Reviewerly/exHarmony huggingface dataset:
A Semi-supervised Learning Approach for Visual Question Answering based on Maximal Correlation

In this paper, we propose a semi-supervised learning approach for the Visual Question Answering (VQA) task based on maximal correlation. Instead of training the VQA model with just classification loss like cross-entropy, we propose a semi-supervised loss function to incorporate Soft-HGR, a training approach based on Hirschfeld-Gebelein-Rényi (HGR) maximal correlation, to realize semi-supervised model training. With Soft-HGR, the high-order correlation from cross-modal common information of VQA image-question pairs is utilized to improve VQA model performance even without discriminative supervision from answer labels. We conduct experiments on the VQA v2 dataset by training the VQA model with different percentages of unlabeled samples. Experimental results show that our approach is efficient and model-agnostic for this semi-supervised learning task.
Personal Data Matters: New Opportunities from Lifelogs

In this position paper, the motivation for, and early progress in lifelog analytics and retrieval are presented. Early progress in the field is reviewed with a specific focus on the challenge of developing personal Memex-style lifelog search engines.
Fingerprinting Tabletop Games

We present some initial work on characterizing games using a visual ‘fingerprint’ generated from several independent optimisation runs over the parameters used in Monte Carlo Tree Search (MCTS). This ‘fingerprint’ provides a useful tool to compare games, as well as highlighting the relative sensitivity of a specific game to algorithmic variants of MCTS. The exploratory work presented here shows that in some games there is a major change in the optimal MCTS parameters when we move from 2-players to 3 or 4-players.
Predicting Post-Editing Effort for English-Chinese Neural Machine Translation

Although the technology of machine translation (MT) has achieved great progress in recent years, the output generated by MT systems still need human post-editing (PE) to meet the quality requirements of many translation tasks. How to measure the PE effort of an MT output is a key problem in PE study. PE time is a metric that can well reflect the overall PE effort and is widely used in practice. This paper studied the method to automatically predict the PE time. We manually post-edited the English-Chinese NMT output in three domains, with PE time recorded and MT error types annotated. Based on the corpus, we took error types as features and used machine learning models to predict PE time. Since the error types in MT output are unavailable in real PE scenario, we also proposed a prediction method based on the translation quality estimation (QE) framework that can make prediction without error information. Experimental results show that both methods achieve higher correlation with the actual PE time than the baseline method.
Between adverbs and particles: A corpus study of selected intensifiers

Abstract In this paper, we present a preliminary study of three intensifiers ( absolutně , naprosto , úplně) based on data from three different corpora, a written corpus SYN2020, a web corpus ONLINE-ARCHIVE, and a spoken corpus ORTOFON 1. Providing a parallel annotation of a random sample of each intensifier, we focus on their functions and meanings in context. We analyse their properties in order to define those features which are relevant to their word class assignment, and to prepare grounds for the future disambiguation tasks.
Solving the N-Queens and Golomb Ruler Problems Using DQN and an Approximation of the Convergence

We build on the Deep Q-Learning Network (DQN) to solve the N-Queens problem to propose a solution to the Golomb Ruler problem, a popular example of a one dimensional constraint satisfaction problem. A comparison of the DQN approach with standard solution approaches to solve constraint satisfaction problems, such as backtracking and branch-and-bound, demonstrates the efficacy of the DQN approach, with significant computational savings as the order of the problem increases. The convergence behavior of the DQN model has been approximated using Locally Weighted Regression and Cybenko Approximation, demonstrating an improvement in the performance of the DQN with episodes, regardless of the order of the problem.
Fast and robust template matching based on ring projection and pseudo-Zernike moments for automatic optical inspection

The traditional template matching methods are very time-consuming and cannot deal with the problems of rotation, scaling, and multi objects matching at the same time. In this paper, we propose a two-stage fast and robust template matching method based on ring projection transformation and pseudo-Zernike moments, which can be used in automatic optical inspection. In the first stage, we use dynamic time warping to estimate the scaling factors and obtain the preliminary candidates according to the extracted ring projection vectors. And then the candidates of the multi objects are obtained by non-maximum suppression. In the second stage, the fine matching process based on pseudo-Zernike moments is performed. Besides, we apply the image pyramid search strategy to improve the computational efficiency. The rotation angle is estimated by the improved ring-shifted method. The experimental results show that the mean errors of rotation, scaling, and translation for the image with or without noise or illumination changes are less than 0.2412°, 0.0037, and 1.0826 pixels, respectively. Compared with the conventional search method, the image pyramid search strategy increases the computational efficiency by 50 times. The experiments of robustness demonstrate that the template matching method we proposed can still obtain high accuracy even in the case of noise or illumination changes. In addition, it can be used for multi objects detection.
INFORMATION TECHNOLOGY IN LANGUAGE TEACHING METHODOLOGY COURSE AT PEDAGOGICAL UNIVERSITY

