Reviewerly/exHarmony huggingface dataset:
How to Exploit the Transferability of Learned Image Compression to Conventional Codecs

Lossy image compression is often limited by the simplicity of the chosen loss measure. Recent research suggests that generative adversarial networks have the ability to overcome this limitation and serve as a multi-modal loss, especially for textures. Together with learned image compression, these two techniques can be used to great effect when relaxing the commonly employed tight measures of distortion. However, convolutional neural network-based algorithms have a large computational footprint. Ideally, an existing conventional codec should stay in place, ensuring faster adoption and adherence to a balanced computational envelope.As a possible avenue to this goal, we propose and investigate how learned image coding can be used as a surrogate to optimise an image for encoding. A learned filter alters the image to optimise a different performance measure or a particular task. Extending this idea with a generative adversarial network, we show how entire textures are replaced by ones that are less costly to encode but preserve a sense of detail.Our approach can remodel a conventional codec to adjust for the MS-SSIM distortion with over 20% rate improvement without any decoding overhead. On task-aware image compression, we perform favourably against a similar but codec-specific approach.
Moderate Automobile Accident Claim Process Automation Using Machine Learning

In modern-day, traditional automobile accident claim process struggles to keep up with the recurring automobile accidents and furthermore, the claim itself is a critical point in which the policyholder may decide to switch to a different automobile insurance provider. In this paper, the authors present a system which can be used to automate the processing of claims for automobiles which were involved in less severe accidents in a much quicker manner. The presented system comprises of four components, each with a model developed using computer vision or machine learning techniques to facilitate the automation process. The models are built and fine-tuned using transfer learning and ensemble learning techniques in order to determine the damaged component of the automobile, determine the make and model of the automobile, compute an accurate repair estimate and also compute the likeliness of the policyholder may churn, to ensure that the policyholder is satisfied with the appraised amount and will be retained by the insurance provider.
Using Mind Maps to motivate the digital generation of students to learn foreign languages

The purpose of this research is to study the possibilities of using mind-mapping technology in modern conditions of digital transformation of the educational process to develop students’ motivation to learn foreign languages. The research methods are the method of static description of the material, analysis, comparison and generalization of scientific information, and experiment. As a result, it was proved that the use of this technology in teaching foreign languages facilitates memorization of lexical material, promotes the development of speech skills, and makes the learning process exciting. It was found that in the course of creation of Mind Maps, students with a high level of digital skills are more successful in overcoming the language barrier, striving to expand the semantic components of Mind Maps. The scientific novelty lies in the fact that for the first time the students’ preferences on the choice of methods of learning foreign languages were studied. To pioneer the use of the method, the study was carried out on special German texts, which were studied by the students by means of two methods (traditional and mind-mapping technology).
Experimental Evaluation of PSO Based Transfer Learning Method for Meteorological Visibility Estimation

Estimation of Meteorological visibility from image characteristics is a challenging problem in the research of meteorological parameters estimation. Meteorological visibility can be used to indicate the weather transparency and this indicator is important for transport safety. This paper summarizes the outcomes of the experimental evaluation of a Particle Swarm Optimization (PSO) based transfer learning method for meteorological visibility estimation method. This paper proposes a modified approach of the transfer learning method for visibility estimation by using PSO feature selection. Image data are collected at fixed location with fixed viewing angle. The database images were gone through a pre-processing step of gray-averaging so as to provide information of static landmark objects for automatic extraction of effective regions from images. Effective regions are then extracted from image database and the image features are then extracted from the Neural Network. Subset of Image features are selected based on the Particle Swarming Optimization (PSO) methods to obtain the image feature vectors for each effective sub-region. The image feature vectors are then used to estimate the visibilities of the images by using the Multiple Support Vector Regression (SVR) models. Experimental results show that the proposed method can give an accuracy more than 90% for visibility estimation and the proposed method is effective and robust.
Catching them red-handed: Real-time Aggression Detection on Social Media

Aggression on social media has evolved into a major point of concern. However, recently proposed machine learning (ML) approaches to detect various types of aggressive behavior fall short, due to the fast and increasing pace of content generation as well as evolution of such behavior over time. This work introduces the first, practical, real-time framework for detecting aggression on Twitter via embracing the streaming ML paradigm. This method adapts its ML binary classifiers in an incremental fashion, while receiving new annotated examples, and achieves similar performance as batch-based ML models, with 82-93% accuracy, precision, and recall. Experimental analysis on real Twitter data reveals how this framework, implemented in Spark Streaming, easily scales to process millions of tweets in minutes.
Prediction of solar direct beam transmittance derived from global irradiation and sunshine duration using anfis

