Reviewerly/exHarmony huggingface dataset:
A Researcher by Vocation, a Science Manager by Duty: to the 50th Anniversary of A.Yu. Samarin

This article charts the career of Alexander Yuryevich Samarin, Deputy Director General for Scientific and Publishing Activities at the Russian State Library (RSL), Doctor of Historical Sciences, and the Honoured Worker of Culture of the Russian Federation. It details his professional journey, starting from his studies at the V.I. Lenin Moscow State Pedagogical University (1990—1995), and concludes with his current position. A.Yu. Samarin has authored more than 560 scientific papers and has received over ten state and public awards. As a scientist, A.Yu. Samarin is recognized for his meticulous study of sources related to his research topics, and for his deep interest in the individuals who have made significant contributions to history. His most prominent administrative achievements include streamlining the operations of the RSL Research Department of Rare Books (Book Museum); his advocacy for establishing a legal framework for book monuments; his efforts to restore library science, bibliography, and book science as part of the Nomenclature of scientific specialties; holding the International Scientific Conference “Library Science in the 21st Century: Content, Organisation, Digitalisation, and Scientometrics” (2021). Finally, the article highlights the release of two editions of a joint monograph titled “Scientific Research in Libraries: Topics, Organisation, and Presentation of Results” in 2021 and 2023.
Chromatic DIC-Assisted Fringe Projection Profilometry for Shape, Deformation, and Strain Measurement With Intensity-Chroma Space Analysis

The recently emerging digital image correlation (DIC)-assisted fringe projection profilometry (FPP) has been demonstrated to be superior in shape and deformation measurement on complex structure. But the contradictory demands on reflectivity of tested surface between FPP and DIC extremely limit its measuring accuracy. In this work, chromatic DIC-assisted FPP is proposed to achieve accurate 3D shape, deformation measurement and strain analysis. Firstly, colorful fluorescent pigment is introduced to fabricate speckle patterns and the optimal fluorescent color is solved to simultaneously ensure high fringes intensity modulation and speckles’ chroma difference. And then, intensity-chroma space analysis method is presented to modulate and demodulate the projected fringe and fabricated speckle information in intensity and chroma space without interference. High-quality fringe and speckle patterns can be simultaneously obtained for accurate shape, deformation and strain measurement. To the best of our knowledge, it for the first time essentially overcomes inherent contradictions between FPP and DIC on requirement of surface reflectivity. Finally, chain rule is introduced to further complete strain analysis with less computational cost. Experimental results demonstrated that the proposed method can simultaneously guarantee shape and deformation measurement accuracy. Meanwhile, both complete and fine surface geometry and its strain distributions can be obtained with simple convolution and algebraic operations, which can provide an alternative method for bridging 3D geometric structures and mechanical state of complex specimens.
Forensic Assignment Stylometry

This chapter discusses the stylometry of portfolios of assignments submitted by individual students from the perspective of evidence gathering in cases of suspected contract cheating. It opens with a discussion of the properties of student-submitted assignments that distinguish those from “normal” texts as written by established authors and how those affect stylometry. Stylometry has evolved with a main focus on established writers writing in their individual styles, whereas students are learners developing their styles across a range of different types of assignments over their periods of study, often spanning several years. Any forensic stylometry of student assignments thus has to factor in such variabilities while discriminating documents written by students from those written by commissioned third parties. The second part of the chapter describes a novel stylometric approach based on information theory, still in development, that seeks to work around variabilities in student assignments. Initial evaluative case studies are presented.
Autocorrelation-Aware Aggregation Network for Salient Object Detection of Strip Steel Surface Defects

