Reviewerly/exHarmony huggingface dataset:
Hybrid morphological-convolutional neural networks for computer-aided diagnosis

Training deep Convolutional Neural Networks (CNNs) presents challenges in terms of memory requirements and computational resources, often resulting in issues such as model overfitting and lack of generalization. These challenges can only be mitigated by using an excessive number of training images. However, medical image datasets commonly suffer from data scarcity due to the complexities involved in their acquisition, preparation, and curation. To address this issue, we propose a compact and hybrid machine learning architecture based on the Morphological and Convolutional Neural Network (MCNN), followed by a Random Forest classifier. Unlike deep CNN architectures, the MCNN was specifically designed to achieve effective performance with medical image datasets limited to a few hundred samples. It incorporates various morphological operations into a single layer and uses independent neural networks to extract information from each signal channel. The final classification is obtained by utilizing a Random Forest classifier on the outputs of the last neural network layer. We compare the classification performance of our proposed method with three popular deep CNN architectures (ResNet-18, ShuffleNet-V2, and MobileNet-V2) using two training approaches: full training and transfer learning. The evaluation was conducted on two distinct medical image datasets: the ISIC dataset for melanoma classification and the ORIGA dataset for glaucoma classification. Results demonstrate that the MCNN method exhibits reliable performance in melanoma classification, achieving an AUC of 0.94 (95% CI: 0.91 to 0.97), outperforming the popular CNN architectures. For the glaucoma dataset, the MCNN achieved an AUC of 0.65 (95% CI: 0.53 to 0.74), which was similar to the performance of the popular CNN architectures. This study contributes to the understanding of mathematical morphology in shallow neural networks for medical image classification and highlights the potential of hybrid architectures in effectively learning from medical image datasets that are limited by a small number of case samples.
National Security Perceptions of Korean University Students

Purpose: Korean university students have a special interest in security issues, understand the importance of Northeast Asian and foreign situations and national security, and have a sense of responsibility. This is because Korean university students recognized the importance of security through service in the military, and they are aware of security threats and espionage activities even after being discharged. Therefore, by publishing this thesis in English, we intend to contribute to prosperity and peace by sharing information on the security awareness of Korean university students with researchers around the world.&#x0D; Method: Frequency analysis was conducted to identify general characteristics using the SPSS/PC+23.0 program, and Cronbach’s α coefficient was calculated to verify the reliability of the questionnaire. In addition, Factor Analysis, Correlation Analysis, and Simple Linear Regression Analysis were conducted on Korean university students’ perception of counterespionage activities, security reporting methods, threats from neighboring countries, and the role of the government.&#x0D; Results: ‘Threat and awareness’ factors were extracted for Korean university students’ perception of counterintelligence activities, and 'public factors' and 'personal factors' were extracted for security reporting methods. As major national threats, 'dictatorship' and 'liberal democracy' were classified and extracted, and the government's role was extracted as requiring 'joint response'. Perception of counterintelligence activities correlated with security reporting methods, threats from neighboring countries, and the role of the government. There was also a significant correlation between security reporting methods, threats from neighboring countries, and the role of the government. As a result of simple linear regression analysis, it was found that counterintelligence activity perception affects the method of reporting security, threats from neighboring countries, and the role of the government.&#x0D; Conclusion: Korean university students are aware of threats from neighboring countries, and Korea’s military service system is important for raising awareness of the importance of national security through mandatory service in the army and training in the reserves. Korean university students have negative perceptions of the dictatorships of neighboring countries North Korea and China, are aware of China's espionage and cyber psychological warfare, and are aware of the reality by watching TV broadcasts featuring North Korean defectors, and are fully aware of the threat. In response to these threats, Korean university students aspire for stability and prosperity in Northeast Asia through joint responses by government departments.
Research on Acceleration Technologies and Recent Advances of Data Center GPUs

Deep learning applications in data centers are significantly computation-intensive and storage-intensive, and require GPUs with high throughput and high energy efficiency. Major manufacturers have successively released a number of high-performance data center GPU accelerators, which comprehensively use a variety of software and hardware optimization technologies to obtain excellent performance. Understanding and using these optimization technologies is of great significance to deeply understand GPUs and fully release their powerful computing capabilities. This paper analyzes and reviews the acceleration technologies and their recent advances used in data center GPU accelerators to improve throughput and energy efficiency, including convolution acceleration, data quantization, tensor/matrix cores, sparsity, high bandwidth and density memory, software stack, and multi-GPU system interconnect. This article also compares and analyzes the computation and storage performance of the latest data center GPU accelerators released by representative vendors such as Nvidia, AMD, Intel and so on.
A stochastic evaluation of quantum Fisher information matrix with generic Hamiltonians

