Reviewerly/exHarmony huggingface dataset:
ESLDL: An Integrated Deep Learning Model for Egyptian Sign Language Recognition

Sign languages is a critical requirement that helps deaf people to express their needs, feelings and emotions using a variety of hand gestures throughout their daily life. This language had evolved in parallel with spoken languages, however, it do not resemble its counterparts in the same way. Moreover, it is as complex as any other spoken language, as each sign language embodies hundreds of signs, that differs from the next by slight changes in hand shape, position, motion direction, face and body parts contributing to each sign. Unfortunately, sign languages are not globally standardized, where the language differs between countries and has its own vocabulary and varies although they might look similar. Furthermore, publicly available datasets are limited in quality and most of the available translation services are expensive, due to the required skilled human personnel. This paper proposes a deep learning approach for sign language detection that is finely tailored for the Egyptian sign language (special case of the generic sign language). The model is built to harnesses the power of convolutional and recurrent networks by integrating them together to better recognize the sign language spatio-temporal data-feed. In addition, the paper proposes the first Egyptian sign language dataset for emotion words and pronouns. The experimental results demonstrated the proposed approach promising results on the introduced dataset using combined CNN with RNN models.
Real-time Aircraft Tracking System: A Survey and A Deep Learning Based Model

Real-time tracking of a maneuvering aircraft is a challenging issue in the literature. For effective tracking, an accurate and complete transfer of data from aviation to ground systems is needed. However, possible data loss caused by telemetry or data acquisition system makes aircraft tracking difficult. To mitigate this problem, efficient tracking systems are proposed in the literature. Accordingly, we first present a brief survey of aircraft tracking systems by splitting them according to their approaches as mathematical, machine learning-based, and deep learning-based approaches. After examining the existing studies, we offer a real-time Deep learning-based Aircraft Tracking (DeepAT) system that enables real-time tracking of an aircraft. Deep learning models are employed to predict the next location of aircraft. Accordingly, the radar angle of the antenna is determined to the antenna or radar system directs to the next location. When the proposed model is analyzed through two potential use case scenarios which are flight tests and military applications, DeepAT offers promising solutions to prevent data loss in different applications.
Extended Reality for Teleoperated Mobile Robots

Human-Robot Interaction (HRI) will be a crucial component of intelligent factories of the future (FoF). Nevertheless, several research indicates that robot teleoperation can be challenging and, therefore, impose high requirements on user training and expertise. The emerging technology of Extended Reality, the umbrella for immersive technologies associated with different types of realities, has recently been applied to create novel developer tools in industrial robotics, with the potential to increase user-friendliness and minimize requirements on user training and operator&#x2019;s previous expertise. This paper evaluates the robot teleoperation efficiency achieved by a number of users performing a given task, using the traditional keyboard method and two forms of extended realities. Results showed the advantages of teleoperation based on extended realities, with respect to user friendliness, less operational errors, and efficiency and effectiveness.
AR-Enabled Mobile Apps to Support Post COVID-19 Tourism

The outbreak of COVID-19 has shown many negative impacts on industry, especially when it comes to travelling and tourism sector. In this paper, it is explored how mobile applications incorporating the elements of augmented reality (AR) can be leveraged in order to revive tourism in post COVID-19 era. As outcome, we present three prototype case studies aiming tourism support: 1) AR-driven exploration and sightseeing 2) historical 3D reconstruction 3) gamification approach to sightseeing. For multiplatform mobile application implementation, the synergy of AppSheet and Google Apps Script technologies is used, while for the elements of location-based AR we rely on AR.js web-based library. According to our findings, AR applications have huge potential in field of tourism.
An Intermittent Fault Data Generation Method Based on LSTM and GAN

Intermittent fault has the characteristic of degradation, and may eventually evolve into permanent failure. In some areas, the data of intermittent fault collected through actual operation cannot meet the large requirement for fault diagnosis and degradation assessment. This paper proposes a novel GAN model based on LSTM. Combined with GAN’s adversarial idea and LSTM’s ability to process time series. The generative model is able to automatically generate intermittent failure data under the condition of very few samples, so as to achieve the enrichment and supplement of intermittent failure data.
Ship Detection via Dilated Rate Search and Attention-Guided Feature Representation

