Reviewerly/exHarmony huggingface dataset:
Challenges facing distance learning during martial law: results of a survey of Ukrainian students

Cataclysms such as the COVID-19 pandemic or the Russian invasion of Ukraine have had an irreversible impact on all spheres of human life. Therefore, it is crucial to monitor the needs of participants in specific processes to be able to respond to them dynamically. In this regard, a survey was conducted with 331 respondents from 13 institutions of higher education in Ukraine. The questionnaire consisted of four blocks: general questions (age, gender, educational institution, year of study, location, etc.), technical block (availability of technical means, access to the Internet, etc.), psychological block (respondents’ state), and educational block (availability of opportunities to continue educational activities). To answer some questions, a sample was made by regions of Ukraine to understand whether there is a significant difference between regions where active hostilities are taking place and regions that are conditionally safer. The article aims to present the results and analysis of the answers and offer suggestions for teachers on implementing educational activities based on the obtained results. The survey showed that teachers of higher education institutions take into account the peculiarities of the current situation in Ukraine. Most institutions implemented blended/distance learning to ensure all participants' safety in the educational process. Among the recommendations are the introduction of video recording of online classes and increased use of non-formal education opportunities, particularly MOOC courses.
MarkBERT: Marking Word Boundaries Improves Chinese BERT

We present a Chinese BERT model dubbed MarkBERT that uses word information in this work. Existing word-based BERT models regard words as basic units, however, due to the vocabulary limit of BERT, they only cover high-frequency words and fall back to character level when encountering out-of-vocabulary (OOV) words. Different from existing works, MarkBERT keeps the vocabulary being Chinese characters and inserts boundary markers between contiguous words. Such design enables the model to handle any words in the same way, no matter they are OOV words or not. Besides, our model has two additional benefits: first, it is convenient to add word-level learning objectives over markers, which is complementary to traditional character and sentence-level pretraining tasks; second, it can easily incorporate richer semantics such as POS tags of words by replacing generic markers with POS tag-specific markers. With the simple markers insertion, MarkBERT can improve the performances of various downstream tasks including language understanding and sequence labeling. (All the codes and models will be made publicly available at https://github.com/ ).
Large-Scale Simulation of Shor’s Quantum Factoring Algorithm

Shor’s factoring algorithm is one of the most anticipated applications of quantum computing. However, the limited capabilities of today’s quantum computers only permit a study of Shor’s algorithm for very small numbers. Here, we show how large GPU-based supercomputers can be used to assess the performance of Shor’s algorithm for numbers that are out of reach for current and near-term quantum hardware. First, we study Shor’s original factoring algorithm. While theoretical bounds suggest success probabilities of only 3–4%, we find average success probabilities above 50%, due to a high frequency of “lucky” cases, defined as successful factorizations despite unmet sufficient conditions. Second, we investigate a powerful post-processing procedure, by which the success probability can be brought arbitrarily close to one, with only a single run of Shor’s quantum algorithm. Finally, we study the effectiveness of this post-processing procedure in the presence of typical errors in quantum processing hardware. We find that the quantum factoring algorithm exhibits a particular form of universality and resilience against the different types of errors. The largest semiprime that we have factored by executing Shor’s algorithm on a GPU-based supercomputer, without exploiting prior knowledge of the solution, is 549,755,813,701 = 712,321 × 771,781. We put forward the challenge of factoring, without oversimplification, a non-trivial semiprime larger than this number on any quantum computing device.
Binary Anarchic Society Optimization for Feature Selection

"Datasets comprise a collection of features; however, not all of these features may be necessary. Feature selection is the process of identifying the most relevant features while eliminating redundant or irrelevant ones. To be effective, feature selection should improve classification performance while reducing the number of features. Existing algorithms can be adapted and modified into feature selectors. In this study, we introduce the implementation of the Anarchic Society Optimization algorithm, a human-inspired algorithm, as a feature selector. This is the first study that utilizes the binary version of the algorithm for feature selection. The proposed Binary Anarchic Society Algorithm is evaluated on nine datasets and compared to three known algorithms: Binary Genetic Algorithm, Binary Particle Swarm Optimization, and Binary Gray Wolf Optimization. Additionally, four traditional feature selection techniques (Info Gain, Gain Ratio, Chi-square, and ReliefF) are incorporated for performance comparison. Our experiments highlight the competitive nature of the proposed method, suggesting its potential as a valuable addition to existing feature selection techniques."
Characterization and experimental verification of the rotating synthetic aperture optical imaging system

