Reviewerly/exHarmony huggingface dataset:
Active learning with missing values considering imputation uncertainty

To perform active learning with incomplete datasets containing missing values, conventional methods first impute the missing values via missing value imputation. Unlabeled instances are then sequentially selected to be labeled based on an acquisition function towards improving the prediction model. However, inaccurate imputations would negatively affect the performance of the prediction model. In this study, we propose a novel query selection method that considers the imputation uncertainty for active learning with missing values. We quantify the imputation uncertainty of each instance using multiple imputation. For query selection, the acquisition function is penalized by the imputation uncertainty. Consequently, inaccurately imputed instances are less likely to be selected for labeling, thereby avoiding superfluous labeling costs. We verified the effectiveness of the proposed method on twenty benchmark datasets using various missing rates and prediction models.
Image convolution: a linear programming approach for filters design

Abstract Image analysis is a branch of signal analysis that focuses on the extraction of meaningful information from images through digital image processing techniques. Convolution is a technique used to enhance specific characteristics of an image, while deconvolution is its inverse process. In this work, we focus on the deconvolution process, defining a new approach to retrieve filters applied in the convolution phase. Given an image I and a filtered image $$I' = f(I)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mi>I</mml:mi> <mml:mo>′</mml:mo> </mml:msup> <mml:mo>=</mml:mo> <mml:mi>f</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>I</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> , we propose three mathematical formulations that, starting from I and $$I'$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>I</mml:mi> <mml:mo>′</mml:mo> </mml:msup> </mml:math> , are able to identify the filter $$f'$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>f</mml:mi> <mml:mo>′</mml:mo> </mml:msup> </mml:math> that minimizes the mean absolute error between $$I'$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>I</mml:mi> <mml:mo>′</mml:mo> </mml:msup> </mml:math> and $$f'(I)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mi>f</mml:mi> <mml:mo>′</mml:mo> </mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>I</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> . Several tests were performed to investigate the applicability of our approaches in different scenarios. The results highlight that the proposed algorithms are able to identify the filter used in the convolution phase in several cases. Alternatively, the developed approaches can be used to verify whether a specific input image I can be transformed into a sample image $$I'$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>I</mml:mi> <mml:mo>′</mml:mo> </mml:msup> </mml:math> through a convolution filter while returning the desired filter as output.
Video Representation Fusion Network For Multi-Label Movie Genre Classification

In this paper, we introduce a Video Representation Fusion Network (VRFN) for movie genre classification. Different from the previous works, which use frame-level features for movie genre classification, our approach uses video classification architecture to create video-level features from a group of frames and fuse these features temporally to learn long-term spatiotemporal information for the movie genre classification task. We use a pre-trained I3D model to generate intermediate video representations and connect it with a C3D-LSTM model for feature fusion and movie genre classification. LMTD-9 dataset which contains 4007 trailers multi-labeled with 9 movie genres is used for training and evaluation of the model. The experimental results demonstrate that learning long-term temporal dependencies by fusing video representations improves the performance in movie genre classification. Our best model outperforms state-of-the-art methods by 3.4% improvement in AUPRC(macro).
On Depth, Robustness and Performance Using the Data Re-Uploading Single-Qubit Classifier

Quantum machine learning (QML) is a new field in its infancy, promising performance enhancements over many classical machine learning (ML) algorithms. Data reuploading is a QML algorithm with a focus on utilizing the power of a singular qubit as an individually capable classifier. Recently, there have been studies set out to explore the concept of data re-uploading in a classification setting, however, important aspects are often not considered in experiments, which may hinder our understanding of the methodology’s performance. In this work, we conduct an analysis of the single-qubit data re-uploading methodology, in relation to the effect that system depth has on classification and robustness performances against the influence of environmental noise during training. This is aimed towards bridging together previous works in order to solidify the concepts of the methodology, and provide reasonable insight into how transferable the methodology is when applied to non-synthetic data. To further demonstrate the findings, we also analyse the results of a case study using a subset of MNIST data. From this work, our experimental results support that an increase in system depth can lead to higher classification performances, as well as improved stability during training in noisy environments, with the sharpest performance improvements seemingly occurring between 1–3 uploading layer repetitions. Leading on from our experimental results, we suggest areas for further exploration, to ensure we can maximize classification performance when using the data re-uploading methodology.
Optimizing Broad Learning System Hyper-parameters through Particle Swarm Optimization for Predicting COVID-19 in 184 Countries

