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This paper reports on a study on delinquent acts in three academic libraries in northern Ghana. A survey method was used for the study and a questionnaire was used to collect data. The study population consisted of staff in the Reader Service Sections of the three libraries. The study found that failure to observe library regulations, keeping overdue materials, eating in the library, littering, reserving seats unjustifiably, and hiding of books for the purpose of monopoly are examples of delinquent acts practiced by library users. According to the participants, the main reasons for delinquent acts are inadequate orientation to users, insufficient copies of books, and indiscipline among library users. The respondents further reported that these actions led to a reduction in library resources and low patronage. To curb deviant behaviors in libraries, the study recommends the adoption of pragmatic approaches including massive orientation for users, imbibing positive library culture that fosters respect, bolstering commitment and cooperation among library users and the staff, increasing library budgets as well as finding more creative ways of acquiring more copies of books, such as using Open Educational Resources (OERs), belonging to consortia and building and/or enhancing existing Institutional Repositories (IR). [ABSTRACT FROM AUTHOR]

Food Recommender Systems (RecSys) are innovative knowledge systems that inform consumers of food choices according to criteria, including nutritional content, health concerns, production method, carbon footprint or other social and ethical considerations. They raise important questions at the intersection of technology accuracy and today evolving consumers' knowledge seeking behaviours, which implies to unpack the epistemic value of food RecSys. This study investigates the drivers of the perceived value of food RSs consumption by proposing a model that establishes via PLS-SEM (n = 253) a positive relationship between the Yuka company's food RecSys' epistemic value and its perceived value. The model demonstrates that Yuka RecSys' epistemic value relies on the disciplinary drivers of compatibility, self-confidence, and consumer innovativeness, and the problematising drivers of memory and learning, which come from using the application. The perceived value of food RecSys is found to relate to RecSys epistemic value beyond the functional accuracy aspects of recommendation algorithms. Results highlight the importance of developing a refined understanding of epistemic value considering the consumption of RecSys. RecSys' developers, retailers, food manufacturers and policy makers must work on better mapping and adjusting information through consumers socialised RecSys' usage to shape the design of the next generation RecSys. [ABSTRACT FROM AUTHOR]

Background: Recommendations for cosmetics are gaining popularity, but they are not being made with consideration of the analysis of cosmetic ingredients, which customers consider important when selecting cosmetics. Aims: This article aims to propose a method for estimating the efficacy of cosmetics based on their ingredients and introduces a system that recommends personalized products for consumers, combined with AI skin analysis. Methods: We constructed a deep neural network architecture to analyze sequentially arranged cosmetic ingredients in the product and incorporated skin analysis models to get the precise skin status of users from frontal face images. Our recommendation system makes decisions based on the results optimized for the individual. Results: Our cosmetic recommendation system has shown its effectiveness through reliable evaluation metrics, and numerous examples have demonstrated its ability to make reasonable recommendations for various skin problems. Conclusion: The result shows that deep learning methods can be used to predict the effects of products based on their cosmetic ingredients and are available for use in personalized cosmetic recommendations. [ABSTRACT FROM AUTHOR]

With the emerging era of E‐commerce and online shopping, people are also in a habit to receive default product recommendations on the web pages that they access. Google is already providing such suggestions. Till now recommendations were made only based on previous sentiments or feedback or ratings, but this research has improved the product recommendation method by including one more parameter for the same. This article represents two parameters for making predictions of product allocation to a new customer. These parameters are ratings given by the existing users for that particular product and the region to which the new customer belongs. Following these parameters, a prediction model and an algorithm, Improved_Collab_Similarity, have been implemented. The dataset has been developed where India as a country along with all its States has been considered for products which are popular for their creation based on regional and ancient skills of the people belonging to that area. Results for the mentioned prediction model have been discussed in this article where generally precision increases with the increase in a number of products but at some points, it does not increase when a smaller number of that product was purchased by the customers. [ABSTRACT FROM AUTHOR]

The industrial internet of things (IIoT) encompasses smart devices, manufacturing systems, humans, and networks for automated productive outcomes. The placement of devices and networks is vulnerable to distributed denial of service (DDOS) attacks that degrade the productivity and efficiency of IIoT. In this article, we propose a checkpoint‐intrigued adversary mitigation scheme (CIAMS) for improving the security features and recommendations of the detection systems. Features that use the recommendation to provide relevant information maintain their security level. A DDoS attack is dealt with at the outset, resulting in increased productivity. The IIoT's smart devices are less productive and efficient because of this DDoS attack. This CIAMS method is designed to address vulnerability and the ability to survive the features checkpoints. The proposed scheme substantiates the security breach and lag in the checkpoint systems against DDOS attacks. The checkpoints' vulnerability level and surviving features are assessed using a classified learning approach. In this assessment, the degrading features are reimbursed by improving the security functions, control, and access methods. Periodic checkpoint replacement and mutual security measures are used for mitigating the prolonging DDOS impact in the network. The proposed scheme's performance is verified using false positives, service distribution, lag, an efficiency score. Improvements have been made to the industrial environment's service delivery and efficiency. By reducing false positives by 10.35%, the proposed scheme improves service distribution ratio and efficiency score by 11.68% and 12.55% for different devices. [ABSTRACT FROM AUTHOR]

