Reviewerly/exHarmony huggingface dataset:
Characterizing Academic Help-seeking Moods for Enrollment Performance of Institutional Online Student

Few could have anticipated the sudden and dramatic impact of COVID-19 on all aspects of life, including online academic help-seeking of institutional education. Academic help-seeking is a quite prevalent phenomenon that supports students to learn knowledge and improve academic performance. This study is aiming to understand learners and associate their performances via exploiting academic help-seeking moods with online learning of institutional education setting. Adopting the relevant theories, we propose a novel research model and identify three different online help-seeking moods, which are namely goal-directed seeker, exploratory seeker and avoidant seeker. Goal-directed seekers are described with preference for more challenging assignments and more posting on the platform discussion board frequently. Exploratory seekers hold the highest achievements during all help-seeking moods. Avoidant seekers are well-distinguished by holding the lowest frequency of posting among all moods and the most average time spent on the platform. Students have collective preferences for assignment submission in each help-seeking mood, and deviation from those preferences increases their probability of dropping academic grade significantly. To the best of our knowledge, this research is the first work that characterizes the help-seeking moods and associates moods with the enrollment performance for online education of institutional student.
Thrifty Label Propagation: Fast Connected Components for Skewed-Degree Graphs

Various concurrent algorithms have been proposed in the literature in recent years that mostly focus on the disjoint set approach to the Connected Components (CC) algorithm. However, these CC algorithms do not take the skewed structure of real-world graphs into account and as a result they do not benefit from common features of graph datasets to accelerate processing.We investigate the implications of the skewed degree distribution of real-world graphs on their connectivity and we use these features to introduce Thrifty Label Propagation as a structure-aware CC algorithm obtained by incorporating 4 fundamental optimization techniques in the Label Propagation CC algorithm.Our evaluation on 15 real-world graphs and 2 different processor architectures shows that Thrifty accelerates the flow of labels and processes only 1.4% of the edges of the graph.In this way, Thrifty is up to 16 × faster than state-of-the-art CC algorithms such as Afforest, Jayanti-Tarjan, and Breadth-First Search CC. In particular, Thrifty delivers 1.5 × −19.9× speedup for graph datasets larger than one billion edges.
AutoAt: A deep autoencoder-based classification model for supervised authorship attribution

Authorship attribution is the task of determining the likely author of a given text, with applications in domains such as literature and literary history, social network analysis, software engineering and cybersecurity. AutoAt, a deep autoencoder-based classification model which exploits the ability of autoencoders to encode meaningful data patterns is proposed to solve this task. Experiments are conducted on a data set of 1571 poems authored by 8 Romanian poets using a distributed document representation. The proposed approach obtains comparable or better results with respect to other machine learning classifiers. Additionally, the formulation of the AutoAt model allows for the computation of the probability that a test instance belongs to a given author class, which may be a useful property in a variety of authorship attribution applications. This aspect and the fact that AutoAt performs well in the difficult task of authorship attribution on poetic data without the step of feature engineering being informed by domain knowledge show that the proposed classifier is a general one, with potential to be used successfully in other fields.
The Predictive Power of Eye-Tracking Data in an Interactive AR Learning Environment

Learning through embodiment is a promising concept, potentially capable to remove many layers of abstraction hindering the learning process. Walk the Graph, our HoloLens2-based AR application, provides an inquiry-based learning setting for understanding graphs through the full-body movement of the user. In this paper, as part of our ongoing work to build an AI framework to quantify and predict the learning gain of the user, we examine the predictive potential of gaze data collected during the app usage. To classify users into groups with different learning gains, we construct a map of areas of interest (AOI) based on the gaze data itself. Subsequently, using a sliding window approach, we extract engineered features from the collected in-app as well as gaze data. Our experimental results have shown that a Support Vector Machine with selected features achieved the highest F1 score (0.658; baseline: 0.251) compared to other approaches including a K-Nearest Neighbor and a Random Forest Classifier although in each of the cases the lion’s share of the predictive power is indeed provided by the gaze-based features.
DCF: An Efficient and Robust Density-Based Clustering Method

