Reviewerly/exHarmony huggingface dataset:
Temporal convolutional autoencoder for unsupervised anomaly detection in time series

Learning temporal patterns in time series remains a challenging task up until today. Particularly for anomaly detection in time series, it is essential to learn the underlying structure of a system’s normal behavior. Periodic or quasiperiodic signals with complex temporal patterns make the problem even more challenging: Anomalies may be a hard-to-detect deviation from the normal recurring pattern. In this paper, we present TCN-AE, a temporal convolutional network autoencoder based on dilated convolutions. Contrary to many other anomaly detection algorithms, TCN-AE is trained in an unsupervised manner. The algorithm demonstrates its efficacy on a comprehensive real-world anomaly benchmark comprising electrocardiogram (ECG) recordings of patients with cardiac arrhythmia. TCN-AE significantly outperforms several other unsupervised state-of-the-art anomaly detection algorithms. Moreover, we investigate the contribution of the individual enhancements and show that each new ingredient improves the overall performance on the investigated benchmark.
AttentionGAN: Unpaired Image-to-Image Translation Using Attention-Guided Generative Adversarial Networks

State-of-the-art methods in the image-to-image translation are capable of learning a mapping from a source domain to a target domain with unpaired image data. Though the existing methods have achieved promising results, they still produce visual artifacts, being able to translate low-level information but not high-level semantics of input images. One possible reason is that generators do not have the ability to perceive the most discriminative parts between the source and target domains, thus making the generated images low quality. In this article, we propose a new Attention-Guided Generative Adversarial Networks (AttentionGAN) for the unpaired image-to-image translation task. AttentionGAN can identify the most discriminative foreground objects and minimize the change of the background. The attention-guided generators in AttentionGAN are able to produce attention masks, and then fuse the generation output with the attention masks to obtain high-quality target images. Accordingly, we also design a novel attention-guided discriminator which only considers attended regions. Extensive experiments are conducted on several generative tasks with eight public datasets, demonstrating that the proposed method is effective to generate sharper and more realistic images compared with existing competitive models. The code is available at https://github.com/Ha0Tang/AttentionGAN.
Precise No-Reference Image Quality Evaluation Based on Distortion Identification

The difficulty of no-reference image quality assessment (NR IQA) often lies in the lack of knowledge about the distortion in the image, which makes quality assessment blind and thus inefficient. To tackle such issue, in this article, we propose a novel scheme for precise NR IQA, which includes two successive steps, i.e., distortion identification and targeted quality evaluation. In the first step, we employ the well-known Inception-ResNet-v2 neural network to train a classifier that classifies the possible distortion in the image into the four most common distortion types, i.e., Gaussian white noise (WN), Gaussian blur (GB), jpeg compression (JPEG), and jpeg2000 compression (JP2K). Specifically, the deep neural network is trained on the large-scale Waterloo Exploration database, which ensures the robustness and high performance of distortion classification. In the second step, after determining the distortion type of the image, we then design a specific approach to quantify the image distortion level, which can estimate the image quality specially and more precisely. Extensive experiments performed on LIVE, TID2013, CSIQ, and Waterloo Exploration databases demonstrate that (1) the accuracy of our distortion classification is higher than that of the state-of-the-art distortion classification methods, and (2) the proposed NR IQA method outperforms the state-of-the-art NR IQA methods in quantifying the image quality.
Complex Thinking in the Framework of Education 4.0 and Open Innovation—A Systematic Literature Review

Today&rsquo;s complex, dynamic, interconnected world presents the field of education (&ldquo;Education 4.0&rdquo;) with significant challenges in developing competencies for reasoning for complexity. This article analyzes complex thinking as a macro-competency with sub-competencies of critical, systemic, scientific, and innovative thinking in educational environments. We worked with the systematic literature review method, selecting 35 articles in the Scopus and Web of Science databases using keywords words of interest and applying inclusion and exclusion criteria. Seven research questions guided the data analysis. The results highlighted that: (a) there are common characteristics of studies linking complex thinking, critical thinking, and creative thinking; (b) there is predominance of the qualitative method in the studies; (c) the critical thinking competency has been the most addressed in the research; (d) the predominant components of Education 4.0 are teaching methods and techniques; and (e) the three challenges that stand out for educational research are project feasibility, research opportunities, and required skills. This article is intended to be of value to academic and social communities and decision-makers interested in developing reasoning for complexity within the framework of Education 4.0.
HyperExtended LightFace: A Facial Attribute Analysis Framework

