Reviewerly/exHarmony huggingface dataset:
YolTrack: Multitask Learning Based Real-Time Multiobject Tracking and Segmentation for Autonomous Vehicles

Modern autonomous vehicles are required to perform various visual perception tasks for scene construction and motion decision. The multiobject tracking and instance segmentation (MOTS) are the main tasks since they directly influence the steering and braking of the car. Implementing both tasks using a multitask learning neural network presents significant challenges in performance and complexity. Current work on MOTS devotes to improve the precision of the network with a two-stage tracking by detection model, which is difficult to satisfy the real-time requirement of autonomous vehicles. In this article, a real-time multitask network named YolTrack based on one-stage instance segmentation model is proposed to perform the MOTS task, achieving an inference speed of 29.5 frames per second (fps) with slight accuracy and precision drop. The YolTrack uses ShuffleNet V2 with feature pyramid network (FPN) as a backbone, from which two decoders are extended to generate instance segments and embedding vectors. Segmentation masks are used to improve the tracking performance by performing logic AND operation with feature maps, proving that foreground segmentation plays an important role in object tracking. The different scales of multiple tasks are balanced by the optimized geometric mean loss during the training phase. Experimental results on the KITTI MOTS data set show that YolTrack outperforms other state-of-the-art MOTS architectures in real-time aspect and is appropriate for deployment in autonomous vehicles.
Detecting virtual concept drift of regressors without ground truth values

Abstract Regression analysis is a standard supervised machine learning method used to model an outcome variable in terms of a set of predictor variables. In most real-world applications the true value of the outcome variable we want to predict is unknown outside the training data, i.e., the ground truth is unknown. Phenomena such as overfitting and concept drift make it difficult to directly observe when the estimate from a model potentially is wrong. In this paper we present an efficient framework for estimating the generalization error of regression functions, applicable to any family of regression functions when the ground truth is unknown. We present a theoretical derivation of the framework and empirically evaluate its strengths and limitations. We find that it performs robustly and is useful for detecting concept drift in datasets in several real-world domains.
Do conventional currencies hedge cryptocurrencies?

We examine the hedge and safe-haven properties of conventional currencies for four cryptocurrencies — Bitcoin, Ethereum, Ripple, and Litecoin. We extend the Baur and McDermott (2010) framework, where the safe-haven role is examined against reverse explosiveness in cryptocurrency prices. Our results suggest that the Japanese yen is the most consistent hedger for cryptocurrencies, followed by the British pound, Chinese yuan, and the Euro. All currencies, except the Euro, perform a safe-haven role for Bitcoin and its fork, Litecoin. The safe-haven potential of the Euro, Japanese Yen, and Chinese Yuan is also confirmed during the negative explosiveness periods of the cryptocurrency market.
Projection Invariant Feature and Visual Saliency-Based Stereoscopic Omnidirectional Image Quality Assessment

In this article, we propose a quality assessment model-based on the projection invariant feature and the visual saliency for Stereoscopic Omnidirectional Images (SOIs). Firstly, the projection invariant monocular and binocular features of SOI are derived from the Scale-Invariant Feature Transform (SIFT) points to tackle the inconsistency between the stretched projection formats and the viewports. Secondly, the visual saliency model, which combines the chrominance and contrast perceptual factors, is used to facilitate the prediction accuracy. Thirdly, according to the characteristics of the panoramic image, we generate the weight map and utilize it as a location prior, which can be adapted to different projection formats. Finally, the proposed SOI quality assessment model fuses the projection invariant features, visual saliency, and location prior. Experimental results on both the NingBo University SOI Database (NBU-SOID) and Stereoscopic OmnidirectionaL Image quality assessment Database (SOLID) demonstrate the proposed metric on equi-rectangular projection format outperforms the state-of-the-art schemes, the pearson linear correlation coefficient and spearman rank order correlation coefficient performance are 0.933 and 0.933 on SOLID, and 0.907 and 0.910 on NBU-SOID, respectively. Meanwhile, the proposed algorithm is extended to another five representative projection formats and achieves superior performance.
Your Identity is Yours: Take Back Control of Your Identity Using GDPR Compatible Self-Sovereign Identity

