Reviewerly/exHarmony huggingface dataset:
SignPose: Sign Language Animation Through 3D Pose Lifting

Sign Language Generation (SLG) is a challenging task in computer animation as it involves capturing intricate hand gestures accurately, for several thousand signs in each sign language. Traditional methods require expensive equipment and considerable human involvement. In this paper, we provide a method to automate this process using only plain RGB images to generate sign poses for an avatar - the first of its kind for SLG. Current state of the art models for human 3D pose estimation do not perform satisfactorily in SLG due to the large difference between tasks. The datasets they are trained on contain only tasks like walking and playing sports, which involve significantly different types of motion compared to signing. Synthetic, manually created 3D animations are available for diverse tasks including sign language performance. Modern 2D pose estimation models which work on real world images are also robust enough to work on these animations accurately. Inspired by this, we formulate a novel method of leveraging animation data, using an intermediate 2D pose representation, to train an SLG animation model that works on real world sign language performance videos. To create the dataset for training, we extend an available animated dataset of signs in the Indian Sign Language (ISL) by permuting different hand and body motions. A novel quaternion based architecture is created to perform the task of lifting the 2D keypoints to 3D. The architecture is simplified to match the requirements of our task as well as to work with our smaller dataset size. We train a model, SignPose, using this architecture on the constructed dataset and demonstrate that it matches or outperforms current models for human pose reconstruction for the Sign Language Generation task. We will release both the dataset as well the model to the public to encourage further research in this field.
Social Recommendation System Based on Hypergraph Attention Network

In recent years, due to the rise of online social platforms, social networks have more and more influence on our daily life, and social recommendation system has become one of the important research directions of recommendation system research. Because the graph structure in social networks and graph neural networks has strong representation capabilities, the application of graph neural networks in social recommendation systems has become more and more extensive, and it has also shown good results. Although graph neural networks have been successfully applied in social recommendation systems, their performance may still be limited in practical applications. The main reason is that they can only take advantage of pairs of user relations but cannot capture the higher-order relations between users. We propose a model that applies the hypergraph attention network to the social recommendation system (HASRE) to solve this problem. Specifically, we take the hypergraph’s ability to model high-order relations to capture high-order relations between users. However, because the influence of the users’ friends is different, we use the graph attention mechanism to capture the users’ attention to different friends and adaptively model selection information for the user. In order to verify the performance of the recommendation system, this paper carries out analysis experiments on three data sets related to the recommendation system. The experimental results show that HASRE outperforms the state-of-the-art method and can effectively improve the accuracy of recommendation.
Path Planning Generator with Metadata through a Domain Change by GAN between Physical and Virtual Environments

Increasingly, robotic systems require a level of perception of the scenario to interact in real-time, but they also require specialized equipment such as sensors to reach high performance standards adequately. Therefore, it is essential to explore alternatives to reduce the costs for these systems. For example, a common problem attempted by intelligent robotic systems is path planning. This problem contains different subsystems such as perception, location, control, and planning, and demands a quick response time. Consequently, the design of the solutions is limited and requires specialized elements, increasing the cost and time development. Secondly, virtual reality is employed to train and evaluate algorithms, generating virtual data. For this reason, the virtual dataset can be connected with the authentic world through Generative Adversarial Networks (GANs), reducing time development and employing limited samples of the physical world. To describe the performance, metadata information details the properties of the agents in an environment. The metadata approach is tested with an augmented reality system and a micro aerial vehicle (MAV), where both systems are executed in an authentic environment and implemented in embedded devices. This development helps to guide alternatives to reduce resources and costs, but external factors limit these implementations, such as the illumination variation, because the system depends on only a conventional camera.
MotifClass: Weakly Supervised Text Classification with Higher-order Metadata Information

We study the problem of weakly supervised text classification, which aims to classify text documents into a set of pre-defined categories with category surface names only and without any annotated training document provided. Most existing classifiers leverage textual information in each document. However, in many domains, documents are accompanied by various types of metadata (e.g., authors, venue, and year of a research paper). These metadata and their combinations may serve as strong category indicators in addition to textual contents. In this paper, we explore the potential of using metadata to help weakly supervised text classification. To be specific, we model the relationships between documents and metadata via a heterogeneous information network. To effectively capture higher-order structures in the network, we use motifs to describe metadata combinations. We propose a novel framework, named MotifClass , which (1) selects category-indicative motif instances, (2) retrieves and generates pseudo-labeled training samples based on category names and indicative motif instances, and (3) trains a text classifier using the pseudo training data. Extensive experiments on real-world datasets demonstrate the superior performance of MotifClass to existing weakly supervised text classification approaches. Further analysis shows the benefit of considering higher-order metadata information in our framework.
Discrete Latent Variable Models