Abstract The rotating synthetic aperture (RSA) optical imaging system employs a rectangular primary mirror for detection. During the imaging process, the primary mirror rotates around the center to achieve the aperture equivalent to the long side of the rectangle at different rotation angles. As a result, the system’s point spread function changes over time, causing periodic time-varying characteristics in the acquired images’ resolution. Moreover, due to the rectangular primary mirror, the images obtained by the RSA system are spatially asymmetric, with a lower resolution in the short side’s direction than in the long side’s direction. Hence, image processing techniques are necessary to enhance the image quality. To provide reference for the study of image quality improvement methods, we first characterize the imaging quality degradation mechanism of the RSA system and the time–space evolution law of the imaging process. We then establish an imaging experiment platform to simulate the dynamic imaging process of the RSA system. We quantify the RSA system’s impact on image degradation using objective indexes. Subsequently, by comparing the imaging experiment results with theoretical analysis, we verify the spatially asymmetric and temporally periodic imaging characteristics of the RSA system. Lastly, we introduce image super-resolution experiments to assess the limitations of directly applying generic deep learning-based single image super-resolution methods to the images captured by the RSA system, thereby revealing the challenges involved in improving image quality for the RSA system.
Sign language recognition using the fusion of image and hand landmarks through multi-headed convolutional neural network

Sign Language Recognition is a breakthrough for communication among deaf-mute society and has been a critical research topic for years. Although some of the previous studies have successfully recognized sign language, it requires many costly instruments including sensors, devices, and high-end processing power. However, such drawbacks can be easily overcome by employing artificial intelligence-based techniques. Since, in this modern era of advanced mobile technology, using a camera to take video or images is much easier, this study demonstrates a cost-effective technique to detect American Sign Language (ASL) using an image dataset. Here, "Finger Spelling, A" dataset has been used, with 24 letters (except j and z as they contain motion). The main reason for using this dataset is that these images have a complex background with different environments and scene colors. Two layers of image processing have been used: in the first layer, images are processed as a whole for training, and in the second layer, the hand landmarks are extracted. A multi-headed convolutional neural network (CNN) model has been proposed and tested with 30% of the dataset to train these two layers. To avoid the overfitting problem, data augmentation and dynamic learning rate reduction have been used. With the proposed model, 98.981% test accuracy has been achieved. It is expected that this study may help to develop an efficient human-machine communication system for a deaf-mute society.
Optimal initial states for quantum parameter estimation based on Jaynes–Cummings model [Invited]

Quantum parameter estimation is a crucial tool for inferring unknown parameters in physical models from experimental data. The Jaynes–Cummings model is a widely used model in quantum optics that describes the interaction between an atom and a single-mode quantum optical field. In this Letter, we systematically investigate the problem of estimating the atom-light coupling strength in this model and optimize the initial state in the full Hilbert space. We compare the precision limits achievable for different optical field quantum states, including coherent states, amplitude- and phase-squeezed states, and provide experimental suggestions with an easily prepared substitute for the optimal state. Our results provide valuable insights into optimizing quantum parameter estimation in the Jaynes–Cummings model and can have practical implications for quantum metrology with hybrid quantum systems.
Design as a cultural phenomenon

The object of the study is culture in terms of its aesthetic component (art and design). The subject of the study is design as a cultural phenomenon. The author suggests that design from a philosophical point of view contains meanings that are not described by the traditional number of aesthetic categories. The author demarcates the categories "design" with the categories "art" and "aesthetic", consistently identifying differences in the following parameters: 1. functional difference; 2. difference of origin; 3. semiotic difference. Having distinguished the design category from the art category, the author reveals the specific semiurgical essence of the design category. Semiurgic design is associated with projective activity, that is, the activity of creating new signs. Design is associated with the design of secondary reality and the gradual doubling of the world. The main conclusion of the study is an attempt to derive the formula of the philosophical content of the category "design". The philosophical category "design" is associated with the aesthetic organization of secondary reality and is characterized by projectivity (orientation to innovation), transitivity (orientation to real application), functionality (orientation to optimal application), immanence (lack of connections with the transcendent) and semiurgicity – the function of creating new signs. The semiurgic nature of design lies in the constant reproduction of itself, since the discourse of design "new" becomes synonymous with design and loses the meaning of the dialectical negation of "old". The design is associated with the symbolic doubling of the world. Moreover, this doubling does not increase the meaning and value of being, but, on the contrary, enhances the illusory nature of being, since design, unlike art, is not associated with pointing to a transcendent reality.
Gottesman-Kitaev-Preskill qubit synthesizer for propagating light