Digital identity has importance in the digital world representing users in a comparable manner to that of the physical identity in the real world. Digital identity comprises certain personal and confidential attributes related to identity owners, managed through an Identity Management (IDM) system. In most IDM systems, identity owners do not control their own identity and its related personal data. However, Self-Sovereign Identity (SSI) is an emerging IDM system which offers users the ownership and full control over their personal data. In the European Union, General Data Protection Regulation (GDPR) is the basic regulatory environment for anyone involved in processing personal data, whilst SSI is concerned with the requirement of managing identity and its associated personal data. If an SSI system could comply with the key GDPR principles then it could become both a desirable and appropriate IDM solution legally and universally. This paper evaluates this aspect of SSI and analyses SSI compliance and alignment with the key principles of GDPR. Furthermore, it investigates two different types of SSI ecosystems public permissionless blockchain based SSI ecosystem uPort and public permissioned blockchain based SSI ecosystem Sovrin, according to the various defined roles and their compatibility with GDPR roles. Finally, this paper performs the comparative analysis of uPort and Sovrin to assess their compliance with the key principles of GDPR.
PyDTNN: A user-friendly and extensible framework for distributed deep learning

We introduce a framework for training deep neural networks on clusters of computers with the following appealing properties: (1) It is developed in Python, exposing an amiable interface that provides an accessible entry point for the newcomer; (2) it is extensible, offering a customizable tool for the more advanced user in deep learning; (3) it covers the main functionality appearing in convolutional neural networks; and (4) it delivers reasonable inter-node parallel performance exploiting data parallelism by leveraging MPI via MPI4Py for communication and NumPy for the efficient execution of (multithreaded) numerical kernels.
A Survey on Deep Learning Based Methods and Datasets for Monocular 3D Object Detection

Owing to recent advancements in deep learning methods and relevant databases, it is becoming increasingly easier to recognize 3D objects using only RGB images from single viewpoints. This study investigates the major breakthroughs and current progress in deep learning-based monocular 3D object detection. For relatively low-cost data acquisition systems without depth sensors or cameras at multiple viewpoints, we first consider existing databases with 2D RGB photos and their relevant attributes. Based on this simple sensor modality for practical applications, deep learning-based monocular 3D object detection methods that overcome significant research challenges are categorized and summarized. We present the key concepts and detailed descriptions of representative single-stage and multiple-stage detection solutions. In addition, we discuss the effectiveness of the detection models on their baseline benchmarks. Finally, we explore several directions for future research on monocular 3D object detection.
Network-Based Earthquake Magnitude Determination via Deep Learning

Abstract The stability and robustness of determining earthquake magnitude are of great significance in earthquake monitoring and seismic hazard assessment. The routine workflow of determining earthquake local magnitude, such as the widely used Richter magnitude, may result in an unreliable measurement of earthquake magnitude because it relies on individual amplitude measurement of a single station, which is prone to be influenced by natural impulsive noise or anthropogenic noise. In this study, we present an automated estimation of earthquake magnitude by applying a deep-learning algorithm named magnitude neural network (MagNet) based on the full-waveform recordings from a network of seismic stations at China seismic experimental site (CSES). The MagNet consists of a compression component that extracts the global features of waveform data and an expansion component that yields a Gaussian probability distribution representing the magnitude estimation. The MagNet is trained with an augmented data set, which includes 21,700 training samples with evenly distributed magnitudes. From the prediction results on the test data set, the mean errors and standard deviations are −0.017 and 0.21, respectively, for 600 moderate earthquakes with magnitudes ranging from 3 to 5.9, and −0.011 and 0.14, respectively, for 70 small earthquakes with magnitudes ranging from 2.3 to 3.5. However, it remains challenging for large earthquakes (magnitude&amp;gt;6.5), due to the lack of sufficient historical large earthquakes as training data. In addition, testing results show that the new method is capable of minimizing the impact of abnormal noises in the data. These results demonstrate the validity and merits of the proposed deep-learning method in predicting earthquake magnitude automatically.
Hough$^2$Map – Iterative Event-Based Hough Transform for High-Speed Railway Mapping

To cope with the growing demand for transportation on the railway system, accurate, robust, and high-frequency positioning is required to enable a safe and efficient utilization of the existing railway infrastructure. As a basis for a localization system we propose a complete on-board mapping pipeline able to map robust meaningful landmarks, such as poles from power lines, in the vicinity of the vehicle. Such poles are good candidates for reliable and long term landmarks even through difficult weather conditions or seasonal changes. To address the challenges of motion blur and illumination changes in railway scenarios we employ a Dynamic Vision Sensor, a novel event-based camera. Using a sideways oriented on-board camera, poles appear as vertical lines. To map such lines in a real-time event stream, we introduce Hough2Map, a novel consecutive iterative event-based Hough transform framework capable of detecting, tracking, and triangulating close-by structures. We demonstrate the mapping reliability and accuracy of Hough2Map on real-world data in typical usage scenarios and evaluate using surveyed infrastructure ground truth maps. Hough2Map achieves a detection reliability of up to 92% and a mapping root mean square error accuracy of 1.1518m.
Focused Bayesian prediction

