Reviewerly/exHarmony huggingface dataset:
Review on Virtual Reality for the Advancement of Architectural Learning

A set of virtual reality (VR) based learning environment is being produced to investigate the possibilities for technological innovations and to discover where and how VR may best assist architecture instructors. VR has been utilized as an instructional aid in education, challenging the idea of what makes a learning environment more effective. Thanks to HD (high-definition) visuals and comprehensive contents given by head mounted displays (HMD), learners have been able to study complex and difficult subjects in ways that conventional teaching approach cannot. This paper reviews several learning domains that advance architectural education to benefit the scholars and academicians for usage of immersive VR technologies and its application to design studio. Research on learning benefits, intervention and evaluation action related to the usage of Virtual reality has been lacking. This literature review stresses that it is necessary for the VR's potential to be understood as a pedagogical approach by identifying relevant evaluation methods, intervention features and learning objectives.
A Comprehensive and Comparative Study of Handwriting Recognition System

Handwriting recognition has made a significant impact in the digital world. Handwriting recognition is used in a wide range of applications such as digitizing documents, recognizing postal addresses, interpreting forms and so on. These systems can convert handwritten text into a digital format which can be easily processed by machines. Handwriting recognition is a sustainable technology that is typically based on deep learning techniques, which involve training a model on a large dataset of handwritten samples. There are various types of handwriting recognition systems such as online systems that analyze handwriting in real-time and offline systems that analyze pre-recorded images of handwriting. The technique selection depends upon the application usage and dataset size. There are also hybrid systems that combine both character-level and word-level recognition. In this paper, 422 Scopus papers are analyzed and a comprehensive and comparative analysis of different techniques are discussed.
Artificial Intelligence based Technique for Solar Irradiance Prediction Model with Improved Performance

Solar energy is one of today's most widely adopted renewable energy sources. The use of solar energy in power grids has many advantages, including the decrease in emissions and greenhouse gases, the decrease in power losses and costs, the ability to directly charge dc loads like laptops, electric vehicles, and phones without the need for converters, and an increase in power quality. Techniques based on artificial intelligence are superior in terms of their ability to provide accurate forecasts or predictions of solar energy. This allows photovoltaic power plants to participate in energy auctions earlier, allowing for more cost-effective resource planning. This research presents efficient machine and deep learning-based techniques to improve forecasting performance. The dataset will be taken from the base paper or the publicly available machine learning repository. The simulation will be done using the python spyder IDE 3.7 version. The simulated result will be shown in terms of performance parameter improvement like root means square error, mean absolute error etc.
Intelligent Mobile Robot Speed Regulation using Metaheuristic Algorithms

With technical advances in robotics and technology, mobile robots (MRs) have become more significant in medical applications that require great subtlety, accuracy and efficiency. MRs are primarily time-varying and nonlinear systems that require intelligent controller rather conventional controllers. When artificial intelligence (AI) is employed in MRs, they become intelligent or autonomous. So, metaheuristic algorithms genetic algorithm (GA), firefly algorithm (FFA) and particle swarm optimization (PSO) have been used in this paper to get the optimal outcome and improve the performance of the controller, resulting in an overall system improvement. The study exhibit that PSO control is superior for time-varying and nonlinear MR than conventional control.
Research on Semantic Segmentation Based on Improved PSPNet

Semantic segmentation covers the fields of machine learning, computer vision, image processing and human-computer interaction, has broad application prospects and great application value. But the existing semantic segmantation models still have complex structure, too mang parameters and missing context semantic features problem. To tackle this problems,we propose an improved method based on PSPNet. Specifically, we replace the traditional ResNet with MobileNetV2. Based on this, we extract and preserve the context features effectively, termed contextual semantic features supplement module. Finally, we regard the introduction of the traditional level set method as after processing to the model. Compared with the traditional method, our method has a significant improvement in speed and accuracy on Baidu people segmentation dataset, Adobe's portal segmentation dataset and Sebastien Marcel static hand posture database dataset. To verify the robustness of our method, we make correlation test of multi objects, complex background and low resolution. Result demonstrate the superiority of the proposed approach.
Non-Conventional Computing using thermal and driven Skyrmion Dynamics

Skyrmions are magnetic whirls which exhibit quasiparticle-behavior, respond strongly nonlinear to external stimuli and have recently been shown to exhibit thermally activated dynamics. These properties open up possible future applications in computing architectures beyond von-Neumann computing. We propose and demonstrate experimentally a conceptually new approach to skyrmion reservoir computing that exploits the thermally activated diffusive motion of skyrmions. By confining the thermal and energetically low, electrically gated skyrmion motion, together with employing spatially resolved input and readout, we find that already a single skyrmion in a confined, triangular geometry suffices to realize non-linearly separable functions, which we demonstrate for the XOR gate along with all other Boolean logic gate operations. Beyond reservoir computing, we design and experimentally realize token-based and stochastic computing devices which also greatly benefit from thermal skyrmion dynamics.
3D Human Reconstruction from A Single Depth Image

