Reviewerly/exHarmony huggingface dataset:
Low-Resource Name Tagging Learned with Weakly Labeled Data

Name tagging in low-resource languages or domains suffers from inadequate training data.Existing work heavily relies on additional information, while leaving those noisy annotations unexplored that extensively exist on the web.In this paper, we propose a novel neural model for name tagging solely based on weakly labeled (WL) data, so that it can be applied in any low-resource settings.To take the best advantage of all WL sentences, we split them into high-quality and noisy portions for two modules, respectively: (1) a classification module focusing on the large portion of noisy data can efficiently and robustly pretrain the tag classifier by capturing textual context semantics; and (2) a costly sequence labeling module focusing on high-quality data utilizes Partial-CRFs with non-entity sampling to achieve global optimum.Two modules are combined via shared parameters.Extensive experiments involving five low-resource languages and fine-grained food domain demonstrate our superior performance (6% and 7.8% F1 gains on average) as well as efficiency 1 .… Barangay Ginebra and Formula Shell forming a rivalry … Barangay Ginebra and Formula Shell forming a rivalry O'Neal Shaquille … Barangay Ginebra and Formula Shell forming a rivalry B-ORG O O O O B-LOC I-ORG Formula shell won game one in Philippines … Weakly Labelled, extensively exists on the web Fully Labelled, expensive to obtain
A Novel Real-Time Penetration Path Planning Algorithm for Stealth UAV in 3D Complex Dynamic Environment

In recent years, stealth aircraft penetration path planning has been a significant research subject in the field of low altitude combat.However, previous works have mainly concentrated on the path planning for stealth unmanned aerial vehicle(UAV) in 2D static environment.In contrast, this paper addresses a novel real-time path planning algorithm for stealth UAV to realize the rapid penetration, which aims to devise a route penetration strategy based on the improved A-Star algorithm to address the problems of replanning for stealth UAV in 3D complex dynamic environment.The proposed method introduces the kinematic model of stealth UAV and detection performance of netted radar in the process of low altitude penetration.Additionally, POP-UP threats are adopted into three different threat scenarios, which is closer to the real combat environment.Moreover, further combined with the prediction technique and route planning algorithm, the multi-step search strategy is developed for stealth UAV to deal with POP-UP threats and complete the replanning of the route in different scenarios.Furthermore, the attitude angle information is integrated into the improved A-Star algorithm, which reflects the characteristics of the dynamic radar cross section(RCS) and conforms to the actual flight requirements for the stealth UAV.Finally, the improved A-Star algorithm, the sparse A-Star search (SAS), and the dynamic A-Star algorithm(D-Star) are respectively adopted to address the problem of penetration route planning for stealth UAV in three different threat scenarios.Numerical simulations are performed to illustrate that the proposed approach can achieve rapid penetration route planning for stealth UAV in a dynamic threat scenario, and verify the validity of the improved A-Star algorithm which is compared to the other two algorithms.
Prospects and limitations of e-learning application in private tertiary institutions amidst COVID-19 lockdown in Nigeria

E-learning has numerous potentials to spur education development in tertiary institutions in Nigeria. It impacts positively on the educational process, unlike the physical chalkboard in the classrooms. The outbreak and fast spread of the COVID-19 led to the closed down of schools. Efforts to revamp education due to prolong lockdown made the government enforce e-learning in tertiary institutions across the country. It is however worthy to know that these directives did not make much change as a result of poor infrastructure and networking. Hence, this study investigated compliance with e-learning during COVID-19 pandemic lockdown by the instructors in the private tertiary institutions in Nigeria vis-à-vis their socio-economic factors and limitations encountered. A systematic sampling technique was adopted to select 180 respondents from the staff list. A validated questionnaire was used to collect data on socio-economic variables (SEV), compliance (ϒ) to e-learning, and limitations (Ls) while multiple linear regression model (R) was used to test the interaction between the compliance and limitations. Results show that age (β = 0.351), educational attainment (β = 0.843) and teaching experience (β = 0.169) influence e-learning compliance at p < 0.05. It was also found that 67.3% compliance with e-learning took place in the Universities compared to 59.1% in the Polytechnics and 52.8% in the Colleges of Education. Regression shows that constraints affected the level of compliance (R2 = 0.73). The study concludes that constraints are major obstacles to the compliance and prospects of e-learning in the Private Tertiary Institutions in Nigeria.
A Benchmark for Data Imputation Methods

