Reviewerly/exHarmony huggingface dataset:
Resolving Position Ambiguity of IMU-Based Human Pose with a Single RGB Camera

Human motion capture (MoCap) plays a key role in healthcare and human–robot collaboration. Some researchers have combined orientation measurements from inertial measurement units (IMUs) and positional inference from cameras to reconstruct the 3D human motion. Their works utilize multiple cameras or depth sensors to localize the human in three dimensions. Such multiple cameras are not always available in our daily life, but just a single camera attached in a smart IP devices has recently been popular. Therefore, we present a 3D pose estimation approach from IMUs and a single camera. In order to resolve the depth ambiguity of the single camera configuration and localize the global position of the subject, we present a constraint which optimizes the foot-ground contact points. The timing and 3D positions of the ground contact are calculated from the acceleration of IMUs on foot and geometric transformation of foot position detected on image, respectively. Since the results of pose estimation is greatly affected by the failure of the detection, we design the image-based constraints to handle the outliers of positional estimates. We evaluated the performance of our approach on public 3D human pose dataset. The experiments demonstrated that the proposed constraints contributed to improve the accuracy of pose estimation in single and multiple camera setting.
Alexa, What classes do I have today? The use of Artificial Intelligence via Smart Speakers in Education

Looking back to the rumours from the early 2000’s, when the world of technology bloomed together with the curiosity towards what was next to come, by 2020, robots should have assisted and supported almost every task from our daily life. While this may seem as a Sci-Fi movie scenario, it is partially a tangible reality, that we quickly got used to, thanks to the introduction of smart speakers. As the world changes, so does the future of our students. In this respects, the evolution of the technology comes up with specific environments for educational purpose. Building smart learning environments supported by e-learning platforms is an important area of research in education domain within our days. The evolution of these smart learning environments is justified by some events (Covid19) that force students to learn remotely. The paper proposes a software application component using Alexa smart speaker, that integrates different services (Amazon Web Services, Microsoft Services) for a proper virtual environment platform, for both students and teachers. It addresses the main concerns of the current educational system, and provides a smart solution through the use of Artificial Intelligence based tools. The proposed approach not only achieves unifying data and knowledge-share mechanisms in a remotely mode, but it brings also a good learning experience, increasing the effectiveness and the efficiency of the learning process.
CPchain: A Copyright-Preserving Crowdsourcing Data Trading Framework Based on Blockchain

Crowdsourcing data trading is a novel paradigm in which the crowdsourcing technology is adopted to collect big data for trading. At present, existing crowdsourcing data trading systems usually depend on a trusted broker and haven't considered the truthfulness and the quality of data (QoD) simultaneously. Besides, copyright protection is the another issue that has not been properly addressed. To tackle these problems, we propose a Copyright-Preserving crowdsourcing data trading framework based on Blockchain, named CPchain, which mainly includes a smart contract. We design an auction algorithm based on semantic similarity to guarantee the truthfulness and individual rationality while ensuring QoD. Moreover, we combine digital fingerprint technology with blockchain to protect data copyright without a third-party certification authority. Furthermore, we develop a simple prototype of our proposed trading framework on the Ethereum test network. We have carried out a lot of experiments to demonstrate the significant performances of our framework.
Energy-Efficient Cluster Head Selection via Quantum Approximate Optimization

This paper proposes an energy-efficient cluster head selection method in the wireless ad hoc network by using a hybrid quantum-classical approach. The wireless ad hoc network is divided into several clusters via cluster head selection, and the performance of the network topology depends on the distribution of these clusters. For an energy-efficient network topology, none of the selected cluster heads should be neighbors. In addition, all the selected cluster heads should have high energy-consumption efficiency. Accordingly, an energy-efficient cluster head selection policy can be defined as a maximum weight independent set (MWIS) formulation. The cluster head selection policy formulated with MWIS is solved by using the quantum approximate optimization algorithm (QAOA), which is a hybrid quantum-classical algorithm. The accuracy of the proposed energy-efficient cluster head selection via QAOA is verified via simulations.
Review of Deployment of Machine Learning in Blockchain Methodology

