Reviewerly/exHarmony huggingface dataset:
White-box Machine learning approaches to identify governing equations for overall dynamics of manufacturing systems: A case study on distillation column

Dynamical equations form the basis of design for manufacturing processes and control systems; however, identifying governing equations using a mechanistic approach is tedious. Recently, Machine learning (ML) has shown promise to identify the governing dynamical equations for physical systems faster. This possibility of rapid identification of governing equations provides an exciting opportunity for advancing dynamical systems modeling. However, applicability of the ML approach in identifying governing mechanisms for the dynamics of complex systems relevant to manufacturing has not been tested. We test and compare the efficacy of two white-box ML approaches (SINDy and SymReg) for predicting dynamics and structure of dynamical equations for overall dynamics in a distillation column. Results demonstrate that a combination of ML approaches should be used to identify a full range of equations. In terms of physical law, few terms were interpretable as related to Fick’s law of diffusion and Henry’s law in SINDy, whereas SymReg identified energy balance as driving dynamics.
The Effect of Simulation Technique on Academic Achievement: A Meta-Analysis Study

This research aims to examine the experimental studies on the impact of simulation technique on students' academic achievement using the meta-analysis method. The previous studies that could be meta-analyzed were examined based on the criteria set out in this study. Finally, 91 studies that were conducted between 2010-2020 years and met the inclusion criteria were subjected to meta-analysis. The sample of the research consisted of 7575 participants. According to the results of the analysis performed using the random-effects model, the mean effect size was calculated as g=0.759 with a standard error of 0.075. Based on the findings, the simulation technique can be said to have a broad impact on students' academic achievement. The results of the publication bias analysis revealed that the present meta-analysis study had no publication bias. On the other hand, the results of the moderator analysis revealed that the impact of the simulation technique on the students' academic achievement did not differ by the teaching levels, course/subject area, and application times, however, it differed by the sample size.
ARTIQ and Sinara: Open Software and Hardware Stacks for Quantum Physics

Sinara is a control system dedicated to quantum applications. It is based on industrial standards and consists of over 50 modules. The hardware is controlled by ARTIQ, which provides a high-level programming language.
Laplacian Pyramid Neural Network for Dense Continuous-Value Regression for Complex Scenes

Many computer vision tasks, such as monocular depth estimation and height estimation from a satellite orthophoto, have a common underlying goal, which is regression of dense continuous values for the pixels given a single image. We define them as dense continuous-value regression (DCR) tasks. Recent approaches based on deep convolutional neural networks significantly improve the performance of DCR tasks, particularly on pixelwise regression accuracy. However, it still remains challenging to simultaneously preserve the global structure and fine object details in complex scenes. In this article, we take advantage of the efficiency of Laplacian pyramid on representing multiscale contents to reconstruct high-quality signals for complex scenes. We design a Laplacian pyramid neural network (LAPNet), which consists of a Laplacian pyramid decoder (LPD) for signal reconstruction and an adaptive dense feature fusion (ADFF) module to fuse features from the input image. More specifically, we build an LPD to effectively express both global and local scene structures. In our LPD, the upper and lower levels, respectively, represent scene layouts and shape details. We introduce a residual refinement module to progressively complement high-frequency details for signal prediction at each level. To recover the signals at each individual level in the pyramid, an ADFF module is proposed to adaptively fuse multiscale image features for accurate prediction. We conduct comprehensive experiments to evaluate a number of variants of our model on three important DCR tasks, i.e., monocular depth estimation, single-image height estimation, and density map estimation for crowd counting. Experiments demonstrate that our method achieves new state-of-the-art performance in both qualitative and quantitative evaluation on the NYU-D V2 and KITTI for monocular depth estimation, the challenging Urban Semantic 3D (US3D) for satellite height estimation, and four challenging benchmarks for crowd counting. These results demonstrate that the proposed LAPNet is a universal and effective architecture for DCR problems.
KS‐FQA: Keyframe selection based on face quality assessment for efficient face recognition in video

Video is considered as one of the most useful and important forms of multimedia data, that is usually used in several applications. Despite its importance, video indexing and retrieval becomes a challenging task. In order to reduce the amount of data and keep only relevant frames, keyframe extraction becomes necessary in a content-based video retrieval (CBVR) system. In this paper, a keyframe extraction method is proposed based on the face image quality for video surveillance systems. Data is reduced by rejecting frames without faces. Then, face images are clustered by identity. After that, a set of candidate frames is selected to be proceeded. The face quality assessment is based on four metrics including pose estimation, sharpness, brightness and resolution, and the frame with the best face quality is considered as a keyframe. Experimental tests were carried on several datasets in order to prove the efficiency of authors' method compared with state-of-the-art approaches.
Deep Learning based Eye Gaze Tracking for Automotive Applications: An Auto-Keras Approach

