Reviewerly/exHarmony huggingface dataset:
Probing fractal spatiotemporal inhomogeneity in a quantum walk

We investigate the transport and entanglement properties exhibited by quantum walks with coin operators concatenated in a space-time fractal structure. Inspired by recent developments in photonics, we choose the paradigmatic Sierpinski gasket. The 0-1 pattern of the fractal is mapped into an alternation of the generalized Hadamard-Fourier operators. This two-state coin-operator approach overcomes the intricacies caused by the utilization of high-dimensional coin operators required in prior studies of discrete-time quantum walks on fractals. In fulfilling the blank space on the analysis of the impact of inhomogeneity in quantum walk properties, specifically, fractal deterministic inhomogeneity, our results show a robust effect of entanglement enhancement as well as an interesting road to superdiffusive spreading with a tunable scaling exponent attaining robust superdiffusion, subballistic though. Explicitly, with this fractal approach it is possible to obtain an increase in quantum entanglement with reduced impact on standard quantum walk theoretical spreading. Alongside those features, we analyze further properties such as the degree of interference and visibility. The present model corresponds to an application of fractals in an experimentally viable setting, namely, the building block for the construction of photonic patterned structures.
Maximal information leakage from quantum encoding of classical data

An alternative measure of information leakage for quantum encoding of classical data is defined. An adversary can access a single copy of the state of a quantum system that encodes some classical data and is interested in correctly guessing a general randomized or deterministic function of the data (e.g., a specific feature or attribute of the data in quantum machine learning) that is unknown to the security analyst. The resulting measure of information leakage, referred to as maximal quantum leakage, is the multiplicative increase of the probability of correctly guessing any function of the classical data upon observing measurements of the quantum state. Maximal quantum leakage is shown to satisfy the postprocessing inequality (i.e., applying a quantum channel reduces information leakage) and independence property (i.e., leakage is zero if the quantum state is independent of the classical data), which are fundamental properties required for privacy and security analysis. It also bounds accessible information. Effects of global and local depolarizing noise models on the maximal quantum leakage are established.
Noisy Demkov-Kunike model

The Demkov-Kunike (DK) model, characterized by a time-dependent Rabi coupling $J\phantom{\rule{0.16em}{0ex}}\text{sech}(t/T)$ and on-site detuning ${\mathrm{\ensuremath{\Delta}}}_{0}+{\mathrm{\ensuremath{\Delta}}}_{1}tanh(t/T)$, has one of the most general forms of an exactly solvable two-state quantum system and therefore it provides a paradigm for coherent manipulations of a qubit's quantum state. Despite its extensive application in noise-free cases, the exploration of the noisy DK model remains limited. Here we extend the coherent DK model to take into account a noisy coupling term $J\ensuremath{\rightarrow}{J}_{\text{noisy}}(t)$. We consider colored Markovian noise sources represented by telegraph noise and Gaussian noise. We present exact solutions for the survival probability ${Q}_{\text{DK}}^{\text{noisy}}$ of the noisy DK model, namely, the probability of the system to remain in its initial state. For slow telegraph noise, we identify parameter regimes where the survival probability ${Q}_{\text{DK}}^{\text{noisy}}$ is suppressed rather than enhanced by noise. In contrast, for slow Gaussian noise, the noise always enhances the survival probability ${Q}_{\text{DK}}^{\text{noisy}}$, due to the absorption of noise quanta across the energy gap. This study not only complements the existing research on the noisy Landau-Zener model but also provides valuable insights into the control of two-level quantum systems.
Multi-modal sarcasm detection based on emotion perception and cross-modality attention fusion

Sarcasm is a rhetorical device commonly used in social media and is prevalent on some social platforms, such as Twitter and Reddit, to dismiss, criticize or ridicule people or events using metaphors and exaggeration. With the rapid growth of social media and internet technology, the way people express their emotions and feelings is not limited to text. Therefore, a multi-modal sarcasm detection task is crucial to understanding people’s real feelings and beliefs. However, most existing models often use implicit fusion and do not significantly align the emotions between modalities explicitly, neglecting the significant role of emotional words in sarcasm detection. In this paper, a model was proposed based on emotion perception and cross-modality attention fusion for multi-modal sarcasm detection. Specifically, an external emotional knowledge was introduced for emotional information enhancement. In addition, the dual-channel BERT-based module and cross-modality interaction fusion were proposed based on an attention mechanism. The experimental results on a public multi-modal sarcasm detection dataset based on Twitter demonstrate the superiority of the proposed model.
Echo State Property upon Noisy Driving Input

