Reviewerly/exHarmony huggingface dataset:
Diagnose Like Doctors: Weakly Supervised Fine-Grained Classification of Breast Cancer

Breast cancer is the most common type of cancers in women. Therefore, how to accurately and timely diagnose it becomes very important. Some computer-aided diagnosis models based on pathological images have been proposed for this task. However, there are still some issues that need to be further addressed. For example, most deep learning based models suffer from a lack of interpretability. In addition, some of them cannot fully exploit the information in medical data, e.g., hierarchical label structure and scattered distribution of target objects. To address these issues, we propose a weakly supervised fine-grained medical image classification method for breast cancer diagnosis, i.e., DLD-Net for short. It simulates the diagnostic procedures of pathologists by multiple attention-guided cropping and dropping operations, making it have good clinical interpretability. Moreover, it cannot only exploit the global information of a whole image, but also further mine the critical local information by generating and selecting critical regions from the image. In light of this, those subtle discriminating information hidden in scattered regions can be exploited. In addition, we also design a novel hierarchical cross-entropy loss to utilize the hierarchical label information in medical images, making the classification results more discriminative. Furthermore, DLD-Net is a weakly supervised network, which can be trained end-to-end without any additional region annotations. Extensive experimental results on three benchmark datasets demonstrate that DLD-Net is able to achieve good results and outperforms some state-of-the-art methods.
DPIVE: A Regionalized Location Obfuscation Scheme with Personalized Privacy Levels

The popularity of cyber-physical systems is fueling the rapid growth of location-based services. This poses the risk of location privacy disclosure. Effective privacy preservation is foremost for various mobile applications. Recently, geo-indistinguishability and expected inference error are proposed for limiting location leakages. In this paper, we argue that personalization means regionalization for geo-indistinguishability, and we propose a regionalized location obfuscation mechanism called DPIVE with personalized utility sensitivities. This substantially corrects the differential and distortion privacy problem of PIVE framework proposed by Yu et al. on NDSS 2017. We develop DPIVE with two phases. In Phase I, we determine disjoint sets by partitioning all possible positions such that different locations in the same set share the Protection Location Set (PLS). In Phase II, we construct a probability distribution matrix in which the rows corresponding to the same PLS have their own sensitivity of utility (PLS diameter). Moreover, by designing QK-means algorithm for more search space in 2-D space, we improve DPIVE with refined location partition and present fine-grained personalization, enabling each location to have its own privacy level endowed with a customized privacy budget. Experiments with two public datasets demonstrate that our mechanisms have the superior performance, typically on skewed locations.
Hidden in the light: Scientists’ online presence on institutional websites and professional networking sites

The visibility of individual scientists and their academic performance plays a major role in gaining the creditability to get funded and to advance in academic positions. Therefore, web presences are increasingly used to boost one’s own visibility, disseminate research results and keep up to date with the research of others. However, previous reports show that these channels are not used equally by all scientists. Our study therefore investigates how faculty members ( N = 868) at all universities in Lower Saxony (Germany) in the disciplines of physics, biology and chemistry present themselves on institutional websites and professional networking sites. We find that online presentations on institutional websites are mostly rudimentary. In contrast, there are more informative self-presentations on professional networking sites for both established (professors) and less-established (only PhD holders) faculty members. Our figures confirm observations that scientists present themselves online, but less-established ones seem to find less-supportive environments in academic institutions.
l-shaped geometry-based pattern descriptor serving shape retrieval

Feature representation patterns serving shape retrieval have gained considerable attention over recent years. Accordingly, a geometry-based characterization arrangement based on the l-shape pattern is adopted in this paper for shape description. The presented l-shape pattern descriptor bounds shape edges for providing highly localized distinct features supporting characterization. Then a novel feature representation scheme fabricates these shapes into histograms subsequently, employed for matching and retrieval. The metric Bull’s Eye Retrieval (BER) rate is deployed for retrieval analysis on the Kimia-99, MPEG-7 and Tari-1000 datasets that reveal a uniform and remarkable performance higher than 90% over its predecessors. The congruence nature of the l-shaped geometrical arrangement ensures its robustness towards diverse affine transformations and warrants increased performance. The complexity associated with descriptor realization along space and time reveals its lightweight simplicity and efficiency.
End-to-end feature diversity person search with rank constraint of cross-class matrix