Personalisation, which tailors to individual preferences, is considered a possible route for improving engagement with digital mental health (DMH) products. Despite claims about the presence and importance of personalised features, evidence about their extent and impact is limited. The study objective was to review evidence from published research to investigate and characterise the contribution of personalisation to engagement and effectiveness.Research papers were retrieved using keywords, with 139 papers being fully examined and 61 meeting our eligibility criteria. Most of the eligible articles reviewing DMH systems have weak to intermediate forms of personalisation (45). Only nine were coded as having strong, adaptive personalisation. Of the 40 articles which evaluated the personalisation effectiveness, 28 were qualitative indicating user preference for personalised features. The 16 controlled, quantitative designs lacked a non-personalised intervention, making it difficult to determine the added value. Effect sizes calculated from available data showed minimal differences in effectiveness compared to non-personalised apps.Our review indicates mixed evidence of personalisation’s performance in DMH interventions. Generally, there is a lack of good quality evidence to isolate specific contributions of personalisation. Opportunities were identified for improved evaluation, however caution is required when implementing more sophisticated methods of DMH personalisation. [ABSTRACT FROM AUTHOR]

In e-commerce recommender systems, the sparsity of user-item rating data limits the quality of semantic embedding representation of users and items, which affects the accuracy of rating prediction. Previous studies have focused on learning attribute features or interaction features of nodes for recommendation and ignore the preference interest of rating tags in terms of attributes. In this paper, we propose a novel fusion representation method of multi-type node feature embedding for recommendation (MultiFEFR). The framework introduces rating preferences in terms of attributes and interactions and develops three kinds of feature presentations including rating-aware interaction feature embedding, rating-aware attribute feature embedding and heterogeneous attribute matched embedding. We design a multi-task joint learning strategy to optimize the accuracy of rating prediction. Experimental results show that the MultiFEFR outperforms the state-of-the-art models in MAE and RMSE metrics. Our findings are that the fusion of multi-type feature embedding can improve the accuracy of recommendation and the preference of attribute and interaction features are very explanatory for score grades. [ABSTRACT FROM AUTHOR]

The diagnosis and recommendation integrated system (DRIS) has such high reliability that it can be used to evaluate many crops. Therefore, in order to have a practical database for these norms, this study was conducted to evaluate the established DRIS norms for plant pineapples (Ananas comosus L.) and to assess their nutrition status in an acid sulfate soil. Healthy and disease-free leaf samples of 8-month-old pineapple plants without fruits were collected from experimental fields with nutritional omission trials and from farmers' fields at four cultivating sites. Nutrient concentrations were analyzed to determine DRIS indices. The result showed that the + 7 leaf was highly reliable to diagnose the macro-nutrition status, while the + 1 leaf and + 3 leaf lacked the nutrient ratios pair to assess P and Mg nutrition status. Based on the average DRIS indices of the four sites, the nutrient surplus was observed in the order P>Mn>Fe>Ca (81.7 > 52.8 > 15.7 > 13.3), while nutrient deficiency varied in the order K>Zn>N>Mg (−88.2<-20.3<-16.4<-10.7) at the + 7 leaf. The DRIS norms needed to be calibrated to obtain nutrient ratio pairs for P in the + 1 leaf, and P and Mg at the + 3 leaf. [ABSTRACT FROM AUTHOR]

Recommender Systems are mainly used in various e-commerce applications, especially online stores threatening users’ privacy. The privacy issues can be overcome by using security solutions, which include blockchain technology for privacy applications. The fusion of the Internet of Things and blockchain technology has fully improved modern distributed systems. The combination guarantees the safety and scalability of the recommender system. We aim to create an authorized secure exchange device using blockchain-enabled multiparty computation by adding smart contracts to the core blockchain protocol. The recommendation structure and Blockchain technology make online shopping more convenient and private. We propose a blockchain-related recommender system using the “movielens” data. The case study includes a smart contract model that recommends movies to buyers. Initially, we tested the model on a small “movielens dataset” and extended it to a 3M movielens dataset. We developed a classifier model for movielens and proposed a Dual light graph convolutional network for movielens data classification. Our results, including ablation analysis, show that blockchain strategies and Dual light graph convolutional networks can effectively improve recommender systems’ privacy. Furthermore, the suggested blockchain technique can be stretched by similar procedures. [ABSTRACT FROM AUTHOR]

Summary: The existing active disturbance rejection control (ADRC) method may not provide sufficient disturbance rejection to multiple mismatched disturbances for the fractional order systems. In this paper, a composite disturbance rejection approach is developed for a class of fractional order uncertain systems, by synthesizing the fractional order ADRC (FOADRC) approach and a disturbance observer (DO)‐based compensation scheme. Taking advantage of more disturbance information and a filter structure, an improved DO is developed to achieve precise estimation of disturbances in the presence of sensor noises. In addition, a state transformation is developed to convert the system into a simple integral chain model with only matched disturbances. Then a composite control law is designed to compensate the disturbances and provide satisfying dynamic performance. The efficiency of the proposed method is demonstrated by a numerical simulation and an actual servo control simulation, as well as the comparison with two kinds of the existing ADRC methods and the commonly used integral sliding mode control (I‐SMC) method. [ABSTRACT FROM AUTHOR]

