Reviewerly/exHarmony huggingface dataset:
Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study

In the realm of computer security, the username/password standard is becoming increasingly antiquated. Usage of the same username and password across various accounts can leave a user open to potential vulnerabilities. Authentication methods of the future need to maintain the ability to provide secure access without a reduction in speed. Facial recognition technologies are quickly becoming integral parts of user security, allowing for a secondary level of user authentication. Augmenting traditional username and password security with facial biometrics has already seen impressive results; however, studying these techniques is necessary to determine how effective these methods are within various parameters. A Convolutional Neural Network (CNN) is a powerful classification approach which is often used for image identification and verification. Quite recently, CNNs have shown great promise in the area of facial image recognition. The comparative study proposed in this paper offers an in-depth analysis of several state-of-the-art deep learning based-facial recognition technologies, to determine via accuracy and other metrics which of those are most effective. In our study, VGG-16 and VGG-19 showed the highest levels of image recognition accuracy, as well as F1-Score. The most favorable configurations of CNN should be documented as an effective way to potentially augment the current username/password standard by increasing the current method’s security with additional facial biometrics.
Claim Matching Beyond English to Scale Global Fact-Checking

Manual fact-checking does not scale well to serve the needs of the internet.This issue is further compounded in non-English contexts.In this paper, we discuss claim matching as a possible solution to scale fact-checking.We define claim matching as the task of identifying pairs of textual messages containing claims that can be served with one fact-check.We construct a novel dataset of WhatsApp tipline and public group messages alongside fact-checked claims that are first annotated for containing "claim-like statements" and then matched with potentially similar items and annotated for claim matching.Our dataset contains content in high-resource (English, Hindi) and lower-resource (Bengali, Malayalam, Tamil) languages.We train our own embedding model using knowledge distillation and a high-quality "teacher" model in order to address the imbalance in embedding quality between the low-and high-resource languages in our dataset.We provide evaluations on the performance of our solution and compare with baselines and existing state-of-the-art multilingual embedding models, namely LASER and LaBSE.We demonstrate that our performance exceeds LASER and LaBSE in all settings.We release our annotated datasets 1 , codebooks, and trained embedding model 2 to allow for further research.Table 1: Example message pairs in our data annotated for claim similarity.
Expert system gradient descent style training: Development of a defensible artificial intelligence technique

Artificial intelligence systems, which are designed with a capability to learn from the data presented to them, are used throughout society. These systems are used to screen loan applicants, make sentencing recommendations for criminal defendants, scan social media posts for disallowed content and more. Because these systems do not assign meaning to their complex learned correlation network, they can learn associations that do not equate to causality, resulting in non-optimal and indefensible decisions being made. In addition to making decisions that are sub-optimal, these systems may create legal liability for their designers and operators by learning correlations that violate anti-discrimination and other laws regarding what factors can be used in different types of decision making. This paper presents the use of a machine learning expert system, which is developed with meaning-assigned nodes (facts) and correlations (rules). Multiple potential implementations are considered and evaluated under different conditions, including different network error and augmentation levels and different training levels. The performance of these systems is compared to random and fully connected networks.
Person re-identification based on multi-scale feature learning

Extracting discriminative pedestrian features is an effective method in person re-identification. Most person re-identification works focus on extracting abstract features from the high-layer of the network, but ignore the middle-layer features, thus reducing the identity accuracy. To solve this problem, we construct a Smooth Aggregation Module (SAM) to extract, align, and fuse the feature maps in the middle-layer of the network to make up for the lack of detailed information in the high-level network features, and propose an Omni-Scale Feature Aggregation method (OSFA)1 to jointly learn the abstract features and local detail features. Considering that the intra-class distance in person re-identification should be less than the inter-class distance, we combine multiple losses to constrain the model. We evaluate the performance of our method on three standard benchmark datasets: Market-1501, CUHK03 (both detected and labeled) and DukeMTMC-reID, and experimental results show that our method is superior to the state-of-the-art approaches.
Analysis of classic assumption test and multiple linear regression coefficient test for employee structural office recommendation