This work describes the application of models to estimate the transmitted fraction of direct solar irradiation into normal incidence (Ktb) as a function of the atmospheric transmissivity (Kt) and the insolation ratio (n/N). In the first model, the values of Ktb and Kt in the hourly (h) and daily (d) partitions were correlated using polynomial regression. In the second model, Ktb and n/N in the daily partition were correlated through linear regression. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is used to estimate the obtained values Ktb. The performance of ANFIS1 (as a function of Kt hourly), ANFIS2 (as a function of daily Kt), ANFIS3 (as a function of daily n/N), and ANFIS4 (as a function of the ratio of Kt and daily n/N) are compared to statistical models. Two databases, defined as a typical year and an atypical year, are adopted to validate the models and ANFIS. The models were validated comparing the estimate and measurements using the statistical indicators rMBE and rRMSE. The statistical models as a function of Kt resulted in hourly average (Ktbh): 〈rMBE〉=−4.08±2.18% and 〈rRMSE〉=25.97±0.90%; daily (Ktbd): 〈rMBE〉=−4.93±4.89% and 〈rRMSE〉=22.06±2.06%. The model as a function of the insolation ratio resulted in a daily average (Ktbd): 〈rMBE〉=−3.01±2.62% and 〈rRMSE〉=24.15±0.30%. The rMBE of models using ANFIS are lower than those of statistical models. The rRMSE value using ANFIS1 model was 〈24.45%〉, while using ANFIS2, ANFIS3, and ANFIS4 models, they resulted on 〈16.47%〉, 〈19.60%〉, and 〈14.60%〉 in average, respectively, lower than the statistical models. ANFIS's performance is superior to statistical models.
FAIR or FAIRer? An Integrated Quantitative FAIRness Assessment Grid for Semantic Resources and Ontologies

In open science, the expression "FAIRness assessment" refers to evaluating to which degree a digital object is Findable, Accessible, Interoperable, and Reusable. Standard vocabularies or ontologies are a key element to achieving a high level of FAIRness (FAIR Principle I2) but as with any other data, ontologies have themselves to be FAIR. Despite the recent interest in the open science and semantic Web communities for this question, we have not seen yet a quantitative evaluation method to assess and score the level of FAIRness of ontologies or semantic resources in general (e.g., vocabularies, terminologies, thesaurus). The main objective of this work is to provide such a method to guide semantic stakeholders in making their semantic resources FAIR. We present an integrated quantitative assessment grid for semantic resources and propose candidate metadata properties–taken from the MOD ontology metadata model–to be used to make a semantic resource FAIR. Aligned and nourished with relevant FAIRness assessment state-of-the-art initiatives, our grid distributes 478 credits to the 15 FAIR principles in a manner which integrates existing generic approaches for digital objects (i.e., FDMM, SHARC) and approaches dedicated to semantic resources (i.e., 5-stars V, MIRO, FAIRsFAIR, Poveda et al.). The credits of the grid can then be used for implementing FAIRness assessment methods and tools.
Impact of Opportunistic Reuse Practices to Technical Debt

Technical debt (TD) has been recognized as an important quality problem for both software architecture and code. The evolution of TD techniques over the past years has led to a number of research and commercial tools. In addition, the increasing trend of opportunistic reuse (as opposed to systematic reuse), where developers reuse code assets in popular repositories, is changing the way components are selected and integrated into existing systems. However, reusing software opportunistically can lead to a loss of quality and induce TD, especially when the architecture is changed in the process. However, to the best of our knowledge, no studies have investigated the impact of opportunistic reuse in TD. In this paper, we carry out an exploratory study to investigate to what extent reusing components opportunistically negatively affects the quality of systems. We use one commercial and one research tool to analyze the TD ratios of three case systems, before and after opportunistically extending them with open-source software.
Renewable energy management system for Saudi Arabia: Methodology and preliminary results

A Renewable Energy Management System (REMS) has been developed for Saudi Arabia to provide the system operator with the daily day-ahead electrical power generation forecasts. The core models included in REMS are: (i) adapted weather research and forecasting (WRF-solar) model that predicts weather conditions and solar irradiance components based on the aerosol (natural and anthropogenic) properties from CHIMERE chemistry transport model, (ii) renewable power prediction schemes that predict electrical power for specific solar technologies (photovoltaic and concentrating solar power plants) at different scales (plant, substation, area, and country land), and (iii) cutting-edge web-interface and geospatial technologies that enable interactive data visualisation on screens of the power plant characteristics and predicted variables over a four-day time frame (two days prior, current day, and a day ahead). The performance of the REMS was evaluated using the actual power output data from Masdar City 10 MW solar photovoltaic power plant and Shams 100 MW concentrated solar power plant in Abu Dhabi, the United Arab Emirates (UAE). The obtained results show the applicability and accuracy of the modelling framework. Currently, REMS simulates the performance of any set of renewable energy scenarios considering daily day-ahead forecasts. But the eventual use of the system in daily operations coupled with decision-making procedures will ensure efficient monitoring and management of renewable assets over time.
Detecting Multilayer Clouds From the Geostationary Advanced Himawari Imager Using Machine Learning Techniques

