Reviewerly/exHarmony huggingface dataset:
A Unified Framework for Graph-Based Multi-View Partial Multi-Label Learning

Multi-view partial multi-label learning (MVPML) is a fundenmental problem where each sample is linked to multiple kinds of features and candidate labels, including ground-truth and noise labels.The key problem of MVPML is how to manipulate the multiple features and recover the ground-truth labels from candidate label set.To this end, this study designs a novel Graph-based Multi-view Partial Multi-label model named as GMPM, which combines the multi-view information detection, valuable label selection and multi-label predictor model learning into a unified optimization model.To be specific, GMPM first exploits the consensus information across multiple views by learning the view-specific similarity graph and fuses multiple graphs into a target one.Then, we divide the observed label set into two parts: the ground-truth part and the noise part, where the latter is associated with a sparse constraint to make sure the former is clean.Furthermore, we embed the learned unified similarity graph into the process of label disambiguation to restore a more reliable ground-truth label matrix.Finally, the resulting multi-label predictive model is learned with the help of ground-truth label matrix.Extensive experiments on six common used datasets demonstrate that the proposed GMPM achieves comparable performance over the state-of-the-arts.
Design Optimization of Noise Filter Using Quantum Annealer

The use of quantum annealers in black-box optimization to obtain the desired properties of a product with a small number of trials has attracted attention. However, the application of this technique to engineering design problems is still limited. Here, we demonstrate the applicability of black-box optimization with a quantum annealer to the design of electric circuit systems, focusing on $\pi$-type noise filters as an example. We develop a framework that uses quantum annealing to find the optimal location of electrical components and conductor paths connecting the components, and confirm that the learning process appropriately works over a number of trials to efficiently search for a design with high performance. The results show the potential applicability of quantum annealing to design problems of electric circuit systems.
ADL-GAN: Data Augmentation to Improve In-the-Wild ADL Recognition Using GANs

The types of Activities of Daily Living (ADL) a person performs or avoids, and underlying patterns can provide insights into physical and mental health, making passive ADL recognition from smartphone sensor data important.However, as people perform ADLs unequally in real life, ADL datasets collected in the wild can be extremely imbalanced, which presents a challenge to Machine Learning (ML) ADL classification.Prior solutions to mitigating imbalance, such as oversampling and instance weighting, reduce but do not completely eliminate the problem.We instead propose ADL-GAN, which utilizes translation Generative Adversarial Networks (GANs), to synthesize smartphone motion and audio sensor data to improve ADL classification performance.ADL-GANs augment the minority ADL of subject A by translating real samples from either 1) other ADLs where subject A has adequate data in Context-transfer ADL-GAN or 2) other subjects with adequate ADL data in Subject-transfer ADL-GAN.ADL-GANs utilize multi-domain and contrastive loss functions to perform many-to-many translations between ADL classes and subjects, respectively.Subject-transfer ADL-GAN outperformed baselines and improved balanced accuracy (BA) on an in-the-wild ADL dataset by 27.9 %, while context-transfer ADL-GAN performed best on a scripted dataset, improving the BA of baselines by 9.58 %.The augmented samples from ADL-GANs were shown to be more realistic and diverse than conditional GAN.
RemoteTouch: Enhancing Immersive 3D Video Communication with Hand Touch

Recent research advance has significantly improved the visual real-ism of immersive 3D video communication. In this work we present a method to further enhance this immersive experience by adding the hand touch capability (“remote hand clapping”). In our system, each meeting participant sits in front of a large screen with haptic feedback. The local participant can reach his hand out to the screen and perform hand clapping with the remote participant as if the two participants were only separated by a virtual glass. A key challenge in emulating the remote hand touch is the realistic rendering of the participant's hand and arm as the hand touches the screen. When the hand is very close to the screen, the RGBD data required for realistic rendering is no longer available. To tackle this challenge, we present a dual representation of the user's hand. Our dual representation not only preserves the high-quality rendering usually found in recent image-based rendering systems but also allows the hand to reach to the screen. This is possible because the dual representation includes both an image-based model and a 3D geometry-based model, with the latter driven by a hand skeleton tracked by a side view camera. In addition, the dual representation provides a distance-based fusion of the image-based and 3D geometry-based models as the hand moves closer to the screen. The result is that the image-based and 3D geometry-based models mutually enhance each other, leading to realistic and seamless rendering. Our experiments demonstrate that our method provides consistent hand contact experience between remote users and improves the immersive experience of 3D video communication.
Non-Interactive Publicly-Verifiable Delegation of Committed Programs

