Reviewerly/exHarmony huggingface dataset:
Visualization and visual analysis of multimedia data in manufacturing: A survey

With the development of production technology and social needs, sectors of manufacturing are constantly improving. The use of sensors and computers has made it increasingly convenient to collect multimedia data in manufacturing. Targeted, rapid, and detailed analysis based on the type of multimedia data can make timely decisions at different stages of the entire manufacturing process. Visualization and visual analytics are frequently adopted in multimedia data analysis of manufacturing because of their powerful ability to understand, present, and analyze data intuitively and interactively. In this paper, we present a literature review of visualization and visual analytics specifically for manufacturing multimedia data. We classify existing research according to visualization techniques, interaction analysis methods, and application areas. We discuss the differences when visualization and visual analytics are applied to different types of multimedia data in the context of particular examples of manufacturing research projects. Finally, we summarize the existing challenges and prospective research directions.
Blind Tone-Mapped HDR Image Quality Measurement by Analysis of Low-Level and High-Level Perceptual Characteristics

Tone-mapping technology aims to transform High Dynamic Range (HDR) images into standard dynamic range images for visualizing on standard screens. However, various visual artifacts will inevitably be induced during the tone-mapping process. To quantify the visual quality degradation for Tone-Mapped Images (TMIs) accurately and automatically, a robust blind TMI quality measurement method is developed by analysis of low-level and high-level perceptual characteristics. Specifically, for low-level visual features, considering that the tone-mapping operator is prone to destroy the local image contrast, which conveys vital structural information, we resort to the joint statistical features based on gradient map and Laplacian of Gaussian response to portray the local image contrast variation in TMIs. In addition, Chromatic Local Binary Pattern (CLBP) is leveraged to measure the colorfulness degradation over four chromatic descriptor maps. For high-level quality-aware features, deep-learned features are utilized by a pre-trained convolutional neural network to characterize the semantics variation. Finally, the extracted low-level and high-level features are combined and mapped into an overall quality score by regression function. Extensive experiments show that our metric achieves a higher performance over two publicly available TMI benchmark databases.
Self-Supervised Language Learning From Raw Audio: Lessons From the Zero Resource Speech Challenge

Recent progress in self-supervised or unsupervised machine learning has opened the possibility of building a full speech processing system from raw audio without using any textual representations or expert labels such as phonemes, dictionaries or parse trees. The contribution of the Zero Resource Speech Challenge series since 2015 has been to break down this long-term objective into four well-defined tasks—Acoustic Unit Discovery, Spoken Term Discovery, Discrete Resynthesis, and Spoken Language Modeling—and introduce associated metrics and benchmarks enabling model comparison and cumulative progress. We present an overview of the six editions of this challenge series since 2015, discuss the lessons learned, and outline the areas which need more work or give puzzling results.
Deep Learning Enhanced UAV Imagery for Critical Infrastructure Protection

Unmanned aerial vehicles (UAVs) have seen a significant increase in their commercial application because of various technological break-throughs. As UAVs are typically used in open environments for purposes such as military applications, surveillance, and delivery of commodities, they rely primarily on the visual signals recorded by the flying UAV. UAVs can be used for critical infrastructure protection where their surveillance capabilities are used for monitoring these sites. While carrying out such missions in open environments, visual degradation is an unavoidable concern. It has a negative impact on the performance and security of the system. We propose a deep-learning-based framework, called Aerialgan, to solve the visual degradation caused by haze in the atmosphere. We trained the proposed model using an adversarial training algorithm which is commonly known as the generative adversarial ntwork (GAN) and aims to enhance the hazy images collected by a UAV and generate a clean, haze-free image of the same scene. In addition, we present the Aerial Non-Homogeneous Hazy (ANHH) dataset, which contains over 66,000 pairs of hazy and ground truth aerial photos with realistic, non-homogeneous haze of various densities. We used performance metrics such as peak signal-to-noise ratio and structural similarity index to evaluate our model on ANHH and compare it with contemporary state-of-the-art techniques in image dehazing. The proposed technique can be very useful in improving the reliability of surveillance where UAVs are used for critical infrastructure protection applications.
BetterPR: A Dataset for Estimating the Constructiveness of Peer Review Comments

