Reviewerly/exHarmony huggingface dataset:
Using optical flow algorithm based on dynamic illumination mode to examine defects on highly reflective turbine blade surface

Notion of optical flow literally refers to the displacements of intensity patterns. In that sense, extracting interested information from 2D scene is analogy to modulation/demodulation in random signal processing. To address the limitations presented in computer vision based on static image, we propose a novel metal component defect detection method, specified as the instance of turbine blade surface detection, using optical flow estimation.To start the specified pattern recognition in 2D presentation, we modulate the brightness constancy assumption equation as illumination varying model, by sampling the second image with function whose frequency was chosen according to the Nyquist sampling theorem, and a sinusoidal factor was introduced as an additive factor. This tunable channel based on 2D image transfers intensity features into optical modes. Then, we implement optical flow estimation on two sequential images. Experimental results reveal grayscale space shows completness in representing the optical modes of turbine blade with various kinds of surface characteristics. By modifying the index of information content, we propose quantitative index to evaluate the performance of our method. Evaluation reveals optical flow algorithm is qualified to examine defects on highly reflective turbine blade, and our method extends the application of optical flow.
Combine unlabeled with labeled MR images to measure acute ischemic stroke lesion by stepwise learning

Acute ischemic stroke is a common threat to human health and may obtain timely treatment by fast localizing and quantitatively evaluating the lesions. Most CNN-based methods try to segment and measure the lesions, however, they require a training on a large number of labeled subjects that are labor-intensive and time-consuming to obtain. In this paper, a method is proposed that can combine limited labeled subjects with abundant unlabeled subjects to alleviate the problem. The proposed method consists of two stages: stepwise learning process and segmentation process. Stepwise learning is used to obtain the pretrained encoder. The pretrained encoder and the proposed decoder are connected into a new end-to-end segmentation network, which is retrained on the labeled subjects in the segmentation process. By using 5 labeled subjects and 79 unlabeled subjects, the proposed method achieves a mean dice coefficient of 0.663 ± $\,{\pm}\,$ 0.205, a mean average symmetric surface distance (ASSD) of 2.17 mm and a mean 95 percentile Hausdorff distance (HD) of 18.38 mm on a clinical MR dataset with 179 subjects. More importantly, it achieves lesion-wise F1 score of 0.857 and a subject-wise detection rate of 0.966.
Solution Existence Theory for Artificial Neural Networks

Artificial neural networks (NNs) have been widely used in creation of various machine learning (ML) models. Training an NN for a given dataset is essentially finding a set of weights and biases, known as learning (or training) parameters. An NN uses the affine transformations to encode the features in the dataset in the form of learning parameters. One may naturally ask fundamental questions: (1) can the NN be trained, meaning that can a solution for a set of learning parameters can be found? and (2) is the solution unique? This paper addresses these questions. First, detailed formulation is presented for a simple NN with just one basic affine transformation unit (ATU) for linear regression problems. Conditions for solution existence and uniqueness will be examined, leading to the Solution Existence Theory. A discussion will be presented when this Solution Existence Theory is extended to general NNs for complex problems, in addition to a discussion on predictability of NNs.
A new modified grasshopper optimization algorithm

The grasshopper algorithm (GOA) is a recent algorithm. It is widely used in many applications and results in a good solution. The algorithm is simple and the accuracy in very high. The GOA has some limitations due to the use of linear comfort zone parameter that causes some difficulties in balancing between the exploration and exploitation which may lead to fall in a local optimum. In this paper a modification is made to improve the operation of GOA. A nonlinear function is developed to replace the linear comfort zone parameter. The benchmark of GOA authors is used for testing the performance improvement of the suggested modified GOA compared to the basic GOA. Results indicate that the MGOA outperforms original GOA, presenting a higher accuracy, faster convergence, and stronger stability. The proposed new modified GOA performs better than the original GOA.
Solar radiation resource

The sun is the main source of renewable energy. It emits radiation with a constant intensity outside the earth's atmosphere. Only direct (beam) radiation is available outside the earth's atmosphere. As solar radiation propagates through the atmospheric matter, it is attenuated, producing direct and diffuse components, which reach the earth surface. Only the direct component can be focused and is, therefore, useful in the development of concentrating solar power (CSP) technologies. Absorption of solar radiation by a receiving surface is maximum when the angle of incidence is zero, and the surface is said to receive direct normal irradiation (DNI). The recommended minimum annual sum of DNI for CST technology to be economically feasible is 2000 kWh m−2, and many locations within the sunbelt meet this threshold. This level of available solar energy resource is attractive for the advancement of the CSP technology.
Toward Efficient Ensemble Learning with Structure Constraints: Convergent Algorithms and Applications