Person search aims to locate and identify specific persons from a series of uncropped images, which has achieved a significant impact on many human-related applications, e.g., person activity understanding and person tracking. Person search includes two sub-tasks: person detection and person re-identification. Person detection focuses on finding the commonality of all identities, while person re-identification focuses on finding the differences among different identities. To mitigate the impact of the different purposes of these two sub-tasks on a person search model, we split the ResNet50 network according to the sub-task, and propose a feature diversity person search (FDPS) framework based on the rank constraint of the cross-class matrix. We first construct a model called the split-baseline (S-bsl), and then introduce the deformable convolution to locate the entire person area. More importantly, a rank perception optimization (RPO) loss is proposed in the FDPS framework to enhance the discrimination and diversity of inter-class features. Experimental results on PRW and CUHK-SYSU datasets demonstrate the effectiveness of the proposed method.
Marine Geology of Kuwait

Abstract Kuwait marine geology can be directly or indirectly influenced by various environmental, anthropogenic, industrial, and geomorphological factors continuously altering its nature and affecting its numerous compartments like fauna/flora, water chemistry, physical oceanography, etc. In this chapter, we attempted to describe salient characteristics about Kuwait onshore, and offshore marine geology from historical and modern perspectives. It highlights unique and dynamic ecological and geological factors that are continuously influencing its nature concerning local and transboundary environmental impacts in addition to the significantly booming oil industry that is persistently transforming Kuwait’s marine geology.
Classification of open source software bug report based on transfer learning

Abstract Currently, the feature richness of text encoding vectors in the bug report classification model based on deep learning is limited by the size of the domain dataset and the quality of the text. However, it is difficult to further enrich the features of text encoding vectors. At the same time, most existing bug report classification methods ignore the submitter's personal information. To solve these problems, we construct nine personal information characteristics of bug report submitters in GitHub by survey. Then, we propose a GitHub bug report classification method named personal information fine‐tuning network (PIFTNet) based on transfer learning and the submitter's personal information. PIFTNet transfers the general text feature vectors in bidirectional encoder representation from transformers (BERT) to the domain of bug report classification by fine‐tuning the pre‐training parameters in BERT. It also combines the text characteristics and the characteristics of the submitter's personal information to construct the classification model. In addition, we propose a two‐stage training method to alleviate the catastrophic changes in the pre‐training parameters and loss of the initially learned knowledge caused by direct training of PIFTNet. We verify the proposed PIFTNet on the dataset extracted from GitHub and empirical results prove the effectiveness of PIFTNet.
Education resource reallocation and innovation: Evidence from university mergers in China

Based on the university mergers policy in China that began in the 1990s, this paper uses a DID regression model to study the impact of higher education resource reallocation on university R&D. It reveals that the university mergers have significantly increased the output of R&D, and the conclusion is consistent in a series of robustness tests. The mechanism analysis results show that the university mergers can affect the R&D by increasing the scientific research funding input in universities, improving the discipline development, and increasing the ability of basic R&D. Heterogeneity test results show that the complementary mergers have a greater impact on R&D in universities. These findings reveal an important mechanism that is conducive to deepening the reform of higher education and catching up with the first-class universities and disciplines.
The Effect of Aging on Delamination Strength Utilizing an Embedded Digital Image Correlation Scheme

We utilize an in situ/embedded digital image correlation (DIC) technique to evaluate the effect of natural aging on interface delamination strength on an idealized model system. The system consists of a single ~500–650 μm glass bead inclusion at the center of a Sylgard 184 dog-bone tensile sample along with an embedded DIC speckle pattern at the midplane of the sample. The speckle pattern enables the measurement of the evolving strain field in the region surrounding the embedded glass bead, while the sample undergoes a tensile load to failure. During the tensile loading of the sample, the applied stresses at the particle-binder interface force a debonding event to occur before the ultimate tensile failure of the sample. The measured strain field at the moment of the forced debonding event is then utilized to characterize the localized stress required to induce a failure at the interface. Through repetition of the experiment, the debonding behavior can be described in a statistically meaningful fashion. This approach is then extended to capture the effect of natural aging on the delamination behavior through a time period of approximately 24 days.
Factors Affecting the Adoption of E-Learning During the COVID-19 Pandemic

