Reviewerly/exHarmony huggingface dataset:
The design and evaluation of a multimodal ubiquitous learning application for EFL writers

This study presents the design of a multimodal ubiquitous learning application (MULA) with different augmenting effects for enhancing English as a Foreign Language (EFL) academic writing instruction. In a pilot project, 17 undergraduates engaged in ubiquitous learning and writing at a green building and provided survey responses and interview feedback about their ubiquitous writing experience. The survey results were categorized into perceptions about (a) the ubiquitous learning mode, (b) effective writing facilitation, (c) motivation, self-efficacy, and attitude, (d) self-regulation, and (e) system usability. Interview results further illustrated how using MULA led to positive and negative experiences during ubiquitous writing. A final synthesis on the findings led to the identification of seven affordances that made MULA a potential tool for enhancing writing instruction in EFL settings. The seven affordances were considered effective design elements for multimodal context-aware ubiquitous learning that served the purpose of enriching English academic writing instruction. Implications about creating multimodal ubiquitous learning applications in higher education are also provided.
Game-Theoretic Planning for Risk-Aware Interactive Agents

Modeling the stochastic behavior of interacting agents is key for safe motion planning. In this paper, we study the interaction of risk-aware agents in a game-theoretical framework. Under the entropic risk measure, we derive an iterative algorithm for approximating the intractable feedback Nash equilibria of a risk-sensitive dynamic game. We use an iteratively linearized approximation of the system dynamics and a quadratic approximation of the cost function in solving a backward recursion for finding feedback Nash equilibria. In this respect, the algorithm shares a similar structure with DDP and iLQR methods. We conduct experiments in a set of challenging scenarios such as roundabouts. Compared to ignoring the game interaction or the risk sensitivity, we show that our risk-sensitive game-theoretic framework leads to more timeefficient, intuitive, and safe behaviors when facing underlying risks and uncertainty.
Vision-Based Gesture Recognition in Human-Robot Teams Using Synthetic Data

Building successful collaboration between humans and robots requires efficient, effective, and natural communication. Here we study a RGB-based deep learning approach for controlling robots through gestures (e.g., "follow me"). To address the challenge of collecting high-quality annotated data from human subjects, synthetic data is considered for this domain. We contribute a dataset of gestures that includes real videos with human subjects and synthetic videos from our custom simulator. A solution is presented for gesture recognition based on the state-of-the-art I3D model. Comprehensive testing was conducted to optimize the parameters for this model. Finally, to gather insight on the value of synthetic data, several experiments are described that systematically study the properties of synthetic data (e.g., gesture variations, character variety, generalization to new gestures). We discuss practical implications for the design of effective human-robot collaboration and the usefulness of synthetic data for deep learning.
Image-Based Crowd Stability Analysis Using Improved Multi-Column Convolutional Neural Network

Crowd stability analysis is an important yet challenging task, as it is difficult to obtain the quantitative information regarding a crowd in motion, for instance, the crowd count and crowd density in a pedestrian merging area. This paper proposes a novel model that can be used to accurately analyze the crowd stability based on images obtained from a real-time video surveillance system (VSS) in dense crowd scenarios. To enhance the accuracy of the human head recognition for the crowd counting and crowd density estimation, we improve the conventional convolution-neural-network (CNN) model with more columns and merged features, obtaining a four-column convolutional neural network (4C-CNN). Using more columns with receptive fields of deferent sizes, more merged features can be learned, to be adaptive to variations in pedestrian head size due to image resolution. Furthermore, the crowd density of different areas is calculated with image rectification against the perspective distortion. By utilizing the stability criterion based on crowd density, we propose a crowd stability analysis model (CSAM) with the capability of quantitative computation dynamically. The results of extensive experiments performed on public datasets indicate that this improved CNN model exhibits a better performance for crowd counting than the typical multi-column CNN models. In addition, the experiment results pertaining to Shanghai Hongqiao Railway Station demonstrate the effectiveness of the crowd stability analysis model. Thus, this integrated approach can simultaneously conduct the processes of crowd counting, image rectification, density map calculation and crowd stability analysis by using dense crowd images from a VSS.
Aspect-Based Sentiment Analysis for Polarity Estimation of Customer Reviews on Twitter

