Reviewerly/exHarmony huggingface dataset:
Non-Parametric Semi-Supervised Learning in Many-Body Hilbert Space with Rescaled Logarithmic Fidelity

In quantum and quantum-inspired machine learning, the very first step is to embed the data in quantum space known as Hilbert space. Developing quantum kernel function (QKF), which defines the distances among the samples in the Hilbert space, belongs to the fundamental topics for machine learning. In this work, we propose the rescaled logarithmic fidelity (RLF) and non-parametric semi-supervised learning in the quantum space, which we name as RLF-NSSL. The rescaling takes advantage of the non-linearity of the kernel to tune the mutual distances of samples in the Hilbert space, and meanwhile avoids the exponentially-small fidelities between quantum many-qubit states. Being non-parametric excludes the possible effects from the variational parameters, and evidently demonstrates the advantages from the space itself. We compare RLF-NSSL with several well-known non-parametric algorithms including naive Bayes classifiers, k-nearest neighbors, and spectral clustering. Our method exhibits better accuracy particularly for the unsupervised case with no labeled samples and the few-shot cases with small numbers of labeled samples. With the visualizations by t-stochastic neighbor embedding, our results imply that the machine learning in the Hilbert space complies with the principles of maximal coding rate reduction, where the low-dimensional data exhibit within-class compressibility, between-class discrimination, and overall diversity. Our proposals can be applied to other quantum and quantum-inspired machine learning, including the methods using the parametric models such as tensor networks, quantum circuits, and quantum neural networks.
Bilevel Continual Learning

Continual Learning (CL) studies the problem of learning a sequence of tasks, one at a time, such that the learning of each new task does not lead to the deterioration in performance on the previously seen ones while exploiting previously learned features. This paper presents Bilevel Continual Learning (BiCL), a general framework for continual learning that fuses bilevel optimization and recent advances in meta-learning for deep neural networks. BiCL is able to train both deep discriminative and generative models under the conservative setting of the online continual learning. Experimental results show that BiCL provides competitive performance in terms of accuracy for the current task while reducing the effect of catastrophic forgetting.
Learning linear non-Gaussian graphical models with multidirected edges

Abstract In this article, we propose a new method to learn the underlying acyclic mixed graph of a linear non-Gaussian structural equation model with given observational data. We build on an algorithm proposed by Wang and Drton, and we show that one can augment the hidden variable structure of the recovered model by learning multidirected edges rather than only directed and bidirected ones. Multidirected edges appear when more than two of the observed variables have a hidden common cause. We detect the presence of such hidden causes by looking at higher order cumulants and exploiting the multi-trek rule. Our method recovers the correct structure when the underlying graph is a bow-free acyclic mixed graph with potential multidirected edges.
An efficient curriculum management ICT-based system to support AUN-QA

Effective curriculum management has been the main concern at Van Lang University over the past few years. With the goal of becoming one of the most admired young universities in Asia by 2030, Van Lang University has strived to enhance its academic quality by a constant process of inspection and self-evaluation in which curriculum management plays a central role. Aiming to suggest a proper way to manage curricula, in this article, the authors present an ICT based solution to digitize curriculum and syllabus content with keen attention to details of expected learning outcomes based on AUN-QA, a standard measurement which was proposed by ASEAN University Network for assessing academic quality at university. The proposed system has been experimentally applied to manage syllabuses of all subjects in one semester at Information Technology faculty. Faculty retrospective has shown that the tool is efficient in managing and visualizing the curriculum and syllabus content, which is useful in promoting, evaluating, as well as comparing the amount of training and learning outcomes among curricula. Based on this research, the researchers hope that Van Lang University, especially the Office of Academic Affairs will be able to manage all current curricula effectively and consistently.
Utilizing Machine Learning for Video Processing

The demand for media content in the form of audio, video, and images is growing at an unprecedented rate. To meet this demand, media content production is increasing rapidly. However, the process of producing, distributing, and monetizing content is often complex, expensive, and timeconsuming. Applying machine learning (ML) capabilities can allow for media companies to augment the work their teams are doing and provide capabilities that were previously prohibitively expensive or not even possible. ML capabilities are made available in one of three ways—built into software as a feature, consumed as an artificial intelligence (AI, of which ML is a subset) service [application programming interfaces (APIs) that can be consumed without ML expertise], and custom-trained models (which require data science expertise to train and deploy models).
BUSCA DE PALAVRAS CHAVE EM IMAGENS DE LIVROS IMPRESSOS USANDO VISÃO COMPUTACIONAL