This paper presents a pioneering work on 3D human reconstruction from a single depth image only. Most of previous works were proposed to use RGB or RGBD images as input, which however suffers from bad illumination and violates privacy protection. Graph convolution network is introduced in our work to fully exploit the topological correlation between the neighboring vertices of graph-structured human body mesh. Parametric Skinned Multi-Person Linear (SMPL) model is further obtained via a regression network. As few datasets contain depth images as well as 3D ground truth, the constraints of 2D keypoints and 2D silhouette, inferred from the given depth image, are combined in loss function to supervise the training of the proposed network. Quantitative and qualitative evaluation on a challenging dataset demonstrated promising performance. And, the effectiveness of main components in our method is validated through a series of ablation studies.
Universal Set of Magnon-Mediated Quantum Gates

We propose a universal set of magnon-mediated quantum gates applied to a hybrid quantum system of mutually noninteracting qubits coupled to an ancilla magnonic mode. The gates are realized through dynamic manipulation of the frequency of the magnon mode and control of its initial state, without directly changing any qubit parameters. We present protocols for arbitrary single-qubit gates and two-qubit controlled phase gate, which together form a universal set and can be combined to perform any unitary transformation on the qubit subsystem. Our results demonstrate principal possibility of realizing a universal quantum computer controlled solely through manipulation of magnon-qubit interactions.
Global Biasing Using a Hardware-Based Artificial Zeeman Term in Spinwave Ising Machines

A spinwave Ising machine (SWIM) is a newly proposed type of time-multiplexed hardware solver for combinatorial optimization that employs feedback coupling and phase sensitive amplification to map an Ising Hamiltonian into phase-binarized propagating spin-wave RF pulses in an Yttrium-Iron-Garnet (YIG) film. In this work, we increase the mathematical complexity of the SWIM by adding a global Zeeman term to a 4-spin MAX-CUT Hamiltonian using a continuous external electrical signal with the same frequency as the spin pulses and phase locked with with one of the two possible states. We are able to induce ferromagnetic ordering in both directions of the spin states despite antiferromagnetic pairwise coupling. Embedding a planar antiferromagnetic spin system in a magnetic field has been proven to increase the complexity of the graph associated to its Hamiltonian and thus this straightforward implementation helps explore higher degrees of complexity as the technology evolves.
A Comprehensive Study Of Metaverse Privacy And Security

The concept of the metaverse has been evolving over time, and it does not have a specific launch date. The term "metaverse" was popularized by Neal Stephenson in his science fiction novel "Snow Crash," published in 1992. The term "metaverse" refers to a virtual reality space or a collective virtual shared space where people can interact with a computer-generated environment and with each other in real-time. It is often described as a digital universe that encompasses various interconnected virtual worlds, augmented reality, and virtual reality experiences. Metaverse privacy refers to the protection of personal information and the preservation of individual privacy within the metaverse or virtual reality environments. As the metaverse becomes more prevalent and people engage in immersive digital experiences, it raises concerns about the collection, use, and sharing of personal data, as well as the potential for privacy breaches and surveillance. In this case study we try to resolve why the security landscape will continually change, and new threats and challenges may emerge.
Novel Topic Models for Parallel Topics Extraction from Multilingual Text

In this work, we propose novel topic models to extract topics from multilingual documents. We add more flexibility to conventional LDA by relaxing some constraints in its prior. We apply other alternative priors namely generalized Dirichlet and Beta-Liouville distributions. Also, we extend finite mixture model to infinite case to provide flexibility in modelling various topics. To learn our proposed models, we deploy variational inference. To evaluate our framework, we tested it on English and French documents and compared topics and similarities by Jaccard index. The outcomes indicate that our proposed model could be considered as promising alternative in topic modeling.
In Support of Push-Based Streaming for the Computing Continuum

Real-time data architectures are core tools for implementing the edge-to-cloud computing continuum since streams are a natural abstraction for representing and predicting the needs of such applications. Over the past decade, Big Data architectures evolved into specialized layers for handling real-time storage and stream processing. Open-source streaming architectures efficiently decouple fast storage and processing engines by implementing stream reads through a pull-based interface exposed by storage. However, how much data the stream source operators have to pull from storage continuously and how often to issue pull-based requests are configurations left to the application and can result in increased system resources and overall reduced application performance. To tackle these issues, this paper proposes a unified streaming architecture that integrates co-located fast storage and streaming engines through push-based source integrations, making the data available for processing as soon as storage has them. We empirically evaluate pull-based versus push-based design alternatives of the streaming source reader and discuss the advantages of both approaches.
Vietnamese Multidocument Summarization Using Subgraph Selection-Based Approach with Graph-Informed Self-attention Mechanism

