Reviewerly/exHarmony huggingface dataset:
Backhand-View-Based Continuous-Signed-Letter Recognition Using a Rewound Video Sequence and the Previous Signed-Letter Information

In sign language, when signed letters are continuously spelled based on backhand view, a previous signed letter influences the trajectory of hand and fingers approaching the pause duration for signing the current signed letter.Since those varied trajectories are regarded as parts of the current signed letter, hand gesture during pause duration of the current signed letter is regarded as insufficient for recognition of the current signed letter.The previous signed letters, and trajectories of hand and fingers between the previous and the current signed letters should be included as data for classification.This paper proposes a method of backhand-view-based continuous-signed-letter recognition using a rewound video sequence with previous signed letter.In the method, a hand shape of previous signed letter and trajectories of finger joints moving from the previous signed letter to the current one are detected, features are then extracted, and finally, the features are classified for signed letter recognition.To evaluate performance of the proposed method, experiments with 10 participants were performed 20 times each, and the results revealed 96.07%accuracy approximately which were improved significantly from the conventional methods using forehand and backhand.
Towards Fine-Grained Prosody Control for Voice Conversion

In a typical voice conversion system, previous works utilized various acoustic features (such as the pitch, voiced/unvoiced flag and aperiodicity) of the source speech to control the prosody of converted speech. However, prosody is related with many factors, such as the intonation, stress and rhythm. It is a challenging task to perfectly describe prosody through hand-crafted acoustic features. To address these difficulties, we propose to use prosody embeddings to describe prosody. These embeddings are learned from the source speech in an unsupervised manner. To verify the effectiveness of our proposed method, we conduct experiments on our Mandarin corpus. Experimental results show that our proposed method can improve the speech quality and speaker similarity of the converted speech. What's more, we observe that our method can even achieve promising results in singing conditions.
The application of augmented reality in elementary school education

The application of Augmented Reality in the education sector has increased, the novelty of Augmented Reality technology has become a special attraction for stake holders to improve the quality of education. However, so far there is still a lack of literature studies that focus on investigating the application of Augmented Reality to students at the basic education level. This study presents a literature review on the use of Augmented Reality technology to students at the basic education level. Articles that were synthesized from 2010 to 2020 indexed by Google Schoolar were 10 articles. Using qualitative content analysis, this study attempts to answer two questions; (1) What is the impact of the application of Augmented Reality on the learning outcomes of Elementary School Students? and (2) how does the Pedagogic Perspective view Augmented Reality?
LSSD: A Controlled Large JPEG Image Database for Deep-Learning-Based Steganalysis “Into the Wild”

For many years, the image databases used in steganalysis have been relatively small, i.e. about ten thousand images. This limits the diversity of images and thus prevents large-scale analysis of steganalysis algorithms. In this paper, we describe a large JPEG database composed of 2 million colour and grey-scale images. This database, named LSSD for Large Scale Steganalysis Database, was obtained thanks to the intensive use of “controlled” development procedures. LSSD has been made publicly available, and we aspire it could be used by the steganalysis community for large-scale experiments. We introduce the pipeline used for building various image database versions. We detail the general methodology that can be used to redevelop the entire database and increase even more the diversity. We also discuss computational cost and storage cost in order to develop images.
Digital video forensics: a comprehensive survey

The wide spread of digital devices and tools causes the simplification of the manipulation of any digital multimedia content. As a result, digital videos and photos are not trusted to be used as evidence in courts. This fact raises the need for finding techniques to ensure the authenticity of digital multimedia contents. Experts in digital-signal processing conducted a huge number of researches to find new strategies, using digital forensics, to verify digital evidences and trace its origins. The aim of this paper is to collect and provide the definitions of the main concepts related to media forensics. Also, this paper gives an overview of the different techniques used in media forensics concentrating on video forensics. Furthermore, it classifies the work done in the field according to the main technique used in the proposed solution approach.
Exploiting hierarchy in medical concept embedding*

