Reviewerly/exHarmony huggingface dataset:
Relationship between Brand Experience, Local Presence, Flow, and Brand Attitude While Applying Spatial Augmented Reality to a Brand Store

AbstractOffline brand spaces strive to incorporate innovative technologies and showcase their other aspects. Spatial augmented reality (SAR) is a type of augmented reality that is directly integrated into the physical space, overcoming display limitations, and providing a more immersive experience. In this study, 85 participants were asked to experience marketing-based SAR experimental stimuli, and the data were analyzed by evaluating flow, local presence, and brand attitude. The results of the study show a positive relationship between experiential elements such as sensory perception, emotion, cognition, behavior, and flow and local presence, which, in turn, impact brand attitude. However, if the target brand differs, the experiment may vary depending on the specifications, potentially impacting the evaluation. Additionally, future research should consider technological advancements and alternative approaches to expanded reality experiences. This study offers valuable insights into the potential marketing applications of SAR in offline brand spaces.Keywords: Immersive experiencespatial augmented realitybrand attitudepresenceflowoffline store Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea [NRF-2021S1A5B5A17054966].Notes on contributorsJungeun ParkJungeun Park is a PhD researcher at the Department of Interior Architecture and Built Environment, Yonsei University. She studies user reactions utilizing interactive media, such as AR and VR. Her research focuses on the impact of these technologies on user experience in the field of the built environment and space marketing.Hyunsoo LeeHyunsoo Lee is a Professor of the Department of Interior Architecture and Built Environment, Yonsei University. His research focuses on spatial design marketing, GIS analysis, and interactive media. He examines the relationship between spatial design and marketing strategies, employing GIS analysis to gain insights.
Offline reinforcement learning in high-dimensional stochastic environments

Abstract Offline reinforcement learning (RL) has emerged as a promising paradigm for real-world applications since it aims to train policies directly from datasets of past interactions with the environment. The past few years, algorithms have been introduced to learn from high-dimensional observational states in offline settings. The general idea of these methods is to encode the environment into a latent space and train policies on top of this smaller representation. In this paper, we extend this general method to stochastic environments (i.e., where the reward function is stochastic) and consider a risk measure instead of the classical expected return. First, we show that, under some assumptions, it is equivalent to minimizing a risk measure in the latent space and in the natural space. Based on this result, we present Latent Offline Distributional Actor-Critic (LODAC), an algorithm which is able to train policies in high-dimensional stochastic and offline settings to minimize a given risk measure. Empirically, we show that using LODAC to minimize Conditional Value-at-Risk (CVaR) outperforms previous methods in terms of CVaR and return on stochastic environments.
Tuning the Weights: The Impact of Initial Matrix Configurations on Successor Features’ Learning Efficacy

The focus of this study is to investigate the impact of different initialization strategies for the weight matrix of Successor Features (SF) on the learning efficiency and convergence in Reinforcement Learning (RL) agents. Using a grid-world paradigm, we compare the performance of RL agents, whose SF weight matrix is initialized with either an identity matrix, zero matrix, or a randomly generated matrix (using the Xavier, He, or uniform distribution method). Our analysis revolves around evaluating metrics such as the value error, step length, PCA of Successor Representation (SR) place field, and the distance of the SR matrices between different agents. The results demonstrate that the RL agents initialized with random matrices reach the optimal SR place field faster and showcase a quicker reduction in value error, pointing to more efficient learning. Furthermore, these random agents also exhibit a faster decrease in step length across larger grid-world environments. The study provides insights into the neurobiological interpretations of these results, their implications for understanding intelligence, and potential future research directions. These findings could have profound implications for the field of artificial intelligence, particularly in the design of learning algorithms.
CETD: Counterfactual Explanations by Considering Temporal Dependencies in Sequential Recommendation

Providing interpretable explanations can notably enhance users’ confidence and satisfaction with regard to recommender systems. Counterfactual explanations demonstrate remarkable performance in the realm of explainable sequential recommendation. However, current counterfactual explanation models designed for sequential recommendation overlook the temporal dependencies in a user’s past behavior sequence. Furthermore, counterfactual histories should be as similar to the real history as possible to avoid conflicting with the user’s genuine behavioral preferences. This paper presents counterfactual explanations by Considering temporal dependencies (CETD), a counterfactual explanation model that utilizes a variational autoencoder (VAE) for sequential recommendation and takes into account temporal dependencies. To improve explainability, CETD employs a recurrent neural network (RNN) when generating counterfactual histories, thereby capturing both the user’s long-term preferences and short-term behavior in their real behavioral history. Meanwhile, CETD fits the distribution of reconstructed data (i.e., the counterfactual sequences generated by VAE perturbation) in a latent space, and leverages learned variance to decrease the proximity of counterfactual histories by minimizing the distance between the counterfactual sequences and the original sequence. Thorough experiments conducted on two real-world datasets demonstrate that the proposed CETD consistently surpasses current state-of-the-art methods.
Nonparametric statistics. Part 3. Correlation coefficients