In Multi-Document Summarization (MDS), the exceedingly large input length is a significant difficulty. In this paper, we designed a graph-based model with Graph-informed Self-attention to capture inter-sentence and inter-document relations in graph. Our models represent the MDS task as a sub-graph selection problem, where the candidate summaries are its subgraphs and the source documents are thought of as a similarity a graph. We add a pair of weight matrix learning parameters that represent the relationship between sentences and the relationship between documents in the cluster, thereby seeing the close relationship and the role of relation types on the graph. Empirical results on the MultiNews and Vietnamese AbMuSu dataset demonstrate that our approach substantially outperforms some baseline models.
An Improvement of Diachronic Embedding for Temporal Knowledge Graph Completion

Knowledge graph completion (KGC) aims to predict missing information in a knowledge graph (KG). Knowledge embedding approaches learn the representations of entities and relations in a static knowledge graph, playing an essential role in knowledge graph completion. However, the previous methods have been designed for static knowledge graphs. A temporal knowledge graph (TKG), on th other hand, contains timestamp-based facts indicating relationships between entities at different times. R. Goel et al. (2020) introduced novel models for temporal knowledge graph completion through existing static models with a diachronic entity embedding function, which are able to provide characteristics of entities at any point in time. Moreover, their method is flexible since it can be potentially combined with any existing static model. In this paper, we propose two models, - (DE-RotatE and DE-RotatE-sinc) -, to combine the diachronic entity embedding with RotatE - the model is introduced by Z. Sun et al. (2019). Through experiments, we show that the results of our proposed models are better than those in the work by R. Goel at al. Our source code is available on Github, https://github.com/anhntt1202/DE-RotatE .
Fuzzy Logic Framework for Ontology Concepts Alignment

The problem of ontology alignment appears when interoperability of independently created ontologies is expected. The task can be described as collecting a set of pairs of elements taken from such ontologies that relate to the same objects from the universe of discourse. In our previous research, we introduced incorporating fuzzy logic in the considered task. It was used to combine several different similarity measures calculated between elements taken from different ontologies to eventually provide an unequivocal decision on whether or not a pair of such elements can be treated as mappable. Up until now, we focused solely on the level of instances and relations. Therefore, in this paper, we propose our novel approach to designating ontology mappings on the level of concepts. The developed methods were experimentally verified, yielding very promising results. We used the widely accepted benchmarks provided by the Ontology Alignment Evaluation Initiative, which are considered the state-of-the-art datasets used to evaluate solutions to any ontology-related problem.
Multi-criteria Approaches to Explaining Black Box Machine Learning Models

The adoption of machine learning algorithms, especially in critical domains often encounters obstacles related to the lack of their interpretability. In this paper we discuss the methods producing local explanations being either counterfactuals or rules. However, choosing the most appropriate explanation method and one of the generated explanations is not an easy task. Instead of producing only a single explanation, the creation of a set of diverse solutions by a specialized ensemble of explanation methods is proposed. Large sets of these explanations are filtered out by using the dominance relation. Then, the most compromise explanations are searched with a multi-criteria selection method. The usefulness of these approaches is shown in two experimental studies carried out with counterfactuals or rule explanations, respectively.
Integrating Ontology-Based Knowledge to Improve Biomedical Multi-Document Summarization Model

Most existing extractive summarization models use the original text’s internal information and calculate each sentence’s importance individually. When applied to specific domains (such as verbal text, biomedical literature, etc.), these models have some drawbacks: the variety of synonym terms, unknown words or terminologies, and the intra-document and inter-document relations between sentences or terms. In this work, we proposed an ontology-based summarization model that leverages many knowledge bases to understand the input documents. Our proposed model was built with an integrated ontology and a signal transmission-based method for extending domain knowledge such as related terms, and relationships between terms and sentences. The proposed model has been proven effective with the highest ROUGE-2 F1 score in the test dataset of the MEDIQA 2021 MAS shared tasks.
Can Ensemble Calibrated Learning Enhance Link Prediction? A Study on Commonsense Knowledge