The Coronavirus Disease 2019 (COVID-19) began to outbreak since December 2019 and widely spread over the world.How to accurately predict the spread of COVID-19 is one of the essential issues for controlling the pandemic.This study establishes a general model that can predict the trend of COVID-19 in a country based on historical COVID-19 data in 184 countries.First, Savitzky-Golay (S-G) filter is utilized to detect multiple waves of COVID-19 in a country.Then, a PSO-SIR (particle swarm optimization susceptible-infected-recovery) model is provided for data augmentation.Finally, a novel PSO-BLS (particle swarm optimization broad learning system) is proposed for predicting the trend of COVID-19.Experimental results show that compared with the deep learning models (ANN, CNN, LSTM, and GRU), the PSO-BLS algorithm has higher accuracy and stability in predicting the number of active infected cases and removed cases.
Dual-Path Deep Supervision Network with Self-Attention for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification(VI-RelD) is an emerging but challenging problem that aims to match pedestrians captured by visible and infrared cameras. Existing studies in this field mainly focus on learning sharable feature representations from the last layer of deep convolution neural networks (CNNs) to handle the cross-modality discrepancies. However, due to the huge differences between visible and infrared images, the last layer's feature representations are less discriminative for VI-ReID. To remedy this, we propose a novel deep supervision learning network, namely Dual-path Deep Supervision Network (DDSN), for VI-ReID. Based on the backbone network, DDSN consists of two key modules, (1) a dual-path deep supervision learning (DDSL) module that is plugged into multiple network layers, and (2) a self-attention module that is developed on top of the backbone network. The backbone network extracts multilevel features at lower middle layers, and several DDSL modules utilize these features to generate more discriminative descriptors. Furthermore, we apply the self-attention module for context modeling to capture useful contextual cues as a supplement. By fusing these descriptors, DDSN can utilize both multi-level information and potential contextual information. Despite the apparent simplification, our method outperforms several state-of- the-art methods on two large-scale datasets: RegDB and SYSU-MM01.
Modality Disentangled Discriminator for Text-to-Image Synthesis

Text-to-image (T2I) synthesis aims at generating photo-realistic images from text descriptions, which is a particularly important task in bridging vision and language. Each generated image consists of two parts: the content part related to the text and the style part irrelevant to the text. The existing discriminator does not distinguish between the content part and the style part. This not only precludes the T2I synthesis models from generating the content part effectively but also makes it difficult to manipulate the style of the generated image. In this paper, we propose a modality disentangled discriminator that distinguishes between the content part and the style part at a specific layer. Specifically, we enforce the early layers of a certain number in the discriminator to become the disentangled representation extractor through two losses. The extracted common representation for the content part can make the discriminator more effective for capturing the text-image correlation, while the extracted modality-specific representation for the style part can be directly transferred to other images. The combination of these two representations can also improve the quality of the generated images. Our proposed discriminator is used to substitute the discriminator of each stage in the representative model AttnGAN and the SOTA model DM-GAN. Extensive experiments are conducted on three widely used datasets, i.e. CUB, Oxford-102, and COCO, for the T2I synthesis task, demonstrating the superior performance of the modality disentangled discriminator over the base models. Code for DM-GAN with our modality disentangled discriminator is available at <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/FangxiangFeng/DM-GAN-MDD</uri></b> .
Fast Morphological Dilation and Erosion for Grey Scale Images Using the Fourier Transform

The basic filters in mathematical morphology are dilation and erosion. They are defined by a flat or non-flat structuring element that is usually shifted pixel-wise over an image and a comparison process that takes place within the corresponding mask. Existing fast algorithms that realise dilation and erosion for grey value images are often limited with respect to size or shape of the structuring element. Usually their algorithmic complexity depends on these aspects. Many fast methods only address flat morphology.
Human Facial Expression Based Video Retrieval with Query Video Using EBCOT and MLP

We can find a massive demand for systems that retrieve video with a query as image or video since the text-based retrieval system (text, images, video) was obsolete and the content-based video retrieval oriented. For many applications, 'it is essential for retrieving videos from the extensive database with image and video query for content linking and brand monitoring. In this paper, a new video retrieval scheme developed for retrieving videos from an extensive database with a video query. The process uses EBCOT (Embedded Block Coding with Optimization Truncation) coding for feature extraction and MLP (Multilayer Perceptron) for classification and retrieval. The process uses videos with human facial expression as query and related videos with similar facial expression can be retrieved. The process carried out with three phases i)facial expression recognition ii)frame indexing iii)video indexing and retrieving. This methodology does video retrieval process in which video gives as a query for which facial expression has been recognized videos in the database are indexed with the corresponding expression and finally retrieval has been performed thus in this method retrieval scalability has been improved significantly.
Hiding in the Crowd: Federated Data Augmentation for On-Device Learning

