Reviewerly/exHarmony huggingface dataset:
Active learning with MaskAL reduces annotation effort for training Mask R-CNN on a broccoli dataset with visually similar classes

The generalisation performance of a convolutional neural network (CNN) is influenced by the quantity, quality, and variety of the training images. Training images must be annotated, and this is time consuming and expensive. The goal of our work was to reduce the number of annotated images needed to train a CNN while maintaining its performance. We hypothesised that the performance of a CNN can be improved faster by ensuring that the set of training images contains a large fraction of hard-to-classify images. The objective of our study was to test this hypothesis with an active learning method that can automatically select the hard-to-classify images. We developed an active learning method for Mask Region-based CNN (Mask R-CNN) and named this method MaskAL. MaskAL involved the iterative training of Mask R-CNN, after which the trained model was used to select a set of unlabelled images about which the model was most uncertain. The selected images were then annotated and used to retrain Mask R-CNN, and this was repeated for a number of sampling iterations. In our study, MaskAL was compared to a random sampling method on a broccoli dataset with five visually similar classes. MaskAL performed significantly better than the random sampling. In addition, MaskAL had the same performance after sampling 900 images as the random sampling had after 2300 images. Compared to a Mask R-CNN model that was trained on the entire training set (14,000 images), MaskAL achieved 93.9% of that model's performance with 17.9% of its training data. The random sampling achieved 81.9% of that model's performance with 16.4% of its training data. We conclude that by using MaskAL, the annotation effort can be reduced for training Mask R-CNN on a broccoli dataset with visually similar classes. Our software is available on https://github.com/pieterblok/maskal.
Quantum advantage with membosonsampling

Quantum computer, harnessing quantum superposition to boost a parallel computational power, promises to outperform its classical counterparts and offer an exponentially increased scaling. The term “quantum advantage” was proposed to mark the key point when people can solve a classically intractable problem by artificially controlling a quantum system in an unprecedented scale, even without error correction or known practical applications. Boson sampling, a problem about quantum evolutions of multi-photons on multimode photonic networks, as well as its variants, has been considered as a promising candidate to reach this milestone. However, the current photonic platforms suffer from the scaling problems, both in photon numbers and circuit modes. Here, we propose a new variant of the problem, membosonsampling, exploiting the scaling of the problem can be in principle extended to a large scale. We experimentally verify the scheme on a self-looped photonic chip inspired by memristor, and obtain multi-photon registrations up to 56-fold in 750,000 modes with a Hilbert space up to 10254. The results exhibit an integrated and cost-efficient shortcut stepping into the “quantum advantage” regime in a photonic system far beyond previous scenarios, and provide a scalable and controllable platform for quantum information processing.
"It's Great to Exercise Together on Zoom!": Understanding the Practices and Challenges of Live Stream Group Fitness Classes

The COVID-19 pandemic greatly changed many people's daily lives. Because of the temporary closure of gyms and fitness centers, many people started to take group fitness classes online. The abrupt transition to synchronous online exercise brought lots of uncertainty and challenges to fitness instructors and participants. This study aims to understand how people teach and participate in live stream group fitness classes, the challenges they have encountered, and their practices to address these challenges. We conducted semi-structured interviews with 11 instructors and 14 participants who had experience teaching or taking live stream group fitness classes on Zoom during the pandemic. Our interviews showed that instructors saw teaching online as a new, more cognitively demanding experience than teaching in-person classes; they found limited resources to create a professional workout space, experienced stress and anxiety, had limited information and approaches to give participants feedback, and faced difficulties with rapport building. Participants found it hard to give real-time reactions while exercising far away from their laptops; they receive less customized instructions, and they have difficulty engaging in private chats. Despite the challenges, we envision CMC in exercise is an opportunity for new design and interactions. We propose design recommendations, including using smartwatches to give real-time reactions and smart garments to generate tactile feedback. Health and biometric data can be shared with instructors and other participants during exercise for safety purposes and to create social connections.
Efficient and Accurate Stitching for 360° Dual-Fisheye Images and Videos