The echo state property (ESP) is a key concept for understanding the working principle of the most widely used reservoir computing model, the echo state network (ESN). The ESP is achieved most of the operation time under general conditions, yet the property is lost when a combination of driving input signals and intrinsic reservoir dynamics causes unfavorable conditions for forgetting the initial transient state. A widely used treatment, setting the spectral radius of the weight matrix below the unity, is not sufficient as it may not properly account for the nature of driving inputs. Here, we characterize how noisy driving inputs affect the dynamical properties of an ESN and the empirical evaluation of the ESP. The standard ESN with a hyperbolic tangent activation function is tested using the MNIST handwritten digit datasets at different additive white Gaussian noise levels. The correlations among the neurons, input mapping, and memory capacity of the reservoir nonlinearly decrease with the noise level. These trends agree with the deterioration of the MNIST classification accuracy against noise. In addition, the ESP index for noisy driving input is developed as a tool to help easily assess ESPs in practical applications. Bifurcation analysis explicates how the noise destroys an asymptotical convergence in an ESN and confirms that the proposed index successfully captures the ESP against noise. These results pave the way for developing noise-robust reservoir computing systems, which may promote the validity and utility of reservoir computing for real-world machine learning applications.
Machine translation: Turkish–English bilingual speakers’ accuracy detection of evidentiality and preference of MT

Machine translation (MT) is the automated process of translating text between different languages, encompassing a wide range of language pairs. This study focuses on non-professional bilingual speakers of Turkish and English, aiming to assess their ability to discern accuracy in machine translations and their preferences regarding MT. A particular emphasis is placed on the linguistically subtle yet semantically meaningful concept of evidentiality. In this experimental investigation, 36 Turkish-English bilinguals, comprising both early and late bilinguals, were presented with simple declarative sentences. These sentences varied in their evidential meaning, distinguishing between firsthand and non-firsthand evidence. The participants were then provided with MT of these sentences in both translation directions (Turkish to English and English to Turkish) and asked to identify the accuracy of these translations. Additionally, participants were queried about their preference for MT in four crucial domains: medical, legal, academic, and daily contexts. The findings of this study indicated that late bilinguals exhibited a superior ability to detect translation accuracy, particularly in the case of firsthand evidence translations, compared to their early bilingual counterparts. Concerning the preference for MT, age of acquisition and the accuracy detection of non-firsthand sentence translations emerged as significant predictors.
COMPARISON OF THE SPEECH TEXTS OF INDONESIAN PRESIDENT JOKO WIDODO AND PRESIDENT SUSILO BAMBANG YUDHOYONO: STUDY USING A CORPUS LINGUISTIC APPROACH

Purpose: This research has several objectives. First, determine lexical density and compare the lexical density. Second, to determine the key lexical density and compare the key lexical density. Third, to test the independence of the relationship between lexical variations and the text of President Joko Widodo's and President Susilo Bambang Yudhoyono's speeches. Theoretical Reference: The theoretical basis used in this research is the lexical analysis approach in linguistics. The application of lexical perspective analysis is expected to be able to review the communication used by each individual. The theoretical lexical discussion will also use a statistical independence analysis approach. The application of a statistical independence analysis approach is used to review a person's individual language abilities. Method: This research uses a qualitative and quantitative corpus linguistics approach. The corpus linguistic application used in this research is the KORTARA application (Korpus Nusantara). The research data is a corpus of 9 texts of President Joko Widodo's speeches and a corpus of 9 texts of President Susilo Bambang Yudhoyono which are official speeches every 16 August before the DPR of the Republic of Indonesia. Results and Conclusion: The results of this research reveal that the text corpus of President Joko Widodo's speech is richer and more varied than the text corpus of President Susilo Bambang Yudhoyono's speech in lexical use. This research also revealed that there is a relationship between lexical variation and the type of text of the President of the Republic of Indonesia's speech with a confidence level of 95%. The difference in lexical variation and frequency between the text corpus of President Joko Widodo's speech and the text corpus of Susilo Bambang Yudhoyono's speech is statistically significant at p &lt; 0.05. Implication of Research: The implication of this research is the realization of the KORTARA corpus linguistic approach (Korpus Nusantara) which can facilitate research for small and large scale data. This research also reveals that the application of a statistical approach provides maximum results in the analysis of large-scale linguistic phenomena. Originality/value: The current study makes a valuable empirical contribution by combining statistical analysis using corpus and qualititative analysis to give comprehensive conclusion. This study is the answer toward the question about the reliability and validity of linguistic studies.
The impact of digital media on the education of different digital generations: contributions and challenges for accelerating information and promoting the learning of connected individuals

