Reviewerly/exHarmony huggingface dataset:
Multi-Purpose Oriented Real-World Underwater Image Enhancement

Images captured underwater usually suffer from weak illumination, color cast, fuzz and noise, which severely degrade the visibility.Numerous methods have been proposed to improve the quality of underwater images, but rarely of them can give a comprehensive consideration to all these problems, which makes them hard to adapt for various and complex real-world underwater scenes.Herein, a novel multi-purpose oriented approach for real-world underwater image enhancement is proposed.To manipulate different information on the corresponding layers, we firstly decompose the input image into illumination layer and reflectance layer.Subsequently, compensation of the brightness is carried out on the illumination layer, while color correction and contrast enhancement are implemented on the reflectance layer through a multi-scale processing strategy.Benefiting from this strategy, the proposed approach is provided with high control flexibility, which can significantly improve the visibility of underwater images while efficiently suppress the amplification of noise.Both qualitative and quantitative evaluations demonstrate that the proposed method has superior robustness, accuracy and effectiveness for complex marine circumstance.
Utilizing time series data embedded in electronic health records to develop continuous mortality risk prediction models using hidden Markov models: A sepsis case study

Continuous mortality risk monitoring is instrumental to manage a patient’s care and to efficiently utilize the limited hospital resources. Due to incompleteness and irregularities of electronic health records (EHR), developing continuous mortality risk prediction using EHR data is a challenge. In this study, we propose a framework to continuously monitor mortality risk, and apply it to the real-world EHR data. The proposed method employs hidden Markov models (temporal technique) that take account of both the previous state of patient’s health and the current value of clinical signs. Following the Sepsis-3 definition, we selected 3898 encounters of patients with suspected infection to compare the performance of temporal and non-temporal methods (Decision Tree (DT), Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM)). The area under receiver operating characteristics (AUROC) curve, sensitivity, specificity and G-mean were used as performance measures. On the selected data, the AUROC of the proposed temporal framework (0.87) is 9–12% greater than the nontemporal methods (DT: 0.78, NB: 0.79, SVM: 0.79, LR: 0.80 and RF: 0.80). The results also show that our model (G-mean=0.78) provides a better balance between sensitivity and specificity compared to clinically acceptable bed-side criteria (G-mean=0.71). The proposed framework leverages the longitudinal data available in EHR and performs better than the non-temporal methods. The proposed method facilitates information related to the time of change of the patient’s health that may help practitioners to plan early and develop effective treatment strategies.
<b>BeSS</b>: An <i>R</i> Package for Best Subset Selection in Linear, Logistic and Cox Proportional Hazards Models

We introduce a new R package, BeSS, for solving the best subset selection problem in linear, logistic and Cox's proportional hazard (CoxPH) models. It utilizes a highly efficient active set algorithm based on primal and dual variables, and supports sequential and golden search strategies for best subset selection. We provide a C++ implementation of the algorithm using an Rcpp interface. We demonstrate through numerical experiments based on enormous simulation and real datasets that the new BeSS package has competitive performance compared to other R packages for best subset selection purposes.
Unmanned aerial vehicle abstraction layer: An abstraction layer to operate unmanned aerial vehicles

This article presents a software layer to abstract users of unmanned aerial vehicles from the specific hardware of the platform and the autopilot interfaces. The main objective of our unmanned aerial vehicle abstraction layer (UAL) is to simplify the development and testing of higher-level algorithms in aerial robotics by trying to standardize and simplify the interfaces with the unmanned aerial vehicles. Unmanned aerial vehicle abstraction layer supports operation with PX4 and DJI autopilots (among others), which are current leading manufacturers. Besides, unmanned aerial vehicle abstraction layer can work seamlessly with simulated or real platforms and it provides calls to issue standard commands such as taking off, landing or pose, and velocity controls. Even though unmanned aerial vehicle abstraction layer is under continuous development, a stable version is available for public use. We showcase the use of unmanned aerial vehicle abstraction layer with a set of applications coming from several European research projects, where different academic and industrial entities have adopted unmanned aerial vehicle abstraction layer as a common development framework.
Privacy-Preserving Unsupervised Domain Adaptation in Federated Setting

The training of deep neural networks relies on massive high-quality labeled data which is expensive in practice.To tackle this problem, domain adaptation is proposed to transfer knowledge from label-rich source domain to unlabeled target domain to learn a classifier that can well classify target data.However, people don't consider privacy issues in domain adaptation.In this paper, we introduce a novel method that builds an effective model without sharing sensitive data between source and target domain.Target domain party can benefit from label-rich source domain without revealing its privacy data.We transfer the traditional domain adaptation into a federated setting, where a global server contains a shared global model.Additionally, homomorphic encryption (HE) algorithm is used to guarantee the computing security.Experiments show that our method performs effectively without reducing the accuracy.Our method can achieve secure knowledge transfer and privacy-preserving domain adaptation.
Food Aid Technology