Back-to-back dual-fisheye cameras are the most cost-effective devices to capture 360° visual content. However, image and video stitching for such cameras often suffer from the effect of fisheye distortion, photometric inconsistency between the two views, and non-collocated optical centers. In this paper, we present algorithms for geometric calibration, photometric compensation, and seamless stitching to address these issues for back-to-back dual-fisheye cameras. Specifically, we develop a co-centric trajectory model for geometric calibration to characterize both intrinsic and extrinsic parameters of the fisheye camera to fifth-order precision, a photometric correction model for intensity and color compensation to provide efficient and accurate local color transfer, and a mesh deformation model along with an adaptive seam carving method for image stitching to reduce geometric distortion and ensure optimal spatiotemporal alignment. The stitching algorithm and the compensation algorithm can run efficiently for 1920×960 images. Quantitative evaluation of geometric distortion, color discontinuity, jitter, and ghost artifact of the resulting image and video shows that our solution outperforms the state-of-the-art techniques.
Comments on "Using k-core Decomposition on Class Dependency Networks to Improve Bug Prediction Model's Practical Performance"

In a very recent paper by Qu et al. (IEEE Transactions on Software Engineering, vol. 47 no. 2, pp. 348-366, Feb. 1 2021, doi:10.1109/TSE.2019.2892959), the authors propose an effective equation, top-core, to improve the performance of effort-aware bug prediction models. A distinctive feature of top-core is that it takes into account the coreness of a class in a Class Dependency Network (CDN) when calculating the relative risk of a class to be buggy. In this comment, we show that Qu et al.&#x0027;s paper contains three shortcomings that may influence the performance of top-core or even have the potential to lead to erroneous results. First, we show that the CDN that they use to calculate the coreness of classes is not very accurate, neglecting many important types of dependency relations between classes such as method call relation, access relation, and instantiates relation. Second, they trained a Logistic Regression model using the scikit-learn framework to predict the probability of a specific class to be buggy. It is actually an L2 regularized Logistic Regression model, which is dependent on the scale of the features. But they neglected to normalize the features, making the obtained results erroneous. Finally, the number of execution times (viz. 10 times in the paper of Qu et al.) they used to reduce the bias caused by the randomness (viz. random split of instances and the process to handle class-imbalance problem) in the experiments is too small to ensure that the obtained results converge to stable values; but they failed to signify the precision level of their results for comparison. In this comment, we provide solutions to the problems by using i) an improved CDN (ICDN) to represent the structure of software systems, ii) the z-score method to normalize the features, and iii) an adaptive mechanism to determine the number of execution times. In the experiments, we find that Qu et al.&#x0027;s approach based on the Logistic Regression model does not perform significantly better than the state-of-the-art approach Ree, which is inconsistent with the conclusion in Qu et al.&#x0027;s work. We also observe that replacing CDN with ICDN does improve the performance of Qu et al.&#x0027;s approach.
A Multitask Multimodal Framework for Sentiment and Emotion-Aided Cyberbullying Detection

Cyberbullying has become more widespread, especially among teens with the growth of the digital sphere and advancement of technology. This article is the first attempt in investigating the role of sentiment and emotion information for identifying cyberbullying in the Indian scenario. From Twitter, a benchmark Hind–English code-mixed corpus called BullySentEmo has been developed as there was no dataset available labeled with bully, sentiment, and emotion. The developed dataset consists of both the modalities, tweet- text and emoji. In India, the majority of communication on different social media platforms is based on Hindi and English, and language switching is a common practice in digital communication. A multitask multimodal framework called MT-MM-Bert+VecMap based on BERT and VecMap embedding schemes with emoji modality, has been developed. Our proposed multitask-multimodal framework outperforms all the single task and unimodal baselines with the highest accuracy values of 82.05(+/- 1.36)%, 77.87(+/- 1.93)%, and 58.05(+/-2.78)% for the cyberbully detection task, sentiment analysis task, and emotion recognition task, respectively.
Seek for Success: A Visualization Approach for Understanding the Dynamics of Academic Careers

How to achieve academic career success has been a long-standing research question in social science research. With the growing availability of large-scale well-documented academic profiles and career trajectories, scholarly interest in career success has been reinvigorated, which has emerged to be an active research domain called the Science of Science (i.e., SciSci). In this study, we adopt an innovative dynamic perspective to examine how individual and social factors will influence career success over time. We propose ACSeeker, an interactive visual analytics approach to explore the potential factors of success and how the influence of multiple factors changes at different stages of academic careers. We first applied a Multi-factor Impact Analysis framework to estimate the effect of different factors on academic career success over time. We then developed a visual analytics system to understand the dynamic effects interactively. A novel timeline is designed to reveal and compare the factor impacts based on the whole population. A customized career line showing the individual career development is provided to allow a detailed inspection. To validate the effectiveness and usability of ACSeeker, we report two case studies and interviews with a social scientist and general researchers.
Financial inclusion and inclusive growth in sub-Saharan Africa