In recent years, salient object detection has made great progress in natural scene images (NSIs), but salient object detection of strip steel defect images (SDIs) in industrial scenes is still an open and challenging problem. Existing detection methods are difficult to segment different types of defects with clutter and shallow contrast. Therefore, we propose a novel Autocorrelation Aware Aggregation Network (A3Net) for salient object detection of strip steel surface defects. Firstly, we use a general residual attention mechanism to enhance the encoder features and accelerate the convergence of the model. In the decoder stage, we propose a global autocorrelation module (GAM) to explore semantic information cues of high-level features to locate and guide low-level information. Then, we deploy the scale interaction module (SIM) to realize the fusion and interaction of feature information between different layers. Finally, we design a local autocorrelation module (LAM) to further refine the edge details of salient objects. We conduct detailed and rich experiments on the public strip steel surface defects dataset, which proves that our method is consistently superior to the state-of-the-art methods. In addition, we build a new challenging strip SDI dataset with multiple defect types for SOD task, which contains 4800 images with pixel-level annotations. Our dataset and code are available at: https://github.com/VDT-2048/A3Net.
Predicting female breast cancer by artificial intelligence: Combining clinical information and BI-RADS ultrasound descriptors

This study aimed to use Artificial Intelligence (AI) Deep Learning (DL) techniques to predict female breast cancer detected by ultrasound based on clinical data and Breast Imaging Reporting and Data System (BI-RADS) Ultrasound (US) descriptors. We retrospectively gathered data on clinical information and BI-RADS US descriptors of breast lesions from 1051 female patients, forming a comprehensive dataset. Two datasets (A and B) were derived by selecting different variables. A BI-RADS DL-based Network (BD-Net) was developed and trained on Dataset A and B, and its performance was evaluated on an external test set. Radiologists also classified Dataset B and the external test set using BI-RADS US. Performance in predicting the probability of malignancy was evaluated by calculating the Area Under Curve (AUC), accuracy, sensitivity, and specificity. BD-Net achieved an accuracy of 92.5% (95%CI, 90.5–94.2) in predicting breast cancer with a sensitivity of 93.0% (95%CI, 90.3–95.4), a specificity of 92.1% (95%CI, 89.7–94.6), and an AUC of 0.97 (95%CI, 0.96–0.98) on the training data set of dataset A. On the external dataset, the BD-Net showed a sensitivity of 93.8% (95%CI, 87.5–98.8), a specificity of 91.0% (95%CI, 85.0–96.0), and an AUC of 0.92 (95%CI, 0.88–0.97) for predicting breast cancer. The radiologists predicted breast cancer on Dataset B and the external test set with AUC values between 0.75 (95%CI, 0.75–0.79) and 0.82 (95%CI, 0.77–0.87). These results indicate that the BD-Net is effective for predicting ultrasound-detected female breast cancer.
Breast cancer detection using Histopathology Image with Mini-Batch Stochastic Gradient Descent and Convolutional Neural Network

Cancer is a devastating disease that has far-reaching effects on our culture and economy, in addition to the human lives it takes. Regarding budgetary responsibility, investing just in cancer treatment is not an option. Early diagnosis is a crucial part of the remedy that sometimes gets overlooked. Malignancy is often diagnosed and evaluated using Histopathology Images (HI), which are widely accepted as the gold standard in the field. Yet, even for experienced pathologists, analysing such images is challenging, which raises concerns of inter- and intra-observer variability. The analysis also requires a substantial investment of time and energy. One way that such an examination may be sped up is by making use of computer-assisted diagnostics devices. The purpose of this research is to create a comprehensive cancer detection system using images of breast and prostate histopathology stained with haematoxylin and eosin (H&amp;E). Proposed here is work on improving colour normalisation methods, constructing an integrated model for nuclei segmentation and multiple objects overlap resolution, introducing and evaluating multi-level features for extracting relevant histopathological image and interpretable information, and developing classification algorithms for tasks such as cancer diagnosis, tumor identification, and tumor class labelling. Mini-Batch Stochastic Gradient Descent and Convolutional Neural Network which obtained statistical kappa value for breast cancer histopathology images shows a high degree of consistency in the classification task, with a range of 0.610.80 for benign and low grades and a range of 0.811.0 for medium and high rates. The Support Vector Machine (SVM), on the other hand, shows an almost perfect degree of consistency (0.811.0) across the several breast cancer picture classifications (benign, low, medium, and high).
Cross-platform verification in quantum networks