Over half of Syrian refugee households in Lebanon are food insecure with some reliant on an electronic voucher (e-voucher) system for food aid. The interplay between the digitisation of food aid, within the socio-technical context of refugees, and community collaborative practices is yet to be investigated. Through design engagements and interviews with refugees and shop owners we explore the experiences of a Syrian refugee community in Lebanon using the e-voucher system. We provide insights into the socio-technical environment in which the e-voucher system is dispensing aid, the information and power asymmetries experienced, refugee collaborative coping practices and how they interplay with the e-voucher system. We highlight the need for: (1) expanding refugee digital capabilities to encompass understandings of aid technologies and identifying trusted intermediaries and (2) for technologies to support in upholding humanitarian principles and mitigating power and information asymmetries. Lastly, we call for CSCW researchers and humanitarian innovators to consider how humanitarian technologies can enable refugee collaborative practices and adopt everyday security as a lens for designing aid technologies. The paper contributes to CSCW knowledge regarding the interplay between aid technologies and refugees? socio-technical contexts and practices that provides a basis for future technological designs for collaborative technologies for refugee food security.
STEAM projects with KIKS format for developing key competences

Secondary education curricula highlight competence-based learning; however, meaningful school-intervention habits have not yet been observed. The initiatives implemented have focused primarily on promoting the combined development of no more than three key competences. This article presents an international study, under the Erasmus+ and H2020 programmes, for analysing the impact of the STEAM project-based learning approach with KIKS format (Kids Inspire Kids for STEAM) on the interwoven development of key competences. The sample included 267 high school students divided into 53 teams from 29 educational centres in Finland, England, Hungary, and Spain. Each team carried out several projects, for no less than two academic years, by means of the following two approaches: STEAM project-based learning and KIKS format. Data from this qualitative study were collected from observations and interviews with students, teachers, and KIKS trainers. Analyses revealed that the combination of these two approaches facilitated the development of all eight key competences. The project-based learning focus essentially fostered the enhancement of the mathematical competence and competence in science, technology, and engineering, while KIKS format promoted literacy and multilingual competences. The remaining competences were encouraged by the combination of both approaches. It is noteworthy that prolonged participation in the programme, as it was implemented, was crucial to achieving the obtained outcomes. Los currículos de educación secundaria resaltan el aprendizaje por competencias, sin embargo, actualmente no se observan cambios sustanciales en los hábitos de intervención en el aula. Las iniciativas implementadas se han centrado principalmente en impulsar el desarrollo conjunto de tres competencias clave como máximo. Este artículo presenta un estudio internacional, bajo los programas Erasmus+ y H2020, con el objetivo de establecer relaciones entre el aprendizaje basado en proyectos STEAM con formato KIKS (Kids Inspire Kids for STEAM) y el desarrollo global de las competencias clave. La muestra incluye 267 estudiantes de secundaria distribuidos en 53 equipos, pertenecientes a 29 centros de Finlandia, Inglaterra, Hungría y España. Cada equipo elaboró varios proyectos, durante al menos dos años académicos, mediante los siguientes enfoques de implementación: aprendizaje basado en proyectos STEAM y formato KIKS. Los datos de este estudio cualitativo se recabaron por medio de observaciones y entrevistas a estudiantes, profesores y formadores KIKS. Los análisis revelaron que la combinación de ambos enfoques facilitó el desarrollo de las ocho competencias clave. El aprendizaje por proyectos favoreció esencialmente el desarrollo de la competencia matemática y competencia en ciencia, tecnología e ingeniería, mientras que el formato KIKS potenció las competencias en lectoescritura y multilingüe. El resto de competencias se vieron estimuladas por la combinación de ambos enfoques. Cabe destacar que la participación prolongada en el programa de implementación fue determinante en los resultados obtenidos.
Rotation-Invariant Siamese Network for Low-Altitude Remote-Sensing Image Registration