Abstract Objective To construct and publicly release a set of medical concept embeddings for codes following the ICD-10 coding standard which explicitly incorporate hierarchical information from medical codes into the embedding formulation. Materials and Methods We trained concept embeddings using several new extensions to the Word2Vec algorithm using a dataset of approximately 600,000 patients from a major integrated healthcare organization in the Mid-Atlantic US. Our concept embeddings included additional entities to account for the medical categories assigned to codes by the Clinical Classification Software Revised (CCSR) dataset. We compare these results to sets of publicly released pretrained embeddings and alternative training methodologies. Results We found that Word2Vec models which included hierarchical data outperformed ordinary Word2Vec alternatives on tasks which compared naïve clusters to canonical ones provided by CCSR. Our Skip-Gram model with both codes and categories achieved 61.4% normalized mutual information with canonical labels in comparison to 57.5% with traditional Skip-Gram. In models operating on two different outcomes, we found that including hierarchical embedding data improved classification performance 96.2% of the time. When controlling for all other variables, we found that co-training embeddings improved classification performance 66.7% of the time. We found that all models outperformed our competitive benchmarks. Discussion We found significant evidence that our proposed algorithms can express the hierarchical structure of medical codes more fully than ordinary Word2Vec models, and that this improvement carries forward into classification tasks. As part of this publication, we have released several sets of pretrained medical concept embeddings using the ICD-10 standard which significantly outperform other well-known pretrained vectors on our tested outcomes.
A Descriptive Study on Cybersecurity Challenges of Working from Home during COVID-19 Pandemic and a Proposed 8 step WFH Cyber-attack Mitigation Plan

Organizations struggle to strike the right balance between flexibility and security for remote work. With the major portion of the workforce in every country working from home during the covid-19 pandemic, employers might not have accounted for this scale of load on their IT infrastructure, not to mention their cybersecurity preparedness.
Smart Surveillance Systems with Edge Intelligence: Convergence of Deep Learning and Edge Computing

With the advancement of cloud computing technologies and availability of cheaper hardware; surveillance systems are getting more reliable and accurate. Unfortunately, most of the existing systems still face a lot of limitations like latency issues, scalability issues, privacy concerns, delayed inference, reliability issues, etc. They hamper the quality and performance of surveillance systems in real-world scenarios like license plate recognition on mobile police cars, autonomous drones, etc. An extremely smart approach to tackle all such issues is to distribute some, if not all, of the computing and analytics to the edge devices. When we try to shift this computing to source of data, edge computing and artificial intelligence (for analytics) can be very helpful (especially deep learning for video-based systems). As artificial intelligence and edge computing naturally converge, their combination gives rise to the technology of the future -- Edge Intelligence. In this paper, we discuss problems with existing surveillance systems; and the use of deep learning and edge computing for the same. We introduce their combination (Edge Intelligence) as the solution to most of the major issues. We also discuss its various benefits and acceleration techniques, along with some applications in real-world scenarios along with relevant research works for further development in Edge Intelligence.
Unsupervised Adversarial Domain Adaptation with Error-Correcting Boundaries and Feature Adaption Metric for Remote-Sensing Scene Classification

Unsupervised domain adaptation (UDA) based on adversarial learning for remote-sensing scene classification has become a research hotspot because of the need to alleviating the lack of annotated training data. Existing methods train classifiers according to their ability to distinguish features from source or target domains. However, they suffer from the following two limitations: (1) the classifier is trained on source samples and forms a source-domain-specific boundary, which ignores features from the target domain and (2) semantically meaningful features are merely built from the adversary of a generator and a discriminator, which ignore selecting the domain invariant features. These issues limit the distribution matching performance of source and target domains, since each domain has its distinctive characteristic. To resolve these issues, we propose a framework with error-correcting boundaries and feature adaptation metric. Specifically, we design an error-correcting boundaries mechanism to build target-domain-specific classifier boundaries via multi-classifiers and error-correcting discrepancy loss, which significantly distinguish target samples and reduce their distinguished uncertainty. Then, we employ a feature adaptation metric structure to enhance the adaptation of ambiguous features via shallow layers of the backbone convolutional neural network and alignment loss, which automatically learns domain invariant features. The experimental results on four public datasets outperform other UDA methods of remote-sensing scene classification.
Adapting training in real time: An empirical test of adaptive difficulty schedules

Adaptive Training (AT) has been shown to be an effective technique for training tasks in multiple domains. Despite the promise AT has shown as a training technique, researchers remain unsure of the specific qualities that improve learning. In this experiment, we examined how adaptation schedule affects the efficacy and efficiency of difficulty adaptation in computer-based training. We used Cognitive Load Theory to guide our predictions about performance gains. In the reported study, we hypothesized that an adaptation schedule that adapts more frequently would lead to superior performance. To test this, we examined two types of difficulty adaptation (i.e., Within-Adaptive & Between-Adaptive) schedules using an audio-visual change detection task over five 10-minute scenarios. The Within-Adaptive condition adapted difficulty throughout the scenario based on trainee performance in real time. The Between-Adaptive condition adapted difficulty of subsequent scenarios based on previous scenario performance. We compared these two conditions to a Control condition, which maintained a constant difficulty throughout the experiment. We identified performance benefits for the Within-Adaptive condition, particularly for individuals whose performance was initially poor. A closer examination of the results suggested that average difficulty was the driving factor for performance gains in the Between-Adaptive condition. The data support that a within-scenario adaptation schedule effectively manages cognitive load to facilitate learning gains.
A block-based method for the remote sensing images cloud detection and removal