Abstract Practical quantum computing requires robust encoding of logical qubits in physical systems to protect fragile quantum information. Currently, the lack of scalability limits the logical encoding in most physical systems, and thus the high scalability of propagating light can be a game changer. However, propagating light also has difficulty in logical encoding due to weak nonlinearity. Here, we propose a synthesizer that encodes Gottesman-Kitaev-Preskill (GKP) qubits in propagating light by exploiting the nonlinearity of photon detectors. This synthesizer is based on an approach what we call Gaussian breeding, leading to the following four advantages: (i) systematic and rigorous synthesis of arbitrary GKP qubits, (ii) use of minimal resources, (iii) high fidelity and high success probability, and (iv) robustness against loss. There has been no protocol that incorporates all these advantages, and thus the proposed synthesizer excels in both performance and feasibility. By employing our method, one can generate GKP qubits using a few to several squeezed light sources, beam splitters and photon detectors.
Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review

Digital data play an increasingly important role in advancing health research and care. However, most digital data in healthcare are in an unstructured and often not readily accessible format for research. Unstructured data are often found in a format that lacks standardization and needs significant preprocessing and feature extraction efforts. This poses challenges when combining such data with other data sources to enhance the existing knowledge base, which we refer to as digital unstructured data enrichment. Overcoming these methodological challenges requires significant resources and may limit the ability to fully leverage their potential for advancing health research and, ultimately, prevention, and patient care delivery. While prevalent challenges associated with unstructured data use in health research are widely reported across literature, a comprehensive interdisciplinary summary of such challenges and possible solutions to facilitate their use in combination with structured data sources is missing. In this study, we report findings from a systematic narrative review on the seven most prevalent challenge areas connected with the digital unstructured data enrichment in the fields of cardiology, neurology and mental health, along with possible solutions to address these challenges. Based on these findings, we developed a checklist that follows the standard data flow in health research studies. This checklist aims to provide initial systematic guidance to inform early planning and feasibility assessments for health research studies aiming combining unstructured data with existing data sources. Overall, the generality of reported unstructured data enrichment methods in the studies included in this review call for more systematic reporting of such methods to achieve greater reproducibility in future studies.
Optimal Generators for Quantum Sensing

We propose a computationally efficient method to derive the unitary evolution that a quantum state is most sensitive to. This allows one to determine the optimal use of an entangled state for quantum sensing, even in complex systems where intuition from canonical squeezing examples breaks down. In this paper we show that the maximal obtainable sensitivity using a given quantum state is determined by the largest eigenvalue of the quantum Fisher information matrix (QFIM) and the corresponding evolution is uniquely determined by the coinciding eigenvector. Since we optimize the process of parameter encoding rather than focusing on state preparation protocols, our scheme is relevant for any quantum sensor. This procedure naturally optimizes multiparameter estimation by determining, through the eigenvectors of the QFIM, the maximal set of commuting observables with optimal sensitivity.
Good Models Borrow, Great Models Steal: Intellectual Property Rights and Generative AI

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Impact of knowledge‐oriented leadership on innovative behavior, and employee satisfaction: The mediating role of knowledge‐centered culture for sustainable workplace

Abstract Knowledge‐oriented leadership phenomenon has captured the interest of scholars and industry leaders given its positive impact on sustainable organizational and employees outcomes. By drawing on affective event theory, this research explores the role of knowledge‐oriented leadership on knowledge‐centered culture, service innovative behavior and employees' job satisfaction and the mediating role of knowledge‐centered culture. The proposed model and hypotheses were tested using survey data gathered from 456 hotels and travel agencies employees in Jordan. The analysis was conducted using the contemporary structural equation modeling modus operandi, the partial least squares. The findings confirm that knowledge‐oriented leadership promotes knowledge‐centered culture in organizations, which in turn, promotes employees' job satisfaction and service innovative behavior. Knowledge‐centered culture concurrently mediates the path linking knowledge‐oriented leadership to job satisfaction and innovative behavior, respectively. The literature is devoid of scholarly work on the impact of knowledge‐oriented leadership and knowledge‐centered culture on employee outcomes in the tourism and hospitality industry context. This study being among the first to contribute to the literature from a developing country's perspective, prescribes empirical evidence, that knowledge‐oriented leadership promotes a knowledge‐centered culture in organizations, which subsequently leads to increased job satisfaction and service innovation. Furthermore, this study provides practical implications for organizations looking to promote a knowledge‐centered culture and enhance their employees' job satisfaction and service innovation.
Consumer Adoption of AI-powered Virtual Assistants (AIVA): An Integrated Model Based on the SEM–ANN Approach