Most consumers read online reviews written by different users before making purchase decisions, where each opinion expresses some sentiment. Therefore, sentiment analysis is currently a hot topic of research. In particular, aspect-based sentiment analysis concerns the exploration of emotions, opinions and facts that are expressed by people, usually in the form of polarity. It is crucial to consider polarity calculations and not simply categorize reviews as positive, negative, or neutral. Currently, the available lexicon-based method accuracy is affected by limited coverage. Several of the available polarity estimation techniques are too general and may not reflect the aspect/topic in question if reviews contain a wide range of information about different topics. This paper presents a model for the polarity estimation of customer reviews using aspect-based sentiment analysis (ABSA-PER). ABSA-PER has three major phases: data preprocessing, aspect co-occurrence calculation (CAC) and polarity estimation. A multi-domain sentiment dataset, Twitter dataset, and trust pilot forum dataset (developed by us by defined judgement rules) are used to verify ABSA-PER. Experimental outcomes show that ABSA-PER achieves better accuracy, i.e., 85.7% accuracy for aspect extraction and 86.5% accuracy in terms of polarity estimation, than that of the baseline methods.
Analysis of Deep Features for Image Aesthetic Assessment

The performance of image aesthetic assessment has been significantly improved by deep learning techniques in comparison to traditional hand-crafted feature-based methods.However, there has not been an attempt to analyze the learned features in deep learning approaches.This paper presents in-depth analysis of the deep models and the learned features by the models for image aesthetic assessment in various viewpoints.We consider binary classification of the mean (average aesthetic level) and standard deviation (subjectivity of aesthetic perception) of aesthetic ratings.In particular, our analysis is based on transfer learning among image classification and aesthetic classifications for comparative analysis.Our results highlight the similarity and transferability of learned features among the classification tasks, and comparison of the trained models and convolutional filters.
Density Matrix Quantum Circuit Simulation via the BSP Machine on Modern GPU Clusters

As quantum computers evolve, simulations of quantum programs on classical computers will be essential in validating quantum algorithms, understanding the effect of system noise, and designing applications for future quantum computers. In this paper, we first propose a new multi-GPU programming methodology called MG-BSP which constructs a virtual BSP machine on top of modern multi-GPU platforms, and apply this methodology to build a multi-GPU density matrix quantum simulator called DM-Sim. We propose a new formulation that can significantly reduce communication overhead, and show that this formula transformation can conserve the semantics despite noise being introduced. We build the tool-chain for the simulator to run open standard quantum assembly code, execute synthesized quantum circuits, and perform ultra-deep and largescale simulations. We evaluated DM-Sim on several state-of-the-art multi-GPU platforms including NVIDIA's PascaUVolta DGX1, DGX-2, and ORNL's Summit supercomputer. In particular, we have demonstrated the simulation of one million general gates in 94 minutes on DGX-2, far deeper circuits than has been demonstrated in prior works. Our simulator is more than 10x faster with respect to the corresponding state-vector quantum simulators on GPUs and other platforms. The DM-Sim simulator is released at: http:llgithub.comlpnnllDM-Sim.
DiPE: Deeper into Photometric Errors for Unsupervised Learning of Depth and Ego-motion from Monocular Videos

Unsupervised learning of depth and ego-motion from unlabelled monocular videos has recently drawn great attention, which avoids the use of expensive ground truth in the supervised one. It achieves this by using the photometric errors between the target view and the synthesized views from its adjacent source views as the loss. Despite significant progress, the learning still suffers from occlusion and scene dynamics. This paper shows that carefully manipulating photometric errors can tackle these difficulties better. The primary improvement is achieved by a statistical technique that can mask out the invisible or nonstationary pixels in the photometric error map and thus prevents misleading the networks. With this outlier masking approach, the depth of objects moving in the opposite direction to the camera can be estimated more accurately. To the best of our knowledge, such scenarios have not been seriously considered in the previous works, even though they pose a higher risk in applications like autonomous driving. We also propose an efficient weighted multi-scale scheme to reduce the artifacts in the predicted depth maps. Extensive experiments on the KITTI dataset show the effectiveness of the proposed approaches. The overall system achieves state-of-the-art performance on both depth and ego-motion estimation.
Image denoising by a novel variable‐order total fractional variation model

The total variation model performs very well for removing noise while preserving edges. However, it gives a piecewise constant solution which often leads to the staircase effect, consequently small details such as textures are filtered out in the denoising process. Fractional‐order total variation method is one of the major approaches to overcome such drawbacks. Unlike their good quality of fractional order, all these methods use a fixed fractional order for the whole of the image. In this paper, a novel variable‐order total fractional variation model is proposed for image denoising, in which the order of fractional derivative will be allocated automatically for each pixel based on the context of the image. This kind of selection is able to capture the edges and texture of the image simultaneously. In this regard, we prove the existence and uniqueness of the presented model. The split Bregman method is adapted to solve the model. Finally, the results illustrate the efficiency of the proposed model that yielded good visual effects and a better signal‐to‐noise ratio.
Online harassment of journalists as a consequence of populism, mis/disinformation, and impunity

