Reviewerly/exHarmony huggingface dataset:
Domain adaptation with a shrinkable discrepancy strategy for cross-domain sentiment classification

Cross-domain sentiment classification (CDSC) is used to predict the sentiment polarity of a text in an unlabeled target domain by analyzing the reviews in the labeled source domain. Domain adaptive approaches have become the preferred solution in recent years to the unsupervised domain migration problem. Among them, adversarial learning aligns the sample distribution of the two domains through domain confusion to transfer sentiment across domains. However, traditional adversarial learning often roughly measures domain discrepancy. Although scholars have attempted to adjust the decision boundary of different categories to eliminate the domain shift, such as maximum classifier discrepancy model, there are still two problems with this approach. First, it ignores the intra-domain structure, which causes the samples distributed on the decision boundary to be easily misclassified. Second, it only realizes coarse-grained sentiment migration and lacks a refined evaluation of the transferable information in the inter-domain, which causes a negative transfer. To solve these problems, we propose domain adaptation with a shrinkable discrepancy strategy (DA-SDS) for the task of CDSC. Specifically, we propose to shrink the category subspace in the intra-domain while building the decision boundary of classifiers, which reduces the misclassification by clustering samples to the category center. We also propose to measure the weighted domain discrepancy in the inter-domain, which mitigates the negative transfer through the refined assessment of domain discrepancy. Extensive evaluations showed that DA-SDS outperformed state-of-the-art methods on the Amazon Review dataset.
Effects of a contextualised reflective mechanism-based augmented reality learning model on students’ scientific inquiry learning performances, behavioural patterns, and higher order thinking

Augmented reality (AR) can represent a contextualised scientific inquiry environment in which students may explore the real world and develop science process skills via interacting with rich information from virtual systems. However, it remains a challenge for most students to complete scientific inquiry tasks without proper support. Research evidence has suggested the potential of reflective scaffolding when applying scientific inquiry. Accordingly, we designed a contextualised reflective mechanism-based AR learning model to assist students in completing scientific inquiry tasks. Guided by the proposed model, we designed four stages of scientific inquiry learning: conceptual understanding, reflective cognition, in-depth inquiry, and knowledge building. A quasi-experiment and lag sequential analysis were conducted by recruiting 81 sixth-grade students to examine the effects of the proposed model on their scientific inquiry learning performances, higher order thinking, and behavioural patterns. The experimental results reveal that the proposed approach improved students’ inquiry learning performances and higher order thinking tendency (problem-solving tendency and metacognitive awareness). Moreover, the evidence from this study also suggests that the students who learned with the proposed approach exhibited more observation, comparison, exploration, and reflection behavioural patterns in the field trip than those who learned without the contextualised reflective mechanism. Implications are discussed.
Dual Attentional Siamese Network for Visual Tracking

Attention mechanism in siamese-based trackers has drawn great attention on visual object tracking. However, we experimentally discover that the feature information after being enhanced by attention models with different styles may be interfered with each other for visual tracking. To address this issue, we propose a Dual Attentional Siamese Network (DASNet) to separate channel attention and spatial attention to alleviate the feature interference. Specifically, we propose a dual attention module to enhance the acquired semantic feature expression ability. Especially in spatial dimension, we introduce spatial pyramid attention on multiscale feature pyramid to capture the long-range interdependencies with different granularity. Moreover, we exploit adaptive decision fusion strategy to integrate the outputs from two parallel tracking-heads along two separate dimensions, channel and spatial, so as to effectively take the advantages of attention models with different styles. Extensive experiments are conducted on publicly challenging benchmarks, where the proposed DASNet achieves new state-of-the-art results, by 0.4299 → 0.4515 and 0.3835 → 0.3934 EAO compared with the strong baseline SiamMask (Wang et al., 2019) on VOT 2016 and VOT 2018. Our source code is available at: https://github.com/jwma0725/DASNet.
VDTR: Video Deblurring With Transformer

