Reviewerly/exHarmony huggingface dataset:
Research on Recognition Method of Social Robot based on T-A-GCNIIT in Metaverse

Social robots are used in intelligent customer service, intelligent chat, intelligent shopping guides, and more because of emotion recognition studies in cognitive psychology. However, determining the user's purpose fast and precisely has proved challenging. Domestic researchers proposed the A-GCNII model to address missing feature information, however it needs a lot of math. This research offers a social robot recognition approach using T-A-GCNIIT model and cognitive psychology to optimize computing complexity and performance. T-A-GCNIIT algorithm processes social network data, and Viola-Jones algorithm improves social robot intelligence to represent social robots in the meta-universe. The model performs well in node classification, link prediction, community discovery, and other tasks, with enhanced accuracy, recall, F1 score value, and other metrics. The model can also better comprehend the user's emotional state using cognitive psychology to better recognize their purpose and propose a fresh notion for enhancing social robots' cognitive psychology.
Personalized Query Expansion with Contextual Word Embeddings

Personalized Query Expansion, the task of expanding queries with additional terms extracted from the user-related vocabulary, is a well-known solution to improve the retrieval performance of a system w.r.t. short queries. Recent approaches rely on word embeddings to select expansion terms from user-related texts. Although promising results have been delivered with former word embedding techniques, we argue that these methods are not suited for contextual word embeddings, which produce a unique vector representation for each term occurrence. In this article, we propose a Personalized Query Expansion method designed to solve the issues arising from the use of contextual word embeddings with the current Personalized Query Expansion approaches based on word embeddings. Specifically, we employ a clustering-based procedure to identify the terms that better represent the user interests and to improve the diversity of those selected for expansion, achieving improvements of up to 4% w.r.t. the best-performing baseline in terms of MAP@100. Moreover, our approach outperforms previous ones in terms of efficiency, allowing us to achieve sub-millisecond expansion times even in data-rich scenarios. Finally, we introduce a novel metric to evaluate the expansion terms’ diversity and empirically show the unsuitability of previous approaches based on word embeddings when employed along with contextual word embeddings, which cause the selection of semantically overlapping expansion terms.
The Proteus Effect: Overview, Reflection, and Recommendations

Avatars are an integral element in most virtual environments including virtual reality and video games, and the Proteus effect initially proposed by Yee and Bailenson (2007) is a framework that explains the psychological effects of individuals’ digital self-representation, which theorizes that the traits of avatars would affect individuals’ behavior and cognition accordingly. Although research on the Proteus effect has been fruitful over the years, this line of research also showed inconsistencies over whether the effect is significant, the direction of how avatars affect individuals, as well as its theoretical explanations, which suggests a lack of elucidation on the cognitive processes underlying the Proteus effect. Hence, this paper provides an overview of the Proteus effect research and offers a reflection on the current literature regarding the theoretical basis, empirical evidence, and methodological approaches of the Proteus effect research. Finally, this study provides three recommendations for future research on the Proteus effect.
REST: Debiased Social Recommendation via Reconstructing Exposure Strategies

The recommendation system, relying on historical observational data to model the complex relationships among users and items, has achieved great success in real-world applications. Selection bias is one of the most important issues of the existing observational data-based approaches, which is actually caused by multiple types of unobserved exposure strategies (e.g., promotions and holiday effects). Though various methods have been proposed to address this problem, they are mainly relying on the implicit debiasing techniques but not explicitly modeling the unobserved exposure strategies. By explicitly Reconstructing Exposure STrategies (REST), we formalize the recommendation problem as the counterfactual reasoning and propose the debiased social recommendation method. In REST, we assume that the exposure of an item is controlled by the latent exposure strategies, the user, and the item. Based on the above generation process, we first provide the theoretical guarantee of our method via identification analysis. Second, we employ a variational auto-encoder to reconstruct the latent exposure strategies, with the help of the social networks and the items. Third, we devise a counterfactual reasoning based recommendation algorithm by leveraging the recovered exposure strategies. Experiments on four real-world datasets, including three published datasets and one private WeChat Official Account dataset, demonstrate significant improvements over several state-of-the-art methods.
ISO25000-Related Metrics for Evaluating the Quality of Complex Information Systems