In this work, we present the first construction of a fully non-interactive publicly-verifiable delegation scheme for committed programs. More specifically, we consider a setting where Alice is a trusted author who delegates to an untrusted worker the task of hosting a program P, represented as a Boolean circuit. Alice also commits to a succinct value based on P. Any arbitrary user/verifier without knowledge of P should be convinced that they are receiving from the worker an actual computation of Alice’s program on a given input x. Before our work, the only object known to imply this challenging form of delegation was a SNARG/SNARK for $$\mathcal{N}\mathcal{P}$$ . This is because from the point of view of the user/verifier, the program P is an unknown witness to the computation. However, constructing a SNARG for $$\mathcal{N}\mathcal{P}$$ from standard assumptions remains a major open problem. In our work, we show how to achieve delegation in this challenging context assuming only the hardness of the Learning With Errors (LWE) assumption, bypassing the apparent need for a SNARG for $$\mathcal{N}\mathcal{P}$$ .
Fa'aliyyah Mawad Ta'lim Maharah al-Qira'ah li Tarqiyah Maharah al-Qira'ah al-Thullab

The purpose of this research was to determine the effectiveness of using teaching materials of reading to improve students’ reading skills at the Institute for Islamic Studies Ma’arif Nahdlatul Ulama (IAIMNU) in Metro Lampung. This research used the comparative quantitative method. For data analysis, the researchers used descriptive statistical analysis to produce the effectiveness of teaching materials for reading skills with a criterion based on descriptive perception in the degree to test the responses in the questionnaire scale. T-test using SPSS with Paired Sample Test was used to find out the differences between the results of before and after the experiment. Based on the statistical analysis, the value of experimental learning model was 14.462.The result of significance was Sig. (2-tailed) 000. The pre-test of academic achievement in reading - the post-test of academic achievement of reading was 8.295. The result of significance was Sig. (2-tailed) 000.The result of this research indicated that Hₒ was not acceptable, and H1was acceptable. The difference in the academic achievement of reading between before and after the experiment was indicative. Hence, the improvement of reading ability occurred after using reading materials designed based on David A. Kolb's experiential learning model.
Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning

Abstract Exploiting the properties of quantum information to the benefit of machine learning models is perhaps the most active field of research in quantum computation. This interest has supported the development of a multitude of software frameworks (e.g. Qiskit, Pennylane, Braket) to implement, simulate, and execute quantum algorithms. Most of them allow us to define quantum circuits, run basic quantum algorithms, and access low-level primitives depending on the hardware such software is supposed to run. For most experiments, these frameworks have to be manually integrated within a larger machine learning software pipeline. The researcher is in charge of knowing different software packages, integrating them through the development of long code scripts, analyzing the results, and generating the plots. Long code often leads to erroneous applications, due to the average number of bugs growing proportional with respect to the program length. Moreover, other researchers will struggle to understand and reproduce the experiment, due to the need to be familiar with all the different software frameworks involved in the code script. We propose QuASK, an open-source quantum machine learning framework written in Python that aids the researcher in performing their experiments, with particular attention to quantum kernel techniques. QuASK can be used as a command-line tool to download datasets, pre-process them, quantum machine learning routines, analyze and visualize the results. QuASK implements most state-of-the-art algorithms to analyze the data through quantum kernels, with the possibility to use projected kernels, (gradient-descent) trainable quantum kernels, and structure-optimized quantum kernels. Our framework can also be used as a library and integrated into pre-existing software, maximizing code reuse.
A simple approach for quantizing neural networks

In this short note, we propose a new method for quantizing the weights of a fully trained neural network. A simple deterministic pre-processing step allows us to quantize network layers via memoryless scalar quantization while preserving the network performance on given training data. On one hand, the computational complexity of this pre-processing slightly exceeds that of state-of-the-art algorithms in the literature. On the other hand, our approach does not require any hyper-parameter tuning and, in contrast to previous methods, allows a plain analysis. We provide rigorous theoretical guarantees in the case of quantizing single network layers and show that the relative error decays with the number of parameters in the network if the training data behave well, e.g., if it is sampled from suitable random distributions. The developed method also readily allows the quantization of deep networks by consecutive application to single layers.
Enhanced contrastive representation in network