With the increasingly frequent use of books in digital format, people search for the desired subjects in a faster way compared to the search in physical books. This work aimed to develop a computational resource in the form of an application for Android smartphones, which, based on an image captured from a page in a book, performs searches by keywords. The purpose of using the application is to help the reader to find the desired information quickly. We use Computer Vision techniques with the aid of the OpenCV library in the development of algorithms to perform segmentation, correction of the perspective of the book page image, identification and rectification of the wavy lines, recognition and character classification. The results shown were promising with a hit rate of over 88%.
Security sharing model of power material logistics information based on blockchain Technology

In order to improve the security of power material logistics information sharing, a security sharing model of power material logistics information based on blockchain technology is designed. The logistics information collection device is designed and installed in the logistics information system of power enterprises to obtain the logistics information in real time. The collected information is stored in the blockchain in chronological order, the information feature space is reconstructed, and the information tamper proof strategy is designed to construct the security sharing model of power material logistics information. The experimental results show that the model can effectively avoid the problem of information disclosure, the response and sharing time of information sharing request is short.
Limits for realizing single photons

Exact single photons cannot be generated on demand due to their infinite tails. To quantify how close realizable optical states can be to some target single photon in one dimension, we argue that there are two natural but incompatible ways to specify the target state. Either it can be expressed as a photon with a chosen positive-frequency spectrum, or it can be described as an (unphysical) photon in a chosen positive-time pulse. The results show that for sufficiently short target pulses, the closest realizable states contain substantial multiphoton components. Upper and lower bounds for the maximum fidelity are derived and are expressed as functions of the size of the target state's tail, for negative time or negative frequency, respectively. We also generalize the bounds to arbitrary photon-number states.
A Network of Object-Oriented Software Metrics’ Parameters

The use of software metrics has become a crucial tool during the software design phase. The quality of software design can be measured using these metrics. Therefore, it is important to understand software metrics in a way that makes it wiser to select a particular metric in the software design assessment process. To calculate the value of a metric, it is needed to have its parameters involved. Hence, each metric consists of one or several parameters. Many of the object-oriented metrics have parameters in common. This means a parameter can be associated with several metrics. In this case, understanding the relations among parameters may help developers when selecting metrics during the software design phase, which makes the assessment process accurate, wiser, and reduce time consumption. In this work, we deeply investigate the relations among object-oriented metrics’ parameters using concepts inspired from complex networks. The dataset used in this paper was collected from trusted and accredited resources in Software Engineering field. The dataset contains the main 104 object-oriented metrics and their parameters. The results showed important facts about the relations among metrics’ parameters. We believe that this is the first kind of work that investigates the relations among object-oriented metrics’ parameters.
Behind The Mask: Masquerading The Reason for Prediction

Ensuring the interpretability of the machine-learning-based prediction models is important for gaining users’ trust. The current representative algorithms for ensuring interpretability, LIME and SHAP, explain the prediction of a given black-box machine learning model based on a common perturbation mechanism to input data. In this study, we propose a masquerade layer to nullify this perturbation and hide the reason for prediction. Our proposed masquerade layer can be attached to any prediction models. It can also be attached without altering the prediction model itself, making it possible to manipulate the explanation provided by the interpretability algorithm in a manner that hardly changes the prediction model behavior. The experimental results show that existing representative perturbation-based interpretability algorithms have a critical weaknesses in terms of their reliability.
Riprap Stockpile Size and Shape Analyses Using Computer Vision

Riprap rocks and large-sized aggregates are being used extensively in geotechnical and hydraulic engineering applications. Knowing the particle size and morphological/shape properties ensures the reliable and sustainable use of aggregate materials at both quarry production lines and construction sites. Traditional methods for assessing aggregates geometric properties involve subjective visual inspection and time-consuming hand measurements, while the use of computer vision analysis techniques is restricted primarily due to the unwieldiness of large-sized aggregates. This paper presents the use of a computer vision system for in-place size and shape evaluation of large aggregates in a stockpile using images of stockpiles conveniently collected using smartphone cameras. The automated image analysis software is comprised of an image segmentation kernel and a morphological analysis module. Comprehensive analyses are conducted on a sample stockpile with multiple view inspections. Based on the findings of this study, the stockpile aggregate image analysis program has great potential as an efficient and innovative application for field-scale and in-place evaluations of aggregate materials.
Short Text Clustering with a Deep Multi-embedded Self-supervised Model