Evaluating complex information systems necessitates deep contextual knowledge of technology, user needs, and quality. The quality evaluation challenges increase with the system’s complexity, especially when multiple services supported by varied technological modules, are offered. Existing standards for software quality, such as the ISO25000 series, provide a broad framework for evaluation. Broadness offers initial implementation ease albeit, it often lacks specificity to cater to individual system modules. This paper maps 48 data metrics and 175 software metrics on specific system modules while aligning them with ISO standard quality traits. Using the ISO25000 series as a foundation, especially ISO25010 and 25012, this research seeks to augment the applicability of these standards to multi-faceted systems, exemplified by five distinct software modules prevalent in modern information ecosystems.
Socioeconomic disadvantage and engagement with digital contact tracing for COVID-19 in Western Sydney: A secondary analysis of surveillance data

COVID-19 outcomes were highly inequitably distributed in Australia and worldwide. The digitalisation of public health interventions offers resource-efficiency and increased capacity for pandemic responses, but risks excluding the elderly and disadvantaged, reinforcing existing inequalities. Despite this, there has been little evaluation of the determinants of uptake of digital contact tracing. This paper describes the use of digital contact tracing for COVID-19 in a population in metropolitan Sydney and the determinants of engagement in this population.Routinely collected surveillance data for residents of Western Sydney Local Health District, returning a positive SARS-CoV-2 result between 1st August 2021 and 12th February 2022, were extracted including responses to a digital contact tracing questionnaire. Individual records were linked to area-level socioeconomic indices of disadvantage. Descriptive analyses explored characteristics of non-responders and geospatial variation. Logistic regression was undertaken to evaluate the effect of age, sex and socioeconomic disadvantage on the odds of response.Of the 133 055 individuals included, 130 645 (98%) were issued a digital contact tracing questionnaire, and 106 432 (81%) responded. Odds of responding were lower in males (odds ratio: 0.79), individuals aged 80+ (odds ratio: 0.17) and the most disadvantaged communities (odds ratio: 0.32).Digital data collection for contact tracing was a scalable and efficient tool in the context of the Western Sydney Local Health District COVID-19 response. However, older people and individuals in disadvantaged communities were less likely to engage.Responses to future pandemics should leverage the resource-efficiency of digital interventions but should avoid compounding existing health inequalities.
Self-Supervised Learning for Industrial Image Anomaly Detection by Simulating Anomalous Samples

Abstract Industrial image anomaly detection (AD) is a critical issue that has been investigated in different research areas. Many works have attempted to detect anomalies by simulating anomalous samples. However, how to simulate abnormal samples remains a significant challenge. In this study, a method for simulating anomalous samples is designed. First, for the object category, patch extraction and patch paste are designed to ensure that the extracted image patches come from the objects and are pasted to the objects in the image. Second, based on the statistical analysis of various anomalies’ presence, a combination of data augmentation is proposed to cover various anomalies as much as possible. The method is evaluated on MVTec AD and BTAD datasets; the experimental results demonstrate that our method achieves an overall detection AUC of 97.6% in MVTec AD datasets, outperforming the baseline by 1.5%, and the improvement over VT-ADL method is 4.3% on the BTAD datasets, demonstrating our method’s effectiveness and generalization.
ASPEK MORALITAS NOVEL RANTAU 1 MUARA KARYA AHMAD FUADI RELEVANSI BAHAN AJAR SASTRA

The research used a qualitative descriptive method; the descriptive presentation was carried out according to facts and phenomena, not in the form of numbers or writing variables with the title "Aspects of Morality of the Rantau 1 Muara Novel by Ahmad Fuadi" aimed at reviewing the point of view contained in the novel Rantau 1 Muara by Ahmad Fuadi. The results of this research analysis using structural theory are as follows: the morality aspect in the Rantau 1 Muara novel includes the human relationship with God, oneself, and other humans, as well as relevance as learning material in high school. This article is able to increase knowledge of literature in relation to society. The aspect of literary morality aims to organize one's personality through moral values ​​taught in moral studies in the world of education so as not to do things that can damage one's morals so that one becomes a good person.
Few-Shot Relation Prediction of Knowledge Graph via Convolutional Neural Network with Self-Attention

Abstract Knowledge graph (KG) has become the vital resource for various applications like question answering and recommendation system. However, several relations in KG only have few observed triples, which makes it necessary to develop the method for few-shot relation prediction. In this paper, we propose the C onvolutional Neural Network with Self- A ttention R elation P rediction (CARP) model to predict new facts with few observed triples. First, to learn the relation property features, we build a feature encoder by using the convolutional neural network with self-attention from the few observed triples rather than background knowledge. Then, by incorporating the learned features, we give an embedding network to learn the representation of incomplete triples. Finally, we give the loss function and training algorithm of our CARP model. Experimental results on three real-world datasets show that our proposed method improves Hits@10 by 48% on average over the state-of-the-art competitors.
Multi-target Weakly Supervised Regression Using Manifold Regularization and Wasserstein Metric

