Reviewerly/exHarmony huggingface dataset:
Machine Learning: A Literature Review for Breast Cancer

Breast cancer is, after lung cancer, the most prevalent form of the disease in the globe. Women are the demographic most likely to be affected by this condition. Breast cancer is the most common kind of cancer to result in a woman's death if she is of childbearing age. Because there is always more to learn and there is room for improvement in every line of work, medical imaging is not an exception to this rule. It is expected that the death rate associated with cancer would decrease if it is discovered early and effectively treated. The diagnosis accuracy of persons working in the health care profession may be improved via the use of machine learning techniques. The technique known as deep learning has the potential to differentiate between breasts that are healthy and those that have cancer also known as neural networking. This method might be used to differentiate between healthy breast tissue and breast tissue affected by illness. Long-term research on the topic aimed, among other things, to examine breast cancer and screening practises among Indian women. This was one of the primary goals of the inquiry. A literature study was carried out with the assistance of several databases along with additional sources. Participants in the study were instructed to use phrases linked to breast cancer such as "breast carcinoma" and "breast cancer awareness," in addition to terms such as "knowledge" and "attitude," as well as the gender neutral term "women." In addition, India had a role in the study that was done. This search does not look for articles that have been published in the English language in the last 12 years.
Analysis of Spatial Characteristics and Equilibrium Degree of Pre-school Education Resource Allocation Based on GIS Technology

The traditional allocation of educational resources is usually based on administrative division, and the primary school, junior high school, high school and university, which is deficient in preschool education. Therefore, using GIS technology, through the establishment of preschool education resource configuration characteristics of space and the degree of equilibrium analysis system, understanding of the study area (hulun buir city hailar district preschool education, the spatial distribution of spatial analysis, it is concluded that the accessibility of the allocation of resources, for the preschool education resources allocation level of ascension has a certain reference value. This system mainly uses Visual Studio 2012 as the main framework of system development to design the spatial analysis system of preschool education resource allocation. It uses C# programming language and combines Arc GIS software, ArcEngine10.2 component technology and related dynamic DLL to design and develop. The experimental results show that: The spatial distribution of preschool education resources in the study area is compact and the balance degree needs to be improved.
Improving Self-supervised Dimensionality Reduction: Exploring Hyperparameters and Pseudo-Labeling Strategies

Dimensionality reduction (DR) is an essential tool for the visualization of high-dimensional data. The recently proposed Self-Supervised Network Projection (SSNP) method addresses DR with a number of attractive features, such as high computational scalability, genericity, stability and out-of-sample support, computation of an inverse mapping, and the ability of data clustering. Yet, SSNP has an involved computational pipeline using self-supervision based on labels produced by clustering methods and two separate deep learning networks with multiple hyperparameters. In this paper we explore the SSNP method in detail by studying its hyperparameter space and pseudo-labeling strategies. We show how these affect SSNP’s quality and how to set them to optimal values based on extensive evaluations involving multiple datasets, DR methods, and clustering algorithms.
PTZ Active Vision Method Based On BEBLID

Abstract The traditional pan-tilt-zoom (PTZ) camera relies on manual operation in control, and it requires a large number of samples for training in the algorithm to achieve higher target recognition accuracy. Therefore, we introduce the Boosted Efficient Binary Local Image Descriptor (BEBLID) propose a pedestrian detection and tracking method applied to the PTZ. By extracting image feature points and feature descriptions, we use Support Vector Machines (SVM) to train fewer samples to complete pedestrian detection. Then we input it into kernel correlation filters (KCF) for subsequent tracking, and use its high frame rate to reduce the feedback steps of the PTZ position loop, which reduces the system running time, and then we control the target image to be always locked in the center of the image. Finally, the experiment is carried out, and the whole system can run normally. Under 700 test samples, the detection effect of this algorithm is the highest, reaching 93.00 %, which is 14.57 % and 3.57 % higher than that of Scale-invariant feature transform (SIFT) and Oriented Brief (ORB).
The impact of the macroeconomic factors over the profitability of the banking sector in the Republic of North Macedonia