Ensemble learning methods, such as boosting, focus on producing a strong classifier based on numerous weak classifiers. In this paper, we develop a novel ensemble learning method called rescaled boosting with truncation (ReBooT) for binary classification by combining well-known rescaling and regularization ideas in boosting. Theoretically, we present some sufficient conditions for the convergence of ReBooT, derive an almost optimal numerical convergence rate, and deduce fast-learning rates in the framework of statistical learning theory. Experimentally, we conduct both toy simulations and four real-world data runs to show the power of ReBooT. Our results show that, compared with the existing boosting algorithms, ReBooT possesses better learning performance and interpretability in terms of solid theoretical guarantees, perfect structure constraints, and good prediction performance. History: Accepted by Ram Ramesh, Area Editor for Data Science &amp; Machine Learning Funding: This work was supported by the National Natural Science Foundation of China [Grants 11971374, 61772374, and 61876133]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.1224 .
Research on calibration device of laser demarcation instruments

The accuracy of Laser Demarcation Instruments (LDIs) determines the efficiency and quality of subsequent measurements. After investigating the existing calibration method of LDIs, a new LDIs calibration device is designed in this paper based on image vision detection technology. The calibration method is convenient, high detection accuracy and high efficiency, and realizes digital calibration. This paper completes the overall design of the calibration system for LDIs, designs the mechanical structure of the image acquisition device and the working platform of the LDIs, and selects the relevant devices. The mechanical structure of the system is composed of an image acquisition device, a standard vertical line and a working platform for LDIs, which can calibrate the laser line width, horizontal tilt error of the laser line, and vertical tilt error of the laser line. Experiments were carried out, and the collected laser line images were processed by the computer to record and save the calibration results. The measurement uncertainty of the measurement results was evaluated.
Multimodal prediction of breast cancer using radiogenomics and clinical trials with decision fusion

Multimodal analysis focuses on the internal and external manifestations of cancer cells to provide physicians, oncologists and surgeons with timely information on personalized diagnosis and treatment for patients. Decision fusion in multimodal analysis reduces manual intervention, and improves classification accuracy facilitating doctors to make quick decisions. Genetic characteristics extracted on biopsies do not, however, provide details on adjacent cells. Images can only provide external observable details of cancer cells. While mammograms can detect breast cancer, region wise details can be obtained from ultrasound images. Hence, different types of imaging techniques are used. Features are extracted using the SelectKbest method in the Wisconsin Breast Cancer, Clinical and gene expression datasets. The features are extracted using Gray Level Co-occurrence Matrix from Histology, Mammogram and Sonogram images. For image datasets, the Convolution Neural Network (CNN) is used as a classifier. The combined features from clinical, gene expression and image datasets are used to train an Integrated Stacking Classifier. The integrated multimodal system’s effectiveness is shown by experimental findings.
Quantum analog of the Kolmogorov-Arnold-Moser theorem in the anisotropic Dicke model and its possible implications in the hybrid Sachdev-Ye-Kitaev models

The classical Kolmogorov-Arnold-Moser (KAM) theorem provides the underlying mechanism for the stability of the solar system under some small chaotic perturbations. Despite many previous efforts, any quantum version of the KAM theorem remains elusive. In this work, we provide a quantum KAM theorem in the context of the anisotropic Dicke model, which is the most important quantum optics model. It describes a single mode of photons coupled to $N$ qubits with both a rotating wave (RW) term and a counter-RW (CRW) term. As the ratio of the CRW over the RW term increases from zero to one, the systems evolves from quantum integrable to quantum chaotic. We establish a quantum KAM theorem to characterize such an evolution quantitatively by both large-$N$ expansion and random matrix theory and find agreement from the two complementary approaches. Connections and differences between the Dicke models and Sachdev-Ye-Kitaev (SYK) or hybrid SYK models are examined. A possible quantum KAM theorem in terms of other quantum chaos criteria such as the quantum Lyapunov exponent is also discussed.
A new algorithm for compression of partially commutative alphabets

In this paper, we will address the problem of how to compress sequences defined in partially commutative alphabets. The use of partial order between symbols may result in far more efficient compression schemes than the most commonly used compression techniques, which use total order. We suggest a new algorithm for compressing sequences defined in alphabets with a partial order. This new algorithm is named the Lempel–Ziv with Partial Order (POLZ) and stands for a Lempel–Ziv (LZ78) generalization. We will show how to compress and decompress sequences by utilizing the POLZ. In addition, we establish the methods for determining the upper bounds for sequence complexity by using the notion of equivalence classes. We will also demonstrate that POLZ is asymptotically optimal, achieving an even greater compression rate when compared to the LZ78 when applied to sequences emanating from concurrent processes. Furthermore, we will show that the complexity obtained by using the LZ78 is also an upper bound for the POLZ complexity. We have developed examples in robotics and real-time systems to show how to use and the benefits of using our algorithm for compression within the context of concurrent systems. When the concurrency of the processes is taken into account, all experimental results show a significant compression gain.
Directed Acyclic Graph networks to characterize phase space evolution with application to musical composition and industrial maintenance

