Reviewerly/exHarmony huggingface dataset:
Winding Through: Crowd Navigation via Topological Invariance

We focus on robot navigation in crowded environments. The challenge of predicting the motion of a crowd around a robot makes it hard to ensure human safety and comfort. Recent approaches often employ end-to-end techniques for robot control or deep architectures for high-fidelity human motion prediction. While these methods achieve important performance benchmarks in simulated domains, dataset limitations and high sample complexity tend to prevent them from transferring to real-world environments. Our key insight is that a low-dimensional representation that captures critical features of crowd-robot dynamics could be sufficient to enable a robot to wind through a crowd smoothly. To this end, we mathematically formalize the act of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">passing</i> between two agents as a rotation, using a notion of topological invariance. Based on this formalism, we design a cost functional that favors robot trajectories contributing higher passing progress and penalizes switching between different sides of a human. We incorporate this functional into a model predictive controller that employs a simple constant-velocity model of human motion prediction. This results in robot motion that accomplishes statistically significantly higher clearances from the crowd compared to state-of-the-art baselines while maintaining competitive levels of efficiency, across extensive simulations and challenging real-world experiments on a self-balancing robot.
A systematic review of the institutional success factors for blockchain-based land administration

The land administration systems have witnessed many innovative technologies in the attempt to revolutionalize the task of recording land and property rights. One such revolution is the transition toward blockchain-based land administration. Like most technologies, blockchain-based land administration projects are not immune to failures, early adopters have encountered challenges, and several pilot cases have failed. Institutional factors have proven to be issues that can hinder the successful adoption of technological innovations such as blockchain-based land administration. This study identifies and analyses institutional success factors for adopting blockchain-based land administration. A systematic literature review of 37 studies resulted in the extraction of 18 related institutional factors critical to adopting blockchain-based land administration projects. Using Scott's three-pillar framework, the factors were divided into three dimensions: regulatory, organisational and cultural environment. Despite the popular opinion that the regulatory environment is integral to adopting blockchain-based land administration, our review suggests that a mutual co-existence and interplay between all the factors is key to the success of blockchain-based land projects. This study concludes that the adoption of blockchain-based land projects can be enhanced by understanding the institutional factors necessary to adopt blockchain-based land administration.
A Comparative Study for Outlier Detection Methods in High Dimensional Text Data

Abstract Outlier detection aims to find a data sample that is significantly different from other data samples. Various outlier detection methods have been proposed and have been shown to be able to detect anomalies in many practical problems. However, in high dimensional data, conventional outlier detection methods often behave unexpectedly due to a phenomenon called the curse of dimensionality. In this paper, we compare and analyze outlier detection performance in various experimental settings, focusing on text data with dimensions typically in the tens of thousands. Experimental setups were simulated to compare the performance of outlier detection methods in unsupervised versus semi-supervised mode and uni-modal versus multi-modal data distributions. The performance of outlier detection methods based on dimension reduction is compared, and a discussion on using k-NN distance in high dimensional data is also provided. Analysis through experimental comparison in various environments can provide insights into the application of outlier detection methods in high dimensional data.
Preserving data privacy in federated learning through large gradient pruning

In federated learning, the server trains a global learning model based on gradient information shared by multiple clients; thus, protecting client data privacy. However, it has been shown that training data can be reconstructed from the shared gradients, which can result in serious privacy breaches (e.g., also known as gradient-based inversion attacks). Popular privacy-preserving methods include those that are perturbation-related, such as differential privacy. However, these methods can result in high utility loss. In this paper, we reveal that large magnitude gradients play an important role in the image reconstructing process, and thus propose two pruning based defense mechanisms (i.e., SLGP and RLGP) for different model architectures. As only very few gradients have been affected, the utility can be maintained. To demonstrate efficiency, We evaluate the impact of our mechanisms on preventing the reconstruction of input images on various model architectures and datasets using state-of-the-art attack methods. The reconstructed images obtained using the gradient processed by our method are unrecognizable while maintaining the original performance of the models.
Information and communication technology (ICT) skills and job satisfaction of primary education teachers in the context of Covid-19. Theoretical model