The introduction of information and communications technology (ICT) in pedagogical universities of Ukraine has considerably optimized the professor’s preparation for different forms of in-class teaching. The aim of this research is to characterize the range of media resources used during the preparation of the teaching foreign languages methodology course at high-specialized school and universities of Ukraine. The predominant method used to check the effectiveness of ICT was content analysis. The method of psychological experiment served to study the link between ICT and the students’ emotional sphere. An anonymous poll has discovered a considerable number of students showing a positive attitude to online instruments and using those resources very frequently or almost every day.
People Detection and Tracking Using a Fisheye Camera Network

In this paper we study techniques for accurate detection, localization, and tracking of multiple people in an indoor scene covered by multiple top-view fisheye cameras. This is a rarely studied setting within the topic of multi-camera object tracking. The experimental results on test videos exhibit good performance for practical use. We also propose methods to account for occlusion by scene objects at different stages of the algorithm that lead to improved results.
Unsupervised Joint Multi-Task Learning of Vision Geometry Tasks

In this paper, we present a novel architecture and training methodology for learning monocular depth prediction, camera pose estimation, optical flow, and moving object segmentation using a common encoder in an unsupervised fashion. We demonstrate that the geometrical relationships between these tasks not only support joint unsupervised learning as shown in previous works but also allow them to share common features. We also show the advantage of using a two-stage learning approach to improve the performance of the base network.
Machine Learning Analysis for Cervical Cancer Prediction, a Systematic Review of the Literature

At present, cervical cancer is still the most complex issue due to the fact that people who suffer from it have a high risk of death. Therefore, it is very important to have an early diagnosis. The present study is a review of the scientific literature, which includes 50 articles from the following databases: ProQuest, IEEE Xplore, PubMed, ScienceDirect, Springer, IopScience and Scopus. Thus, showing that the research that has been developed with machine learning facilitates the control, follow-up and monitoring of the disease. The systematic review shows that the model that had the highest accuracy is Convolutional Neural Network and the most used tool is R Studio, these two factors are determinant in cervical cancer, according to the research conducted with 50 articles, where more research on this topic was recorded is the continent of Asia and specifically in the countries of India and China.
Profiling Attacks against ECC: Side Channel Analysis Based on Deep Learning for Curve-25519

This paper combines the deep learning technology and the side-channel analysis method to achieve effective cross-technology analysis for the ECC Curve-25519 algorithm on an STM32F407 (ARM-CortexM4) chip. The implementation of the target algorithm, which is protected with point randomization and constant-time operations (Montgomery powering ladder), uses security countermeasures. Therefore, the analysis explores the leakages from memory-related operations (conditional swap), which happen before each point addition or point doubling operation. The side-channel analysis method is used to determine and label the swap bits values of the TOE (target of evaluation), and then the electromagnetic radiation energy traces of the ECC Curve-25519 algorithm are fed into the convolutional neural network (CNN) for iterative training. The experiment shows that both the AlexNet and the VGG-13 network can effectively analyze the ECC Curve-25519 algorithm with security countermeasures, and the analysis accuracy rate reaches 96.11% and 98.61%, respectively.
Multi-Channel Local Binary Pattern Guided Convolutional Neural Network for Breast Cancer Classification

Background: The advancement in convolutional neural network (CNN) has reduced the burden of experts using the computer-aided diagnosis of human breast cancer. However, most CNN networks use spatial features only. The inherent texture structure present in histopathological images plays an important role in distinguishing malignant tissues. This paper proposes an alternate CNN network that integrates Local Binary Pattern (LBP) based texture information with CNN features. Methods: The study propagates that LBP provides the most robust rotation, and translation-invariant features in comparison with other texture feature extractors. Therefore, a formulation of LBP in context of convolution operation is presented and used in the proposed CNN network. A non-trainable fixed set binary convolutional filters representing LBP features are combined with trainable convolution filters to approximate the response of the convolution layer. A CNN architecture guided by LBP features is used to classify the histopathological images. Result: The network is trained using BreKHis datasets. The use of a fixed set of LBP filters reduces the burden of CNN by minimizing training parameters by a factor of 9. This makes it suitable for the environment with fewer resources. The proposed network obtained 96.46% of maximum accuracy with 98.51% AUC and 97% F1-score. Conclusion: LBP based texture information plays a vital role in cancer image classification. A multi-channel LBP futures fusion is used in the CNN network. The experiment results propagate that the new structure of LBP-guided CNN requires fewer training parameters preserving the capability of the CNN network’s classification accuracy.
Hybrid pooling with wavelets for convolutional neural networks