Multiple-view change caused by small unmanned aerial vehicles (UAVs) monitoring the ground, resulting in image distortion, multiview transformation, and low overlap. Thus, such change has a strong effect on the accuracy of image registration. In this study, we utilize a Siamese network to deal with the complexity registration of low-altitude remote-sensing images. A robust neighbor-guided patch representation is designed to describe feature points based on neighborhood relation reconstruction, and patch selection. The network is trained based on rotation-invariant layer to solve the inevitable rotation, and nonrigid deformation caused by multiview images in low-altitude remote-sensing images. With only three training images involving 4500 putative matches, the experiment results demonstrated that the learned network can process the scenarios of yaw rotation, pitch rotation, mixture, and extreme (e.g., mixture, scaling, and distortion occur simultaneously) of UAV better than other six state-of-the-art methods.
CNN-SSPSO: A Hybrid and Optimized CNN Approach for Peripheral Blood Cell Image Recognition and Classification

White blood cells (WBCs) play a main role in identifying the health condition and disease characteristics of a normal person. An automated classification system is capable of recognizing white blood cells that may help doctors to diagnose several diseases like malaria, anemia, leukemia, etc. Automated blood cell analysis allows fast and accurate outcomes and often involves broad data without performance negotiation. The state-of-the-art systems use a lot of different stages (feature extraction, segmentation, pre-processing, etc.) to provide the automated blood cell analysis using blood smear images which is a lengthy process. To overcome these problems, this paper presents an efficient peripheral blood cell image recognition and classification using a combination of the salp swarm algorithm and the cat swarm optimization (SSPSO) algorithm-based optimized convolutional neural networks (SSPSO-CNN) method. This paper uses the CNN approach to classify five peripheral blood cells such as eosinophil, basophil, lymphocytes, monocytes, and neutrophils without any human intervention. The other objective of this paper is to propose an improved version of salp swarm optimizer (SSO) using particle swarm optimization (PSO) to attain competitive classification performance over the database of the blood cell images. In this paper, the CNN uses VGG19 architecture for training purposes. The accuracy of the classification achieved with VGG19 models is 98%. The proposed model based on the CNN approach optimized by SSPSO achieves high classification accuracy and provides automatic peripheral blood cell classification. This method establishes the fine-tuning process to develop a classifier trained using 10 674 images obtained from medical practice. The proposed method augmented the performance in terms of high precision and [Formula: see text]1-score and obtained an overall classification accuracy of 99%.
End-to-End Spoken Language Understanding Without Full Transcripts

An essential component of spoken language understanding (SLU) is slot filling: representing the meaning of a spoken utterance using semantic entity labels. In this paper, we develop end-to-end (E2E) spoken language understanding systems that directly convert speech input to semantic entities and investigate if these E2E SLU models can be trained solely on semantic entity annotations without word-for-word transcripts. Training such models is very useful as they can drastically reduce the cost of data collection. We created two types of such speech-to-entities models, a CTC model and an attention-based encoder-decoder model, by adapting models trained originally for speech recognition. Given that our experiments involve speech input, these systems need to recognize both the entity label and words representing the entity value correctly. For our speech-to-entities experiments on the ATIS corpus, both the CTC and attention models showed impressive ability to skip non-entity words: there was little degradation when trained on just entities versus full transcripts. We also explored the scenario where the entities are in an order not necessarily related to spoken order in the utterance. With its ability to do re-ordering, the attention model did remarkably well, achieving only about 2% degradation in speech-to-bag-of-entities F1 score.
DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases

Keyphrase extraction from documents is useful to a variety of applications such as information retrieval and document summarization. This paper presents an end-to-end method called DivGraphPointer for extracting a set of diversified keyphrases from a document. DivGraphPointer combines the advantages of traditional graph-based ranking methods and recent neural network-based approaches. Specifically, given a document, a word graph is constructed from the document based on word proximity and is encoded with graph convolutional networks, which effectively capture document-level word salience by modeling long-range dependency between words in the document and aggregating multiple appearances of identical words into one node. Furthermore, we propose a diversified point network to generate a set of diverse keyphrases out of the word graph in the decoding process. Experimental results on five benchmark data sets show that our proposed method significantly outperforms the existing state-of-the-art approaches.
Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling

By positing a relationship between naturalistic reading times and information-theoretic surprisal, surprisal theory (Hale, 2001; Levy, 2008) provides a natural interface between language models and psycholinguistic models. This paper re-evaluates a claim due to Goodkind and Bicknell (2018) that a language model’s ability to model reading times is a linear function of its perplexity. By extending Goodkind and Bicknell’s analysis to modern neural architectures, we show that the proposed relation does not always hold for Long Short-Term Memory networks, Transformers, and pre-trained models. We introduce an alternate measure of language modeling performance called predictability norm correlation based on Cloze probabilities measured from human subjects. Our new metric yields a more robust relationship between language model quality and psycholinguistic modeling performance that allows for comparison between models with different training configurations.
Code recommendation for exception handling