We propose a deep neural network-based gaze sensing method in which the design of the neural architecture is performed automatically, through a network architecture search algorithm called Auto-Keras. First, the neural model is generated using the Columbia Gaze Data Set. Then, the performance of the solution is estimated on an online scenario and proves the generalization ability of our model. In comparison to a geometrical approach, which uses dlib facial landmarks, filtering and morphological operators for gaze estimation, the proposed method provides superior results and certain advantages.
The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT

This paper describes the development of a new benchmark for machine translation that provides training and test data for thousands of language pairs covering over 500 languages and tools for creating state-of-the-art translation models from that collection. The main goal is to trigger the development of open translation tools and models with a much broader coverage of the World’s languages. Using the package it is possible to work on realistic low-resource scenarios avoiding artificially reduced setups that are common when demonstrating zero-shot or few-shot learning. For the first time, this package provides a comprehensive collection of diverse data sets in hundreds of languages with systematic language and script annotation and data splits to extend the narrow coverage of existing benchmarks. Together with the data release, we also provide a growing number of pre-trained baseline models for individual language pairs and selected language groups.
Automated Source Code Generation and Auto-Completion Using Deep Learning: Comparing and Discussing Current Language Model-Related Approaches

In recent years, the use of deep learning in language models has gained much attention. Some research projects claim that they can generate text that can be interpreted as human writing, enabling new possibilities in many application areas. Among the different areas related to language processing, one of the most notable in applying this type of modeling is programming languages. For years, the machine learning community has been researching this software engineering area, pursuing goals like applying different approaches to auto-complete, generate, fix, or evaluate code programmed by humans. Considering the increasing popularity of the deep learning-enabled language models approach, we found a lack of empirical papers that compare different deep learning architectures to create and use language models based on programming code. This paper compares different neural network architectures like Average Stochastic Gradient Descent (ASGD) Weight-Dropped LSTMs (AWD-LSTMs), AWD-Quasi-Recurrent Neural Networks (QRNNs), and Transformer while using transfer learning and different forms of tokenization to see how they behave in building language models using a Python dataset for code generation and filling mask tasks. Considering the results, we discuss each approach’s different strengths and weaknesses and what gaps we found to evaluate the language models or to apply them in a real programming context.
Learning occlusion-aware view synthesis for light fields

We present a novel learning-based approach to synthesize new views of a light field image. In particular, given the four corner views of a light field, the presented method estimates any in-between view. We use three sequential convolutional neural networks for feature extraction, scene geometry estimation and view selection. Compared to state-of-the-art approaches, in order to handle occlusions we propose to estimate a different disparity map per view. Jointly with the view selection network, this strategy shows to be the most important to have proper reconstructions near object boundaries. Ablation studies and comparison against the state of the art on Lytro light fields show the superior performance of the proposed method. Furthermore, the method is adapted and tested on light fields with wide baselines acquired with a camera array and, in spite of having to deal with large occluded areas, the proposed approach yields very promising results.
Ensemble deep learning for aspect-based sentiment analysis

Sentiment analysis is a subfield of Natural Language Processing (NLP) which tries to process a text to extract opinions or attitudes towards topics or entities. Recently, the use of deep learning methods for sentiment analysis has received noticeable attention from researchers. Generally, different deep learning methods have shown superb performance in sentiment analysis problem. However, deep learning models are different in nature and have different strengths and limitations. For example, convolutional neural networks are useful for extracting local structures from data, while recurrent models are able to learn order dependence in sequential data. In order to combine the advantages of different deep models, in this paper we have proposed a novel approach for aspect-based sentiment analysis which utilizes deep ensemble learning. In the proposed method, we first build four deep learning models, namely CNN, LSTM, BiLSTM and GRU. Then the outputs of these models are combined using stacking ensemble approach where we have used logistic regression as meta-learner. The results of applying the proposed method on the real datasets show that our method has increased the accuracy of aspect-based prediction by 5% to 20% compared to the basic deep learning methods.
Full Reference Video Quality Assessment Metric on Base Human Visual System Consistent with PSNR

Quality assessment of video compression with a focus upon the perceptibly of distortions introduced by compression from the point of view of human perception is an important area of video quality research. Although the subjective assessment of video quality is more accurate regarding human perception of video than objective, it necessitates the very large design space of the subjective experiment. As the result in recent years, there has been a growing interest in the development of improved models of objective video quality. Based on the researches of fundamental limit to human vision, here we offer experiments of visual perception and measure the characteristics of the human visual systems on the “Research of the characteristics of the visual systems” software and test equipment we created. Also, we represent a new quality measuring method we developed, which takes into account the features of the Human Visual System and will be represented comparison estimates this method with current models used by quality assessment reference, and with subjective scores.
File System Performance Comparison in Full Hardware Virtualization with ESXi, KVM, Hyper-V and Xen Hypervisors