Artificial intelligence (AI) has lured consumers to orchestrate their routine activities relying on such technologies. Though AI-powered virtual assistants (AIVAs) have gained traction among service providers, these are still lagging on the demand front. This study intends to develop an ‘AIVA adoption model’ delineated under a holistic framework based on structural equation modelling and deep neural network incorporating multilayer perceptron algorithm. The sensitivity analysis designated ‘effort expectancy’ as the most dominant antecedent of AIVA adoption, followed by ‘perceived innovativeness’. While ‘perceived risk’ held high relevance, the tech users were equally concerned about the performance of AIVA in conjunction with its anthropomorphic response; however, they gave the least consideration to subjective norms. The parallel mediation analysis revealed that the adopters preferred transactional relationships with AIVA more than the communal one, while the simultaneous application of both the perspectives better generates loyal customers. The moderation analysis unveiled that the uncanny valley paradigm could not always be supportive, especially in the context of AIVA. The developed model may serve the basis to generate as well as sustain adoption and loyalty of the specified technology.
ChatGPT for PLC/DCS Control Logic Generation

Large language models (LLMs) providing generative AI have become popular to support software engineers in creating, summarizing, optimizing, and documenting source code. It is still unknown how LLMs can support control engineers using typical control programming languages in programming tasks. Researchers have explored GitHub CoPilot or DeepMind AlphaCode for source code generation but did not yet tackle control logic programming. A key contribution of this paper is an exploratory study, for which we created 100 LLM prompts in 10 representative categories to analyze control logic generation for of PLCs and DCS from natural language. We tested the prompts by generating answers with ChatGPT using the GPT-4 LLM. It generated syntactically correct IEC 61131-3 Structured Text code in many cases and demonstrated useful reasoning skills that could boost control engineer productivity. Our prompt collection is the basis for a more formal LLM benchmark to test and compare such models for control logic generation.
Artificial intelligence as enabler of future library services: how prepared are librarians in African university libraries

Purpose The purpose of this study is to investigate artificial intelligence (AI) as enabler of future library services, with consideration to how prepared are librarians in African university libraries. Design/methodology/approach This study applied the interpretive content/document analysis of literature harvested from different databases of Scopus and Web of Science. AI could be used to perform daily routines in circulation, serial, reference and selective dissemination of information among others. It could also be applied to the provision of innovative services of recognition of library activities such as answering research quarries, cataloguing and classification of library materials and management of library system software of different databases within the library systems. Findings It could be deduced from the study that AI would continue to serve as a panacea to future library services irrespective of its geographical context. Due to the evolving nature of knowledge growth, AI having its roots in the field of engineering has been found useful to support future library services. The support accrued from library service delivery in the library profession has made librarians continue to interact with other intelligent machines that can demonstrate human behaviour even though they are not real human beings. The behaviour of machines and AI where human beings play a significant role has brought many renovations in the management of complex tasks of processing, communication, knowledge representation, decision making and suggestions, on potentials of diverse work operations. Practical implications The understanding that the present paper portrays in the context of future library services is that there is no way the AI could function without a human interaction perspective when drawing an analogy from computer science, information science and information systems fields of study. Social implications The interest of users across their background would be strengthen if AI advances transformed the handling complex tasks of processing, communication, knowledge representation, decision-making and giving suggestions, among other things. The possibilities of diverse work operations from empirical evidence of studies consulted in recent times gave the authors the impetus to consider AI as the enabler of future library services. Originality/value The increasing demands from library patrons have prompted librarians to adapt their methods of delivering services. These emerging technologies have also brought about shifts in approaches to teaching and learning. Consequently, the recent surge in digital technology-driven service innovations has ushered in a fresh paradigm for education and research. In response to these changes, librarians are actively seeking novel and innovative technologies to enhance user experiences within their libraries. They serve as catalysts for introducing modern and advanced technologies, consistently adapting to contemporary tools that enhance their offerings.