Short text clustering is challenging in the field of Natural Language Processing (NLP) since it is hard to learn the discriminative representations with limited information. In this paper, fused multi-embedded features are employed to enhance the representations of short texts. Then, a denoising autoencoder with an attention layer is adopted to extract low-dimensional features from the multi-embeddings against the disturbance of noisy texts. Furthermore, we propose a novel distribution estimation with jointly utilizing soft cluster assignment and the prior target distribution transition to better fine-tune the encoder. Combining the above work, we propose a deep multi-embedded self-supervised model(DMESSM) for short text clustering. We compare our DMESSM with the state-of-the-art methods in head-to-head comparisons on benchmark datasets, which indicates that our method outperforms them.
Reinforcement Learning with Potential Functions Trained to Discriminate Good and Bad States

Reward shaping is an efficient way to incorporate domain knowledge into a reinforcement learning agent. Nev-ertheless, it is unpractical and inconvenient to require prior knowledge for designing shaping rewards. Therefore, learning the shaping reward function by the agent during training could be more effective. In this paper, based on the potential-based reward shaping framework, which guarantees policy invariance, we propose to learn a potential function concurrently with training an agent using a reinforcement learning algorithm. In the proposed method, the potential function is trained by examining states that occur in good and in bad episodes. We apply the proposed adaptive potential function while training an agent with Q-learning and develop two novel algorithms. One is APF-QMLP, which applies the good/bad state potential function combined with Q-learning and multi-layer perceptrons (MLPs) to estimate the Q-function. The other is APF-Dueling-DQN, which combines the novel potential function with Dueling DQN. In particular, an autoencoder is adopted in APF-Dueling-DQN to map image states from Atari games to hash codes. We evaluated the created algorithms empirically in four environments: a six-room maze, CartPole, Acrobot, and Ms-Pacman, involving low-dimensional or high-dimensional state spaces. The experimental results showed that the proposed adaptive potential function improved the performances of the selected reinforcement learning algorithms.
Foreground Segmentation Improvement by Image Denoising Preprocessing Applied to Noisy Video Sequences

This work deals with the problem of recognizing the relevant pixels in a noisy video sequence (foreground segmentation). Instead of creating a new complex noise-resistant foreground segmentation method, the video frames are preprocessed to remove noise before inputting them to simpler and faster existing foreground segmentation methods. Autoencoder neural networks based on dense and convolutional layers as well as classical methods such as Mean and Median filters are selected as denoising methods in the preprocessing phase. The aim of the experiments is twofold: to verify whether old methods working on preprocessed images can be as effective as recent methods in the state of the art working on non-preprocessed images, and to study whether autoencoders can be adequate for noise removal.
Fault injection in optical path - detection quality degradation analysis with Wasserstein distance

The goal of this paper is to present results of analysis of artificially generated disturbances imitating real defects of camera that occurs in the process of testing autonomous vehicles both during rides and later, in vehicle software simulation and their impact on quality of object detection. We focus on one perception module responsible for detection of other moving vehicles on the road. Injected faults are obliteration by particles and pulsating blinding. At the same time, we want to propose an examination approach scheme that will provide detailed information about distribution of quality in this comparative experiment. The method can be reused for different perception modules, faults, scene sets and also in order to compare new releases of main recognition software. To do so we combine statistical methods (Welch's ANOVA) and topological analysis (Clusterization over space of distributions, Wasserstein metric). Work provides a summary of the experiment for all data used and described by mentioned tools and examples of certain cases that illustrate general conclusions.
Research on improving students’ interest in learning based on CiteSpace.5.7.R2

To comprehensively understand the research situation and research trend in the field of "learning interest", this paper uses CiteSpace.5.7.R2 information visualization software as a research tool, selects 2400 literature data from the Web of Science core database from 1975 to 2021 and 984 literature data from the CNKI database from 1900 to 2021 for visual analysis, respectively from the aspects of national cooperation, institutional cooperation, keyword co-occurrence, co-citation network, etc. The results show that the current international research hotspot is the interest development model, the application of virtual reality technology, and so forth. The research hotspot in China is teaching reform. Finally, the paper puts forward the prospect of the research in the field of "learning interest" in China: strengthening the cooperation among colleges and universities; using science and technology in teaching, such as virtual reality, to improve students’ interest in learning.
Quality Histories of Past Extract Method Refactorings