Density-based clustering methods have been shown to achieve promising results in modern data mining applications. A recent approach, Density Peaks Clustering (DPC), detects modes as points with high density and large distance to points of higher density, and hence often fails to detect low-density clusters in the data. Furthermore, DPC has quadratic complexity. We here develop a new clustering algorithm, aiming at improving the applicability and efficiency of the peak-finding technique. The improvements are threefold: (1) the new algorithm is applicable to large datasets; (2) the algorithm is capable of detecting clusters of varying density; (3) the algorithm is competent at deciding the correct number of clusters, even when the number of clusters is very high. The clustering performance of the algorithm is greatly enhanced by directing the peak-finding technique to discover modal sets, rather than point modes. We present a theoretical analysis of our approach and experimental results to verify that our algorithm works well in practice. We demonstrate a potential application of our work for unsupervised face recognition.
Using Artificial Intelligence to Achieve Auxiliary Training of Table Tennis Based on Inertial Perception Data

Compared with optical sensors, wearable inertial sensors have many advantages such as low cost, small size, more comprehensive application range, no space restrictions and occlusion, better protection of user privacy, and more suitable for sports applications. This article aims to solve irregular actions that table tennis enthusiasts do not know in actual situations. We use wearable inertial sensors to obtain human table tennis action data of professional table tennis players and non-professional table tennis players, and extract the features from them. Finally, we propose a new method based on multi-dimensional feature fusion convolutional neural network and fine-grained evaluation of human table tennis actions. Realize ping-pong action recognition and evaluation, and then achieve the purpose of auxiliary training. The experimental results prove that our proposed multi-dimensional feature fusion convolutional neural network has an average recognition rate that is 0.17 and 0.16 higher than that of CNN and Inception-CNN on the nine-axis non-professional test set, which proves that we can better distinguish different human table tennis actions and have a more robust generalization performance. Therefore, on this basis, we have better realized the enthusiast of table tennis the purpose of the action for auxiliary training.
Prototypical Interaction Graph for Unsupervised Domain Adaptation in Surgical Instrument Segmentation

Surgical instrument segmentation is fundamental for the advanced computer-assisted system. The variability of the surgical scene, a major obstacle in this task, leads to the domain shift problem. Unsupervised domain adaptation (UDA) technique can be employed to solve this problem and adapt the model to various surgical scenarios. However, existing UDA methods ignore the relationship among different categories, hindering the model learning discriminative features from a global view. Additionally, the adversarial strategy utilized in these methods only narrows down the domain gap at the end of the network, leading to the poor feature alignment. To tackle above mentioned problems, we advance a semantic-prototype interaction graph (SePIG) framework for surgical instrument type segmentation to grasp the category-level relationship and further align the feature distribution. The proposed framework consists of prototypical inner-interaction graph (PI-Graph) and prototypical cross-interaction graph (PC-Graph). In PI-Graph, EM-Grouping module is designed to generate multi-prototypes representing the semantic information adequately. Then, propagation is performed upon these multi-prototypes to communicate semantic information inner each domain. Aiming at narrowing down the domain gaps, the PC-Graph constructs hierarchical graphs upon multi-prototypes and category centers, and conducts dynamic reasoning to exchange the correlated information among two domains. Extensive experiments on the EndoVis Instrument Segmentation 2017 \(\rightarrow \) 2018 scenarios demonstrate the superiority of our SePIG framework compared with state-of-the-art methods. Code is available at https://github.com/CityU-AIM-Group/SePIG.
Curved layer path planning on a 5-axis 3D printer

Purpose This paper aims to provide an approach to print shell-type objects using a 5-axis printer. The proposed approach takes advantage of the two additional printer degrees of freedom to provide a curved layer path planning strategy. Design/methodology/approach This paper addresses curved layer path planning on a 5-axis printer. This printer considers movements along the three usual axes together with two additional axes at the printing table (rotation and tilt), allowing to build more complex and reliable objects. Curved layer path planning is considered where polygons obtained from the slicing stage are approximated by linear and cubic splines. The proposed printing strategy consists in building an inner core supporting structure followed by outer curved layers. Findings The curved layer path planning strategy is validated for shell-type objects by considering a 5-axis printer simulator. An example with an aeronautic object is presented to illustrate the proposed approach. Originality/value The paper presents an approach to curved layer path planning on a 5-axis printer, for shell-type objects.
Which and Where to Focus: A Simple yet Accurate Framework for Arbitrary-Shaped Nearby Text Detection in Scene Images