Review comments play an important role in the improvement of scientific articles. There are typically many rounds of review-revision before the different reviewers with varying backgrounds arrive at a consensus on a submission. However, not always the reviews are helpful. Sometimes the reviewers are unnecessarily critical of the work without justifying their comments. Peer reviews are always meant to be critical yet constructive feedback on the scientific merit of a submitted article. However, with the rising number of paper submissions leading to the involvement of novice or less experienced reviewers in the reviewing process, the reviewers tend to spend less expert time on their voluntary reviewing job. This results in lackluster reviews where the authors do not have many takeaways from their reviews. The entire scientific enterprise is heavily dependent on this very human peer-review process. In this paper, we make an attempt to automatically distinguish between constructive and non-constructive peer reviews. We deem constructive comment to be the one that, despite being critical, is polite and provides feedback to the authors to improve their submissions. To this end, we present BetterPR, a manually annotated dataset to estimate the constructiveness of peer review comments. Further, we benchmark BetterPR with standard baselines and analyze their performance. We collect the peer reviews from open access forums and design an annotation scheme to label whether a review comment is constructive or non-constructive. We provide our dataset and codes ( https://github.com/PrabhatkrBharti/BetterPR.git ) for further exploration by the community.
GC3558: An open-source annotated dataset of Ghana currency images for classification modeling

The field of deep learning has led to remarkable advancements in many areas, including banking. Identifying currency denomination type and model is challenging due to intraclass variation and different illumination conditions. Although, in this domain, many datasets regarding currency denomination type and model, e.g., Indian Currency, Thai Currency, Chinese Currency, U.K. currency, etc., have already been experimented with by different researchers. More datasets are needed from a variety of currencies, especially Ghana currency (cedi). This article presents the Ghana Currency image dataset (GC3558) of 3558 color images in 13 classes created from a high-resolution camera. The dataset is comprised of only genuine currency. The class consists of coin and paper notes: 10 pesewas coin, 20 pesewas coin, 50 pesewas coin, 1 cedi coin, 2 cedis coin, 1 cedi note, 2 cedis note, 5 cedis note, 10 cedis note, 20 cedis note, 50 cedis note, 100 cedis note and 200 cedis note. All images are de-identified, validated, and freely available for download to A.I. researchers. The dataset will help researchers evaluate their machine learning models on real-world data.
A Novel Discrete Group Teaching Optimization Algorithm for TSP Path Planning with Unmanned Surface Vehicles

A growing number of researchers are interested in deploying unmanned surface vehicles (USVs) in support of ocean environmental monitoring. To accomplish these missions efficiently, multiple-waypoint path planning strategies for survey USVs are still a key challenge. The multiple-waypoint path planning problem, mathematically equivalent to the traveling salesman problem (TSP), is addressed in this paper using a discrete group teaching optimization algorithm (DGTOA). Generally, the algorithm consists of three phases. In the initialization phase, the DGTOA generates the initial sequence for students through greedy initialization. In the crossover phase, a new greedy crossover algorithm is introduced to increase diversity. In the mutation phase, to balance the exploration and exploitation, this paper proposes a dynamic adaptive neighborhood radius based on triangular probability selection to apply in the shift mutation algorithm, the inversion mutation algorithm, and the 3-opt mutation algorithm. To verify the performance of the DGTOA, fifteen benchmark cases from TSPLIB are implemented to compare the DGTOA with the discrete tree seed algorithm, discrete Jaya algorithm, artificial bee colony optimization, particle swarm optimization-ant colony optimization, and discrete shuffled frog-leaping algorithm. The results demonstrate that the DGTOA is a robust and competitive algorithm, especially for large-scale TSP problems. Meanwhile, the USV simulation results indicate that the DGTOA performs well in terms of exploration and exploitation.
A multi-level methodology for the automated translation of a coreference resolution dataset: an application to the Italian language

Abstract In the last decade, the demand for readily accessible corpora has touched all areas of natural language processing, including coreference resolution. However, it is one of the least considered sub-fields in recent developments. Moreover, almost all existing resources are only available for the English language. To overcome this lack, this work proposes a methodology to create a corpus for coreference resolution in Italian exploiting knowledge of annotated resources in other languages. Starting from OntonNotes, the methodology translates and refines English utterances to obtain utterances respecting Italian grammar, dealing with language-specific phenomena and preserving coreference and mentions. A quantitative and qualitative evaluation is performed to assess the well-formedness of generated utterances, considering readability, grammaticality, and acceptability indexes. The results have confirmed the effectiveness of the methodology in generating a good dataset for coreference resolution starting from an existing one. The goodness of the dataset is also assessed by training a coreference resolution model based on BERT language model, achieving the promising results. Even if the methodology has been tailored for English and Italian languages, it has a general basis easily extendable to other languages, adapting a small number of language-dependent rules to generalize most of the linguistic phenomena of the language under examination.
Implementation of Immersive Technology on Medical Education