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Bounding Box-Free Instance Segmentation Using Semi-Supervised Iterative Learning for Vehicle Detection

Vehicle classification is a hot computer vision topic, with studies ranging from ground-view up to top-view imagery. In remote sensing, the usage of top-view images allows for understanding city patterns, vehicle concentration, traffic management, and others. However, there are some difficulties when aiming for pixel-wise classification: (a) most vehicle classification studies use object detection methods, and most publicly available datasets are designed for this task, (b) creating instance segmentation datasets is laborious, and (c) traditional instance segmentation methods underperform on this task since the objects are small. Thus, the present research objectives are: (1) propose a novel semi-supervised iterative learning approach using GIS software, (2) propose a box-free instance segmentation approach, and (3) provide a city-scale vehicle dataset. The iterative learning procedure considered: (1) label a small number of vehicles, (2) train on those samples, (3) use the model to classify the entire image, (4) convert the image prediction into a polygon shapefile, (5) correct some areas with errors and include them in the training data, and (6) repeat until results are satisfactory. To separate instances, we considered vehicle interior and vehicle borders, and the DL model was the U-net with the Efficient-net-B7 backbone. When removing the borders, the vehicle interior becomes isolated, allowing for unique object identification. To recover the deleted 1-pixel borders, we proposed a simple method to expand each prediction. The results show better pixel-wise metrics when compared to the Mask-RCNN (82% against 67% in IoU). On per-object analysis, the overall accuracy, precision, and recall were greater than 90%. This pipeline applies to any remote sensing target, being very efficient for segmentation and generating datasets.
Is Bitcoin a hedge? How extreme volatility can destroy the hedge property

A large literature tests whether Bitcoin can hedge portfolio risk, i.e. reduce the risk if added to a portfolio. Intuitively, given the extreme volatility and thus risk of Bitcoin and cryptocurrencies, the idea that Bitcoin is a hedge may be puzzling. Indeed, we show that for extreme levels of volatility, Bitcoin does not reduce the risk if added to a benchmark equity portfolio. This is not only true on average but also holds for sub-samples, including the COVID-19 crisis period. We conclude that a focus on correlations is not sufficient for extreme levels of volatility.
Ethereum transaction tracking: Inferring evolution of transaction networks via link prediction

Blockchain is an emerging technology which has attracted wide attention in recent years. As one of the blockchain applications, cryptocurrency has developed rapidly in recent years, attracting criminals to commit fraud and money laundering. Therefore, to better protect the legitimate interests of users and help formulate an effective supervision, it is necessary to track and follow transaction records on blockchain-based systems. This paper studies the problem of transaction tracking in Ethereum from a network perspective, aiming to study explainable strategies for money flow generation. We first collect the space-intensive transaction data from Ethereum blockchain and model them as temporal weighted multi-digraphs. A variety of tracking strategies considering different transaction factors (i.e., frequency and amount) are proposed, and the corresponding random-walk based link predictions method are designed for evaluation. Our method gets explainable results from the experiments, demonstrating that both transaction frequency and amount influence the generation of new transactions in Ethereum. This means when tracking the money flow among Ethereum accounts, we should pay more attention to those transaction paths having a shorter time interval and a larger amount. From these transaction features, the proposed random-walk based link prediction framework is found to be an effective method for transaction tracking. Furthermore, we show an application of transaction tracking via link prediction effectively enhance the ability to detect the suspicious accounts in Ethereum.
Robust audio watermarking scheme based on fractional Charlier moment transform and dual tree complex wavelet transform

The popular embedded watermarking method in transform domains such as DWT, LWT, DCT, and SVD achieves copyright protection of audio signals by embedding watermarks into the original audio signals. The main weaknesses of the existing methods include low payload and low robustness against common audio processing operations and attacks, especially, vulnerability to shifting attack. This paper proposes a new robust, secure, and blind audio watermarking scheme based on a novel hybrid transform which is a combination of the dual tree complex wavelet transform (DTCWT) and the fractional Charlier moment transform (FrCMT). After segmenting the original audio signal into several segments of the same length, the 3-level DTCWT is applied on each segment using filters designed specifically to have shift invariance, then FrCMT is applied on the approximation coefficients of DTCWT. Finally, a sequence of watermark bits is embedded in FrCMT coefficients by quantizing their relative energy. The fractional order in FrCMT is chosen in our scheme in a way to obtain high imperceptibility and robustness against most common audio processing operations and attacks. Moreover, it is used as a security key to ensure a high security level of the proposed scheme. The comparative analysis confirms the high robustness, security, and efficiency of the proposed watermarking method.
Explainable AI toward understanding the performance of the top three TADPOLE Challenge methods in the forecast of Alzheimer’s disease diagnosis