Facial attribute analysis from facial images has always been a challenging task. Its practical use cases are very different. This paper mentioned how to build machine learning models with facial image data for age, gender, facial expression, and race/ethnicity prediction tasks. A fully coded open-source software framework was also developed and published with those functionalities.
The Role of Machine Learning Techniques in Internet of Things-Based Cloud Applications

Today’s Machine Learning (ML) in a blend with Internet of Things (IoT)-based cloud applications plays a significant role in our everyday life. As indicated by Gartner’s recent study, there are around 25 billion devices and a gadget interfacing with IoT including wearables and automated vehicles to smart homes and smart cities applications. All such connected (smart) devices generate immense data that needs to be examined and analysed, to ensure that they continually learn from the available datasets and better themselves without any manual interference. This is where the prerequisite for machine learning comes into being. Several ML algorithms and techniques are introduced in a short time to easily evaluate big data measurements, increasing the IoT’s productivity. Similarly, special ML techniques, such as decision trees, clustering and neural and Bayesian networks, allow devices and gadgets to discern trends from various sources in different kinds of datasets and take appropriate decisions based on their analysis. It would really be difficult for smart devices to make smart decisions gradually without including and enforcing ML. The IoT helps to interconnect various hardware devices, such as houses, cars, electronic gadgets and other devices that are integrated with actuators, sensors and software, so that data can be collected and shared. As various organizations understand the progressive capability of the IoT, they have begun finding various obstructions they have to deliver to use it productively. Numerous organizations and businesses use ML to exploit the IoT’s latent capacity. This chapter evaluates the different methods of machine learning that deal with the challenges posed in the handling of IoT data. Note that this big data is generated through the communication of Internet of Things/smart devices, and this data stored at cloud. The taxonomy of machine learning algorithms is described in this chapter, explaining how different techniques are applied to data generated using IoT devices. It will also address the future problems of machine learning for IoT data analytics.
Revisiting Bitcoin’s carbon footprint

Alex de Vries is a researcher at the School of Business and Economics at the Vrije Universiteit Amsterdam. In 2014 he founded digiconomist.net, which is best known for featuring the Bitcoin Energy Consumption Index since late 2016 and has played a major role in the global discussion regarding the sustainability of cryptocurrencies and blockchain technology.Ulrich Gallersdörfer is a research associate in the Department of Informatics at the Technical University of Munich. His research focuses on the intersection of blockchain technology and adjacent fields. He is a co-founder of CCRI, a company providing GHG emissions estimates for investments in cryptocurrencies, blockchain, and other technologies.Lena Klaaßen is a PhD student in the Climate Finance and Policy group at ETH Zurich. Her research focuses on energy finance for the low-carbon transition as well as the environmental impact of companies and technologies. She also co-founded CCRI, a company providing GHG emissions estimates for investments in cryptocurrencies, blockchain, and other technologies.Christian Stoll is a research affiliate at the Center for Energy and Environmental Policy Research at the Massachusetts Institute of Technology and at the Center for Energy Markets of the Technical University of Munich. His research focuses on the implications of climate change from an economic point of view. Alex de Vries is a researcher at the School of Business and Economics at the Vrije Universiteit Amsterdam. In 2014 he founded digiconomist.net, which is best known for featuring the Bitcoin Energy Consumption Index since late 2016 and has played a major role in the global discussion regarding the sustainability of cryptocurrencies and blockchain technology. Ulrich Gallersdörfer is a research associate in the Department of Informatics at the Technical University of Munich. His research focuses on the intersection of blockchain technology and adjacent fields. He is a co-founder of CCRI, a company providing GHG emissions estimates for investments in cryptocurrencies, blockchain, and other technologies. Lena Klaaßen is a PhD student in the Climate Finance and Policy group at ETH Zurich. Her research focuses on energy finance for the low-carbon transition as well as the environmental impact of companies and technologies. She also co-founded CCRI, a company providing GHG emissions estimates for investments in cryptocurrencies, blockchain, and other technologies. Christian Stoll is a research affiliate at the Center for Energy and Environmental Policy Research at the Massachusetts Institute of Technology and at the Center for Energy Markets of the Technical University of Munich. His research focuses on the implications of climate change from an economic point of view.
Adaptive soft erasure with edge self-attention for weakly supervised semantic segmentation: Thyroid ultrasound image case study