Due to the complexity of scene interference and the variability of ship scale and position, automatic ship detection in remote sensing images makes for challenging research. The existing deep networks rarely design receptive fields that fit the target scale based on training data. Moreover, most of them ignore the effective retention of position information in the feature extraction process, which reduces the contribution of features to subsequent classification. To overcome these limitations, we propose a novel ship detection framework combining the dilated rate selection and attention-guided feature representation strategies, which can efficiently detect ships of different scales under the interference of complex environments such as clouds, sea clutter and mist. Specifically, we present a dilated convolution parameter search strategy to adaptively select the dilated rate for the multi-branch extraction architecture, adaptively obtaining context information of different receptive fields without sacrificing the image resolution. Moreover, to enhance the spatial position information of the feature maps, we calculate the correlation of spatial points from the vertical and horizontal directions and embed it into the channel compression coding process, thus generating the multi-dimensional feature descriptors which are sensitive to direction and position characteristics of ships. Experimental results on the Airbus dataset demonstrate that the proposed method achieves state-of-the-art performance compared with other detection models.
Validating Social Distancing through Deep Learning and VR-Based Digital Twins

The Covid-19 pandemic resulted in a catastrophic loss to global economies, and social distancing was consistently found to be an effective means to curb the virus's spread. However, it is only as effective when every individual partakes in it with equal alacrity. Past literature outlined scenarios where computer vision was used to detect people and to enforce social distancing automatically. We have created a Digital Twin (DT) of an existing laboratory space for remote monitoring of room occupancy and automatically detecting violation of social distancing. To evaluate the proposed solution, we have implemented a Convolutional Neural Network (CNN) model for detecting people, both in a limited-sized dataset of real humans, and a synthetic dataset of humanoid figures. Our proposed computer vision models are validated for both real and synthetic data in terms of accurately detecting persons, posture, and intermediate distances among people.
Hybrid Quantum Deep Learning with Differential Privacy for Botnet DGA Detection

In the DNS query-based botnet domain generation algorithm (DGA) detection, one might argue that domain names in DNS query data might disclose sensitive information related to browsing histories. User privacy preservation is important in the current personal data protection (PDP) era. This paper proposed implementing the differential privacy approach to the hybrid quantum deep learning model for botnet DGA detection. The proposed model consists of traditional deep learning layers and a quantum layer by combining angle embedding and random layer circuits from the Pennylane framework. We used ten botnet DGA datasets: Conficker, Cryptolocker, Goz, Matsnu, New_Goz, Pushdo, Ramdo, and Rovnix. We conducted experiments with considering noise models of eight IBM quantum devices: (ibmq_5_yorktown, ibmq_armonk, ibmq_athens, ibmq_belem, ibmq_lima, ibmq_quito, ibmq_santiago, and ibmqx2). We found that our proposed hybrid quantum model delivers a satisfactory performance (92.4% of maximum accuracy), superior to the classical deep learning counterpart. However, the hyperparameters of the differential privacy implementations (l2_norm_clip, noise_multiplier, microbatches, and learning_rate) still need to be tuned to improve the privacy guarantee of our proposed models.
An improved gas extraction model during stepwise crushing: New perspectives on fluid geochronology and geochemistry

Trapped fossil fluids in mineral-hosted fluid inclusions preserve pivotal information on the evolution of paleo geological systems, particularly the hydrothermal mineralization systems. However, identification of the sources of the extracted gases during bulk analysis of fluid inclusions is challenging because multiple generations, types, and phases of fluid inclusions may exist in the same mineral. To better identify the gas sources during stepwise crushing we report an improved gas extraction model of fluid inclusions. Stepwise extracted gases from fluid inclusions in quartz and cassiterite were measured with a quadrupole mass spectrometer and compared with Raman analysis of different types of secondary (SFIs) and primary (PFIs) fluid inclusions. We found that H2O-poor and CO2-rich gases with elevated CO2/CH4 ratios, H2O and formamide-rich gases, and CH4-rich gases were extracted sequentially during stepwise crushing. Such compositional variations of the released gases suggest that the extraction of fluid inclusions starts from SFIs with high vapor-filling degrees (f) and CO2-bearing tri-phase high-f fluid inclusions, which is followed by large liquid-rich SFIs and mid-sized high-f PFIs that are rich in CO2 and formamide, and ends with small CH4-rich low-f PFIs. These findings suggest that the degassing pattern of hydrothermal minerals during stepwise crushing is mostly governed by the physicochemical characteristics of the fluid inclusions, in particular, their vapor-filling degrees, sizes, compositions (which can lead to different inner pressures and densities), and associations with micro-cracks. This updated model of gas release patterns strengthens the theoretical basis for differentiating and identifying the sources of gases extracted during stepwise crushing experiments, which is key to understanding the fluid geochronology and geochemistry of hydrothermal deposits with multi-stage hydrothermal evolution in general.
TAGen: Generating Trigger-Action Rules for Smart Homes by Mining Event Traces