Immersive technology has developed and impacted the revolution in the medical education sector, especially during the COVID-19 pandemic, which required adaptation to new learning methods. Medical education requires intensive learning, such as face-to-face meetings with teachers and supporting equipment. During the pandemic, most learning activities use online video meetings and video learning. The learning method may be helpful for theory-based learning, but in practice-based education, several aspects, such as the absence of supporting equipment, must be considered. In this paper, the author designs a learning scenario in the medical field, namely a simulation of infusion installation on the web XR platform and VR applications. Here, we will show how these two immersive technologies differ and a more effective platforms for use in virtual simulation-based learning scenarios. The Native VR applications can only be run on VR devices, while WebXR is a multi-cross platform that can run on PCVR, Android as AR devices, Oculus Quest 2 as VR devices, Magic Leap 1, and HoloLens 2 as MR devices. The author will also evaluate using the PIECES framework to get test results between the two platforms. The satisfaction predicate on VR application has a higher overall average with 4.7 meanwhile the WebXR overall average value is 4.35.
Color-Perception-Guided Display Power Reduction for Virtual Reality

Battery life is an increasingly urgent challenge for today's untethered VR and AR devices. However, the power efficiency of head-mounted displays is naturally at odds with growing computational requirements driven by better resolution, refresh rate, and dynamic ranges, all of which reduce the sustained usage time of untethered AR/VR devices. For instance, the Oculus Quest 2, under a fully-charged battery, can sustain only 2 to 3 hours of operation time. Prior display power reduction techniques mostly target smartphone displays. Directly applying smartphone display power reduction techniques, however, degrades the visual perception in AR/VR with noticeable artifacts. For instance, the "power-saving mode" on smartphones uniformly lowers the pixel luminance across the display and, as a result, presents an overall darkened visual perception to users if directly applied to VR content. Our key insight is that VR display power reduction must be cognizant of the gaze-contingent nature of high field-of-view VR displays. To that end, we present a gaze-contingent system that, without degrading luminance, minimizes the display power consumption while preserving high visual fidelity when users actively view immersive video sequences. This is enabled by constructing 1) a gaze-contingent color discrimination model through psychophysical studies, and 2) a display power model (with respect to pixel color) through real-device measurements. Critically, due to the careful design decisions made in constructing the two models, our algorithm is cast as a constrained optimization problem with a closed-form solution, which can be implemented as a real-time, image-space shader. We evaluate our system using a series of psychophysical studies and large-scale analyses on natural images. Experiment results show that our system reduces the display power by as much as 24% (14% on average) with little to no perceptual fidelity degradation.
A Tale of Two Tasks: Automated Issue Priority Prediction with Deep Multi-task Learning

Background. Issues are prevalent, and identifying the correct priority of the reported issues is crucial to reduce the maintenance effort and ensure higher software quality. There are several approaches for the automatic priority prediction, yet they do not fully utilize the related information that might influence the priority assignment. Our observation reveals that there are noticeable correlations between an issue's priority and its category, e.g., an issue of bug category tends to be assigned with higher priority than an issue of document category. This correlation motivates us to employ multi-task learning to share the knowledge about issue's category prediction and facilitating priority prediction.
DP-Parse: Finding Word Boundaries from Raw Speech with an Instance Lexicon

Abstract Finding word boundaries in continuous speech is challenging as there is little or no equivalent of a ‘space’ delimiter between words. Popular Bayesian non-parametric models for text segmentation (Goldwater et al., 2006, 2009) use a Dirichlet process to jointly segment sentences and build a lexicon of word types. We introduce DP-Parse, which uses similar principles but only relies on an instance lexicon of word tokens, avoiding the clustering errors that arise with a lexicon of word types. On the Zero Resource Speech Benchmark 2017, our model sets a new speech segmentation state-of-the-art in 5 languages. The algorithm monotonically improves with better input representations, achieving yet higher scores when fed with weakly supervised inputs. Despite lacking a type lexicon, DP-Parse can be pipelined to a language model and learn semantic and syntactic representations as assessed by a new spoken word embedding benchmark. 1
Changing the Culture on Data Management and Sharing: Overview and Highlights from a Workshop Held by the National Academies of Sciences, Engineering, and Medicine

The U.S. National Institutes of Health (NIH) released a broad new policy on data management and sharing on October 29, 2020, mandating that all data from NIH-funded or conducted research must be managed and most must be shared.This policy requires the submission of data management and
Face mask detection and social distance monitoring system for COVID-19 pandemic