The evolution of blockchain methodology has been a remarkable, highly transformative and trend-setting platform in current years.BT's accessible platform reinforces data protection and confidentiality.In addition, the consensus framework in it ensures system is protected and accurate.Nevertheless, it introduces additional security challenges such as invasion by the majority and double consumption.Data analysis on encrypted data centered on blockchain is crucial to manage the existing challenges.Insights on these results elevates the value of emerging of Machine Learning technique.It covers the fair quantity of data needed to make specific choices.Consistency of data and its distribution are very critical in ML to increase findings reliability.The fusion of these two techniques will produce extremely accurate outcomes.In this article, we describe a thorough analysis on ML implementation to make smart applications based on BT further robust to threats.There are numerous standard ML approaches such as Support Vector Machines (SVM), Clustering, Bagging, and Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN) and Long-Term Memory (LSTM) that can be employed to evaluate the threats on a block chain network.Finally, we discuss how two different techniques can be implemented in a number of smart applications like Unmanned Aerial Vehicle (UAV), Smart Grid (SG), medical care and Smart cities.
Evolutionary diversity optimization and the minimum spanning tree problem

In the area of evolutionary computation the calculation of diverse sets of high-quality solutions to a given optimization problem has gained momentum in recent years under the term evolutionary diversity optimization. Theoretical insights into the working principles of baseline evolutionary algorithms for diversity optimization are still rare. In this paper we study the well-known Minimum Spanning Tree problem (MST) in the context of diversity optimization where population diversity is measured by the sum of pairwise edge overlaps. Theoretical results provide insights into the fitness landscape of the MST diversity optimization problem pointing out that even for a population of μ = 2 fitness plateaus (of constant length) can be reached, but nevertheless diverse sets can be calculated in polynomial time. We supplement our theoretical results with a series of experiments for the unconstrained and constraint case where all solutions need to fulfill a minimal quality threshold. Our results show that a simple (μ + 1)-EA can effectively compute a diversified population of spanning trees of high quality.
Anonymous Electronic Health Record Sharing Scheme Based on Decentralized Hierarchical Attribute-Based Encryption in Cloud Environment

The rapid development of communication technologies, the network, advanced computing methods and wireless medical sensors gives rise to a modern medical system. In this system, large-scale electronic health records (EHRs) are often outsourced to be stored at the third parties, such as cloud service providers (CSPs). However, CSPs are not trustworthy, that is, serious security and privacy concerns about cloud service exist because it may expose the user's sensitive data to CSPs or unauthorized users in transmission, storage and sharing. To prevent the privacy disclosure of patients better and realize information sharing more effectively, this paper proposes an anonymous EHRs sharing scheme based on decentralized hierarchical attribute-based encryption (ABE). In the proposed scheme, (1) Multiple attribute authority (AA) ABE is leveraged to achieve fine-grained and scalable data access control and avoid bottleneck. Meanwhile, hierarchical access tree is used to encrypt multiple files in one operation, thereby saving calculation and storage load greatly. Moreover, the hidden access policy enhances user privacy protection. (2) The global identifier (GID) of a user is introduced to resist the collusion attack of users. Subsequently, an anonymous key generation mechanism is equipped to prevent multiple AAs from building a full profile using the user's GID. (3) To ensure the correctness and integrity of EHRs, users can conduct double verification based on the verification tag and convergent key. Finally, the efficiency analysis and experiments show that the scheme meets the security requirements of key management and privacy preservation in cloud and is proven secure and efficient in practice under the decisional bilinear Diffie-Hellman (DBDH) assumption.
An Empirical Analysis of BERT Embedding for Automated Essay Scoring

Automated Essay Scoring (AES) is one of the most challenging problems in Natural Language Processing (NLP).The significant challenges include the length of the essay, the presence of spelling mistakes affecting the quality of the essay and representing essay in terms of relevant features for the efficient scoring of essays.In this work, we present a comparative empirical analysis of Automatic Essay Scoring (AES) models based on combinations of various feature sets.We use 30manually extracted features, 300-word2vec representation, and 768-word embedding features using BERT model and forms different combinations for evaluating the performance of AES models.We formulate an automated essay scoring problem as a rescaled regression problem and quantized classification problem.We analyzed the performance of AES models for different combinations.We compared them against the existing ensemble approaches in terms of Kappa Statistics and Accuracy for rescaled regression problem and quantized classification problem respectively.A combination of 30-manually extracted features, 300-word2vec representation, and 768-word embedding features using BERT model results up to 77.2 ± 1.7 of Kappa statistics for rescaled regression problem and 75.2 ± 1.0 of accuracy value for Quantized Classification problem using a benchmark dataset consisting of about 12,000 essays divided into eight groups.The reporting results provide directions to the researchers in the field to use manually extracted features along with deep encoded features for developing a more reliable AES model.
Developing a Task-Based Dialogue System for English Language Learning