We review the discrete latent variable approach, which is very popular in statistics and related fields. It allows us to formulate interpretable and flexible models that can be used to analyze complex datasets in the presence of articulated dependence structures among variables. Specific models including discrete latent variables are illustrated, such as finite mixture, latent class, hidden Markov, and stochastic block models. Algorithms for maximum likelihood and Bayesian estimation of these models are reviewed, focusing, in particular, on the expectation–maximization algorithm and the Markov chain Monte Carlo method with data augmentation. Model selection, particularly concerning the number of support points of the latent distribution, is discussed. The approach is illustrated by summarizing applications available in the literature; a brief review of the main software packages to handle discrete latent variable models is also provided. Finally, some possible developments in this literature are suggested.
Moderator factors of software security and performance verification

Security and performance are critical software non-functional requirements. Therefore, verification activities should be included in the development process to identify related defects, avoiding failures after deployment. However, there is a lack of understanding on factors moderating the security and performance verification, which jeopardizes organizations to improve their verification activities to assure the releasing of software fulfilling these requirements. To identify moderator factors influencing security and performance verification and actions to promote them. Case study to identify security and performance moderators factors. Rapid Literature Reviews with Snowballing to strengthen moderator factors confidence. Practitioners Survey to classify the moderator factors relevance. Identification of eight security and performance moderator factors regarding organizational awareness, cross-functional team, suitable requirements, support tools, verification environment, verification methodology, verification planning, and reuse practices. Rapid Reviews confirmed the moderator factors and revealed actions to promote each. A survey with 37 practitioners allowed us to classify the moderator factors and their actions regarding their relevancy. The moderator factors can be considered key points to software development organizations implement/improve security and performance verification activities in regular software systems. Further investigation is necessary to support the understanding of these moderator factors when building modern software systems.
Learning an augmentation strategy for sparse datasets

The limited quantity of training data can hamper supervised machine learning methods, that generally need large amounts of data to avoid overfitting. Data augmentation has a long history of use with machine learning algorithms and is a straightforward method to overcome overfitting and improve model generalisation. However, data augmentation schemes are typically designed by hand and demand substantial domain knowledge to create suitable data transformations. This paper introduces a GAN based method that automatically learns an augmentation strategy appropriate for sparse datasets and can improve pixel-level semantic segmentation accuracy by filling the gaps in the training set. Our method can also be combined with other augmentation techniques to further improve performance. We evaluate the proposed method's feasibility on four datasets and three semantic segmentation models, leading to improvement in the mean intersection-over-union (mIoU) score of between 0.5 and 14 percentage points, under different circumstances.
What’s in a Name? Answer Equivalence For Open-Domain Question Answering

A flaw in QA evaluation is that annotations often only provide one gold answer.Thus, model predictions semantically equivalent to the answer but superficially different are considered incorrect.This work explores mining alias entities from knowledge bases and using them as additional gold answers (i.e., equivalent answers).We incorporate answers for two settings: evaluation with additional answers and model training with equivalent answers.We analyse three QA benchmarks: Natural Questions, TriviaQA and SQuAD.Answer expansion increases the exact match score on all datasets for evaluation, while incorporating it helps model training over real-world datasets.We ensure the additional answers are valid through a human post hoc evaluation. 1
Gradient-Descent for Randomized Controllers Under Partial Observability

Randomization is a powerful technique to create robust controllers, in particular in partially observable settings. The degrees of randomization have a significant impact on the system performance, yet they are intricate to get right. The use of synthesis algorithms for parametric Markov chains (pMCs) is a promising direction to support the design process of such controllers. This paper shows how to define and evaluate gradients of pMCs. Furthermore, it investigates varieties of gradient descent techniques from the machine learning community to synthesize the probabilities in a pMC. The resulting method scales to significantly larger pMCs than before and empirically outperforms the state-of-the-art, often by at least one order of magnitude.
Automatic Text Evaluation through the Lens of Wasserstein Barycenters

