Reviewerly/exHarmony huggingface dataset:
SSR-MGTI: Self-attention Sequential Recommendation Algorithm Based on Movie Genre Time Interval

As an important part of the recommendation system, movie recommendation system can recommend movies to users accurately according to their preferences. Traditional movie recommendation systems simply treat user-movie interactions as a time-ordered sequence, without considering the time intervals between movies of the same genre. The genre time interval can reflect the user’s preference for a particular genre and determine whether the algorithm can fully capture the user’s interests and the time characteristics of the movie, which plays an important role in the accuracy of the movie recommendation. Therefore, in this paper, we propose a Self-Attention Sequential Recommendation algorithm based on Movie Genre Time Interval (SSR-MGTI). Specifically, a multi-head self-attention mechanism is used to model the same genre time interval information. Then, an absolute position is added to the multi-head self-attention mechanism model to solve the problem that multi-head self-attention mechanism does not consider the sequence. In addition, the convolutional neural network is used to convert the model from linear to non-linear and extract local information of user-movie interaction sequences. It is interesting to show that the proposed SSR-MGTI can accurately predict the movie that the user will watch next time. Experimental results on MovieLens and Amazon datasets demonstrate the superiority of our SSR-MGTI over state-of-the-art movie recommendation methods.
Action Segmentation Based on Encoder-Decoder and Global Timing Information

Action segment has made significant progress, but segmenting and recognizing actions from untrimmed long videos remains a challenging problem. Most state-of-the-art (SOTA) methods focus on designing models based on temporal convolution. However, the limitations of modeling long-term temporal dependencies and the inflexibility of temporal convolutions restrict the potential of these models. To address the issue of over-segmentation in existing action segmentation algorithms, which leads to prediction errors and reduced segmentation quality, this paper proposes an action segmentation algorithm based on Encoder-Decoder and global temporal information. The action segmentation algorithm based on Encoder-Decoder and global timing information proposed in this paper uses the global timing information captured by LSTM to assist the Encoder-Decoder structure in judging the action segmentation point more accurately and, at the same time, suppress the excessive segmentation phenomenon caused by the Encoder-Decoder structure. The algorithm proposed in this paper achieves 93% frame accuracy on the constructed real Taiji action data set. The experimental results prove that this model can accurately and efficiently complete the long video action segmentation task.
Algorithm for the Accelerated Calculation of Conceptual Distances in Large Knowledge Graphs

Conceptual distance refers to the degree of proximity between two concepts within a conceptualization. It is closely related to semantic similarity and relationships, but its measurement strongly depends on the context of the given concepts. DIS-C represents an advancement in the computation of semantic similarity/relationships that is independent of the type of knowledge structure and semantic relations when generating a graph from a knowledge base (ontologies, semantic networks, and hierarchies, among others). This approach determines the semantic similarity between two indirectly connected concepts in an ontology by propagating local distances by applying an algorithm based on the All Pairs Shortest Path (APSP) problem. This process is implemented for each pair of concepts to establish the most effective and efficient paths to connect these concepts. The algorithm identifies the shortest path between concepts, which allows for an inference of the most relevant relationships between them. However, one of the critical issues with this process is computational complexity, combined with the design of APSP algorithms, such as Dijkstra, which is 𝒪n3. This paper studies different alternatives to improve the DIS-C approach by adapting approximation algorithms, focusing on Dijkstra, pruned Dijkstra, and sketch-based methods, to compute the conceptual distance according to the need to scale DIS-C to analyze very large graphs; therefore, reducing the related computational complexity is critical. Tests were performed using different datasets to calculate the conceptual distance when using the original version of DIS-C and when using the influence area of nodes. In situations where time optimization is necessary for generating results, using the original DIS-C model is not the optimal method. Therefore, we propose a simplified version of DIS-C to calculate conceptual distances based on centrality estimation. The obtained results for the simple version of DIS-C indicated that the processing time decreased 2.381 times when compared to the original DIS-C version. Additionally, for both versions of DIS-C (normal and simple), the APSP algorithm decreased the computational cost when using a two-hop coverage-based approach.
Transmission Removal from a Single OSN-Shared Glass Mixture Image

Photographs taken through glass often reflect the photographer or the surroundings, which is very helpful in uncovering information about the photograph. Various lossy operations performed on images over online social networks (OSNs), such as compression and resampling, pose a great challenge for transmission layer removal. This paper proposes a self-attention-based architecture for image enhancement over OSNs, to ensure that the downloaded glass mixture image can show more information about the reflection layer than the original image. Transmission layer removal is then achieved using a two-stage generative adversarial network. We also add attention to the transmission layer in the mixture image and use the gradient and color block information in the next stage to extract the reflection layer. This method yielded a gain of 0.46 dB in PSNR, 0.016 in SSIM, and 0.057 in LPIPS, resulting in an effective improvement in the visual quality of the final extracted reflection layer images.
Steelmaking Predictive Analytics Based on Random Forest and Semantic Reasoning