This research involved the design of a task-based dialogue system and evaluation of its learning effectiveness. Dialogue training still heavily depends on human communication with instant feedback or correction. However, it is not possible to provide a personal tutor for every English learner. With the rapid development of information technology, digitized learning and voice communication is a possible solution. The goal of this research was to develop an innovative model to refine the task-based dialogue system, including natural language understanding, disassembly intention, and dialogue state tracking. To enable the dialogue system to find the corresponding sentence accurately, the dialogue system was designed with machine learning algorithms to allow users to communicate in a task-based fashion. Past research has pointed out that computer-assisted instruction has achieved remarkable results in language reading, writing, and listening. Therefore, the direction of the discussion is to use the task-oriented dialogue system as a speaking teaching assistant. To train the speaking ability, the proposed system provides a simulation environment with goal-oriented characteristics, allowing learners to continuously improve their language fluency in terms of speaking ability by simulating conversational situational exercises. To evaluate the possibility of replacing the traditional English speaking practice with the proposed system, a small English speaking class experiment was carried out to validate the effectiveness of the proposed system. Data of 28 students with three assigned tasks were collected and analyzed. The promising results of the collected students’ feedback confirm the positive perceptions toward the system regarding user interface, learning style, and the system’s effectiveness.
Dissociation of proprioceptive drift and feelings of ownership in the somatic rubber hand illusion

The sense of self is a complex phenomenon, comprising various sensations of bodily self-consciousness. Interestingly, the experience of possessing a body – ‘embodiment’ – and locating the body within space may be modulated by the Rubber Hand Illusion (RHI). Measures of the RHI include proprioceptive drift (PD), the extent to which the hand is mis-localised towards the rubber hand, and subjective questionnaires. Although these measures often correlate, research from the visual RHI suggests that they reflect separate underlying processes. We investigated whether increasing the duration of tactile stimulation would affect PD and questionnaires differently during the somatic RHI. Participants experienced 30 s, 2 min, or 5 min of synchronous or asynchronous tactile stimulation. Increasing duration affected only PD, with increased drift following 5 min vs 30 s of stimulation. Our findings suggest that PD and questionnaires are not proxies for one another, but reflect separate underlying processes of the somatic RHI.
Deep Architecture Enhancing Robustness to Noise, Adversarial Attacks, and Cross-Corpus Setting for Speech Emotion Recognition

Speech emotion recognition systems (SER) can achieve high accuracy when the training and test data are identically distributed, but this assumption is frequently violated in practice and the performance of SER systems plummet against unforeseen data shifts.The design of robust models for accurate SER is challenging, which limits its use in practical applications.In this paper we propose a deeper neural network architecture wherein we fuse Dense Convolutional Network (DenseNet), Long shortterm memory (LSTM) and Highway Network to learn powerful discriminative features which are robust to noise.We also propose data augmentation with our network architecture to further improve the robustness.We comprehensively evaluate the architecture coupled with data augmentation against (1) noise, (2) adversarial attacks and (3) cross-corpus settings.Our evaluations on the widely used IEMOCAP and MSP-IMPROV datasets show promising results when compared with existing studies and state-of-the-art models.
Integrated correction of optical distortions for global HR-EBSD techniques

Optical distortions caused by camera lenses affect the accuracy of the elastic strains and lattice rotations measured by high-angular resolution techniques. This article introduces an integrated correction of optical distortions for global HR-EBSD/HR-TKD approaches. The digital image correlation analysis is directly applied to optically distorted patterns, avoiding the pattern pre-processing step conducted so far while preserving the numerical efficiency of the Gauss-Newton algorithm. The correction implementation is first described and its numerical cost is assessed considering a homography-based HR-EBSD approach. The correction principle is validated numerically for various levels of first-order radial distortion over a wide range of disorientation angles (0 to 14°) and elastic strain (0 to 5×10−2). The errors induced when neglecting such distortions as well as the influence of both the radial distortion coefficient and the pattern centre and optical centre locations are quantified. Even when both reference and target patterns are distorted, the correction appears necessary whatever the disorientation between those patterns. The required accuracy on the true distortion parameters for an effective correction is consequently determined.
Model Fusion via Optimal Transport