Video deblurring is still an unsolved problem due to the challenging spatio-temporal modeling process. While existing convolutional neural network (CNN)-based methods show a limited capacity of effective spatial and temporal modeling for video deblurring. This paper presents VDTR, an effective Transformer-based model that makes the first attempt to adapt pure Transformer for video deblurring. VDTR exploits the superior long-range and relation modeling capabilities of Transformer for both spatial and temporal modeling. However, it is challenging to design an appropriate Transformer-based model for video deblurring due to the complicated non-uniform blurs, misalignment across multiple frames and the high computational costs for high-resolution spatial modeling. To address these problems, VDTR advocates performing attention within non-overlapping windows and exploiting the hierarchical structure for long-range dependencies modeling. For frame-level spatial modeling, we propose an encoder-decoder Transformer that utilizes multi-scale features for deblurring. For multi-frame temporal modeling, we adapt Transformer to fuse multiple spatial features efficiently. Compared with CNN-based methods, the proposed method achieves highly competitive results on both synthetic and real-world video deblurring benchmarks, including DVD, GOPRO, REDS and BSD. We hope such a Transformer-based architecture can serve as a powerful alternative baseline for video deblurring and other video restoration tasks. The source code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ljzycmd/VDTR</uri> .
Effects of Field of View on Dynamic Out-of-View Target Search in Virtual Reality

We present a study of the effects of field of view (FOV), target movement, and number of targets on visual search performance in virtual reality. We compared visual search tasks in two FOVs (~65&#x00B0;, ~32.5&#x00B0;) under two target movement speeds (static, dynamic) while varying the visible target count, with targets potentially out of the user&#x2019;s view. We examined the expected linear relationship between search time and number of items, to explore how moving and/or out-of-view targets affected this relationship. Overall, search performance increased with a wide FOV, but decreased when targets were moving and with more visible targets. FOV more strongly influenced search performance than target movement. Neither FOV nor target movement meaningfully altered the linear relationship between visual search time and number of items. Participants also rated perceived workload for each condition; FOV and target movement both negatively affected the perceived workload, with target movement being a more significant factor.
Exploring Empathy with Digital Humans

Digital humans are autonomously-animated virtual people whose social interactions are driven by artificial intelligence. They are increasingly being deployed in applications such as healthcare, customer service, and education, and they may have a place in the metaverse. For digital humans to have effective social relationships with users, it is important that they are capable of empathetic interactions. This research aims to evaluate and build upon the autonomous empathy system of a digital human through five experimental studies. Psychological and physiological data will be collected, and the effects will be compared in an Augmented Reality environment and cross-culturally. This paper presents the research agenda, and discusses considerations and challenges for empathetic interactions with digital humans.
Enhanced Brain Storm Optimization Algorithm Based on Modified Nelder–Mead and Elite Learning Mechanism

Brain storm optimization algorithm (BSO) is a popular swarm intelligence algorithm. A significant part of BSO is to divide the population into different clusters with the clustering strategy, and the blind disturbance operator is used to generate offspring. However, this mechanism is easy to lead to premature convergence due to lacking effective direction information. In this paper, an enhanced BSO algorithm based on modified Nelder–Mead and elite learning mechanism (BSONME) is proposed to improve the performance of BSO. In the proposed BSONEM algorithm, the modified Nelder–Mead method is used to explore the effective evolutionary direction. The elite learning mechanism is used to guide the population to exploit the promising region, and the reinitialization strategy is used to alleviate the population stagnation caused by individual homogenization. CEC2014 benchmark problems and two engineering management prediction problems are used to assess the performance of the proposed BSONEM algorithm. Experimental results and statistical analyses show that the proposed BSONEM algorithm is competitive compared with several popular improved BSO algorithms.
Discriminant Feature Extraction by Generalized Difference Subspace

In this paper, we reveal the discriminant capacity of orthogonal data projection onto the generalized difference subspace (GDS), both theoretically and experimentally. In our previous work, we demonstrated that the GDS projection works as a quasi-orthogonalization of class subspaces, which is an effective feature extraction for subspace based classifiers. Here, we further show that GDS projection also works as a discriminant feature extraction through a similar mechanism to the Fisher discriminant analysis (FDA). A direct proof of the connection between GDS projection and FDA is difficult due to the significant difference in their formulations. To circumvent the complication, we first introduce geometrical Fisher discriminant analysis (gFDA) based on a simplified Fisher criterion. It is derived from a heuristic yet practically plausible assumption: the direction of the sample mean vector of a class is largely aligned to the first principal component vector of the class, given that the principal component analysis (PCA) is applied without data centering. gFDA works stably even under few samples, bypassing the small sample size (SSS) problem of FDA. We then prove that gFDA is equivalent to GDS projection with a small correction term. This equivalence ensures GDS projection to inherit the discriminant ability from FDA via gFDA. Furthermore, we discuss two useful extensions of these methods, 1) a nonlinear extension by kernel trick, 2) a combination with CNN features. The equivalence and the effectiveness of the extensions have been verified through extensive experiments on the extended Yale B+, CMU face database, ALOI, ETH80, MNIST, and CIFAR10, mainly focusing on image recognition under small samples.
Development of Personalized Non-Invasive Ventilation Interfaces for Neonatal and Pediatric Application Using Additive Manufacturing