The need to detect and classify objects correctly is a constant challenge, being able to recognize them at different scales and scenarios, sometimes cropped or badly lit is not an easy task. Convolutional neural networks (CNN) have become a widely applied technique since they are completely trainable and suitable to extract features. However, the growing number of convolutional neural networks applications constantly pushes their accuracy improvement. Initially, those improvements involved the use of large datasets, augmentation techniques, and complex algorithms. These methods may have a high computational cost. Nevertheless, feature extraction is known to be the heart of the problem. As a result, other approaches combine different technologies to extract better features to improve the accuracy without the need of more powerful hardware resources. In this paper, we propose a hybrid pooling method that incorporates multiresolution analysis within the CNN layers to reduce the feature map size without losing details. To prevent relevant information from losing during the downsampling process an existing pooling method is combined with wavelet transform technique, keeping those details "alive" and enriching other stages of the CNN. Achieving better quality characteristics improves CNN accuracy. To validate this study, ten pooling methods, including the proposed model, are tested using four benchmark datasets. The results are compared with four of the evaluated methods, which are also considered as the state-of-the-art.
Domain-Adaptation Person Re-Identification via Style Translation and Clustering

To solve the two challenges of the high cost of manual labeling data and significant degradation of cross-domain performance in person re-identification (re-ID), we propose an unsupervised domain adaptation (UDA) person re-ID method combining style translation and unsupervised clustering method. Our model framework is divided into two stages: 1) In the style translation stage, we can get the labeled source image with the style of the target domain; 2) In the UDA person re-ID stage, we use Wasserstein distance as the evaluation index of the distribution difference between domains. In addition, to solve the problem of source domain ID labels information loss in the process of style translation, a feedback mechanism is designed to feedback the results of person re-ID to the style translation network, to improve the quality of image style translation and the accuracy of ID labels and make the style translation and person re-ID converge to the best state through closed-loop training. The test results on Market-1501, DukeMTMC, and MSMT17 show that the proposed method is more efficient and robust.
Rec-clusterGCN: An Efficient Graph Convolution Network for Recommendation

With more Graph Convolutional Network (GCN) models applied to recommendation tasks, graph data of tremendous scalability becomes much harder to train for existing models. Many existing works of adapting GCN models to recommendation tasks often try to improve accuracy by aggregating messages from all high-order neighboring nodes, which can result in insufficient computational efficiency. To tackle with data with large scalability, we propose a novel recommendation algorithm&#x2014;Rec-clusterGCN, which utilizes the cluster structure of graphs, and is suitable for SGD-based training. It goes as follows: at every step, a dense subgraph of user-item interaction will be constructed, and each node will only aggregate messages from neighboring nodes within the subgraph. Next, our model accepts the LightGCN model structure. It can reduce a huge amount of computational time cost and make training of larger graph possible without sacrificing too much accuracy. In addition, by using our node enhancement technique, the performance of Rec-clusterGCN is further improved. The experimental results also indicate that the proposed algorithm outperforms most baseline algorithms. Significant improvement has been made in computational efficiency (on average about 30.0% relative improvement for different layers in the Gowalla dataset).
Audio Compression Using Transform Coding with LZW and Double Shift Coding

The need for audio compression is still a vital issue, because of its significance in reducing the data size of one of the most common digital media that is exchanged between distant parties. In this paper, the efficiencies of two audio compression modules were investigated; the first module is based on discrete cosine transform and the second module is based on discrete wavelet transform. The proposed audio compression system consists of the following steps: (1) load digital audio data, (2) transformation (i.e., using bi-orthogonal wavelet or discrete cosine transform) to decompose the audio signal, (3) quantization (depend on the used transform), (4) quantization of the quantized data that separated into two sequence vectors; runs and non-zeroes decomposition to apply the run length to reduce the long-run sequence. Each resulted vector is passed into the entropy encoder technique to implement a compression process. In this paper, two entropy encoders are used; the first one is the lossless compression method LZW and the second one is an advanced version for the traditional shift coding method called the double shift coding method. The proposed system performance is analyzed using distinct audio samples of different sizes and characteristics with various audio signal parameters. The performance of the compression system is evaluated using Peak Signal to Noise Ratio and Compression Ratio. The outcomes of audio samples show that the system is simple, fast and it causes better compression gain. The results show that the DSC encoding time is less than the LZW encoding time.
Machine Learning-based Anomaly Detection for Particle Accelerators