Scene text detection has drawn the close attention of researchers. Though many methods have been proposed for horizontal and oriented texts, previous methods may not perform well when dealing with arbitrary-shaped texts such as curved texts. In particular, confusion problem arises in the case of nearby text instances. In this paper, we propose a simple yet effective method for accurate arbitrary-shaped nearby scene text detection. Firstly, a One-to-Many Training Scheme (OMTS) is designed to eliminate confusion and enable the proposals to learn more appropriate groundtruths in the case of nearby text instances. Secondly, we propose a Proposal Feature Attention Module (PFAM) to exploit more effective features for each proposal, which can better adapt to arbitrary-shaped text instances. Finally, we propose a baseline that is based on Faster R-CNN and outputs the curve representation directly. Equipped with PFAM and OMTS, the detector can achieve state-of-the-art or competitive performance on several challenging benchmarks.
Compositional learning of mutually recursive procedural systems

Abstract This paper presents a compositional approach to active automata learning of Systems of Procedural Automata (SPAs), an extension of Deterministic Finite Automata (DFAs) to systems of DFAs that can mutually call each other. SPAs are of high practical relevance, as they allow one to efficiently learn intuitive recursive models of recursive programs after an easy instrumentation that makes calls and returns observable. Key to our approach is the simultaneous inference of individual DFAs for each of the involved procedures via expansion and projection: membership queries for the individual DFAs are expanded to membership queries of the entire SPA, and global counterexample traces are transformed into counterexamples for the DFAs of concerned procedures. This reduces the inference of SPAs to a simultaneous inference of the DFAs for the involved procedures for which we can utilize various existing regular learning algorithms. The inferred models are easy to understand and allow for an intuitive display of the procedural system under learning that reveals its recursive structure. We implemented the algorithm within the LearnLib framework in order to provide a ready-to-use tool for practical application which is publicly available on GitHub for experimentation.
User Feedback to Improve the Performance of a Cyberattack Detection Artificial Intelligence System in the e-Health Domain

New and evolving threats emerge every day in the e-Health industry. The safety of e-Health’s telemonitoring systems is becoming a prominent task. In this work, starting from a CADS (Cyberattack Detection System) model that uses artificial intelligence techniques to detect anomalies, we focus on the activity of interacting with data. Using a User Interaction Engine, a dashboard allows you to visually explore and view data from suspected attacks on healthcare professionals for a threat reaction. In particular, a User Feedback module is presented to interact with healthcare personnel and ask for a response on the anomaly detected.
Discriminative Feature Mining Based on Frequency Information and Metric Learning for Face Forgery Detection

Face forgery detection has received considerable attention due to security concerns about abnormal faces generated by face forgery technology. While recent researches have made prominent progress, they still suffer from two limitations: a) the learned features supervised by softmax loss are insufficiently discriminative, since the softmax loss fails to explicitly boost inter-class separability and intra-class compactness; b) hand-crafted features are unable to effectively mine forgery patterns from frequency domain. To address the two problems, this paper proposes a novel frequency-aware discriminative feature learning framework. Specifically, we design an innovative single-center loss which compresses mere intra-class variations of natural faces while encouraging inter-class differences between natural and manipulated faces in the embedding space. Supervised by such a loss, more discriminative features can be learned with less optimization difficulty. As for frequency-related features, a frequency feature adaptively generated module is developed to capture frequency clues in a data-driven manner. Besides, to better fuse the features of both RGBdomain and frequency domain, this paper devises a fusion module based on positional correlation of features. The effectiveness and superiority of our framework have been proved by extensive experiments and our approach achieves state-of-the-art performance in both in-dataset and cross-dataset evaluation
Optimal Training Configurations of a CNN-LSTM-Based Tracker for a Fall Frame Detection System