This paper proposes a human-in-the-loop framework that integrates machine learning models with semantic technologies to aid decision making in the domain of steelmaking. To achieve this, we convert a random forest (RF) into rules in a Semantic Web Rule Language (SWRL) format and represent real-world data as a knowledge graph in a Resource Description Framework (RDF) format, capturing the meta-data as part of the model. A rule engine is deployed that applies logical inference on the knowledge graph, resulting in a semantically enriched classification. This new classification is combined with external domain-expert knowledge to provide improved, knowledge-guided assistance for the human-in-the-loop system. A case study in the steel manufacturing domain is introduced, where this application is used for real-world predictive analytic purposes.
Bilateral co-transfer for unsupervised domain adaptation

Labeled data scarcity of an interested domain is often a serious problem in machine learning. Leveraging the labeled data from other semantic-related yet co-variate shifted source domain to facilitate the interested domain is a consensus. In order to solve the domain shift between domains and reduce the learning ambiguity, unsupervised domain adaptation (UDA) greatly promotes the transferability of model parameters. However, the dilemma of over-fitting (negative transfer) and under-fitting (under-adaptation) is always an overlooked challenge and potential risk. In this paper, we rethink the shallow learning paradigm and this intractable over/under-fitting problem, and propose a safer UDA model, coined as Bilateral Co-Transfer (BCT), which is essentially beyond previous well-known unilateral transfer. With bilateral co-transfer between domains, the risk of over/under-fitting is therefore largely reduced. Technically, the proposed BCT is a symmetrical structure, with joint distribution discrepancy (JDD) modeled for domain alignment and category discrimination. Specifically, a symmetrical bilateral transfer (SBT) loss between source and target domains is proposed under the philosophy of mutual checks and balances. First, each target sample is represented by source samples with low-rankness constraint in a common subspace, such that the most informative and transferrable source data can be used to alleviate negative transfer. Second, each source sample is symmetrically and sparsely represented by target samples, such that the most reliable target samples can be exploited to tackle under-adaptation. Experiments on various benchmarks show that our BCT outperforms many previous outstanding work.
Interactive Relative Pose Estimation for 360° Indoor Panoramas through Wall-Wall Matching Selections

We present an interactive approach to estimating the relative camera pose of two panoramas shot in the same indoor environment. Compared to the trivial interactive baseline, which would require the user to precisely select 8 or more pairs of matching points by mouse clicks, our method just needs the user to select a pair of matching walls with two mouse clicks or keyboard strokes. Our method is based on the key observation that, in most cases, there exist at least one or multiple pairs of roughly matched walls in the room layouts estimated by neural networks - which alone are sufficient to generate accurate relative camera poses. Tested on a real-world indoor panorama dataset, our method outperforms current state-of-the-art automatic methods by large margins, compensating the additional human efforts. Through user studies, we found that matched wall-wall pairs can be easily recognized and selected by humans in relatively short time, indicating that such an interactive approach is practical.
Exploring Embodiment and Usability of Autonomous Prosthetic Limbs through Virtual Reality

We propose the utilization of motion capture and immersive virtual reality to explore embodiment and user perception associated with prosthetic limbs. We developed a virtual reality simulation where a user could control an amputated avatar in first-person view with full body motion capture to experimentally investigate how the movement speed of an autonomous prosthetic limb affects its embodiment, usability, competence, warmth, and discomfort. In a within-subjects design experiment, participants performed a reaching task using a virtual prosthetic lower arm that moved at six different speeds in a minimum jerk trajectory. Results showed that extremely fast movements and extremely slow movements equally reduce embodiment, usability, and competence while movements at moderate speeds maximize embodiment and usability while reducing discomfort. Our findings provide insights into developing autonomous prosthetic limbs with higher user satisfaction that take embodiment, user perception, and usability into consideration.
SCOOT:Self-supervised Centric Open-set Object Tracking