The integration of graph embedding technology and collaborative filtering algorithms has shown promise in enhancing the performance of recommendation systems. However, existing integrated recommendation algorithms often suffer from feature bias and lack effectiveness in personalized user recommendation. For instance, users' historical interactions with a certain class of items may inaccurately lead to recommendations of all items within that class, resulting in feature bias. Moreover, accommodating changes in user interests over time poses a significant challenge. This study introduces a novel recommendation model, RCKFM, which addresses these shortcomings by leveraging the CoFM model, TransR graph embedding model, backdoor tuning of causal inference, KL divergence, and the factorization machine model. RCKFM focuses on improving graph embedding technology, adjusting feature bias in embedding models, and achieving personalized recommendations. Specifically, it employs the TransR graph embedding model to handle various relationship types effectively, mitigates feature bias using causal inference techniques, and predicts changes in user interests through KL divergence, thereby enhancing the accuracy of personalized recommendations. Experimental evaluations conducted on publicly available datasets, including "MovieLens-1M" and "Douban dataset" from Kaggle, demonstrate the superior performance of the RCKFM model. The results indicate a significant improvement of between 3.17% and 6.81% in key indicators such as precision, recall, normalized discount cumulative gain, and hit rate in the top-10 recommendation tasks. These findings underscore the efficacy and potential impact of the proposed RCKFM model in advancing recommendation systems. [ABSTRACT FROM AUTHOR]

A text recommendation system helps language learners find suitable reading materials. Similar to graded readers, most systems assign difficulty levels or school grades to the documents in their database, and then identify the documents that best match the language proficiency of the learner. This graded approach has two main limitations. First, the common grade scale assumes that all learners find the same words difficult, ignoring different orders in vocabulary acquisition. Second, it cannot cater to the incremental vocabulary expansion typical of language learners, since discrete grades do not facilitate fine-grained adaptation in text recommendation. This article evaluates a personalized and adaptive method for text recommendation that aims to mitigate these limitations. This method recommends documents according to the new-word density preferred by the user. It estimates a document's new-word density with an open learner model that predicts the user's vocabulary knowledge. Users can dynamically update the learner model as they acquire new vocabulary. We conducted a user study involving ten learners of Chinese as a foreign language. Results shows that this method can estimate the learner model at 77.6% accuracy, outperforming the graded approach. Further, a user simulation demonstrates that it can recommend documents within 0.22% of the target new-word density, satisfying user preferences more accurately than the graded approach. [ABSTRACT FROM AUTHOR]

A distributed state observer is designed for state estimation and tracking of mobile robots amidst dynamic environments and occlusions within distributed LiDAR sensor networks. The proposed novel framework enhances three-dimensional bounding box detection and tracking utilizing a consensus-based information filter and a region of interest for state estimation of mobile robots. The framework enables the identification of the input to the dynamic process using remote sensing, enhancing the state prediction accuracy for low-visibility and occlusion scenarios in dynamic scenes. Experimental evaluations in indoor settings confirm the effectiveness of the framework in terms of accuracy and computational efficiency. These results highlight the benefit of integrating stationary LiDAR sensors' state estimates into a switching consensus information filter to enhance the reliability of tracking and to reduce estimation error in the sense of mean square and covariance. [ABSTRACT FROM AUTHOR]

Fairness has become a critical value online, and the latest studies consider it in many problems. In recommender systems, fairness is important since the visibility of items is controlled by systems. Previous fairness-aware recommender systems assume that sufficient relationship data between users and items are available. However, it is common that new users and items are frequently introduced, and they have no relationship data yet. In this paper, we study recommendation methods to enhance fairness in a cold-start state. Fairness is more significant when the preference of a user or the popularity of an item is unknown. We propose a meta-learning-based cold-start recommendation framework called FaRM to alleviate the unfairness of recommendations. The proposed framework consists of three steps. We first propose a fairness-aware meta-path generation method to eliminate bias in sensitive attributes. In addition, we construct fairness-aware user representations through the meta-path aggregation approach. Then, we propose a novel fairness objective function and introduce a joint learning method to minimize the trade-off between relevancy and fairness. In extensive experiments with various cold-start scenarios, it is shown that FaRM is significantly superior in fairness performance while preserving relevance accuracy over previous work. [ABSTRACT FROM AUTHOR]

Search engines serve as information gatekeepers on a multitude of topics dealing with different aspects of society. However, the ways search engines filter and rank information are prone to biases related to gender, ethnicity, and race. In this article, we conduct a systematic algorithm audit to examine how one specific form of bias, namely, sexualization, is manifested in Google's text search results about different national and gender groups. We find evidence of the sexualization of women, particularly those from the Global South and East, in search outputs in both organic and sponsored search results. Our findings contribute to research on the sexualization of people in different forms of media, bias in web search, and algorithm auditing as well as have important implications for the ongoing debates about the responsibility of transnational tech companies for preventing systems they design from amplifying discrimination. [ABSTRACT FROM AUTHOR]

Community detection is a well-known area of research that yields essential results in various fields such as social media, biological networks, pandemic spread, recommendation systems, etc. There are two important areas for improvement in existing community detection algorithms: quality of community formed should be improved and a parallel approach to community detection is needed to handle massive data. In this paper, we have proposed a three-step parallel algorithm, Par-Com, using the concept of k Clique and modularity optimization to address both of the above issues. The proposed algorithm increases the execution speed and also improves the quality of the community formed by optimizing modularity. Par-Com uses dynamic load balancing on the multicore architecture of Supercomputer ParamShivay. We have also evaluated Par-Com's performance against nine sequential and three parallel community detection algorithms on varying size datasets, i.e. karate, macaque, email, immuno, soc-epinions, facebook, and com-Friendster. The experiment result shows that Par-Com outperforms other algorithms under consideration with up to 45% increase in modularity and up to 84% increase in execution speed. Par-Com is also capable of detecting overlapping communities, fuzzy membership of each node, most influential node in each community formed, and outlier nodes. Nodes within a community that have the highest influence are deemed as experts. The choices made by an expert in a particular community are served as recommendations to other users within that community. [ABSTRACT FROM AUTHOR]