Summary We propose a new method for conducting Bayesian prediction that delivers accurate predictions without correctly specifying the unknown true data generating process. A prior is defined over a class of plausible predictive models. After observing data, we update the prior to a posterior over these models, via a criterion that captures a user‐specified measure of predictive accuracy. Under regularity, this update yields posterior concentration onto the element of the predictive class that maximizes the expectation of the accuracy measure. In a series of simulation experiments and empirical examples, we find notable gains in predictive accuracy relative to conventional likelihood‐based prediction.
Sentiment Classification Algorithm Based on Multi-Modal Social Media Text Information

The issue of sentiment classification in short-term and small-scale data scenarios is considered in this paper.It is a hot topic because the text sentiment classification task in the public opinion analysis scene has two characteristics: short time and small data scale.Existing work focused on improving the accuracy at the cost of data and training time, without considering scenarios where time and data are lacked.The most commonly used method to solve the problem of small data scale is to use multi-modal information such as pictures, sounds and videos, which will lead to unbearable training time.The shorter training time determines that the classification model is generally selected as a deep neural network with fewer layers, such as TextCNN, TextRNN, and so on.However, such models are limited by the structure and have a low classification accuracy.In order to solve both short-term and small-scale data problems, a common information user attribute on social media is added to the model as multimodal information, which includes twelve attributes such as user age, location, and posting time.This paper proposed a sentiment classification algorithm based on multi-modal social media text information.The algorithm makes use of parallel convolutional neural networks (CNN) and recurrent neural network (RNN) to process text information and user attributes respectively, and combines the feature vectors of the two models for classification, which is called User attributes Convolutional and Recurrent Neural Network (UCRNN).The addition of user attributes can improve accuracy, and the CNN network used to extract user attributes features has fewer parameters, which proves that the algorithm can achieve high accuracy under short-term and small-scale data.Experiments verify that the training time of this model is slightly less than TextRNN.The classification accuracy can reach 90.2%, which is the state-of-the-art in the field of short-term and smallscale data sentiment classification.
Gender inclusive intermediary education, financial stability and female employment in the industry in Sub-Saharan Africa

The study examines how financial stability modulates the effect of inclusive intermediary education on female employment in the industry for the period 2008-2018 in Sub-Saharan Africa. The empirical evidence is based on Tobit, Ordinary Least Squares (OLS) and Quantile regressions. There are positive interactive or conditional effects between inclusive intermediary education and financial stability in the Tobit, OLS and bottom quantiles estimations. A net positive (negative) effect is apparent in the 10th quantitle (median) of female employment in the industry distribution. Implications are discussed.
Deep Selective Memory Network With Selective Attention and Inter-Aspect Modeling for Aspect Level Sentiment Classification

Aspect level sentiment classification aims to recognize the sentiment polarity of each aspect term in a sentence. However, most of the existing methods usually applied the attention mechanism over position-weighted memory and did not consider inter-aspect information. To address these issues, we propose a novel framework for aspect level sentiment classification, Deep Selective Memory Network (DSMN), which selects the context memory dynamically for better guiding the multi-hop attention mechanism and integrates inter-aspect information with deep memory network. By designing a selective attention mechanism based on the distance information between an aspect and its context, DSMN focuses on different parts of the context memory in different memory network layers to capture abundant aspect-aware context information. Besides, to make full use of the inter-aspect information, we also design effective inter-aspect modeling modules to generate both semantic and relation information of the nearby aspects for the desired aspect. We evaluate the advantages of our framework on three benchmark datasets, and experiment results show that our framework achieves state-of-the-art performance.
Fusion-Based Multidetection Multitarget Tracking With Random Finite Sets

