Reviewerly/exHarmony huggingface dataset:
Online Learning from Evolving Feature Spaces with Deep Variational Models

In this paper, we explore a novel online learning setting, where the online learners are presented with “doubly-streaming” data. Namely, the data instances constantly streaming in are described by feature spaces that over-time evolve, with new features emerging and old features fading away. The main challenge of this problem lies in the fact that the newly emerging features are described by very few samples, resulting in weak learners that tend to make error predictions. A seemingly plausible idea to overcome the challenge is to establish a relationship between the old and new feature spaces, so that an online learner can leverage the knowledge learned from the old features to better the learning performance on the new features. Unfortunately, this idea does not scale up to high-dimensional feature spaces that entail very complex feature interplay. Specifically. a tradeoff between onlineness, which biases shallow learners, and expressiveness, which requires deep models, is inevitable. Motivated by this, we propose a novel paradigm, named Online Learning Deep models from Data of Double Streams (OLD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> S), where a shared latent supspace is discovered to summarize information from the old and new feature spaces, building an intermediate feature mapping relationship. A key trait of OLD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> S is to treat the model capacity as a learnable semantics, aiming to yield optimal model depth and parameters jointly in accordance with the complexity and non-linearity of the input data streams in an online fashion. To ablate its efficacy and applicability, two variants of OLD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> S are proposed namely, OLD-Linear that learns the relationship by a linear function; and OLD-FD learns that two consecutive feature spaces pre-and-post evolution with fixed deep depth. Besides, instead of re-starting the entire learning process from scratch, OLD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> S learns multiple newly emerging feature spaces in a lifelong manner, retaining the knowledge from the learned and vanished feature space to enjoy a jump-start of the new features' learning process. Both theoretical analysis and empirical studies supstantiate the viability and effectiveness of our proposed approach. The code is available online at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">github.com/X1aoLian/OLD3S-L</uri> .
Trajectory Planning for Autonomous Driving in Unstructured Scenarios Based on Deep Learning and Quadratic Optimization

Trajectory planning for autonomous driving is challenging in unstructured scenarios such as mining sites. Existing studies mainly resort to a heuristic search-based planner to find a feasible trajectory. In the case of the narrow area, the heuristic function of the planner suffers from over-expansion problems, which may result in heavy computation burden and memory usage as well as potential failure. Therefore, a novel trajectory planning approach, namely learning and optimization-based trajectory planning (LOTP), is proposed, which is featured by a hierarchical structure consisting of two modules: (1) path searching, (2) speed profile generation. Firstly, a path searching method based on deep learning and Monte-Carlo tree search is proposed to generate a coarse path connecting starting and terminal points. Then, the path is smoothed using path optimization and provided to the speed-planning module as the reference. Next, a speed planning method based on quadratic optimization is developed, which allows to seek maximum driving comfort and energy saving. Last, extensive simulation experiments were conducted in the real environment of mining sites. The results verified that LOTP enhances the computational efficiency and success rate of path planning and helps generate an optimal speed profile. Furthermore, LOTP exhibits desirable potential for the practical application of autonomous driving at mining sites. Source implementation will be released as an open-source code.
Channel Pyramidal Transformer Network for Single Image Deraining

In recent years, Convolutional Neural Networks (CNNs) and Visual Transformers have shown remarkable performance in image deraining tasks. However, these state-of-the-art (SOTA) methods exhibit high computational costs in addition to excellent performance. This would hinder the analytical comparison of methods and limit their practical application. We argue that the high computational cost mainly stems from the explosion in the number of parameters due to the surge of feature dimensions. To achieve better results with fewer parameters. By reconstructing the multi-head attention mechanism and feed-forward network, we propose a multi-scale hierarchical Transformer network with a change of width resembling a pyramid, called CPTransNet. The key idea of CPTransNet is to slowly increase the feature dimension during the feature extraction process. This avoids parameter wastage due to feature dimension surge. CPTransNet achieves 33.25 dB PSNR on the classical dataset of image deraining, exceeding the previous state-of-the-art 0.22 dB PSNR with only 19.4% of its computational cost.
Multimodal Deep Learning Approaches to Breast Tumor Characterization using Ultrasound B-Mode and Nakagami Parametric Images