The performance appraisal process in Religious High Court Bandar Lampung has not been carried out objectively, but rather a subjectivity element (relationship closeness). Some employees occupy structural positions but do not fulfil competence and promotion principles, so that it has an impact on providing promotion to a position in the judiciary. Multiple Linear Regression method can provide a predictive model for employee recommendations entitled to occupy positions in the agency. The method implementation using SPSS produces an equation Y = 74.177 + 0.035X1 + 0.020X2 - 0.026X3 + 0.045X4 + 0.001X5. This equation is applied to the employee performance values, and it is obtained from 40 employees 26 employees deserve to be given recommendations promotion. Regression performance testing results using 10-cross validation get the correlation coefficient value is 80.66% with MAE value of 2.24% and RMSE 3.88%, which mean has good performance.
HybridTabNet: Towards Better Table Detection in Scanned Document Images

Tables in document images are an important entity since they contain crucial information. Therefore, accurate table detection can significantly improve the information extraction from documents. In this work, we present a novel end-to-end trainable pipeline, HybridTabNet, for table detection in scanned document images. Our two-stage table detector uses the ResNeXt-101 backbone for feature extraction and Hybrid Task Cascade (HTC) to localize the tables in scanned document images. Moreover, we replace conventional convolutions with deformable convolutions in the backbone network. This enables our network to detect tables of arbitrary layouts precisely. We evaluate our approach comprehensively on ICDAR-13, ICDAR-17 POD, ICDAR-19, TableBank, Marmot, and UNLV. Apart from the ICDAR-17 POD dataset, our proposed HybridTabNet outperformed earlier state-of-the-art results without depending on pre- and post-processing steps. Furthermore, to investigate how the proposed method generalizes unseen data, we conduct an exhaustive leave-one-out-evaluation. In comparison to prior state-of-the-art results, our method reduced the relative error by 27.57% on ICDAR-2019-TrackA-Modern, 42.64% on TableBank (Latex), 41.33% on TableBank (Word), 55.73% on TableBank (Latex + Word), 10% on Marmot, and 9.67% on the UNLV dataset. The achieved results reflect the superior performance of the proposed method.
Graph Based Network with Contextualized Representations of Turns in Dialogue

Dialogue-based relation extraction (RE) aimsto extract relation(s) between two arguments that appear in a dialogue.Because dialogues have the characteristics of high personal pronoun occurrences and low information density, and since most relational facts in dialogues are not supported by any single sentence, dialogue-based relation extraction requires a comprehensive understanding of dialogue.In this paper, we propose the TUrn COntext awaRE Graph Convolutional Network (TUCORE-GCN) modeled by paying attention to the way people understand dialogues.In addition, we propose a novel approach which treats the task of emotion recognition in conversations (ERC) as a dialoguebased RE.Experiments on a dialogue-based RE dataset and three ERC datasets demonstrate that our model is very effective in various dialogue-based natural language understanding tasks.In these experiments, TUCORE-GCN outperforms the state-of-theart models on most of the benchmark datasets.Our code is available at https://github.com/BlackNoodle/TUCORE-GCN.
Deep Multi-Model Fusion for Human Activity Recognition Using Evolutionary Algorithms

Machine recognition of the human activities is an active research area in computer vision.In previous study, either one or two types of modalities have been used to handle this task.However, the grouping of maximum information improves the recognition accuracy of human activities.Therefore, this paper proposes an automatic human activity recognition system through deep fusion of multi-streams along with decision-level score optimization using evolutionary algorithms on RGB, depth maps and 3d skeleton joint information.Our proposed approach works in three phases, 1) space-time activity learning using two 3D Convolutional Neural Network (3DCNN) and a Long Sort Term Memory (LSTM) network from RGB, Depth and skeleton joint positions 2) Training of SVM using the activities learned from previous phase for each model and score generation using trained SVM 3) Score fusion and optimization using two Evolutionary algorithm such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO) algorithm.The proposed approach is validated on two 3D challenging datasets, MSRDailyActivity3D and UTKinectAction3D.Experiments on these two datasets achieved 85.94% and 96.5% accuracies, respectively.The experimental results show the usefulness of the proposed representation.Furthermore, the fusion of different modalities improves recognition accuracies rather than using one or two types of information and obtains the state-of-art results.
Paradigm shifts in e-book-supported learning: Evidence from the Web of Science using a co-citation network analysis with an education focus (2010–2019)