This study develops a machine learning (ML)-based multilayer cloud detection algorithm for the passive Advanced Himawari Imager (AHI) aboard the geostationary Himawari-8 satellite. AHI measurements in the 0.64-, 1.6-, 2.3-, 3.9-, 7.3-, 8.6-, 11.2-, and 12.4- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> channels, their combinations, geolocations, and observational geometries are used as predictors, and collocated active CloudSat and CALIPSO data are used to accurately label multilayer cloud pixels as the reference/truth of the predictand. We develop an ML-based daytime model (ML-Day) that utilizes all the aforementioned predictors and an all-time one (ML-All) that excludes the solar channel-dependent variables. Among four ML algorithms, the random forest (RF) performs slightly better than the artificial neural network, K-nearest neighbor, and support vector machines. By comparing with the merged CloudSat and CALIPSO product, the ML-Day model correctly identifies ~89% single-layer clouds and ~70% multilayer clouds, outperforming the Moderate Resolution Imaging Spectroradiometer (MODIS) operational multilayer cloud product (~80% and ~40% given by Marchant <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ). The success rates of ML-All for single-layer and multilayer clouds also reach ~85% and ~64%, respectively. The misclassification of our algorithm is mostly caused by missing optically thin clouds, a drawback of most radiometers without the 1.38- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> channel. Furthermore, with multilayer cloud pixels well detected by our algorithm, the AHI operational cloud top height retrievals are found to be larger biased due to multilayer cloud occurrence and might be improved by considering cloud vertical structures.
Exemplars-Guided Empathetic Response Generation Controlled by the Elements of Human Communication

Empathy is fundamental to humans amongst other animals.It is key to strong social cohesion, cornerstone of health and success of societies.Thus, empathy could be an important component of effective human-computer interactions through conversations.This has motivated a whole sub-field of research focused on empathetic response generation.The majority of existing methods for empathetic response generation rely on the emotion of the context to generate empathetic responses.However, empathy is much more than generating responses with an appropriate emotion.It also often entails subtle expressions of understanding and personal resonance with the situation of the other interlocutor.Unfortunately, such qualities are difficult to quantify, and the datasets lack the relevant annotations.To address this issue, in this paper we propose an approach that relies on exemplars to cue the generative model on fine stylistic properties that signal empathy to the interlocutor.To this end, we employ dense passage retrieval to extract relevant exemplary responses from the training set.Three elements of human communication-emotional presence, interpretation, and exploration-and sentiment are additionally introduced using synthetic labels to guide the generation towards empathy.The human evaluation is also extended by these elements of human communication.We empirically show that these approaches yield significant improvements in empathetic response quality in terms of both automated and human-evaluated metrics.The implementation is available at https://github.com/declare-lab/exemplary-empathy.
Fostering participation in digital contact tracing

Digital contact tracing is a promising digital public health intervention to manage epidemics. However, in order to reach its full potential, the technology has to be widely adopted and used. During the SARS-CoV-2 pandemic, this has not necessarily been the case. We review the literature with a focus on how participation in digital contact tracing could be fostered and provide policy recommendations on how to increase its adoption and usage as well as recommendations for further research.
Smart, comfortable wearable system for recognizing Arabic Sign Language in real-time using IMUs and features-based fusion

Automatic sign language recognition systems have been presented in the literature to avail as a helpful device for deaf and mute individuals to incorporate them into the community without impediments. Gesture recognition is usually done by using vision-based techniques or hand-attached sensors that track hand and finger movements. This paper presents a sensors-based Arabic Sign Language recognition system. The proposed system depends on the information fusion from a 3-axis accelerometer and gyroscope Inertial Measurement Units (IMUs) sensors. The architecture of the proposed system consists of two main modules: a wearable device (glove), which includes sensors, a communication unit, and micro-controller unit, and a software module running on a mobile device. The proposed glove system utilizes six IMUs sensors where five of them placed at the fingers and the sixth sensor placed at the back of the palm. The collected data from sensors are transmitted to a mobile device through a Bluetooth low-energy wireless transmission for data analysis and gesture recognition. For each gesture, the beginning and endpoints of the significative gesture part are discovered automatically using the intensity of the gyroscope signal. The system employs a features-based sensor fusion that combines accelerometer and gyroscope time-domain and frequency-domain features. In a comprehensive set of experiments with seven participants, the system was trained and tested with 28 Arabic alphabets, and the best recognition accuracy achieved was 98.6% for the user-dependent data and 96% for the user-independent data under support vector machine classifier. The achieved results are outperforming existing systems that have been presented in recent decades for alphabet-level gesture recognition.
Deep Reinforcement Learning with Automated Label Extraction from Clinical Reports Accurately Classifies 3D MRI Brain Volumes