[S U M M A R Y] Weakly supervised segmentation for medical images ease the reliance of models on pixel-level annotation while advancing the field of computer-aided diagnosis. However, the differences in nodule size in thyroid ultrasound images and the limitations of class activation maps in weakly supervised segmentation methods typically lead to under- and/or over-segmentation problems in real predictions. To alleviate this problem, we propose a weakly supervised segmentation neural network approach. This new method is based on a dual branch soft erase module that expands the foreground response region while constraining the erroneous expansion of the foreground region by the enhancement of background features. The sensitivity of this neural network to the nodule scale size is further enhanced by the scale feature adaptation module, which in turn generates integral and high-quality segmentation masks. In addition, while the nodule area can be significantly expanded through soft erase module and scale feature adaptation module, the activation effect in the nodule edge area is still not satisfactory, so that we further add an edge-based attention mechanism to strengthen the nodule edge segmentation effect. The results of experiments performed on the thyroid ultrasound image dataset showed that our new approach significantly outperformed existing weakly supervised semantic segmentation methods, e.g., 5.9% and 6.3% more accurate than the second-based results in terms of Jaccard and Dice coefficients, respectively.
Distribution Bias Aware Collaborative Generative Adversarial Network for Imbalanced Deep Learning in Industrial IoT

The impact of Internet of Things (IoT) has become increasingly significant in smart manufacturing, while deep generative model (DGM) is viewed as a promising learning technique to work with large amount of continuously generated industrial Big Data in facilitating modern industrial applications. However, it is still challenging to handle the imbalanced data when using conventional Generative Adversarial Network (GAN) based learning strategies. In this article, we propose a distribution bias aware collaborative GAN (DB-CGAN) model for imbalanced deep learning in industrial IoT, especially to solve limitations caused by distribution bias issue between the generated data and original data, via a more robust data augmentation. An integrated data augmentation framework is constructed by introducing a complementary classifier into the basic GAN model. Specifically, a conditional generator with random labels is designed and trained adversarially with the classifier to effectively enhance augmentation of the number of data samples in minority classes, while a weight sharing scheme is newly designed between two separated feature extractors, enabling the collaborative adversarial training among generator, discriminator, and classifier. An augmentation algorithm is then developed for intelligent anomaly detection in imbalanced learning, which can significantly improve the classification accuracy based on the correction of distribution bias using the rebalanced data. Compared with five baseline methods, experiment evaluations based on two real-world imbalanced datasets demonstrate the outstanding performance of our proposed model in tackling the distribution bias issue for multiclass classification in imbalanced learning for industrial IoT applications.
Thinking Outside the Block: Projected Phases of Blockchain Integration in the Accounting Industry

Abstract This paper aims to propound a thorough and circumspect analysis of the implications of blockchain technology in the accounting profession and its broader industry. The analysis begins with a summary of early developments by first movers and how they are harnessing blockchain technology to improve business practices. Concomitantly, the paper will go on to discuss how this technology will streamline accounting processes, specifically as the technology approaches critical mass. Finally, a discussion of its long‐term implications will follow through a more philosophical and conceptual dialogue. Throughout the paper, criticisms will be raised to address concerns regarding blockchain's widespread use.
Analysis of the Bitcoin UTXO Set

Bitcoin relies on the Unspent Transaction Outputs (UTXO) set to efficiently verify new generated transactions. Every unspent output, no matter its type, age, value or length is stored in every full node. In this paper we introduce a tool to study and analyze the UTXO set, along with a detailed description of the set format and functionality. Our analysis includes a general view of the set and quantifies the difference between the two existing formats up to the date. We also provide an accurate analysis of the volume of dust and unprofitable outputs included in the set, the distribution of the block height in which the outputs where included, and the use of non-standard outputs.
Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning

Management and mission planning over a swarm of unmanned aerial vehicle (UAV) remains to date as a challenging research trend in what regards to this particular type of aircrafts. These vehicles are controlled by a number of ground control station (GCS), from which they are commanded to cooperatively perform different tasks in specific geographic areas of interest. Mathematically the problem of coordinating and assigning tasks to a swarm of UAV can be modeled as a constraint satisfaction problem, whose complexity and multiple conflicting criteria has hitherto motivated the adoption of multi-objective solvers such as multi-objective evolutionary algorithm (MOEA). The encoding approach consists of different alleles representing the decision variables, whereas the fitness function checks that all constraints are fulfilled, minimizing the optimization criteria of the problem. In problems of high complexity involving several tasks, UAV and GCS, where the space of search is huge compared to the space of valid solutions, the convergence rate of the algorithm increases significantly. To overcome this issue, this work proposes a weighted random generator for the creation and mutation of new individuals. The main objective of this work is to reduce the convergence rate of the MOEA solver for multi-UAV mission planning using weighted random strategies that focus the search on potentially better regions of the solution space. Extensive experimental results over a diverse range of scenarios evince the benefits of the proposed approach, which notably improves this convergence rate with respect to a naïve MOEA approach.
Weakly Supervised Salient Object Detection With Spatiotemporal Cascade Neural Networks