Intermediate-scale quantum devices are becoming more reliable and may soon be harnessed to solve useful computational tasks. At the same time, common classical methods used to verify their computational output become intractable due to a prohibitive scaling of required resources with system size. Inspired by recent experimental progress, here we describe and analyze efficient cross-platform verification protocols for quantum states and show how these can be used to verify computations. We focus on the pairwise comparison between distant nodes of a quantum network, identify the most promising protocols, and discuss possible implementations in laboratory settings. As a proof of principle, we implement basic versions of these schemes on available quantum processors.
Fraud Detection of Credit Cards Using Supervised Machine Learning Techniques

Credit card fraud encompasses illicit activities aimed at unlawfully obtaining confidential information to enable unauthorized individuals to engage in illegal transactions. As technology advances, fraudsters have honed their skills in evading security measures, presenting a formidable challenge in fraud detection. To address this issue, an array of algorithms and analytical techniques has emerged to identify and mitigate instances of fraud. This research aimed to identify the most appropriate supervised machine learning algorithm for credit card fraud detection. Logistic Regression, Random Forest, Support Vector Machine, and Decision Trees were implemented and compared. Due to the imbalanced nature of the dataset, the SMOTE (Synthetic Minority Oversampling Technique) technique was employed to rectify the data imbalance by oversampling the minority class. The performance of the trained models was evaluated using various metrics, including the confusion matrix, accuracy, precision, recall, f1-score, Matthews Correlation Coefficient (MCC), and Area Under the Curve (AUC). The results of the analysis revealed that Random Forests exhibited exceptional performance, achieving an impressive recall score of 84% and surpassing other algorithms. This research provides the groundwork for future investigations involving diverse deep-learning techniques applied to real-time and dynamic datasets, enabling continuous enhancements in fraud detection and prevention mechanisms.
FedRich: Towards efficient federated learning for heterogeneous clients using heuristic scheduling

Federated Learning (FL) always involves a large number of heterogeneous clients (both in statistic and resource heterogeneity) when collaboratively training a model, leading to a compromise in the model performance. Recent research has focused on customizing FL frameworks to address the issues. However, compared to models trained on independent and identically distributed (IID) data, these methods still face performance degradation in non-IID scenarios. Moreover, resource consumption is also somewhat expensive. In this work, we present an efficient FL framework named FedRich to tackle the statistic and resource heterogeneity. The key idea of FedRich is adaptive segmentation of the model and heuristic scheduling of the active clients. Adaptive segmentation enables resource-dependent customization of the model, which is conducive to clients with varying resource budgets to conduct local training. The heuristic scheduling strategy appropriately selects clients to participate in federated training, mitigating statistical heterogeneity. Moreover, FedRich incorporates a hierarchical aggregation mechanism to stably aggregate heterogeneous models of different sophistication. Extensive experimental results on three benchmark datasets demonstrate that FedRich outperforms state-of-the-art heterogeneous FL approaches.
RELEVANT CONSEQUENCE RELATIONS: AN INVITATION

Abstract We generalize the notion of consequence relation standard in abstract treatments of logic to accommodate intuitions of relevance . The guiding idea follows the use criterion , according to which in order for some premises to have some conclusion(s) as consequence(s), the premises must each be used in some way to obtain the conclusion(s). This relevance intuition turns out to require not just a failure of monotonicity, but also a move to considering consequence relations as obtaining between multisets . We motivate and state basic definitions of relevant consequence relations, both in single conclusion (asymmetric) and multiple conclusion (symmetric) settings, as well as derivations and theories, guided by the use intuitions, and prove a number of results indicating that the definitions capture the desired results (at least in many cases).
Sharing the knowledge: exploring cultural heritage through an ontology-based platform

