Reviewerly/exHarmony huggingface dataset:
Blockchain Enablers for Supply Chains: How to Boost Implementation in Industry

Supply chain management is considered one of the main sectors of development for blockchain technology.This study provides solid contributions to understanding blockchain innovation and presents some main features and guidelines for how to boost blockchain implementation in industry.As explorative research, this paper presents a grounded theory analysis based on 18 expert interviews.The pool of interviewees is composed of academics and business and institutional representatives with relevant technological knowledge on blockchain and innovation management.Renowned worldwide experts provided us with powerful input to run this analysis and with a general overview of the current situation.Blockchain development of course impacts supply chains, but currently, the analysis shows that it does not seem to be a disruptive technology.In accordance with C.M. Christensen, blockchain presents all the features to be a sustaining innovation rather than disruptive.For this reason, as outcomes, we present five enablers that can foster prompt adoption in industry.
COVID-19 infection: Disease detection and mobile technology

A pneumonia outbreak of unknown etiology took place in Wuhan, Hubei province, China and spread quickly worldwide in December 2019. Chinese Center for Disease Control and Prevention identified a novel beta-coronavirus called 2019-nCoV, now officially known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) that is responsible for the pandemic. The coronavirus COVID-19 affected 215 countries and territories around the world and more than 99 hundred thousand people at present (Nature Nanotechnology, 2020). At present, there are no specific vaccines or treatments available for COVID-19. However, there are many ongoing clinical trials evaluating potential treatments. At this time the experts recommend precautions such as social distancing, hand washing, and wearing face masks to reduce disease transmission. This review article aims to improve the readers' awareness towards the important role of mobile technology for SARS-CoV-2.To achieve this objective, we performed a COVID-19 literature review from various sources that include data from the published articles as well as World Health Organization reports on coronavirus disease and how mobile technology is useful to fight against this disease.Mobile technology can be helpful in mapping disease spread and provides an easy way to provide awareness that promotes safety and adoption of necessary precautions to mitigate and stop community transmission.The spread rate of COVID-19 is very high and until now no vaccines are available to control this disease. To this end we should leverage other avenues such as digital technologies to protect ourselves from this disease. Mobile technology such as smartphones are playing an important role in this pandemic, by launching apps to track coronavirus infected people. These apps are very easy to use and provide self-isolation guidelines as well as other safety tips.
Improving the quantification of sediment source contributions using different mathematical models and spectral preprocessing techniques for individual or combined spectra of ultraviolet–visible, near- and middle-infrared spectroscopy

In recent years, several sediment fingerprinting studies have used ultraviolet–visible (UV–Vis), near-infrared (NIR) and middle-infrared (MIR) spectroscopy as a low cost, non-destructive and fast alternative to obtain tracer properties to estimate sediment source contributions. For this purpose, partial last square regression (PLSR) has often been used to build predictive parametric models. However, spectra preprocessing and more robust and non-parametric models such as support vector machines (SVM) has gained little attention in these studies. Accordingly, the objectives of the current research were to evaluate (i) the accuracy of two multivariate methods (PLSR and SVM), (ii) the effect of eight spectra preprocessing techniques, and (iii) the effect of using the information contained in the UV–Vis, NIR and MIR regions considered either separately or in combination on sediment source apportionment. The estimated source contribution was then compared with contributions obtained by the conventional fingerprinting approach based on geochemical tracers. This study was carried out in the Arvorezinha catchment (1.23 km2) located in southern Brazil. Forty soil samples were collected in three main potential source (cropland surface, unpaved roads and stream channels) and twenty-nine suspended sediment samples collected at the catchment outlet during nine rainfall-runoff events were used in this study. Both PLSR and SVM models showed a higher accuracy when calibrated and validated with the spectra submitted to spectral processing when compared to the direct use of the raw spectra. The best model results were obtained with PLSR and SVM mathematical models associated with the spectral preprocessing techniques 1st derivative Savitzky-Golay (SGD1), normalization (NOR) and combining NOR + SGD1 in the UV–Vis + NIR + MIR. The lowest errors were observed when the UV–Vis + NIR + MIR bands were combined due to the gain in information and, consequently, the increase in discriminant power achieved by the models. Despite the good accuracy of the models calibrated and validated with the artificial mixtures, significant errors remain when results of source contributions are compared to those obtained with the conventional sediment fingerprinting technique based on geochemical tracers. Nevertheless, the magnitude of the contributions calculated by the spectroscopy and geochemical approaches remains very similar for all sources, especially when using the SVM-UV–Vis + NIR + MIR model. Therefore, spectroscopy proved to be a fast, cheap and accurate technique, offering an alternative to the conventional geochemical approach for discriminating sediment source contributions in agricultural catchments located in subtropical regions.
UX information in the daily work of an agile team: A distributed cognition analysis