This paper aims to provide research-based evidence for highlighting the paradigm shifts of e-book research. The most popular e-book-related keywords (e.g., electronic book, e-book) were used to retrieve the most relevant and quality journal articles published in the recent decade (2010–2109) from the Web of Science. Unlike most e-book review studies based on content analysis, this present paper employed a co-citation network analysis to examine the bibliographic relationships for the development of e-book-supported learning research. While the co-citation analysis is to identify the most highly joint-referenced e-book articles, social network analysis is to visualize the e-book co-citation networks with the main characteristics of the research interest (e.g., the focused research subjects and variables that measured learning outcomes). After careful screening and reviewing of the search data, a total of 55 high-quality e-book articles with 1203 citations were collected and analyzed. The result of the e-book research networks across two periods provided a decade overview of the development of e-book-supported learning research. (1) Early research (2010–2014) focused on improving the emerging literacy of preschool children and the learning effects of e-books. A sub-stream of high-risk students' e-book use was also identified. (2) In recent years (2015–2019), e-books were used at various education levels and were adopted in more diverse learning contexts. For young children's e-book use, researchers focused on e-book reading for early literacy, especially in the situation of co-reading with an adult. For university students, researchers focused on developing e-book-based systems to achieve better results and reduce their anxiety during formal learning. Discussion and implications are provided.
MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers

While Math Word Problem (MWP) solving has emerged as a popular field of study and made great progress in recent years, most existing methods are benchmarked solely on one or two datasets and implemented with different configurations. In this paper, we introduce the first open-source library for solving MWPs called MWPToolkit, which provides a unified, comprehensive, and extensible framework for the research purpose. Specifically, we deploy 17 deep learning-based MWP solvers and 6 MWP datasets in our toolkit. These MWP solvers are advanced models for MWP solving, covering the categories of Seq2seq, Seq2Tree, Graph2Tree, and Pre-trained Language Models. And these MWP datasets are popular datasets that are commonly used as benchmarks in existing work. Our toolkit is featured with highly modularized and reusable components, which can help researchers quickly get started and develop their own models. We have released the code and documentation of MWPToolkit in https://github.com/LYH-YF/MWPToolkit.
Application of Hyperledger in the Hospital Information Systems: A Survey

Hyperledger is considered to be the most mature consortium chain technology. Compared with other blockchain technologies, Hyperledger focuses on the application development of enterprise-level standards. Due to its unique permission management, fine-grained access control, pluggable consensus algorithm and higher transaction performance, Hyperledger has been increasingly used in the hospital information systems. Although there are more and more applications of Hyperledger in the field of health care, there is currently no research to comprehensively classify and compare them. This article obtained the latest 222 articles related to Hyperledger from the of Web of science, Wordlib, and EBSCO databases. And this article used manual method to filter out the literates needed for this review. This article had comprehensively analyzed and compared the above literature, then summarized four research directions related to hospital information systems, including medical drug traceability, medical records, medical images and other medical fields (financial benefits, medical insurance, medical system performance and dynamic processing). Through in-depth analysis of all solutions, this article reconstructed the architecture diagrams of each solution that focus on Hyperledger, and showed the most intuitive comparison results. In addition, we summarized each solution, and analyzed their implementation form four aspects, including traceability, monitoring, security and privacy. Finally, the future opportunities and prospects were discussed. Four potential medical research directions of Hyperledger in DNA research, pathological image sharing, protein folding calculation and machine learning had been identified. Finally, this article discussed the opportunities and prospects for the future of hospital information systems, and summarized the applications of Hyperledger in four potential directions, including DNA research, pathological image sharing, protein folding calculation and machine learning.
Bayesian U-Net: Estimating Uncertainty in Semantic Segmentation of Earth Observation Images

In recent years, numerous deep learning techniques have been proposed to tackle the semantic segmentation of aerial and satellite images, increase trust in the leaderboards of main scientific contests and represent the current state-of-the-art. Nevertheless, despite their promising results, these state-of-the-art techniques are still unable to provide results with the level of accuracy sought in real applications, i.e., in operational settings. Thus, it is mandatory to qualify these segmentation results and estimate the uncertainty brought about by a deep network. In this work, we address uncertainty estimations in semantic segmentation. To do this, we relied on a Bayesian deep learning method, based on Monte Carlo Dropout, which allows us to derive uncertainty metrics along with the semantic segmentation. Built on the most widespread U-Net architecture, our model achieves semantic segmentation with high accuracy on several state-of-the-art datasets. More importantly, uncertainty maps are also derived from our model. While they allow for the performance of a sounder qualitative evaluation of the segmentation results, they also include valuable information to improve the reference databases.
Any-To-Any Connected Cavity-Mediated Architecture for Quantum Computing with Trapped Ions or Rydberg Arrays