Abstract The exponential growth of data has made ontology modeling an up-and-coming research area for knowledge representation. One of the domains of interest is represented by Cultural Heritage, in which modeling of environments, such as archaeological parks, could lead to their protection and enhancement. In this scenario, the valuable dataset on the Naples Urban Archaeological Park (PAUN) is of particular interest. Due to its peculiarity, the database could benefit from innovative techniques for retrieving the information, representing a large part of the information contained within the DatabencArt platform, which collects data on Campania's cultural heritage. This paper aims to introduce an ontology-based approach to improve data retrieval, which could help expert users in the field (archaeologists, art historians, geologists, etc.). Scholars need to be able to easily compare the information. To this end, a semantic search is able to transform a vast amount of data into linked concepts. We mapped all concepts according to the ArCo ontology and equipped the DatabencArt platform with a semantic query layer, making the information accessible through ArCo ontology's properties. To evaluate the proposed approach, we designed an experimental campaign involving expert users to assess the results obtained in the system's queries through Sparql queries. The obtained results are promising.
Transformer-Based Approaches for Multilingual Visual Question Answering

Visual Question Answering (VQA) is a difficult task that has steadily gained popularity and made significant progress. VQA is also one of the potential tasks with a combination of Computer Vision and Computational Linguistics. Through the prism of the English language, VQA has mostly been researched. But using the same strategy to VQA in other languages would demand a significant investment in resources. Therefore, finding solutions to the multilingual VQA (mVQA) task is crucial. This topic is very challenging because it requires the model not only to comprehend the visual context of the image but also to understand the language and provide the correct answer to the input question in that language. Utilizing the outstanding development and remarkable performance of the Vision-Language Pre-training models, this paper presents our approach to the Multilingual Visual Question Answering shared tasks (EVJVQA) ( https://vlsp.org.vn/vlsp2022/eval/evjvqa ) of the Vietnamese Language and Speech Processing (VLSP) Challenge 2022. We propose a transformer-based framework with a specialized custom encoder consisting of two modules for question–answer embedding and visual embedding utilizing the Vision-Language Pre-training models. Without using any data augmentation techniques, our strategy performed better than the organizer’s baseline model and produced results that were competitive with those of other approaches on the public and private tests of the UIT-EVJVQA benchmark. Our highest result on the private test set is 34.67% and 41.15% in the two metrics BLEU and F1-score, respectively.
Daily tourism forecasting through a novel method based on principal component analysis, grey wolf optimizer, and extreme learning machine

Abstract Accurate forecasting tourism demand is crucial for improving the economic benefits of tourist attractions, but it is a challenging task. In this paper, we propose an effective daily tourism forecast model, principal component analysis‐grey wolf optimizer‐extreme learning machine (PCA‐GWO‐ELM), based on Baidu index data, holiday data, and weather data. Our model uses PCA to reduce the dimensionality of the data and employs the GWO to optimize the number of neural networks in the hidden layer of the ELM model, improving its forecast performance. We conduct an empirical study using the collected tourist data of Mount Siguniang. The results show that the proposed hybrid forecasting model outperforms other models in daily tourism demand forecasting, making it a potential candidate method for practitioners and researchers studying tourism demand forecasting.
The development and validation of a C-test and a pseudo-character test for online CFL assessment

Abstract Taking inspiration from a popular online English test, this study first documents the development of an achievement test containing a newly developed C-test and pseudo-character test for beginner-level Chinese learners. Then, based on the test results collected from 53 university learners and using statistical tools, analyses were conducted on its content validity, construct validity, criterion-related validity, difficulty, discrimination power, and reliability. Results indicate that this new achievement test has relatively satisfactory reliability and validity, with only minor issues that need to be addressed in future studies. Situated in the context of language assessment, the study sheds light on the application of the C-test and a pseudo-character section in both face-to-face and at-distance Chinese as a foreign language (CFL) classrooms. Furthermore, the study also provides practical and applicable methods for CFL teachers to develop their own assessments.
Differential evolution for population diversity mechanism based on covariance matrix