A measure of correlation or strength of association between random variables is the correlation coefficient. In scientific research, correlation analysis is most often carried out using various correlation coefficients without explaining why this particular coefficient was chosen and what the resulting value of this coefficient means. The article discusses Spearman correlation coefficient, Kendall correlation coefficient, phi (Yule) correlation coefficient, Cramér’s correlation coefficient, Matthews correlation coefficient, Fechner correlation coefficient, Tschuprow correlation coefficient, rank-biserial correlation coefficient, point-biserial correlation coefficient, as well as association coefficient and contingency coefficient. The criteria for applying each of the coefficients are given. It is shown how to establish the significance (insignificance) of the resulting correlation coefficient. The scales in which the correlated variables should be located for the coefficients under consideration are presented. Spearman rank correlation coefficient and other nonparametric indicators are independent of the distribution law, and that is why they are very useful. They make it possible to measure the contingency between such attributes that cannot be directly measured, but can be expressed by points or other conventional units that allow ranking the sample. The benefit of rank correlation coefficient also lies in the fact that it allows to quickly assess the relationship between attributes regardless of the distribution law. Examples are given and step-by-step application of each coefficient is described. When analyzing scientific research and evaluating the results obtained, the strength of association is most commonly assessed by the correlation coefficient. In this regard, a number of scales are given (Chaddock scale, Cohen scale, Rosenthal scale, Hinkle scale, Evans scale) grading the strength of association for correlation coefficient, both widely recognized and not so well known.
Sentiment Analysis on Algerian Dialect with Transformers

The task of extracting sentiment from text has been widely studied in the field of natural language processing. However, little work has been conducted specifically on the Arabic language with the Algerian dialect. In this research, we aim to make a significant contribution to the field of sentiment analysis on the Algerian dialect by creating a custom and relatively large dataset with a tailored deep learning model. The dataset was extracted from Algerian YouTube channels and manually annotated by the research team. We then utilize this dataset to train a state-of-the-art deep learning model for natural language processing called BERT, which is a type of Transformer model. Using this model, we were able to achieve an F1-score of 78.38% and an accuracy of 81.74% on the testing set. This demonstrates the effectiveness of our approach and the potential of using BERT for sentiment analysis on the Algerian dialect. Our model can be used to infer sentiment from any Algerian text, thus providing a valuable tool for understanding the opinions and emotions of the population. This research highlights the importance of studying the Algerian dialect and the potential of using state-of-the-art deep learning models for natural language processing in this area.
Problem-specific classical optimization of Hamiltonian simulation

Nonequilibrium time evolution of large quantum systems is a strong candidate for quantum advantage. Variational quantum algorithms have been put forward for this task, but their quantum optimization routines suffer from trainability and sampling problems. Here, we present a classical preprocessing routine for variational Hamiltonian simulation that circumvents the need for a quantum optimization by expanding rigorous error bounds in a perturbative regime for suitable time steps. The resulting cost function is efficiently computable on a classical computer. We show that there always exists potential for optimization with respect to a Trotter sequence of the same order and that the cost value has the same scaling as Trotter in simulation time and system size. Unlike previous work on classical preprocessing, the method is applicable to any Hamiltonian system independent of locality and interaction lengths. Via numerical experiments for spin-lattice models, we find that our approach significantly improves digital quantum simulation capabilities with respect to Trotter sequences for the same resources. For short times, we find accuracy improvements of more than three orders of magnitude for our method as compared to Trotter sequences of the same gate number. Moreover, for a given gate number and accuracy target, we find that the preoptimization we introduce enables simulation times that are consistently more than ten times longer for a target accuracy of 0.1%.
Joint Audio-Visual Attention with Contrastive Learning for More General Deepfake Detection