Monitoring and assessment of the profitability of banks and the banking sector as a whole, presents a serious challenge, especially in the environment in which they operate today.The needs and demands of the clients are constantly increasing and changing, as well as the regulatory requirements, which influence the banking operations process, their efficiency and effectiveness.The purpose of this research is to analyze the performance of the banking sector in the Republic of North Macedonia, considering the trends of 15 Banks in banking industry between 2009 and 2019.This research considers an empirical model by applying various methods, in order to perceive the results of the sector and to identify trends in the key indicators and the macroeconomic factors affecting the performance.For the empirical model it is used secondary data on the performances for the banking sector published by the NBRM, as well as data from the banks' financial statements.The results show a strong connection of the profitability, inflation and average net salary growth, and weak connection between the profitability and GDP growth, where the reference rate of the CB Bills as a factor is statistically insignificant.
A Self-supervised 3D/2D Registration Method for Incomplete DSA Vessels

For vascular interventional surgery, the preoperative 3D computed tomography (CT) has complete information of vessels but is not convenient for obvervation, while the intraoperative 2D digital subtraction angiography (DSA) is easy for doctors to monitor the vascular conditions real-timely but information is incomplete in each frame. As a result, 2D/3D registration, which is the technology to fuse information from images of different modal, is useful for the guidance of vascular interventional surgery. In this paper, we proposed a self-supervised 2D/3D vascular registration method to improve the performance on DSAs with incomplete vessels. The proposed method contains a rigid and an elastic registration stage, for regressing the 6-dim parameters to obtain a center image and fine-tuning respectively. In addition, a patch-based content loss is introduced to the rigid registration step to give an appropriate similarity measure for images with incomplete vessels, and a masked elastic module is introduced to simulate the incompletion and deformation caused by breath or heart beats on the real vessels in elastic registration. We evaluated our method on both simulated and real images. Experiments prove that our proposed method is effective to register CT and DSA images.
An Empirical Study on the Professional Development of Middle School English Teachers in Ethnic Minority Areas of Yunnan

Based on the reality of teacher professional development in ethnic minority areas in Yunnan Province, this paper used the empirical quantitative questionnaire survey, and 439 middle school English teachers from 8 minority prefectures in Yunnan Province filled out the online questionnaire. The data collected were treated with SPSS 22.0 and the following conclusions are drawn: (1) The general situation of professional development of the subjects was generally good; (2) The professional development of English teachers was significantly, positively but weakly correlated with their English proficiency and teaching ability; (3) English teachers’ professional development showed significant differences in age and weekly class hours.
The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detection

Graph-based fraud detection has heretofore received considerable attention. Owning to the great success of Graph Neural Networks (GNNs), many approaches adopting GNNs for fraud detection has been gaining momentum. However, most existing methods are based on the strong inductive bias of homophily, which indicates that the context neighbors tend to have same labels or similar features. In real scenarios, fraudsters often engage in camouflage behaviors in order to avoid detection system. Therefore, the homophilic assumption no longer holds, which is known as the inconsistency problem. In this paper, we argue that the performance degradation is mainly attributed to the inconsistency between topology and attribute. To address this problem, we propose to disentangle the fraud network into two views, each corresponding to topology and attribute respectively. Then we propose a simple and effective method that uses the attention mechanism to adaptively fuse two views which captures data-specific preference. In addition, we further improve it by introducing mutual information constraints for topology and attribute. To this end, we propose a Disentangled Information Graph Neural Network (DIGNN) model, which utilizes variational bounds to find an approximate solution to our proposed optimization objective function. Extensive experiments demonstrate that our model can significantly outperform state-of-the-art baselines on real-world fraud detection datasets.
Density peaks clustering by granular computing with label propagation

Density peaks clustering (DPC) algorithm identifies the density peaks as cluster centers and forms clusters by assigning each object to its nearest higher local density neighbor. However, DPC suffers from two main issues: its local density estimation is unreliable in complex real datasets, and its cluster assignment strategy has a risk of ’chain error’. To overcome these two issues, this paper proposes a novel density peaks clustering by granular computing with label propagation. At first, each object is coarsened into the fuzzy semantic cell. The local density estimation in DPC can be interpreted from learning fuzzy semantic cells and converted into an optimization problem. We provide an alternative reliable local density estimation method. Subsequently, we allocate an initial label matrix by learned fuzzy semantic cells and then utilize the label propagation algorithm to let the label of each prototype be updated from its k nearest neighbors. The adopted label propagation method can significantly improve the cluster assignment strategy compared to DPC. To validate the proposed method, we conduct comprehensive experiments on various datasets. The results prove that our method outperforms the other state-of-art DPC-related methods.
A KG-Based Integrated UAV Approach for Engineering Semantic Trajectories in the Cultural Heritage Documentation Domain

