Reviewerly/exHarmony huggingface dataset:
Ultrasound image de-speckling by a hybrid deep network with transferred filtering and structural prior

Deep neural-network has been widely used in natural image denoising. However, due to the lack of label of real ultrasound (US) B-mode image for de-speckling, the deep neural network is greatly restricted in US image de-speckling. In this paper, we propose to use transfer learning and two types of prior knowledge to construct a hybrid neural network structure for de-speckling. Firstly, based on a given US image model, the speckle noise is similar to Gaussian distribution in the logarithmic transformation domain, called Gaussian prior knowledge. The distribution parameters are estimated in the logarithmic transformation domain based on four typical traditional US image de-speckling methods with maximum likelihood estimation. Secondly, depending on the prior parameters, a transferable denoising network is trained with clean natural image dataset. Finally, a VGGNet is used to extract the structure boundaries before and after US image de-speckling based on the transfer network, and we call it structural prior knowledge. The structural boundaries of a US image should be unchanged after the de-speckling, and hence we use this constraint to fine-tune the transfer network. The proposed de-speckling framework is verified on artificially generated phantom (AGP) images and real US images, and the results demonstrate its effectiveness.
“All Around Me Are Synthetic Faces”: The Mad World of AI-Generated Media

Advances in artificial intelligence and deep neural networks have led to a rise in synthetic media, i.e., automatically and artificially generated or manipulated photo, audio, and video content. Synthetic media today is highly believable and “true to life”; so much so that we will no longer be able to trust what we see or hear is unadulterated and genuine. Among the different forms of synthetic media, the most concerning forms are deepfakes and general adversarial networks (GANs). For IT professionals, it is important to understand what these new phenomena are. In this article, we explain what deepfakes and GANs are, how they work and discuss the threats and opportunities resulting from these forms of synthetic media.
Parallelization of $Top_{k}$ Algorithm Through a New Hybrid Recommendation System for Big Data in Spark Cloud Computing Framework

In the era of big data, parallel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Top_{k}$</tex-math></inline-formula> query processing under information retrieval has received increasing attention from both the industry and academia. This query handling allows users to retrieve the most useful data objects in a set of choices. This problem is compounded by the use of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Top_{k}$</tex-math></inline-formula> in cases of multiple dimensions and extensive data analytics. In this article, we provide a novel parallel algorithm in a distributed recommender system based on the Apache Spark platform. The purpose of this approach was to implement the multicriteria decision aiding support and dominating query approach run by using matrix factorization and singular value decomposition (SVD)-based model as a sophisticated machine learning technique. Simultaneously, applying the resilient distributed datasets paradigm in cloud computing, which presents a favorable environment for big data management. Extensive experimental results in terms of accuracy, and scalability indicated the new algorithm’s advantage compared to other <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Top_{k}$</tex-math></inline-formula> algorithms. Accordingly, our recommender system based on the conceived algorithm achieved high precision (62%–82%, depending on the data) to verify the profoundly positive effect of the use of the Spark framework and the SVD-based model while applying the commonly used evaluation metrics in the recommendation systems.
A survey of live Virtual Machine migration techniques

Live Virtual Machine migration is the process of moving a running VM from one physical host to another with minimal disruption to ongoing services. It is a powerful tool that facilitates hardware maintenance, load balancing, fault tolerance, and power saving in clusters and data centers. A key challenge with live migration is that it is difficult to simultaneously meet the goals of minimizing downtime, total migration time, and the CPU and network resources used. In this paper, we discuss weaknesses and strengths of state-of-the-art live migration techniques, and the impact of workload characteristics on the suitability of different migration mechanisms.
Social fidelity in virtual agents: Impacts on presence and learning

Teaching and training are increasingly moving from real world venues to computerized environments, with human instructors often being replaced or joined by virtual pedagogical agents. While system fidelity and immersive properties of virtual learning environments are frequently discussed in the literature, less often addressed is the fidelity of the social components of teaching and their inclusion in pedagogical agent design. Teaching is inherently a social process, making the social fidelity of virtual agents a potential factor affecting learning outcomes. In this paper, we explore the concept of social fidelity as it pertains to the teaching effectiveness of pedagogical agents. We define the term, distinguish two subcategories, and discuss representative examples of research in these domains. Promising avenues for improving learning outcomes with social fidelity include personalized language, politeness, personality, attention, feedback, social memory, and gestures. Key conclusions are that: Social fidelity is important to learning in non-social domains, tradeoffs exist when implementing certain forms of social fidelity, individual user differences need to be more widely considered, and more focused studies are needed to compare different levels of social fidelity to uncover how they impact learning outcomes.
Flounder-Net: An efficient CNN for crowd counting by aerial photography

