Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence-Assisted Design and Virtual Diagnostic for the Initial Condition of a Storage-Ring-Based Quantum Information System

Developments in Artificial Intelligence (AI) are helping to solve complex physical problems that otherwise may be too computationally demanding to solve using traditional approaches. Universal Approximation Theorems tell us that we can model any physical system if we can approximate the system with some continuous function (i.e., compact convergence topology and algorithmically generated sets of functions, such as the convolutional neural network), whether for an arbitrary depth or arbitrary width neural network. We consider the problem of solving a set of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> coupled algebraic equations as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> becomes very large and apply machine learning (ML) to solve this problem for any value of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> . The physical problem we are focusing on is to model the equilibrium positions of ions in an ion trap. A storage ring quantum computer could contain well over tens of thousands of ions. Quickly determining the equilibrium positions will be important to minimize the time to target and observe each ion. As each ion serves as a single qubit, this is important for setting and measuring the individual qubit states. The phonon modes from a collection of ions acts as another qubit, useful for gate operations. Measuring the phonon modes, where ions are oscillating around their respective equilibrium positions also means understanding the equilibrium positions very well. Turning all of this into a virtual diagnostic allows real time prediction and comparison to ensure unique definition of each ion.
Using Result Profiles to Drive Meta-learning

Knowledge gained by meta-learning processes is valuable when it can be successfully used in solving algorithm selection problems. There is still strong need for automated tools for learning from data, performing model construction and selection with little or no effort from human operator. This article provides evidence for efficacy of a general meta-learning algorithm performing validations of candidate learning methods and driving the search for most attractive models on the basis of an analysis of learning results profiles. The profiles help in finding similar processes performed for other datasets and pointing to promising learning machines configurations. Further research on profile management is expected to bring very attractive automated tools for learning from data. Here, several components of the framework have been examined and an extended test performed to confirm the possibilities of the method. The discussion also touches on the subject of testing and comparing the results of meta-learning algorithms.
Metalearning for Deep Neural Networks

Abstract Deep neural networks have enabled large breakthroughs in various domains ranging from image and speech recognition to automated medical diagnosis. However, these networks are notorious for requiring large amounts of data to learn from, limiting their applicability in domains where data is scarce. Through metalearning, the networks can learn how to learn, allowing them to learn from fewer data. In this chapter, we provide a detailed overview of metalearning for knowledge transfer in deep neural networks. We categorize the techniques into (i) metric-based, (ii) model-based, and (iii) optimization-based techniques, cover the key techniques per category, discuss open challenges, and provide directions for future research such as performance evaluation on heterogeneous benchmarks.
Design Flaws and Suggested Improvement of Secure Medical Data Sharing Scheme Based on Blockchain

The fast development of information technology and communication includes blockchain technology, artificial intelligence, and cloud computing. Medical cyber systems increasingly need secure data protection. Cloud computing provides the solution to the complexity of data but fails to provide secure data transmission. In blockchain technology, the decentralization specification provides secure authentication and secure data transmission. In this paper, we have reviewed Cheng et al. scheme which is published in the Journal of Medical Systems ( https://doi.org/10.1007/s10916-019-1468-1 ). In Cheng et al. scheme, we found security weaknesses and some possible security attacks such as clock synchronization attack, DoS attack, impersonation attack, fails to protect the session keys, and stolen verifier attack. Thus, Cheng et al. scheme is not satisfactory for medical secure data sharing for medical communication systems. At last, we suggest some improvements for Cheng et al. protocol.
A Fuzzy-Based Multiobjective Cat Swarm Optimization Algorithm: A Case Study on Single-Cell Data

The microRNAs and single-cell expression data embed relevant and unrelevant information about the genes. Finding the hidden gene expression pattern helps us to understand the functionality of a gene at molecular level. Particularly, gene expression pattern varies with the effect of external stimuli. Therefore, in this situation, soft clustering technique helps to detect multiple patterns of a gene at one time. In this work, a form of soft clustering approach is presented in assimilation with the fuzzy and cat swarm optimization (CSO) algorithm to find an optimal cluster center for clustering. Here, the task of clustering is formulated as the optimization of multiple cluster validity indexes. The proposed clustering algorithm (multiobjective fuzzy-based CSO) is then compared with some of state-of-the-art clustering technique on different gene expression dataset. A case study is conducted to identify the transcriptome or marker gene in each cell types of human urinary bladder single-cell RNA sequencing data. Experimental analysis of multiobjective fuzzy-based CSO clustering algorithm shows its effectiveness over other state-of-the-art clustering algorithm such as AL, K-means, GA-FCM, DE-FCM, PSO-FCM, and GWO-FCM.
Application of Augmented Reality Technology in Smart Cartoon Character Design and Visual Modeling

