Reviewerly/exHarmony huggingface dataset:
Practical high-dimensional quantum key distribution protocol over deployed multicore fiber

Quantum key distribution (QKD) is a secure communication scheme for sharing symmetric cryptographic keys based on the laws of quantum physics, and is considered a key player in the realm of cyber-security. A critical challenge for QKD systems comes from the fact that the ever-increasing rates at which digital data are transmitted require more and more performing sources of quantum keys, primarily in terms of secret key generation rate. High-dimensional QKD based on path encoding has been proposed as a candidate approach to address this challenge. However, while proof-of-principle demonstrations based on lab experiments have been reported in the literature, demonstrations in realistic environments are still missing. Here we report the generation of secret keys in a 4-dimensional hybrid time-path-encoded QKD system over a 52-km deployed multicore fiber link forming by looping back two cores of a 26-km 4-core optical fiber. Our results indicate that robust high-dimensional QKD can be implemented in a realistic environment by combining standard telecom equipment with emerging multicore fiber technology.
Exploring language alternation and participation in an ‘in-between learning scenario’: a case study of a WhatsApp chat with secondary students of English

The study of participation from a socio-interactional perspective relates to the exploration of the interactional practices displayed by interactants and the close examination of the multimodal resources they employ. Here we observe the language choices made by 2 teachers and 10 students who participate in a WhatsApp chat over the summer break. Since language choice is a social category-bound activity linked to social forms of participation, our analysis focuses on the negotiation participants engage in to determine their practiced language policy and to select their medium of interaction. Our analysis reveals that language choices reshape and construct what we would refer to as an ‘in-between learning scenario’. This scenario creates tensions between the teachers’ and the students’ agenda, observed through their alignment with other interactants and their interpretation of the task at hand. Language selection is not a strict, personal choice. Instead, it is socially situated and depends on how speakers co-construct the communicative event they participate in and who holds the control of topic selection.
Occupying Another’s Digital Space: Privacy of Smartphone Users as a Situated Practice

Abstract A smartphone’s screen is commonly regarded as a private space, and the action of looking at it is usually considered a violation of one’s privacy both by researchers and designers. However, our study demonstrates how participants in the interaction themselves negotiate moment by moment and achieve an understanding of someone’s screen space as public or private. In this paper, we analyze the interactional sequences of uninvited looks at another participant’s phone. Drawing on visual ethnography and ethnomethodologically informed multimodal interaction analysis, we video-recorded and analyzed everyday interactions between friends and acquaintances. Our findings show that looking at someone’s smartphone display is often performed and oriented to as a resource in interaction rather than an invasion of privacy. We therefore characterize the interactional functions of gazes and glances at another’s screen. We also discuss the research and design implications of approaching privacy as a situated practice.
A Quick Fix for Creating Stimulating Lessons Using Images, Videos, and Audios from the Library of Congress Website

Online learning has put pressure on instructors to create more stimulating lessons using images, videos, and the like. While the Internet offers resources, many sites require educators to pay in order to use them legally. Furthermore, instructors need images representing a diversity of cultures to be inclusive. One website that overcomes these challenges and offers a quick fix for creating invigorating lessons is the Library of Congress website.
Physics‐Aware Analytic‐Gradient Training of Photonic Neural Networks

Abstract Photonic neural networks (PNNs) have emerged as promising alternatives to traditional electronic neural networks. However, the training of PNNs, especially the chip implementation of analytic gradient descent algorithms that are recognized as highly efficient in traditional practice, remains a major challenge because physical systems are not differentiable. Although training methods such as gradient‐free and numerical gradient methods are proposed, they suffer from excessive measurements and limited scalability. State‐of‐the‐art in situ training method is also cost‐challenged, requiring expensive in‐line monitors and frequent optical I/O switching. Here, a physics‐aware analytic‐gradient training (PAGT) method is proposed that calculates the analytic gradient in a divide‐and‐conquer strategy, overcoming the difficulty induced by chip non‐differentiability in the training of PNNs. Multiple training cases, especially a generative adversarial network, are implemented on‐chip, achieving a significant reduction in time consumption (from 31 h to 62 min) and a fourfold reduction in energy consumption, compared to the in situ method. The results provide low‐cost, practical, and accelerated solutions for training hybrid photonic‐digital electronic neural networks.
A multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision

Abstract Three‐dimensional displacement monitoring over long distances has been a long‐standing concern in the structural health monitoring industry. In this study, a multi‐degree‐of‐freedom slope displacement monitoring method is developed by fusing computer vision and the 3D point triangulation method. Attributed to this method, the problems of outdoor binocular camera calibration, multi‐target mismatching, and outdoor illumination effects were solved. First, a two‐stage camera calibration method is proposed to accurately calibrate intrinsic and extrinsic camera parameters under a large field of view and long working distance conditions. Second, the adaptive spatial‐frequency method is proposed to calculate the coding and pixel coordinates of the monitored target. In this step, to solve the problem of mismatching monitored points in different camera frames, the Augmented Reality University of Cordoba code is introduced to provide a unique identity code for each monitored point. To mitigate the impact of illumination and other factors on pixel coordinate calculation, an adaptive pixel coordinate calculation method that combines information from the spatial and frequency domains is proposed., Third, based on the intrinsic and extrinsic parameters of the stereo camera and the pixel coordinates of the monitored points, the 3D coordinates of the monitored points are obtained through triangulation. Finally, the accuracy experiments and stability experiments are conducted. According to the results of the experiments, the measurement distance is positively correlated with the measurement error. And the baseline length is negatively correlated with the measurement error in the z ‐direction. Ultimately, we suggest that the ratio of baseline length to measurement distance should be greater than 40%. When the recommended value is satisfied, the measurement error is less than 1 mm when the measurement distance is less than 40 m. When the measurement distance is equal to 90 m, the measurement error is less than 5 mm. Meanwhile, stability experiments of the algorithm were carried out, and in a period of outdoor validation experiments, the fluctuations were only sub‐millimeter, demonstrating good anti‐interference performance. Moreover, the method proposed in this study successfully monitored a landslide disaster in Guangxi, which demonstrated its outstanding practical application capabilities.
A Personalized Federated Learning Method Based on Clustering and Knowledge Distillation

Federated learning (FL) is a distributed machine learning paradigm under privacy preservation. However, data heterogeneity among clients leads to the shared global model obtained after training, which cannot fit the distribution of each client’s dataset, and the performance of the model degrades. To address this problem, we proposed a personalized federated learning method based on clustering and knowledge distillation, called pFedCK. In this algorithm, each client has an interactive model that participates in global training and a personalized model that is only trained locally. Both of the models perform knowledge distillation with each other through the feature representation of the middle layer and the soft prediction of the model. In addition, in order to make an interaction model only obtaining the model information from the client, which has similar data distribution and avoids the interference of other heterogeneous information, the server will cluster the clients according to the similarity of the amount of parameter variation uploaded by different interaction models during every training round. By clustering clients, interaction models with similar data distributions can cooperate with each other to better fit the local dataset distribution. Thereby, the performance of personalized model can be improved by obtaining more valuable information indirectly. Finally, we conduct simulation experiments on three benchmark datasets under different data heterogeneity scenarios. Compared to the single model algorithms, the accuracy of pFedCK improved by an average of 23.4% and 23.8% over FedAvg and FedProx, respectively; compared to typical personalization algorithms, the accuracy of pFedCK improved by an average of 0.8% and 1.3%, and a maximum of 1.0% and 2.9% over FedDistill and FML.
A Practical Approach to Automatic Earthquake Catalog Compilation in Local OBS Networks Using Deep-Learning and Network-Based Algorithms

Abstract In land-based seismology, modern automatic earthquake detection and phase picking algorithms have already proven to outperform classic approaches, resulting in more complete catalogs when only taking a fraction of the time needed for classic methods. For marine-based seismology, similar advances have not been made yet. For ocean-bottom seismometer (OBS) data, additional challenges arise, such as a lower signal-to-noise ratio and fewer labeled data sets available for training deep-learning models. However, the performance of available deep-learning models has not yet been extensively tested on marine-based data sets. Here, we apply three different modern event detection and phase picking approaches to an ∼12 month local OBS data set and compare the resulting earthquake catalogs and location results. In addition, we evaluate their performance by comparing different subcatalogs of manually detected events and visually revised picks to their automatic counterparts. The results show that seismicity patterns from automatically compiled catalogs are comparable to a manually revised catalog after applying strict location quality control criteria. However, the number of such well-constrained events varies between the approaches and catalog completeness cannot be reliably determined. We find that PhaseNet is more suitable for local OBS networks compared with EQTransformer and propose a pick-independent event detection approach, such as Lassie, as the preferred choice for an initial event catalog compilation. Depending on the aim of the study, different schemes of manual repicking should be applied because the automatic picks are not yet reliable enough for developing a velocity model or interpreting small-scale seismicity patterns.
Development and Practical Applications of Computational Intelligence Technology