The integration of agile software development and user experience (UX) design has been a topic of investigation for practitioners and researchers for many years, and agile teams have become increasingly aware of the importance of UX design. Most studies have focused so far on the integration of UX theories and methods with agile practices. The objective of this research is to investigate whether and how UX information is embedded in the daily work of an agile team. We conducted an ethnographic study of an agile team based in the UK. We performed a qualitative analysis using different data sources and three complementary analytical lenses: Distributed Cognition of Teamwork, Garrett's set of UX elements and planes, and Hassenzahl's content-oriented model of UX. This combination provided an understanding of the different types of UX information available to the agile team through artefacts and face-to-face meetings, how the information flowed within and around the agile team, and the type of engagement they have with UX information. The findings reveal that: (1) agile team members were consumers of UX information not producers; (2) the most common type of UX information found in the system related to how the user interacts with the product rather than to user goals or needs; (3) information focusing on the user perspective appears in verbal communication rather than being captured in artefacts; and, (4) the flow of UX information around the team is complex. In combination, these factors indicate a potential breakdown in the communication of UX information. We argue that these findings have relevance for other agile teams because the artefacts and methods used by this team are commonly used by other agile teams. To improve the situation, we suggest a number of recommendations to engage agile team members in UX work, and reduce the complexity of UX information flow.
Dual-Teacher++: Exploiting Intra-Domain and Inter-Domain Knowledge With Reliable Transfer for Cardiac Segmentation

Annotation scarcity is a long-standing problem in medical image analysis area. To efficiently leverage limited annotations, abundant unlabeled data are additionally exploited in semi-supervised learning, while well-established cross-modality data are investigated in domain adaptation. In this paper, we aim to explore the feasibility of concurrently leveraging both unlabeled data and cross-modality data for annotation-efficient cardiac segmentation. To this end, we propose a cutting-edge semi-supervised domain adaptation framework, namely Dual-Teacher++. Besides directly learning from limited labeled target domain data (e.g., CT) via a student model adopted by previous literature, we design novel dual teacher models, including an inter-domain teacher model to explore cross-modality priors from source domain (e.g., MR) and an intra-domain teacher model to investigate the knowledge beneath unlabeled target domain. In this way, the dual teacher models would transfer acquired inter- and intra-domain knowledge to the student model for further integration and exploitation. Moreover, to encourag reliable dual-domain knowledge transfer, we enhance the inter-domain knowledge transfer on the samples with higher similarity to target domain after appearance alignment, and also strengthen intra-domain knowledge transfer of unlabeled target data with higher prediction confidence. In this way, the student model can obtain reliable dual-domain knowledge and yield improved performance on target domain data. We extensively evaluated the feasibility of our method on the MM-WHS 2017 challenge dataset. The experiments have demonstrated the superiority of our framework over other semi-supervised learning and domain adaptation methods. Moreover, our performance gains could be yielded in bidirections, i.e., adapting from MR to CT, and from CT to MR. Our code will be available at https://github.com/kli-lalala/Dual-Teacher-.
Social-aware Pedestrian Trajectory Prediction via States Refinement LSTM

In the task of pedestrian trajectory prediction, social interaction could be one of the most complicated factors since it is difficult to be interpreted through simple rules. Recent studies have shown a great ability of LSTM networks in learning social behaviors from datasets, e.g., introducing LSTM hidden states of the neighbors at the last time step into LSTM recursion. However, those methods depend on previous neighboring features which lead to a delayed observation. In this paper, we propose a data-driven states refinement LSTM network (SR-LSTM) to enable the utilization of the current intention of neighbors through a message passing framework. Moreover, the model performs in the form of self-updating by jointly refining the current states of all participants, rather than an input-output mechanism served by feature concatenation. In the process of states refinement, a social-aware information selection module consisting of an element-wise motion gate and a pedestrian-wise attention is designed to serve as the guidance of the message passing process. Considering the pedestrian walking space as a graph where each pedestrian is a node and each pedestrian pair with an edge, spatial-edge LSTMs are further exploited to enhance the model capacity, where two kinds of LSTMs interact with each other so that states of them are interactively refined. Experimental results on four widely used pedestrian trajectory datasets, ETH, UCY, PWPD, and NYGC demonstrate the effectiveness of the proposed model.
On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries

Large-scale semantic parsing datasets annotated with logical forms have enabled major advances in supervised approaches.But can richer supervision help even more?To explore the utility of fine-grained, lexical-level supervision, we introduce SQUALL, a dataset that enriches 11,276 WIKITABLEQUESTIONS English-language questions with manually created SQL equivalents plus alignments between SQL and question fragments.Our annotation enables new training possibilities for encoderdecoder models, including approaches from machine translation previously precluded by the absence of alignments.We propose and test two methods: (1) supervised attention;(2) adopting an auxiliary objective of disambiguating references in the input queries to table columns.In 5-fold cross validation, these strategies improve over strong baselines by 4.4% execution accuracy.Oracle experiments suggest that annotated alignments can support further accuracy gains of up to 23.9%.
Fast Distance-Based Anomaly Detection in Images Using an Inception-Like Autoencoder

The goal of anomaly detection is to identify examples that deviate from normal or expected behavior. We tackle this problem for images. We consider a two-phase approach. First, using normal examples, a convolutional autoencoder (CAE) is trained to extract a low-dimensional representation of the images. Here, we propose a novel architectural choice when designing the CAE, an Inception-like CAE. It combines convolutional filters of different kernel sizes and it uses a Global Average Pooling (GAP) operation to extract the representations from the CAE's bottleneck layer. Second, we employ a distanced-based anomaly detector in the low-dimensional space of the learned representation for the images. However, instead of computing the exact distance, we compute an approximate distance using product quantization. This alleviates the high memory and prediction time costs of distance-based anomaly detectors. We compare our proposed approach to a number of baselines and state-of-the-art methods on four image datasets, and we find that our approach resulted in improved predictive performance.
Detecting Blockchain Security Threats

In many organizations, permissioned blockchain networks are currently transitioning from a proof-of-concept stage to production use. A crucial part of this transition is ensuring awareness of potential threats to network operations. Due to the plethora of software components involved in distributed ledgers, threats may be difficult or impossible to detect without a structured monitoring approach. To this end, we conduct a survey of attacks on permissioned blockchains and develop a set of threat indicators. To gather these indicators, a data processing pipeline is proposed to aggregate log information from relevant blockchain components, enriched with data from external sources. To evaluate the feasibility of monitoring current blockchain frameworks, we determine relevant data sources in Hyperledger Fabric. Our results show that the required data is mostly available, but also highlight significant improvement potential with regard to threat intelligence, chaincode scanners and built-in metrics.
Quality Diversity Through Surprise

Quality diversity is a recent family of evolutionary search algorithms which focus on finding several well-performing (quality) yet different (diversity) solutions with the aim to maintain an appropriate balance between divergence and convergence during search. While quality diversity has already delivered promising results in complex problems, the capacity of divergent search variants for quality diversity remains largely unexplored. Inspired by the notion of surprise as an effective driver of divergent search and its orthogonal nature to novelty this paper investigates the impact of the former to quality diversity performance. For that purpose we introduce three new quality diversity algorithms which employ surprise as a diversity measure, either on its own or combined with novelty, and compare their performance against novelty search with local competition, the state of the art quality diversity algorithm. The algorithms are tested in a robot navigation task across 60 highly deceptive mazes. Our findings suggest that allowing surprise and novelty to operate synergistically for divergence and in combination with local competition leads to quality diversity algorithms of significantly higher efficiency, speed and robustness.
A Survey on federated learning

Federated learning (FL) is an emerging setting which implement machine learning in a distributed environment while protecting privacy. Research activities relating to FLhave grown at a fast rate recently in control. Exactly what activities have been carrying the research momentum forward is a question of interest to the research community. This study finds these research activities and optimization path of FL based on survey. Thus, this study aims to review related studies of FL to base on the baseline a universal definition gives a guiding for the future work. Besides, this study presents the prevailing FL applications and the evolution of federated learning. In the end, this study also identifies four research fronts to enrich the FL literature and help advance our understanding of the field. A comprehensive taxonomy of FL can also be developed through analyzing the results of this review.
Exploiting Morphological and Phonological Features to Improve Prosodic Phrasing for Mongolian Speech Synthesis