This paper conceptualizes a consumer-centric, regenerative artificial intelligence ("ReGenAI") model for the Fast-Moving Consumer Goods ("FMCG") retailing channel. The system uses its awareness of context, time, and users to (re)generate customer touchpoints and other marketing communications. Its output provides deep insights into regular and altered FMCG customer journeys, such as shopping behaviors under stressors like lifestyle choices or cataclysmic socio-economic and weather events. The recursive model advances from current, generative AI systems. It uses "tired or inspired" as a simplified bifurcated grocery shopper taxonomy to operationalize customers' purchasing and consumption behaviors into actionable data for demand planning and retail operations. [ABSTRACT FROM AUTHOR]

Recently, there has been a surge in the popularity of online groups on event-based social networks (EBSNs) like Meetup and Douban Event. These groups cater to individuals who share common interests, provide comments, and engage in various activities. Our research focuses on online group recommendations, based on which users can conveniently join groups and participate in offline events organized by the groups. Traditional group recommendation methods do not work well in addressing this problem because they lack the ability to deal with the challenges posed by dynamic user interests, sparse supervision signals, and heterogeneous networks simultaneously. The self-attention model with contrastive learning for online group recommendation (SCL4GR) presented in this study exploits user-group sequential data, online and offline networks in a unified framework to predict user preferences for groups. First, a graph encoder is used to capture the high-order social interaction between users. Then, the pattern of dynamic interests is captured by sequence model Transformer. Furthermore, the contrastive learning is employed to derive self-supervision signals from both online and offline networks. We conduct experiments on three real-world datasets. Experimental results show that our SCL4GR consistently outperforms state-of-the-art methods for online group recommendation in EBSNs. [ABSTRACT FROM AUTHOR]

E-commerce providers increasingly rely on recommender systems to curate online consumption. EU law requirements for such recommender systems were initially introduced in privacy legislation, but recent years have seen several new provisions on this topic in consumer protection legislation and platform regulation. However, the latter regulations are limited to online intermediaries and, just like privacy legislation, they merely require businesses that use recommender systems to provide transparency on this to consumers. Transparency is not a solution for the challenges that recommender systems pose because it is difficult to achieve (especially towards consumers) and is not enough to allow users to control recommendations. Furthermore, most of these tools rely on private enforcement, which has proved to be less than effective. [ABSTRACT FROM AUTHOR]

The online content, including live streaming and short videos, provides abundant visual and textual product information to users, which offers insights into users' multiple and changeable preferences toward product style, brand, color, etc. These dynamic preferences are helpful to the product recommender system to extract users' purchasing patterns. However, existing sequential recommendation fails to combine with items' multimodal characteristics and dynamic preferences. This paper proposes the Online Content-Based Sequence Recommendation method (named OCBSR), where dynamic preferences and multimodal representation such as visual images, textual descriptions, and item-ID of products are leveraged with convolutional autoencoder to map multimodal data into a unified latent space. We devise a multimodal transformer mechanism to aggregate the multimodal representation. A novel contrastive infomax loss is constructed through contrastive prototype representations and noise-contrastive sequences. The loss enhances the correlation between single-modality and multimodality preferences and learns the contrastive representation. Then, users' dynamic preferences are extracted from the contrastive representation in each time step using the gated recurrent unit with skip connections. Experiments are conducted on two real-life multimodal datasets, H&M and KuaiRec. The hit ratio (HR) and normalized discounted cumulative gain (NDCG) results indicate that our approach noticeably outperforms the current advanced recommenders. The HR increases by above 20% and 25% on H&M and KuaiRec datasets, respectively; the NDCG rises about 12% and 24% on H&M and KuaiRec datasets, respectively. The ablation study verifies each module's effectiveness. This research contributes to online content literature and multimodal sequential recommendation in practice. [ABSTRACT FROM AUTHOR]

As a knowledge representation method, knowledge graph is widely used in intelligent question answering systems and recommendation systems. At present, the research on knowledge graph mainly focuses on information query and retrieval based on knowledge graph. In some domain knowledge graphs, specific subgraph structures (patterns) have specific physical meanings. Aiming at this problem, this paper proposes a method and framework of knowledge graph pattern mining based on gat. Firstly, the patterns with specific physical meaning were transformed into subgraph structures containing topological structures and entity attributes. Secondly, the subgraph structure of the pattern is regarded as the query graph, and the knowledge graph is regarded as the data graph, so that the problem is transformed into an approximate subgraph matching problem. Then, the improved relational graph attention network is used to fuse the adaptive edge deletion mechanism to realize the approximate subgraph matching of subgraph structure and attribute, so as to obtain the best matching subgraph. The proposed method is trained in an end-to-end manner. The approximate subgraph matching is realized on the existing data set, and the research work of key pattern mining of complex geological structure knowledge graph is carried out. [ABSTRACT FROM AUTHOR]