Modern programming environments offer the Extract Method refactoring as a way to improve software quality by moving a source code fragment into a new method. This refactoring comes with an immediate positive feedback by shortening the refactored method. It can also increase code re-usage and encourage developers to remove code clones. The impact of refactorings on the software quality has been the topic of many research efforts. However, these refactorings are usually studied in groups. Therefore the metrics evaluated and the observation are not tailored to a specific refactoring, thus hiding a valuable insight on how practitioners use a refactoring in particular. In this paper, we conduct an assessment of the quality impact resulting from the Extract Method refactoring. Our results statistically confirm the tendency of the Extract Method to improve complexity and slightly worsen cohesion, respectively in 46% and 70% of the refactoring. In addition, we observe that the Extract Method favors re-use and reduces occurrences of code clones in 56% of the extracted methods. However, our results also show that some specific cases are contrary to the previously mentioned trends and that it is therefore necessary to study refactorings at a low granularity.
Modern WebQuest Models: Applications in Education

The paper presents the modeling of an educational WebQuest as a component of didactic information technology with special attention to the criteria and levels of success in its applications. A WebQuest is a problem-oriented task that requires the use of information resources available online. The relevance of using a WebQuest in the educational process is connected with the implementation of the competence-based approach, the main requirement of which is effective knowledge and skills management. The authors have developed primary mathematical models of a WebQuest based on set theory. Sample applications of these modern models in education are provided.
Neural Plasticity Networks

Neural plasticity is an important functionality of human brain, in which number of neurons and synapses can shrink or expand in response to stimuli throughout the span of life. We model this dynamic learning process as an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{0}$</tex> - norm regularized binary optimization problem, in which each unit of a neural network (e.g., weight, neuron or channel, etc.) is attached with a stochastic binary gate, whose parameters determine the level of activity of a unit in the network. At the beginning, only a small portion of binary gates (therefore the corresponding neurons) are activated, while the remaining neurons are in a hibernation mode. As the learning proceeds, some neurons might be activated or deactivated if doing so can be justified by the cost-benefit tradeoff measured by the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{0}$</tex> -norm regularized objective. As the training gets mature, the probability of transition between activation and deactivation will diminish until a final hardening stage. We demonstrate that all of these learning dynamics can be modulated by a single parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> seamlessly. Our neural plasticity network (NPN) can prune or expand a network depending on the initial capacity of network provided by the user; it also unifies dropout (when <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k=0$</tex> ), traditional training of DNNs (when <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k=\infty$</tex> ) and interpolates between these two. To the best of our knowledge, this is the first learning framework that unifies network sparsification and network expansion in an end-to-end training pipeline. Extensive experiments on synthetic dataset and multiple image classification benchmarks demonstrate the superior performance of NPN. We show that both network sparsification and network expansion can yield compact models of similar architectures, while retaining competitive accuracies of the original networks. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Our source code is available at https://github.com/leo-yangli/npns.
Implementation of Graph Isomorphism Algorithm on Shared Memory Architecture

In this research work, the parallel graph isomorphism algorithms for larger size graphs on multiple core processors have been discussed. To perform comparisons and matching of graphs using adjacency matrix, we require to perform Euclidean operation on thousands of nodes. The OpenMP is the most common API for parallel programming and provides different application features. Tiling and data decomposition technique is used for performing graph isomorphism between input graph and graph dataset. The new proposed algorithms for graph isomorphism are named as A2 and A3 which are found to be better in performance than the serial implementation. The performance evaluation of these algorithms is studied and compared on multicore architecture. Experimental implementation serially using OpenMP for A2 algorithm shows the speedup performance as 6.28× using general graph datasets, 6.10× using m2D dataset, 6.21x using m3D dataset, and 6.13× using m4D dataset. Experimental implementation serially using OpenMP for A3 algorithm shows the speedup performance as 9.98× using general graph datasets, 8.52× using m2D dataset, 9.47× using m3D dataset, and 8.66× using m4D dataset. The speedup achieved by algorithm A2 and A3 is improvised. By increasing graph size and utilizing more cores available, these algorithms show better speedup.