Abstract Quantum Fisher information matrix (QFIM) is a fundamental quantity in quantum physics, which closely links to diverse fields such as quantum metrology, phase transitions, entanglement witness, and quantum speed limit. It is crucial in quantum parameter estimation, central to the ultimate Cramér-Rao bound. Recently, the evaluation of QFIM using quantum circuit algorithms has been proposed for systems with multiplicative parameters Hamiltonian. However, systems with generic Hamiltonians still lack these proposed schemes. This work introduces a quantum-circuit-based approach for evaluating QFIM with generic Hamiltonians. We present a time-dependent stochastic parameter-shift rule for the derivatives of evolved quantum states, whereby the QFIM can be obtained. The scheme can be executed in universal quantum computers under the family of parameterized gates. In magnetic field estimations, we demonstrate the consistency between the results obtained from the stochastic parameter-shift rule and the exact results, while the results obtained from a standard parameter-shift rule slightly deviate from the exact ones. Our work sheds new light on studying QFIM with generic Hamiltonians using quantum circuit algorithms.
Methods for Inclusive Underwriting of Breast Cancer Risk with Machine Learning and Innovative Algorithms

Introduction.—Due to early detection and improved therapies, the prevalence of long-term breast cancer survivors is increasing. This has increased the need for more inclusive underwriting in individuals with a history of breast cancer. Herein, we developed a method using algorithm aiming facilitating the underwriting of multiple parameters in breast cancer survivors. Methods.—Variables and data were extracted from the SEER database and analyzed using 4 different machine learning based algorithms (Logistic Regression, GA2M, Random Forest, and XGBoost) that were compared with Kaplan Meier survival estimates. The performances of these algorithms have been compared with multiple metrics (Log Loss, AUC, and SMR). In situ (non-invasive) and metastatic breast cancer were excluded from this analysis. Results.—Parameters included the pathological subtype, pTNM staging (T: tumor size, N; number of nodes; M presence or absence of metastases), Scarff-Bloom-Richardson grading, the expression of estrogen and progesterone hormone receptors were selected to predict the individual outcome at any time point from diagnosis. While all models had identical performance in terms of statistical metrics (AUC, Log Loss, and SMR), the logistic regression was the one and only model that respects all business constraints and was intelligible for medical and underwriting users. Conclusion.—This study provides insight to develop algorithms to set underwriter-friendly calculators for more accurate risk estimations that can be used to rationalize insurance pricing for breast cancer survivors. This study supports the development of a more inclusive underwriting based on models that can encompass the heterogeneity of several malignancies such as breast cancer.
ASPFL: Efficient Personalized Federated Learning for Edge Based on Adaptive Sparse Training

One of the primary challenges in cloud-edge environments is efficiently utilizing significant amounts of data on edge devices for machine learning tasks, enabling adaptation to increasingly complex computing and service scenarios. Federated Learning (FL) is a machine learning paradigm that enables collaborative training of models involving multiple data warehouses in a privacy-preserving manner. However, classical federated learning has poor convergence on highly heterogeneous data, which limits its performance of global model on each edge device. The emergence of Personalized Federated Learning (PFL) effectively alleviates data heterogeneity, but learning a personalized model may incur greater overheads. In this paper, we propose an efficient FL framework named as ASPFL, which uses dynamic sparse training for personalized federated learning to maintain model performance while reducing computational and communication overheads in cloud-edge environments. By adaptively allocating the dynamic sparsity from a global perspective to explore sparse network structure during training, ASPFL improves the independent parameter exploration process of local sparse training to adapt to various heterogeneous situations and solves the Non-IID challenge of FL. The abundant experimental results show that ASPFL outperforms state-of-the-art methods in performance, overheads, and convergence speed in PFL.
OpenDrift: Online Evolving Fraud Detection for Open-Category and Concept-Drift Transactions