To cope with the lack of on-device machine learning samples, this article presents a distributed data augmentation algorithm, coined federated data augmentation (FAug). In FAug, devices share a tiny fraction of their local data, i.e., seed samples, and collectively train a synthetic sample generator that can augment the local datasets of devices. To further improve FAug, we introduce a multihop-based seed sample collection method and an oversampling technique that mixes up collected seed samples. Both approaches enjoy the benefit from the crowd of devices, by hiding data privacy from preceding hops and feeding diverse seed samples. In the image classification tasks, simulations demonstrate that the proposed FAug frameworks yield stronger privacy guarantees, lower communication latency, and higher on-device ML accuracy.
A survey on different dimensions for graphical keyword extraction techniques

The transmission from offline activities to online activities due to the social disorder evolved from COVID-19 pandemic lockdown has led to increase in the online economic and social activities. In this regard, the Automatic Keyword Extraction (AKE) from textual data has become even more interesting due to its application over different domains of Natural Language Processing (NLP). It is observed that the Graphical Keyword Extraction Techniques (GKET) use Graph of Words (GoW) in literature for analysis in different dimensions. In this article, efforts have been made to study these different dimensions for GKET, namely, the GoW representation, the statistical properties of GoW, the stability of the structure of GoW, the diversity in approaches over GoW for GKET, and the ranking of nodes in GoW. To elucidate these different dimensions, a comprehensive survey of GKET is carried in different domains to make some inferences out of the existing literature. These inferences are used to lay down possible research directions for interdisciplinary studies of network science and NLP. In addition, the experimental results are analysed to compare and contrast the existing GKET over 21 different dataset, to analyse the Word Co-occurrence Networks (WCN) for 15 different languages, and to study the structure of WCN for different genres. In this article, some strong correspondences in different disciplinary approaches are identified for different dimensions, namely, GoW representation: 'Line Graphs' and 'Bigram Words Graphs'; Feature extraction and selection using eigenvalues: 'Random Walk' and 'Spectral Clustering'. Different observations over the need to integrate multiple dimensions has open new research directions in the inter-disciplinary field of network science and NLP, applicable to handle streaming data and language-independent NLP.
Electromyography Gesture Identification Using CNN-RNN Neural Network for Controlling Quadcopters

Abstract Purpose : This paper presents a CNN-RNN neural network approach towards electromyography. Here the neural network is employed to identify different gestures from the reading of signals from human’s hand muscles. The identified gestures are then utilized to control a drone. Methodology : Implementation is made by using Myo, an 8-channel Electromyography (EMG) data acquisition device. 14000 datasets of 9 different gestures are collected and used to train the CNN-RNN models. The trained models are then tested on a drone using dronekit to translate the gestures to drone commands and send them from Python to the drone. Results : The results show that a CNN-RNN approach is very effective in identifying gestures from raw muscle data, resulting in an average of 96.60% positive identification for each gesture. The identified gesture shows an effective drone control from the simulated drone. Applications/Originality/Value : The high rate of positive identification opens the possibility to use a wearable device that is able to give the current state of the hand’s muscle tensions, translating them into a specific command to control devices or machines. With a rich information and real-time human control, it is expected that humans will experience a more intuitive approach towards devices or machines control.
An intelligent particle filter with resampling of multi-population cooperation

The particle filter (PF) has excellent estimation performance for nonlinear non-Gaussian systems. However, this method misleads the results due to sample impoverishment and the sensitivity on the initial values of state and process noise variance. To overcome this, an intelligent PF method with a resampling of multi-population cooperation is presented in this paper. Firstly, an intelligent resampling mechanism based on a circular collaborative structure is proposed. In this mechanism, the particles are divided into multiple populations, which are generated by importance densities with different initial state values and variances individually. Secondly, a collaborative strategy based on Gaussian mutation is designed to improve particle diversity so as to raise estimation accuracy of PF. This strategy reserves the high-weight particles in each population, and replaces the low-weight ones with the particles generated by Gaussian mutation on high-weight particles in the previous population based on the circular collaborative structure. Finally, three systems are introduced to test the performance of the proposed method. The results illustrate that the proposed method can effectively improve the estimation accuracy and robustness of PF when the initial values of state and variance of process noise are unknown compared with three existing methods.
Explaining Neural Networks by Decoding Layer Activations