Image classification is probably the most fundamental task in radiology artificial intelligence. To reduce the burden of acquiring and labeling data sets, we employed a two-pronged strategy. We automatically extracted labels from radiology reports in Part 1. In Part 2, we used the labels to train a data-efficient reinforcement learning (RL) classifier. We applied the approach to a small set of patient images and radiology reports from our institution. For Part 1, we trained sentence-BERT (SBERT) on 90 radiology reports. In Part 2, we used the labels from the trained SBERT to train an RL-based classifier. We trained the classifier on a training set of [Formula: see text] images. We tested on a separate collection of [Formula: see text] images. For comparison, we also trained and tested a supervised deep learning (SDL) classification network on the same set of training and testing images using the same labels. Part 1: The trained SBERT model improved from 82 to [Formula: see text] accuracy. Part 2: Using Part 1's computed labels, SDL quickly overfitted the small training set. Whereas SDL showed the worst possible testing set accuracy of 50%, RL achieved [Formula: see text] testing set accuracy, with a [Formula: see text]-value of [Formula: see text]. We have shown the proof-of-principle application of automated label extraction from radiological reports. Additionally, we have built on prior work applying RL to classification using these labels, extending from 2D slices to entire 3D image volumes. RL has again demonstrated a remarkable ability to train effectively, in a generalized manner, and based on small training sets.
COINS: Dynamically Generating COntextualized Inference Rules for Narrative Story Completion

Despite recent successes of large pre-trained language models in solving reasoning tasks, their inference capabilities remain opaque.We posit that such models can be made more interpretable by explicitly generating interim inference rules, and using them to guide the generation of task-specific textual outputs.In this paper we present COINS, a recursive inference framework that i) iteratively reads context sentences, ii) dynamically generates contextualized inference rules, encodes them, and iii) uses them to guide task-specific output generation.We apply COINS to a Narrative Story Completion task that asks a model to complete a story with missing sentences, to produce a coherent story with plausible logical connections, causal relationships, and temporal dependencies.By modularizing inference and sentence generation steps in a recurrent model, we aim to make reasoning steps and their effects on next sentence generation transparent.Our automatic and manual evaluations show that the model generates better story sentences than SOTA baselines, especially in terms of coherence.We further demonstrate improved performance over strong pre-trained LMs in generating commonsense inference rules.The recursive nature of COINS holds the potential for controlled generation of longer sequences.
SUPERVEGAN: Super Resolution Video Enhancement GAN for Perceptually Improving Low Bitrate Streams

This paper presents a novel model family that we call SUPERVEGAN, for the problem of video enhancement for low bitrate streams by simultaneous video super resolution and removal of compression artifacts from low bitrates (e.g. 250Kbps). Our strategy is fully end-to-end, but we upsample and tackle the problem in two main stages. The first stage deals with removal of streaming compression artifacts and performs a partial upsampling, and the second stage performs the final upsampling and adds detail generatively. We also use a novel progressive training strategy for video together with the use of perceptual metrics. Our experiments shown resilience to training bitrate and we show how to derive real-time models. We also introduce a novel bitrate equivalency test that enables the assessment of how much a model improves streams with respect to bitrate. We demonstrate efficacy on two publicly available HD datasets, LIVE-NFLX-II and Tears of Steel (TOS). We compare against a range of baselines and encoders and our results demonstrate our models achieve a perceptual equivalence which is up to two times over the input bitrate. In particular our 4X upsampling outperforms baseline methods on the LPIPS perceptual metric, and our 2X upsampling model also outperforms baselines on traditional metrics such as PSNR.
A Performance Comparison of RESTful Applications Implemented in Spring Boot Java and MS.NET Core

Abstract RESTful is an emergent architectural style to develop web services/applications due to its lightweight nature. Though more and more organizations are adopting RESTful services, when it comes to choosing the right platform to implement one, teams usually gravitate towards reusing the existing skillset and prefer the platform that they currently support instead of looking for a better performing alternative. Considering that internal architecture and implementation of each platform can drastically affect its efficiency and performance, the objective of this research paper is to assess and compare the performance of RESTful web applications implemented in Spring Boot Java and MS.NET Core. An identical business use case to provide the basic four Create, Read, Update, and Delete (CRUD) operations was developed in both implementations. Apache JMeter 5.2.1, an automated performance testing tool is used in this experiment to create a swamp of concurrent virtual users to load both the applications at regular intervals. The comprehensive experimental results are presented at the end of the research paper.