This chapter aims to examine additional factors associated with populism, disinformation and online harassment of journalists in an environment of impunity, though the relationship between digital communication and populism has been analysed since the late 1990s. It considers strategies that have been utilised or suggested to combat online harassment of journalists on the path forward. Online harassment of women journalists has been documented more frequently than of male journalists, who often are attacked because of their coverage. Online harassment has risen in recent years via comment sections under online news articles barraging journalists’ emails and social media accounts with defamatory, threatening, demeaning, or even pornographic material. The Organization for Security and Cooperation in Europe recommends that countries consider prosecuting the online harassment of journalists through existing harassment laws. One cross-country study found that online harassment disrupts routine practices and the extent that women journalists can interact with audiences.
Demonstration of Federated Learning over Edge-Computing Enabled Metro Optical Networks

We demonstrate the benefits of a federated learning framework for (re)training of global ML models over geo-distributed data sources. The demonstration is carried out on a live edge computing enabled optical networking test-bed.
Accelerated Training for Convolutional Neural Networks

Handwritten character recognition (HCR) is one of the ongoing research field in Artificial Intelligence. The recognition of handwritten characters is based on pattern recognition and image processing. One of the most common machine learning methods for solving this problem is Convolutional Neural Networks (CNN). To implement such tasks, a multilayer neural network is often used. CNN require as much data as possible to ensure high accuracy, while parallel processing can help us save time when neural network training. Such training time can be reduced by improving training operations using parallel computing technology and multi-core platforms. In this paper, exemplary parallelization of CNN training by dint of OpenMP technology and its libraries has been implemented. English alphabets were chosen as an experiment for the handwritten character recognition system, and CNN was used for its better accuracy. Each character data set contains 26 alphabets. That is, the image size of each character ranges from 16×16 to 256×256 pixels, and these pixels are taken as features for training the CNN. We analyzed how the speedup of CNN training depends on image size. Three multi-core processors with different specifications were chosen for the hardware implementation. The results show that the proposed parallel approach using OpenMP technology gives good acceleration, which reduces training time.
An Augmented Reality Interaction Interface for Autonomous Drone

Human drone interaction in autonomous navigation incorporates spatial interaction tasks, including reconstructed 3D map from the drone and human desired target position. Augmented Reality (AR) devices can be powerful interactive tools for handling these spatial interactions. In this work, we build an AR interface that displays the reconstructed 3D map from the drone on physical surfaces in front of the operator. Spatial target positions can be further set on the 3D map by intuitive head gaze and hand gesture. The AR interface is deployed to interact with an autonomous drone to explore an unknown environment. A user study is further conducted to evaluate the overall interaction performance.
Gamification in Learning using Quizizz Application as Assessment Tools

Abstract Nowadays, Teaching and learning activities in the world of education must always follow the development of technology. The use of these technologies will make these activities more effective and efficient. Gamification is part of innovation in education. In this research, gamification is used as a tool for studying activities in project management information systems subject. The method used is technology-based applied research. The Assessment Tool in this study uses Quizizz. Quizizz is used on midterms. This exam was attended by 29 students of the information systems department STMIK Royal. The questionnaire was made using a Mentimeter. The use of Quizizz has a positive impact. The level of student answers questions correctly is 51%. Then 66% prefer Quizizz as assessment tool compared to paper and google forms.
Ship Detection and Fine-Grained Recognition in Large-Format Remote Sensing Images Based on Convolutional Neural Network

Ship detection and fine-grained recognition in large-format remote sensing image are an important research direction in the field of remote sensing image detection. But less research has been done in this area. Aiming at this problem, this paper constructs a large-format remote sensing image ship target dataset with ship category information, and proposes a background filtering network and a ship fine-grained classification network. The background filtering network is used to quickly filter out the background area, and the ship fine-grained classification network is used to detect ship targets and distinguish ship categories. Compared with the previous method, the method proposed in this paper can significantly improve the efficiency of ship target detection in large-format remote sensing images, while also improving the detection accuracy.
Aircraft skin gap and flush measurement based on seam region extraction from 3D point cloud