We present a ‘CLAssifier-DECoder’ architecture (ClaDec) which facilitates the comprehension of the output of an arbitrary layer in a neural network (NN). It uses a decoder to transform the non-interpretable representation of the given layer to a representation that is more similar to the domain a human is familiar with. In an image recognition problem, one can recognize what information is represented by a layer by contrasting reconstructed images of ClaDec with those of a conventional auto-encoder(AE) serving as reference. We also extend ClaDec to allow the trade-off between human interpretability and fidelity. We evaluate our approach for image classification using Convolutional NNs. We show that reconstructed visualizations using encodings from a classifier capture more relevant information for classification than conventional AEs. Relevant code is available at https://github.com/JohnTailor/ClaDec.
Importance of data preparation when analysing written responses to open-ended questions: An empirical assessment and comparison with manual coding

In a world where consumer texts grow more numerous each day, automated text analysis can deliver valuable insights about consumer attitudes and behaviours. The present research was methodological in nature and focused on pre-processing of text data, which generally is the most time-consuming stage of analysis. Using responses to an open-ended question from 4341 consumers, document-term matrices (DTM) were created from varying combinations of n-grams (unigrams, bigrams, trigrams and combinations hereof), stemming (yes or no) and low-frequency term thresholding (retaining all terms or excluding those used < 0.1%, <1% or < 5%). By comparison to a fixed standard – manually derived content coded of respondents’ answers – the relative impact of the three pre-processing steps were assessed. PLS-DA was used to do so, and classifier performance was evaluated using AUC-ROC scores. Inclusion of bigrams and trigrams in DTMs did not influence classification performance and stemming had only a minor impact. Inclusion of all and very rare features (<0.1%) improved classification performance. The results were invariant of sample size and replicated in subsets of 2000, 1000 and 500 participants. The results may be specific to the short length of the answers (median words = 4), although they held in a sub-sample of the 500 longest answers (median words = 41). Future research should directly test the influence of these pre-processing steps, for example, through topic modelling.
New Models for Understanding and Reasoning about Speculative Execution Attacks

Spectre and Meltdown attacks and their variants exploit hardware performance optimization features to cause security breaches. Secret information is accessed and leaked through covert or side channels. New attack variants keep appearing and we do not have a systematic way to capture the critical characteristics of these attacks and evaluate why they succeed or fail.In this paper, we provide a new attack-graph model for reasoning about speculative execution attacks. We model attacks as ordered dependency graphs, and prove that a race condition between two nodes can occur if there is a missing dependency edge between them. We define a new concept, “security dependency”, between a resource access and its prior authorization operation. We show that a missing security dependency is equivalent to a race condition between authorization and access, which is a root cause of speculative execution attacks. We show detailed examples of how our attack graph models the Spectre and Meltdown attacks, and is generalizable to all the attack variants published so far. This attack model is also very useful for identifying new attacks and for generalizing defense strategies. We identify several defense strategies with different performance-security tradeoffs. We show that the defenses proposed so far all fit under one of our defense strategies. We also explain how attack graphs can be constructed and point to this as promising future work for tool designers.
Cross-Dimension Mining Model of Public Opinion Data in Online Education Based on Fuzzy Association Rules

Abstract The multi-dimensional characteristics of public opinion in online education lead to the difficulty of data cross-dimensional mining. To solve this problem, this paper designs a cross-dimensional data mining model of public opinion in online education based on fuzzy association rules. Based on the public opinion subject, object, and ontology to analyze the characteristics of public opinion in online education, Yaahp software is used to calculate the influence factor weight of public opinion in online education. According to the weight analysis results, the relationship between the dimensions of various public opinion data is clarified by using data semantic association. This paper introduces the fuzzy set theory into the database and uses crawlers to obtain public opinion data and stores them in the database, to complete the data preprocessing through distributed text preprocessing, feature selection distributed computing, and text vectorization distributed computing. Taking the cloud computing platform as the core, the cross-dimension mining model of public opinion in online education data is constructed according to the dimension correlation analysis and preprocessing results. The simulation results show that the model has the advantages of wide range, fast speed, and high accuracy, and can provide data support for education reform.