Reviewerly/exHarmony huggingface dataset:
Semantic-Preserving Metric Learning for Video-Text Retrieval

Video-text retrieval requires finding an optimal space for comparing the similarity of two different modalities. Most approaches adopt ranking loss as a primary training objective to find the space. The loss is only interested in bringing the samples annotated as pairs closer to each other without considering the semantic relevance of different samples. This rather causes even semantically similar pairs not to get close. To deal with the problem, we propose semantic-preserving metric learning. The proposed method entails the metric space where the similarity ratio between samples is proportional to semantic relevance between annotations. In the extensive experiments on video-text datasets, the proposed method presents a close alignment between the learned metric space and the semantic space. It also demonstrates state-of-the-art retrieval performance.
Big data: some ways to solve the problems of higher education

Abstract The article deals with issues related to the transition to digitalization of higher professional education, as well as with the widespread use of distance education technologies. The analysis of methods for monitoring the quality of education of university students is carried out. There are five types of data that require the application of big data analysis methods: personal data; data on the interaction of students with and between e-learning systems; data on the effectiveness of educational material; administrative data; forecasts. To solve a number of problems, we propose the use of Education Data Mining technology, which will allow us to design digital education management systems based on data and ways to systematize them for making organizational, pedagogical and managerial decisions. The analysis of educational data is based on the prediction of models, the identification of learning structures, and the identification of relationships. The possible ways of developing the individual trajectory of a student studying at the university, his further caree.
The good, the bad, and the ugly: mining for patterns in student source code

Research on source code mining has been explored to discover interesting structural regularities, API usage patterns, refactoring opportunities, bugs, crosscutting concerns, code clones and systematic changes. In this paper we present a pattern mining algorithm that uses frequent tree mining to mine for interesting good, bad or ugly coding idioms made by undergraduate students taking an introductory programming course. We do so by looking for patterns that distinguish positive examples, corresponding to the more correct answers to a question, from negative examples, corresponding to solutions that failed the question. We report promising initial results of this algorithm applied to the source code of over 500 students. Even though more work is needed to fine-tune and validate the algorithm further, we hope that it can lead to interesting insights that can eventually be integrated into an intelligent recommendation system to help students learn from their errors.
“All the research says”: manufactured consensus and the burden of proof in the racialized police violence controversy

This article reconstructs and analyzes the argument strategies used by right-wing public intellectuals, journalists, and political figures to delegitimize the controversy over racialized police violence. I demonstrate how right-wing advocates aim to shift the issue to the technical sphere, claiming to represent an expert consensus and depicting antiracist advocates as misunderstanding, if not intentionally misusing, technical data. This case provides a rich opportunity to deepen rhetoric and argument study of, first, how advocates disguise racist and post-racial discourses in the terms of technical expertise, and, second, how the burden of proof is assigned and negotiated within racial controversies. Claiming to represent an expert consensus would seem to carry a much higher burden of proof than would amplifying technical uncertainty. I argue, however, that defenders of the police mitigate this burden through heavy reliance on argument from ignorance, insinuating that the supposed lack of evidence of officer bias means that police use of lethal force must be racially fair.
Navigation in Multi-Agent System With Side Preference Path Optimizer

Multi-agent path planning aims to find a set of collision-free global path in the joint configuration space to move each agent from the given start states to the goal states and keep the total cost of the path to a minimum. However, the multi-agent systems in the real-world turn out to be a non-communication and partially observable system where the goal states and the planning strategy of other agents are usually unknown. In order to solve the multi-agent path planning problem in a more general environment, a social-aware navigation method is proposed in this paper. By imitating the most basic human road rules, left/right-handed rules, we add side preference to the global path planning by using a path optimizer so that each agent only needs to consider its own planning while the system is approximately optimal. The proposed path optimizer is tested in benchmark maps, robot-robot and robot-human interaction environment, respectively. With the side preference path optimizer, the success rate of multi-agent navigation with 6 agents increase from 0% to 78% at the cost of only extra 0.1 seconds planning time. The proposed method makes the navigation in human-robot interaction scenes become easier.
Joint estimation of target location and relative altitude from angle measurements