This paper designs and implements a cartoon character design and visual modeling system based on an augmented reality platform, allowing children to create a three-dimensional cartoon scene from their two-dimensional hand drawings and perform interactive operations in the augmented reality environment. In order to enhance the realistic visual experience in the virtual scene, modeling and behavior control of the characters in the 3D virtual animation design are carried out. Create a three-dimensional virtual animation character model in the POSER software, create a three-dimensional virtual animation character skeleton in the 3D software and skin the bones to realize the animation character modeling. The skeletal animation technology is used to simulate the movement behavior of the character, adjust the movement and drive the bones in the 3D software.
Research on Military Equipment Entity Recognition and Knowledge Graph Construction Method Based on ALBERT-Bi-LSTM-CRF

Aiming at the problems of complex and sparse military weapon data and weak association between data, this paper proposes a research method for unstructured data named entity recognition based on the ALBERT-Bi-LSTM-CRF model. Preprocess the ALBERT model to generate a word vector based on contextual information, and then input the trained word vector into the BiLSTM-CRF model for further training processing, and finally build a knowledge map based on the military weapon field and complete the knowledge question and answer function. The experiment evaluates the performance of entity recognition by adding training data to the ALBERT model several times. The results show that the model has a good advantage in entity relationship recognition in Baidu Baike and Canglang Corpus, with F1 values reaching 93.76% and 92.76%, respectively. It provides a better way to solve the existing problems.
Digital dreams have become nightmares

research-article Share on Digital dreams have become nightmares: UX for ethical technology use Author: Ronald M. Baecker University of Toronto University of TorontoView Profile Authors Info & Claims InteractionsVolume 29Issue 2March - April 2022 pp 50–53https://doi.org/10.1145/3512990Published:25 February 2022Publication History 1citation745DownloadsMetricsTotal Citations1Total Downloads745Last 12 Months745Last 6 weeks45 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A weakly supervised method for improving the resolution of seismic data based on cycle generative adversarial network

PreviousNext No AccessSEG 2021 Workshop: 4th International Workshop on Mathematical Geophysics: Traditional & Learning, Virtual, 17–19 December 2021A weakly supervised method for improving the resolution of seismic data based on cycle generative adversarial networkAuthors: Wenli NiuYanhui ZhouXiaokai WangZhensheng ShiCheng WangDawei LiuWenchao ChenWenli NiuXi'an Jiaotong UniversitySearch for more papers by this author, Yanhui ZhouXi'an Jiaotong UniversitySearch for more papers by this author, Xiaokai WangXi'an Jiaotong UniversitySearch for more papers by this author, Zhensheng ShiXi'an Jiaotong UniversitySearch for more papers by this author, Cheng WangResearch Institute of Exploration and Development of Daqing Oilfield Company Ltd.Search for more papers by this author, Dawei LiuXi'an Jiaotong UniversitySearch for more papers by this author, and Wenchao ChenXi'an Jiaotong UniversitySearch for more papers by this authorhttps://doi.org/10.1190/iwmg2021-08.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract In order to solve the shortcomings of traditional methods in improving the resolution of seismic data, and the problem that the strongly supervised methods rely too much on labels, in this paper, we proposed a weakly supervised method to improve the resolution of 3D seismic data by using a cycle generative adversarial network, the label is a high-resolution seismic data from another work area that is not paired with low resolution data. The network learns a distribution from the low-resolution seismic data to the high-resolution seismic data, and generates a high-resolution result similar to the high-resolution label. That is, by learning two unrelated high-and low-resolution data to achieve the weakly supervised learning process. Experiments prove that the weakly supervised method can obtain similar result as the strongly supervised method. Keywords: time slice, deconvolution, filtering, amplitude, mappingPermalink: https://doi.org/10.1190/iwmg2021-08.1FiguresReferencesRelatedDetails SEG 2021 Workshop: 4th International Workshop on Mathematical Geophysics: Traditional & Learning, Virtual, 17–19 December 2021ISSN (online):2159-6832Copyright: 2022 Pages: 193 publication data© 2022 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 24 Feb 2022 CITATION INFORMATION Wenli Niu, Yanhui Zhou, Xiaokai Wang, Zhensheng Shi, Cheng Wang, Dawei Liu, and Wenchao Chen, (2022), "A weakly supervised method for improving the resolution of seismic data based on cycle generative adversarial network," SEG Global Meeting Abstracts : 29-32. https://doi.org/10.1190/iwmg2021-08.1 Plain-Language Summary Keywordstime slicedeconvolutionfilteringamplitudemappingPDF DownloadLoading ...
Video Quality Loss Model on Communication Networks: An Approach Based on Frame Loss