Prosodic phrasing is an important factor that affects naturalness and intelligibility in text-to-speech synthesis. Studies show that deep learning techniques improve prosodic phrasing when large text and speech corpus are available. However, for low-resource languages, such as Mongolian, prosodic phrasing remains a challenge for various reasons. First, the database suitable for system training is limited. Second, word composition knowledge that is prosody-informing has not been used in prosodic phrase modeling. To address these problems, in this article, we propose a feature augmentation method in conjunction with a self-attention neural classifier. We augment input text with morphological and phonological decompositions of words to enhance the text encoder. We study the use of self-attention classifier, that makes use of global context of a sentence, as a decoder for phrase break prediction. Both objective and subjective evaluations validate the effectiveness of the proposed phrase break prediction framework, that consistently improves voice quality in a Mongolian text-to-speech synthesis system.
A Survey of Motion and Task Planning Techniques for Unmanned Multicopter Systems

Unmanned aerial systems provide many applications with the ability to perform flying tasks autonomously, and hence have received significant research and commercial attention in the past decade. One of the most popular unmanned aerial platforms for such tasks is the small-scale rotorcraft with multiple rotors, commonly known as multicopters. In order for these platforms to perform fully autonomous missions and tasks, they require a sophisticated low-level flight control system that is integrated with advanced task and motion planning modules, which combine together to form the complete unmanned aerial system (UAS). In this paper, the planning module of unmanned multicopter systems is discussed in detail, and a comprehensive survey on techniques for both motion and task planning reported in literature and by the Unmanned Systems Research Group at the National University of Singapore is presented.
Temporal Distinct Representation Learning for Action Recognition

Motivated by the previous success of Two-Dimensional Convolutional Neural Network (2D CNN) on image recognition, researchers endeavor to leverage it to characterize videos. However, one limitation of applying 2D CNN to analyze videos is that different frames of a video share the same 2D CNN kernels, which may result in repeated and redundant information utilization, especially in the spatial semantics extraction process, hence neglecting the critical variations among frames. In this paper, we attempt to tackle this issue through two ways. 1) Design a sequential channel filtering mechanism, i.e., Progressive Enhancement Module (PEM), to excite the discriminative channels of features from different frames step by step, and thus avoid repeated information extraction. 2) Create a Temporal Diversity Loss (TD Loss) to force the kernels to concentrate on and capture the variations among frames rather than the image regions with similar appearance. Our method is evaluated on benchmark temporal reasoning datasets Something-Something V1 and V2, and it achieves visible improvements over the best competitor by $$2.4\%$$ and $$1.3\%$$ , respectively. Besides, performance improvements over the 2D-CNN-based state-of-the-arts on the large-scale dataset Kinetics are also witnessed.
An overview and a benchmark of active learning for outlier detection with one-class classifiers

Active learning methods increase classification quality by means of user feedback. An important subcategory is active learning for outlier detection with one-class classifiers. While various methods in this category exist, selecting one for a given application scenario is difficult. This is because existing methods rely on different assumptions, have different objectives, and often are tailored to a specific use case. All this calls for a comprehensive comparison, the topic of this article. This article starts with a categorization of the various methods. Interestingly, many assumptions in the literature are implicit, and their impact has not been discussed so far. Based on this, we propose a novel approach to evaluate active learning results by quantifying how classification results evolve with more user feedback, in a compact and nuanced manner. We run over 84,000 experiments to compare state-of-the-art one-class active learning methods, for a broad variety of scenarios. One key finding is that there is no single active learning method that stands out in a competitive evaluation. Instead, we found that selecting a good query strategy alone is not sufficient, since results hinge significantly on other factors, such as the selection of hyperparameter values. Our results show that some configurations are more robust than others. We conclude by phrasing our findings as guidelines on how to select active learning methods for outlier detection with one-class classifiers.
Vision: A Critique of Immunity Passports and W3C Decentralized Identifiers

Due to the widespread COVID-19 pandemic, there has been a push for ‘immunity passports’ and even technical proposals. Although the debate about the medical and ethical problems of immunity passports has been widespread, there has been less inspection of the technical foundations of immunity passport schemes. These schemes are envisaged to be used for sharing COVID-19 test and vaccination results in general. The most prominent immunity passport schemes have involved a stack of little-known standards, such as Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) from the World Wide Web Consortium (W3C). Our analysis shows that this group of technical identity standards are based on under-specified and often non-standardized documents that have substantial security and privacy issues, due in part to the questionable use of blockchain technology. One concrete proposal for immunity passports is even susceptible to dictionary attacks. The use of ‘cryptography theater’ in efforts like immunity passports, where cryptography is used to allay the privacy concerns of users, should be discouraged in standardization. Deployment of these W3C standards for ‘self-sovereign identity’ in use-cases like immunity passports could just as well lead to a dangerous form identity totalitarianism.