This study investigates the factors influencing of adoption of e-learning at Dhofar University in Oman. This study is carried out after five semesters since the beginning of the COVID-19 pandemic. Structural equation modelling (SEM) has been used in the present investigation to assess the hypotheses and model. The findings showed infrastructure factor (INF) and economic and technological factors (ETF) have an effect on e-learning system (ELS) during the COVID-19 pandemic. In contrast, this study revealed the lack of effect of the human factors represented by skills and knowledge (SKF) and compatibility factor (COF) on the e-learning system in light of this unprecedented pandemic.
Blockchain and Big Data for a Smart Healthcare Model

Blockchain technology offers great properties such as scalability, decentralization, immutability, and security. These properties attracted many new fields such as healthcare, education, finance, and much more. This chapter proposes combining blockchain with big data technologies in order to provide a smart and efficient healthcare model. First, the authors propose to use the blockchain to save medical records (transactions) for patients on a large-scale system while preserving the privacy for patient. Blockchain can be used to keep track about each medical record for a patient. Since blockchains can save small amounts of data and since medical records can become very lengthy and complex to analyze, the authors propose to use big data technology in combination with blockchain. The big data technology can store large files and can handle heterogeneous files. Big data technology can apply sophisticated analysis on the data collected from blockchain.
Persian Optical Character Recognition Using Deep Bidirectional Long Short-Term Memory

Optical Character Recognition (OCR) is a system of converting images, including text,into editable text and is applied to various languages such as English, Arabic, and Persian. While these languages have similarities, their fundamental differences can create unique challenges. In Persian, continuity between Characters, the existence of semicircles, dots, oblique, and left-to-right characters such as English words in the context are some of the most important challenges in designing Persian OCR systems. Our proposed framework, Bina, is designed in a special way to address the issue of continuity by utilizing Convolution Neural Network (CNN) and deep bidirectional Long-Short Term Memory (BLSTM), a type of LSTM networks that has access to both past and future context. A huge and diverse dataset, including about 2M samples of both Persian and English contexts,consisting of various fonts and sizes, is also generated to train and test the performance of the proposed model. Various configurations are tested to find the optimal structure of CNN and BLSTM. The results show that Bina successfully outperformed state of the art baseline algorithm by achieving about 96% accuracy in the Persian and 88% accuracy in the Persian and English contexts.
Considering digital art museums in the era of mass extinction: exploring digital zoos and aquariums through posthuman thinking

ABSTRACTABSTRACTThis paper discusses the possibility of digital art museums, which have recently been gaining popularity, examining how they function as new educational facilities for enlightenment in the age of mass extinction. It then considers two digital amusement facilities of teamLab. These digital installations change the traditional asymmetrical relationship humans have with animals and plants, for example, by forming a pseudo-parent–child dynamic, thus creating an interactive and cross-species connection based on equality. Moreover, compared to conventional zoos and botanical gardens, which exhibit animals and plants in pursuit of realism, digital museums create virtual natural environments based on a nonrealistic orientation and can become models for sustainable museums. Moreover, they can be places for the inheritance of lost memories by reconstructing extinct animals and plants and transmitting their digital data to the next generation. One problem with these digital amusement facilities; however, is the aesthetic and romantic modeling of digital creatures, which reflects anthropocentric consciousness as well as the maintenance of a panoramic and divine perspective. It is difficult to create a digital museum that deconstructs this much-desired divine perspective. Concerning these problems, a new model for an ecologically sustainable digital museum as an educational facility is discussed.KEYWORDS: Digital art museumposthumanteamLabenlightenmenteducation AcknowledgementsThis research was supported by a KAKEN grant (19K00487) from the Japan Society for the Promotion of Science, as well as the research fund of the College of Commerce, Nihon University. The two reviewers' insightful advice and suggestions were of great help in revising this paper, and I would like to express my gratitude to both of them.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by JSPS: [Grant Number (19K00487)].Notes on contributorsAsuka YamazakiAsuka Yamazaki, Ph.D. is associate professor of European Theater, Art, Literature, and German Language Teaching at the College of Commerce, Nihon University, in Japan. Among her publications are The Development of an Actor's Cosmopolitan and Enlightened Identity: Their Intermediate and Educational Function of Building a Peaceful World and a Prosperous Urban Culture (Königshausen & Neumann, 2022), Das deutsche Nationalbewusstsein des 19. Jahrhunderts und Richard Wagners Tristan und Isolde (Königshausen & Neumann, 2013), as well as "On Rhetoric Education in Leibniz's Theory of Prince Education" (Leibniz Studies 6, 2020) etc.Correction StatementThis article has been corrected with minor changes. These changes do not impact the academic content of the article.
Style-transfer counterfactual explanations: An application to mortality prevention of ICU patients