The objective of this study was to present a methodology and manufacturing workflow for non-invasive ventilation interfaces (NIV) for neonates and small infants. It aimed to procure a fast and feasible solution for personalized NIV produced in-house with the aim of improving fit and comfort for the patient. Three-dimensional scans were obtained by means of an intraoral (Trios 3) and a facial scanner (3dMd Flex System). Fusion 360 3D-modelling software was employed to automatize the design of the masks and their respective casting molds. These molds were additively manufactured by stereolithography (SLA) and fused filament fabrication (FFF) technologies. Silicone was poured into the molds to produce the medical device. In this way, patient individualized oronasal and nasal masks were produced. An automated design workflow and use of additive manufacturing enabled a fast and feasible procedure. Despite the cost for individualization likely being higher than for standard masks, a user-friendly workflow for in-house manufacturing of these medical appliances proved to have potential for improving NIV in neonates and infants, as well as increasing comfort.
MEDEP: Maintenance Event Detection for Multivariate Time Series Based on the PELT Approach

Predictive Maintenance (PdM) is one of the most important applications of advanced data science in Industry 4.0, aiming to facilitate manufacturing processes. To build PdM models, sufficient data, such as condition monitoring and maintenance data of the industrial application, are required. However, collecting maintenance data is complex and challenging as it requires human involvement and expertise. Due to time constraints, motivating workers to provide comprehensive labeled data is very challenging, and thus maintenance data are mostly incomplete or even completely missing. In addition to these aspects, a lot of condition monitoring data-sets exist, but only very few labeled small maintenance data-sets can be found. Hence, our proposed solution can provide additional labels and offer new research possibilities for these data-sets. To address this challenge, we introduce MEDEP, a novel maintenance event detection framework based on the Pruned Exact Linear Time (PELT) approach, promising a low false-positive (FP) rate and high accuracy results in general. MEDEP could help to automatically detect performed maintenance events from the deviations in the condition monitoring data. A heuristic method is proposed as an extension to the PELT approach consisting of the following two steps: (1) mean threshold for multivariate time series and (2) distribution threshold analysis based on the complexity-invariant metric. We validate and compare MEDEP on the Microsoft Azure Predictive Maintenance data-set and data from a real-world use case in the welding industry. The experimental outcomes of the proposed approach resulted in a superior performance with an FP rate of around 10% on average and high sensitivity and accuracy results.
Multi-attention multiple instance learning

A new multi-attention based method for solving the MIL problem (MAMIL), which takes into account the neighboring patches or instances of each analyzed patch in a bag, is proposed. In the method, one of the attention modules takes into account adjacent patches or instances, several attention modules are used to get a diverse feature representation of patches, and one attention module is used to unite different feature representations to provide an accurate classification of each patch (instance) and the whole bag. Due to MAMIL, a combined representation of patches and their neighbors in the form of embeddings of a small dimensionality for simple classification is realized. Moreover, different types of patches are efficiently processed, and a diverse feature representation of patches in a bag by using several attention modules is implemented. A simple approach for explaining the classification predictions of patches is proposed. Numerical experiments with various datasets illustrate the proposed method.
Age estimation from facial images based on Gabor feature fusion and the CIASO‐SA algorithm

Abstract Aiming at the problem of long time‐consuming and low accuracy of existing age estimation approaches, a new age estimation method using Gabor feature fusion, and an improved atomic search algorithm for feature selection is proposed. Firstly, texture features of five scales and eight directions in the face region are extracted by Gabor wavelet transform. The statistical histogram is introduced to encode and fuse the directional index with the largest feature value on Gabor scales. Secondly, a new hybrid feature selection algorithm chaotic improved atom search optimisation with simulated annealing (CIASO‐SA) is presented, which is based on an improved atomic search algorithm and the simulated annealing algorithm. Besides, the CIASO‐SA algorithm introduces a chaos mechanism during atomic initialisation, significantly improving the convergence speed and accuracy of the algorithm. Finally, a support vector machine (SVM) is used to get classification results of the age group. To verify the performance of the proposed algorithm, face images with three resolutions in the Adience dataset are tested. Using the Gabor real part fusion feature at 48 × 48 resolution, the average accuracy and 1‐off accuracy of age classification exhibit a maximum of 60.4% and 85.9%, respectively. Obtained results prove the superiority of the proposed algorithm over the state‐of‐the‐art methods, which is of great referential value for application to the mobile terminals.
Enhancing CTR Prediction with Context-Aware Feature Representation Learning