Phase space networks built on proximity of reconstructed state space points from time series are useful for system identification and characterization. We consider causal proximity networks where directed edges connect vertices corresponding to future states to past states. Here, we introduce a new network which captures how induced proximity network subgraphs evolve in time. Due to the causal nature of the proximity network these subgraphs are directed acyclic graphs (DAG) and the new network is a transition network. This network can be summarized using entropy-like properties. We show how to incorporate this new network into a data analysis pipeline and highlight its potential to detect dynamical change and the evolving geometry of state space. Applications to music composition and industrial maintenance are used to illustrate the promise of the approach.
Light field extraction from a conventional camera

This paper presents a single-shot light field (LF) acquisition method using a conventional camera. The proposed method does not require any modification in the camera design and instead involves an offline calibration process. The calibration step involves the estimation of the point spread function (PSF) for different perspectives at all sampled depths. PSF relates perspective image captured with a narrow aperture opening to the central image captured with wider aperture opening containing multiple perspectives of the same scene superimposed on each other. During LF extraction, different perspective images are obtained by deconvolving the wide aperture input image with the set of PSFs estimated in the offline calibration phase. The experimental results demonstrate that the proposed method generates high angular and spatial resolution LF.
Evolving multi-label fuzzy classifier with advanced robustness respecting human uncertainty

Multi-label classification has attracted much attention in the machine learning community to address the problem of assigning single samples to more than one class at the same time. We propose an evolving multi-label fuzzy classifier (EFC-ML-FWU) which is able to self-adapt and self-evolve its structure and consequent parameters in the form of multiple hyper-planes with new incoming multi-label samples in an incremental, single-pass manner and which especially addresses the intrinsic curse of dimensionality as well as human label uncertainty problems, often apparent in multi-label classification problems, to ensure the advanced robustness of the learned structure and parameters. The former is achieved by integrating feature weights into the learning process, specifically designed for online multi-label classification problems in an incremental manner, measuring the impact of features with respect to their discriminatory power. The features are integrated (i) into the rule evolution criterion, leading to a shrinkage of distances along unimportant dimensions, which reduces the likelihood of unnecessary rule evolution and thus decreases over-fitting due to the curse of dimensionality, (ii) into the first consequent learning part by a variable-regularized RFWLS approach realized through an incremental coordinate descent algorithm, and (iii) into the second consequent learning part employing correlation-based preservation learning by using weight-based thresholds (extending the classical Lipschitz constant-based threshold) within soft shrinkage operations to optimize a feature-based weighted L1-norm on the consequent parameters. Uncertainty in class labels is handled by an integration of sample weights, where lower weights indicate a higher uncertainty in the labels carried by a sample. This leads to (i) a weighted updating of the incremental feature weights, (ii) a weighted update of the rule antecedent space through a weighted incremental clustering process, and (iii) a specific weighted update of the consequent parameters exploring a single-label and a multi-label view of uncertainty. Our approach was evaluated on several data sets from the MULAN repository and showed significantly improved classification accuracy and average precision trend lines compared to alternative (evolving) one-versus-rest or classifier chaining concepts, and especially improved the native EFC-ML method without feature weights and uncertainty handling with performance gains up to 17% in the AUC of the accuracy trends. Furthermore, interesting insights into an improved robustness of the multi-label classifier (i) in the case of wrong labels due to low user experience levels and (ii) in the case of low label certainties but potentially correct labels were obtained.
Efficient mining of concept-hierarchy aware distinguishing sequential patterns

Distinguishing sequential patterns are sequential patterns that have much higher frequencies in one target group of sequences (concerning a given phenomenon of interest) than in a contrasting group of sequences. Distinguishing sequential patterns are useful for many machine learning tasks, as well as for the explanation and characterization of the phenomenon underlying the target group of sequences. However, previous studies on mining distinguishing sequential patterns did not consider the hierarchical relationship among elements in sequences. To fill the gap, this paper investigates the mining of distinguishing sequential patterns in the presence of concept hierarchies among sequence elements. The associated patterns will be called concept-hierarchy aware distinguishing sequential patterns (hDSPs). After presenting the challenges on mining hDSPs, we present hDSP-Miner, a method with effective pruning techniques, for mining hDSPs. Our empirical study using real-world protein sequences demonstrates that hDSP-Miner is effective and efficient, and it can discover more novel distinguishing sequential patterns than previous algorithms for mining distinguishing sequential patterns.
Energy-driven reference selection for hierarchical light field compression

