Reviewerly/exHarmony huggingface dataset:
Rhythm-Aware Sequence-to-Sequence Learning for Labanotation Generation With Gesture-Sensitive Graph Convolutional Encoding

Labanotation is a professional dance notation system widely used in dance education and choreography preservation. Automatically generatingLabanotation dance scores from motion capture data can save a huge amount of manual time and effort. Recently, the sequence-to-sequence (seq2seq) model is applied to the automatic Labanotation generation. This model is based on an encoder-decoder structure, which encodes the input motion sequence to a fixed-length vector and then decodes it to generate the target sequence. However, the encoding of spatial skeleton structure of motion data is not considered in the existing work. Besides, it is challenging to align between the input motion data and the output Laban symbol sequences due to the severe imbalance of sequence lengths. Therefore, in this paper, we present a new seq2seq model for more effective Labanotation generation. In the encoder, we propose a new gesture-sensitive graph convolutional network with learned adaptive joint weights and non-physical connections to learn both spatial and temporal patterns from motion data sequences. In the decoder, we exploit motion rhythm information and propose a novel rhythm-aware attention mechanism to learn a good alignment between motion sequences and Laban symbol sequences, so that we can focus on relevant parts of the input motion sequence without searching in the whole sequence when predicting a target Laban symbol. Extensive experiments on two real-world datasets show that the proposed method achieves a better performance compared with the state-of-the-art approaches on the task of automatic Labanotation generation.
VERTIGo: A Visual Platform for Querying and Exploring Large Multilayer Networks

Many real world data can be modeled by a graph with a set of nodes interconnected to each other by multiple relationships. Such a rich graph is called multilayer graph or network. Providing useful visualization tools to support the query process for such graphs is challenging. Although many approaches have addressed the visual query construction, few efforts have been done to provide a contextualized exploration of query results and suggestion strategies to refine the original query. This is due to several issues such as i) the size of the graphs ii) the large number of retrieved results and iii) the way they can be organized to facilitate their exploration. In this article, we present VERTIGo, a novel visual platform to query, explore and support the analysis of large multilayer graphs. VERTIGo provides coordinated views to navigate and explore the large set of retrieved results at different granularity levels. In addition, the proposed system supports the refinement of the query by visual suggestions to guide the user through the exploration process. Two examples and a user study demonstrate how VERTIGo can be used to perform visual analysis (query, exploration, and suggestion) on real world multilayer networks.
An efficient multistage CBIR based on Squared Krawtchouk-Tchebichef polynomials

Abstract Image databases are increasing exponentially because of rapid developments in social networking and digital technologies. To search these databases, an efficient search technique is required. CBIR is considered one of these techniques. This paper presents a multistage CBIR to address the computational cost issues while reasonably preserving accuracy. In the presented work, the first stage acts as a filter that passes images to the next stage based on SKTP, which is the first time used in the CBIR domain. While in the second stage, LBP and Canny edge detectors are employed for extracting texture and shape features from the query image and images in the newly constructed database. The proposed CBIR was tested against existing algorithms on well-known database (Wang’s database), where Manhattan distance is used as a similarity metric. The improvement ratio in terms of computation time between the proposed system and existing system achieves 73.99%, which is considered a promising result.
Contrastive and consistent feature learning for weakly supervised object localization and semantic segmentation

Weakly supervised learning attempts to construct predictive models by learning with weak supervision. In this paper, we concentrate on weakly supervised object localization and semantic segmentation tasks. Existing methods are limited to focusing on narrow discriminative parts or overextending the activations to less discriminative regions even on backgrounds. To mitigate these problems, we regard the background as an important cue that guides the feature activation to cover the entire object to the right extent, and propose two novel objective functions: 1) contrastive attention loss and 2) foreground consistency loss. Contrastive attention loss draws the foreground feature and its dropped version close together and pushes the dropped foreground feature away from the background feature. Foreground consistency loss favors agreement between layers and provides early layers with a sense of objectness. Using both losses leads to balanced improvements over localization and segmentation accuracy by boosting activations on less discriminative regions but restraining the activation in the target object extent. For better optimizing the above losses, we use the non-local attention blocks to replace channel-pooled attention leading to enhanced attention maps considering the spatial similarity. Finally, our method achieves state-of-the-art localization performance on CUB-200-2011, ImageNet, and OpenImages benchmarks regarding top-1 localization accuracy, MaxBoxAccV2, and PxAP. We also demonstrate the effectiveness of our method in improving segmentation performance measured by mIoU on the PASCAL VOC dataset.
Regularization with multilevel non-stationary tight framelets for image restoration