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge is the most comprehensive challenge to date with regard to the number of subjects, considered features, and challenge participants. The initial objective of TADPOLE was the identification of the most predictive data, features, and methods for the progression of subjects at risk of developing Alzheimer's. The challenge was successful in recognizing tree-based ensemble methods such as gradient boosting and random forest as the best methods for the prognosis of the clinical status in Alzheimer's disease (AD). However, the challenge outcome was limited to which combination of data processing and methods exhibits the best accuracy; hence, it is difficult to determine the contribution of the methods to the accuracy. The quantification of feature importance was globally approached by all the challenge participant methods. In addition, TADPOLE provided general answers that focused on improving performance while ignoring important issues such as interpretability. The purpose of this study is to intensively explore the models of the top three TADPOLE Challenge methods in a common framework for fair comparison. In addition, for these models, the most meaningful features for the prognosis of the clinical status of AD are studied and the contribution of each feature to the accuracy of the methods is quantified. We provide plausible explanations as to why the methods achieve such accuracy, and we investigate whether the methods use information coherent with clinical knowledge. Finally, we approach these issues through the analysis of SHapley Additive exPlanations (SHAP) values, a technique that has recently attracted increasing attention in the field of explainable artificial intelligence (XAI).
Otsu-Kmeans gravity-based multi-spots center extraction method for microlens array imaging system

We propose an Otsu-Kmeans gravity-based center extraction method for multi-spot images captured by the microlens array imaging system. We develop a 4-connected-region-based region of interest location method to recognize and segment light spot areas. We design an Otsu-Kmeans gravity-based threshold selection algorithm to realize the multi-spot center accurate position. Both numerical and actual experiments are implemented. The effectiveness and feasibility of the proposed method are validated by comparing with the results from the traditional centroid method, the Hough circle method, the Gaussian fitting method, and the Otsu thresholding centroid method. The results show that the proposed method can realize high-precision positioning and can be used for the sub-image segmentation of the microlens array imaging system.
A Compression-Based Multiple Subword Segmentation for Neural Machine Translation

In this study, we propose a simple and effective preprocessing method for subword segmentation based on a data compression algorithm. Compression-based subword segmentation has recently attracted significant attention as a preprocessing method for training data in Neural Machine Translation. Among them, BPE/BPE-dropout is one of the fastest and most effective method compared to conventional approaches. However, compression-based approach has a drawback in that generating multiple segmentations is difficult due to the determinism. To overcome this difficulty, we focus on a probabilistic string algorithm, called locally-consistent parsing (LCP), that has been applied to achieve optimum compression. Employing the probabilistic mechanism of LCP, we propose LCP-dropout for multiple subword segmentation that improves BPE/BPE-dropout, and show that it outperforms various baselines in learning from especially small training data.
A New Feature Selection Method Based on Hybrid Approach for Colorectal Cancer Histology Classification

Colorectal cancer (CRC) is one of the most common malignant cancers worldwide. To reduce cancer mortality, early diagnosis and treatment are essential in leading to a greater improvement and survival length of patients. In this paper, a hybrid feature selection technique (RF-GWO) based on random forest (RF) algorithm and gray wolf optimization (GWO) was proposed for handling high dimensional and redundant datasets for early diagnosis of colorectal cancer (CRC). Feature selection aims to properly select the minimal most relevant subset of features out of a vast amount of complex noisy data to reach high classification accuracy. Gray wolf optimization (GWO) and random forest (RF) algorithm were utilized to find the most suitable features in the histological images of the human colorectal cancer dataset. Then, based on the best-selected features, the artificial neural networks (ANNs) classifier was applied to classify multiclass texture analysis in colorectal cancer. A comparison between the GWO and another optimizer technique particle swarm optimization (PSO) was also conducted to determine which technique is the most successful in the enhancement of the RF algorithm. Furthermore, it is crucial to select an optimizer technique having the capability of removing redundant features and attaining the optimal feature subset and therefore achieving high CRC classification performance in terms of accuracy, precision, and sensitivity rates. The Heidelberg University Medical Center Pathology archive was used for performance check of the proposed method which was found to outperform benchmark approaches. The results revealed that the proposed feature selection method (GWO-RF) has outperformed the other state of art methods where it achieved overall accuracy, precision, and sensitivity rates of 98.74%, 98.88%, and 98.63%, respectively.