Computational intelligence (CI) uses applied computational methods for problem-solving inspired by the behavior of humans and animals. Biological systems are used to construct software to solve complex problems, and one type of such system is an artificial immune system (AIS), which imitates the immune system of a living body. AISs have been used to solve problems that require identification and learning, such as computer virus identification and removal, image identification, and function optimization problems. In the body’s immune system, a wide variety of cells work together to distinguish between the self and non-self and to eliminate the non-self. AISs enable learning and discrimination by imitating part or all of the mechanisms of a living body’s immune system. Certainly, some deep neural networks have exceptional performance that far surpasses that of humans in certain tasks, but to build such a network, a huge amount of data is first required. These networks are used in a wide range of applications, such as extracting knowledge from a large amount of data, learning from past actions, and creating the optimal solution (the optimization problem). A new technique for pre-training natural language processing (NLP) software ver.9.1by using transformers called Bidirectional Encoder Representations (BERT) builds on recent research in pre-training contextual representations, including Semi-Supervised Sequence Learning, Generative Pre-Training, ELMo (Embeddings from Language Models), which is a method for obtaining distributed representations that consider context, and ULMFit (Universal Language Model Fine-Tuning). BERT is a method that can address the issue of the need for large amounts of data, which is inherent in large-scale models, by using pre-learning with unlabeled data. An optimization problem involves “finding a solution that maximizes or minimizes an objective function under given constraints”. In recent years, machine learning approaches that consider pattern recognition as an optimization problem have become popular. This pattern recognition is an operation that associates patterns observed as spatial and temporal changes in signals with classes to which they belong. It involves identifying and retrieving predetermined features and rules from data; however, the features and rules here are not logical information, but are found in images, sounds, etc. Therefore, pattern recognition is generally conducted by supervised learning. Based on a new theory that deals with the process by which the immune system learns from past infection experiences, the clonal selection of immune cells can be viewed as a learning rule of reinforcement learning.
A Design of Digital Bag for Pilgrim's Safety

The study aimed to design digital bags that comply with the functional requirements of Hajj and Umrah pilgrims in the light of ergonomics and then to identify the differences between specialists in achieving design patterns of bags made for pilgrims' safety. It also aimed to measure the extent to which Hajj and Umrah pilgrims are satisfied with the proposed designs. The research employed a mixed-methods approach, with a study sample of (301) individuals who had participated in Hajj and Umrah pilgrimages. The study's findings indicated statistically significant variations, at a significance level of (0.01) among the six suggested designs for smart bags, based on the feedback from Hajj and Umrah pilgrims. When testing the direction of significance, the two researchers found that design no. 1 was the best among all designs, followed by design no. 3, then design no. 6. Results also showed statistically significant differences at the level of (0.01) among the six suggested designs for smart bags based on the experts' assessments. When testing the direction of significance, the researchers found that design no. 1 stood out as the optimal choice, followed by design no. 6, then design no. 2. The study concluded that the ergonomic standards should be applied in the smart design, taking into consideration the safety factor, as the two researchers followed the ergonomic standards within the framework of designing smart bags through several elements, namely locating the bag, combating thefts, and enabling bags with RFID technology to identify the user's identity. The study provided an overview of recent trends in the ergonomic field to improve human performance and achieve product development. It resulted in a practical guide to understanding the ergonomic guidelines in smart design. The two researchers recommended considering the ergonomic aspects of fashion design and its supplements, especially the smart ones. They also recommended conducting several studies dealing with the impact of ergonomic considerations on fashion design and its supplements.
Intent Identification by Semantically Analyzing the Search Query

Understanding and analyzing the search intent of a user semantically based on their input query has emerged as an intriguing challenge in recent years. It suffers from small-scale human-labeled training data that produce a very poor hypothesis of rare words. The majority of data portals employ keyword-driven search functionality to explore content within their repositories. However, the keyword-based search cannot identify the users’ search intent accurately. Integrating a query-understandable framework into keyword search engines has the potential to enhance their performance, bridging the gap in interpreting the user’s search intent more effectively. In this study, we have proposed a novel approach that focuses on spatial and temporal information, phrase detection, and semantic similarity recognition to detect the user’s intent from the search query. We have used the n-gram probabilistic language model for phrase detection. Furthermore, we propose a probability-aware gated mechanism for RoBERTa (Robustly Optimized Bidirectional Encoder Representations from Transformers Approach) embeddings to semantically detect the user’s intent. We analyze and compare the performance of the proposed scheme with the existing state-of-the-art schemes. Furthermore, a detailed case study has been conducted to validate the model’s proficiency in semantic analysis, emphasizing its adaptability and potential for real-world applications where nuanced intent understanding is crucial. The experimental result demonstrates that our proposed system can significantly improve the accuracy for detecting the users’ search intent as well as the quality of classification during search.
Scene text recognition with context-aware autonomous bidirectional iterative models