Variational regularization models are one of the popular and efficient approaches for image restoration. The regularization functional in the model carries prior knowledge about the image to be restored. The prior knowledge, in particular for natural images, are the first-order (i.e. variance in luminance) and second-order (i.e. contrast and texture) information. In this paper, we propose a model for image restoration, using a multilevel non-stationary tight framelet system that can capture the image's first-order and second-order information. We develop an algorithm to solve the proposed model and the numerical experiments show that the model is effective and efficient as compared to other higher-order models.
A novel method for clinical risk prediction with low-quality data

In real-world data, predictive models for clinical risks (such as adverse drug reactions, hospital readmission, and chronic disease onset) are constantly struggling with low-quality issues, namely redundant and highly correlated features, extreme category imbalances, and most importantly, a large number of missing values. In most existing work, each patient is represented as a value vector with the fixed-length from some feature space, and missing values are forced to be imputed, which introduces much noise for prediction if the data set is highly incomplete. Besides, other challenges are either remaining unresolved or only partially solved when modeling, but without a systematic approach. In this paper, we propose a novel framework to address these low-quality problems, that we first treat patients as bags with the various number of feature-value pairs, called instances, and map them to an embedding space through our proposed feature embedding method to learn from it directly. In this way, predictive models can avoid the negative impact of missing data naturally. A novel multi-instance neural network is then connected, using two computational modules to deal with the problems of correlated and redundant features: multi-head attention and attention-based multi-instance pooling. They are capable of capturing the instance correlations and locating valuable information in each instance or bag. The feature embedding and multi-instance neural network are parameterized and optimized jointly in an end-to-end manner. Moreover, the training process is under both main and auxiliary supervision with focal loss functions to avoid the caveat of a highly imbalanced label set. This proposed framework is named AMI-Net3. We evaluate it on three suitable data sets from real-world settings with different clinical risk prediction tasks: adverse drug reaction of risperidone, schizophrenia relapse, and invasive fungi infection, respectively. The comprehensive experimental results demonstrate the effectiveness and superiority of our proposed method over competitive baselines.
Can you design the perfect condom? Engaging young people to inform safe sexual health practice and innovation

This paper describes the process of engaging young people in a user centred, co-design strategy to define their perfect condom. Our aims were 1) to find a way to destigmatise discussions about sexuality and contraception and 2) to provide information about what characteristics a perfect condom might embody for adolescents. We used arts-based methods to introduce creativity and enjoyment into discussing sensitive topics related to sexual and reproductive health, which is often avoided in relation to youth and usually nuanced by themes of risk and danger. Using theories of objects becoming things through their sociality, we explored what a condom could be to young people through our method of embedding the condom design process into narratives of young people’s lives and integration of these ideas into the development of a next-generation condom.
CMT: Cross Mean Teacher Unsupervised Domain Adaptation for VHR Image Semantic Segmentation

Semantic segmentation of remote sensing images has achieved superior results with the supervised deep learning models. However, their performance to unseen data domains could be very bad due to the domain shift between different domains. Recently, a series of unsupervised domain adaptation (UDA) methods has been developed to solve the domain shift problem in semantic segmentation. Most of them use adversarial learning to achieve global cross-domain alignment and use a self-training (ST) strategy to generate pseudo-labels for classwise alignment. However, these methods ignore the pixels that are not assigned pseudo-labels. Those pixels are mostly at the boundaries, which are vital to the final segmentation results. To solve this problem, this letter proposes a cross mean teacher (CMT) UDA method. The whole framework consists of two parts. On the one hand, the global cross-domain distribution alignment is performed, and then, reliable pseudo-labels are assigned to the target data. On the other hand, a cross teacher–student network (CTSN) is developed to effectively use those pixels with and without pseudo-labels. This network contains two student networks ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{1}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{2}$ </tex-math></inline-formula> ) and two teacher networks ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T_{1}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T_{2}$ </tex-math></inline-formula> ) for cross-consistency constraints that supervises <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{2}$ </tex-math></inline-formula> (or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{1}$ </tex-math></inline-formula> ) by the prediction results of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T_{1}$ </tex-math></inline-formula> (or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T_{2}$ </tex-math></inline-formula> ). The cross supervision by CTSN is helpful to prevent performance bottlenecks caused by the high coupling of teacher–student network in existing methods. Extensive experiments on three different remote sensing adaptation scenes verify the effectiveness and superiority of the proposed method.
Robust Segmentation and Classification of Moving Objects from Surveillance Video