The present work is the result of a bibliographic research, based on the discussion about the effectiveness of the use of digital media in the classroom, promoting comparative relations with the current reality and exposing some primordial media resources to enhance the school teaching process of the Digital Generation. The purpose was the analog realization of how learning was transmitted to the students of the past, establishing a reflection of how it is given to the transmission of knowledge in contemporary times, evidencing that the education of the XXI century, has advanced significantly with the advent and influences related to the use of the New Digital Technologies based on the challenge of educating the generation born in the Digital Age. The methodological procedure covered the writings and analysis of scientific articles through a critical reflection on the generation of Screenagers and Education.The bibliographic investigation provided the expansion of knowledge on the subject, supported by authors who address the teaching-learning process mediated by the New Digital Technologies, demonstrating the possibilities and limits of using digital technologies in school as a pedagogical resource in the generation of the Digital Age, contributing to a quality and transformative teaching..
Recoverability of quantum channels via hypothesis testing

Abstract A quantum channel is sufficient with respect to a set of input states if it can be reversed on this set. In the approximate version, the input states can be recovered within an error bounded by the decrease of the relative entropy under the channel. Using a new integral representation of the relative entropy in Frenkel (Integral formula for quantum relative entropy implies data processing inequality, Quantum 7 , 1102 (2023)), we present an easy proof of a characterization of sufficient quantum channels and recoverability by preservation of optimal success probabilities in hypothesis testing problems, equivalently, by preservation of $$L_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>L</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> -distance.
Resource analysis and modifications of quantum computing with noisy qubits for elliptic curve discrete logarithms

We estimate the number of physical qubits and execution time by decomposing an implementation of Shor's algorithm for elliptic curve discrete logarithms into universal gate units at the logical level when surface codes are used. We herein also present modified quantum circuits for elliptic curve discrete logarithms and compare our results with those of the original quantum circuit implementations at the physical level. Through the analysis, we show that the use of more logical qubits in quantum algorithms does not always lead to the use of more physical qubits. We assumed using rotated surface code and logical qubits with all-to-all connectivity. The number of physical qubits and execution time are expressed in terms of bit length, physical gate error rate, and probability of algorithm failure. In addition, we compare our results with the number of physical qubits and execution time of Shor's factoring algorithm to assess the risk of attack by quantum computers in RSA and elliptic curve cryptography.
Analysing environmental opinion using highly customisable visualisation tools to understand citizens’ attitudes and barriers

Abstract Knowledge of public opinion is key to understanding citizens' attitudes towards environmental policies. However, large polls and surveys generate complex datasets from which it is not always easy to draw conclusions. In addition, tailor-made solutions for analysing public opinion face the challenge of handling too many layers of information, which can easily lead to an overwhelming user experience and impair decision-making. Service design methodologies can support the design of ad hoc visualisation tools focused on user needs. We present Op-e-nion , a case study of a visualisation tool for the analysis of public opinion regarding environmental issues, aimed at administrations and public institutions. The involvement of experts from different fields allowed for the identification of the main metrics necessary to target the least engaged socio-demographic groups as well as the barriers that limited their environmental actions. Experts also highlighted useful aspects of the design process and the final prototype to help them define more effective campaigns and policies to address social challenges and promote citizen action. An innovative step was introduced in the methodology by involving non-state actors in the evaluation of the tool, ensuring problem detection and enhancing the sustainability of the final product. Important aspects for the visualisation of multi-categorical data included simplifying the interaction with the tool while prioritising relevant information, and using highly customizable visualisations to answer specific user requirements and changing needs (i.e. analytical vs. managerial tasks). Improved visualisations of public opinion data will, in turn, better support the development of policies shaped by citizens’ concerns.
Thin-film transistor for temporal self-adaptive reservoir computing with closed-loop architecture

Reservoir computing is a powerful neural network-based computing paradigm for spatiotemporal signal processing. Recently, physical reservoirs have been explored based on various electronic devices with outstanding efficiency. However, the inflexible temporal dynamics of these reservoirs have posed fundamental restrictions in processing spatiotemporal signals with various timescales. Here, we fabricated thin-film transistors with controllable temporal dynamics, which can be easily tuned with electrical operation signals and showed excellent cycle-to-cycle uniformity. Based on this, we constructed a temporal adaptive reservoir capable of extracting temporal information of multiple timescales, thereby achieving improved accuracy in the human-activity-recognition task. Moreover, by leveraging the former computing output to modify the hyperparameters, we constructed a closed-loop architecture that equips the reservoir computing system with temporal self-adaptability according to the current input. The adaptability is demonstrated by accurate real-time recognition of objects moving at diverse speed levels. This work provides an approach for reservoir computing systems to achieve real-time processing of spatiotemporal signals with compound temporal characteristics.
Category-Aware Saliency Enhance Learning Based on CLIP for Weakly Supervised Salient Object Detection