Knowledge graphs are now widely used in various domains, including Question-and-answer systems, intelligent search and recommendation systems, and intelligent decision-making systems. However, knowledge graphs inevitably contain inaccurate and incomplete knowledge during the creation process, which leads to a reduction in the usefulness of knowledge graphs. Therefore, to assess the usefulness of knowledge graphs based on specific application requirements, quality assessment is particularly important. Among them, accuracy assessment, as a necessary dimension, reflects the degree of correctness of the triples. However, in the actual assessment process, the existing assessment methods do not consider the user's needs and do not implement the concept of "Fitness for Use". Meanwhile, it takes a lot of labor cost to evaluate the accuracy of large-scale knowledge graphs. Therefore, to ensure the accuracy of the assessment in a cost-saving way while meeting the needs of users, we propose and implement a novel accuracy assessment method that focuses on the requirements of users by designing an effective sampling method to obtain accurate assessment results that are more practical and instructive for users. Finally, the performance of our proposed method is evaluated by comparing it with the real accuracy rate, and the experimental results show that the accuracy rate obtained by the proposed method is very close to the real accuracy rate, and the sample size is minimized. [ABSTRACT FROM AUTHOR]

Knowledge graph (KG) shows great potential in improving recommendation systems. Recent studies have focused on developing end-to-end models based on graph neural networks. However, these previous approaches do not fully utilize collaborative signals, which are the collective information and feedback provided by multiple users in a recommendation system. Specifically, we think that collaborative signals could be better utilized in two points. Firstly, users' collective information leads to differences in node importance in KG, offering opportunities to improve item representation. Secondly, an item's collaborative filtering (CF) feature can reveal the multiple interests of a user, helping to gain fine-grained user representation. In this paper, we highlight the effect of node importance estimation and CF-based item clustering and propose the collaborative signals injection module. We integrate this module in our Collaborative Signals Enhanced Knowledge Graph recommendation (CSEKG) model. CSEKG outperforms state-of-the-art methods like KGIN (Wang et al. in Learning intents behind interactions with knowledge graph for recommendation. CoRR arXiv:2102.07057, 2021) , KGCL (Yang et al. in Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, 2022) , and KGRec (Yang et al. in Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining, 2023) through improving recall@20 by 3.86 % , 5.15 % , and 2.88 % on Last.FM, Amazon-Book and MovieLens-1M, respectively. Furthermore, we flexibly integrate the collaborative signals injection module into existing methods, such as LightGCN, KGAT and KGIN, which improves their performance. Further visualizing recommendation cases show the interpretability benefits brought by collaborative signals. [ABSTRACT FROM AUTHOR]

The more science advances, the more questions are asked. This compounding growth can make it difficult to keep up with current research directions. Furthermore, this difficulty is exacerbated for junior researchers who enter fields with already large bases of potentially fruitful research avenues. In this paper, we propose a novel task and a recommender system for research directions, RecSOI, that draws from statements of ignorance (SOIs) found in the research literature. By building researchers' profiles based on textual elements, RecSOI generates personalized recommendations of potential research directions tailored to their interests. In addition, RecSOI provides context for the recommended SOIs, so that users can quickly evaluate how relevant the research direction is for them. In this paper, we provide an overview of RecSOI's functioning, implementation, and evaluation, demonstrating its effectiveness in guiding researchers through the vast landscape of potential research directions. [ABSTRACT FROM AUTHOR]

Crop Recommendation Systems are invaluable tools for farmers, assisting them in making informed decisions about crop selection to optimize yields. These systems leverage a wealth of data, including soil characteristics, historical crop performance, and prevailing weather patterns, to provide personalized recommendations. In response to the growing demand for transparency and interpretability in agricultural decision-making, this study introduces XAI-CROP an innovative algorithm that harnesses eXplainable artificial intelligence (XAI) principles. The fundamental objective of XAI-CROP is to empower farmers with comprehensible insights into the recommendation process, surpassing the opaque nature of conventional machine learning models. The study rigorously compares XAI-CROP with prominent machine learning models, including Gradient Boosting (GB), Decision Tree (DT), Random Forest (RF), Gaussian Naïve Bayes (GNB), and Multimodal Naïve Bayes (MNB). Performance evaluation employs three essential metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2). The empirical results unequivocally establish the superior performance of XAI-CROP. It achieves an impressively low MSE of 0.9412, indicating highly accurate crop yield predictions. Moreover, with an MAE of 0.9874, XAI-CROP consistently maintains errors below the critical threshold of 1, reinforcing its reliability. The robust R2 value of 0.94152 underscores XAI-CROP's ability to explain 94.15% of the data's variability, highlighting its interpretability and explanatory power. [ABSTRACT FROM AUTHOR]

