sequence elements are associated with complex features. As a result, we discuss different deep discriminative models in SBRS in this chapter, such as variants of RNNs and CNNs.
Quantum BER estimation modelling and analysis for satellite‐based quantum key distribution scenarios

Abstract The quantum communication channel is considered to be eavesdropped when the signal Quantum Bit Error Rate (QBER) exceeds a defined theoretical limit and is thus considered a figure of merit parameter for assessing the security of a quantum channel. This work presents a general mathematical model considering device imperfections and various sources of errors for estimating signal QBER in polarisation encoded satellite‐based QKD systems. QBER performance for satellite‐to‐ground downlink scenarios has been investigated for multiple sky brightness conditions (day time and night time operations), two operating wavebands: 800 and 1550 nm as well as for different quantum transmitter and quantum receiver architectures. Further, a novel QBER estimation analysis for inter‐satellite QKD links has also been presented. The estimation results obtained from the developed model have been validated against and found in good agreement with the measured results of the only reported satellite‐to‐ground QKD experiments till date. The presented QBER modelling and analysis will aid in system engineering and efficient design of future satellite‐based QKD systems.
Information Potential of the Epistolary Heritage of P. Kulish in Funds of V. I. Vernadsky National Library of Ukraine

The aim of the article is to define the information potential of the epistolary heritage of P. Kulish in the funds of V. I. Vernadsky National Library of Ukraine by analysing and establishing its documentary content, which makes it possible to reproduce retrospective information that complements and expands already known facts about the personality.&#x0D; Research methods. Along with general scientific methods of analysis and synthesis, the research uses historical, systematic, typological and aesthetic ones, as well as methods of archival and source scholar heuristics, source scholar criticism of sources, their textual analysis and information reconstruction.&#x0D; The scientific novelty of the study consists in highlighting the information resources representing the epistolary heritage of P. Kulish in V. I. Vernadsky National Library of Ukraine funds. The existing document multitude, primarily letters and telegrams, in the form of manuscripts and copies (in particular, typewritten articles by researchers of P. Kulish), which are of scientific interest for studying the writer’s life and creativity, is analyzed in detail.&#x0D; Main conclusions. In the funds of IR NBUV, there are about one thousand and a half letters of P. Kulish, which cover a long chronological range and present the names of many correspondents, both well-known and unknown, as well as letters without specifying the names of the addressees. The archive of P. Kulish is not consolidated in one fund, but scattered among different funds of the scientific institution: I, II, VII, X, 63, 95, 112, 131, 170, 172, 251, 315, 338 (the total number is 12 funds). Complex funds No. I “Literary materials”, No. III “Correspondence”, and fund No. X “Archives of the Ukrainian Academy of Sciences”, in which the artist’s epistolary documents predominate, deserve special attention. The document collection of P. Kulish, which is kept in No. 315, and is represented by the author’s epistolary block, is also noteworthy. Fund No. I presents about one thousand letters of P. Kulish, many of which do not contain information about the addressees (it is a damaged document or an unidentified addressee), which prevents the full reproduction of retrospective information, but still complements and expands already known facts. The epistles of P. Kulish to his addressees recorded information of a biographical nature, which is a valuable addition to his own prosopographical portrait. For the most part, the writer’s letters are characterised by informative content, as they are aimed to be read by a definite person. In addition, at that time, correspondence was one of the most important means of communication.