Reviewerly/exHarmony huggingface dataset:
Automated progress tomography using genetic algorithm for quantum key distribution

Polarization maintenance of quantum communication links is one of the keys to the stable and long-term operation of optical quantum communication. At present, many solutions are based on link process tomography result for polarization compensation. To automate process tomography for automated polarization compensation when the link is changed, this paper presents an automated optical path process tomography method based on genetic algorithm. This method uses sensors to automatically obtain input and output states and depends on genetic algorithm to optimize the post-processing of process tomography. In this way, the method can automatically solve the transmission process in the optical path. Besides, the experimental verification is completed by using a single-mode fiber simulation optical path black-box. The experimental results demonstrate that the fidelity of polarized light can reach an average of 0.9966. This method can automate the process tomography of the optical path Therefore, the method has the potential to improve the efficiency and reliability of polarization compensation in quantum communication.
Benchmarking Whole Knowledge Graph Embedding Techniques

Knowledge Graphs (KGs) are gaining popularity and are being widely used in a plethora of applications. They owe their popularity to the fact that KGs are an ideal form to integrate and retrieve data originating from various sources. Using KGs as input for Machine Learning (ML) tasks allows to perform predictions on these popular graph structures. However, KGs cannot directly be used as ML input in their graph representation, they first require to be transformed to a vector space representation through an embedding technique. As ML techniques are data-driven, they can generalize over unseen input data that deviates to some extent from the data they were trained upon. To fully exploit the generalization capabilities of ML algorithms when using embedded KGs as input, small changes in the KGs should also result in small changes in the embedding space. Various embedding techniques for graphs in general exist, however, they have not been tailored towards embedding whole KGs, while KGs can be considered a special kind of graph that adheres to a certain KG schema. This paper evaluates if these existing embedding techniques that embed the whole graphs can represent the similarity between KGs in their embedding space, allowing ML algorithms to generalize over their input. We compare the similarities between KGs in terms of changes in size, entity labels, and KG schema. We found that most techniques were able to represent the similarities in terms of size and entity labels in their embedding space, however, none of the techniques were able to capture the similarities in KG schema.
Making computing visible &amp; tangible: A paper-based computing toolkit for codesigning inclusive computing education activities

MCVT (Making Computing Visible and Tangible) Cards are a toolkit of paper-based computing cards intended for use in the codesign of inclusive computing education. Working with groups of teachers and students over multiple design sessions, we share our toolkit, design drivers and material considerations; and use cases drawn from a week-long codesign workshop where seven teachers made and adapted cards for their future classroom facilitation. Our findings suggest that teachers valued the MCVT toolkit as a resource for their own learning and perceived the cards to be useful for supporting new computational practices, specifically for learning through making and connecting to examples of everyday computing. Critically reviewed by teachers during codesign workshops, the toolkit however posed some implementation challenges and constraints for learning through making and troubleshooting circuitry. From teacher surveys, interviews, workshop video recordings, and teacher-constructed projects, we show how teachers codesigned new design prototypes and pedagogical activities while also adapting and extending paper-based computing materials so their students could take advantage of the unique technical and expressive affordances of MCVT Cards. Our design research contributes a new perspective on using interactive paper computing cards as a medium for instructional materials development to support more inclusive computing education.
A novel variational robust filter with Gaussian mixture model for unknown non-Gaussian noises

In numerous practical applications, the presence of unpredictable abnormal behaviors often leads to random measurement outliers, causing the random measurement noises are no longer Gaussian but unknown non-Gaussian distributions, and it is intractable to obtain accurate a priori information about the noises. To address this challenge and enable nonlinear state estimation under such measurement noises, we propose a novel robust filter based on Gaussian mixture model (GMM). Firstly, a GMM is introduced to model the unknown non-Gaussian measurement likelihood probability density function (ML-PDF). Without relying on any prior noise statistics, the GMM can accurately model the ML-PDF utilizing a set of model parameters adaptively learned by the variational Bayesian (VB) method. Then, considering the high computational burden associated with the existing variational iterative process, we develop an improved VB method, which independently calculates the posterior distributions of state and unknown model parameters, thereby improving the computational efficiency. Finally, a new robust filter is derived by integrating the GMM with the improved VB approach, The effectiveness and superiority of the filter are demonstrated through both numerical simulations and real-world data analysis.
Procedural applications of total quality management in university education in light of the COVID-19 pandemic