With the increasing importance and complexity of data pipelines, data quality became one of the key challenges in modern software applications. The importance of data quality has been recognized beyond the field of data engineering and database management systems (DBMSs). Also, for machine learning (ML) applications, high data quality standards are crucial to ensure robust predictive performance and responsible usage of automated decision making. One of the most frequent data quality problems is missing values. Incomplete datasets can break data pipelines and can have a devastating impact on downstream ML applications when not detected. While statisticians and, more recently, ML researchers have introduced a variety of approaches to impute missing values, comprehensive benchmarks comparing classical and modern imputation approaches under fair and realistic conditions are underrepresented. Here, we aim to fill this gap. We conduct a comprehensive suite of experiments on a large number of datasets with heterogeneous data and realistic missingness conditions, comparing both novel deep learning approaches and classical ML imputation methods when either only test or train and test data are affected by missing data. Each imputation method is evaluated regarding the imputation quality and the impact imputation has on a downstream ML task. Our results provide valuable insights into the performance of a variety of imputation methods under realistic conditions. We hope that our results help researchers and engineers to guide their data preprocessing method selection for automated data quality improvement.
Factors Affecting the Organizational Adoption of Blockchain Technology: Extending the Technology–Organization–Environment (TOE) Framework in the Australian Context

Blockchain technology (BCT) has been gaining popularity due to its benefits for almost every industry. However, despite its benefits, the organizational adoption of BCT is rather limited. This lack of uptake motivated us to identify the factors that influence the adoption of BCT from an organizational perspective. In doing this, we reviewed the BCT literature, interviewed BCT experts, and proposed a research model based on the TOE framework. Specifically, we theorized the role of technological (perceived benefits, compatibility, information transparency, and disintermediation), organizational (organization innovativeness, organizational learning capability, and top management support), and environmental (competition intensity, government support, trading partners readiness, and standards uncertainty) factors in the organizational adoption of BCT in Australia. We confirmed the model with a sample of adopters and potential adopter organizations in Australia. The results show a significant role of the proposed factors in the organizational adoption of BCT in Australia. Additionally, we found that the relationship between the influential factors and BCT adoption is moderated by “perceived risks”. The study extends the TOE framework by adding factors that were ignored in previous studies on BCT adoption, such as perceived information transparency, perceived disintermediation, organizational innovativeness, organizational learning capability, and standards uncertainty.
LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup

We propose a local adversarial disentangling network (LADN) for facial makeup and de-makeup. Central to our method are multiple and overlapping local adversarial discriminators in a content-style disentangling network for achieving local detail transfer between facial images, with the use of asymmetric loss functions for dramatic makeup styles with high-frequency details. Existing techniques do not demonstrate or fail to transfer high-frequency details in a global adversarial setting, or train a single local discriminator only to ensure image structure consistency and thus work only for relatively simple styles. Unlike others, our proposed local adversarial discriminators can distinguish whether the generated local image details are consistent with the corresponding regions in the given reference image in cross-image style transfer in an unsupervised setting. Incorporating these technical contributions, we achieve not only state-of-the-art results on conventional styles but also novel results involving complex and dramatic styles with high-frequency details covering large areas across multiple facial features. A carefully designed dataset of unpaired before and after makeup images is released at https://georgegu1997.github.io/LADN-project-page.
Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits

Domain adaptation performance of a learning algorithm on a target domain is a function of its source domain error and a divergence measure between the data distribution of these two domains. We present a study of various distance-based measures in the context of NLP tasks, that characterize the dissimilarity between domains based on sample estimates. We first conduct analysis experiments to show which of these distance measures can best differentiate samples from same versus different domains, and are correlated with empirical results. Next, we develop a DistanceNet model which uses these distance measures, or a mixture of these distance measures, as an additional loss function to be minimized jointly with the task's loss function, so as to achieve better unsupervised domain adaptation. Finally, we extend this model to a novel DistanceNet-Bandit model, which employs a multi-armed bandit controller to dynamically switch between multiple source domains and allow the model to learn an optimal trajectory and mixture of domains for transfer to the low-resource target domain. We conduct experiments on popular sentiment analysis datasets with several diverse domains and show that our DistanceNet model, as well as its dynamic bandit variant, can outperform competitive baselines in the context of unsupervised domain adaptation.
Interoperability and Synchronization Management of Blockchain-Based Decentralized e-Health Systems