To improve the accuracy of multiple target tracking in the clutter environment, a new joint probabilistic data association (JPDA) algorithm based on variational Bayesian adaptive moment estimation is proposed. First, considering the existence of measurements, the posterior distribution of the target state in JPDA is composed of two parts of probability weighting, that is, the posterior distribution of the target state that the real measurement exists in the association gate and the posterior distribution of the target state that the real measurement does not exist in the association gate. By combining the conjugate properties of the prior and posterior distributions, the prior distributions of the target state in the two cases are classified to provide more accurate a priori information to filter, so as to improve the accuracy of data association. Second, considering the coupling effect between state estimation and data association process, combined with variational Bayesian inference, the problem of minimizing Kullback–Leibler divergence is transformed into the problem of maximizing the evidence lower bound, thereby effectively measuring the distance between the posterior distribution of target state estimation and the real posterior distribution, so as to improve the accuracy of data association again from the perspective of optimizing nonlinear filter. Finally, the adaptive momentum estimation strategy is introduced to iteratively solve the variable distribution that meets the maximization of the evidence lower bound, and the optimization of the posterior distribution of the target state is completed. Theoretical derivation and simulation experiments are conducted to verify the feasibility and effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Background: People with polycystic ovary syndrome suffer from many symptoms and are at risk of developing diseases such as hypertension and diabetes in the future. Therefore, the importance of self-care doubles. It is mainly to modify the lifestyle, especially following the principles of healthy eating. The purpose of this study is to review artificial intelligence-based systems for providing management recommendations, especially food recommendations. Materials and methods: This study started by searching three databases: PubMed, Scopus, and Web of Science, from inception until 6 June 2023. The result was the retrieval of 15,064 articles. First, we removed duplicate studies. After the title and abstract screening, 119 articles remained. Finally, after reviewing the full text of the articles and considering the inclusion and exclusion criteria, 20 studies were selected for the study. To assess the quality of articles, we used criteria proposed by Malhotra, Wen, and Kitchenham. Out of the total number of included studies, seventeen studies were high quality, while three studies were moderate quality. Results: Most studies were conducted in India in 2021. Out of all the studies, diagnostic recommendation systems were the most frequently researched, accounting for 86% of the total. Precision, sensitivity, specificity, and accuracy were more common than other performance metrics. The most significant challenge or limitation encountered in these studies was the small sample size. Conclusion: Recommender systems based on artificial intelligence can help in fields such as prediction, diagnosis, and management of polycystic ovary syndrome. Therefore, since there are no nutritional recommendation systems for these patients in Iran, this study can serve as a starting point for such research. [ABSTRACT FROM AUTHOR]

Real-world listening settings often consist of multiple concurrent sound streams. To limit perceptual interference during selective listening, the auditory system segregates and filters the relevant sensory input. Previous work provided evidence that the auditory cortex is critically involved in this process and selectively gates attended input toward subsequent processing stages. We studied at which level of auditory cortex processing this filtering of attended information occurs using functional magnetic resonance imaging (fMRI) and a naturalistic selective listening task. Forty-five human listeners (of either sex) attended to one of two continuous speech streams, presented either concurrently or in isolation. Functional data were analyzed using an inter-subject analysis to assess stimulus-specific components of ongoing auditory cortex activity. Our results suggest that stimulus-related activity in the primary auditory cortex and the adjacent planum temporale are hardly affected by attention, whereas brain responses at higher stages of the auditory cortex processing hierarchy become progressively more selective for the attended input. Consistent with these findings, a complementary analysis of stimulus-driven functional connectivity further demonstrated that information on the to-be-ignored speech stream is shared between the primary auditory cortex and the planum temporale but largely fails to reach higher processing stages. Our findings suggest that the neural processing of ignored speech cannot be effectively suppressed at the level of early cortical processing of acoustic features but is gradually attenuated once the competing speech streams are fully segregated. [ABSTRACT FROM AUTHOR]

Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone. On the other hand, computational approaches, especially the machine learning-based frameworks, have shown remarkable application potential in drug discovery. In this work, we propose Progeni, a novel machine learning-based framework for target identification. In addition to fully exploiting the known heterogeneous biological networks from various sources, Progeni integrates literature evidence about the relations between biological entities to construct a probabilistic knowledge graph. Graph neural networks are then employed in Progeni to learn the feature embeddings of biological entities to facilitate the identification of biologically relevant target candidates. A comprehensive evaluation of Progeni demonstrated its superior predictive power over the baseline methods on the target identification task. In addition, our extensive tests showed that Progeni exhibited high robustness to the negative effect of exposure bias, a common phenomenon in recommendation systems, and effectively identified new targets that can be strongly supported by the literature. Moreover, our wet lab experiments successfully validated the biological significance of the top target candidates predicted by Progeni for melanoma and colorectal cancer. All these results suggested that Progeni can identify biologically effective targets and thus provide a powerful and useful tool for advancing the drug discovery process. Author summary: The selection of a disease target, a biological entity with which potential drugs interact to treat diseases, is often the first step of a drug discovery project. Known to be extremely costly, drug discovery projects spend tremendous resources on failed drug instances, most of which result from inadequate choices of targets. To help address the costliness problem of drug discovery, we developed a machine learning-based framework that identifies biologically effective targets using a probabilistic knowledge graph built from both biological network data and literature evidence. Our method not only outperforms state-of-the-art baseline methods on the target prediction task, but can also identify targets with high biological relevance, as shown by the strong support of the literature for the predicted target candidates and wet lab experiments that validate the significance of the predicted target candidates for melanoma and colorectal cancer, These results suggest that our method can identify effective targets and therefore benefit drug discovery. [ABSTRACT FROM AUTHOR]

Today, due to the expansion of the Internet and social networks, people are faced with a vast amount of dynamic information. To mitigate the issue of information overload, recommender systems have become pivotal by analyzing users' activity histories to discern their interests and preferences. However, most available social image recommender systems utilize a static strategy, meaning they do not adapt to changes in user preferences. To overcome this challenge, our paper introduces a dynamic image recommender system that leverages a deep reinforcement learning (DRL) framework, enriched with a novel set of features including emotion, style, and personality. These features, uncommon in existing systems, are instrumental in crafting a user's characteristic vector, offering a personalized recommendation experience. Additionally, we overcome the challenge of state representation definition in reinforcement learning by introducing a new state representation. The experimental results show that our proposed method, compared to some related works, significantly improves Recall@k and Precision@k by approximately 7%–10% (for the top 100 images recommended) for personalized image recommendation. [ABSTRACT FROM AUTHOR]