This letter proposes a mathematical model capable of estimating video quality loss in error-prone networks through Peak Signal-to-Noise (PSNR ) ratio metric. Also, it presents experimental results by correlating the frame losses, the video resolution, and the received video visual complexity to obtain video quality loss models. The results show that frame loss satisfies the model to predict the PSNR loss for three resolutions.
Gaze Estimation Based on Difference Residual Network

Because of the variabilities in eye shapes and inner eye structures among individuals, the universal models that regress gaze directions directly from a single face or eye image through the neural network obtain limited accuracies. There is a person-specific gaze bias caused by the kappa angle between the model’s predicted and the actual gaze direction. Currently, people-specific models are usually established through calibration to increase accuracy. This paper proposes a novel estimation method of gaze direction based on the Differential Residual Network (D-ResNet),Which works by training the D-ResNet to predict gaze difference between two eye images (the same eye of the same subject) to eliminate gaze bias. At test time, only a few person - specific calibration samples are needed to infer the new eye image’s gaze direction. Our model also incorporates the head pose vector to improve the robustness to free-head. Moreover, our left and right eye models are trained and tested separately which can select the dominant eye model based on the test error to predict gaze. Experiments on 2 public datasets validate our approach outperforms state-of-the-art person-independent and person- specific gaze estimation models. In particular, our model doesn’t fail to calibrate even if there is only one calibration sample.
A multi-agent based model for user interest mining on Sina Weibo

User interest mining on Sina Weibo is the basis of personalized recommendations, advertising, marketing promotions, and other tasks. Although great progress has been made in this area, previous studies have ignored the differences among users: the varied behaviors and habits that lead to unique user data characteristics. It is unreasonable to use a single strategy to mine interests from such varied user data. Therefore, this paper proposes an adaptive model for user interest mining based on a multi-agent system whose input includes self-descriptive user data, microblogs and correlations. This method has the ability to select the appropriate strategy based on each user's data characteristics. The experimental results show that the proposed method performs better than the baselines.
Bengali Visual Genome: A Multimodal Dataset for Machine Translation and Image Captioning

Multimodal machine translation (MMT) refers to the extraction of information from more than one modality aiming at performance improvement by utilizing information collected from the modalities other than pure text. The availability of multimodal datasets, particularly for Indian regional languages, is still limited, and thus, there is a need to build such datasets for regional languages to promote the state of MMT research. In this work, we describe the process of creation of the Bengali Visual Genome (BVG) dataset. The BVG is the first multimodal dataset consisting of text and images suitable for English-to-Bengali multimodal machine translation tasks and multimodal research. We also demonstrate the sample use-cases of machine translation and region-specific image captioning using the new BVG dataset. These results can be considered as the baseline for subsequent research.
A Method for Modeling the Neural Network for Term Extraction Based on Bilingual Sentence Alignment Corpus

The implementation of the current term extraction model is mainly dependent on the training on manual tagging of a bilingual term corpus. It will take a great amount of manpower to tag terms in a massive corpus. In order to solve this problem, this paper proposes a method for modeling the neural network for term extraction based on bilingual sentence alignment corpus, which implies that GIZA++ is used to tag terms in terms of bilingual word alignment based on bilingual sentence alignment corpus, thus generating the tagged corpus for bilingual word alignment as the initial training input of neural network. In order to fully explore the potential language features between sentences, from the perspective of deep learning, an unsupervised training algorithm for word alignment based on Word2vec training word vector and integrated into the neural network was proposed. The experiment was carried out based on English-Chinese patent and standard sentence alignment corpus, with an accuracy of 0.8662.
Characterising eye movement events with an unsupervised hidden markov model

Eye-tracking allows researchers to infer cognitive processes from eye movements that are classified into distinct events. Parsing the events is typically done by algorithms. Here we aim at developing an unsupervised, generative model that can be fitted to eye-movement data using maximum likelihood estimation. This approach allows hypothesis testing about fitted models, next to being a method for classification. We developed gazeHMM, an algorithm that uses a hidden Markov model as a generative model, has few critical parameters to be set by users, and does not require human coded data as input. The algorithm classifies gaze data into fixations, saccades, and optionally postsaccadic oscillations and smooth pursuits. We evaluated gazeHMM's performance in a simulation study, showing that it successfully recovered hidden Markov model parameters and hidden states. Parameters were less well recovered when we included a smooth pursuit state and/or added even small noise to simulated data. We applied generative models with different numbers of events to benchmark data. Comparing them indicated that hidden Markov models with more events than expected had most likely generated the data. We also applied the full algorithm to benchmark data and assessed its similarity to human coding and other algorithms. For static stimuli, gazeHMM showed high similarity and outperformed other algorithms in this regard. For dynamic stimuli, gazeHMM tended to rapidly switch between fixations and smooth pursuits but still displayed higher similarity than most other algorithms. Concluding that gazeHMM can be used in practice, we recommend parsing smooth pursuits only for exploratory purposes. Future hidden Markov model algorithms could use covariates to better capture eye movement processes and explicitly model event durations to classify smooth pursuits more accurately.
Multi-modal learning with missing data for cancer diagnosis using histopathological and genomic data