Multidetection (MD) systems are characterized by multiple observation modes (OMs), and hence, simultaneously produce multiple measurements for each target. The key challenge in exploiting MD systems for multitarget tracking (MTT), compared to single-detection (SD) systems, is the significant amount of extra computational burden involved in order to solve the resulting multidimensional assignment problem among measurements, targets, and OMs. This article presents a novel computationally efficient MTT framework for MD systems, wherein the multitarget state is modeled as a random finite set (RFS), and a bank of OM-dependent MTT RFS filters with SD model are employed to recursively provide OM-dependent posteriors. The latter, which contain both real and false targets, are then suitably fused so as to enhance consensus on the true targets while weakening trust on the existence of the false ones. In this way, the computational complexity is significantly reduced compared to existing MTT algorithms with the MD model. Two representative RFS filters, i.e., unlabeled probability hypothesis density (PHD) and labeled multi-Bernoulli (LMB), are considered in the proposed framework and the computational complexity of the resulting MD MTT algorithms is analyzed. Performance of the proposed approach is assessed by simulation experiments in both over-the-horizon-radar (OTHR) and single-frequency-network passive radar (SFN-PR) MTT applications.
Towards Privacy-assured and Lightweight On-chain Auditing of Decentralized Storage

How to audit outsourced data in centralized storage like cloud is well-studied, but it is largely under-explored for the rising decentralized storage network (DSN) that bodes well for a billion-dollar market. To realize DSN as a usable service in a fully decentralized manner, the blockchain comes in handy - to record and verify audit trails in forms of proof of storage, and based on that, to enforce fair payments with necessary dispute resolution. Leaving the audit trails on the blockchain offers transparency and fairness, yet it 1) sacrifices privacy, as they may leak information about the data under audit, and 2) overwhelms onchain resources, as they may be practically large in size and expensive to verify. Prior auditing designs in centralized settings are not directly applicable here. A handful of proposals targeting DSN cannot satisfactorily address these issues either. We present an auditing solution that addresses on-chain privacy and efficiency, from a synergy of homomorphic linear authenticators with polynomial commitments for succinct proofs, and the sigma protocol for provable privacy. The solution results in, per audit, 288-byte proof written to the blockchain, and constant verification cost. It can sustain long-term operation and easily scale to thousands of users on Ethereum.
Faster and efficient cloud-server-aided data de-duplication scheme with an authenticated key agreement for Industrial Internet-of-Things

The volume of the outsourced IIoT data to the cloud servers is exponentially increasing due to the increase in the number of IIoT devices and duplicity in their outsourced data. This has therefore increased the cost and storage overheads in IIoT and cloud server, respectively. Aside from this, outsourcing IIoT data to semi-trusted distributed cloud servers increases the security challenges in IIoT. Although, many encryption techniques had been proposed to perform secure de-duplication over outsourced data in the cloud server. However, some of these schemes are computational intensive, do not allow data searching over the ciphertext, and could not enforce necessary security requirements especially Inside Keyword Guess Attack (IKGA). In this paper, we propose a novel faster server-aided data de-duplication scheme with an efficient authenticated key agreement. The scheme enforces data singularity, confidentiality, integrity in the IIoT and allows secure data searching over the ciphertext in the semi-trusted cloud server. The effectiveness of the scheme revolves around its capability to generate a tokenized-tag and trapdoor for every uploaded data on the server.
Experiencing Distance: Wearable Engagements with Remote Relationships

Living dislocated from family, friends or partners can result in negative emotions and a lack of physical, bodily closeness. We focus on materialising the negative consequences associated with living far apart in a textile form, and manifest those in two pairs of wearable artefacts: first, WARMTH, which simulates the bodily distance between remote people through a decrease in felt temperature, and second, BREATH, which embodies the bodily absence of a remote other through a decline in mechanical movement of textiles. The wearables are ‘discussion artefacts’ that enable conversation about, reflection on and exchange of personal, negative, vulnerable and challenging emotions connected to living far apart. The stance taken in this pictorial emphasises the necessity to focus not only on overcoming and bridging the distance between remote people through interactive artefacts; but also, to consider and manifest melancholic and possibly negative personal experiences.
A new approach to (key) keywords analysis: Using frequency, and now also dispersion

A widely-used method in corpus-linguistic approaches to discourse analysis, register/text type/genre analysis, and educational/curriculum questions is that of keywords analysis, a simple statistical method aiming to identify words that are key to, i.e. characteristic for, certain discourses, text types, or topic domains. The vast majority of keywords analyses relied on the same statistical measure that most collocation studies are using, the log-likelihood ratio, which is performed on frequencies of occurrence in two corpora under consideration. In a recent paper, Egbert and Biber (2019) advocated a different approach, one that involves computing log-likelihood ratios for word types based on the range of their distribution rather than their frequencies in the target and reference corpora under consideration. In this paper, I argue that their approach is a most welcome addition to keywords analysis but can still be profitably extended by utilizing both frequency and dispersion for keyness computations. I am presenting a new two-dimensional approach to keyness and exemplifying it on the basis of the Clinton-Trump Corpus and the British National Corpus.