There is a growing need for recommender systems and other ML-based systems as an abundance of data is now available across all industries. Various industries are currently using recommender systems in slightly different ways. These programs utilize algorithms to propose appropriate products to consumers based on their prior choices and interactions. Moreover, Systems for recommending events to users suggest pertinent happenings that they might find interesting. As opposed to an object recommender that suggests books or movies; event-based recommender systems typically require distinct algorithms. A developed event recommendation method is introduced which includes two stages: feature extraction and recommendation. In stage, I, a Set of features like personal willingness, community willingness, informative content, edge weight, and node interest degree are extracted. Stage II of the event recommendation system performs a hybrid classification by combining LSTM and CNN. In the LSTM classifier, optimal tuning is done by Improvised Cat and Mouse optimization (ICMO) algorithm. The results of the ICMO technique at an 80% training percentage have the maximum sensitivity value of 95.19%, whereas those of the existing approaches SSA, DINGO, BOA, and CMBO have values of 93.89%, 93.35%, 92.36%, and 92.24%. Finally, the best result is then determined by evaluating the whole performance. [ABSTRACT FROM AUTHOR]

Selective attention refers to the ability to focus on goal-relevant information while filtering out irrelevant information. In a multisensory context, how do people selectively attend to multiple inputs when making categorical decisions? Here, we examined the role of selective attention in cross-modal categorization in two experiments. In a speed categorization task, participants were asked to attend to visual or auditory targets and categorize them while ignoring other irrelevant stimuli. A response-time extended multinomial processing tree (RT-MPT) model was implemented to estimate the contribution of attentional focusing on task-relevant information and attentional filtering of distractors. The results indicated that the role of selective attention was modality-specific, with differences found in attentional focusing and filtering between visual and auditory modalities. Visual information could be focused on or filtered out more effectively, whereas auditory information was more difficult to filter out, causing greater interference with task-relevant performance. The findings suggest that selective attention plays a critical and differential role across modalities, which provides a novel and promising approach to understanding multisensory processing and attentional focusing and filtering mechanisms of categorical decision-making. [ABSTRACT FROM AUTHOR]

Massive open online courses (MOOCs; online courses delivered over the Internet) enable distance learning without time and place constraints. MOOCs are popular; however, active participation level among students who take MOOCs is generally lower than that among students who take in-person courses. Students who take MOOCs often lack guidance, and the courses often fail to provide personalized learning materials. Artificial intelligence (AI) has been applied to manage increasing amounts of learning data in learners' online activity records. Driven by the trend in big data, AI technology has drawn increasing attention in various fields. AI-based recommendation systems (RSs) are powerful tools for improving resource acquisition through supply customization, and they can provide personalized learning materials as study guides. In this study, a personalized learning path for MOOCs based on long short-term memory (LSTM) was proposed to meet students' personal needs for learning. According to students' video-watching behaviors, we proposed an MOOC material RS that identifies students with similar learning behaviors through clustering and then uses the clustering results and the learning paths of each group of students to construct an LSTM model to recommend learning paths. The system's learning path recommendations can effectively improve the online participation of learners, and students who received recommendations progressed from the slow-progress group to the medium-progress or fast-progress group. In addition, the learning attitude questionnaire results indicated that the proposed system not only motivated learners to continue learning and achieve high learning capacity but also supported their study planning according to their individual learning needs. [ABSTRACT FROM AUTHOR]

As the volume of data continues to grow, the significance of text classification is on the rise. This vast amount of data majorly exists in the form of texts. Effective data preparation is essential to extract sentiment data from this vast amount of text, as irrelevant and redundant information can impede valuable insights. Feature selection is an important step in the data preparation phase as it eliminates irrelevant and insignificant features from the huge features set. There exist a large body of work related to feature selection for image processing but limited research is done for text data. While some studies recognize the significance of feature selection in text classification, but there is still need for more efficient sentiment analysis models that optimize feature selection and reduce computational. This manuscript aims to bridge these gaps by introducing a hybrid multi-objective evolutionary algorithm as a feature selection mechanism, combining the power of multiple objectives and evolutionary processes. The approach combines two feature selection techniques within a binary classification model: a filter method, Information Gain (IG), and an evolutionary wrapper method, Binary Multi-Objective Grey Wolf Optimizer (BMOGWO). Experimental evaluations are conducted across six diverse datasets. It achieves a reduction of over 90 percent in feature size while improving accuracy by nearly nine percent. These results showcase the model's efficiency in terms of computational time and its efficacy in terms of higher classification accuracy which improves sentiment analysis performance. This improvement can be beneficial for various applications, including recommendation systems, reviews analysis, and public opinion observation. However, it's crucial to acknowledge certain limitations of this study. These encompass the need for broader classifier evaluation, and scalability considerations with larger datasets. These identified limitations serve as directions for future research and the enhancement of the proposed approach. [ABSTRACT FROM AUTHOR]

Occlusion or posture change of the face in natural scenes has typical asymmetry; however, an asymmetric face plays a key part in the lack of information available for facial expression recognition. To solve the problem of low accuracy of asymmetric facial expression recognition, this paper proposes a fusion of channel global features and a spatial local information expression recognition network called the "Channel–Spatial Integration Network" (CSINet). First, to extract the underlying detail information and deepen the network, the attention residual module with a redundant information filtering function is designed, and the backbone feature-extraction network is constituted by module stacking. Second, considering the loss of information in the local key area of face occlusion, the channel–spatial fusion structure is constructed, and the channel features and spatial features are combined to enhance the accuracy of occluded facial recognition. Finally, before the full connection layer, more local spatial information is embedded into the global channel information to capture the relationship between different channel–spatial targets, which improves the accuracy of feature expression. Experimental results on the natural scene facial expression data sets RAF-DB and FERPlus show that the recognition accuracies of the modeling approach proposed in this paper are 89.67% and 90.83%, which are 13.24% and 11.52% higher than that of the baseline network ResNet50, respectively. Compared with the latest facial expression recognition methods such as CVT, PACVT, etc., the method in this paper obtains better evaluation results of masked facial expression recognition, which provides certain theoretical and technical references for daily facial emotion analysis and human–computer interaction applications. [ABSTRACT FROM AUTHOR]