A new metric BaryScore to evaluate text generation based on deep contextualized embeddings (e.g., BERT, Roberta, ELMo) is introduced.This metric is motivated by a new framework relying on optimal transport tools, i.e., Wasserstein distance and barycenter.By modelling the layer output of deep contextualized embeddings as a probability distribution rather than by a vector embedding; this framework provides a natural way to aggregate the different outputs through the Wasserstein space topology.In addition, it provides theoretical grounds to our metric and offers an alternative to available solutions (e.g., Mover-Score and BertScore).Numerical evaluation is performed on four different tasks: machine translation, summarization, data2text generation and image captioning.Our results show that BaryScore outperforms other BERT based metrics and exhibits more consistent behaviour in particular for text summarization.
An efficient interactive multi-label segmentation tool for 2D and 3D medical images using fully connected conditional random field

Image segmentation is a crucial and fundamental step in many medical image analysis tasks, such as tumor measurement, surgery planning, disease diagnosis, etc. To ensure the quality of image segmentation, most of the current solutions require labor-intensive manual processes by tracing the boundaries of the objects. The workload increases tremendously for the case of three dimensional (3D) image with multiple objects to be segmented.In this paper, we introduce our developed interactive image segmentation tool that provides efficient segmentation of multiple labels for both 2D and 3D medical images. The core segmentation method is based on a fast implementation of the fully connected conditional random field. The software also enables automatic recommendation of the next slice to be annotated in 3D, leading to a higher efficiency.We have evaluated the tool on many 2D and 3D medical image modalities (e.g. CT, MRI, ultrasound, X-ray, etc.) and different objects of interest (abdominal organs, tumor, bones, etc.), in terms of segmentation accuracy, repeatability and computational time.In contrast to other interactive image segmentation tools, our software produces high quality image segmentation results without the requirement of parameter tuning for each application. Both the software and source code are freely available for research purpose1. 1Software and source code download: https://drive.google.com/file/d/1JIzWkT3M-X7jeB8tTwVcEw240TGbJAvj/view?usp=sharing.
Resources for Turkish dependency parsing: introducing the BOUN Treebank and the BoAT annotation tool

In this paper, we introduce the resources that we developed for Turkish dependency parsing, which include a novel manually annotated treebank (BOUN Treebank), along with the guidelines we adopted, and a new annotation tool (BoAT). The manual annotation process that we employed was shaped and implemented by a team of four linguists and five Natural Language Processing (NLP) specialists. Decisions regarding the annotation of the BOUN Treebank were made in line with the Universal Dependencies (UD) framework as well as our recent efforts for unifying the Turkish UD treebanks through manual re-annotation. To the best of our knowledge, the BOUN Treebank is the largest Turkish UD treebank. It contains a total of 9761 sentences from various topics including biographical texts, national newspapers, instructional texts, popular culture articles, and essays. In addition, we report the parsing results of a state-of-the-art dependency parser obtained over the BOUN Treebank as well as two other treebanks in Turkish. Our results demonstrate that the unification of the Turkish annotation scheme and the introduction of a more comprehensive treebank lead to improved performance with regards to dependency parsing.
A Proxy-Authorized Public Auditing Scheme for Cyber-Medical Systems Using AI-IoT

Artificial intelligence based Internet of Things enables autonomous communication among social networks and IoT to leverage the promising solution in the modern paradigms. It can provide an interactive platform across the globe to enrich the quality of networking services to the end users. Of late, the expansion of information-centric networking has brought an incredible technique, known as public auditing scheme, for IoT-enabled sensor technologies. It uses a cloud-based medical cyber-physical system (M-CPS) to rely on cloud computing that ensures fast computing and reliable data storage. Since the medical file is so vital to involve precise diagnoses, data integrity and verification have lately become the data auditing tool. To exploit proxy authorizer and trusted auditor, an identity-based proxy authorized outsourcing with public auditing (ID-PAOPA) is proposed. It uses proxy authorization and verification to upload medical data over cloud-based M-CPS. To substantiate the finding, this article provides security proof based on the EC-DLP assumption. Finally, the performance analysis proves that the proposed ID-PAOPA achieves less computation and auditing timing to fulfill the objectives of cloud-based M-CPS.
Improving GAN Equilibrium by Raising Spatial Awareness