An improved THREE-Stage Extended Kalman Filter (3S-EKF) is presented in this paper for slow target tracking by collecting azimuth and elevation angle measurements on a single platform. The 3S-EKF is derived by measurement decouple and accurate prediction of relative height. Since the one-stage extended Kalman filter has measurement coupling issues, a TWO-Stage Extended Kalman Filter (2S-EKF) is proposed. It first estimates the 2D-projected target position using the azimuth angle only followed by updating relative height using elevation measurements. However, 2S-EKF ignores the geometrical relationship between the 2D-projected target position and relative altitude, making the prediction of relative height inaccurate. Thus, the 3S-EKF algorithm is proposed in this paper based on the exploration of the geometrical constraint to improve the prediction accuracy of relative height. The proposed 3S-EKF algorithm can achieve the Bayesian Cramér-Rao lower bound. Furthermore, the theoretical bias is analyzed with various target speeds. Simulation results demonstrate that the 3S-EKF algorithm has better estimation accuracy.
Zero in on Shape: A Generic 2D-3D Instance Similarity Metric Learned from Synthetic Data

We present a network architecture which compares RGB images and untextured 3D models by the similarity of the represented shape. Our system is optimised for zero-shot retrieval, meaning it can recognise shapes never shown in training. We use a view-based shape descriptor and a siamese network to learn object geometry from pairs of 3D models and 2D images.Due to scarcity of datasets with exact photograph-mesh correspondences, we train our network with only synthetic data.Our experiments investigate the effect of different qualities and quantities of training data on retrieval accuracy and present insights from bridging the domain gap. We show that increasing the variety of synthetic data improves retrieval accuracy and that our system’s performance in zero-shot mode can match that of the instance-aware mode, as far as narrowing down the search to the top 10% of objects.
Towards Automated Superconducting Circuit Calibration using Deep Reinforcement Learning

This paper presents an innovative way of quantum circuit optimization; we propose an automated superconducting circuit calibration using deep reinforcement learning (ASCC-DRL). A simplified model of a transmon qubit coupled to a cavity resonator is used to demonstrate a quantum circuit. We analyzed the circuit through its equivalent two-mode lumped-element representation of the distributed circuit and derived the equivalent Hamiltonian translation of the circuit with the non-linearity of the Josephson Junctions. We adopted the parametrized qubit junctions and used the component physical value to tune the circuits to maximize the fidelity of the desired entangle state and the resulting quantum circuit state. However, quantum systems are susceptible to environmental parasitics and noise; therefore, a reliable optimization technique is essential to maintain high fidelity across the system. For optimization, we integrate deep reinforcement learning using deep deterministic policy gradient techniques as our mechanism for our AI module to optimize the fidelity of the superconducting quantum circuit. We demonstrate the feasibility of this approach by training the agent in three distinct environments to achieve the highest possible best rewards. We achieved an average fidelity (best rewards) of 0.92, which is 94% of the statically tuned circuit.
Bottom-Up Saliency Meets Top-Down Semantics For Object Detection

While convolution neural networks (CNNs) successfully boost the accuracy of object detection algorithms, it is still challenging to detect far-away objects since they can be tiny in an image. To enhance CNNs’ ability for tiny object detection, this paper presents an algorithm called Saliency-Guided Feature Module (SGFM). Based on low-level image features, we compute the saliency over the image to indicate where foreground objects could be, and use SGFM to enhance the CNN’s feature maps that focus on similar areas. We also present a new dataset named Camera Monitoring System Driving Dataset (CMS-DD) where images were captured from the view angle of side mirrors on driving vehicles so far-away objects can look even tinier. Our experiments show that SGFMs can further improve a recent state-of-the-art object detector in practical driving scenes like CMS-DD.
End-To-End Pairwise Human Proxemics from Uncalibrated Single Images

In this work, we address the ill-posed problem of estimating pairwise metric distances between people using only a single uncalibrated image. We propose an end-to-end model, DeepProx, that takes as inputs two skeletal joints as a set of 2D image coordinates and outputs the metric distance between them. We show that an increased performance is achieved by a geometrical loss over simplified camera parameters provided at training time. Further, DeepProx achieves a remarkable generalisation over novel viewpoints through domain generalisation techniques. We validate our proposed method quantitatively and qualitatively against baselines on public datasets for which we provided groundtruth on interpersonal distances.
QoE Analysis for Improving Multimedia Services Based on Different Operating Situations on Cellular Networks