This paper aims to effectively detect and remove clouds on remote sensing (RS) images. The proposed algorithm has a two-stage procedure: (a) an efficient cloud detection algorithm and (b) an efficient inpainting algorithm for image reconstruction. We introduce an inpainting method to recover the images with cloud occlusion from the remote sensing image's surfaces using block-matching. The proposed approach has been evaluated on the remote sensing RGB image dataset with various aerial sceneries. Compared with traditional inpainting methods, both qualitative and quantitative results show that our approach has advantages over state-of-the-art methods.
Optimal Singular Value Decomposition Based Big Data Compression Approach in Smart Grids

The smart grid is a fully automatic delivery grid for electricity power with a two-way reliable flow of electricity and information among different equipment on the grid. Smart meters and sensors monitoring the system provide a huge amount of data in various part of smart grid. To logically manage this trouble, a new lossy data compression approach for big data compression is proposed. The optimal singular value decomposition (SVD) is applied to a matrix that achieves the optimal number of singular values to the sending process, and the other ones will be neglected. This goal is done due to the quality of retrieved data and the compression ratio. In the presented scheme, to implement the optimization framework, various intelligent optimization methods are used to determine the number of optimal values in the elimination stage. The efficiency and capabilities of the proposed method are examined using a wide range of data types, from electricity market data to image processing benchmarks. The comparisons show that the compression level obtained by the proposed method can dominate the points given by the existing SVD rank reduction methods. Also, as the other finding of this article, the performance of the rank reduction methods depends on the application and data types. It means that a rank reduction method can reveal a good performance in one application and performs unacceptably for another purpose. So, the optimized rank reduction can pave the way toward a robust and reliable performance.
Immersive Education for Chronic Condition Self-Management

Chronic conditions represent a significant twenty first century challenge. Education and self-management training are the mainstay of clinical intervention for such conditions since care is dependent on health literacy and self-management. This intervention not only imparts the necessary understanding and skills for self-management, but also helps people to overcome personal barriers to positive behavioral change, such as low self-efficacy. Moreover, education maximizes dignity, by enabling shared decision-making. A plethora of research supports the role of education and self-management training in the management of chronic conditions, whilst at the same time highlighting that not all approaches lead to meaningful behavioral change. Immersive virtual reality (VR) offers a unique set of features and tools for delivering these interventions. For example, the immersive nature focuses attention and promotes engagement; the ability to simulate authentic and interactive real-world scenarios can be used to promote the benefits of active learning; and the ability to facilitate embodiment of avatars with distinct appearance and capability can be used to bias new perceptions and behaviors in-line with the avatar's characteristics. Moreover, the ability to use VR independent of a clinician renders a potential solution to instances where significant barriers to healthcare access exist. This short perspective paper will discuss how VR may be used to host education and self-management interventions in the domain of chronic condition management. Further, it will outline considerations for developers and conclude with a call for the co-creation of new VR-based education and self-management interventions.
Evaluación de una formación online basada en Flipped classroom

This research analyses the effectiveness of the Flipped classroom pedagogical model in the performance and satisfaction of future teachers, students of the Master’s Degree in Teacher Training in a completely online environment. A quantitative methodology is followed with a quasi-experimental design with a non-equivalent control group. The control group is made up of 103 students and the experimental group is made up of 119. The academic performance of both groups is compared, before and after the intervention, with a knowledge questionnaire and evaluating the results of their learning throughout the semester. In addition, their opinion on the experience was studied by means of a satisfaction questionnaire at the end of the course. Both groups show an equivalent level of knowledge at the pretest stage, and differences have been found upon completion of the intervention, where the experimental group reaches higher levels, being the medium effect size (r=0.66). The activities utilized to evaluate the results of the learning show the existence of statistically significant differences, being the average ranges higher in the experimental group in all activities, with a very large effect size in three of them (r=0.89, r=0.90 and r=0.96) and large in another (r=0.68). In relation to the degree of satisfaction of the students with the experience developed, all the dimensions analyzed obtain scores above 3.56 (on a scale of 1 to 4). After the implementation of the Flipped classroom model, a highly positive effect has been determined in the improvement of student learning, both in performance and in motivation, commitment and interaction between students and teacher. The need to incorporate the Flipped classroom model in the initial training of the teaching staff is concluded.
Language Modelling as a Multi-Task Problem

