Reviewerly/exHarmony huggingface dataset:
Integration of U-Net, ResU-Net and DeepLab Architectures with Intersection Over Union metric for Cells Nuclei Image Segmentation

Identifying cells nuclei is the starting point for most biomedical analyzes, because cells contain a nucleus filled with DNA, automating the detection of cells nuclei will speed up disease research and help find cures. The goal is to integrate Neural network architectures such as U-Net, ResU-Net and DeepLab with the Intersection Over Union (IoU) quality measure for the segmentation of images of cells nuclei. The dataset is made up of images of cells nuclei found in the Data Science Bowl 2018 which were predicted and successfully segmented by the U-Net architecture. The U-Net IoU metric was identified as having good values, approximately 0.90, which indicates a good response to image modifications, ResU-Net has achieved satisfactory results, but without surpassing U-Net performance. On the other hand, DeepLab did not show satisfactory results which can be improved through network modifications.
Generating Synthetic Data for Deep Learning using VR Digital Twin

The ongoing Covid-19 pandemic has made it challenging for large scale data collection, in particular for Convolutional Neural Network (CNN)-based computer vision systems. Additionally, there are numerous circumstances where security, privacy, and limitations pertaining to the accessibility of the required equipment make it arduous to validate computer vision systems with real-world datasets. In this paper, we investigated the possibilities of using synthetic datasets, generated from Virtual Environments (VE) for training and validation of CNN models. We present two use cases where the above-mentioned circumstances play a vital role in preparing the datasets and validating the model with large-scale datasets. By developing and leveraging a three-dimensional Digital Twin (DT), we produce large scale datasets for validating social distancing in workspaces; and in the context of semi-autonomous vehicles, we evaluate how a CNN-based object detection model would perform in an Indian road scenario.
Naive automated machine learning

Abstract An essential task of automated machine learning ( $$\text {AutoML}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mtext>AutoML</mml:mtext> </mml:math> ) is the problem of automatically finding the pipeline with the best generalization performance on a given dataset. This problem has been addressed with sophisticated $$\text {black-box}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mtext>black-box</mml:mtext> </mml:math> optimization techniques such as Bayesian optimization, grammar-based genetic algorithms, and tree search algorithms. Most of the current approaches are motivated by the assumption that optimizing the components of a pipeline in isolation may yield sub-optimal results. We present $$\text {Naive AutoML}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mtext>Naive AutoML</mml:mtext> </mml:mrow> </mml:math> , an approach that precisely realizes such an in-isolation optimization of the different components of a pre-defined pipeline scheme. The returned pipeline is obtained by just taking the best algorithm of each slot. The isolated optimization leads to substantially reduced search spaces, and, surprisingly, this approach yields comparable and sometimes even better performance than current state-of-the-art optimizers.
Outlier Detection Using Convolutional Neural Network for Wireless Sensor Network

Over the recent years, deep learning has been considered as one of the primary choices for handling huge amounts of data. Having deeper hidden layers, it surpasses classical methods for detection of outliers in wireless sensor networks. The convolutional neural network (CNN) is a biologically-inspired computational model which is one of the most popular deep learning approaches. It comprises neurons that self-optimize through learning. EEG generally known as electroencephalography is a tool used for investigation of brain function, and EEG signal gives time-series data as output. In this paper, the authors propose a state-of-the-art technique designed by processing the time-series data generated by the sensor nodes stored in a large dataset into discrete one-second frames, and these frames are projected onto 2D map images. A convolutional neural network (CNN) is then trained to classify these frames. The result improves detection accuracy.
150 years of written Dutch

In this article, we present a new corpus spanning 163 years of written Dutch. This Dutch Corpus of Contemporary and late Modern Periodicals (Dutch C-CLAMP) comprises 47,738 part-of-speech tagged articles published in Dutch periodicals from 1837 until 1999, totaling approximately 200 million tokens in size. We explain the measures we took to overcome the shortcomings of existing corpora of historical Dutch covering the same period. We provide a detailed description of how the corpus has been compiled and enriched. Several aspects are covered: text-markup, preprocessing of the data, including foreign language recognition and spelling normalization, and the enrichment of both textual data as well as metadata of the authors of the corpus files. We also carry out two case studies to illustrate the reliability of the corpus.
Understanding Usability and User Experience Cloud-Based Learning Management System From Teacher Review