Particle accelerators are complex systems composed of multiple subsystems that must work together to produce high quality beams employed for physics experiments. A fault or an anomalous behaviour in one of such subsystems can lead to expensive downtime for the whole facility. Thus, it is of paramount importance to be able to promptly detect anomalies. Given the vast amount of streaming data generated by accelerator field sensors, Machine Learning (ML)-based tools are promising candidates for efficient monitoring of such systems: an approach based on unsupervised ML techniques exploiting the data from a Radio Frequency tuning system is here proposed. Feature importance is exploited to guide the definition of the optimal windowing for feature extraction. The proposed approach is here validated on real-world data related to the ALPI accelerator at Legnaro National Laboratories in Italy.
Vector Field for Following Elliptic Paths

This paper introduces a new vector field guidance method for following elliptic paths. The proposed vector field commands the course angle expressed as a combination of tangent to the path and a nonlinear function of the error. Further analysis deduces the curvature variation for the elliptic path following vector field. Stability of the proposed method is shown using Lyapunov indirect method. Considering a two-dimensional kinematic model of a UAV, simulation results are presented in support.
An Adaptative and Compliant Forensics Admissibility Metrics Generation Methodology

The paper aims to solve the lack or absence of effective metrics that reflect the sized system’s forensics readiness, providing intrinsic weights to the gathered evidence. This paper proposes a new adaptive methodology to elicit forensics metrics that measure the evidence admissibility attributes. It considers the legislation difference between countries and required procedures. It also helps in measuring the computing system forensics soundness to generate admissible evidence. The article presents an application of the methodology within Cloud computing services as a demonstration example.
Hierarchical Graph Classification Method Based on Graph Pool Topology Learning

Graph neural network extends deep neural network to graph structured data, and realizes many tasks related to graphs. However, most of the existing models focus on design graph convolution operations, and the important role of graph pools in hierarchical learning representation is often ignored. In addition, the existing Top-K graph pooling method will cause the feature information of unselected nodes lost, which inevitably leads to the loss of a large amount of graph topology feature information. In order to solve the above problems, improved &#x201C;intra-layer&#x201D; &amp; &#x201C;inter-layer&#x201D; joint feature extraction structure is proposed, which introduces a structural feature learning mechanism, combines the graph adaptive pooling operator with the existing GNN architecture. To mitigate the problem of insufficient feature extraction by LSTM, BiLSTM is used to extract the inter-layer feature. Experiment results show that our method can achieves better performance on benchmark data sets by using a reasonable number of parameters.
Coupled Harmonic Oscillator in a System of Free Particles

The coupled quantum harmonic oscillator is one of the most researched and important model systems in quantum optics and quantum informatics. This system is often investigated for quantum entanglement in the environment. As a result, such studies are complex and can only be carried out using numerical methods that do not reveal the general pattern of such systems. In this work, the external environment is considered to be two independent particles interacting with coupled harmonic oscillators. It is shown that such a system has an exact analytical solution to the dynamic Schrödinger equation. The analysis of this solution is carried out, and the main parameters of this system are revealed. The solutions obtained can be used to study more complex systems and their quantum entanglement.
MOtion Human Parsing: A New Benchmark for 3D Human Parsing

Human parsing is one of the most fundamental tasks in analyzing humans and has huge potentials in upper-level applications. Since the introduction of 3D sensors, 3D human parsing has attracted increasing attention. Unfortunately, existing datasets lack motion data and have limited semantic annotations, such as sparse annotations and insufficient label categories. To tackle this issue, we propose a point cloud dataset, MOtion Human Parsing(MOHP), containing 28,800 RGB-D images and 7,200 color point clouds of moving humans with accurate point-wise semantic annotations, captured by 4 synchronized depth cameras at a speed of 15 frames per second. To the best of our knowledge, this is the first color motion human point cloud dataset for 3D human parsing.
Modeling Analysis and Control Improvement of VSCMG Frame Harmonic Deceleration Drive System

The drive mode of the VSCMG frame servo system adopts the three-phase permanent magnet synchronous motor three closed loop control (speed, position, current three closed loop), in order to suppress the phenomenon of large output speed errors at low speeds, the system adds PID control to reduce the output speed error. However, due to the excessive number of nonlinear variables, the error increases. Therefore, this paper proposes a permanent magnet DC brushless motor drive system with adaptive robust three closed loop control using rotational speed, position and current to reduce the error caused by nonlinear factors and prolong the life of harmonic reducer.