In this paper, we consider the weakly supervised multi-target regression problem where the observed data is partially or imprecisely labelled. The model of the multivariate normal distribution over the target vectors represents the uncertainty arising from the labelling process. The proposed solution is based on the combination of a manifold regularisation method, the use of the Wasserstein distance between multivariate distributions, and a cluster ensemble technique. The method uses a low-rank representation of the similarity matrix. An algorithm for constructing a co-association matrix with calculation of the optimal number of clusters in a partition is presented. To increase the stability and quality of the ensemble clustering, we use k-means with different distance metrics. The experimental part presents the results of numerical experiments with the proposed method on artificially generated data and real data sets. The results show the advantages of the proposed method over existing solutions.
Searching for Distance Graph Embeddings and Optimal Partitions of Compact Sets in Euclidean Space

We consider three problems in combinatorial geometry in which the search for counterexamples and improvement of known estimates is reduced to a finite-dimensional multi-extremal optimization problem with piecewise-smooth constraints. The first problem is to find a distance embedding of some graph into a given surface, i.e. to find a set of points for which a part of pairwise distances and some additional condition are given. The other two problems consist in minimization of some functional computed for partitions of a compact set into a given number of subsets. The solutions found have improved some quantitative estimates in generalizations of the Borsuk hypothesis and variants of the Hadwiger–Nelson–Erdös problem on the chromatic number of space.
Assemblage + Waste + Memory = Jewel

This reflection provides further insight into the interaction between Jewellery Design and Art, through the exploration of an applied research methodology that did not exclude intuition, experimentation around materials and techniques, and the enhance of emotion and memory. Jewellery and Art gradually approached over the 20th century, a more affirmative aspect since the 60s, with the pioneering use of industrial materials, often intersected in assemblages and the adoption of the multiple, in denial of the single product. New relationships between body and object as well as new meanings were explored, stimulated by even futuristic jewellery concepts that provided a new experience to the user and freed themselves from the exclusive use of precious metals and gems. This involvement has contributed to the affirmation of jewel, one of the least changeable products that communicate something about us and the way we see ourselves in the world, more actively or more implicitly. The literature review and instruments such as exploratory sketches and prototypes play a key role in our applied research, focused on strategies that provide new experiences to the jewelry owner, minimize the environmental impact through options such as upcycling, and affirm a line of research focused on body-object relationship and memory.
k-NN-SSc: An Effective Similarity Score for k-NN Classifier

This paper proposes a new similarity score known as k-NN-SSc that is incorporated into the traditional k-NN classifier. The proposed similarity score computes the similarity between two instances, and the score is used to find the k-nearest neighbors of an unknown instance for the k-NN classifier. The effectiveness of the proposed k-NN-SSc similarity score measure is compared with Euclidean, Minkowski, Manhattan, Chebyshev, and Cosine distance measures. From the experimental results, we observed that the proposed measure performs better as compared to other competing measures in terms of Accuracy, F1 Score, and Matthew’s Correlation Coefficient (MCC) on 20 UCI repository datasets.
A Comprehensive Study on Natural Language Processing, It’s Techniques and Advancements in Nepali Language

Natural Language Processing is the ability of a system to understand, interpret, and analyze spoken words, text files, etc. There have been plethora of models that have been developed for language processing which consist of rule-based approach, Neural Network approach, and Traditional Machine Learning. NLP has a wide range of applications, including speech recognition, machine translation, sentiment analysis, chatbots, and intelligent personal assistants. In recent years, NLP has made significant progress, thanks to the development of deep learning models, which have greatly improved the performance of NLP systems. To make the machine understand the speech, the machine should be able to understand the sentences, which can only be achieved when the sentences are well-structured, which is ensured by Grammar. In this paper, a survey is done on various natural language processing works that have been done for the Nepali language, different resources, and work available.
Performance Analysis of the AdaGrad Family of Algorithms

Optimization algorithms in Machine Learning play a vital role in proper convergence during function evaluation to get better results, thereby forming one of the most important parts of Machine Learning. The AdaGrad family of algorithms, particularly is noteworthy for several reasons. Their performance on certain types of data distributions is yet to be studied for robust adaptation. In this paper, we looked into the performance of these algorithms when pitted against different types of data distributions, the effect of cosine annealing on different hyperparameters, and how one can improve upon them. We found that cosine annealing improves the overall error rate average of the AdaGrad family by a factor of 2.5. These results would be vastly helpful in noise modeling and removal techniques, primarily in image and signal processing.
Association Rule Mining in Distributed Environment: A Survey

