Reviewerly/exHarmony huggingface dataset:
Stationary states of a dissipative two-qubit quantum channel and their applications for quantum machine learning

Entangled-state preparation and preservation are the cornerstones of any quantum information platform. However, the strongest adversaries in quantum information science are unwanted environmental effects such as decoherence and dissipation. Here, we address how to control and harness these unwanted effects that arise from the coupling of a system with its environment, to provide stationary entangled states for quantum machine learning (QML). To do so, we design a dissipative quantum channel, i.e., a two-qubit system interacting with a squeezed vacuum field reservoir, and study the output state of the channel by solving the corresponding master equation, especially, in the small squeezing regime. We show that the time-dependent output state of the channel is the so-called two-qubit X-states that generalize many families of entangled two-qubit states. Also, by considering a general Bell diagonal state as the initial state of the system, we reveal that this dissipative channel generates two well-known classes of entangled mixed state and Werner-like states in the steady-state regime. Moreover, this channel provides an efficient way to determine whether a given initial state results in a stationary entangled state or not. Finally, we examine the potential application of the designed two-qubit channel for QML. In this line, we propose a general theoretical scheme for quantum neural networks (QNNs) implemented with the variational quantum circuits, which encode data in continuous variables (CVs) of the two-qubit states. The linear (also referred to as affine) and nonlinear (activation function) transformations are enacted in the QNN using the stationary states of the two-qubit channel and measurement process, respectively. Finally, we proceed to test our proposed model by solving some supervised binary classification tasks. Integrating the non-unitary transformation and parallel-processed neural computing on such a two-qubit channel establishes the requirements for a meaningful QNN. Such a CV-QNN model with sufficient layers may execute any algorithm implementable on a universal CV quantum computer.
Systematic scoping review of the implementation, adoption, use, and effectiveness of digital contact tracing interventions for COVID-19 in the Western Pacific Region

A systematic scoping review of digital contact tracing (DCT) interventions for COVID-19 was conducted to describe the implementation, adoption, use and effectiveness of DCT interventions implemented as part of the COVID-19 response in the Western Pacific Region (WPR). A systematic search identified 341 studies and 128 grey literature sources, of which 18 studies and 41 grey literature sources were included. 17 (46%) WPR countries and areas implemented DCT interventions. Adoption ranged from 14.6% to 92.7% in different adult populations and epidemiological contexts. Trust in authorities, and privacy concerns and beliefs, were the most frequent determinants of adoption and use. Only two studies analysed DCT effectiveness, which showed limited to no effectiveness of DCT interventions in low transmission settings. Overall, there is limited evidence available to evaluate the contribution of DCT to mitigating COVID-19 in the WPR. Preparedness for future health emergencies should include developing robust frameworks for DCT effectiveness evaluations.
Chosen ciphertext correlation power analysis on Kyber

The post-quantum cryptography (PQC) standardization process of the National Institute of Standards and Technology (NIST) has entered the final round. Kyber is one of the four key encapsulation mechanism (KEM) finalists. The coefficient multiplication is vulnerable to side-channel attacks. In this paper, we analyzed the multiplication part of Kyber and proposed the methods for constructing ciphertext. By using the chosen ciphertext, more functions can be attacked in the reference implementation of Kyber512. The attacks succeed on the pqm4 implementation with chosen ciphertext while failing with random ciphertext in our experiment environment. For all the attacks, the full secret key recovery can be achieved in a few minutes.
A Comparative Study on the Effort of Human Translation and Post-Editing in Relation to Text Types: An Eye-Tracking and Key-Logging Experiment

With the increasing utilization of Machine Translation, it is worth exploring in what areas it actually reduces translators’ effort. This study focuses on the English-Chinese language pair and compares the effort of human translation (HT) and that of post-editing (PE) in relation to text types, covering advertising, news, legal, and literary texts, via an eye-tracking and key-logging experiment and a follow-up questionnaire survey. It refers to Krings’ framework and explores effort in terms of temporal, cognitive, and technical dimensions. Data were obtained from 33 Chinese student translators, and data analyses lead to the following conclusions. First, PE involves less effort than HT, and PE increases productivity and improves translation quality. Second, the tendency that PE involves less effort is seen in the advertising, news, and literary text types, but the measures of effort show variation for the legal text. Third, the objective measures, including Seconds per Target Word, Edits per Target Word, Pause Count per Target Word, Pause Duration per Target Word, Source Fixation Duration per Source Word, and Source Fixation Count per Source Word, are reliable and well correlated with subjective measures.
DreamNet: A Deep Riemannian Manifold Network for SPD Matrix Learning