This paper focus is the mathematical modeling of the file system performance in virtual environment when using type-1 hypervisors. The modeling provides a set of hypotheses related to ...
Applications of artificial intelligence to improve patient flow on mental health inpatient units - Narrative literature review

BackgroundDespite a growing body of research into both Artificial intelligence and mental health inpatient flow issues, few studies adequately combine the two. This review summarises findings in the fields of AI in psychiatry and patient flow from the past 5 years, finds links and identifies gaps for future research.MethodsThe OVID database was used to access Embase and Medline. Top journals such as JAMA, Nature and The Lancet were screened for other relevant studies. Selection bias was limited by strict inclusion and exclusion criteria.Research3,675 papers were identified in March 2020, of which a limited number focused on AI for mental health unit patient flow. After initial screening, 323 were selected and 83 were subsequently analysed. The literature review revealed a wide range of applications with three main themes: diagnosis (33%), prognosis (39%) and treatment (28%). The main themes that emerged from AI in patient flow studies were: readmissions (41%), resource allocation (44%) and limitations (91%). The review extrapolates those solutions and suggests how they could potentially improve patient flow on mental health units, along with challenges and limitations they could face.ConclusionResearch widely addresses potential uses of AI in mental health, with some focused on its applicability in psychiatric inpatients units, however research rarely discusses improvements in patient flow. Studies investigated various uses of AI to improve patient flow across specialities. This review highlights a gap in research and the unique research opportunity it presents.
Leveraging Lead Bias for Zero-shot Abstractive News Summarization

A typical journalistic convention in news articles is to deliver the most salient information in the beginning, also known as the lead bias. While this phenomenon can be exploited in generating a summary, it has a detrimental effect on teaching a model to discriminate and extract important information in general. We propose that this lead bias can be leveraged in our favor in a simple and effective way to pre-train abstractive news summarization models on large-scale unlabeled news corpora: predicting the leading sentences using the rest of an article. We collect a massive news corpus and conduct data cleaning and filtering via statistical analysis. We then apply self-supervised pre-training on this dataset to existing generation models BART and T5 for domain adaptation. Via extensive experiments on six benchmark datasets, we show that this approach can dramatically improve the summarization quality and achieve state-of-the-art results for zero-shot news summarization without any fine-tuning. For example, in the DUC2003 dataset, the ROUGE-1 score of BART increases 13.7% after the lead-bias pre-training. We deploy the model in Microsoft News and provide public APIs as well as a demo website for multi-lingual news summarization.
Parameter Space Factorization for Zero-Shot Learning across Tasks and Languages

Abstract Most combinations of NLP tasks and language varieties lack in-domain examples for supervised training because of the paucity of annotated data. How can neural models make sample-efficient generalizations from task–language combinations with available data to low-resource ones? In this work, we propose a Bayesian generative model for the space of neural parameters. We assume that this space can be factorized into latent variables for each language and each task. We infer the posteriors over such latent variables based on data from seen task–language combinations through variational inference. This enables zero-shot classification on unseen combinations at prediction time. For instance, given training data for named entity recognition (NER) in Vietnamese and for part-of-speech (POS) tagging in Wolof, our model can perform accurate predictions for NER in Wolof. In particular, we experiment with a typologically diverse sample of 33 languages from 4 continents and 11 families, and show that our model yields comparable or better results than state-of-the-art, zero-shot cross-lingual transfer methods. Our code is available at github.com/cambridgeltl/parameter-factorization.
AUTOMATIC MUSIC COMPOSITION USING GENETIC ALGORITHM AND ARTIFICIAL NEURAL NETWORKS

The aim of this paper is to automatically compose new pleasing music from randomly generated notes without human intervention. To achieve this goal, Genetic Algorithm was implemented to generate random notes. The Neural Network was trained on a set of melodies to learn their regularity of patterns and then it is used as a fitness evaluator for the generated music from the Genetic Algorithm. Four Genetic Algorithms (using different combinations of tournament, roulette-wheel selections and one-point, two-point crossovers) were used in generating music to compare them according to which one is the most suitable for music composition. The experiments show that using tournament selection and two-point crossover produces better music patterns than using other combinations by 57%. The experiments show that the generated music was good and the results were promising. For evaluation, 10 music experts were asked to listen and evaluate four samples of the generated music; two of them were evaluated high from the Neural Network and two were evaluated low. Then we compared their results with the results from the Neural Network. The results show that the error rate for Neural Network was 16.7% and accuracy was 83.3%.
CBDCs beyond borders: results from a survey of central banks