Recently, deep learning techniques have substantially boosted the performance of salient object detection in still images. However, the salient object detection in videos by using traditional handcrafted features or deep learning features is not fully investigated, probably due to the lack of sufficient manually labeled video data for saliency modeling, especially for the data-driven deep learning. This paper proposes a novel weakly supervised approach to the salient object detection in a video, which can learn a robust saliency prediction model by using very limited manually labeled data and a large amount of weakly labeled data that could be easily generated in a supervised approach. Furthermore, we propose a spatiotemporal cascade neural network architecture for saliency modeling, in which two fully convolutional networks are cascaded to evaluate the visual saliency from both spatial and temporal cues to lead the optimal video saliency prediction. The proposed approach is extensively evaluated on the widely used challenging data sets, and the experiments demonstrate that our proposed approach substantially outperforms the state-of-the-art salient object detection models.
Efficient CNN based summarization of surveillance videos for resource-constrained devices

The widespread usage of surveillance cameras in smart cities has resulted in a gigantic volume of video data whose indexing, retrieval and management is a challenging issue. Video summarization tends to detect important visual data from the surveillance stream and can help in efficient indexing and retrieval of required data from huge surveillance datasets. In this research article, we propose an efficient convolutional neural network based summarization method for surveillance videos of resource-constrained devices. Shot segmentation is considered as a backbone of video summarization methods and it affects the overall quality of the generated summary. Thus, we propose an effective shot segmentation method using deep features. Furthermore, our framework maintains the interestingness of the generated summary using image memorability and entropy. Within each shot, the frame with highest memorability and entropy score is considered as a keyframe. The proposed method is evaluated on two benchmark video datasets and the results are encouraging compared to state-of-the-art video summarization methods.
Efficient solution of Otsu multilevel image thresholding: A comparative study

Multi-level thresholding of a gray image is one of the basic operations in computer vision, with applications in image enhancement and segmentation. Various criteria for the selection of threshold level values were proposed. One of these criterion is the Otsu criterion that uses maximization of between-class variance approach. Although applying multi-level thresholding to an image is a straightforward operation, computation of the threshold levels with Otsu criterion is a computationally expensive process. In this paper, we revisit a dynamic programming algorithm that provides exact and efficient solution to the problem and compare it with modern meta-heuristic algorithms. We provide a rigorous proof for the correctness of the algorithm. The algorithm computational cost is linear in the number of threshold levels. We compare the algorithm with state of the art algorithms and verify its superior performance. The experiments show that we could gain speedup up to 2.45 × .
Scene Understanding in Deep Learning-Based End-to-End Controllers for Autonomous Vehicles

Deep learning techniques have been widely used in autonomous driving community for the purpose of environment perception. Recently, it starts being adopted for learning end-to-end controllers for complex driving scenarios. However, the complexity and nonlinearity of the network architecture limits its interpretability to understand driving scenarios and judge the importance of certain visual regions in sensory scenes. In this paper, based on the convolutional neural network (CNN), we propose two complementary frameworks to automatically determine the most contributive regions of the input scenes, offering intuitive knowledge of how a trained end-to-end autonomous vehicle controller understands driving scenarios. In the first framework, a feature map-based method is proposed by leveraging current progress in CNN visualization, in which the deconvolution approach recovers the feature maps to extract features that contribute most to understand driving scenes. In the second framework, the importance level of regions is ranked using the error map between the labeled and predicted control inputs generated by occluding different parts of input scenes, thus providing a pixel-wise rank of importance. Test data sets with extracted contributive regions are input to the CNN controller. Then, different CNN controllers trained with the new data sets preprocessed using our proposed frameworks are verified via closed-loop tests. Results show that both the features identified from the first framework and the regions identified from the second framework are of crucial importance to scene understanding for the controller and can significantly affect the performance of CNN controllers.
Does the introduction of futures improve the efficiency of Bitcoin?

The introduction of futures on Bitcoin eases the access of institutional investors to the market and offers an efficient way to short the cryptocurrency. We investigate the effect of this event on the market’s price efficiency and find the Bitcoin market to turn efficient. We conduct commonly used tests for market efficiency and check the robustness of our results by investigating Bitcoin Cash, a hard fork of Bitcoin, where we do not find a change in market’s efficiency.