We propose a novel and comprehensive general-purpose object tracking system named Self-supervised Centric Open-set Object Tracking or ‘SCOOT’. Our SCOOT encompasses a self-supervised appearance model, a fusion module for combining textual and visual features, and an object association algorithm based on reconstruction and observation. Through this system, we unlock new possibilities for enhancing the capability of open-set object tracking with the aid of language cues in real-world scenarios.
Quantifying display lag and its effects during Head-Mounted Display based Virtual Reality

poster Free Access Share on Quantifying display lag and its effects during Head-Mounted Display based Virtual Reality Authors: Peter Wagner Faculty of Medicine and Health, School of Optometry and Vision Science, Sensory Processes Research Laboratory, University of New South Wales, Australia Faculty of Medicine and Health, School of Optometry and Vision Science, Sensory Processes Research Laboratory, University of New South Wales, Australia 0000-0001-7835-3357Search about this author , Juno Kim Faculty of Medicine and Health, School of Optometry and Vision Science, Sensory Processes Research Laboratory, University of New South Wales, Australia Faculty of Medicine and Health, School of Optometry and Vision Science, Sensory Processes Research Laboratory, University of New South Wales, Australia 0000-0003-1300-9875Search about this author , Robert S. Allison Electrical Engineering and Computer Science, School of Engineering, York University, Canada Electrical Engineering and Computer Science, School of Engineering, York University, Canada 0000-0002-4485-2665Search about this author , Stephen Palmisano School of Psychology, University of Wollongong, Australia School of Psychology, University of Wollongong, Australia 0000-0002-9140-5681Search about this author Authors Info & Claims SA '23: SIGGRAPH Asia 2023 PostersNovember 2023Article No.: 29Pages 1–3https://doi.org/10.1145/3610542.3626139Published:28 November 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Avatars for Good Drinking: An Exploratory Study of The Effects of Avatar's Body Shape on Beverage Perception

The Proteus effect is known as the effect that the self-image evoked by an avatar influences the behavior. Previous studies have shown that the Proteus effect influences food-related behaviors, but it is not clear whether it influences perceptions of food. Therefore, in this study, we investigated whether the avatar’s body shape affects beverage perception when drinking a beverage in a virtual environment. Specifically, we measured the sense of body ownership toward the avatar, the taste of the beverage, the amount of the beverage consumed, and the purchase intention of the beverage. The results showed that a gradual transition in body shape with drinking produced a significant improvement in the sense of body ownership, particularly in terms of feeling that the virtual body belonged to oneself. We also report the larger body shape significantly increases the purchase intention. Furthermore, we found that image congruence between the avatar and cola induces better taste perception.
Improving Solar Radiation Forecasting Utilizing Data Augmentation Model Generative Adversarial Networks with Convolutional Support Vector Machine (GAN-CSVR)

The accuracy of solar radiation forecasting depends greatly on the quantity and quality of input data. Although deep learning techniques have robust performance, especially when dealing with temporal and spatial features, they are not sufficient because they do not have enough data for training. Therefore, extending a similar climate dataset using an augmentation process will help overcome the issue. This paper proposed a generative adversarial network model with convolutional support vector regression, which is named (GAN-CSVR) that combines a GAN, convolutional neural network, and SVR to augment training data. The proposed model is trained utilizing the Multi-Objective loss function, which combines the mean squared error and binary cross-entropy. The original solar radiation dataset used in the testing is derived from three locations, and the results are evaluated using two scales, namely standard deviation (STD) and cumulative distribution function (CDF). The STD and the average error value of the CDF between the original dataset and the augmented dataset for these three locations are 0.0208, 0.1603, 0.9393, and 7.443981, 4.968554, and 1.495882, respectively. These values show very significant similarity in these two datasets for all locations. The forecasting accuracy findings show that the GAN-CSVR model produced augmented datasets that improved forecasting from 31.77% to 49.86% with respect to RMSE and MAE over the original datasets. This study revealed that the augmented dataset produced by the GAN-CSVR model is reliable because it provides sufficient data for training deep networks.
Location-Aware Social Network Recommendation via Temporal Graph Networks

In the data-driven era, recommendations have become indispensable across various systems. Graphs, as versatile data structures, shine at abstracting complex systems. Many real-world scenarios effortlessly translate into graphs, representing individuals and their relationships as nodes and edges. Link prediction, a cornerstone of recommendations, excels in forecasting future network connections based on current structures. Its applications span diverse domains, including social networks, biological networks, and network security. Previous studies have leveraged classification algorithms like logistic regression and random forest, often complemented by node embedding techniques, yielding impressive results in addressing the challenge of link prediction. Today's dynamic networks continually reshape connections, introducing new links and nodes while removing others. Furthermore, the inclusion of location information associated with nodes provides a new opportunity. Adapting models to this dynamism necessitates capturing spatial and temporal dependencies for sustained effectiveness. In this paper, we undertake a comprehensive evaluation of various algorithms for link prediction. Subsequently, we further enriched the continuous-time dynamic graph networks by incorporating essential location information. This strategic enhancement results in a remarkable performance improvement, highlighting the crucial role of location-based temporal data in improving recommendations. It emphasizes the untapped potential of location and temporal information in refining user recommendations within interconnected networks.
Application of a Multifractal Model for Identification of Lithology and Hydrothermal Alteration in the Dasuji Porphyry Mo Deposit in Inner Mongolia, China