The Covid-19 pandemic has shown that educational systems must have an online component or even a substitute. However, the efforts for this necessary transition fall largely on the teaching staff, who have been forced to quickly adapt their activities to a virtual environment. In this study, a theoretical model for analyzing how teachers’ information and communications technology (ICT) skills and the integration of these technologies influence the improvement of teaching and teacher job satisfaction is introduced. The model also pays special attention to the gender gap related to the use of ICT in teaching. At the empirical level, the model is validated on a sample of 257 Spanish primary school teachers, using the partial least squares (PLS) structural equation method. The results of the analysis show that, although the teachers’ ICT skills help them improve their teaching activities, such skills do not have a direct impact on teachers’ job satisfaction. However, teachers who integrate ICTs into their teaching activities not only improve their results and lighten their workload but also enjoy higher job satisfaction, which translates into more motivated and committed teachers. In addition, teachers’ ICT skills influence job satisfaction in different ways depending on gender. Although, for female teachers, the integration of ICT increases their job satisfaction, the results show that, for male teachers, this integration should generate improvements in teaching to yield enhanced job satisfaction. As the main implication, it is recommended to invest in teachers’ ICT skills, as these lead to enhanced efficiency and motivation and enable the change toward an ICT-based teaching model.
Impact of Organizational Culture on Academics’ Readiness and Behavioral Intention to Implement eLearning Changes in Kuwaiti Universities during COVID-19

A comprehensive review of the literature indicates that there is a lack of research on the influence of all the organizational culture (OC) types on academics’ readiness and behavioral intention to implement eLearning changes in higher education institutions (HEIs). To address this gap, this study empirically investigates the impact of OC on academics’ readiness and behavioral intention to implement eLearning changes within HEIs in Kuwait during the COVID-19 pandemic. A 218 self-administered survey was distributed to public and private HEIs. The analysis of collected data reveal that the most prominent OCs are hierarchy and market types which support readiness for change. The contribution of this study lies in further understanding the impact of OC on academics’ readiness for eLearning changes and behavioral intention to support eLearning implementation. The findings further offer an original contribution by confirming the mediating role of academics’ readiness for eLearning changes in the relationship between OC and behavioral intention. This paper adds to the change management literature by collecting data during the COVID-19 pandemic within the interesting context of HEIs in the Gulf Cooperation Council (GCC), which is currently scarce. The implications of this study contribute to the sustainability of Kuwaiti HEIs.
Security and Performance Analysis of Quorum-based Blockchain Consensus Protocols

Consensus protocols for blockchain-based systems are crucial and the most complex part of the blockchain. The proof-of-work consensus protocol of Bitcoin cryptocurrency is the most popular, but it presents a low transaction rate and high energy consumption. This paper analyzes the main quorum-based consensus protocols for blockchains that are alternatives with higher throughput and energy-efficient. The paper focuses on Byzantine fault-tolerant protocols as they are more robust to security attacks. We classify and analyze quorum-based consensus protocols according to their main characteristics and performance, highlighting the flaws and the specific attacks that affect each consensus protocol and presenting countermeasures. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
UCRNet: Underwater color image restoration via a polarization-guided convolutional neural network

Underwater images always suffer from low contrast and color distortion due to the wavelength-dependent scattering and absorption effects caused by particles existing in turbid water, especially in high turbidity conditions. Based on the polarization properties of the backscattering light, polarimetric methods can estimate the intensity level of the backscattering and the transmittance of the media. Accordingly, they can separate the target signal from the undesired ones to achieve high-quality imaging. In addition, learning-based polarimetric methods are effective for gray-model image restoration, but the learning-based polarimetric technique for color image restoration has yet to be considered. In this paper, we propose a 3- dimensional convolutional neural network, which maintains the correlation of polarization information among different polarization channel images as well as embodies polarization constraints, for underwater color image restoration. The experimental results verify that the proposed solution improves the image quality (i.e., the image contrast, details, and color) and outperforms other existing methods, especially when the turbidity of scattering media is high. The proposed solution can be readily applied to practical applications and potentially realize the clear vision in other scattering media, including biomedical imaging and remote sensing.
Program targeting with machine learning and mobile phone data: Evidence from an anti-poverty intervention in Afghanistan

Can mobile phone data improve program targeting? By combining rich survey data from a “big push” anti-poverty program in Afghanistan with detailed mobile phone logs from program beneficiaries, we study the extent to which machine learning methods can accurately differentiate ultra-poor households eligible for program benefits from ineligible households. We show that machine learning methods leveraging mobile phone data can identify ultra-poor households nearly as accurately as survey-based measures of consumption and wealth; and that combining survey-based measures with mobile phone data produces classifications more accurate than those based on a single data source.
Designing interactive augmented reality application for student's directed learning of continuous distillation process