Abstract The current study aimed to establish the basic infrastructure to facilitate the implementation of the procedural applications of total quality management, through the introduction of a fully functional total quality management unit in colleges and scientific departments, in line with internationally recognized standards, by meeting ISO 21001: standards for the requirements of the management system of educational institutions 2018 in light of the coronavirus crisis. A questionnaire was printed including all applications and 40 actions distributed to three categories of volunteers: undergraduate students, graduate students, and faculty members at the university. The current study involved the calculation of several key metrics, including the average number of volunteers, the average level of satisfaction across all choices, and the proportion of overall satisfaction attributable to the mean values observed across all groups. The results indicate a relatively low rating of 15 and a satisfaction level of 25.125 among the sample of undergraduates who participated in the study. In the context of postgraduate education, the scale reached a numerical value of 12.1, resulting in a satisfaction rate of 48.4. The responses or alternatives provided by respected faculty members at the university indicated a high level of confidence and clarity. The results indicate that a large percentage of the respondents 62.322 expressed their satisfaction unequivocally in the categorical response of “exactly yes” with a similar frequency of 8.725. The rationale behind their selection can be attributed to various factors, such as their proximity to the deanship, their great experience in university work, their active participation in decision-making processes, and the limited impact of the Corona pandemic. The satisfaction rate of university professors reached 80.57% overall. The graduate student group ranked second, peaking at 70.56%. In contrast, the statistical figures related to undergraduate students were relatively lower, as they ranked third with a rate of 55.21%.
A Comparative Study of the Typing Performance of Two Mid-Air Text Input Methods in Virtual Environments

Inputting text is a prevalent requirement among various virtual reality (VR) applications, including VR-based remote collaboration. In order to eliminate the need for complex rules and handheld devices for typing within virtual environments, researchers have proposed two mid-air input methods-the trace and tap methods. However, the specific impact of these input methods on performance in VR remains unknown. In this study, typing tasks were used to compare the performance, subjective report, and cognitive load of two mid-air input methods in VR. While the trace input method was more efficient and novel, it also entailed greater frustration and cognitive workload. Fortunately, the levels of frustration and cognitive load associated with the trace input method could be reduced to the same level as those of the tap input method via familiarity with VR. These findings could aid the design of virtual input methods, particularly for VR applications with varying text input demands.
Spatial distribution of rock uplift in the Bongarà district (Peruvian Andes) and implications for the genesis of supergene ore deposits

Topography and river landscape morphometry of two hydrographic basins, the Utcubamba River and the Chiriaco River basins (Bongará district, Amazonas region) allowed to determine the relationship between the irregular distribution of vertical motions (e.g., rock uplift) and the genesis of supergene Zn deposits. The latter derives from variable weathering of pre-existing carbonate-hosted Zn sulfides orebodies, in three distinct localities: Florida Canyon, Mina Grande and Cristal. The research was carried out through the evaluation of the parameters such as elevation, local relief, swath profile, river longitudinal profiles and transformed river profiles (χ-long profiles), and slope/area analysis to derive the normalized channel steepness index (ksn). The NE-ward sharp increase in mean and minimum elevation coupled with a jump in the mean ksn values, suggests that the Chiriaco River basin has experienced more recent rock uplift than the Utcubamba River basin. In fact, the Utcubamba River experienced a long-lasting uplift, resulting in a rugged local relief and lowering the mean and minimum elevations, and exhumation and weathering of the Triassic-hosted sulfide mineral deposits at Florida Canyon. In the Chiriaco River basin, the younger uplift has promoted the weathering of the more surficial Jurassic-hosted Mina Grande and Cristal mineral deposits. The morphometric approach proved to be effective in recognizing fertile areas with near-surface orebodies also in regions characterized by distinct types of ore deposits.
Combined query image retrieval based on hybrid coding of CNN and Mix-Transformer

Convolutional Neural Network (CNN) is commonly used to extract reference image features in combined query image retrieval algorithms. CNN extracts image features by calculating the relationship between pixels in a small area through convolution kernel, which covers only a small part of the image each time. Therefore, CNN pays more attention to the local information of the image, which is easy to cause the loss of global information of the image. In order to enrich the feature information of reference images and improve the retrieval performance of network model, this paper proposes the following improvements: (1) Multi-Headed Self-Attention (MSA) in Vision Transformer (ViT) only pays attention to the correlation within samples, ignoring the correlation between samples, this makes it is difficult to obtain a global condition of the dataset, which limits the ability of ViT to capture reference image information. To solve this problem, we propose Group External Attention (GEA) module and replace MSA in ViT with it, thus proposing Mix-Transformer. Further, we build a hybrid network of CNN and Mix-Transformer to capture the reference image features. (2) We introduce Shuffle Attention (SA) module to reaggregate reference image features extracted by CNN in groups by channel, so that information can flow between different groups, which can effectively enhance the richness and relevance of reference image feature information. A large number of experiments have been conducted on Fashion200k and MIT-States publicly available datasets, and experimental results confirm our algorithm’s outstanding performance.
Machine learning-based ensemble approach in prediction of lung cancer predisposition using XRCC1 gene polymorphism