Central bank digital currencies (CBDCs) could ease current frictions in cross-border payments – and particularly so if central banks factor an international dimension into CBDC design from the outset. Based on a survey of 50 central banks in the first quarter of 2021, this paper explores initial thinking on the cross-border use of CBDCs. While most central banks have yet to take a firm decision on issuing a CBDC, the survey responses show a tentative inclination towards allowing use of a future CBDC by tourists and other non-residents domestically. They have a cautious approach to allowing use of a CBDC beyond their own jurisdiction. Concerns about the economic and monetary implications of cross-border CBDC use and about private sector global stablecoins are taken seriously. At the wholesale level, 28% of surveyed central banks are considering options to make CBDCs interoperable by forming multi-CBDC arrangements. This involves arrangements that enhance compatibility, interlink or even integrate multiple CBDCs into a single payments system. Finally, almost 14% of respondents are considering an active role for the central bank in FX conversion.
A Survey on Facial Wrinkles Detection and Inpainting: Datasets, Methods, and Challenges

Automatic facial wrinkles detection and inpainting algorithms have gained attention of researchers in cosmetics, forensics and computer vision. The majority of current inpainting algorithms was implemented on the whole face, despite the fact that only imperfections need inpainting. In general, the definition of an imperfection is sign of ageing, spot, scar and freckles. This paper focuses on wrinkles as it is an obvious sign of ageing. We survey the computer vision techniques in facial wrinkles localisation from detection to inpainting. We present a comprehensive literature review on benchmark datasets, automated wrinkles detection algorithms and facial inpainting algorithms. Due to limited study on wrinkle inpainting, we inpaint the wrinkle regions using three state-of-the-art inpainting algorithms, namely flood-fill, Coherence Sensitivity Hashing and exemplar-based method. To assess the realism of inpainting results, we present the original and inpainted images to 40 participants, where they provide rating on the realism scale and age group of each image. The result shows that flood-fill method preserved the realism but there was no significant difference in age prediction. Finally, we conclude the paper by proposing some future directions to advance this field.
Distributed Path Planning of Unmanned Aerial Vehicle Communication Chain Based on Dual Decomposition

Limited by the insufficiency of single UAV’s load and flight time capabilities, the multi-UAV (unmanned aerial vehicle) collaboration to improve mission efficiency and expand mission functions has become the focus of current UAV theory and application research. In this paper, the research on UAV global path planning is carried out using the ant colony algorithm, and an indoor UAV path planning model based on the ant colony algorithm is constructed. In order to improve the efficiency of the algorithm, enhance the adaptability and robustness of the algorithm, a distributed path planning algorithm based on the dual decomposition UAV communication chain is proposed. This algorithm improves the basic ant colony algorithm from the aspects of path selection, pheromone update, and rollback strategy in view of the inherent shortcomings of the ant colony algorithm. In order to achieve the best performance of the algorithm, this paper analyzes each parameter in the ant colony algorithm in depth and obtains the optimal combination of parameters. The construction method of the Voronoi diagram was improved, and the method was simulated to verify that the method can obtain a Voronoi diagram path that is safer than the original method under certain time conditions. Through the principle analysis and simulation verification of the Dijkstra algorithm and the dual decomposition ant colony algorithm, it is concluded that the dual decomposition ant colony algorithm is more efficient in pathfinding. Finally, through simulation, it was verified that the dual decomposition ant colony algorithm can plan a safe and reasonable flight path for multiple UAV formation flights in an offline state and achieve offline global obstacle avoidance for multiple UAVs.
Fusion: Towards Automated ICD Coding via Feature Compression

ICD coding aims to automatically assign International Classification of Diseases (ICD) codes from unstructured clinical notes or discharge summaries, which saves human labor and reduces errors.Although several studies are proposed to solve this challenging task, none distinguishes the importance of different phrases with a word window.Intuitively, informative phrases should be useful for the prediction.This paper proposes a feature compressed ICD coding model named Fusion to address this issue.In particular, we propose an attentive soft-pooling approach to compress the sparse and redundant word representations into informative and dense ones as local features.Next, we use the key-query attention mechanism for modeling the inner relations among local features to generate the global features, which are further used to predict ICD codes.Experiments on two widely used datasets demonstrate that Fusion is comparable with baselines.We also find that none of the state-of-the-art approaches significantly perform better than others.Thus, automated ICD coding is still a challenging task.