Cellular networks are constantly evolving to support the new context and demands of users with increasing internet traffic. The key to a service provider’s success is a direct response to endpoints to retain existing users and attract new users. For this reason, Mobile Network Operators (MNOs) must reach user satisfaction under a user-centered perspective with Quality of Experience (QoE) to achieve a Return on Investment (RoI). In the latest QoE modelling, our previous method performed QoE estimation and synthesis using an Artificial Neural Network (ANN). Nevertheless, such work did not prove the characteristics of the QoE model built on different working situations. The essential features of the area and user diversities need to be considered in detail to summarize the proper model. In this paper, the proposed framework focuses on analyzing QoE model characteristics under different operating situations for comprehensive implementation. The experimental results reveal that the created QoE models from areas of different densities affect the different predictive variables. Inter-Rater Reliability (IRR) analysis exposes the reliability of datasets gathered from users of different groups to distinguish the effects on the response of significant parameters. Additionally, the summary specifies the characteristics of the appropriate model by area and user group as a guideline for improving the multimedia services.
Hardmix: A Regularization Method to Mitigate the Large Shift in Few-Shot Domain Adaptation

Few-Shot Domain Adaptation aims to transfer knowledge learned from a known domain to a closely related novel domain with only a few training data available for each class. The limited number of target training data makes it challenging to bridge the domain gap and can easily lead to overfitting. In this paper, we proposed HardMix as a regularization technique which interpolates the data in feature space and assigns augmented features with ‘hard’ labels to eliminate the domain discrepancy. In order to generate a better decision boundary and a more compact intra-class distribution, an adaptive triplet loss is proposed to constrain the ‘hard’ samples near the decision boundary. We demonstrated its effectiveness by comparing our results with the state-of-the-art methods on several benchmark datasets.
Semantic Graphical Dependence Parsing Model in Improving English Teaching Abilities

It is a very difficult problem to achieve high-order functionality for graphical dependency parsing without growing decoding difficulties. To solve this problem, this article offers a way for Semantic Graphical Dependence Parsing Model (SGDPM) with a language-dependency model and a beam search to represent high-order functions for computer applications. The first approach is to scan a large amount of unnoticed data using a baseline parser. It will build auto-parsed data to create the Language-dependence Model (LDM). The LDM is based on a set of new features during beam search decoding, where it will incorporate the LDM features into the parsing model and utilize the features in parsing models of bilingual text. Our approach has main benefits, which include rich high-order features that are described given the large size and the additional large crude corpus for increasing the difficulty of decoding. Further, SGDPM has been evaluated using the suggested method for parsing tasks of mono-parsing text and bi-parsing text to carry out experiments on the English and Chinese data in the mono-parsing text function using computer applications. Experimental results show that the most accurate Chinese data is obtained with the best known English data systems and their comparable accuracy. Furthermore, the lab-scale experiments on the Chinese/General bilingual information in the bitext parsing process outperform the best recorded existing solutions.
Non-Local Feature Aggregation on Graphs via Latent Fixed Data Structures

In contrast to image/text data whose order can be used to perform non-local feature aggregation in a straight-forward way using the pooling layers, graphs lack the tensor representation and mostly the element-wise max/mean function is utilized to aggregate the locally extracted feature vectors. In this paper, we present a novel approach for global feature aggregation in Graph Neural Networks (GNNs) which utilizes a Latent Fixed Data Structure (LFDS) to aggregate the extracted feature vectors. The locally extracted feature vectors are sorted/distributed on the LFDS and a latent neural network (CNN/GNN) is utilized to perform feature aggregation on the LFDS. The proposed approach is used to design several novel global feature aggregation methods based on the choice of the LFDS. We introduce multiple LFDSs including loop, 3D tensor (image), sequence, data driven graphs and an algorithm which sorts/distributes the extracted local feature vectors on the LFDS. While the computational complexity of the proposed methods are linear with the order of input graphs, they achieve competitive or better results.
Su-Sampling Based Active Learning For Large-Scale Histopathology Image

Expensive annotation cost has always been a critical obstacle in deep learning systems, in particular for the applications requiring domain experts’ knowledge, such as medical image analysis. Active learning has attracted widespread attention by decreasing the quantity and difficulty in annotation with query strategies. To make a query for labeling, contextual features of candidates’ relation are often considered essential. In this paper, we propose an innovative method that incorporating spatial distribution approximation in the uncertainty sampling for whole-slide histopathology image annotation. The active query selection combines the measure of spatial representativeness and histopathological tissue informativeness. With the assumption that the labeling cost of individual instances is non-biased, we use three independent features, namely loss prediction query, Monte Carlo distribution query, and loss estimation enhanced by spatial information for the active learning task. The experiments were validated on large cohorts of The Cancer Genome Atlas (TCGA) and the ”100,000 histological images of human colorectal cancer and healthy tissue” dataset. Empirically, the proposed method can outperform the other annotation strategies on the histopathology datasets. The annotation cost is reduced by an obvious margin of 40% to retain an accurate classifier. This novel annotation strategy provides the potential to efficiently label and classify histopathology images with a patch-based convolutional neural network.
State Estimation in Linear Dynamical Systems By Partial Update Kalman Filtering