Crowd counting on aerial images using the embedded system is a challenging task, due to high-definition images, low computing power, and limited memory. To tackle this task, we propose an efficient deep learning model named Flounder-Net structured like a flounder. In the Flounder-Net, a novel interleaved group convolution is proposed to eliminate the redundancy of network, and a rapid shrink of feature maps is employed to tackle the high-resolution problem. Since we would like to investigate the case of online aerial surveillance, we use the embedded system of a drone to run our algorithm. We also use the vision system of this drone to collect a set of high-definition aerial photographs as a benchmark. Extensive experiments on existing datasets and our aerial dataset show that Flounder-Net achieves FCN-level accuracy with three types of photograph devices: handheld cameras, surveillance cameras, and drone-based cameras. Additionally, Flounder-Net has 17× fewer parameters and 20× faster speed than FCN and allows an input image with arbitrary sizes.
A Deep-Ensemble-Level-Based Interpretable Takagi–Sugeno–Kang Fuzzy Classifier for Imbalanced Data

Existing research reveals that the misclassification rate for imbalanced data depends heavily on the problematic areas due to the existence of small disjoints, class overlap, borderline, and rare data samples. In this study, by stacking zero-order Takagi-Sugeno-Kang (TSK) fuzzy subclassifiers on the minority class and its problematic areas in the deep ensemble, a novel deep-ensemble-level-based TSK fuzzy classifier (IDE-TSK-FC) for imbalanced data classification tasks is presented to achieve both promising classification performance and high interpretability of zero-order TSK fuzzy classifiers. Simultaneously, according to the stacked generalization principle, the proposed classifier lifts up oversampling from the data level to the deep ensemble level with a guarantee of enhanced generalization capability for class imbalance learning. In the structure of IDE-TSK-FC, the first interpretable zero-order TSK fuzzy subclassifier is built on the original training dataset. After that, several successive zero-order TSK fuzzy subclassifiers are stacked layer by layer on the newly identified problematic areas from the original training dataset plus the corresponding interpretable predictions obtained by the averaging strategy on all previous layers. IDE-TSK-FC simply takes the classical K -nearest neighboring algorithm at each layer to identify its problematic area that consists of the minority samples and its surrounding K majority neighbors. After randomly neglecting certain input features and randomly selecting the five Gaussian membership functions for all the chosen input features and the augmented feature in the premise of each fuzzy rule, each subclassifier can be quickly obtained by using the least learning machine to determine the consequent part of each fuzzy rule. The experimental results on both the public datasets and a real-world healthcare dataset demonstrate IDE-TSK-FC's superiority in class imbalanced learning.
Objective and subjective risks of investing into cryptocurrencies

New empirical evidence on cryptocurrencies emerges rapidly, and it is thus necessary to consolidate the knowledge gained and identify the gaps therein. We provide a focused, systematic literature analysis of the objective and perceived risks of investing in cryptocurrencies using 50 papers from both the academic and practice-oriented literature. As an additional contribution, we identify three important and promising research avenues, which we contend should be the focus of future research: (i) subjective perception of risks, (ii) adoption of cryptocurrencies implementing innovation research and (iii) non-standard financial risks.
SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures

Ensuring safety and explainability of machine learning (ML) is a topic of increasing relevance as data-driven applications venture into safety-critical application domains, traditionally committed to high safety standards that are not satisfied with an exclusive testing approach of otherwise inaccessible black-box systems. Especially the interaction between safety and security is a central challenge, as security violations can lead to compromised safety. The contribution of this paper to addressing both safety and security within a single concept of protection applicable during the operation of ML systems is active monitoring of the behavior and the operational context of the data-driven system based on distance measures of the Empirical Cumulative Distribution Function (ECDF). We investigate abstract datasets (XOR, Spiral, Circle) and current security-specific datasets for intrusion detection (CICIDS2017) of simulated network traffic, using distributional shift detection measures including the Kolmogorov-Smirnov, Kuiper, Anderson-Darling, Wasserstein and mixed Wasserstein-Anderson-Darling measures. Our preliminary findings indicate that there is a meaningful correlation between ML decisions and the ECDF-based distances measures of the input features. Thus, they can provide a confidence level that can be used for a) analyzing the applicability of the ML system in a given field (safety/security) and b) analyzing if the field data was maliciously manipulated. (Our preliminary code and results are available at https://github.com/ISorokos/SafeML .)
Incremental learning based multi-domain adaptation for object detection