In this paper, we propose to study language modelling as a multi-task problem, bringing together three strands of research: multitask learning, linguistics, and interpretability.Based on hypotheses derived from linguistic theory, we investigate whether language models adhere to learning principles of multi-task learning during training.To showcase the idea, we analyse the generalisation behaviour of language models as they learn the linguistic concept of Negative Polarity Items (NPIs).Our experiments demonstrate that a multi-task setting naturally emerges within the objective of the more general task of language modelling.We argue that this insight is valuable for multitask learning, linguistics and interpretability research and can lead to exciting new findings in all three domains.
Flightmare: A Flexible Quadrotor Simulator

State-of-the-art quadrotor simulators have a rigid and highly-specialized structure: either are they really fast, physically accurate, or photo-realistic. In this work, we propose a novel quadrotor simulator: Flightmare. Flightmare is composed of two main components: a configurable rendering engine built on Unity and a flexible physics engine for dynamics simulation. Those two components are totally decoupled and can run independently of each other. This makes our simulator extremely fast: rendering achieves speeds of up to 230 Hz, while physics simulation of up to 200,000 Hz on a laptop. In addition, Flightmare comes with several desirable features: (i) a large multi-modal sensor suite, including an interface to extract the 3D point-cloud of the scene; (ii) an API for reinforcement learning which can simulate hundreds of quadrotors in parallel; and (iii) integration with a virtual-reality headset for interaction with the simulated environment. We demonstrate the flexibility of Flightmare by using it for two different robotic tasks: quadrotor control using deep reinforcement learning and collision-free path planning in a complex 3D environment.
Multilingual Machine Translation: Closing the Gap between Shared and Language-specific Encoder-Decoders

State-of-the-art multilingual machine translation relies on a universal encoder-decoder, which requires retraining the entire system to add new languages.In this paper, we propose an alternative approach that is based on language-specific encoder-decoders, and can thus be more easily extended to new languages by learning their corresponding modules.So as to encourage a common interlingua representation, we simultaneously train the N initial languages.Our experiments show that the proposed approach outperforms the universal encoder-decoder by 3.28 BLEU points on average, while allowing to add new languages without the need to retrain the rest of the modules.All in all, our work closes the gap between shared and language-specific encoderdecoders, advancing toward modular multilingual machine translation systems that can be flexibly extended in lifelong learning settings.
The Missing Goal-Scorers in the Artificial Intelligence Team: Of Big Data, the Fundamental Right to Research and the failed Text and Data Mining Limitations in the CSDM Directive

This article argues that recent strategies of the European Union in the field of Artificial Intelligence (AI) resemble a football team missing a goal-scorer to win any of the competitions with other jurisdictions having more flexible limitations to copyright, in particular with those allowing robust text and data mining (TDM) activities. It analyses the TDM limitations newly introduced in EU copyright law by the Directive on Copyright in the Digital Single Market to show that these provisions not only fail to take duly into account the right to research grounded in the fundamental right to information, but also will not allow the European Union to provide a competitive environment for the development of AI and data-driven innovations. As a conclusion, the article calls for a prompt revision of the copyright framework for TDM activities at EU and international levels, combined with an implementation of the directive by Member States that would be compliant with the fundamental rights framework of the EU and the objective advanced by European policy makers.
Bayesian Structure Learning and Sampling of Bayesian Networks with the <i>R</i> Package <b>BiDAG</b>

The R package BiDAG implements Markov chain Monte Carlo (MCMC) methods for structure learning and sampling of Bayesian networks. The package includes tools to search for a maximum a posteriori (MAP) graph and to sample graphs from the posterior distribution given the data. A new hybrid approach to structure learning enables inference in large graphs. In the first step, we define a reduced search space by means of the PC algorithm or based on prior knowledge. In the second step, an iterative order MCMC scheme proceeds to optimize the restricted search space and estimate the MAP graph. Sampling from the posterior distribution is implemented using either order or partition MCMC. The models and algorithms can handle both discrete and continuous data. The BiDAG package also provides an implementation of MCMC schemes for structure learning and sampling of dynamic Bayesian networks.