The success of Generative Adversarial Networks (GANs) is largely built upon the adversarial training between a generator (G) and a discriminator (D). They are expected to reach a certain equilibrium where D cannot distinguish the generated images from the real ones. However, such an equilibrium is rarely achieved in practical GAN training, instead, D almost always surpasses G. We attribute one of its sources to the information asymmetry between D and G. We observe that D learns its own visual attention when determining whether an image is real or fake, but G has no explicit clue on which regions to focus on for a particular synthesis. To alleviate the issue of D dominating the competition in GANs, we aim to raise the spatial awareness of G. Randomly sampled multi-level heatmaps are encoded into the intermediate layers of G as an inductive bias. Thus G can purposefully improve the synthesis of certain image regions. We further propose to align the spatial awareness of G with the attention map induced from D. Through this way we effectively lessen the information gap between D and G. Extensive results show that our method pushes the two-player game in GANs closer to the equilibrium, leading to a better synthesis performance. As a byproduct, the intro-duced spatial awareness facilitates interactive editing over the output synthesis. Demo video and code are available at https://genforce.github.io/eqgan-sa/
Fuzzy Expert System of Information Security Risk Assessment on the Example of Analysis Learning Management Systems

The rapid development and application of new digital technologies has, on the one hand, opened up new opportunities for more efficient management of technological and business processes. On the other hand, this leads to a significant increase in security threats, increasing the vulnerability of businesses and organisations to cybercriminals. In recent years, the rapid growth of incidents of various kinds has shown that traditional approaches to information security (IS) are insufficient. Consequently, software product information security risk assessment has become an important task for most organisations. Several models have been proposed to help different enterprises deal with the challenges of building information security. This paper proposes a new hierarchical structured model for information security risk assessment using fuzzy logic. A new method for information security risk assessment of software is also described using the example of automated control systems or enterprise resource planning (ERP) systems (using learning management systems as an example). The proposed new risk assessment model has been software implemented using fuzzy logic in the form of 15 fuzzy machines. In a series of experiments, we have scrutinised the information security risk assessment of various software products. The proposed method should solve the problem of flexible risk assessment.
Viewpoint Robustness of Automated Facial Action Unit Detection Systems

Automatic facial action detection is important, but no previous studies have evaluated pre-trained models on the accuracy of facial action detection as the angle of the face changes from frontal to profile. Using static facial images obtained at various angles (0°, 15°, 30°, and 45°), we investigated the performance of three automated facial action detection systems (FaceReader, OpenFace, and Py-feat). The overall performance was best for OpenFace, followed by FaceReader and Py-Feat. The performance of FaceReader significantly decreased at 45° compared to that at other angles, while the performance of Py-Feat did not differ among the four angles. The performance of OpenFace decreased as the target face turned sideways. Prediction accuracy and robustness to angle changes varied with the target facial components and action detection system.
Performance Analysis of the IOTA DAG-Based Distributed Ledger

Distributed ledgers (DLs) provide many advantages over centralized solutions in Internet of Things projects, including but not limited to improved security, transparency, and fault tolerance. To leverage DLs at scale, their well-known limitation (i.e., performance) should be adequately analyzed and addressed. Directed acyclic graph-based DLs have been proposed to tackle the performance and scalability issues by design. The first among them, IOTA, has shown promising signs in addressing the preceding issues. IOTA is an open source DL designed for the Internet of Things. It uses a directed acyclic graph to store transactions on its ledger, to achieve a potentially higher scalability over blockchain-based DLs. However, due to the uncertainty and centralization of the deployed consensus, the current IOTA implementation exposes some performance issues, making it less performant than the initial design. In this article, we first extend an existing simulator to support realistic IOTA simulations and investigate the impact of different design parameters on IOTA’s performance. Then, we propose a layered model to help the users of IOTA determine the optimal waiting time to resend the previously submitted but not yet confirmed transaction. Our findings reveal the impact of the transaction arrival rate, tip selection algorithms, weighted tip selection algorithm randomness, and network delay on the throughput. Using the proposed layered model, we shed some light on the distribution of the confirmed transactions. The distribution is leveraged to calculate the optimal time for resending an unconfirmed transaction to the DL. The performance analysis results can be used by both system designers and users to support their decision making.