In recent years, machine learning methods have been rapidly adopted in the medical domain. However, current state-of-the-art medical mining methods usually produce opaque, black-box models. To address the lack of model transparency, substantial attention has been given to developing interpretable machine learning models. In the medical domain, counterfactuals can provide example-based explanations for predictions, and show practitioners the modifications required to change a prediction from an undesired to a desired state. In this paper, we propose a counterfactual solution MedSeqCF for preventing the mortality of three cohorts of ICU patients, by representing their electronic health records as medical event sequences, and generating counterfactuals by adopting and employing a text style-transfer technique. We propose three model augmentations for MedSeqCF to integrate additional medical knowledge for generating more trustworthy counterfactuals. Experimental results on the MIMIC-III dataset strongly suggest that augmented style-transfer methods can be effectively adapted for the problem of counterfactual explanations in healthcare applications and can further improve the model performance in terms of validity, BLEU-4, local outlier factor, and edit distance. In addition, our qualitative analysis of the results by consultation with medical experts suggests that our style-transfer solutions can generate clinically relevant and actionable counterfactual explanations.
Evaluation of teachers' teaching quality based on analytic hierarchy and grey correlation projection

In view of the limitations of the current teaching quality evaluation methods of teachers in various colleges and universities, this paper at first takes five teachers in our institute as an example to establish a scientific evaluation index system according to the standards of teacher teaching quality evaluation. After the weight of each indicator is determined through the hierarchical analysis method, it overcomes the subjectivity of directly giving weights to a certain extent. Then by using the grey correlation projection method to evaluate this teaching quality indicators of the five teachers were comprehensively evaluated and sorted by the evaluation results.
Traffic sign detection based on improved YOLOv5

Aiming at the problems of locating and identifying impediment for minor targets as well as unpromising detection accuracy, a traffic sign detection algorithm based on improved YOLOv5 is propounded in this paper. First, the Addition of the Coordinate Attention Mechanism module behind the backbone network enables the model to effectively capture the relationship between location information and channels, thereby more accurately locates the area of interest. Then a detection layer is introduced into the network structure, and the feature map is continued to be upsampled after the 17th layer of the original algorithm network structure, so that the feature map continues to expand, and a feature map of 160 *160 size is obtained at the 19th layer and the feature map of the second layer of the backbone network is spliced and integrated. This results in a larger feature map for the detection of small targets. Finally, α -CIoU loss is used to replace the original CIoU positioning loss function in the model training stage to obtain higher quality anchor frame. Experiments are performed on the TT100K dataset, compared to the original YOLOv5 algorithm, the proposed algorithm improves accuracy by 3.9%, recall rate by 5.5% and mAP by 4.6% under the premise of less speed reduction, which still meets the requirements of real-time detection.
sympy2c: From symbolic expressions to fast C/C++ functions and ODE solvers in Python

Computer algebra systems play an important role in science as they facilitate the development of new theoretical models. The resulting symbolic equations are often implemented in a compiled programming language in order to provide fast and portable codes for practical applications. We describe sympy2c, a new Python package designed to bridge the gap between the symbolic development and the numerical implementation of a theoretical model. sympy2c translates symbolic equations implemented in the SymPy Python package to C/C++ code that is optimized using symbolic transformations. The resulting functions can be conveniently used as an extension module in Python. sympy2c is used within the PyCosmo Python package to solve the Einstein–Boltzmann equations, a large system of ODEs describing the evolution of linear perturbations in the Universe. After reviewing the functionalities and usage of sympy2c, we describe its implementation and optimization strategies. This includes, in particular, a novel approach to generate optimized ODE solvers making use of the sparsity of the symbolic Jacobian matrix. We demonstrate its performance using the Einstein–Boltzmann equations as a test case. sympy2c is general and potentially useful for various areas of computational physics. sympy2c is publicly available at https://cosmology.ethz.ch/research/software-lab/sympy2c.html.