The employment of machine learning approaches has shown promising results in predicting cancer. In the current study, polymorphisms data of five single nucleotide polymorphisms (SNPs) of DNA repair gene XRCC1 (XRCC1 399, XRCC1 194, XRCC1 206, XRCC1 632, XRCC1 280) of the north Indian population along with four smoking status data is considered as an input to the proposed ensemble model to predict the risk of individual susceptibility to the lung cancer. The prediction accuracy of the proposed ensemble model for cancer predisposition was found to be 85%. The model performance is also evaluated using sensitivity, specificity, precision and the Gini index, which is found in the range of 0.83-0.87. The proposed model also outperformed in all evaluation parameters when compared with the individual Model (LM, SVM, RF, KNN and baseline neural net). Collectively, current results suggest the potential of the proposed ensemble model in predicting the risk of cancer based on XRCC1 SNPs data.Communicated by Ramaswamy H. Sarma.
Constructing an Investment Scam Detection Model Based on Emotional Fluctuations Throughout the Investment Scam Life Cycle

ABSTRACTIn recent years, the COVID-19 pandemic has increased investment scams as individuals seek alternative investment opportunities. This study employs emotion analysis to examine the emotional expressions of scammers in investment scams and proposes an investment scam detection model to identify fraudulent behavior. Using data from seven real-world investment scam chat rooms, we perform a two-stage analysis to evaluate the emotional fluctuations of scammers and construct a machine learning-based investment scam detection model. Our findings reveal distinct patterns of emotional fluctuation in investment scam chat rooms according to the established Investment Scam Life Cycle (ISLC), which portrays the progression of fraudulent behavior. We confirm the applicability of machine learning in scam detection, and the emotion analysis presented in this study can assist researchers and fraud examiners in identifying scam scenarios and reducing financial losses. Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsChin KuoChin Kuo is a Section Chief Auditor at the Far Eastern Group in Taiwan. He specializes in operation and information security audits, fraud detections, and investigations. He is a Certified Fraud Examiner (CFE) and the director of the Association of Certified Fraud Examiners (ACFE) Taiwan chapter. He holds an MBA from the National Ilan University, Taiwan, and a Ph.D. from the Graduate Institute of Management at the National Taiwan University of Science and Technology, Taiwan. He is currently an Executive MBA student at Quantic School of Business and Technology, USA. His primary teaching and research interests include criminology, fraud schemes, anomaly behavior, and white-collar crime.Seng-Su TsangSeng-Su Tsang is Professor of Business Strategy in the Department of Business Administration at the National Taiwan University of Science and Technology since 2008. His current research interests include innovations and strategies in services involving artificial intelligence applications. He is also engaged with an international research team in developing business strategies with respect to retailing. He received BS in chemical engineering from Tsinghua University, Taiwan, MBA from Sun Yat-sen University, Taiwan, and Ph.D. in public policy and management from Carnegie Mellon University, USA. He is a member of Phi Tau Phi, branch of Sun Yat-sen University in 1989. He published peer-reviewed papers in areas including retailing, technology applications, and services innovation.
A netnographic study with social media comments for investigating service failures

Abstract This study demonstrates a netnographic study to investigate airline service failure types and to test differences of linguistic patterns among complaint types on social media user comments. User comments in an airline's Facebook community were scraped and service failure types were systematically classified with the measurement developed by thematic coding and literature review. The results presented frequently occurring service failure categories in airline service operations. A T ‐Test was conducted to confirm the significant differences in linguistic characteristics between complaint comments and non‐complaint comments. Word frequency analysis showed distinctive word themes between the two comment groups. Research and managerial implications were provided.
An Improved Sparrow Search Algorithm for Global Optimization with Customization-Based Mechanism