In responding to the Covid-19 pandemic, Indonesia has issued a policy regarding the implementation of distance learning at all levels of education. Several distance learning strategies using technology have emerged, one of them has become a trend, namely the Learning Management System (LMS). There are Some advantages offered by LMS, such as self-guided, portable, complete learning features, etc., to make a connection between teachers and students. To ensure a proper learning process, a quality LMS is needed. Usability and UX are important aspects as determinants of a quality system. The ideal LMS can provide services that are easy to learn, easy to operate and provide a satisfying user experience. This study aims to find out and understand how usability and user experience are by comparing three LMSs Cloud-based that are popularly used in Indonesia, including Google Classroom, Schoology, and Edmodo. The sampling technique used in this research is an incidental sampling from teachers who are experienced in operating them. Usability testing is known through System Usability Scale (SUS) method, while UX testing is carried out through the User Experience Questionnaire (UEQ). Based on the usability and UX, the best cloud-based LMS in order, are Google Classroom, Edmodo, and Schoology.
Limitations on Transversal Gates for Hypergraph Product Codes

In a fault tolerant quantum computer, quantum codes are expected to serve the conflicting purposes of protecting quantum information while also allowing that information to be manipulated by fault-tolerant gates. We introduce a new technique for placing limitations on such gates, and apply this technique to a class of quantum codes known as hypergraph product codes contained within the vertical sector. These codes are constructed from input which is a pair of classical linear codes, and generalize the Kitaev surface code which is the hypergraph product of classical repetition codes. We provide a necessary condition on these input codes, under which the resulting hypergraph product code has transversal gates limited to the Clifford group. We conjecture that this condition is satisfied by all <inline-formula> <tex-math notation="LaTeX">$[n,k,d]$ </tex-math></inline-formula> Gallagher codes with <inline-formula> <tex-math notation="LaTeX">$d\ge 3$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$k\le n/2$ </tex-math></inline-formula>. This work is a generalization of an argument due to Bravyi and K&#x00F6;nig, and we also conjecture this is a refinement of the recent notion of disjointness due to Jochym-O&#x2019;Connor <i>et al.</i>
Generalized Pseudorandom Secret Sharing and Efficient Straggler-Resilient Secure Computation

Secure multiparty computation (MPC) enables n parties, of which up to t may be corrupted, to perform joint computations on their private inputs while revealing only the outputs. Optimizing the asymptotic and concrete costs of MPC protocols has become an important line of research. Much of this research focuses on the setting of an honest majority, where \(n \ge 2t+1\), which gives rise to concretely efficient protocols that are either information-theoretic or make a black-box use of symmetric cryptography. Efficiency can be further improved in the case of a strong honest majority, where \(n>2t+1\).
Inspecting the Process of Bank Credit Rating via Visual Analytics

Bank credit rating classifies banks into different levels based on publicly disclosed and internal information, serving as an important input in financial risk management. However, domain experts have a vague idea of exploring and comparing different bank credit rating schemes. A loose connection between subjective and quantitative analysis and difficulties in determining appropriate indicator weights obscure understanding of bank credit ratings. Furthermore, existing models fail to consider bank types by just applying a unified indicator weight set to all banks. We propose RatingVis to assist experts in exploring and comparing different bank credit rating schemes. It supports interactively inferring indicator weights for banks by involving domain knowledge and considers bank types in the analysis loop. We conduct a case study with real-world bank data to verify the efficacy of RatingVis. Expert feedback suggests that our approach helps them better understand different rating schemes.
A Closer-to-Reality Model for Comparing Relevant Dimensions of Recommender Systems, with Application to Novelty

Providing fair and convenient comparisons between recommendation algorithms—where algorithms could focus on a traditional dimension (accuracy) and/or less traditional ones (e.g., novelty, diversity, serendipity, etc.)—is a key challenge in the recent developments of recommender systems. This paper focuses on novelty and presents a new, closer-to-reality model for evaluating the quality of a recommendation algorithm by reducing the popularity bias inherent in traditional training/test set evaluation frameworks, which are biased by the dominance of popular items and their inherent features. In the suggested model, each interaction has a probability of being included in the test set that randomly depends on a specific feature related to the focused dimension (novelty in this work). The goal of this paper is to reconcile, in terms of evaluation (and therefore comparison), the accuracy and novelty dimensions of recommendation algorithms, leading to a more realistic comparison of their performance. The results obtained from two well-known datasets show the evolution of the behavior of state-of-the-art ranking algorithms when novelty is progressively, and fairly, given more importance in the evaluation procedure, and could lead to potential changes in the decision processes of organizations involving recommender systems.
Zero-Shot Cross-Lingual Transfer in Legal Domain Using Transformer Models