A smart home facilities human daily lives by orchestrating IoT devices through trigger-action (TA) rules. However, creating TA rules is challenging for novice users as (1) it requires comprehensive domain knowledge, (2) the created rules often deviate from user intents, and (3) errors are usually inevitable. To address these challenges, this paper proposes TAGen, an approach to automating TA rule generations by mining historical event traces. TAGen augments events and event traces with contextual information based on a deep understanding of a smart home system. It synthesizes TA rules by identifying frequent event pairs, inferring potential conditions, and heuristically filtering and ranking rule candidates. We implement a prototype and evaluate it with a preliminary experiment, and the experimental results show that TAGen effectively generates TA rules aligned with user behaviors.
Agents’ ontologies negotiation in cohesive hybrid intelligent multi-agent systems

Abstract The paper proposes the model for negotiating intelligent agents’ ontologies in cohesive hybrid intelligent multi-agent systems. Intelligent agent in this study will be called relatively autonomous software entity with developed domain models and goal-setting mechanisms. When such agents have to work together within single hybrid intelligent multi-agent systems to solve some problem, the working process “go wild”, if there are significant differences between the agents’ “points of view” on the domain, goals and rules of joint work. In this regard, in order to reduce labor costs for integrating intelligent agents into a single system, the concept of cohesive hybrid intelligent multi-agent systems was proposed that implement mechanisms for negotiating goals, domain models and building a protocol for solving the problems posed. The presence of these mechanisms is especially important when building intelligent systems from intelligent agents created by various independent development teams.
Proposal for a Crop Protection Information System for Rural Farmers in Tanzania

Crop protection information, such as how to control emergent and outbreak crop diseases and pests, as well as the latest research, regulations, and quality control measures for pesticides and fertilizers, is important to farmers. Rural smallholder farmers in Tanzania have traditionally relied on government agricultural officers who visit them in their villages to provide this crop protection information. However, these officers are few and cannot reach all the farmers on time. This means that farmers fail to make critical farming decisions on time, which can lead to low crop productivity. In this study, we aim to provide farmers with reliable and instant crop protection information by developing a system based on the Short Message Service (SMS) and the Web. This system automatically replies to farmers’ requests for the latest crop protection information in the Swahili language through SMS on a mobile phone or a Web system. The findings reveal that our proposed system can provide farmers with crop protection information at lower cost (500 times cheaper) than the existing Tigo Kilimo system. Furthermore, our proposed system’s deep learning model is effective in understanding and processing Swahili natural language SMS queries for crop protection information with an accuracy of 96.43%. This crop protection information will help farmers make better critical farming decisions on time and improve crop productivity.
Statistical modeling and inference in the era of Data Science and Graphical Causal modeling

Abstract The paper discusses four paradigm shifts in statistics since the 1920s with a view to compare their similarities and differences, and evaluate their effectiveness in giving rise to ‘learning from data' about phenomena of interest. The first is Fisher's 1922 recasting of Karl Pearson's descriptive statistics into a model‐based [ ] statistical induction that dominates current statistics (frequentist and Bayesian). A crucial departure was Fisher's replacing the curve‐fitting perspective guided by goodness‐of‐fit measures with a model‐based perspective guided by the statistical adequacy : the validity of the probabilistic assumptions comprising . Statistical adequacy is pivotal in securing trustworthy evidence since it underwrites the reliability of inference. The second is the nonparametric turn in the 1970s aiming to broaden by replacing its distribution assumption with weaker mathematical conditions relating to the unknown density function underlying . The third is a two‐pronged development initiated in Artificial Intelligence (AI) in the 1990s that gave rise to Data Science (DS) and Graphical Causal (GC) modeling. The primary objective of the paper is to compare and evaluate the other competing approaches with a refined/enhanced version of Fisher's model‐based approach in terms of their effectiveness in giving rise to genuine “learning from data;” excellent goodness‐of‐fit/prediction is neither necessary nor sufficient for statistical adequacy, or so it is argued.
A Discussion of Machine Learning Approaches for Clinical Prediction Modeling

While machine learning has occupied a niche in clinical medicine for decades, continued method development and increased accessibility of medical data have led to broad diversification of approaches. These range from humble regression-based models to more complex artificial neural networks; yet, despite heterogeneity in foundational principles and architecture, the spectrum of machine learning approaches to clinical prediction modeling have invariably led to the development of algorithms advancing our ability to provide optimal care for our patients. In this chapter, we briefly review early machine learning approaches in medicine before delving into common approaches being applied for clinical prediction modeling today. For each, we offer a brief introduction into theory and application with accompanying examples from the medical literature. In doing so, we present a summarized image of the current state of machine learning and some of its many forms in medical predictive modeling.
An initial evaluation of research data management online training at the University of New South Wales