Cross-domain object detection uses knowledge from source domain tasks to enhance the object detection in target domain. It can reduce the workload of data annotations in the new domain and significantly improve the adaptation ability of the network. We consider a more realistic transfer scenario, that is, our target domain samples cannot be obtained at the same time so that learners cannot forget the old domain knowledge while learning the new domain knowledge, which is consistent with the basic assumption of incremental learning. However, the existing methods do not consider the sequential learning process of the multiple target domains which may cause performance degradation. To address this issue, we propose a novel multi-domain adaptation method for object detection based on incremental learning. Specifically, the incremental learning network saves the knowledge of multiple domains and makes the model to fuse the knowledge of different domains during the training effectively. To make it work better, the progressive training strategy is proposed to make the model gradually adapt to multiple domains. Moreover, we use a multi-level feature alignment module to ensure that domain alignment is realized on features at various levels. We perform experiments on two sets of 6 datasets, which demonstrate that our model can effectively solve the problem of domain knowledge forgetting in multi-target domain adaptation and significantly improve the detection accuracy in each domain.
Entrepreneurship, fear of failure, and economic policy

The previous literature finds that self-reported 'fear of failure' has a significant negative effect on individuals' choice to become entrepreneurs. We hypothesize this effect is lessened in economies with a larger number of additional, alternative, entrepreneurial opportunities to pursue if a failure occurs. Prior literature also concludes the number of entrepreneurial opportunities is enhanced significantly by having policies and institutions consistent with higher levels of economic freedom. We therefore test and confirm that fear of failure hurts the entrepreneurial process less when levels of economic freedom are higher as there are more additional chances for failed entrepreneurs to pursue.
Improving Future Teachers’ Digital Competence Using Active Methodologies

Contemporary society demands a university education based on active and participatory educational models that enable the development of competences, with digital competence being amongst the most demanded ones. This work presents the results of an educational innovation at the university level. It intends to analyse whether the implementation of an active methodology supported by technological tools in a virtual classroom contributes to students’ digital development. A quantitative methodology with a pre-experimental pretest-posttest design was used. The sample comprised 30 students studying the Curriculum Design module on the Biology and Geology Specialism of the Master’s in Teacher Training at the Universidad Internacional de la Rioja. The results show an improvement in the five areas of the digital competence specified by the Common Framework for Teachers’ Digital Competence (MCCDD) established by Spain’s National Institute of Educational Technologies and Teacher Training (INTEF), with a large effect size. It is concluded that the educational experiment implemented has enabled an increment in the level of digital competence of future teachers.
Fusion of Multiple Lidars and Inertial Sensors for the Real-Time Pose Tracking of Human Motion

Today, enhancement in sensing technology enables the use of multiple sensors to track human motion/activity precisely. Tracking human motion has various applications, such as fitness training, healthcare, rehabilitation, human-computer interaction, virtual reality, and activity recognition. Therefore, the fusion of multiple sensors creates new opportunities to develop and improve an existing system. This paper proposes a pose-tracking system by fusing multiple three-dimensional (3D) light detection and ranging (lidar) and inertial measurement unit (IMU) sensors. The initial step estimates the human skeletal parameters proportional to the target user’s height by extracting the point cloud from lidars. Next, IMUs are used to capture the orientation of each skeleton segment and estimate the respective joint positions. In the final stage, the displacement drift in the position is corrected by fusing the data from both sensors in real time. The installation setup is relatively effortless, flexible for sensor locations, and delivers results comparable to the state-of-the-art pose-tracking system. We evaluated the proposed system regarding its accuracy in the user’s height estimation, full-body joint position estimation, and reconstruction of the 3D avatar. We used a publicly available dataset for the experimental evaluation wherever possible. The results reveal that the accuracy of height and the position estimation is well within an acceptable range of ±3–5 cm. The reconstruction of the motion based on the publicly available dataset and our data is precise and realistic.
Disentangling Monocular 3D Object Detection: From Single to Multi-Class Recognition