Zero-shot cross-lingual transfer is an important feature in modern NLP models and architectures to support low- resource languages. In this work, We study zero-shot cross-lingual transfer from English to French and German under Multi-Label Text Classification, where we train a classifier using English training set, and we test using French and German test sets. We extend EURLEX57K dataset, the English dataset for topic classification of legal documents, with French and German official translation. We investigate the effect of using some training techniques, namely Gradual Unfreezing and Language Model finetuning, on the quality of zero-shot cross-lingual transfer. We find that Language model finetuning of multi-lingual pre-trained model (M-DistilBERT, M-BERT) leads to 32.0-34.94%, 76.15- 87.54% relative improvement on French and German test sets correspondingly. Also, Gradual unfreezing of pre-trained model&#x2019;s layers during training results in relative improvement of 38- 45% for French and 58-70% for German. Compared to training a model in Joint Training scheme using English, French and German training sets, zero-shot BERT-based classification model reaches 86% of the performance achieved by jointly-trained BERT-based classification model.
Interacting with Climate Change: A Survey of HCI and Design Projects and Their Use of Transmedia Storytelling

Climate change is arguably one of the most debated issues today. The scale and global reach of this crisis doesn't afford a universal solution and requires widespread global mobilization. Public engagement is essential for the success of any initiative on this topic. However, sometimes communicating the facts is not enough. Interactive storytelling and transmedia narratives have an important part to play in communicating climate change, especially in shifting from a mere transmission of data to a narrative that is more engaging, positive and action focused, that considers diverse audiences and active participation. Following this premise, we conducted a survey on climate change applied research projects addressing the general public to understand how the fields of interactive media, HCI and Design are using transmedia narratives. The intention of this study is to gather what has been done regarding these topics and the strategies used, to further the debate among the community and inform future research.
A brain extraction algorithm for infant T2 weighted magnetic resonance images based on fuzzy c-means thresholding

It is challenging to extract the brain region from T2-weighted magnetic resonance infant brain images because conventional brain segmentation algorithms are generally optimized for adult brain images, which have different spatial resolution, dynamic changes of imaging intensity, brain size and shape from infant brain images. In this study, we propose a brain extraction algorithm for infant T2-weighted images. The proposed method utilizes histogram partitioning to separate brain regions from the background image. Then, fuzzy c-means thresholding is performed to obtain a rough brain mask for each image slice, followed by refinement steps. For slices that contain eye regions, an additional eye removal algorithm is proposed to eliminate eyes from the brain mask. By using the proposed method, accurate masks for infant T2-weighted brain images can be generated. For validation, we applied the proposed algorithm and conventional methods to T2 infant images (0-24 months of age) acquired with 2D and 3D sequences at 3T MRI. The Dice coefficients and Precision scores, which were calculated as quantitative measures, showed the highest values for the proposed method as follows: For images acquired with a 2D imaging sequence, the average Dice coefficients were 0.9650 ± 0.006 for the proposed method, 0.9262 ± 0.006 for iBEAT, and 0.9490 ± 0.006 for BET. For the data acquired with a 3D imaging sequence, the average Dice coefficient was 0.9746 ± 0.008 for the proposed method, 0.9448 ± 0.004 for iBEAT, and 0.9622 ± 0.01 for BET. The average Precision was 0.9638 ± 0.009 and 0.9565 ± 0.016 for the proposed method, 0.8981 ± 0.01 and 0.8968 ± 0.008 for iBEAT, and 0.9346 ± 0.014 and 0.9282 ± 0.019 for BET for images acquired with 2D and 3D imaging sequences, respectively, demonstrating that the proposed method could be efficiently used for brain extraction in T2-weighted infant images.
Text Generation From Data With Dynamic Planning

Transcribing structural data into readable text (data-to-text) is a fundamental language generation task. One of its challenges is to plan the input records for text realization. Recent works tackle this problem with a static planner, which performs record planning in advance for text realization. However, they cannot revise plans to cope with unexpected realized text and require golden plans for supervised training. To address these issues, we first propose a model that contains a dynamic planner. It decomposes text generation into two alternately procedures, record planning and text realization. We also devise a novel likelihood-driven training strategy for the planner. This strategy exploits sentence likelihood to select input records, which requires no annotated plan. Besides, we design a metric based on the set similarity to evaluate the quality of predicted plans. We conduct comprehensive experiments on two data-to-text datasets, E2E and EPW. Our best model considerably outperforming previous works on both text metrics and plan metrics. The likelihood-driven strategy also exhibits competitiveness for training the dynamic planner.
Hardware-friendly Log-scale Quantization for CNNs with Activation Functions Containing Negative Values

Recently, with the development of deep learning and GPU, various convolutional neural network (CNN)-based object detection studies are accelerating, and accordingly, research on network compression including quantization is being actively conducted. Due to the characteristics of CNN that accompanies multiple multiplication operations, it is difficult for the existing linear scale quantization method to maximize the benefits of network compression in the accelerator implementation process. On the other hand, log scale quantization has a good quantization effect when implementing an accelerator, but causes a relatively large decrease in accuracy. To address this problem, this paper proposes a technique that minimizes the accuracy degradation of CNNs due to quantization by re-scaling the distribution in consideration of the activation function with negative values in the log scale quantization process. As a result of the experiment, the proposed quantization technique can achieve an accuracy improvement of 1.6% compared to the existing log scale quantization, which can reduce hardware resources by more than 87.52% while maintaining a similar accuracy degradation compared to the existing linear scale quantization.
FoodTemi: The AI-Oriented Catering Service Robot