This study proposes a multimodal deep learning architecture for breast tumor characterization that takes as input both ultrasound B-Mode and ultrasound Nakagami parametric images in the form of stacked 2D data-maps. Ultrasound data from 831 patients, among which 582 had benign and 249 had malignant lesions, was used in the study to train, validate and test the networks. The multimodal architecture obtained statistically significantly higher AUC, sensitivity and accuracy compared to networks taking an input of B-Mode or Nakagami image data by themselves. The obtained results suggest that B-Mode and Nakagami images contain complimentary information that is valuable for breast tumor characterization if utilized together in a deep learning framework and demonstrates the potential of this approach in improving breast cancer diagnosis.
Performance Impact Analysis of Homomorphic Encryption: A Case Study Using Linear Regression as an Example

In recent years, the trend has increasingly been to store and process data in the cloud. However, this is based on the premise that cloud providers treat the data in a trustworthy manner. One way of using the data in the cloud without the provider having access to it is homomorphic encryption. However, since this encryption has only recently become practicable, analysis of its for practical applications is still in its infancy. Therefore, we investigate the performance of homomorphic encryption using a real-world application, namely linear regression. Our main finding is that although the homomorphic computation of linear regression is in the range of minutes and thus slower than in the non-homomorphic case, linear regression can be computed homomorphic and is therefore suitable for use cases where data security is the top priority.
Quantum state-channel duality for the calculation of Standard Model scattering amplitudes

Recent instances of successful application of quantum information techniques to particle physics problems invite for an analysis of the mathematical details behind such connection. In this paper, we identify the Choi-Jamiolkowski isomorphism, or state-channel duality, as a theoretical principle enabling the application of the theory of quantum information to the scattering amplitudes associated with Standard Model processes.
PertCF: A Perturbation-Based Counterfactual Generation Approach

Post-hoc explanation systems offer valuable insights to increase understanding of the predictions made by black-box models. Counterfactual explanations, an instance-based post-hoc explanation method, aim to demonstrate how a model’s prediction can be changed with minimal effort by presenting a hypothetical example. In addition to counterfactual explanation methods, feature attribution techniques such as SHAP (SHapley Additive exPlanations) have also been shown to be effective in providing insights into black-box models. In this paper, we propose PertCF, a perturbation-based counterfactual generation method that benefits from the feature attributions. Our approach combines the strengths of perturbation-based counterfactual generation and feature attribution to generate high-quality, stable, and interpretable counterfactuals. We evaluate PertCF on two open datasets and show that it has promising results over state-of-the-art methods regarding various evaluation metrics like stability, proximity, and dissimilarity.
Advanced Objects In Kubernetes Clusters

After thoroughly discussing the two most fundamental objects of Kubernetes, containers and pods, in the previous chapter, we will start our discussion of more advanced Kubernetes objects such as Deployment, ReplicaSet, StatefulSet, etc.
Objects Important For A Secure Kubernetes Cluster

After building a solid foundation of Kubernetes workload resources such as Deployment, ReplicaSet, etc., in the previous chapter, in this chapter we will start our discussion of objects that are important for creating a secure Kubernetes cluster.
Kubernetes Deep Dive

After building a solid foundation of containerization concepts in the previous chapter, in this chapter, we will kick off our Kubernetes journey. We will start our discussion by going through the history of Kubernetes, including who developed and designed Kubernetes. You will also learn about the role of the Linux Foundation and the Cloud Native Computing Foundation (CNCF) in the evolution of Kubernetes.
The Construction Paths of Blended Teaching Practice Base of "Comprehensive English"

"Comprehensive English" is a professional basic course for undergraduate English majors, with the overall goal of cultivating learners' basic language skills, cross-cultural communication skills, and critical thinking abilities. Blended teaching combines the advantages of traditional offline teaching and online teaching to enhance interaction between students and teachers, thereby enhancing students' interest in learning. Taking the collaborative education project of industry and university as an opportunity, carrying out the construction of a blended teaching practice base for "Comprehensive English" has important research significance and application prospects. This article proposes the principles and paths for the construction of a blended teaching practice base for "Comprehensive English". The construction principles include: mutual benefit, professional alignment, complementarity and sharing, dynamic development, and demonstration and leadership; The construction path includes: clarifying construction goals, formulating construction plans, setting up organizational structures, formulating management measures, constructing practical conditions, developing teaching resources, developing practical teaching platforms, and strengthening guarantee measures.
The Relationship Between Vocabulary Learning Strategies and Reading Comprehension Based on Gender