Continuous distillation is an important separation process in chemical engineering curriculum. Conventionally, it is often taught as black box diagrams that lack user visibility, thus preventing students from developing a deeper understanding and comprehension. This paper presents the technical development of the mobile AR application and its implementation in the curriculum of Chemical Engineering Unit Operations at Nanyang Technological University Singapore. The mobile AR application is to provide interactive visualization and real-time numerical simulation to promote students' active learning in the classroom. The results of the pre-test and post-test indicated that the AR application helped students gain a better understanding of the principle and fundamental concepts of the distillation process. The evaluation survey with 6-point likert scale questions shows that students had a positive experience using the mobile AR application and it has enhanced their learning experience, as suggested by a mean evaluation score of 5.18 out of 6.0.
Active learning for efficient analysis of high-throughput nanopore data

As the third-generation sequencing technology, nanopore sequencing has been used for high-throughput sequencing of DNA, RNA, and even proteins. Recently, many studies have begun to use machine learning technology to analyze the enormous data generated by nanopores. Unfortunately, the success of this technology is due to the extensive labeled data, which often suffer from enormous labor costs. Therefore, there is an urgent need for a novel technology that can not only rapidly analyze nanopore data with high-throughput, but also significantly reduce the cost of labeling. To achieve the above goals, we introduce active learning to alleviate the enormous labor costs by selecting the samples that need to be labeled. This work applies several advanced active learning technologies to the nanopore data, including the RNA classification dataset (RNA-CD) and the Oxford Nanopore Technologies barcode dataset (ONT-BD). Due to the complexity of the nanopore data (with noise sequence), the bias constraint is introduced to improve the sample selection strategy in active learning. Results: The experimental results show that for the same performance metric, 50% labeling amount can achieve the best baseline performance for ONT-BD, while only 15% labeling amount can achieve the best baseline performance for RNA-CD. Crucially, the experiments show that active learning technology can assist experts in labeling samples, and significantly reduce the labeling cost. Active learning can greatly reduce the dilemma of difficult labeling of high-capacity nanopore data. We hope active learning can be applied to other problems in nanopore sequence analysis.The main program is available at https://github.com/guanxiaoyu11/AL-for-nanopore.Supplementary data are available at Bioinformatics online.
Practical and scalable decoder for topological quantum error correction with an Ising machine

Quantum error correction is one of the most important milestones for the realization of large-scale quantum computation. To achieve this, it is essential not only to integrate a large number of qubits with high fidelity but also to build a scalable classical system that can perform error correction. Here, we propose an efficient and scalable decoder for quantum error correction using ``Fujitsu Digital Annealer'' (DA). Specifically, the error correction problem of stabilizer codes is mapped into an Ising-type optimization problem, so-called quadratic unconstrained binary optimization (QUBO) problem, which is solved by DA. In particular, we implement the proposed DA decoder for the surface code and perform detailed numerical experiments for various code distances to see its performance and scalability. We observe that computational scaling for the DA decoder has a lower order of polynomials than the decoding methods using simulated annealing (SA) and minimum-weight perfect matching (MWPM) algorithm under all tested conditions. It is also shown that the DA decoder has advantages over the Union-Find (UF) decoder from a variety of perspectives including hardware implementation. Furthermore, the threshold behavior of the logical error probability for the DA decoder is analyzed and the resultant threshold lies between 9.4% and 9.8%, which is very close to that obtained by the MWPM decoder. This result clearly shows the high potential of the DA decoder for quantum error correction.
Mining Adverse Drug Reactions from Unstructured Mediums at Scale

Adverse drug reactions/events (ADR/ADE) have a major impact on patient health and health care costs. While most ADR’s are not reported via formal channels, they are often documented in a variety of unstructured conversations such as social media posts or customer support call transcripts. In this paper, we propose a natural language processing (NLP) solution that detects ADR’s in such unstructured free-text conversations, which improves on previous work in three ways. First, a new Named Entity Recognition (NER) model obtains state-of-the-art accuracy for ADR and Drug entity extraction on the ADE, CADEC, and SMM4H benchmark datasets (91.75, 78.76, and 83.41% F1 scores respectively). Second, two new Relation Extraction (RE) models are introduced—one based on BioBERT while the other utilizing crafted features over a Fully Connected Neural Network (FCNN)—perform on par with existing state-of-the-art models, and outperform them when trained with a supplementary clinician-annotated RE dataset. Third, a new text classification model, obtains new state-of-the-art accuracy on the CADEC dataset (86.69% F1 score). The complete solution is implemented as a unified NLP pipeline in a production-grade library built on top of Apache Spark, making it natively scalable for processing millions of records on commodity clusters.
An exploratory study of the perceptions of auditors on the impact on Blockchain technology in the United Arab Emirates