The Dasuji giant porphyry molybdenum deposit is one of the largest ore deposits recently discovered along the Yinshan–Yanshan–Liaoning molybdenum belt in China. Using this deposit along the Yinshan–Yanshan–Liaoning molybdenum belt as the study area, the present study proposed a two-stage approach aimed at marking out the hydrothermally altered anomalies in the study area for the guidance of future prospecting in other regions. First of all, the Principal Component Analysis (PCA) and specific Band Ratio methods were applied to the ASTER images from different acquisition dates to extract ferric oxides and hydroxyl alterations related to the porphyry molybdenum deposit. Then, the Fractal-Aided Anomaly-Overlaying Selection model was adopted to recognize two ferric and hydroxyl alteration layers for separating anomalies from the interferences caused by geology and random noise from the data. Furthermore, for lithological differentiation in the previously marked off area, the Random Forest Classifier (RFC) was applied to the composite data obtained via the ASTER, ETM, and DEM, and it is demonstrated that the DEM can significantly improve lithological mapping in areas with complex vegetation cover and topography. Based on field verification and comparison with geological maps, the research revealed that the suggested two-stage approach may effectively reduce erroneously recognized anomalies produced during the first stage while retaining ore-related anomalies for gigantic porphyry molybdenum deposit prospecting in the Dasuji area, which showed the good application potential of the proposed model to extract actual hydrothermally altered anomalies adopted for lithological discrimination and mapping.
The Effect of Wearing Knee Supporters on the Applicable Gain of Redirected Walking

Redirected Walking (RDW) is a locomotion technique that enables users to explore extensive virtual environments while confined to limited real-world environment by manipulating orientation and coordinates within the virtual environment. Previous research has shown that RDW can be made more effective by wearing a knee-tightening device. In this study, we examined the effects of two different knee supporters—band-type and soft-type—on the applicable gain of RDW. A significant difference in applicable gain between the two supporter conditions was confirmed. This result indicates that knee tightening affects the applicable gains of RDW and suggest that a more comfortable RDW experience may be possible with the use of appropriately shaped supporters.
SAR-HUB: Pre-Training, Fine-Tuning, and Explaining

Since the current remote sensing pre-trained models trained on optical images are not as effective when applied to SAR image tasks, it is crucial to create sensor-specific SAR models with generalized feature representations and to demonstrate with evidence the limitations of optical pre-trained models in downstream SAR tasks. The following aspects are the focus of this study: pre-training, fine-tuning, and explaining. First, we collect the current large-scale open-source SAR scene image classification datasets to pre-train a series of deep neural networks, including convolutional neural networks (CNNs) and vision transformers (ViT). A novel dynamic range adaptive enhancement method and a mini-batch class-balanced loss are proposed to tackle the challenges in SAR scene image classification. Second, the pre-trained models are transferred to various SAR downstream tasks compared with optical ones. Lastly, we propose a novel knowledge point interpretation method to reveal the benefits of the SAR pre-trained model with comprehensive and quantifiable explanations. This study is reproducible using open-source code and datasets, demonstrates generalization through extensive experiments on a variety of tasks, and is interpretable through qualitative and quantitative analyses. The codes and models are open source.
Crossing Narrative: Exploring the Possibilities of Crossing the Virtuality and Reality in Interactive Narrative Experiences

With the development of mixed reality technology, the convergence of virtual and real experiences has emerged as a trend in the interactive narratives. In this paper, we introduce “Crossing Narrative”, an interactive narrative experience that seamlessly blends virtuality and reality by utilizing real-world views and bystanders. We discuss specific methods for designing cross-reality narrative experience, focusing on three key aspects of cross-reality interactions: diegetic objects, transition effects, and bystander's avatar. Our design methods aim to enhance audience social interactions and understanding of the immersive narrative.
Register differences and intra-register variation of elicited texts

Abstract This study examines functional differences between texts elicited under different scenarios (both between- and intra-register variation). Text elicitation is used (in linguistics and other disciplines) to control for the conditions of production and in the hopes of observing different reactions to scenarios emulating real-life situations. However, it entails a series of questions: How well does the collected data correspond to real-world situations? How to design scenarios to be meaningfully distinct in terms of the language they elicit? In order to examine the linguistic variability of scenarios and to assess their ecological validity, the present study maps Czech elicited texts onto a previously established general-purpose multi-dimensional model of register variability. One of the takeaways is that scenarios mimicking informal situations are particularly conducive to obtaining responses with high intra-register variation, which makes them more likely than formal ones to reflect variability induced by for example different psychological characteristics of participants.