The rapid growth of electronic commerce brings convenience to modern life but comes with security risks by various cybercrimes in online payment services. Most existing security methods for fraud detection depend on the static learning paradigm, which trains a model over a static training dataset and deploys the trained model for inference with the frozen model parameters under the i.i.d. assumption. Unfortunately, this paradigm becomes incommensurate with the increasingly complicated and varying fraud patterns due to the untimely and delayed responses in the offline environment. Without sensing the evolution of fraud timely, it is challenging to train and deploy targeted countermeasures. The emerging means of fraud are not only reflected in the openness of their category, but also in the drift of their superimposed risk features. The interweaving of open-category and concept drift accelerates the process of existing methods becoming powerless. In this paper, we propose EvoFD, an online evolving fraud detection framework to enable continual learning to cope with undercurrent surges of evolving fraud. The core idea of EvoFD is to weaken the bias caused by the anchoring effect on the learned information. It learns in an online streaming fashion by using instructive representations as anchors. Specially, we maintain the progressively updatable class anchors and optimize the representation network to embed features and class anchors into a unified normalized space, where the training and predicting can be conducted simultaneously or independently. In the framework, we preserve the balanced replay memory for each class to accumulate knowledge and avoid forgetting. The advantages of our method are validated by extensive experiments over the real-world dataset from a prestigious bank.
Physico-chemical and Microbiological (Bacteria and Protozoa) Assessment of Halabja Drinking Water, Kurdistan, Iraq

Drinking water has directly related to human health so, it must have high quality; free from any constituents harmful to human health such as microorganisms, minerals, and organic substances. Most water born outbreaks due to inadequate sanitation and contamination of drinking water with bacteria and protozoa, which cause global health problems, so the drinking water must be monitored every month. The current research expected to evaluate the consumption water for the microbiological and chemical pollutant of Halabja. In diffirent sources of drinking water samples were collected and they subjected for physical, chemical and microbiological examination each season. The consequences of the current research indicated that Halabja Drinking water was free from waterborne microbial pathogenes. Furthermore, the highest values of the following parameters were found in autumn, although they were within international standards; total dissolved solids (382mg/I), Cl- concentration (103 mg/I), free chloride (0.58mg/I), Ec (595µS/cm), Mg+2 (103mg/I) and total hardness (392 mg/I). Therefore this water canbeused for drinking and daily work, while the sources of Halabja drinking water need treatment and management regularly and it should be monitored systematically.
A Texture Classification Method Based on Improved Graph Scattering Transform

Texture classification is an important topic in image processing and has a significant impact on picture analysis. The graph scattering transform network is an untrained, interpretive neural network that can extract graph-level characteristics from textures for categorization. We have worked to develop the network in order to overcome its flaws, which include the inability to extract multi-level information from the graph and insufficiently rich output feature information. In order to perform multi-level information acquisition of the graph, we first implement graph coarsening modules in the graph scattering transform network to coarsen the graph to various degrees before feeding it into the network to extract features. Second, we combine the output features obtained by the overall maximum and average operation to enhance the expressiveness of the output coefficients. Classification experiments on texture datasets KTH-TIPS, UMD and UIUC show that the improved network proposed in this paper achieves superior outcomes.
Nonlinear Filtering With Sporadically Missing Sensor Data

The traditional Gaussian filtering adjustment is restricted with the assumption that the sensor data are received at every sampling instant. In practice, however, the observed data are often missing; thereupon, the estimates provided by the Gaussian filters are more likely unreliable. This letter addresses the irregularity of partially missing sensor data and, subsequently, designs an advanced Gaussian filter to tackle this irregularity. First, it proposes a modified measurement model that stochastically incorporates the partially missing data phenomenon. Subsequently, it reformulates the relevant parameters in reference to the modified measurement model. The newly derived parameters substitute the respective ones in the traditional Gaussian filtering and the proposed filtering method ensues. The simulation results obtained for two numerical examples conclude an improved filtering accuracy of the proposed filter in the presence of partially missing sensor data.
Dynamic Frame Interpolation in Wavelet Domain

Video frame interpolation is an important low-level vision task, which can increase frame rate for more fluent visual experience. Existing methods have achieved great success by employing advanced motion models and synthesis networks. However, the spatial redundancy when synthesizing the target frame has not been fully explored, that can result in lots of inefficient computation. On the other hand, the computation compression degree in frame interpolation is highly dependent on both texture distribution and scene motion, which demands to understand the spatial-temporal information of each input frame pair for a better compression degree selection. In this work, we propose a novel two-stage frame interpolation framework termed WaveletVFI to address above problems. It first estimates intermediate optical flow with a lightweight motion perception network, and then a wavelet synthesis network uses flow aligned context features to predict multi-scale wavelet coefficients with sparse convolution for efficient target frame reconstruction, where the sparse valid masks that control computation in each scale are determined by a crucial threshold ratio. Instead of setting a fixed value like previous methods, we find that embedding a classifier in the motion perception network to learn a dynamic threshold for each sample can achieve more computation reduction with almost no loss of accuracy. On the common high resolution and animation frame interpolation benchmarks, proposed WaveletVFI can reduce computation up to 40% while maintaining similar accuracy, making it perform more efficiently against other state-of-the-arts.
Design and Development of IOT &amp; AI Enabled Smart Entrance Monitoring Device