To solve the problems of the original sparrow search algorithm’s poor ability to jump out of local extremes and its insufficient ability to achieve global optimization, this paper simulates the different learning forms of students in each ranking segment in the class and proposes a customized learning method (CLSSA) based on multi-role thinking. Firstly, cube chaos mapping is introduced in the initialization stage to increase the inherent randomness and rationality of the distribution. Then, an improved spiral predation mechanism is proposed for acquiring better exploitation. Moreover, a customized learning strategy is designed after the follower phase to balance exploration and exploitation. A boundary processing mechanism based on the full utilization of important location information is used to improve the rationality of boundary processing. The CLSSA is tested on 21 benchmark optimization problems, and its robustness is verified on 12 high-dimensional functions. In addition, comprehensive search capability is further proven on the CEC2017 test functions, and an intuitive ranking is given by Friedman's statistical results. Finally, three benchmark engineering optimization problems are utilized to verify the effectiveness of the CLSSA in solving practical problems. The comparative analysis shows that the CLSSA can significantly improve the quality of the solution and can be considered an excellent SSA variant.
A lightweight deep learning model for real‐time face recognition

Abstract Lightweight deep learning models for face recognition are becoming increasingly crucial for deployment on resource‐constrained devices such as embedded systems or mobile devices. This paper presents a highly efficient and compact deep learning (DL) model that achieves state‐of‐the‐art performance on various face recognition benchmarks. The developed DL model employs one‐ or few‐shot learning to obtain effective feature embeddings and draws inspiration from FaceNet with significant refinements to achieve a memory size of only 3.5 MB—about 30 times smaller than FaceNet—while maintaining high accuracy and real‐time performance. The study demonstrates the model's effectiveness through extensive experiments, which include testing on public datasets and the model's ability to recognize occluded faces in uncontrolled environments using grayscale input images. Compared to the state‐of‐the‐art lightweight models, the proposed model requires fewer FLOPs (0.06G), has a smaller number of parameters (1.2 M), and occupies a smaller model size (3.5 MB) while achieving a competitive level of recognition accuracy and real‐time performance. The results show that the model is well‐suited for deployment in embedded domains, including live entrance security checks, driver authorization, and in‐class attendance systems. The entire code of FN8 is available on GitHub .
Continual adaptation of federated reservoirs in pervasive environments

When performing learning tasks in pervasive environments, the main challenge arises from the need of combining federated and continual settings. The former comes from the massive distribution of devices with privacy-regulated data. The latter is required by the low resources of the participating devices, which may retain data for short periods of time. In this paper, we propose a setup for learning with Echo State Networks (ESNs) in pervasive environments. Our proposal focuses on the use of Intrinsic Plasticity (IP), a gradient-based method for adapting the reservoir's non-linearity. First, we extend the objective function of IP to include the uncertainty arising from the distribution of the data over space and time. Then, we propose Federated Intrinsic Plasticity (FedIP), which is intended for client–server federated topologies with stationary data, and adapts the learning scheme provided by Federated Averaging (FedAvg) to include the learning rule of IP. Finally, we further extend this algorithm for learning to Federated Continual Intrinsic Plasticity (FedCLIP) to equip clients with CL strategies for dealing with continuous data streams. We evaluate our approach on an incremental setup built upon real-world datasets from human monitoring, where we tune the complexity of the scenario in terms of the distribution of the data over space and time. Results show that both our algorithms improve the representation capabilities and the performance of the ESN, while being robust to catastrophic forgetting.
Enhanced subgraph matching for large graphs using candidate region-based decomposition and ordering

The subgraph matching problem associated with large graphs is an emerging research challenge in graph search due to the growing size of the web, social, and metabolic graphs, and the wide availability of graph databases. Such problems involve finding all instances (aka embedding) of the small-sized query graph in the associated large-sized reference graph. Many state-of-the-art algorithms, including VF3, RI, CFL-Match, and Glasgow, exist to solve subgraph matching problem. RI is one of the fastest subgraph matching algorithms focusing mainly on time efficiency performance measures. However, other performance measures, such as the number of found instances of the query graph (embedding count), the method of ordering the query graph’s vertices, and the number of recursive calls, are crucial for the efficiency and effectiveness of the subgraph matching. In this paper, the RI+ algorithm is proposed as an enhanced version of RI, which has been designed using candidate region-based decomposition and ordering. Three novel candidate region orderings have been introduced, namely vertex-count, density, and average-path-length, based on the structural properties of the candidate regions. On empirical analysis of RI+ on real-world data sets, it was observed that RI+ shows significant improvement in efficiency and effectiveness over RI on both performance evaluation measures, namely, embedding count and search time. The influence of the proposed candidate region orderings on the search time of RI+ was also analyzed, revealing that a suitable candidate region ordering has the potential to improve the search time of the proposed algorithm.