Numerous prior works have shown how we can use knowledge graph embedding (KGE) models for ranking unseen facts that are likely to be true. Though these KGE models have been shown to make good performance on the ranking task with standard benchmark datasets, in practice only a subset of the top-k ranked list is indeed correct. This is due to the fact that most knowledge graphs are built under the open world assumption, while state-of-the-art KGE exploit the closed world assumption to build negative samples for training. In this paper, we show to address this problem by ensembling calibrated learning, following the principle that multiple calibrated models can make a stronger one. In experiments on the ConceptNet of commonsense knowledge base, we show significant improvement over each individual baseline model. This suggests that ensembling calibrated learning is a promising technique to improve link prediction with KGEs.
Hybrid Approaches to Sentiment Analysis of Social Media Data

Today, social media is accessible all around the world. It has become an online place for reviewing products or services. Social media is an appealing resource for enterprises looking to monitor user attitudes because of the massive volume of posts. It has gathered much attention in the field of sentiment analysis. Sentiment analysis models could be classified into three categories: Lexicon-based, classical Machine Learning, and Deep Learning model. The classification methods have received a lot of attention in the research area. Although supervised learning algorithms are crucial components, the preprocessing step greatly impacts the accuracy of the sentiment analysis task. In preprocessing, the negation handling method needs to get more attention as the most popular technique creates redundant features. This research proposes a negation handling method that utilizes the lexicon-model VADER (Valence Aware Dictionary for sEntiment Reasoning) for concentrating on high-sentiment words. The proposed negation handling method improves the accuracy of the classical machine learning algorithms Logistic Regression and Naive Bayes. In addition, the research proposes an approach to handling elongated words and emoji characters instead of removing them. An experiment is conducted for a comparison of the proposed methods and the chosen base techniques.
Discovering Prevalent Co-location Patterns Without Collecting Co-location Instances

Discovering prevalent co-location patterns (PCPs) is a process of finding a set of spatial features in which their instances frequently occur in close geographic proximity to each other. Most of the existing algorithms collect co-location instances to evaluate the prevalence of spatial co-location patterns, that is if the participation index (a prevalence measure) of a pattern is not smaller than a minimum prevalence threshold, the pattern is a PCP. However, collecting co-location instances is the most expensive step in these algorithms. In addition, if users change the minimum prevalence threshold, they have to re-collect all co-location instances for obtaining new results. In this paper, we propose a new prevalent co-location pattern mining framework that does not need to collect co-location instances of patterns. First, under a distance threshold, all cliques of an input dataset are enumerated. Then, a co-location hashmap structure is designed to compact all these cliques. Finally, participation indexes of patterns are efficiently calculated by the co-location hashmap structure. To demonstrate the performance of the proposed framework, a set of comparisons with the previous algorithm which is based on collecting co-location instances on both synthetic and real datasets is made. The comparison results indicate that the proposed framework shows better performance.
GIFT4Rec: An Effective Side Information Fusion Technique Apply to Graph Neural Network for Cold-Start Recommendation

Recommendation systems are highly interested in technology companies nowadays. The businesses are constantly growing users and products, causing the number of users and items to continuously increase over time, to very large numbers, this leads to the cold-start problem. Recommending purely cold-start users is a long-standing and fundamental challenge in the recommendation systems where systems are unable to recommend relevant items to the users due to unavailability of adequate information about them. To solve this problem, extensive studies have been carried out using the side information techniques (user information, item information, ...). However, we argue that this work will affect the user/product group that had a lot of interaction, using this side information can reduce the performance of the model when just focusing on learning based on the side information. In this paper, we propose a combination of global and local side Information Fusion Techniques based on attention algorithm applied to Graph neural network-based models for cold-start users recommendation, and we call this architecture GIFT4Rec.
Transfer Learning for Abnormal Behaviors Identification in Examination Room from Surveillance Videos: A Case Study in Vietnam

The examination evaluates the learners’ ability to achieve a specific goal, representing a crucial assessment of the knowledge acquired during the learning process. To attain success in the examination, it is imperative to prevent cheating in the examination hall. Despite this, cheating still occurs due to the limited availability of human resources. Therefore, we have collected surveillance videos of examination halls from a high school in Vietnam to analyze and implement deep learning architectures such as You Only Look Once (YOLO) and Single Shot Detector (SSD) MobileNet V2 to detect anomalous behavior among students during the examination. Our study focuses on detecting five common abnormal behaviors, including looking around, bending over the desk, putting one or two hands under the table, waving, and standing up. YOLO achieved the best results, with a performance of 83.55%, 99.65%, 97%, 99.2%, and 98.0866% in Intersection over Union (IoU), Mean Average Precision (mAP), Precision, Recall, and F1-Score, respectively, across 2639 images. This approach is expected to assist educators and teachers in detecting and preventing cheating activities in examination rooms.