Complex networks contain numerous unlabeled data. Extracting the information in these data and obtaining appropriate node representation poses a significant challenge. Recently, contrastive learning has gained wide acceptance as one of the most important methods in self-supervised learning. It has shown exceptional ability in capturing both the attribute and structural information in network, that provides a new way for network representation. In this paper, we propose the enhanced contrastive representation in network (ECRN). First, we recognize the issue that contrastive learning ignores the network structure in calculating the similarity of the joint distribution. To address the problem, ECRN proposes a feature combination method with additional structure information. Second, we retain the connection relationship of nodes in the low-dimensional embedding by constraining each layer of neural network. Third, we propose a new graph neural network framework, which independently compares each layer, and realizes hierarchical contrastive learning. ECRN obtains information-rich node representations by accumulating representations in each layer, enhancing the performance of downstream tasks. We tested the ECRN model on real datasets and compared its performance with well-known algorithms. Experiments show that the ECRN outperforms state-of-art methods in link prediction, clustering, and node classification tasks.
Exploring graph capsual network and graphormer for graph classification

Graph Neural Networks (GNNs) have achieved significant success in many scenarios. However, scalar-based features in GNN may be insufficient for the generation of graph representation via node representation, resulting in a lack of graph properties. Furthermore, the message aggregation operation of the original GNN suffers from not only a lack of global and structural information in graph but also the over-smoothing problem. As a result, we make efforts to alleviate the above problems of the original GNN. Inspired by the idea of capsule, we convert the node features into the form of capsules and utilize dynamic routing mechanism to generate the graph capsules from various perspectives and attention mechanism to highlight important graph capsules. Additionally, to empower the graph capsules with the ability to capture global and structural information in graph and semantic information between nodes, we introduce the Transformer architecture specific to graph data with GraphNorm to enhance the expressiveness of graph capsules and perform the interpretability analysis for the message passing between capsules and the generation process of capsules by visualization technology. Finally, we conduct extensive experiments on six common graph datasets to demonstrate CapsTrans superiority on the graph classification task over existing State-of-the-Art (SOTA) methods.
Representation learning of knowledge graphs with correlation-based methods

Knowledge graphs (KGs) are intrinsically incomplete. Knowledge representation learning methods deal with embedding components of a knowledge graph (i.e., entities and relations) into a low-dimensional vector space with the aim of predicting the missing links. The KG Embedding models use gradient descent-based optimization techniques to optimize their parameters, but they suffer from late convergence, making them inefficient for dynamic knowledge graphs. Moreover, generating and modeling negative samples has a great impact on the accuracy of the prediction model. To address these challenges, we propose a correlation-based knowledge representation learning (CKRL) method. We utilize three semantic spaces, namely entity, relation, and canonical spaces. The parameter values of these spaces, including transformation matrices and embedding of KG components, are determined by defining four optimization problems. Adapting least-squares and Lagrange multiplier techniques, these optimization problems are solved by time-efficient direct methods. To model positive and negative samples, we use two distinct relation spaces. Negative samples are generated by defining the concept of relational patterns used to measure the similarity between positive and negative samples. Evaluation results on link prediction task and training time analysis demonstrate the superiority of the proposed method in comparison with the state-of-the-art methods.
BHI: Embedded invisible watermark as adversarial example based on Basin-Hopping improvement

Traditional research on generating adversarial examples has mainly focused on artificially adding imperceptible perturbations to input examples. Typically, the perturbation information in adversarial examples has no practical meaning. This study proposes a novel scheme for generating adversarial examples by embedding invisible watermarks based on basin-hopping improvement (BHI). To produce adversarial examples, the proposed BHI scheme is implemented by embedding invisible watermarks into original images based on a data-hiding technique. Specifically, the BHI scheme determines the corresponding coordinates of host images and watermark sizes. By considering the specific coordinates and size, the BHI scheme invisibly embeds the watermarks into the host images to generate adversarial examples. Experimental results show that the attack success rate of the BHI scheme reaches 93.5%. The proposed BHI scheme not only completes the function of an adversarial attack but also protects the copyright of adversarial examples. Moreover, the adversarial examples exhibit outstanding visual and robust performance, providing additional visual protection to avoid the risk of attack.
Investigating diffraction phenomena with low-cost material and augmented reality

So far, there have only been a few articles in this column that discussed diffraction or augmented reality (AR) enhancements. In this article, we want to bring both aspects together and describe an experiment that can be used to investigate diffraction phenomena with low-cost materials and augmented reality in the classroom. Diffraction experiments in schools often have (at least) two difficulties:
Optimum phase estimation with two control qubits