The methods of symmetric positive definite (SPD) matrix learning have attracted considerable attention in many pattern recognition tasks, as they are eligible to capture and learn appropriate statistical features while respecting the Riemannian geometry of SPD manifold where the data reside on. Accompanied with the advanced deep learning techniques, several Riemannian networks (RiemNets) for SPD matrix nonlinear processing have recently been studied. However, it is pertinent to ask, whether greater accuracy gains can be realized by simply increasing the depth of RiemNets. The answer appears to be negative, as deeper RiemNets may be difficult to train. To explore a possible solution to this issue, we propose a new architecture for SPD matrix learning. Specifically, to enrich the deep representations, we build a stacked Riemannian autoencoder (SRAE) on the tail of the backbone network, i.e., SPDNet [23]. With this design, the associated reconstruction error term can prompt the embedding functions of both SRAE and of each RAE to approach an identity mapping, which helps to prevent the degradation of statistical information. Then, we implant several residual-like blocks using shortcut connections to augment the representational capacity of SRAE, and to simplify the training of a deeper network. The experimental evidence demonstrates that our DreamNet can achieve improved accuracy with increased depth.
PathTR: Context-Aware Memory Transformer for Tumor Localization in Gigapixel Pathology Images

With the development of deep learning and computational pathology, whole-slide images (WSIs) are widely used in clinical diagnosis. A WSI, which refers to the scanning of conventional glass slides into digital slide images, usually contains gigabytes of pixels. Most existing methods in computer vision process WSIs as many individual patches, where the model infers the patches one by one to synthesize the final results, neglecting the intrinsic WSI-wise global correlations among the patches. In this paper, we propose the PATHology TRansformer (PathTR), which utilizes the global information of WSI combined with the local ones. In PathTR, the local context is first aggregated by a self-attention mechanism, and then we design a recursive mechanism to encode the global context as additional states to build the end to end model. Experiments on detecting lymph-node tumor metastases for breast cancer show that the proposed PathTR achieves the Free-response Receiver Operating Characteristic Curves (FROC) score of 87.68%, which outperforms the baseline and NCRF method with +8.99% and +7.08%, respectively. Our method also achieves a significant 94.25% sensitivity at 8 false positives per image.
HiCo: Hierarchical Contrastive Learning for Ultrasound Video Model Pretraining

The self-supervised ultrasound (US) video model pretraining can use a small amount of labeled data to achieve one of the most promising results on US diagnosis. However, it does not take full advantage of multi-level knowledge for learning deep neural networks (DNNs), and thus is difficult to learn transferable feature representations. This work proposes a hierarchical contrastive learning (HiCo) method to improve the transferability for the US video model pretraining. HiCo introduces both peer-level semantic alignment and cross-level semantic alignment to facilitate the interaction between different semantic levels, which can effectively accelerate the convergence speed, leading to better generalization and adaptation of the learned model. Additionally, a softened objective function is implemented by smoothing the hard labels, which can alleviate the negative effect caused by local similarities of images between different classes. Experiments with HiCo on five datasets demonstrate its favorable results over state-of-the-art approaches. The source code of this work is publicly available at https://github.com/983632847/HiCo .
Skeleton estimation of directed acyclic graphs using partial least squares from correlated data

Directed acyclic graphs (DAGs) are directed graphical models that are well known for discovering causal relationships between variables in a high-dimensional setting. When the DAG is not identifiable due to the lack of interventional data, the skeleton can be estimated using observational data, which is formed by removing the direction of the edges in a DAG. In real data analyses, variables are often highly correlated due to some form of clustered sampling, and ignoring this correlation will inflate the standard errors of the parameter estimates in the regression-based DAG structure learning framework. In this work, we propose a two-stage DAG skeleton estimation approach for highly correlated data. First, we propose a novel neighborhood selection method based on sparse partial least squares (PLS) regression, and a cluster-weighted adaptive penalty is imposed on the PLS weight vectors to exploit the local information. In the second stage, the DAG skeleton is estimated by evaluating a set of conditional independence hypotheses. Simulation studies are presented to demonstrate the effectiveness of the proposed method. The algorithm is also tested on publicly available datasets, and we show that our algorithm obtains higher sensitivity with comparable false discovery rates for high-dimensional data under different network structures.
Adaptive risk-sensitive filter for Markovian jump linear systems