With the continuous advancement of deepfake technology, there has been a surge in the creation of realistic fake videos. Unfortunately, the malicious utilization of deepfake poses a significant threat to societal morality and political security. Therefore, numerous researchers have proposed various deepfake detection methods. However, traditional deepfake approaches tend to focus on specific forgery features, such as artifacts or inconsistent actions, which can be vulnerable to specialized countermeasures. Recent studies show an intrinsic correlation between facial and audio cues, which can be exploited for deepfake detection. To address these challenges and enhance the robustness and generalization of deepfake detection algorithms, we propose a novel joint audio-visual deepfake detection model named AVA-CL, which is capable of detecting deepfakes in both audio and visual domains. Furthermore, exploiting the inherent correlation and consistency between audio and visual enhances the effectiveness of deepfake detection significantly. Through extensive experiments, we demonstrate that our proposed AVA-CL model outperforms many state-of-the-art (SOTA) methods with superior robustness and generalization capabilities. This research presents a promising approach for deepfake detection and reducing the harm caused by malicious use.
METHODOLOGICAL APPROACHES TO MOTIVATION AND STIMULATION OF THE PROJECT TEAM

The article substantiates the relevance of the issues of motivation and stimulation of the activities of project team members, due to the high significance of motivation and involvement of project team members from the point of view of achieving the final results of project activities. The staff is one of the most important resources of the team, because only the presence of employees with the necessary professional and personal qualities, united by common values, will ensure effective teamwork to achieve the goals of the team. The article examines the specifics of the motivation of project team members, considers the possibilities and limitations of using well-known motivational theories to motivate the project team; systematizes typical mistakes of project managers when motivating team members and ways to overcome them; offers recommendations for increasing the motivation of project team members; it is shown that the complex and multidimensional motivation of project team members necessitates the use of a full range of directions and methods of stimulating their activities, including material (monetary and non-monetary) and non-material incentives.
Hybridization model of frame semantics and deep learning for text semantic similarity calculation

Text semantic similarity computation is a fundamental problem in the field of natural language processing. In recent years, text semantic similarity algorithms based on deep learning have become the mainstream research paradigm, but they suffer from the problem of insufficient understanding of text semantics and thus unclear interpretation of computational results. In this paper, we propose a text semantic encoding model combining frame semantic theory and deep learning, which enriches the semantic representation within sentences by making full use of the frame, frame elements, lexical units, and inter-frame relations in the frame semantic knowledge base and combining them with the Bert model, and interacts with the semantics between sentences through Siamese transformer encoders. The experiments are validated on MSRParaphraseCorpus and quora-question-pairs, and the results show that the model proposed in this paper outperforms similar models in terms of F1 values.
Word game prediction and difficulty classification based on machine learning

This article focuses on the Wordle game, a word puzzle game that has become extremely popular on social media. Based on the research objectives, three models have been proposed to solve the problem: a model for predicting the number of players, a model for predicting the number of attempts, and a model for classifying word difficulty. For the problem of predicting the number of players, the (Autoregressive Integrated Moving Average-Support Vector Machine) ARIMA-SVM model was proposed. The results showed that the ARIMA-SVM model performed about 17% better than the ARIMA model. To address the problem of predicting the number of attempts, the Random Forest Regression model was proposed. In regard to the problem of classifying word difficulty, the GA-FCM clustering model was proposed. Finally, this paper contributes to the difficulty classification of words based on different word attributes, and the experimental results showed that (Genetic Algorithm-Fuzzy C-Means) GA-FCM performed significantly better than the FCM model in all indicators.
Research on image features extraction based on graph convolutional network

With the continuous development and progress of information technology and image sensors, the amount of data contained in digital images is increased. Therefore, the image feature extraction and downstream applications have begun to receive widespread attention by computer vision field. However, the majority of existing contributions are concentrated on the utilization of machine learning models and ignore the graph convolution theory can also be utilized to extract the small differences features from input images. In this paper, we utilize the graph convolution model to dispose the issue that small differences between classes make sample differentiation difficult in the process of feature extraction with optimization loss functions in the feature space. From our experimental results, we can conclude that our proposed model can achieve the image feature extraction and validate the identification accuracy reaching to approximately 95% for image classification task with reasonable system costs.
Research and implementation of key technologies of simultaneous interpreting speech translation

Simultaneous Interpretation Speech Translation is an emerging technology that has been widely used in many fields. It is a technology that combines Automatic Speech Recognition (ASR), Machine Translation (MT) and Natural Language Processing (NLP). In this paper, we will discuss the key technologies related to simultaneous interpretation speech translation, including ASR, MT, NLP, and their application scenarios. We will also explore the challenges and opportunities of this technology, as well as the existing solutions. In this paper, we propose a position-based attention to model the interaction between two words regarding positions that can improve the self-attention networks by better integrating sequential relations, which is essential for modeling natural languages.
Dependency parsing masking and BERT for sentiment analysis