In this paper we introduce a method for multi-class, monocular 3D object detection from a single RGB image, which exploits a novel disentangling transformation and a novel, self-supervised confidence estimation method for predicted 3D bounding boxes. The proposed disentangling transformation isolates the contribution made by different groups of parameters to a given loss, without changing its nature. This brings two advantages: i) it simplifies the training dynamics in the presence of losses with complex interactions of parameters; and ii) it allows us to avoid the issue of balancing independent regression terms. We further apply this disentangling transformation to another novel, signed Intersection-over-Union criterion-driven loss for improving 2D detection results. We also critically review the AP metric used in KITTI3D and resolve a flaw which affected and biased all previously published results on monocular 3D detection. Our improved metric is now used as official KITTI3D metric. We provide extensive experimental evaluations and ablation studies on the KITTI3D and nuScenes datasets, setting new state-of-the-art results. We provide additional results on all the classes of KITTI3D as well as nuScenes datasets to further validate the robustness of our method, demonstrating its ability to generalize for different types of objects.
A novel two-stage approach for cryptocurrency analysis

Modelling and quantifying the underlying characteristics of the cryptocurrency market has drawn increasing attention since Bitcoin went online in 2009. This study proposes a two-stage decomposition and composition method (2SDC) that begins with a Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) for better interpreting cryptocurrency formations. This study involves daily closing price data from six cryptocurrencies (i.e., Bitcoin, Ethereum, Bitcoin Cash, Litecoin, Monero and Dash) from July 23rd, 2017 to July 23rd, 2019. In the first stage, six time series are jointly decomposed into 10 independent intrinsic mode functions (IMF) from high to low frequency plus one residual. In the second stage, the IMFs for each cryptocurrency are composed into three components based on Wilcoxon signed-rank test, including high and low frequency components and a long-term trend. These three multi-scale components can be interpreted as short-term fluctuations caused by investor sentiment and micro-structure, the effect of significant events and fundamental values. Furthermore, we demonstrated that the low and high frequency compositions are determining factors of cryptocurrency prices, which supports for the existing evidence (e.g. Bouoiyour, Selmi, Tiwari, & Olayeni, 2016; Ji, Bouri, Lau, & Roubaud, 2019).
Multi-Lingual Optical Character Recognition System Using the Reinforcement Learning of Character Segmenter

In this article, we present a new multi-lingual Optical Character Recognition (OCR) system for scanned documents.In the case of Latin characters, current open source systems such as Tesseract provide very high accuracy.However, the accuracy of the multi-lingual documents, including Asian characters, is usually lower than that for Latin-only documents.For example, when the document is the mix of English, Chinese and/or Korean characters, the OCR accuracy is lowered than English-only because the character/text properties of Chinese and Korean are quite different from Latin-type characters.To tackle these problems, we propose a new framework using three neural blocks (a segmenter, a switcher, and multiple recognizers) and the reinforcement learning of the segmenter: The segmenter partitions a given word image into multiple character images, the switcher assigns a recognizer for each sub-image, and the recognizers perform the recognition of assigned sub-images.The training of recognizers and switcher can be considered traditional image classification tasks and we train them with a supervised learning method.However, the supervised learning of the segmenter has two critical drawbacks: Its objective function is sub-optimal and its training requires a large amount of annotation efforts.Thus, by adopting the REINFORCE algorithm, we train the segmenter so as to optimize the overall performance, i.e., we minimize the edit distance of final recognition results.Experimental results have shown that the proposed method significantly improves the performance for multi-lingual scripts and large character set languages without using character boundary labels.
A Systematic Literature Review of Medical Chatbot Research from a Behavior Change Perspective

From a perspective of behavioral change, we review medical chatbot literature included in top peer-reviewed journals and conferences, and build up a comprehensive picture. We examine literature on how people feel about using a medical chatbot and based on that how far chatbots are useful to change harmful behavior. To structure the review we use the theory of planned behavior and the theory of reasoned action. Based on this we conclude five design-oriented recommendations. We expect this research to identify behavioral aspects that contribute to the acceptance, usage and effectiveness of medical chatbots in future.