E-health systems have witnessed widespread usage in the last few years, mainly due to advancements in health monitoring hardware and the availability of remote diagnostic services. Given the numerous benefits, there are two main challenges: management of the e-health ecosystem and security and privacy of sensitive data. The former results from nonunified, redundant, and often replicated information across numerous independent e-health service providers. While the later stems from sensitive information stored in centralized systems that can be compromised. Blockchain has emerged as a promising technology, which can be used to secure access and privacy of data and provide an umbrella management solution to large-scale distributed and decentralized enterprise systems. In this article, we present a unified system for migrating independent conventional e-health systems to a single blockchain-based ecosystem. More specifically, we address the issues of difference in data structures for conventional relational databases and blockchain file databases. The solution describes the conversion process and synchronization of information in a unified system for large-scale e-health data. The implementation and analysis show that significant improvements in data storage, access control, and seamless migration can be achieved.
Coherently driving a single quantum two-level system with dichromatic laser pulses

The excitation of individual two-level quantum systems using an electromagnetic field is an elementary tool of quantum optics, with widespread applications across quantum technologies. The efficient excitation of a single two-level system usually requires the driving field to be at the same frequency as the transition between the two quantum levels. However, in solid-state implementations, the scattered laser light can dominate over the single photons emitted by the two-level system, imposing a challenge for single-photon sources. Here, we propose a background-free method for the coherent excitation and control of a two-level quantum system using a phase-locked dichromatic electromagnetic field with no spectral overlap with the optical transition. We demonstrate this method experimentally by stimulating single-photon emission from a single quantum dot embedded in a micropillar, reaching single-photon purity of 0.988(1) and indistinguishability of 0.962(6). The phase-coherent nature of our two-colour excitation scheme is demonstrated by the dependence of the resonance fluorescence intensity on the relative phase between the two pulses. Our two-colour excitation method represents an additional and useful tool for the study of atom–photon interaction, and the generation of spectrally isolated indistinguishable single photons. A quantum two-level system can be coherently excited by a phase-locked dichromatic electromagnetic field. This technique can make single-photon generation more efficient as the pump light does not overlap in frequency with the emitted single photons.
Training Question Answering Models From Synthetic Data

Question and answer generation is a data augmentation method that aims to improve question answering (QA) models given the limited amount of human labeled data.However, a considerable gap remains between synthetic and human-generated question-answer pairs.This work aims to narrow this gap by taking advantage of large language models and explores several factors such as model size, quality of pretrained models, scale of data synthesized, and algorithmic choices.On the SQUAD1.1 question answering task, we achieve higher accuracy using solely synthetic questions and answers than when using the SQUAD1.1 training set questions alone.Removing access to real Wikipedia data, we synthesize questions and answers from a synthetic text corpus generated by an 8.3 billion parameter GPT-2 model and achieve 88.4 Exact Match (EM) and 93.9 F1 score on the SQUAD1.1 dev set.We further apply our methodology to SQUAD2.0 and show a 2.8 absolute gain on EM score compared to prior work using synthetic data.
Fully Convolutional Networks for Continuous Sign Language Recognition

Continuous sign language recognition (SLR) is a challenging task that requires learning on both spatial and temporal dimensions of signing frame sequences. Most recent work accomplishes this by using CNN and RNN hybrid networks. However, training these networks is generally non-trivial, and most of them fail in learning unseen sequence patterns, causing an unsatisfactory performance for online recognition. In this paper, we propose a fully convolutional network (FCN) for online SLR to concurrently learn spatial and temporal features from weakly annotated video sequences with only sentence-level annotations given. A gloss feature enhancement (GFE) module is introduced in the proposed network to enforce better sequence alignment learning. The proposed network is end-to-end trainable without any pre-training. We conduct experiments on two large scale SLR datasets. Experiments show that our method for continuous SLR is effective and performs well in online recognition.
Convergence of Blockchain and IoT for Secure Transportation Systems in Smart Cities