Phase estimation is used in many quantum algorithms, particularly in order to estimate energy eigenvalues for quantum systems. When using a single qubit as the probe (used to control the unitary we wish to estimate the eigenvalue of), it is not possible to measure the phase with a minimum mean-square error. In standard methods, there would be a logarithmic (in error) number of control qubits needed in order to achieve this minimum error. Here, we show how to perform this measurement using only two control qubits, thereby reducing the qubit requirements of the quantum algorithm. To achieve this task, we prepare the optimal control state one qubit at a time, at the same time as applying the controlled unitaries and inverse quantum Fourier transform. As each control qubit is measured, it is reset to |0⟩ then entangled with the other control qubit, so only two control qubits are needed.
Synthetic aperture radar images denoising based on multi-scale attention cascade convolutional neural network

Abstract Synthetic aperture radar (SAR) images are often affected by speckle noise, which can hinder accurate interpretation and subsequent use of the images in applications such as target detection and segmentation. To address this issue, we propose a denoising algorithm based on a multi-scale attention cascade convolutional neural network (MSAC-Net). Our algorithm employs multi-scale asymmetric convolution to extract image features and an attention mechanism to integrate these features. Additionally, we designed a multi-layer deep cascade convolutional network to enhance the generalization ability of the model features. Experimental results show that our proposed MSAD-Net model significantly outperforms state-of-the-art SAR image denoising algorithms. Specifically, it achieves a significant improvement in peak signal-to-noise ratio, with an increase of about 0.81–13.97 dB, and structural similarity index measure, with an increase of about 0.01–0.14. Overall, our study presents a novel denoising algorithm for SAR images that greatly improves the accuracy of subsequent image applications.
Comparing cost sensitive classifiers by the false-positive to false- negative ratio in diagnostic studies

Nowadays researchers want to be cautious about cost of building models which can generate false positives and false negatives in unexpected ways. They keep on searching for various measures for controlling such behavior depending upon the underlying datasets. Cost sensitive classifiers are the models to check the total cost due to misclassifications. In this article, the cost sensitive classifiers are tried for the first time endowed with a new measure ‘cost ratio’ to monitor such misclassifications in the sensitive diagnostic studies. The scheme for variations of such ratio is introduced and its influence on the loss is investigated. This cost ratio, a rational number, ρ is made up of the integers for the cost of false positive by its’ frequency of occurrences, in the numerator and the similar cost of false negative in the denominator. We apply this novel cost monitoring measure for learning the sample dataset of sensitive nature in the context of in vitro fertilization (IVF) dataset indicating the success or failure of fertilization depending on the attributes like Age, Anti-Müllerian hormone (AMH), Right ovary (RO), Left Ovary (LO), Number of eggs, No of Inseminations, No of fertilized and Egg quality. This article mainly makes focus on variations of different ranges of cost ratio ρ and establishes the possibility of reducing errors in the predictions made.
Integration of Context-Based Learning with Informative Chatbot for Grassroots Farmers

With frustrating environmental changes, many farmers struggle with their productivity and quality of their crops. As they were tempted to use chemicals to their farms, they were required to update their knowledge and skills in order to achieve proper use and safety for their crops, their lands and their lives. This study was a part of a rice farmer training project which aimed to achieve effective training program that could enhance farmers’ knowledge in rice farming when the chemicals were inevitable for some areas. An explorative study was conducted to investigate insight and needs of knowledge of chemical usage. Then the insight was used in designing and developing an active lesson with informative chatbot, so called ‘Smart Farmer’. This chatbot has served as a knowledge-based agent to facilitate the active lesson with Reflect-Connect-Apply context-based approach for the grassroots farmers in Thailand. The in-dept interview and questionnaire were conducted after the context-based lesson. The results show that a considerate design with most to least frequent rankings of chemicals usage and farming troubles should be incorporated in the context of class instructive flow of the training program as well as in the design of chatbot and other instructive materials.
Human’s Reaction Time Based Score Calculation of Self-practice Dynamic Yoga System for User’s Feedback by OpenPose and Fuzzy Rules

In this study, the proposed self-practice dynamic Yoga system at home with instructor-user synchronization provided a new score calculation as user’s feedback. By following the guide of the instructor’s video, a proper score feedback indicates whether the user’s dynamic Yoga is good or bad, and helps users to understand how to improve their movements and postures. In the new score calculation, it considers two types of scores. The first type of scores is "Shape_scores", and the system compared the similarity of static postures between the instructor and user, and then the "Shape_scores" is calculated through the scoring system based on fuzzy rules with the angle difference values between the instructor and user. The second type of scores is "Delay_scores" by appending on the human’s average reaction time and average recognition time as thresholds to set the five different regions to give the scores for delays. By weighting these two scores and then summing them up, a new score is calculated finally. In the experimental results, the score schemes between the "Shape_scores" and the "Delay_scores" are discussed, and the tester is a lazy mode user who means the user simulates the elderly to observe the influence on the two different scores calculations.