Differential evolution (DE) is a heuristic global search algorithm based on population. It has exhibited great adaptability in solving continuous-domain problems, but sometimes suffered from insufficient local search ability and being trapped in local optimum when dealing with complicated optimization problems. To solve these problems, an improved differential evolution algorithm with population diversity mechanism based on covariance matrix (CM-DE) is proposed. First, a new parameter adaptation strategy is used to adapt the control parameters, in which the scale factor F is updated according to the improved wavelet basis function in the early stage and Cauchy distribution in the later stage and the crossover rate CR is generated according to normal distribution. The diversity of population and convergence speed are improved by employing the method above. Second, the perturbation strategy is incorporated into crossover operator to enhance the search ability of DE. Finally, the covariance matrix of the population is constructed, where the variance in the covariance matrix is used as indicator to measure the similarity between individuals in the population in order to prevent the algorithm from falling into local optimum resulted by low population diversity. The CM-DE is compared with the state-of-art DE variants including LSHADE (Tanabe and Fukunaga, 2014), jSO [1], LPalmDE [2], PaDE [3] and LSHADE-cnEpSin [4] under 88 test functions from CEC2013 [5], CEC2014 [6] and CEC2017 (Wu et al., 2017) test suites. From the experiment results, it is obvious that among 30 benchmark functions from CEC2017 on 50D optimization, the CM-DE algorithm has 22, 20, 24, 23, 28 better performances comparing with LSHADE, jSO, LPalmDE, PaDE, and LSHADE-cnEpsin. For CEC2017 on 30D optimization, the proposed algorithm secures better performance on 19 out of 30 benchmark functions in terms of convergence speed. In addition, a real-world application is also used to verify the feasibility of the proposed algorithm. The experiment results validate the highly competitive performance in terms of solution accuracy and convergence speed.
High-speed 360° 3D shape measurement based on virtual stereo phase unwrapping method

360° three-dimensional (3D) shape measurement is of great significance in the fields of defect detection and reverse modeling. Fringe projection profilometry (FPP) is widely used due to the advantages of high-accuracy and full-field measurement, but the movement of object corrupts the phase measurement. Stereo phase unwrapping (SPU) technique is adopted to improve imaging efficiency, but requires multi-cameras which resulting in high system complexity and cost. In this work, we proposed virtual stereo phase unwrapping (VSPU) method for high-speed 360° 3D shape measurement. This method uses the virtual camera-projector systems formed by the rotation of object for phase unwrapping which only requires single camera-projector to achieve 360° 3D shape measurement. The phase consistency checking in SPU method is modified. Experiments demonstrate that proposed method has strong robustness and can achieve 360° 3D shape measurement in 47 s with an accuracy of 100 μm during continuous rotation of the object.
Haptic technology in society: A sentiment analysis of public engagement

With the rapid rise of metaverse spaces, the integration of haptic technology has become an integral part of users' immersion and engagement in different domains. This study shades the light on public engagement with haptic technology applications in various contexts. The YouTube Application Programming Interface was used to extract data about haptic technology within a specific time period. Public engagement was estimated based on users' cognitive and behavioral engagement with haptic-video materials. A topic modeling approach was used to extract the main topics associated with haptic technology. The main types of users' emotions and their word association were extracted using the association rules mining technique. The results showed that public engagement with haptic technology was mostly observed in few categories (e.g., gaming). Joy and positive sentiments were strongly associated with the extracted topics. The findings offer new insights into the tendency of utilizing haptic technology across communities. The outcomes can help industries and decision makers understand ways to improve the integration of haptic technology in according to the needs of specific domains.