Coronavirus triggers several respirational infections such as sneezing, coughing, and pneumonia, which transmit humans to humans through airborne droplets. According to the guidelines of the World Health Organization, the spread of COVID-19 can be mitigated by avoiding public interactions in proximity and following standard operating procedures (SOPs) including wearing a face mask and maintaining social distancing in schools, shopping malls, and crowded areas. However, enforcing the adaptation of these SOPs on a larger scale is still a challenging task. With the emergence of deep learning-based visual object detection networks, numerous methods have been proposed to perform face mask detection on public spots. However, these methods require a huge amount of data to ensure robustness in real-time applications. Also, to the best of our knowledge, there is no standard outdoor surveillance-based dataset available to ensure the efficacy of face mask detection and social distancing methods in public spots. To this end, we present a large-scale dataset comprising of 10,000 outdoor images categorized into a binary class labeling i.e., face mask, and non-face masked people to accelerate the development of automated face mask detection and social distance measurement on public spots. Alongside, we also present an end-to-end pipeline to perform real-time face mask detection and social distance measurement in an outdoor environment. Initially, existing state-of-the-art single and multi-stage object detection networks are fine-tuned on the proposed dataset to evaluate their performance in terms of accuracy and inference time. Based on better performance, YOLO-v3 architecture is further optimized by tuning its feature extraction and region proposal generation layers to improve the performance in real-time applications. Our results indicate that the presented pipeline performed better than the baseline version, showing an improvement of 5.3% in terms of accuracy.
Fuzzy-clustering and fuzzy network based interpretable fuzzy model for prediction

Abstract Interpretability is the dominant feature of a fuzzy model in security-oriented fields. Traditionally fuzzy models based on expert knowledge have obtained well interpretation innately but imprecisely. Numerical data based fuzzy models perform well in precision but not necessarily in interpretation. To utilize the expert knowledge and numerical data in a fuzzy model synchronously, this paper proposed a hybrid fuzzy c-means (FCM) clustering algorithm and Fuzzy Network (FN) method-based model for prediction. The Mamdani rule-based structure of the proposed model is identified based on FCM algorithm from data and by expert-system method from expert knowledge, both of which are combined by FN method. Particle swarm optimization (PSO) algorithm is utilized to optimize the fuzzy set parameters. We tested the proposed model on 6 real datasets comparing the results with the ones obtained by using FCM algorithm. The results showed that our model performed best in interpretability, transparency, and accuracy.
Unguided virtual-reality training can enhance the oral presentation skills of high-school students

Public speaking is fundamental in our daily life, and it happens to be challenging for many people. Like all aspects of language, these skills should be encouraged early on in educational settings. However, the high number of students per class and the extensive curriculum both limit the possibilities of the training and, moreover, entail that students give short in-class presentations under great time pressure. Virtual Reality (VR) environments can help speakers and teachers meet these challenges and foster oral skills. This experimental study employs a between-subjects pre- and post-training design with four Catalan high-school student groups, a VR group ( N = 30) and a Non-VR group ( N = 20). Both groups gave a 2-min speech in front of a live audience before (pre-training) and after (post-training) 3 training sessions (one session per week) in which they practiced public speaking either in front of a VR audience or alone in a classroom (Non-VR). Students assessed their anxiety measures right before performing every speech and filled out a satisfaction questionnaire at the end. Pre- and post-training speeches were assessed by 15 raters, who analyzed the persuasiveness of the message and the charisma of the presenter. Speeches were also analyzed for prosodic features and gesture rate. First, results showed that self-assessed anxiety got significantly reduced at post-training for both conditions. Second, acoustic analyses of both groups' speeches show that the VR group has, unlike the Non-VR group, developed a more clear and resonant voice quality in the post-training speeches, in terms of higher cepstral-peak prominence (CPP) (although no significant differences in f0- related parameters as a function of training were obtained), as well as significantly less erosion effects than the Non-VR group. However, these differences across groups did not trigger a direct improvement on the participants' gesture rate, persuasiveness and charisma at post-training. Furthermore, students perceived the training in the VR environment to be more useful and beneficial for their future oral presentations than the Non-VR group. All in all, short unguided VR training sessions can help students feel less anxious, promote a more clear and resonant voice style, and can prevent them from experiencing an erosion effect while practicing speeches in front of a real audience.
Deterministic Loading of Microwaves onto an Artificial Atom Using a Time-Reversed Waveform

Loading quantum information deterministically onto a quantum node is an important step towards a quantum network. Here, we demonstrate that coherent-state microwave photons, with an optimal temporal waveform, can be efficiently loaded onto a single superconducting artificial atom in a semi-infinite one-dimensional (1D) transmission-line waveguide. Using a weak coherent state (average photon number N<<1 with an exponentially rising waveform, whose time constant matches the decoherence time of the artificial atom, we demonstrate a loading efficiency of above 94% from 1D semi-free space to the artificial atom. We also show that Fock-state microwave photons can be deterministically loaded with an efficiency of 98.5%. We further manipulate the phase of the coherent state exciting the atom, enabling coherent control of the loading process. Our results open up promising applications in realizing quantum networks based on waveguide quantum electrodynamics (QED).