Lakes, as integral social‐ecological systems, are hotspots for exploring climatic and anthropogenic impacts, with crucial pathways revealed by continuous sediment records. However, the response of multi‐proxies in large shallow lakes to typical abrupt events and sustained drivers since the Anthropocene remains unclear. Here, we explored the driver‐identification relationships between multi‐proxy peaks and natural and anthropogenic events as well as the attribution of short‐term perturbations and long‐term pressures. To this end, sediment core records, socio‐ecological data, and documented events from official records were integrated into a large shallow lake (Dongting Lake, China). Significant causal cascades and path effects (goodness‐of‐fit: 0.488; total effect: −1.10; p <.001) were observed among catchment environmental proxies, lake biogenic proxies, and mixed‐source proxies. The peak‐event identification rate (PEIR) and event‐peak driving rate were proposed, and values of 28.57%–46.43% and 50%–81.25% were obtained, respectively. The incomplete accuracy of depicting event perturbations using sediment proxies was caused by various information filters both inside and outside the lake. PEIRs for compound events were 1.41 (±0.72) and 1.09 (±0.46) times greater than those for anthropogenic‐dominated and natural‐dominated events, respectively. Furthermore, socio‐economic activity, hydrologic dynamics, land‐use changes, and agriculture exerted significant and persistent pressures, cumulatively contributing 55.3%–80.9% to alterations in sediment proxies. Relatively synergistic or antagonistic trends in temporal contributions of these forces were observed after 2000, which were primarily attributed to the "Grain for Green" project and the Three Gorges Dam. This study represents one of the few investigations to distinguish the driver–response relationship of multiple proxies in large shallow lakes under typical event perturbations and long‐term sustained pressures since the Anthropocene. The findings will help policymakers and managers address ecological perturbations triggered by climate change and human activities over long‐term periods. [ABSTRACT FROM AUTHOR]

Collaborative filtering (CF), a fundamental technique in personalized Recommender Systems, operates by leveraging user–item preference interactions. Matrix factorization remains one of the most prevalent CF-based methods. However, recent advancements in deep learning have spurred the development of hybrid models, which extend matrix factorization, particularly with autoencoders, to capture nonlinear item relationships. Despite these advancements, many proposed models often neglect dynamic changes in the rating process and overlook user features. This paper introduces IUAutoTimeSVD++, a novel hybrid model that builds upon autoTimeSVD++. By incorporating item–user features into the timeSVD++ framework, the proposed model aims to address the static nature and sparsity issues inherent in existing models. Our model utilizes a contractive autoencoder (CAE) to enhance the capacity to capture a robust and stable representation of user-specific and item-specific features, accommodating temporal variations in user preferences and leveraging item characteristics. Experimental results on two public datasets demonstrate IUAutoTimeSVD++'s superiority over baseline models, affirming its effectiveness in capturing and utilizing user and item features for temporally adaptive recommendations. [ABSTRACT FROM AUTHOR]

This paper examines the potential impact of AI-based language models on education, with a specific focus on the ChatGPT model developed by OpenAI. The study begins by providing an overview of the current state of AI in education, including the challenges and opportunities presented by these models. It then delves into an analysis of ChatGPT, including its capabilities, strengths and limitations. The paper also conducts a thorough analysis of potential implications and offers suggestions for assessment design to prevent students from utilising AI-based language models in educational environments. Finally, the paper concludes with a set of recommendations for assessment design, considering the specific characteristics of ChatGPT and the potential impact of AI-based language models on tourism education. [ABSTRACT FROM AUTHOR]

Background: Social risk factors, such as food insecurity and financial needs, are associated with increased risk of cardiovascular diseases, health conditions that are highly prevalent in rural populations. A better understanding of rural Veterans' experiences with social risk factors can inform expansion of Veterans Health Administration (VHA) efforts to address social needs. Objective: To examine social risk and need from rural Veterans' lived experiences and develop recommendations for VHA to address social needs. Design: We conducted semi-structured interviews with participants purposively sampled for racial diversity. The interview guide was informed by Andersen's Behavioral Model of Health Services Use and the Outcomes from Addressing Social Determinants of Health in Systems framework. Participants: Rural Veterans with or at risk of cardiovascular disease who participated in a parent survey and agreed to be recontacted. Approach: Interviews were recorded and transcribed. We analyzed transcripts using directed qualitative content analysis to identify themes. Key Results: Interviews (n = 29) took place from March to June 2022. We identified four themes: (1) Social needs can impact access to healthcare, (2) Structural factors can make it difficult to get help for social needs, (3) Some Veterans are reluctant to seek help, and (4) Veterans recommended enhancing resource dissemination and navigation support. Conclusions: VHA interventions should include active dissemination of information on social needs resources and navigation support to help Veterans access resources. Community-based organizations (e.g., Veteran Service Organizations) could be key partners in the design and implementation of future social need interventions. [ABSTRACT FROM AUTHOR]