In this paper, we present divergence-based minimax methods to derive the adaptive risk-sensitive filter for Markovian jump linear systems with parameter uncertainties. The risk-sensitive parameter is treated as a random variable rather than chosen as a prior, which reflects the hypothesis merging property. To tackle the proposed joint probability density function due to the introduction of the random variable, a method that projects orthogonally the original probability measure into a new independent conditional probability space in the Kullback–Leibler (KL) sense is then presented. The exact close-form expression can be obtained via optimization that is of a form suitable in a recursive procedure. The proposed filter can improve robustness by increasing the mixed covariance, whose risk-sensitive like parameter can be updated adaptively using the measurements to fit different situations well. Simulation examples illustrate that the proposed methods can be regarded as competitive alternatives to existing robust approaches against modeling uncertainties.
Feature Sampling based on Multilayer Perceptive Neural Network for image quality assessment

Image quality assessment (IQA) has a vital issue in image processing to measure the perceptual quality of the image. This aims at the human visual system (HVS) by viewing for distorted images from reference images. HVS is the ultimate receiver of visual data in major applications. But, quality assessment is generally a time-consuming and luxurious method. In our work, the novel Bilateral Smoothing Filtered Feature Sampling-Based Tanimoto Index’ve Multilayer Perceptive Neural Network (BSFFS-TIMPNN) technique is developed. The main contribution of the designed BSFFS-TIMPNN technique is for enhancing the quality of the assessment performance with full reference and no reference images with multiple layers. At first, the original and reference image is taken to the input layer and sent to the first hidden layer. Followed by, image preprocessing is performed in the first hidden layer by using the novelty of a nonlinear bilateral smoothing filtering technique for eradicating noisy pixels with a higher-quality image. Next, patch extraction is executed in the second hidden layer with the novelty of the nearest neighbor sampling approach to split images into various patches. Then, the different features of the image such as shape, color, and texture are extracted in the third hidden layer. Finally, classification is carried out output layer by applying the innovation of the Tanimoto similarity index to examine the extracted features for IQA. We estimate the proposed BSFFS-TIMPNN technique on the Tampere Image Database (TID2013) dataset with qualitative and quantitative results analysis. The proposed BSFFS-TIMPNN technique is employed to compute image quality with maximum accuracy and minimum time and memory consumption when compared to other related methods by MATLAB simulator. The observed quantitative result demonstrates the better performance of the proposed BSFFS-TIMPNN technique by full reference and no reference with higher PSNR by 8%, accuracy by 15%, 17%, lesser memory consumption by 15%, 17, and faster prediction time by 9%, 9% than the state of art methods respectively.
An overview of blockchain efficient interaction technologies

The successful operation of Bitcoin has made its underlying blockchain technology receive wide attention. As the application scenarios of blockchain technology are enriched, the requirements for its performance are getting higher. Therefore, it is of utmost importance to effectively solve the problem of high-performance data interaction in the blockchain. In this paper, based on relevant domestic and foreign research literature, we start from the development history of blockchain technology and review the relevant research work on improving the performance of blockchain from three perspectives: on-chain interaction technology, off-chain interaction technology, and cross-chain interaction technology in turn. The on-chain and off-chain interaction technologies improve performance by improving the architecture of the blockchain system. The performance improvement solution of on-chain interaction technology is to modify and optimize the basic protocol and architecture of the blockchain itself to achieve a performance improvement. Still, the impact of this approach is limited in terms of performance improvement. The performance improvement solution of off-chain interaction technology is to transfer part of the data processing to off-chain and only return the final result to on-chain for storage and recording, which reduces the burden of on-chain operation and improves the efficiency of data processing. In terms of cross-chain interaction technology, this paper analyses four mainstream technology, namely, Notary Scheme, Side chain and Chain relay, Hash-Locking, and Distributed Private Key Control, and ultimately concludes through comparative analysis that cross-chain technology has a significant impact on improving blockchain performance. Finally, the paper provides a systematic overview of the above and an outlook on the possible future development of technologies related to enhancing blockchain performance.
Improving Students' Speaking Ability in Expressing Opinion through Think Pair Share