The purpose of this paper is to explore the prospects of blockchain technology (BCT) on audit practice. Semi‐structured interviews were conducted with practitioners from the Big Four and non‐Big Four firms; their responses were analysed thematically. The findings are significant regarding the three main themes identified: namely, audit practice, procedures and the challenges associated with the adoption of BCT. The adoption of BCT has the potential to impact audit procedures to a certain extent. The interviewees believed that BCT would transfer Audit 3.0 to Audit 4.0. For instance, external confirmations could be enhanced by the move towards a more automated verification process. In addition, the audit budget time could be decreased with the help of this technology in the long run. The need for physical observation could be diminished because of this technology, as it could track observations on a real‐time basis. This technology could also facilitate analytical procedures, as it has the potential to incorporate other analytical tools simultaneously. Furthermore, it could reduce the costs associated with fraud detection to a great extent, as it provides a tamper‐proof, immutable audit trail.
Scale-Aware Squeeze-and-Excitation for Lightweight Object Detection

Lightweight object detection can promote intelligent robotics to recognize surroundings objects with limited computational resources, and thus receives increasing attention in robotics communities. Recently, high-resolution networks (HRNets) can learn high-resolution representation and it obtains excellent performance as the backbones of current cutting-edge object detectors. However, two crucial issues remain with regard to applying HRNet-based detectors to mobile devices—insufficient local feature interactions and multiscale feature fusion. In this work, we propose a scale-aware squeeze-and-excitation (SASE) module that utilizes SE operations to fully explore feature interactions without increasing network complexity; this is followed by a scale-aware attention (SAA) mechanism, which adaptively fuses multiscale features by estimating the importance of each scale. The SASE module can serve as the basic block for the HRNet, which facilitates the use of HRNet as a backbone for lightweight object detection. Extensive experiments conducted on Microsoft COCO and Pascal VOC demonstrate that the proposed method has a good tradeoff between accuracy and model complexity. With similar numbers of parameters and calculations, the mean average precision (mAP) achieved on the COCO dataset is improved by 3.7% over that of Lite-HRNet.
VideoReTalking: Audio-based Lip Synchronization for Talking Head Video Editing In the Wild

We present VideoReTalking, a new system to edit the faces of a real-world talking head video according to input audio, producing a high-quality and lip-syncing output video even with a different emotion. Our system disentangles this objective into three sequential tasks: (1) face video generation with a canonical expression; (2) audio-driven lip-sync; and (3) face enhancement for improving photo-realism. Given a talking-head video, we first modify the expression of each frame according to the same expression template using the expression editing network, resulting in a video with the canonical expression. This video, together with the given audio, is then fed into the lip-sync network to generate a lip-syncing video. Finally, we improve the photo-realism of the synthesized faces through an identity-aware face enhancement network and post-processing. We use learning-based approaches for all three steps and all our modules can be tackled in a sequential pipeline without any user intervention. Furthermore, our system is a generic approach that does not need to be retrained to a specific person. Evaluations on two widely-used datasets and in-the-wild examples demonstrate the superiority of our framework over other state-of-the-art methods in terms of lip-sync accuracy and visual quality.
Systematic Construction of Knowledge Graphs for Research-Performing Organizations

Research-Performing Organizations (e.g., research centers, universities) usually accumulate a wealth of data related to their researchers, the generated scientific results and research outputs, and publicly and privately-funded projects that support their activities, etc. Even though the types of data handled may look similar across organizations, it is common to see that each institution has developed its own data model to provide support for many of their administrative activities (project reporting, curriculum management, personnel management, etc.). This creates obstacles to the integration and linking of knowledge across organizations, as well as difficulties when researchers move from one institution to another. In this paper, we take advantage of the ontology network created by the Spanish HERCULES initiative to facilitate the construction of knowledge graphs from existing information systems, such as the one managed by the company Universitas XXI, which provides support to more than 100 Spanish-speaking research-performing organizations worldwide. Our effort is not just focused on following the modeling choices from that ontology, but also on demonstrating how the use of standard declarative mapping rules (i.e., R2RML) guarantees a systematic and sustainable workflow for constructing and maintaining a KG. We also present several real-world use cases in which the proposed workflow is adopted together with a set of lessons learned and general recommendations that may also apply to other domains. The next steps include researching in the automation of the creation of the mapping rules, the enrichment of the KG with external sources, and its exploitation though distributed environments.