The vast amount of light field data taken by plenoptic camera poses a great challenge for compression. Researchers attempt to design various sub-aperture image (SAI) prediction algorithms to reduce the redundancy inside the light field. However, they ignore the fact that it may limit the performance of light field compression when selecting the reference SAIs without regard to the scene content. In this paper, we propose a novel reference SAIs selection algorithm for a hierarchical compression structure from the perspective of light field energy maximization. Specifically, we firstly apply a low-rank model to the feature domain of light field, in which the features are extracted by the difference of Gaussian. In this way, a few SAIs containing sufficient light field energy are selected as the references, and the light field hierarchical structure is accordingly constructed by these selected SAIs and the remainder SAIs. Then, for better prediction of remainder SAIs in the hierarchical structure, we propose a compressive sensing-based weight determination algorithm, which computes the weight parameters for each selected reference SAI in pursuit of minimizing the prediction errors. Experimental results demonstrate that the hierarchical light field compression structure with the proposed reference SAIs selection algorithm can achieve better compression performance, as well as superior refocusing and depth-of-field extending capabilities compared to state-of-the-art methods.
A Quantum Group Signature Based on Quantum Walk in d Dimensions

In this paper, a group signature scheme based on quantum walk for quantum messages is proposed. Our scheme uses long step quantum walk-based teleportation and modified quantum one-time pad to authenticate the quantum messages respectively. In our scheme, the signer in a group signs the quantum messages by quantum walk-based teleportation and modified quantum one-time pad. The verifier can verify the signature via quantum walk-based teleportation while the group manager verifies the signature and identifies the signer via modified quantum one-time pad. The security analysis shows that the scheme can reach the properties of group signature. Compared to the teleportation via EPR pairs or Bell-like states, quantum walks are more flexible and use less measurement resources. Quantum teleportation via long-step quantum walk is more secure than that via one-step quantum walk in previous related works. The scheme can apply to arbitrary finite dimensional quantum systems and can also be possible to realize in practice.
Hypernetwork Representation Learning With the Transformation Strategy

In real life, there are many cases that cannot be described by the network abstracted as the graph, but can be described perfectly by the hypernetwork abstracted as the hypergraph. Different from the network, the hypernetwork structure is more complex and poses a great challenge to the existing network representation learning methods. Therefore, in order to overcome the challenge of the hypernetwork structure, a hypernetwork representation learning method with the transformation strategy is proposed. Firstly, as three types of transformation strategies from the hypergraph to the graph, line graph, incidence graph and 2-section graph are combined into three types of integral graphs with the hyperedge information, namely incidence graph + 2-section graph, line graph + incidence graph and line graph + incidence graph + 2-section graph. Secondly, a shallow neural network algorithm is trained respectively on five types of networks abstracted as incidence graph, 2-section graph, incidence graph + 2-section graph, line graph + incidence graph and line graph +incidence graph + 2-section graph to obtain node representation vectors. Finally, the evaluation experiment is conducted on four different types of hypernetwork datasets. The experimental results demonstrate that the node classification performance of 2-section graph is better than that of other graphs, and the link prediction performance of incidence graph + 2-section graph is better than that of other graphs.
Higher Education in the Philippines

This chapter presents an overview of the current state of the higher education sector in the Philippines. It also highlights key challenges, including access, inclusion and equity, and quality of higher education. Inequitable and inadequate household income issues bring about the abovementioned challenges, the lack and poor implementation of laws and policies for inclusion and equity, the profit-seeking nature of nonsectarian private higher education institutions, and the varying quality of higher education institutions and programs. Recent developments in the sector include the Universal Tertiary Education Act of 2017 (granting free tuition and miscellaneous fees for first-degree students studying at public higher education institutions), ongoing international benchmarking (with the shift to outcomes-based competency standards), and multistakeholder participation in developing minimum curriculum standards. Such initiatives have partly addressed access, quality, and relevance issues in Philippine higher education. The Government’s role through the Commission on Higher Education and the alignment of the higher education sector with the Philippine Development Goals and international commitments, including the UN 2030 Sustainable Development Goals, are presented. The key trends in Philippine higher education include an increased focus on the internationalization of higher education, enhancing global and regional partnerships, and the shift to outcomes-based competency standards. The utilization of Information and Communication Technology (ICT), which the COVID-19 pandemic has accentuated, is also discussed.