In this letter, we develop a partial update Kalman filtering (PUKF) algorithm to solve the state of a discrete-time linear stochastic dynamical system. In the proposed algorithm, only a subset of the state vector is updated at every iteration, which reduces its computational complexity, compared to the original KF algorithm. The required conditions for the stability of the algorithm are discussed. A closed-form expression for steady-state mean-square deviation is also derived. Numerical examples are used to validate the correctness of the provided analysis. They also reveal the PUKF algorithm exhibits a trade-off between the estimation accuracy and the computational load which is extremely profitable in practical applications.
A Parameter Efficient Multi-Scale Capsule Network

Capsule networks consider spatial relationships in an input image. The relationship-based feature propagation in capsule networks shows promising results. However, a large number of trainable parameters limit their widespread use. In this paper, we propose Decomposed Capsule Network (DCN) to reduce the number of training parameters in the primary capsule generation stage. Our DCN represents a capsule as a combination of basis vectors. Generating basis vectors and their coefficients notably reduce the total number of training parameters. Moreover, we introduce an extension of the DCN architecture, named Multi-scale Decomposed Capsule Network (MDCN). The MDCN architecture integrates features from multiple scales to synthesize capsules with fewer parameters. Our proposed networks show better performance on the Fashion-MNIST dataset and the CIFAR10 dataset with fewer parameters than the original network.
Choose Settings Carefully: Comparing Action Unit Detection At Different Settings Using A Large-Scale Dataset

In this paper, we investigate the impact of some of the commonly used settings for (a) preprocessing face images, and (b) classification and training, on Action Unit (AU) detection performance and complexity. We use in our investigation a large-scale dataset, consisting of ~55K videos collected in the wild for participants watching commercial ads. The preprocessing settings include scaling the face to a fixed resolution, changing the color information (RGB to gray-scale), aligning the face, and cropping AU regions, while the classification and training settings include the kind of classifier (multi-label vs. binary) and the amount of data used for training models. To the best of our knowledge, no work had investigated the effect of those settings on AU detection. In our analysis we use CNNs as our baseline classification model.
Fighting-Game Gameplay Generation Using Highlight Cues

In this article, we propose a fighting-game artificial intelligence (AI) that selects its actions from the perspective of highlight generation using Monte Carlo tree search with three highlight cues in the evaluation function. The intended use of this proposed AI is to generate gameplay in live streaming platforms such as Twitch and YouTube where a large number of spectators watch gameplay to entertain themselves. A gameplay analysis and user study are conducted using FightingICE, a fighting-game platform in an international fighting-game AI competition. Results show that gameplay generated by two AI players of the proposed method has more promising characteristics than not only gameplay generated according to an existing method but also gameplay by the top two AI players in the 2019 competition and that it is more entertaining than the latter gameplay.
Coiflet wavelet-homotopy solution of free convection in a closed cavity subjected to an inclined external magnetic field

The Coiflet wavelet solutions are given to the classic cavity flow problem describing natural convection in cavities under the effect of an inclined external magnetic field. The governing partial differential equations are solved by a brand-new and very efficient computational method, namely, the Coiflet wavelet-homotopy method. This approach not only inherits the capability of homotopy analysis to deal with strongly nonlinear problems, but also maintains the orthogonality and high-precision local expression property of the wavelet method. The Coiflet wavelet solutions are obtained via a very efficient homotopy iteration. Rigid comparison with previous studies for a wide range of various physical parameters shows excellent computational efficiency and accuracy of the proposed method.
Unpaired Night-To-Day Translation: Image Restoration And Style Transfer Under Low Illumination

The faint vision at low illumination affects the performance of intelligent surveillance systems and induces criminals to sin under the cover of darkness. Night-to-day translation is an ideal way to handle this problem, but hard to achieve due to the lack of information at night. We propose a novel approach that combines DCGAN(deep convolutional generative adversarial network) and image processing algorithms to find out the mapping from night to day. Images from night domain are enhanced with MSRCP(multi-scale retinex with chromaticity preservation) algorithm before they’re put into DCGAN to generate bright and clear daylight images without paired supervision. At the same time emerging issues of image atomization and local over-exposure are handled to ensure the quality of output. The experimental results show that our approach can be applied to different conditions and dig out sensitive information from the darkness.