Abstract Weakly supervised salient object detection (SOD) using image-level category labels has been proposed to reduce the annotation cost of pixel-level labels. However, existing methods mostly train a classification network to generate a class activation map, which suffers from coarse localization and difficult pseudo-label updating. To address these issues, we propose a novel Category-aware Saliency Enhance Learning (CSEL) method based on contrastive vision-language pre-training (CLIP), which can perform image-text classification and pseudo-label updating simultaneously. Our proposed method transforms image-text classification into pixel-text matching and generates a category-aware saliency map, which is evaluated by the classification accuracy. Moreover, CSEL assesses the quality of the category-aware saliency map and the pseudo saliency map, and uses the quality confidence scores as weights to update the pseudo labels. The two maps mutually enhance each other to guide the pseudo saliency map in the correct direction. Our SOD network can be trained jointly under the supervision of the updated pseudo saliency maps. We test our model on various well-known RGB-D and RGB SOD datasets. Our model achieves an S-measure of 87.6 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> on the RGB-D NLPR dataset and 84.3 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> on the RGB ECSSD dataset. Additionally, we obtain satisfactory performance on the weakly supervised E-measure, F-measure, and mean absolute error metrics for other datasets. These results demonstrate the effectiveness of our model.
A Novel Procedure for the AHP Method for the Site Selection of Solar PV Farms

This study proposes a novel approach to enhance the analytic hierarchy process (AHP) for the selection of suitable sites for solar photovoltaic (PV) farms. This approach is particularly beneficial when it is possible to establish a predefined objective relation in the final weights of the AHP method. The methodology focuses on achieving this predefined relation introducing a systematic revision of the constants of related constraints. In this study, the costs of constructing a unit transmission line and road in the Kayseri Province are objectively related, and the initial constant matrix of the AHP method is iteratively revised until the relation of the final weights converges to the predefined one. The suitability of solar PV farm locations is classified into five classes, revealing approximately 28% (40-100% of suitability) of the province as favorably suitable and designating about 67% as restricted zones. The findings reveal notable distinctions between the revised weights and those derived from the conventional AHP method. The disparity in weights for various constraints varies from 13.5% to 7.2%. Consequently, the alterations in the area of suitability regions range from 3.4% to 50%. The revision of AHP weights results in a reduction in higher-suitability areas, coupled with a significant expansion in the region exhibiting lower suitability. Notably, the extent of change in the suitability map increases when the difference in ratios between two criteria obtained from the AHP and the predefined objective relation is high. The proposed method demonstrates its applicability in regions like Kayseri where an objective relation between criteria can be established. Given the inherent subjectivity of the AHP method, the proposed procedure becomes essential to attain more objective weights. Since the methodology objectively adjusts weights based on known ratios, it increases the accuracy and reliability of site selection studies.
Factors Influencing the Positive Thinking of the Future Teacher

The article is devoted to the analysis of scientific literature on the problem under study, positive psychology and socio-psychological problems of the formation of a positive mood in professional activity. Research objectives: to determine factors influencing positive thinking among future teachers and to analyze psychological problems. Research materials and methods: analysis of statements by scientist psychologists about positive thinking and psychological problems such as tension, hopelessness, lack of self-esteem, conflict at work, self-flagellation, painful emotions, poor health, dissatisfaction with oneself as a subject of education, the presence of constant negative thoughts, lack of optimism. Research results: the basis for positive thinking of a future teacher is the ability to evaluate negativity through the prism of optimism and constructiveness. Conclusions: the development of positive thinking of a future teacher during the period of study at a higher educational institution depends on the dynamics of certain characteristics.
Scientometric visualization of data on artificial intelligence and marketing: Analysis of trends and themes

<h2>Abstract</h2> Artificial Intelligence (AI) technologies are being used in marketing to make automated judgments based on data gathering, data analysis, and further insights into audience or market indicators that may influence marketing activities. The study utilized Biblioshiny, a web-based interface, to do a bibliometric analysis of AI and marketing and its associated studies found in the Scopus database. The time span of the analyzed data is 40 years (1984–2024).The results were analyzed based on many categories, including data set, sources, authors, documents, conceptual structure, intellectual structure, and social structure. The findings revealed satisfactory literature in the selected research domain. The annual scientific production of work related to AI and marketing elevated after 2016. The disciplines of Natural Language Processing, Decision Support Systems etc. garnered the most substantial focus in marketing and were positioned at the core of the subject matter. Based on the investigation of research in AI and marketing, this study will be useful for future researchers and policymakers as a preliminary step to locate pertinent studies.