In recent years, there has been an immense amount of research into fall event detection. Generally, a fall event is defined as a situation in which a person unintentionally drops down onto a lower surface. It is crucial to detect the occurrence of fall events as early as possible so that any severe fall consequences can be minimized. Nonetheless, a fall event is a sporadic incidence that occurs seldomly that is falsely detected due to a wide range of fall conditions and situations. Therefore, an automated fall frame detection system, which is referred to as the SmartConvFall is proposed to detect the exact fall frame in a video sequence. It is crucial to know the exact fall frame as it dictates the response time of the system to administer an early treatment to reduce the fall's negative consequences and related injuries. Henceforth, searching for the optimal training configurations is imperative to ensure the main goal of the SmartConvFall is achieved. The proposed SmartConvFall consists of two parts, which are object tracking and instantaneous fall frame detection modules that rely on deep learning representations. The first stage will track the object of interest using a fully convolutional neural network (CNN) tracker. Various training configurations such as optimizer, learning rate, mini-batch size, number of training samples, and region of interest are individually evaluated to determine the best configuration to produce the best tracker model. Meanwhile, the second module goal is to determine the exact instantaneous fall frame by modeling the continuous object trajectories using the Long Short-Term Memory (LSTM) network. Similarly, the LSTM model will undergo various training configurations that cover different types of features selection and the number of stacked layers. The exact instantaneous fall frame is determined using an assumption that a large movement difference with respect to the ground level along the vertical axis can be observed if a fall incident happened. The proposed SmartConvFall is a novel technique as most of the existing methods still relying on detection rather than the tracking module. The SmartConvFall outperforms the state-of-the-art trackers, namely TCNN and MDNET-N trackers, with the highest expected average overlap, robustness, and reliability metrics of 0.1619, 0.6323, and 0.7958, respectively. The SmartConvFall also managed to produce the lowest number of tracking failures with only 43 occasions. Moreover, a three-stack LSTM delivers the lowest mean error with approximately one second delay time in locating the exact instantaneous fall frame. Therefore, the proposed SmartConvFall has demonstrated its potential and suitability to be implemented for a real-time application that could help to avoid any crucial fall consequences such as death and internal bleeding if the early treatment can be administered.
Progress towards Analytically Optimal Angles in Quantum Approximate Optimisation

The Quantum Approximate Optimisation Algorithm is a $p$ layer, time-variable split operator method executed on a quantum processor and driven to convergence by classical outer loop optimisation. The classical co-processor varies individual application times of a problem/driver propagator sequence to prepare a state which approximately minimizes the problem's generator. Analytical solutions to choose optimal application times (called angles) have proven difficult to find, whereas outer loop optimisation is resource intensive. Here we prove that optimal Quantum Approximate Optimisation Algorithm parameters for $p=1$ layer reduce to one free variable and in the thermodynamic limit, we recover optimal angles. We moreover demonstrate that conditions for vanishing gradients of the overlap function share a similar form which leads to a linear relation between circuit parameters, independent on the number of qubits. Finally, we present a list of numerical effects, observed for particular system size and circuit depth, which are yet to be explained analytically.
Immersive Virtual-Reality-Based Streaming Distance Education System for Solar Dynamics Observatory: A Case Study

The combination of immersive virtual reality (VR) environments and distance education has led to a new educational paradigm. In this study, an immersive VR-based distance education system is proposed to enable multiple remote users to send, share, and experience images from the Solar Dynamics Observatory (SDO) via streaming. In contrast to the conventional system in which only experts use SDO data, the proposed system provides a head-mounted-display-based visualization platform that can be easily used by experts and non-experts. Real-time SDO image streaming must be possible to realistically observe changes in the Sun and increase involvement. Thus, multichannel-based SDO image transmission was applied to increase the network bandwidth utilization. To improve the social presence of participants, realistic avatar models controlled by the motions synchronized with the user are provided. In addition, free communication is possible through verbal interactions. This allows multiple remote users to participate simultaneously without having to be physically present. A user study with 20 subjects showed that the participants could observe SDO images in a more immersive manner by using the proposed system. In addition, they experienced social presence because of the user avatar models and an enriched educational experience by conversing with and listening to experts.
Collaborative Exploration and Reinforcement Learning between Heterogeneously Skilled Agents in Environments with Sparse Rewards

A critical goal in Reinforcement Learning is the minimization of the time needed for an agent to learn to solve a given environment. In this context, collaborative reinforcement learning refers to the improvement of this learning process through the interaction between agents, which usually yields better results than training each agent in isolation. Most studies in this area have focused on the case with homogeneous agents, namely, agents equally skilled for undertaking their task. By contrast, heterogeneity among agents could arise due to the particular capabilities on how they sense the environment and/or the actions they could perform. Those differences eventually hinder the learning process and information sharing between agents. This issue becomes even more complicated to address over hard exploration scenarios where the extrinsic rewards collected from the environment are sparse. This work sheds light on the impact of leveraging collaborative learning strategies between heterogeneously skilled agents over hard exploration scenarios. Our study gravitates on how to share and exploit knowledge between the agents so as to mutually improve their learning procedures, further considering mechanisms to cope with sparse rewards. We assess the performance of these strategies via extensive simulations over modifications of the ViZDooM environment, which allow examining their benefits and drawbacks when dealing with agents endowed with different behavioral policies. Our results uncover the inherent problems of not considering the skill heterogeneity of the agents in the knowledge sharing strategy, and unleash a manifold of research directions aimed at circumventing these noted issues.