In response to low research data management engagement at the University of New South Wales, Australia, an introductory research data management online training was developed and rolled out to all newly enrolled Higher Degree Research candidates. This article outlines the development process of the research data management online training and provides an initial evaluation of the training from the perspectives of the candidates and the university. As such, this article joins up with existing literature on research data management training to assist institutions and research data management stakeholders with the development of research data management training to help researchers and research students enact research data management best practices. Overall, the majority of the candidates ( n = 643) were satisfied with the quality of the training and found it helpful. The benefits the training brought to the university are mapped out by linking institutional research data management problems, the research data management online training’s design and findings into a coherent narrative.
End-to-End Learning for Fair Ranking Systems

The learning-to-rank problem aims at ranking items to maximize exposure of those most relevant to a user query. A desirable property of such ranking systems is to guarantee some notion of fairness among specified item groups. While fairness has recently been considered in the context of learning-to-rank systems, current methods cannot provide guarantees on the fairness of the predicted rankings. This paper addresses this gap and introduces Smart Predict and Optimize for Fair Ranking (SPOFR), an integrated optimization and learning framework for fairness-constrained learning to rank. The end-to-end SPOFR framework includes a constrained optimization sub-model and produces ranking policies that are guaranteed to satisfy fairness constraints, while allowing for fine control of the fairness-utility tradeoff. SPOFR is shown to significantly improve on current state-of-the-art fair learning-to-rank systems with respect to established performance metrics.
What Does Your Computational Imaging Algorithm Not Know?: A Plug-and-Play Model Quantifying Model Uncertainty

Plug-and-Play is an algorithmic framework developed to solve image recovery problems. Thanks to the empirical success of convolutional neural network (CNN) denoisers, numerous Plug-and-Play algorithms utilizing CNN denoisers have been proposed to solve various image recovery tasks. Unfortunately, those Plug-and-Play algorithms lack representing the uncertainty on the parameters of CNN denoisers because their training procedure yields only a point estimate for the parameters of the CNN denoiser. In this paper, we present a novel Plug-and-Play model that quantifies the uncertainty on the parameters of the CNN denoiser. The proposed model places a probability distribution on the parameters of the CNN denoiser and carries out approximate Bayesian inference to obtain the posterior distribution of the parameters to characterize their uncertainty. The uncertainty information provided by the proposed Plug-and-Play model allows characterizing how certain the model is for a given input. The proposed Plug-and-Play model is applicable to a broad set of computational imaging problems, with the requirement that the data fidelity term is differentiable, and has a simple implementation in deep learning frameworks. We evaluate the proposed Plug-and-Play model on a magnetic resonance imaging reconstruction problem and demonstrate its uncertainty characterization capability.
Privacy-Preserving Worker Recruitment Under Variety Requirement in Spatial Crowdsourcing

With the rapid growth of spatial crowdsourcing applications, more and more people are benefiting from it. The idea of spatial crowdsourcing is recruiting a set of workers to finish the spatial tasks. Existing worker recruitment mechanisms do not consider the variety requirement, which is easy to meet if the Spatial Crowdsourcing (SC) platform has full knowledge of the data of each worker. Since the SC platform is not fully trusted, workers are concerned about the privacy of their data. To prevent information leaks, workers’ data needs to be specially processed before it can be sent to untrusted platforms for task assignment. The data specially processed by existing privacy-preserving processing methods cannot be used directly to complete such variety tasks with high quality. To solve this problem, we propose a new variety optimization method based on the classical local differential privacy (LDP) mechanism. It can efficiently select the sets of workers with variety of categorical attributes while providing privacy protection for workers. In addition, we also propose a two-step LDP perturbation protocol that can improve the optimization result in the case of uneven distribution of worker attributes. Extensive experiments on synthetic and real datasets show that our methods can efficiently select variety worker subset with better task quality than baseline and close to optimal selection results.
The Virtual Reality Technology (VR) Application in College Physical Education and Training

Computer VR technology is a relatively advanced science and technology. Because this technology has good effects, it has been applied to many fields, and it also has its presence in the field of education. In college physical education, computer VR technology has been put into full play. Using its technical advantages, it is well integrated with sports practice to improve students' sports technical level and physical fitness, and effectively assist colleges Teaching activities provide relatively large and objective data for physical education, provide an effective basis for the development of physical education, and cultivate more talents for Chinese sports.