This study develops a catering service robot system for a self-serviced restaurant. The overall system integrates the Dobot arm, a linear motion robot, and Temi mobile robot. By ordering the drinks via the panel on robots, the Dobot arm and linear motion robot will prepare for the beverage. When the meal is ready, FoodTemi will automatically fetch and deliver the meal to the customer.
Online Anomaly Detection with Streaming Data based on Fine-grained Feature Forecasting

In the industrial applications like fault diagnosis and health management, monitoring data generally reaches sequentially in a streaming form. To recognize fault occurrence in real time without system halt, it is necessary to improve the accuracy and stability of anomaly detection with streaming data. To solve this problem, a new online anomaly detection method with streaming data is proposed based on fine-grained feature forecasting. First, to conduct fine-grained decomposition of features, a denoising autoencoder network is run to extract multiple-dimensional deep features of online data in the initial period of normal state. Second, a forecasting model with tensor Tucker decomposition and ARIMA is conducted to predict the fluctuation trend of all feature sequences. Finally, the deviation degree between the prediction values and sequentially-arrived data is calculated, and an alarm threshold is built according to the 95% confidence interval of the maximum deviation. Then the anomalous state data can be detected in real time. This paper adopts the problem of bearing early fault online detection as an example, and run comparative experiments on the IEEE PHM Challenge 2012 bearing dataset. The results show that the proposed method has good detection accuracy and is with no false alarm, while the model training does not rely on any offline data. Then the proposed method is applicable to the problem of online anomaly detection
Building a Stereo and Wide-View Hybrid RGB/FIR Imaging System for Autonomous Vehicle

In this work, a novel paradigm is proposed to build a hybrid RGB/FIR camera, which outputs pixel-wisely aligned and temporally synchronized RGB and FIR frames simultaneously. Compared with the existing schemes, we innovate in several ways to make it compact, practical, and extendable for real-world applications. First, we propose a new structure layout design of the hybrid camera, and we also design a removable adjuster to detach the hybrid camera from the base platform. For RGB and FIR image registration, we have achieved a pixel-wise spatial registration of the FIR and RGB frames by a two-step alignment, where we propose a ghost-effect removal scheme to remove the ghost effect from the rolling-shutter-based FIR camera. Finally, multiple hybrid cameras are extended to a hybrid camera array, through which we can obtain wide-view spatially aligned FIR, RGB and disparity images simultaneously. The experimental results demonstrate the accuracy of the hybrid camera and the effectiveness of the designed stereo wide-view hybrid camera system.
What Drives Readership? An Online Study on User Interface Types and Popularity Bias Mitigation in News Article Recommendations

Personalized news recommender systems support readers in finding the right and relevant articles in online news platforms. In this paper, we discuss the introduction of personalized, content-based news recommendations on DiePresse, a popular Austrian online news platform, focusing on two specific aspects: (i) user interface type, and (ii) popularity bias mitigation. Therefore, we conducted a two-weeks online study that started in October 2020, in which we analyzed the impact of recommendations on two user groups, i.e., anonymous and subscribed users, and three user interface types, i.e., on a desktop, mobile and tablet device. With respect to user interface types, we find that the probability of a recommendation to be seen is the highest for desktop devices, while the probability of interacting with recommendations is the highest for mobile devices. With respect to popularity bias mitigation, we find that personalized, content-based news recommendations can lead to a more balanced distribution of news articles' readership popularity in the case of anonymous users. Apart from that, we find that significant events (e.g., the COVID-19 lockdown announcement in Austria and the Vienna terror attack) influence the general consumption behavior of popular articles for both, anonymous and subscribed users.
Multiple-Output Channel Simulation and Lossy Compression of Probability Distributions

We consider a variant of the channel simulation problem with a single input and multiple outputs, where Alice observes a probability distribution P from a set of prescribed probability distributions $\mathcal{P}$, and sends a prefix-free codeword W to Bob to allow him to generate n i.i.d. random variables $X_{1}, X_{2}, \ldots, X_{n}$ which follow the distribution P. This can also be regarded as a lossy compression setting for probability distributions. This paper describes encoding schemes for three cases of $P: P$ is a distribution over positive integers, P is a continuous distribution over $[0,1]$ with a non-increasing pdf, and P is a continuous distribution over $[0, \infty)$ with a nonincreasing pdf. We show that the growth rate of the expected codeword length is sub-linear in n when a power law bound is satisfied. An application of multiple-outputs channel simulation is the compression of probability distributions. A full version of this paper is accessible at: https://arxiv.org/pdf/2105.01045.pdf