Abstract Object segmentation and classification in the video sequence is a classical and critical problem that is constantly addressed due to vast real-time applications such as autonomous vehicles, smart surveillance, etc. Segmentation and classification of moving objects in video sequences captured from real-time non-constraint sequences is a challenging task. This paper presents a moving object segmentation and classification method for video sequences captured in a real-time environment. The key contributions of the paper include a method to detect and segment motion regions by applying the non-parametric Kolmogorov–Smirnov statistical test in the Spatio-temporal domain and a probabilistic neural network-based classification method to classify the moving objects into various classes. Promising results are obtained by experimentation using challenging PETS and Change Detection datasets. To corroborate the efficacy, a comparative analysis with contemporary method is also performed.
Dead Man's Switch: Forensic Autopsy of the Nintendo Switch

The Nintendo Switch is a popular handheld gaming console that is used for a variety of purposes. The most common is that of gaming, however, there are supporting activities such as social networking, media consumption and internet connectivity. In this paper, we have detailed the processes that must be conducted in order to extract forensic evidence from Nintendo Switch devices. We extracted a number of different forensic artefacts from a NAND dump of several Nintendo Switch devices. We discovered several key artefacts, notably personally identifiable information, network connection history and displays that have been connected. We also assessed the forensic value of each artefact extracted from the device. We developed software to automate the process of dumping and extracting the content of the NAND memory. Additionally, we developed modules for the forensic software Autopsy and released these as open source software to automate the process of ingestion and analysis.
EnvBERT: Multi-Label Text Classification for Imbalanced, Noisy Environmental News Data

Imbalanced and noisy classification problems pose a challenge for predictive modeling as most of the machine learning algorithms used for classification were designed around the assumption of an equal number of non-noisy examples for each class. Models with these problems cause classification errors. We propose a multi-label text classification model based on BERT, EnvBERT, which includes multi-label features in text classification and has good predictive performance for imbalanced, noisy environmental news data. EnvBERT is based on the KoBERT model pre-trained with Korean text data. We used the data oversampling technique to resolve the imbalanced characteristics of multi-label data and fine-tuned while setting a global threshold for label prediction. As a result, we show that EnvBERT improves classification performance by more than 80% on the imbalanced and noisy environmental news data.
Evolutionary Algorithms and Efficient Data Analytics for Image Processing

Steganography algorithms facilitate communication between a source and a destination in a secret manner. This is done by embedding messages/text/data into images without impacting the appearance of the resultant images/videos. Ste-ganalysis is the science of determining if an image has secret messages embedded/hidden in it. Because there are numerous steganography algorithms, and since each one of them requires a different type of steganalysis, the steganalysis process is extremely challenging. Thus, researchers aim to develop one universal steganalysis to detect all steganography algorithms. Universal steganalysis extracts a large number of features to distinguish stego images from cover images. However, this leads to the problem of the curse of dimensionality (CoD), which is considered to be an NP-hard problem. Generating a machine learning based model also takes a long time which makes real-time processing appear impossible in any optimization for time-intensive fields such as visual computing. In this study, we investigate previously developed evolutionary algorithms for boosting real-time image processing and argue that they provide the most promising solutions for the CoD problem.
Lexical Acquisition and Information Retrieval

Most approaches to full-text information retrieval are based on indexing documents by the words they contain. One of the problems with this approach is that words are ambiguous, and can therefore cause many documents to be retrieved which are not relevant. We believe that documents should instead be indexed by word senses, and that these senses should be taken from a machine-readable dictionary. This chapter discusses this approach from the viewpoint of lexical acquisition.
Application of Particle Swarm Optimization for Software Defect Prediction Using Object Oriented Metrics