The global health system is being devastated by the outburst of COVID-19 pandemic. The outburst caused worldwide lockdown. The study found that usage of face mask in crowded areas greatly minimizes the chance of virus transmission. But many people avoid wearing face mask. Specially if people wear a mask while entering a shopping mall or a place with heavy gathering like railway stations, stadiums etc., the chances of infections can be reduced. So, in the present work, the authors have constructed an IoT and AI-enabled Smart Entrance Monitoring Device with an RFID reader for counting number of people who are entering a crowded place with or without mask. This system will measure body temperature as well. Any commercial places like mall, hotel, stadium, railway station or residential entry can benefit from the recommended strategy. This automatic face mask detection system will be cost-effective and require zero manpower for detecting body temperature.
Adversarial Network with External Knowledge for Zero-Shot Stance Detection

Zero-shot stance detection intends to detect previously unseen targets’ stances in the testing phase. However, achieving this goal can be difficult, as it requires minimizing the domain transfer between different targets, and improving the model’s inference and generalization abilities. To address this challenge, we propose an adversarial network with external knowledge (ANEK) model. Specifically, we adopt adversarial learning based on pre-trained models to learn transferable knowledge from the source targets, thereby enabling the model to generalize well to unseen targets. Additionally, we incorporate sentiment information and common sense knowledge into the contextual representation to further enhance the model’s understanding. Experimental results on several datasets reveal that our method achieves excellent performance, demonstrating its validity and feasibility.
Computing All-vs-All MEMs in Grammar-Compressed Text

We describe a compression-aware method to find all-vs-all maximal exact matches (MEM) among strings of a repetitive collection $$\mathcal {T}$$ . The key concept in our work is the construction of a fully-balanced grammar $$\mathcal {G}$$ from $$\mathcal {T}$$ that meets a property that we call fix-free: the expansions of the nonterminals that have the same height in the parse tree form a fix-free set (i.e., prefix-free and suffix-free). The fix-free property allows us to compute the MEMs of $$\mathcal {T}$$ incrementally over $$\mathcal {G}$$ using a standard suffix-tree-based MEM algorithm, which runs on a subset of grammar rules at a time and does not decompress nonterminals. By modifying the locally-consistent grammar of Christiansen et al. [7], we show how we can build $$\mathcal {G}$$ from $$\mathcal {T}$$ in linear time and space. We also demonstrate that our MEM algorithm runs on top of $$\mathcal {G}$$ in $$O(G +occ)$$ time and uses $$O((G+occ)\log G)$$ bits, where G is the grammar size, and occ is the number of MEMs in $$\mathcal {T}$$ . In the conclusions, we discuss how to modify our idea to perform approximate pattern matching in compressed space.
A Socio-cultural Analysis of Carnivalesque Languages on Corruption Incidents in Chinese Digital Context

Discussion on corrupted cadres is heated in the Chinese digital context, which is categorized as being carnivalesque by the Chinese academy. However, it did not draw enough attention from English literature. Under Carnivalesque theory, this paper tends to fill the gap. The objective of this study is to explore the language production of the carnivalesque vernacular in the Chinese digital context. it collects 325 comments on Chinese social media on the expose of corruption and conducts in-depth interviews with 16 informants as attenders of the online Carnival. The results of the study revealed a landmark compilation of the hot debates on corruption in public discourses and illustrates the content and form of carnivalesque languages in a Chinese context. In content, it serves as a vehicle of a mentality of hatred for the rich behind a complexity of emotions and assists in negotiating with authoritarian censorship mechanisms through egao production. Besides, it is a complex of disengagement from and return to the compared language in reality.
Evaluation of the library management program as a learning resources with Discrepancy Model

Monitoring and evaluation activities in this study focused on evaluating the school library management program as a learning resource at SMA Sumatra 40 Bandung using the discrepancy model. The research method used is a qualitative approach and a literature review that is processed descriptively. Based on the results of the study, it is known that several aspects evaluated at the Sumatra 40 Bandung High School Library are related to the implementation of the library management program, which has reached high school library standards following Peraturan Kepala Perpustakaan Nasional Republik Indonesia Nomor 12 Tahun 2017 tentang Standar Nasional Perpustakaan Sekolah Menengah Atas/Madrasah Aliyah. However, the aspect of library human resources has not met the standards applied. Recommendations that should be considered by the SMA Sumatra 40 Bandung Library are adding library staff and expanding the library space, and using information and communication technology so that the library management program as a learning resource can be maximized.