Gap and flush measurement is a challenging task in the process of aircraft assembly, which significantly affects the reliability of aircraft aerodynamic shape. To ensure the quality of aircraft assembly, an accurate gap and flush measurement method is essential. This paper presents a novel simple yet effective method, which is suitable for various kinds of scanner data. Given an unstructured scanned point cloud, our method first detects and segments the seam area based on the local point density discrepancy. In the second stage, a projection operation is applied based on the segmentation results to convert 3D measurement to 2D measurement to reduce the computational complexity and improve the accuracy of the measurement. According to the geometric distribution of the projection points generated in stage two, the feature points defined by the gap and flush calculation model are extracted to complete the computation. Experimental results show that the accuracy is comparable to some state-of-the-art methods. Concerning the robustness to different types of scanner devices data, the proposed method clearly outperforms the others.
Let CONAN tell you a story: Procedural quest generation

This work proposes an engine for the Creation Of Novel Adventure Narrative (CONAN), which is a procedural quest generator that uses a planning approach. The engine is tested on its ability to create quests, which are sets of actions that must be performed in order to achieve a certain goal. The engine takes in a world description represented as a set of facts, including characters, locations, and items, and generates quests according to the state of the world and the preferences of the characters in it. We evaluate quests through the classification of the motivations behind the quests. We also compare world descriptions of two different sizes. Compared to human structural quest classification, the current engine was found to be able to replicate the quest structures found in commercial video game quests.
SAIoT: Scalable Anomaly-Aware Services Composition in CloudIoT Environments

Among the novel IT paradigms, cloud computing and the Internet of Things (CloudIoT) are two complementary areas designed to support the creation of smart cities and application services. The CloudIoT not only presents ubiquitous services through IoT nodes but it also provides virtually unlimited resources through services composition. The services composition problem aims to find a set of services among functionally equivalent services with different Quality of Service (QoS) concerning users' constraints. To this aim, previous studies calculate QoS values through service logs without considering the presence of anomalies in the existing QoS values; however, the dynamicity of distributed service environments and communication networks in CloudIoT environments causes anomalies in the QoS values. Therefore, existing approaches fail to model QoS values accurately that leads to service-level agreement (SLA) violation and penalties for service broker. To address this challenge, we propose a scalable anomaly-aware approach (SAIoT) including two main components: the first component models QoS values based on a machine learning anomaly detection technique, to remove the existing abnormal QoS records, and the second component finds a near-optimal composition by using an effective and efficient metaheuristic algorithm. The experimental results based on real-world data sets show that our approach achieves 30.64% of the average improvement in the QoS value of a composite plan with equal or even less price compared to the previous works, such as information theory-based and advertised QoS-based methods.
In Defense of Knowledge Distillation for Task Incremental Learning and Its Application in 3D Object Detection

Making robots learn skills incrementally is an efficient way to design real intelligent agents. To achieve this, researchers adopt knowledge distillation to transfer old-task knowledge from old models to new ones. However, when the length of the task sequence increases, the effectiveness of knowledge distillation to prevent models from forgetting old-task knowledge degrades, which we call the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">long-sequence effectiveness degradation (LED)</i> problem. In this letter, we analyze the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LED</i> problem in the task-incremental-learning setting, and attribute it to the inevitable data distribution differences among tasks. To address this problem, we propose to correct the knowledge distillation for task incremental learning with a Bayesian approach. It additionally maximizes the posterior probability related to the data distributions of all seen tasks. To demonstrate its effectiveness, we further apply our proposed corrected knowledge distillation to 3D object detection. The comparison between the results of increment-at-once and increment-in-sequence experiments shows that our proposed method solves the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LED</i> problem. Besides, it reaches the upper-bound performance in the task-incremental-learning experiments on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KITTI</i> dataset. The code and supplementary materials are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://sites.google.com/view/c-kd/</uri> .
Co-clustering Vertices and Hyperedges via Spectral Hypergraph Partitioning

We propose a novel method to co-cluster the vertices and hyperedges of hypergraphs with edge-dependent vertex weights (EDVWs). In this hypergraph model, the contribution of every vertex to each of its incident hyperedges is represented through an edge-dependent weight, conferring the model higher expressivity than the classical hypergraph. In our method, we leverage random walks with EDVWs to construct a hypergraph Laplacian and use its spectral properties to embed vertices and hyperedges in a common space. We then cluster these embeddings to obtain our proposed co-clustering method, of particular relevance in applications requiring the simultaneous clustering of data entities and features. Numerical experiments using real-world data demonstrate the effectiveness of our proposed approach in comparison with state-of-the-art alternatives.