Data recordings of the movement of vehicles can be enriched with heterogeneous and multimodal data beyond latitude, longitude, and timestamp and enhanced with complementary segmentations, constituting a semantic trajectory. Semantic Web (SW) technologies have been extensively used for the semantic integration of heterogeneous and multimodal movement-related data, and for the effective modeling of semantic trajectories, in several domains. In this paper, we present an integrated solution for the engineering of cultural heritage semantic trajectories generated from unmanned aerial vehicles (UAVs) and represented as knowledge graphs (KGs). Particularly, this work is motivated by, and evaluated based on, the application domain of UAV missions for documenting regions/points of cultural heritage interest. In this context, this research work extends our previous work on UAV semantic trajectories, contributing (a) an updated methodology for the engineering of semantic trajectories as KGs (STaKG), (b) an implemented toolset for the management of KG-based semantic trajectories, (c) a refined ontology for the representation of knowledge related to UAV semantic trajectories and to cultural heritage documentation, and (d) the application and evaluation of the proposed methodology, the developed toolset, and the ontology within the domain of UAV-based cultural heritage documentation. The evaluation of the integrated UAV solution was achieved by exploiting real datasets collected during three UAV missions to document sites of cultural interest in Lesvos, Greece, i.e., the UNESCO-protected petrified forest of Lesvos Petrified Forest/Geopark, the village of Vrissa, and University Hill.
Concise and interpretable multi-label rule sets

Multi-label classification is becoming increasingly ubiquitous, but not much attention has been paid to interpretability. In this paper, we develop a multi-label classifier that can be represented as a concise set of simple “if-then” rules, and thus, it offers better interpretability compared to black-box models. Notably, our method is able to find a small set of relevant patterns that lead to accurate multi-label classification, while existing rule-based classifiers are myopic and wasteful in searching rules, requiring a large number of rules to achieve high accuracy. In particular, we formulate the problem of choosing multi-label rules to maximize a target function, which considers not only discrimination ability with respect to labels, but also diversity. Accounting for diversity helps to avoid redundancy, and thus, to control the number of rules in the solution set. To tackle the said maximization problem we propose a 2-approximation algorithm, which relies on a novel technique to sample high-quality rules. In addition to our theoretical analysis, we provide a thorough experimental evaluation, which indicates that our approach offers a trade-off between predictive performance and interpretability that is unmatched in previous work.
On Punctuation Marks and the Importance of Adequate Teaching

Abstract We focused in this paper on the importance of punctuation marks and its teaching methods in Romanian language, underlining those cases when incorrect punctuation can even change the whole meaning of a sentence. We also illustrate some situations of incorrect use of comma and exclamation mark and we suggest some possible solutions to the problem of incorrect punctuation such as revision lessons, more applications on different texts, tests.
Conditional Independence Testing via Latent Representation Learning

Detecting conditional independencies plays a key role in several statistical and machine learning tasks, especially in causal discovery algorithms, yet it remains a highly challenging problem due to dimensionality and complex relationships presented in data. In this study, we introduce LCIT (Latent representation based Conditional Independence Test) -a novel method for conditional independence testing based on representation learning. Our main contribution involves a hypothesis testing framework in which to test for the independence between X and Y given Z, we first learn to infer the latent representations of target variables X and Y that contain no information about the conditioning variable Z. The latent variables are then investigated for any significant remaining dependencies, which can be performed using a conventional correlation test. The empirical evaluations show that LCIT outperforms several state-of-the-art baselines consistently under different evaluation metrics, and is able to adapt really well to both non-linear and high-dimensional settings on a diverse collection of synthetic and real data sets.
Optimal Quantum Circuit Decomposition of Reversible Gates on IBM quantum computer