Exception handling is an effective mechanism to avoid unexpected runtime errors. However, novice programmers might fail to handle exceptions properly, causing serious errors like system crashing or resource leaking. In this paper, we introduce FuzzyCatch, a code recommendation tool for handling exceptions. Based on fuzzy logic, FuzzyCatch can predict if a runtime exception would occur in a given code snippet and recommend code to handle that exception. FuzzyCatch is implemented as a plugin for Android Studio. The empirical evaluation suggests that FuzzyCatch is highly effective. For example, it has top-1 accuracy of 77% on recommending what exception to catch in a try catch block and of 70% on recommending what method should be called when such an exception occurs. FuzzyCatch also achieves a high level of accuracy and outperforms baselines significantly on detecting and fixing real exception bugs.
Solving Long-Tailed Recognition with Deep Realistic Taxonomic Classifier

Long-tail recognition tackles the natural non-uniformly distributed data in real-world scenarios. While modern classifiers perform well on populated classes, its performance degrades significantly on tail classes. Humans, however, are less affected by this since, when confronted with uncertain examples, they simply opt to provide coarser predictions. Motivated by this, a deep realistic taxonomic classifier (Deep-RTC) is proposed as a new solution to the long-tail problem, combining realism with hierarchical predictions. The model has the option to reject classifying samples at different levels of the taxonomy, once it cannot guarantee the desired performance. Deep-RTC is implemented with a stochastic tree sampling during training to simulate all possible classification conditions at finer or coarser levels and a rejection mechanism at inference time. Experiments on the long-tailed version of four datasets, CIFAR100, AWA2, Imagenet, and iNaturalist, demonstrate that the proposed approach preserves more information on all classes with different popularity levels. Deep-RTC also outperforms the state-of-the-art methods in longtailed recognition, hierarchical classification, and learning with rejection literature using the proposed correctly predicted bits (CPB) metric.
DeepBurning-GL

Building FPGA-based graph learning accelerators is very time-consuming due to the low-level RTL programming and the complicated design flow of FPGA development. It also requires the architecture and hardware expertise from the Graph Neural Network (GNN) application developers to tailor efficient accelerator designs on FPGAs. This work proposes an automation framework, DeepBurning-GL, which is compatible with state-of-the-art graph learning frameworks such as Deep Graph Library so that the developers can easily generate application-specific GNN accelerators from the software-described models. First, DeepBurning-GL employs a GNN performance analyzer to locate the performance bottleneck of specific GNN applications and decide the major design architectures and parameters that meet the user-specified constraints. Second, DeepBurning-GL provides a series of pre-built design templates such as computing templates and memory templates, which can be parameterized and fused to generate the final accelerator design. It also includes an optimizer that conducts automatic optimization by adjusting the accelerator architectural parameters. In evaluation, we use DeepBurning-GL to generate customized accelerators on three different FPGA platforms for various GNN models and workloads. The experimental results show that the generated accelerators achieve 179.4X and 40.1X energy-efficiency boost over the CPU and GPU solutions on average and deliver a 6.28X speedup and 6.73X energy-efficiency improvement on average compared to the latest GNN accelerator HyGCN on Alveo U50.
Knowledge-Based Video Question Answering with Unsupervised Scene Descriptions

To understand movies, humans constantly reason over the dialogues and actions shown in specific scenes and relate them to the overall storyline already seen. Inspired by this behaviour, we design ROLL, a model for knowledge-based video story question answering that leverages three crucial aspects of movie understanding: dialog comprehension, scene reasoning, and storyline recalling. In ROLL, each of these tasks is in charge of extracting rich and diverse information by 1) processing scene dialogues, 2) generating unsupervised video scene descriptions, and 3) obtaining external knowledge in a weakly supervised fashion. To answer a given question correctly, the information generated by each inspired-cognitive task is encoded via Transformers and fused through a modality weighting mechanism, which balances the information from the different sources. Exhaustive evaluation demonstrates the effectiveness of our approach, which yields a new state-of-the-art on two challenging video question answering datasets: KnowIT VQA and TVQA+.
A graded dependent type system with a usage-aware semantics

Graded Type Theory provides a mechanism to track and reason about resource usage in type systems. In this paper, we develop GraD, a novel version of such a graded dependent type system that includes functions, tensor products, additive sums, and a unit type. Since standard operational semantics is resource-agnostic, we develop a heap-based operational semantics and prove a soundness theorem that shows correct accounting of resource usage. Several useful properties, including the standard type soundness theorem, non-interference of irrelevant resources in computation and single pointer property for linear resources, can be derived from this theorem. We hope that our work will provide a base for integrating linearity, irrelevance and dependent types in practical programming languages like Haskell.