This research aimed to determine The Relationship between Vocabulary Learning Strategies and Reading Comprehension based on the Gender. This is a quantitative research using the correlation method. Data were collected from online questionnaires and test. This research uses bivariate correlation analysis as a research design. Respondents in this research were 8th grade students of SMP Muhammadiyah 4 Kebomas of the 2020/2021 academic year in Gresik, East Java, Indonesia. This study found that students’ Vocabulary Learning Strategies had a positive and significant correlation to students’ Reading Comprehension with the results indicate that the significance value in male students’ is 0.828 &gt; 0.265 (rtable value) and the results indicate that the significant value in female students’ is 0.751&gt; 0.265 (rtable value) at the level of significance 0.05.
DIGITAL FACILITIES UTILIZATION AND COMPUTER SKILLS AMONG SCIENCE AND ARTS STUDENTS

The computer world expanded into each and every sector in the current digital era including the education field and rapidly targeting students to acquaint computer and digital knowledge not only for entertaining but also to enhance academic skills. The basics of digital technology are in reach of the people through different sources such as smart phones, desktop computers, laptops and tablets. Recently, the curricular and competency levels are gradually incorporating to the students to acquire digital skills by motivating to various modes of pedagogy such as seminars, assignments and project works. The present research was mainly focused on understanding the digital devices owning by the students, studying knowledge levels of computer skills and frequency of utilizing digital services. The study was carried out from the randomly selected college students of both science and arts streams from the university and colleges students located at Tirupati urban. The sample size of the present investigation comprised of 60 students from each stream of science and arts with total sample of 120 members. The findings well demonstrated that absolutely all the students owned the smart phone and surprisingly one third of them were even planned to purchase the new mobile within one year. The data indicated that around one fourth of science students had desktop and tablets and about one tenth in arts students. The computer skills and levels of confidence also noticed at higher levels in science students against their arts students’ counterparts probably the science students regular academic activities might be intricately enabled the basic computer skills. The information gathered from the students strongly highlighted the high frequency utilization of smart phone by students of both streams. Educating students, creating awareness and organizing digital training programmes by colleges will surely helpful to enhance the digital skills among the students and to excel their academic and future career opportunities.
An orientation-free ring feature descriptor with stain-variability normalization for pathology image matching

Comprehensively analyzing the corresponding regions in the images of serial slices stained using different methods is a common but important operation in pathological diagnosis. To help increase the efficiency of the analysis, various image registration methods are proposed to match the corresponding regions in different images, but their performance is highly influenced by the rotations, deformations, and variations of staining between the serial pathology images. In this work, we propose an orientation-free ring feature descriptor with stain-variability normalization for pathology image matching. Specifically, we normalize image staining to similar levels to minimize the impact of staining differences on pathology image matching. To overcome the rotation and deformation issues, we propose a rotation-invariance orientation-free ring feature descriptor that generates novel adaptive bins from ring features to build feature vectors. We measure the Euclidean distance of the feature vectors to evaluate keypoint similarity to achieve pathology image matching. A total of 46 pairs of clinical pathology images in hematoxylin-eosin and immunohistochemistry straining to verify the performance of our method. Experimental results indicate that our method meets the pathology image matching accuracy requirements (error ¡ 300μm), especially competent for large-angle rotation cases common in clinical practice.
Class-Incremental Learning for Recognition of Complex-Valued Signals

Signal recognition, essential in both military and civilian applications, often deals with an expanding array of signal classes due to the emergence of new communication devices. Current class-incremental learning (CIL) approaches, primarily devised for image-based tasks, prove less efficient when handling complex-valued signals. Moreover, global fine-tuning is not feasible due to its high computational cost. This paper proposes a complex-valued CIL framework, coined as C-SRCIL, engineered to identify complex-valued signals. C-SRCIL features a decoupled feature extractor to limit catastrophic forgetting and updating costs while ensuring the effectiveness of feature representation for CIL with complex-valued neural networks and a carefully designed integrated loss function. During the incremental stage, C-SRCIL modifies the classifier with an adaptive node fusion-based complex-valued CIL adapter, effectively accommodating the increasing signal classes. This paper also proposes an ambiguous boundary indication method for C-SRCIL which solely depends on the weight correlation of the complex-valued classifier to pinpoint the potential ambiguity of signals. Experimental results on benchmark datasets reveal that C-SRCIL outperforms contemporary techniques, highlighting its capacity to expand classification boundaries of previous models with lower overhead. The ambiguous boundary indication method has also been empirically validated, showing its capability to augment predictive information in C-SRCIL.
Building care gerontotechnologies in the context of elderly person with Alzheimer's disease