With the growing number of software applications being developed for every small challenge, the importance of devising efficient software defect prediction models is imperative. Over the years, various machine learning techniques have been utilized to develop defect prediction model and have managed to achieve good results. In all defect prediction models, the task of correcting imbalanced data and feature selection has been of great significance. In this paper we have tried to analyze the working of the oversampling technique SMOTE along with feature selection using Particle Swarm Optimization on Object Oriented metrics. The selected features were then used to train the datasets one of the most popular classification techniques-Support Vector Machine to predict defects. The four datasets used for this study are of different Apache applications whose source code was obtained from open-source platforms and the raw data was pre-processed to obtain Object Oriented metrics. The performance measures used to record the results were Area Under ROC Curve, Recall and F-Measure values which showed that the Support Vector Classifier performed better on the dataset that had been balanced using SMOTE and acted upon by Particle Swarm Optimization for selecting feature set.
Unscented Particle Filter Algorithm Based on Divide-and-Conquer Sampling for Target Tracking

Unscented particle filter (UPF) struggles to completely cover the target state space when handling the maneuvering target tracing problem, and the tracking performance can be affected by the low sample diversity and algorithm redundancy. In order to solve this problem, the method of divide-and-conquer sampling is applied to the UPF tracking algorithm. By decomposing the state space, the descending dimension processing of the target maneuver is realized. When dealing with the maneuvering target, particles are sampled separately in each subspace, which directly prevents particles from degeneracy. Experiments and a comparative analysis were carried out to comprehensively analyze the performance of the divide-and-conquer sampling unscented particle filter (DCS-UPF). The simulation result demonstrates that the proposed algorithm can improve the diversity of particles and obtain higher tracking accuracy in less time than the particle swarm algorithm and intelligent adaptive filtering algorithm. This algorithm can be used in complex maneuvering conditions.
Constructing Dependable Data-Driven Software With Machine Learning

Many data-driven software systems present dependability challenges caused by the distribution and dynamicity of their application environments. We investigate machine learning-driven construction techniques combined with pattern-based architecture, showing that system dependability is linked to data and function quality.
A novel algorithm for high compression rates focalized on electrical power quality signals

<h2>Abstract</h2> This research proposes a high-performance algorithm for the compression rate of electrical power quality signals, using wavelet transformation. To manage the massive amount of data the telecommunications networks are constantly acquiring it is necessary to study techniques for data compression, which will save bandwidth and reduce costs extensively by avoiding having massive data storage facilities. First biorthogonal wavelet level six transform is applied, however after compression, the reconstructed signal will have a different amplitude and it will be shifted when compared to the original one. Then, normalization is used (for amplitude correction between the original signal and reconstructed one) by multiplying the reconstructed signal by the result of the division between the original signal maximum magnitude and the reconstructed signal maximum magnitude. Thirdly, the ripple in the reconstructed signal is eliminated by applying a moving average filter. Finally, the shifting is corrected by finding the difference between the maximum points in a cycle of the original signal and the reconstructed one. After the compression algorithm was performed the best rates are 99.803% for compression rate, RTE 99.9479%, NMSE 0.000434, and Cross-Correlation 0.999925. Finally, this works presents two new performance criteria, compression time and recovery time, both of them in a real scenario will determinate how fast the algorithm can perform.
Positioning and focusing of light sources in light concentrating systems using convolutional neural network modelling

Light energy concentrating systems such as High Flux Solar Simulators (HFSS) offer notable advantages in renewable energy research. Despite their expanding usage, they face some operational challenges that provide opportunities for new designs and further exploration. Accordingly, this study investigated the development and performance of a machine learning model based on convolutional neural networks (CNNs) for HFSS operation and control. Specifically, the hypothesis proposed is that a CNN can predict three HFSS parameters (the relative ×, y, z position of the light source) using imaging and computer vision techniques with an accuracy equal to or better than the operator. First, the HFSS output was characterized in detail to set the overall study expectations and serve as the baseline metric. Then, the optical modelling of the HFSS using Monte Carlo Ray Tracing was employed to generate more than 2,500 images for the training and validation of the CNN. Consequently, the hypothesis was validated as the CNN accurately predicted the source position within 0.07, 0.11, and 0.07 mm in the x-, y-, and z-directions, respectively (a Euclidean distance of ~ 0.249 mm). This is comparable to the mechanical system’s accuracy, which allows the positioning of the light source with a Euclidian distance of approximately 0.24 mm. Remarkably, this achievement allows for full automation and better control of light concentrating facilities and the development of more innovative energy harvesting systems.