Smart cities provide citizens with smart and advanced services to improve their quality of life. However, it has been observed that the collection, storage, processing, and analysis of heterogeneous data that are usually borne by citizens will bear certain difficulties. The development of the Internet of Things, cloud computing, social media, and other Industry 4.0 influencers pushed technology into a smart society’s framework, bringing potential vulnerabilities to sensor data, services, and smart city applications. These vulnerabilities lead to data security problems. We propose a decentralized data management system for smart and secure transportation that uses blockchain and the Internet of Things in a sustainable smart city environment to solve the data vulnerability problem. A smart transportation mobility system demands creating an interconnected transit system to ensure flexibility and efficiency. This article introduces prior knowledge and then provides a Hyperledger Fabric-based data architecture that supports a secure, trusted, smart transportation system. The simulation results show the balance between the blockchain mining time and the number of blocks created. We also use the average transaction delay evaluation model to evaluate the model and to test the proposed system’s performance. The system will address residents’ and authorities’ security challenges of the transportation system in smart, sustainable cities and lead to better governance.
Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural Networks

In recent years, visual recognition on challenging long-tailed distributions, where classes often exhibit extremely imbalanced frequencies, has made great progress mostly based on various complex paradigms (e.g., meta learning). Apart from these complex methods, simple refinements on training procedures also make contributions. These refinements, also called tricks, are minor but effective, such as adjustments in the data distribution or loss functions. However, different tricks might conflict with each other. If users apply these long-tail related tricks inappropriately, it could cause worse recognition accuracy than expected. Unfortunately, there has not been a scientific guideline of these tricks in the literature. In this paper, we first collect existing tricks in long-tailed visual recognition and then perform extensive and systematic experiments, in order to give a detailed experimental guideline and obtain an effective combination of these tricks. Furthermore, we also propose a novel data augmentation approach based on class activation maps for long-tailed recognition, which can be friendly combined with re-sampling methods and shows excellent results. By assembling these tricks scientifically, we can outperform state-of-the-art methods on four long-tailed benchmark datasets, including ImageNet-LT and iNaturalist 2018. Our code is open-source and available at https://github.com/zhangyongshun/BagofTricks-LT.
Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.
Reconfigurable training and reservoir computing in an artificial spin-vortex ice via spin-wave fingerprinting

Strongly interacting artificial spin systems are moving beyond mimicking naturally occurring materials to emerge as versatile functional platforms, from reconfigurable magnonics to neuromorphic computing. Typically, artificial spin systems comprise nanomagnets with a single magnetization texture: collinear macrospins or chiral vortices. By tuning nanoarray dimensions we have achieved macrospin-vortex bistability and demonstrated a four-state metamaterial spin system, the 'artificial spin-vortex ice' (ASVI). ASVI can host Ising-like macrospins with strong ice-like vertex interactions and weakly coupled vortices with low stray dipolar field. Vortices and macrospins exhibit starkly differing spin-wave spectra with analogue mode amplitude control and mode frequency shifts of Δf = 3.8 GHz. The enhanced bitextural microstate space gives rise to emergent physical memory phenomena, with ratchet-like vortex injection and history-dependent non-linear fading memory when driven through global magnetic field cycles. We employed spin-wave microstate fingerprinting for rapid, scalable readout of vortex and macrospin populations, and leveraged this for spin-wave reservoir computation. ASVI performs non-linear mapping transformations of diverse input and target signals in addition to chaotic time-series forecasting.
Physical Activity Classification for Elderly People in Free-Living Conditions

Physical activity is strongly linked with mental and physical health in the elderly population and accurate monitoring of activities of daily living (ADLs) can help improve quality of life and well-being. This study presents and validates an inertial sensors-based physical activity classification system developed with older adults as the target population. The dataset was collected in free-living conditions without placing constraints on the way and order of performing ADLs. Four sensor locations (chest, lower back, wrist, and thigh) were explored to obtain the optimal number and combination of sensors by finding the best tradeoff between the system's performance and wearability. Several feature selection techniques were implemented on the feature set obtained from acceleration and angular velocity signals to classify four major ADLs (sitting, standing, walking, and lying). A support vector machine was used for the classification of the ADLs. The findings show the potential of different solutions (single sensor or multisensor) to correctly classify the ADLs of older people in free-living conditions. Considering a minimal set-up of a single sensor, the sensor worn at the L5 achieved the best performance. A two-sensor solution (L5 + thigh) achieved a better performance with respect to a single-sensor solution. By contrast, considering more than two sensors did not provide further improvements. Finally, we evaluated the computational cost of different solutions and it was shown that a feature selection step can reduce the computational cost of the system and increase the system performance in most cases. This can be helpful for real-time applications.