Currently, computing paradigm is ruled by the laws of classical physics and will continue till the dimensions of transistors reaches the size of atomic particles. Quantum computing (QC) is a new technology that employs laws of quantum mechanics to address issues such as irreversibility and power dissipation that are outside the scope of conventional computing models. QC offers a powerful stage for solving complex and compound problems like searching and number factoring. A critical task in utilizing quantum physics in many application fields is circuit design using reversible quantum gates. Unitary decomposition is a popular scheme for mapping quantum algorithms to any set of elementary quantum operations. Using decomposition techniques enables transformation of unitary matrices into fundamental quantum gates, which is critical for running algorithms on prevailing quantum computers. Any 3x3 reversible quantum gate can be decomposed into single-qubit rotation gates and two qubit CNOT gates. This paper is the first to present quantum implementations of FRSG1 and JTF1 gates into CNOT gates and single qubit U3 gates with different optimization levels on a platform provided by IBM. These gates are important in a variety of practical applications like Stochastic computing and parity generation circuits. In FRSG1 the implementation count of single qubit gates decreases by 56% and the count of two qubit gates by 15% after optimization, whereas in JTF1 gate the single qubit gate count reduces by 71% and the number of two qubit gates reduces by 41%.
Decision Support System for Inheritance Distribution According to Islamic Law Using the Forward Chaining Method

The rapid development of information technology in all aspects of the field in this digital era is very important, such as in the fields of business economics, agriculture, culture and religion. In the religious field, technological advances will make it easier for people to learn and seek information in the religious field, as is the case in the issue of inheritance distribution which so far not all people understand it, and also the limited number of experts in the field of inheritance. Religious science is expected to provide a way to learn about inheritance easily, and with this expert system the community will be facilitated in terms of inheritance distribution problems using the forward chaining method, the system will be tested with a 100% accuracy level value in 10 tests with variables different heirs, from the test results, it is found that the system can be used properly.
Structural Robust Label Propagation on Homogeneous Graphs

Label propagation and graph neural networks are two main methods for the semi-supervised node classification problem on graphs. They share similar idea of propagating information over the network, exhibiting promising performance on the node classification task. Despite effectiveness, the limitations of these propagation methods are still not well understood. From the perspective of label propagation then training, we observe three major challenges of these propagation methods. The observations from both theoretical analyses and empirical studies reveal that the propagation operations can degrade performance on certain labels and suffers from structure noise, which is described by edges with two nodes belonging to distinct labels. To address the above issues, we propose a new method termed Robust Label Propagation (RLP). RLP contains two novel strategies, Robust Training and Ada-Mixup. Robust Training can utilize more attribute information to have the overall training correction ability and alleviate the impact of structure noise significantly. Ada-Mixup can help RLP mine useful structure information by integrating information before and after the propagation adaptively. Extensive empirical studies on real-world datasets demonstrate that RLP outperforms the mainstream baselines on the node classification task in terms of effectiveness, efficiency and robustness.
Knowledge-Guided Semantics Adjustment for Improved Few-Shot Classification

In few-shot image classification, it is challenging for deep neural networks to infer the true class of an image when it contains multiple class-unrelated objects and its label carries no semantic meanings, such as Class 1 or Class 2. In contrast, knowing what are not important in a typical classification task, humans can quickly identify the right class objects with very few images. In this paper, we propose to extract semantic features from a given dataset to filter out class-unrelated objects in a few-shot task. Each semantic feature is meta-learned and represents a common object or pattern shared by many tasks. The strengths of these features in a given image are adjusted by an importance kernel encoding the meta-learned knowledge such that class-unrelated objects can be suppressed, and the few-shot classification performance can be improved. To facilitate learning and identifying semantic features in an image, we further propose an image representation decomposition module to decouple complex correlations between objects in an image embedding. The experimental analysis demonstrates the effectiveness of our method, especially in the extremely low-shot cases.
Active Heterogeneous Graph Neural Networks with Per-step Meta-Q-Learning

Recent years have witnessed the superior performance of heterogeneous graph neural networks (HGNNs) in dealing with heterogeneous information networks (HINs). Nonetheless, the success of HGNNs often depends on the availability of sufficient labeled training data, which can be very expensive to obtain in real scenarios. Active learning provides an effective solution to tackle the data scarcity challenge. For the vast majority of the existing work regarding active learning on graphs, they mainly focus on homogeneous graphs, and thus fall in short or even become inapplicable on HINs. In this paper, we study the active learning problem with HGNNs and propose a novel meta-reinforced active learning framework MetRA. Previous reinforced active learning algorithms train the policy network on labeled source graphs and directly transfer the policy to the target graph without any adaptation. To better exploit the information from the target graph in the adaptation phase, we propose a novel policy transfer algorithm based on meta-Q-learning termed per-step MQL. Empirical evaluations on HINs demonstrate the effectiveness of our proposed framework. The improvement over the best baseline is up to 7% in Micro-F1.