Objective: to build care gerontotechnologies to help family caregivers with the difficulties experienced in the context of elderly people with Alzheimer's disease. Methods: strategic action research carried out with seven family caregivers of elderly people with Alzheimer's disease and 12 health academics from a university. Data was gathered from the family caregivers using a semi-structured interview. Three focus groups were held with the academics. The data was submitted to the discursive textual analysis technique. Results: eight difficulties experienced by family caregivers emerged, corresponding to cognitive aspects, Activities of Daily Living and issues relating to family care. It was possible to construct 14 care gerontotechnologies in product form. Conclusion: gerontotechnologies were built to help family caregivers with the difficulties experienced by elderly people living with Alzheimer's disease. Contributions to practice: the gerontotechnologies developed have the potential to contribute to the care process, since they can be used by family caregivers on a daily basic and can be adapted to each reality, based on the unique needs of each elderly person.
The Effect of Providing a Technological Integration Package with Course Subjects in Improving Digital Literacy and Social-Emotional Competence of Fifth Grade Elementary Students

The Effect of Providing a Technological Integration Package with Course Subjects in Improving Digital Literacy and Social-Emotional Competence of Fifth Grade Elementary Students
Research on color image encryption scheme based on high-dimensional chaotic system and DNA computing

There are various methods of image encryption techniques, including key-based encryption, chaos theory-based encryption, wavelet transform-based encryption, etc. Although low-dimensional chaotic systems are fast, the key space is small and the security is not high. High-dimensional chaotic systems have large key space and high security, which are difficult for attackers to break, but the encryption speed is not good. In order to solve the problem of encryption efficiency of high-dimensional chaotic systems, this paper introduces a chaotic state selection system to dynamically select the state variables so as to reduce the number of iterations in the generation of the secret key, which further improves the security of the algorithm while increasing the encryption speed, making the algorithm fast and in line with the practical application scenarios.
EOG Based Eye Blink Detection using VB GUI for Eye Writing Applications

Usage of bio-signals from Electro-Oculo-Gram (EOG) in Human Computer Interaction (HCI) systems is one of the burgeoning study areas as of now. In order to provide support for physically challenged persons (or keypad Contactless HCI) in operating basic computer functions without making any physical touch with the computer keypad or mouse, we propose a framework utilization concept that changes movement of cursor on the computer screen using virtual keypad based on Visual Basic Graphical User Interface (VB GUI) with the eye movement detection through webcam of the system for different computer functions. In this work, VB GUI platform is used for a web browsing application and corresponding platform operation steps have been included. With the VB GUI, the basic cursor movements in the directions of left, right, up, and down to access the web links on the web page which is treated as eye writing in the concept of human computer interaction. Similarly, we can access different computer Apps such as MS-Doc, XL sheets, Audio and video files etc. Further, this paper focus is on prototype development of different home automation system applications.
10 Things I Get From You<b>: The Benefits of Partnerships in the Pursuit of Improvement and Sustainability in Libraries</b>

Abstract Globalization and growth in digitalization have led to a greater connectivity between individuals, groups, organizations, and libraries. This article will share ten practical outcomes from one librarian’s personal experience with partnerships. A clear example of these collaborative benefits may be seen in the Czech-German Library Next Door partnership, as well as the individual partnership between the Stadtbibliothek Chemnitz and Ústí Regional Library. Partnerships and collaborations allow all involved members to overcome their own shortcomings, strengthen others around them, and excel at their greatest strengths.
Ensemble learning based health care claim fraud detection in an imbalance data environment

&lt;p&gt;&lt;span&gt;Healthcare fraud has become a common encounter in the healthcare finance industry. The financial security of healthcare payers and providers is seriously impacted by healthcare fraud. When incorrect or exaggerated medical services are invoiced for reimbursement, fraudulent healthcare claims result. The effective operation of the healthcare system depends on the detection of such fraudulent actions. This paper develops a healthcare claim fraud detection method based on ensemble learning. Stack ensemble learning algorithm performance is compared to that of methods such as multi-layer perceptron (MLP) classifier, support vector classifier (SVC), logistic regression (LR), and decision tree (DT) algorithm. Because of the healthcare data imbalance, the normal transaction is significantly higher than the fraudulent transaction. The machine learning (ML) algorithm performs poorly because imbalanced data causes it to be biased toward the majority class. As a result, the data is unsampled using the synthetic minority oversampling technique (SMOTE) technique to provide balanced data. The experimental results show that for the identification of healthcare claim fraud, the ensemble learning strategy greatly &lt;span&gt;outperforms single learning algorithms. The stack ensemble learning outperforms all the area under the curve for the receiver-operating characteristic (AUC ROC) curves from various algorithms, and the AUC-ROC curve is determined to be producing results that are adequate for all approaches.&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;