Combining different models is a widely used paradigm in machine learning applications. While the most common approach is to form an ensemble of models and average their individual predictions, this approach is often rendered infeasible by given resource constraints in terms of memory and computation, which grow linearly with the number of models. We present a layer-wise model fusion algorithm for neural networks that utilizes optimal transport to (soft-) align neurons across the models before averaging their associated parameters. We show that this can successfully yield one-shot knowledge transfer (i.e, without requiring any retraining) between neural networks trained on heterogeneous non-i.i.d. data. In both i.i.d. and non-i.i.d. settings , we illustrate that our approach significantly outperforms vanilla averaging, as well as how it can serve as an efficient replacement for the ensemble with moderate fine-tuning, for standard convolutional networks (like VGG11), residual networks (like ResNet18), and multi-layer perceptrons on CIFAR10, CIFAR100, and MNIST. Finally, our approach also provides a principled way to combine the parameters of neural networks with different widths, and we explore its application for model compression. The code is available at the following link, this https URL.
Collaborative Filtering Recommendation Algorithm Based on Attention GRU and Adversarial Learning

Aiming at the problem that the traditional collaborative filtering algorithm using shallow models cannot learn the deep features of users and items, and the recommendation model is very susceptible to the counter-interference of its parameters; this paper proposes a matrix-factorization recommendation model that combines adversarial learning and attention-gated recurrent units (AGAMF).Firstly, the gated recurrent unit based on the attention mechanism is used to extract the user's latent vector from the user's auxiliary side information.Secondly, the convolutional neural network is used to extract the item's latent vector from the item's auxiliary side information.Finally, adversarial disturbances are introduced on the latent factors of users and items to quantify the loss of the model under parameter disturbances, and the latent vectors of users and items are integrated into the probability matrix factorization to predict the user's rating of the item.Experiments were performed on two real data sets MovieLens-1M and MovieLens-10M, and the RMSE, MAE and Recall indicators were used for evaluation.Experiments prove that the model proposed in this paper is robust and can effectively alleviate the problem of data sparsity.Compared with other related recommendation algorithms, our model has a significant improvement in recommendation performance.
Random Reshuffling: Simple Analysis with Vast Improvements

Random Reshuffling (RR) is an algorithm for minimizing finite-sum functions that utilizes iterative gradient descent steps in conjunction with data reshuffling. Often contrasted with its sibling Stochastic Gradient Descent (SGD), RR is usually faster in practice and enjoys significant popularity in convex and non-convex optimization. The convergence rate of RR has attracted substantial attention recently and, for strongly convex and smooth functions, it was shown to converge faster than SGD if 1) the stepsize is small, 2) the gradients are bounded, and 3) the number of epochs is large. We remove these 3 assumptions, improve the dependence on the condition number from $\kappa^2$ to $\kappa$ (resp. from $\kappa$ to $\sqrt{\kappa}$) and, in addition, show that RR has a different type of variance. We argue through theory and experiments that the new variance type gives an additional justification of the superior performance of RR. To go beyond strong convexity, we present several results for non-strongly convex and non-convex objectives. We show that in all cases, our theory improves upon existing literature. Finally, we prove fast convergence of the Shuffle-Once (SO) algorithm, which shuffles the data only once, at the beginning of the optimization process. Our theory for strongly-convex objectives tightly matches the known lower bounds for both RR and SO and substantiates the common practical heuristic of shuffling once or only a few times. As a byproduct of our analysis, we also get new results for the Incremental Gradient algorithm (IG), which does not shuffle the data at all.
Design of an affordable IoT open-source robot arm for online teaching of robotics courses during the pandemic contingency

This article explains the design and construction of an affordable, open-source robot arm for online teaching of robotics courses. The main goal of the proposed robotic prototype is to deal with the current situation of pandemic contingency, where students and instructors cannot attend laboratory facilities in person. The robotic system has four main components: an electromechanical robot arm structure, a control system, a Wi-Fi communications module, and a human-machine interface. The IoT (Internet of Things) robot arm can be used to demonstrate important robotics topics such as direct and inverse kinematics, which are shown by programming simple and complex motions using the Denavit-Hartenberg (DH) methodology. The capabilities of the robotic system are empowered by IoT technology, which is demonstrated with an HMI interface deployed in a smartphone using wireless Wi-Fi communication through an ESP32 microcontroller. The arm's purpose is to be a low-cost and replicable robot that aids the comprehension of robotics design through project-based learning, from the theoretical aspects to the actual coding and construction of a prototype.
The Role of Reentrancies in Abstract Meaning Representation Parsing

Meaning Representation (AMR) parsing aims at converting sentences into AMR representations.These are graphs and not trees because AMR supports reentrancies (nodes with more than one parent).Following previous findings on the importance of reentrancies for AMR, we empirically find and discuss several linguistic phenomena responsible for reentrancies in AMR, some of which have not received attention before.We categorize the types of errors AMR parsers make with respect to reentrancies.Furthermore, we find that correcting these errors provides an increase of up to 5% Smatch in parsing performance and 20% in reentrancy prediction.