A difficult speaking in English has become one of the student problems in English learing. The application of using Think Pair Share method is needed for students to improve students’ speaking ability in expressing opinion.. This is classroom action research which has purpose to describe whether think pair share method can improve students’ speaking ability in expressing opinion at eighth grade of SMPN1 Kotabumi. The research procedure consist of four steps, namely: planning, implementation, observation and reflection. Each cycle consist of two meetings and one assessment to measure students’ speaking ability in expressing opinion, each meeting lasted for 70 minutes. The second cycle was a remedial of the first cycle by revising some components that used in the learning process. To collect the data, the researcher use d oral test and observation sheet. The data is analyzed by summing the score and counting the percentage. The percentage is used as learning indicator that has been approved by the researcher and collaborator. The standart score of speaking is 70. If the targeted percerntage is reached 90% or more it means that the implementation of Think Pair Share method in expressing opinion said succeed. From the data analysis, the percentage of students’ activity in the first cycle is 72%, and it increases into 100% in the second cycle. Meanwhile, the teacher’s activity also increases from 75% in cycle 1 into 97,5% in cycle 2. So, it can be concluded that Think Pair Share method can improve students’ speaking ability in expressing opinion at eighth grade of SMPN 1 Kotabumi.
Discrete learning-based intelligent methodology for heart disease diagnosis

Classification is one of the most frequently used data mining approaches which has been broadly applied in different fields of sciences, such as engineering, finance, energy, environments, transportation, etc., especially medicine, successfully. Over the years, various intelligent modeling techniques with different properties have been proposed to yield more accurate and more efficient classification results. However, in spite of the different appearance of all of the developed models, the same basic methodology is applied to the learning processes. Based on this methodology, a continuous distance-based cost function is considered and optimized for estimating the unknown parameters in the learning procedures. While using a continuous cost function in the classification field in which the goal function is discrete, is unreasonable or at least quite inefficient. In this paper, in contrast to conventional continuous distance-based methodologies, a novel discrete learning-based methodology is proposed for classification purposes. The main difference between the proposed learning methodology rather than conventional versions is its cost function. In the proposed learning methodology, a mismatching function is considered as a cost function, which is dissimilar to previously developed ones, which are continuous functions based on distance, is a discrete function based on direction. In this way, in the proposed learning process, unknown parameters are discretely adjusted and at once jumped to the target. This is in contrast to conventional continuous learning algorithms in which the unknown parameters are continuously adjusted and step-by-step near the target. The multilayer perceptron (MLP) which is one of the most widely-used intelligent classification approaches, is exemplarily chosen in order to implement the proposed methodology. Although it can be generally demonstrated that the classification rate of the proposed discrete learning-based MLP (DIMLP) model will not be worse than its conventional continuous learning-based one. However, in order to determine the superiority of the proposed DIMLP model, it is exemplarily evaluated on the heart disease diagnosis benchmark data set and several other medical datasets, and its performance is compared to the classic multilayer perceptron model. Empirical results illustrate that, as pre-expected, the classification rate of the proposed model is higher than its conventional version in all data sets. Obtained results indicate that the proposed DIMLP classifier can yield a 94.27 % classification rate in heart disease diagnosis, which approximately indicates a 9.35 % improvement over the classic version, which can only produce an 86.21 % classification rate. Therefore, the proposed methodology is an appropriate and effective alternative learning process for intelligent classification approaches, especially when more accurate results and/or a more reasonable model are required.
Knowledge discovery assistants for crash simulations with graph algorithms and energy absorption features

Abstract We propose the representation of data from finite element car crash simulations in a graph database to empower analysis approaches. The industrial perspective of this work is to narrow the gap between the uptake of modern machine learning methods and the current computer-aided engineering-based vehicle development workflow. The main goals for the graph representation are to achieve searchability and to enable pattern and trend investigations in the product development history. In this context, we introduce features for car crash simulations to enrich the graph and to provide a summary overview of the development stages. These features are based on the energy output of the finite element solver and, for example, enable filtering of the input data by identifying essential components of the vehicle. Additionally, based on these features, we propose fingerprints for simulation studies that assist in summarizing the exploration of the design space and facilitate cross-platform as well as load-case comparisons. Furthermore, we combine the graph representation with energy features and use a weighted heterogeneous graph visualization to identify outliers and cluster simulations according to their similarities. We present results on data from the real-life development stages of an automotive company.
Conversational natural language processing for automated customer support services

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Amritpal Kaur, Amrit Singh; Conversational natural language processing for automated customer support services. AIP Conf. Proc. 27 February 2023; 2427 (1): 020101. https://doi.org/10.1063/5.0100875 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search