This research focuses on Scene Text Recognition (STR), a crucial component in various applications of artificial intelligence such as image retrieval, office automation, and intelligent traffic systems. Recent studies have shown that semantic-aware approaches significantly improve the performance of STR tasks, with context-aware STR methods becoming mainstream. Among these, the fusion of visual and language models has shown remarkable effectiveness. We propose a novel method (PABINet) that incorporates three key components: a Visual-Language Decoder, a Language Model, and a Fusion Model. First, during training, the Visual-Language Decoder masks the original labels in the Transformer decoder using permutation masks, with each mask being unique. This enhances word memorization and learning through contextual semantic information, resulting in robust semantic knowledge. During the inference stage, the Visual-Language Decoder employs autonomous Autoregressive model (AR) inference to generate results. Subsequently, the Language Model scrutinizes and corrects the output of the Visual-Language Encoder using a cloze mask approach, achieving context-aware, autonomous, bidirectional inference. Finally, the Fusion Model concatenates and refines the outputs of both models through iterative layers.Experimental results demonstrate that our PABINet performs exceptionally well when handling various quality images. When trained with synthetic data, PABINet achieves a new STR benchmark (average accuracy of 92.41%), and when trained with real data, it establishes new state-of-the-art results (average accuracy of 96.28%).
Digital transformation in higher education: A comprehensive review of e-learning adoption

BACKGROUND: Data-driven practices are now crucial in the digital age for streamlining operations and boosting output across a range of industries. With e-Learning emerging as a cutting-edge and promising strategy to improve educational delivery and student engagement, higher education institutions are also adopting this trend. Despite the potential advantages, e-Learning adoption in higher education is still a developing topic with scant published research. OBJECTIVE: This study aims to conduct a thorough analysis of existing literature, seeking to determine the present status of e-Learning adoption in higher education while also identifying promising avenues for further research. METHODS: In this study, a systematic literature review methodology and a bibliometric approach are utilized to examine 63 papers sourced from the Scopus database, encompassing the years 2017 to 2021. These papers were specifically chosen due to their pertinence to e-Learning adoption in higher education institutions, following the exclusion and inclusion criteria outlined in the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework. The analysis techniques employed include descriptive overviews, citation analysis, and keyword co-occurrence. RESULTS: The systematic review of the literature revealed a significant increase in e-Learning adoption within higher education institutions (HEIs) in recent times. The findings highlighted several crucial factors influencing e-Learning adoption, including perceived ease of use, usefulness, learner attitude, self-efficacy, and facilitating conditions. However, further investigation is needed to better understand the impact of perceived learning and teaching styles on e-Learning acceptance in higher education settings. CONCLUSION: This study suggests conducting future investigations on e-Learning in developing countries, with a specific focus on intention, usage, adoption, and impact. Additionally, it emphasizes the importance of exploring the influence of learning and teaching styles on e-Learning acceptance within higher education institutions. These valuable insights will facilitate the development of effective strategies for the successful implementation of e-Learning in higher education settings.
Efficient bosonic nonlinear phase gates

Abstract Continuous-variable (CV) quantum information processing harnesses versatile experimental tools that leverage the power of infinite-dimensional oscillators controlled by a single qubit. Increasingly available elementary Rabi gates have been proposed as a resource for implementing universal CV gates, but the requirement of many weak, non-commuting gates is a bottleneck in scaling up such an approach. In this study, we propose a resource-efficient technique using Fourier expansion to implement arbitrary non-linear phase gates in a single oscillator. This method reduces the number of sequentially required gates exponentially. These gates represented by cubic, quartic, and other arbitrary nonlinear potentials have applications in CV quantum information processing with infinite-dimensional oscillators controlled by a single qubit. Our method outperforms previous approaches and enables the experimental realization of a wide range of applications, including the development of bosonic quantum sensors, simulations, and computation using trapped ions and superconducting circuits.