CTR prediction has been widely used in the real world. Many methods model feature interaction to improve their performance. However, most methods only learn a fixed representation for each feature without considering the varying importance of each feature under different contexts, resulting in inferior performance. Recently, several methods tried to learn vector-level weights for feature representations to address the fixed representation issue. However, they only produce linear transformations to refine the fixed feature representations, which are still not flexible enough to capture the varying importance of each feature under different contexts. In this paper, we propose a novel module named Feature Refinement Network (FRNet), which learns context-aware feature representations at bit-level for each feature in different contexts. FRNet consists of two key components: 1) Information Extraction Unit (IEU), which captures contextual information and cross-feature relationships to guide context-aware feature refinement; and 2) Complementary Selection Gate (CSGate), which adaptively integrates the original and complementary feature representations learned in IEU with bit-level weights. Notably, FRNet is orthogonal to existing CTR methods and thus can be applied in many existing methods to boost their performance. Comprehensive experiments are conducted to verify the effectiveness, efficiency, and compatibility of FRNet.
Can Machine Translation be a Reasonable Alternative for Multilingual Question Answering Systems over Knowledge Graphs?

Providing access to information is the main and most important purpose of the Web. However, despite available easy-to-use tools (e.g., search engines, chatbots, question answering) the accessibility is typically limited by the capability of using the English language. This excludes a huge amount of people. In this work, we discuss Knowledge Graph Question Answering (KGQA) systems that aim at providing natural language access to data stored in Knowledge Graphs (KG). While several KGQA systems have been proposed, only very few have dealt with a language other than English. In this work, we follow our research agenda of enabling speakers of any language to access the knowledge stored in KGs. Because of the lack of native support for many languages, we use machine translation (MT) tools to evaluate KGQA systems regarding questions in languages that are unsupported by a KGQA system. In total, our evaluation is based on 8 different languages (including some that never were evaluated before). For the intensive evaluation, we extend the QALD-9 dataset for KGQA with Wikidata queries and high-quality translations. The extension was done in a crowdsourcing manner by native speakers of the different languages. By using multiple KGQA systems for the evaluation, we were enabled to investigate and answer the main research question: “Can MT be an alternative for multilingual KGQA systems?”. The evaluation results demonstrated that the monolingual KGQA systems can be effectively ported to the new languages with MT tools.
FenceNet: Fine-grained Footwork Recognition in Fencing

Current data analysis for the Canadian Olympic fencing team is primarily done manually by coaches and analysts. Due to the highly repetitive, yet dynamic and subtle movements in fencing, manual data analysis can be inefficient and inaccurate. We propose FenceNet as a novel architecture to automate the classification of fine-grained footwork techniques in fencing. FenceNet takes 2D pose data as input and classifies actions using a skeleton-based action recognition approach that incorporates temporal convolutional networks to capture temporal information. We train and evaluate FenceNet on the Fencing Footwork Dataset (FFD), which contains 10 fencers performing 6 different footwork actions for 10-11 repetitions each (652 total videos). FenceNet achieves 85.4% accuracy under 10-fold cross-validation, where each fencer is left out as the test set. This accuracy is within 1% of the current state-of-the-art method, JLJA (86.3%), which selects and fuses features engineered from skeleton data, depth videos, and inertial measurement units. BiFenceNet, a variant of FenceNet that captures the "bidirectionality" of human movement through two separate networks, achieves 87.6% accuracy, outperforming JLJA. Since neither FenceNet nor BiFenceNet requires data from wearable sensors, unlike JLJA, they could be directly applied to most fencing videos, using 2D pose data as input extracted from off-the-shelf 2D human pose estimators. In comparison to JLJA, our methods are also simpler as they do not require manual feature engineering, selection, or fusion.