Sentiment Analysis (SA) is one of the most challenging tasks in natural language processing. It is the process of analyzing, processing, generalizing, and reasoning about subjective text with emotional overtones. To cope with sentiment analysis tasks, current deep learning approaches typically use large-scale pre-trained language models and attention mechanisms that apply computed attention weights without any restrictions on attention allocation. This mechanism is not an ideal model for dealing with the task of sentiment analysis, because in all kinds of language used in life, a semantically complete sentence is composed of words with different semantics connected to a complex and diverse grammar. Factors influencing the sentiment tendency of a sentence include the meaning of words and the change in semantics by the superposition of grammars with different structures. This paper proposes a sentiment analysis model that incorporates information about dependency syntax structure. Dependent syntactic information treats words as nodes and establishes syntactic grammatical structures between words by establishing node-to-node relationships. The undirected graph composed of the dependent syntactic structure information screens the text embedding derived from the attention mechanism in multiple layers, so that each layer focuses only on the associated text, and the sentiment classifier uses the text information containing the syntactic structure of the grammar in the classification. Experiments on the Online_Shopping_10_Cats dataset show a significant improvement in the final results compared to current state-of-the-art sentiment analysis models.
Research on the application of artificial intelligence in computer networks

The effective use of high technology in all sectors of society can achieve the purpose of technological empowerment and facilitate the development of education, medical, aerospace, biomedical and other fields to overcome the difficulties. The rational use of advanced technology to achieve business development goals is one of the issues that all sectors are concerned about and must study. Artificial intelligence is a technology developed based on computer technology, through which computers can imitate human consciousness and improve their "cognitive" ability, enabling them to deal with various situations in a continuous cognitive manner. The article explores the application strategies of artificial intelligence in computer networks with a view to providing references for effective optimization of computer networks.
Hybrid particle swarm optimization algorithm based on arena competitive learning strategy

The research on optimization algorithms has kept pace with the times, and one of the hot spots that has attracted much attention is the improvement of Particle Swarm Optimization algorithm (PSO) in the field of swarm intelligence. In this paper, a hybrid PSO algorithm using Adaptive Learning strategy (ALPSO) is introduced, and a hybrid PSO based on Arena Competitive Learning strategy (ACLPSO) is proposed from the perspective of search space and candidate particle generation, effectively enriches population diversity, expands the initial particle swarm search space, and improves convergence speed and accuracy.
Design and implementation of full-LCD virtual instrument graphical user interface based on GL Studio

This paper describes the development trend of automotive full-LCD instrument GUI application, analyzes the hierarchical structure of full-LCD instrument system, demonstrates the design and development process of GUI application based on GL Studio, and generates a full-LCD instrument GUI example with real-time rendering functions of dynamic graphics components and static graphics components. It effectively verifies the design idea of GUI application hierarchical architecture based on MVC architecture pattern and the feasibility of GUI application interface layout, visual communication, graphic component management and data-driven development strategy based on GL Studio, which has a certain reference value for the development of full-LCD instrument GUI design theory and engineering practice innovation.
Efficient transformer for human-object interaction detection

The challenge of detecting Human-Object Interaction (HOI) in photos is addressed in this study through the introduction of an effective Transformer approach. The Transformer decoder incorporates Spatially Modulated Co-Attention (SMCA) to preset the position of the target (human or object) in the image, narrow the search range of the query vector and accelerate the convergence of the model. In order to fuse multi-scale features and increase model recognition accuracy, at the same time, the intra-scale self-attention mechanism is introduced into the encoder as an auxiliary operator to fuse multi-scale features. The model adopts multiple Multilayer Perceptron (MLP) structures to locate and identify HOI instances. The loss function of this model is the combination of classification loss and bounding-box regression loss. The HICO-DET dataset had a detection accuracy of 27.19%mAP on average, while the V-COCO dataset had a detection accuracy of 53.2%mAP.
Research on deep learning recommendation algorithm integrating user preferences

With the explosive growth of internet information and the increasing demand for service quality from users, personalized recommendations have become increasingly important. This article proposes a recommendation algorithm based on deep learning models. Utilize the powerful learning ability of deep learning models to mine more hidden information in data and fully extract features of users and projects. At the same time, in order to solve the problem of data sparsity and the dynamic change of user preferences over time, in the prediction generation stage, user preferences are integrated and updated in a timely manner. The experimental results on the dataset show that the algorithm has achieved good results in prediction accuracy and recommendation quality.