We propose a hardware architecture and protocol for connecting many local quantum processors contained within an optical cavity. The scheme is compatible with trapped ions or Rydberg arrays, and realizes teleported gates between any two qubits by distributing entanglement via single-photon transfers through a cavity. Heralding enables high-fidelity entanglement even for a cavity of moderate quality. For processors composed of trapped ions in a linear chain, a single cavity with realistic parameters successfully transfers photons every few μs, increasing the interchain entanglement rate over 2 orders of magnitude beyond current methods and eliminating a major bottleneck for scaling trapped-ion systems. For one realistic scenario, we outline how to achieve the any-to-any entanglement of 20 ion chains containing a total of 500 qubits in 200μs, with both fidelities and rates limited only by local operations and ion readout. For processors composed of Rydberg atoms, our method fully connects a large array of thousands of neutral atoms. The connectivity afforded by our architecture is extendable to tens of thousands of qubits using multiple overlapping cavities, expanding capabilities for noisy intermediate-scale quantum era algorithms and Hamiltonian simulations, as well as enabling more robust high-dimensional error-correcting schemes.Received 21 September 2021Accepted 18 January 2022DOI:https://doi.org/10.1103/PRXQuantum.3.010344Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum gatesQuantum information architectures & platformsQuantum information processingQuantum simulationPhysical SystemsAtomsTechniquesAtom & ion coolingCavity resonatorsJaynes-Cummings modelQuantum InformationAtomic, Molecular & Optical
Revisiting Adversarial Robustness Distillation: Robust Soft Labels Make Student Better

Adversarial training is one effective approach for training robust deep neural networks against adversarial attacks. While being able to bring reliable robustness, adversarial training (AT) methods in general favor high capacity models, i.e., the larger the model the better the robustness. This tends to limit their effectiveness on small models, which are more preferable in scenarios where storage or computing resources are very limited (e.g., mobile devices). In this paper, we leverage the concept of knowledge distillation to improve the robustness of small models by distilling from adversarially trained large models. We first revisit several state-of-the-art AT methods from a distillation perspective and identify one common technique that can lead to improved robustness: the use of robust soft labels – predictions of a robust model. Following this observation, we propose a novel adversarial robustness distillation method called Robust Soft Label Adversarial Distillation (RSLAD) to train robust small student models. RSLAD fully exploits the robust soft labels produced by a robust (adversarially-trained) large teacher model to guide the student’s learning on both natural and adversarial examples in all loss terms. We empirically demonstrate the effectiveness of our RSLAD approach over existing adversarial training and distillation methods in improving the robustness of small models against state-of-the-art attacks including the AutoAttack. We also provide a set of understandings on our RSLAD and the importance of robust soft labels for adversarial robustness distillation. Code: https://github.com/zibojia/RSLAD.
3D Object Detection with SLS-Fusion Network in Foggy Weather Conditions

The role of sensors such as cameras or LiDAR (Light Detection and Ranging) is crucial for the environmental awareness of self-driving cars. However, the data collected from these sensors are subject to distortions in extreme weather conditions such as fog, rain, and snow. This issue could lead to many safety problems while operating a self-driving vehicle. The purpose of this study is to analyze the effects of fog on the detection of objects in driving scenes and then to propose methods for improvement. Collecting and processing data in adverse weather conditions is often more difficult than data in good weather conditions. Hence, a synthetic dataset that can simulate bad weather conditions is a good choice to validate a method, as it is simpler and more economical, before working with a real dataset. In this paper, we apply fog synthesis on the public KITTI dataset to generate the Multifog KITTI dataset for both images and point clouds. In terms of processing tasks, we test our previous 3D object detector based on LiDAR and camera, named the Spare LiDAR Stereo Fusion Network (SLS-Fusion), to see how it is affected by foggy weather conditions. We propose to train using both the original dataset and the augmented dataset to improve performance in foggy weather conditions while keeping good performance under normal conditions. We conducted experiments on the KITTI and the proposed Multifog KITTI datasets which show that, before any improvement, performance is reduced by 42.67% in 3D object detection for Moderate objects in foggy weather conditions. By using a specific strategy of training, the results significantly improved by 26.72% and keep performing quite well on the original dataset with a drop only of 8.23%. In summary, fog often causes the failure of 3D detection on driving scenes. By additional training with the augmented dataset, we significantly improve the performance of the proposed 3D object detection algorithm for self-driving cars in foggy weather conditions.