Due to the increasing popularity of digital mediums and easy access to Internet technology, society has become dependent on digital resources. This generates a huge amount of data and identifying relationships or patterns among these data has become a predominant field of research. Traditional rule-mining techniques are primarily centralized, which makes these techniques ineffective when dealing with distributed databases. Distributed Association Rule Mining (D-ARM) is being developed for finding association rules from distributed databases stored in different locations. We have explored various D-ARM algorithms based on FP-Growth and Apriori, and we also examined the advantages and limitations of these algorithms.
Time-Related Natural Language Query Handling to Extract Autism Spectrum Disorder Information from Temporal Database

A temporal database may contain time-related data and handling such kind of database is a challenging task. It is difficult to extract data from a temporal database using natural language (NL) queries. Many NL queries to database interfaces have been developed but these interfaces are not suitable to handle time-related NL queries. The proposed system can handle time-related NL queries in the healthcare domain. A temporal database has been created where various Autism Spectrum Disorders (ASD) real-life problem statements have been stored with time. The proposed system is able to extract data from a temporal database using time-related NL queries with aggregation. Time tick has been assigned for handling temporal queries whereas aggregation methods have been used for handling computational type queries. Auxiliary verbs are used as an indication of whether the NL query is a temporal query. The proposed system has the capacity to produce structured query language (SQL) from temporal and computational types of queries which are frequently occurring in NL. This system will help the parents of an autistic child to enhance their knowledge about autism after getting many real-life autism problems according to time.
Multi-criteria-Based Page Ranking Using Metaheuristic Swarm Optimization

The size of data on the internet has grown enormously and also growing continuously. Finding any information for a user in response to query has become a challenging task. In this, search engines are playing a vital role. Yet, these are unable to a retrieve the relevant information. These search engines use some limited criteria to fetch the results in response to the user's query after ranking and indexing the pages. In this paper, a multi-criteria-based approach has been proposed to rank web pages. These criteria include keywords, user access count, unique visitors, stay time, hubs, and authority values. The proposed approach uses particle swarm optimization to find the optimal results. And, this algorithm has been compared with some existing algorithms based on iterative improvement and genetic algorithm. It has been observed that the proposed approach is able to find the most relevant top-ranked pages.
Performance Analysis of Clustering Using Modified Grey Wolf Optimization

Data clustering is the widely used technique in academia and industry to analyse large volumes of data with unknown patterns. Data clustering approaches that draw inspiration from biology are increasingly widely used. In this research, we propose a parallelized automated data clustering using a modified Grey Wolf Optimization technique based on the hunting style of the grey wolf which involves Tracking, chasing and approaching the prey. It will find the optimal solution from the generated ‘N’ solutions. However, the nature of massive data available in the repositories is unknown. So, it is a tedious task to guess the right number of clusters for the massive data. By repeating the procedure with clusters K = 2 to N, the suggested technique determines the best number of clusters. The ideal number of clusters are detected based on the best values of Silhouette index, Davies-Bouldin index and Calinski-Harabasz index. This research aims to propose a more efficient Intelligent clustering framework. The suggested approach operates in both the scenarios, i.e. with a predetermined number of clusters and an uncertain number of clusters. The user can either fix the number of clusters or let the system identify the optimal number of clusters. The proposed method parallelizes and automates cluster analysis in the most effective manner for determining the best clusters and forming natural clusters.
Towards Subderivative-Based Zeroing Neural Networks

Zeroing Neural Networks (ZNN) are dynamic systems suitable for studying and solving time-varying problems. The advantage of this particular type of recurrent neural networks (RNNs) is their global and exponential convergence property, which can be accelerated to a finite-time convergence. The dynamic flow of the ZNN requires the use of an appropriate error (Zhang) function E(t), which can be in matrix, vector, or scalar form, and the element-wise time derivative of $$\dot{E}(t)$$ at each time instant t. A possible difficulty arises in all cases where the time derivative $$\dot{E}(t)$$ does not exist, for any element of E(t) and any time instant $$t_0$$ from a predefined time interval [0, T]. In this research, we propose improvements to the ZNN formula for the case where the time-derivative of the Zhang function does not exist at some points. The non-differentiability occurs in various forms in several cases and occurs frequently. One possible solution in convex and non-differentiable environments is based on the use of subderivatives instead of the time derivative. Another solution is applicable in nonconvex cases and situations with discontinuity, and it is based on shifting in singular points to avoid the division by zero (DBZ) problem that often occurs in division with time-varying expressions.