Multi-modal learning (e.g., integrating pathological images with genomic features) tends to improve the accuracy of cancer diagnosis and prognosis as compared to learning with a single modality. However, missing data is a common problem in clinical practice, i.e., not every patient has all modalities available. Most of the previous works directly discarded samples with missing modalities, which might lose information in these data and increase the likelihood of overfitting. In this work, we generalize the multi-modal learning in cancer diagnosis with the capacity of dealing with missing data using histological images and genomic data. Our integrated model can utilize all available data from patients with both complete and partial modalities. The experiments on the public TCGA-GBM and TCGA-LGG datasets show that the data with missing modalities can contribute to multi-modal learning, which improves the model performance in grade classification of glioma cancer.
Endometrial Cell Images Segmentation: A Comparative Study

Uterine cancer, also known as endometrial cancer, is a form of cancer that affects the female reproductive system.Nowadays, there are 2 step methods that the physician or health care provider tend to use to diagnose cancer, which is using ultrasound technique and endometrial biopsy.The biopsy procedure is used to extract the cell and sent to the pathologist for histopathological image analysis.The histopathological image analysis is the crucial step in all the procedures because it determines the situation for the patient, whether positive or negative.They are two types of cell images known as high grade squamous intraepithelial lesion (HSIL) and low grade squamous intraepithelial lesion (LSIL).The problem occurs when both LSIL and HSIL are different, needing different medical treatment techniques but showing slighter differences in nucleus size cell histopathological image analysis.Therefore, the pathologist usually requires more time to identify whether it is LSIL or HSIL.Based on the limitation, the paper aims to compare a few popular detection methods, which are the Wolf method, Bernsen method, Otsu method and Feng method.Based on the Image Quality Assessment (IQA), the Wolf method shows good performance compared to the others.In a precise term, this finding could benefit the health care community to reduce the diagnosis time to categorize the cell and lead to early treatment of endometrial cancer.
Presto Pack: Resource-aware Workload Placement for Higher Performance and Reliability

Presto is one of the most popular OLAP (Online Analytical Processing) query engines. A Presto cluster consists of multiple worker nodes that execute tasks. As of today, Presto's workload placement is not resource-aware, which leads to wildly unbalanced resource usages across workers, jeopardizing business-important metrics such as reliability, performance and efficiency. We aim to add intelligence to Presto workload placement by making it resource-aware. Referred to as Presto Pack, this feature is mainly implemented on Presto coordinator. It takes into account of two resource types: memory and CPU on worker nodes. Evaluation results show that Presto Pack can achieve much more balanced CPU and memory usage across workers, less reliability issues, and higher throughput. Specifically, Presto Pack can improve overall query throughput by 23% on a per-server basis, leading to higher levels of concurrency and reliability.
Solving Sensor Ontology Metamatching Problem with Compact Flower Pollination Algorithm

To implement co-operation among applications on the Internet of Things (IoT), we need to describe the meaning of diverse sensor data with the sensor ontology. However, there exists a heterogeneity issue among different sensor ontologies, which hampers their communications. Sensor ontology matching is a feasible solution to this problem, which is able to map the identical ontology entity pairs. This work investigates the sensor ontology meta-matching problem, which indirectly optimizes the sensor ontology alignment’s quality by tuning the weights to aggregate different ontology matchers. Due to the largescale entity and their complex semantic relationships, swarm intelligence (SI) based techniques are emerging as a popular approach to optimize the sensor ontology alignment. Inspired by the success of the flower pollination algorithm (FPA) in the IoT domain, this work further proposes a compact FPA (CFPA), which introduces the compact encoding mechanism to improve the algorithm’s efficiency, and on this basis, the compact exploration and exploitation operators are proposed, and an adaptive switching probability is presented to trade-off these two searching strategies. The experiment uses the ontology alignment evaluation initiative (OAEI)’s benchmark and the real sensor ontologies to test CFPA’s performance. The statistical comparisons show that CFPA significantly outperforms other state-of-the-art sensor ontology matching techniques.