Multi-trait Essay Scoring (MES) aims to evaluate the quality of essays across multiple traits (e.g., Language, Content, and Organization). The task can be summarized into three crucial steps: essay content encoding, trait feature learning, and multi-trait scoring. However, previous methods fall short in these steps due to neglecting essential scoring-oriented knowledge, leading to suboptimal performance. To solve these issues, we propose a novel multi-trait scoring framework with multi-knowledge enhancement. Specifically, linguistic knowledge is used to model syntactic structural relations between words, highlighting structurally-informed essay encoding. We learn trait knowledge by capturing the knowledge dependencies between traits to enhance trait-specific features. Further, score-aware ordinal knowledge is integrated to promote ordinal alignment in trait-specific features associated with score rankings, improving scoring performance. Extensive experiments show that our proposed method achieves significant performance.

Explaining machine learning (ML) predictions has become crucial as ML models are increasingly deployed in high-stakes domains such as healthcare. While SHapley Additive exPlanations (SHAP) is widely used for model interpretability, it fails to differentiate between causality and correlation, often misattributing feature importance when features are highly correlated. We propose Causal SHAP, a novel framework that integrates causal relationships into feature attribution while preserving many desirable properties of SHAP. By combining the Peter-Clark (PC) algorithm for causal discovery and the Intervention Calculus when the DAG is Absent (IDA) algorithm for causal strength quantification, our approach addresses the weakness of SHAP. Specifically, Causal SHAP reduces attribution scores for features that are merely correlated with the target, as validated through experiments on both synthetic and real-world datasets. This study contributes to the field of Explainable AI (XAI) by providing a practical framework for causal-aware model explanations. Our approach is particularly valuable in domains such as healthcare, where understanding true causal relationships is critical for informed decision-making.

Feature attribution XAI algorithms enable their users to gain insight into the underlying patterns of large datasets through their feature importance calculation. Existing feature attribution algorithms treat all features in a dataset homogeneously, which may lead to misinterpretation of consequences of changing feature values. In this work, we consider partitioning features into controllable and uncontrollable parts and propose the Controllable fActor Feature Attribution (CAFA) approach to compute the relative importance of controllable features. We carried out experiments applying CAFA to two existing datasets and our own COVID-19 non-pharmaceutical control measures dataset. Experimental results show that with CAFA, we are able to exclude influences from uncontrollable features in our explanation while keeping the full dataset for prediction.

Cognitive inhibition, a key constituent of healthy cognition, has been shown to be susceptible to age-related cognitive declines. Research has shown that cognitive rehabilitation training can facilitate older adults to maintain healthy cognitive functions. Compared to cognitive rehabilitation alone, the combination of physical and cognitive exercises is more effective to train older adults’ cognitive functions. Focusing on the training of older adults’ cognitive inhibition, we design the Ping Pong exergame in this work, which incorporates the traditional cognitive task with physical exercises in the game environment to improve older adults’ cognitive inhibition. A longitudinal study was conducted to evaluate the usability of Ping Pong exergame and its effectiveness on training older adults’ cognitive inhibition. The results show that the Ping Pong exergame received a good usability score and players presented significantly better performance in cognitive tasks after playing the exergame.

The ultimate goal of recommender systems is to suggest appealing items that users are interested in. Traditional recommender systems are built based on a general consensus that users’ preferences reflect their underlying interests. Therefore, various collaborative filtering techniques have been proposed to discover items that best match users’ preferences through estimating ratings for items accurately. However, determining the interestingness of items based on user preferences alone is not sufficient. In human psychology, researchers have found an important intrinsic motivation, i.e., curiosity, for seeking interestingness in social context. Instead of focusing on users’ preferences, curiosity highlights the impact of the unknown and unexpectedness on a person’s feeling of interestingness. In light of this, we propose a novel recommendation model which recommends items by taking consideration of the target users’ curiosity in addition to their personal preferences. To model user curiosity, we adopt a psychologically inspired approach and transpose Berlyne’s theory of curiosity into a computational process. Three key curiosity-stimulating factors, including surprise, uncertainty and conflict, are modelled to estimate user’s curiosity for each item. The proposed recommendation model is evaluated with two large-scale real world datasets and the experimental results highlight that the consideration of social curiosity significantly improves recommendation precision, coverage and diversity.

Most of the current recommender systems focus on estimating user preferences. However, a person's interest in an item is not determined by his/her preference alone. Psychological research has shown that curiosity is a critical motivation relating to a person's interests and driving explorative behaviours. Motivated as above, we aim to model user curiosity in social recommendation context. In this work, we model uncertainty driven curiosity, wherein uncertainty is a well acknowledged factor that stimulates human curiosity. We model user uncertainty based on two well-known theories of uncertainty, i.e., Shannon entropy and Damster-Shafter theory. Then, we rank items by consolidating both user preference and user uncertainty using weighted Borda count. The proposed model is evaluated with two large-scale real world datasets, Douban and Flixster. The experimental results highlight that uncertainty driven curiosity has a positive impact on personalized ranking, by remarkably improving recommendation precision and diversity.

The current generation of senior citizens are enjoying unparalleled levels of good health than previous generations. The need for personal fulfilment after retirement has driven many of them to participate in productive aging activities such as volunteering. This paper outlines the Silver Productive (SP) mobile app, a system powered by the RTS-P intelligent personalized task sub-delegation approach with dynamic worker effort pricing functions. It provides an algorithmic crowdsourcing platform to enable seniors to contribute their effort through productive aging activities and help organizations efficiently utilize seniors' collective productivity.

Activity recognition is a key problem in multi-sensor systems. In this work, we introduce Computational Assumption-based Argumentation, an argumentation approach that seamlessly combines sensor data processing with high-level inference. Our method gives classification results comparable to machine learning based approaches with reduced training time while also giving explanations.

Identifying and rewarding truthful workers are key to the sustainability of crowdsourcing platforms. In this paper, we present a clustering based rewarding mechanism that rewards workers based on their truthfulness while accommodating the differences in workers' preferences. Experimental results show that the proposed approach can effectively discover subcrowds under various conditions, and truthful workers are better rewarded than less truthful ones.

With the prevalence of social networks, social recommendation is rapidly gaining popularity. Currently, social information has mainly been utilized for enhancing rating prediction accuracy, which may not be enough to satisfy user needs. Items with high prediction accuracy tend to be the ones that users are familiar with and may not interest them to explore. In this paper, we take a psychologically inspired view to recommend items that will interest users based on the theory of social curiosity and study its impact on important dimensions of recommender systems. We propose a social curiosity inspired recommendation model which combines both user preferences and user curiosity. The proposed recommendation model is evaluated using large scale real world datasets and the experimental results demonstrate that the inclusion of social curiosity significantly improves recommendation precision, coverage and diversity.

Reputation systems aggregate the ratings provided by buyers to gauge the reliability of sellers in e-marketplaces. The evaluation accuracy of seller reputation significantly impacts the sellers' future utility. The existence of unfair ratings is well-recognized to negatively affect the accuracy of reputation evaluation. Most of the existing approaches dealing with unfair ratings focus on filtering/discounting/aligning the possible unfair ratings caused by malicious attacks or subjective difference. However, these approaches are not effective against unfair ratings in the form of misreporting (e.g., a well-behaving buyer misjudged a seller and provided a negative rating to a transaction which deserves a positive one, and the buyer is willing to revert the misreported negative rating). In this case, how should the buyer undo the damage caused by such misreported ratings and help the seller recover utility loss? In this paper, we propose a reputation revision mechanism to mitigate the negative effects of the misreported ratings. The proposed mechanism temporarily inflates the reputation of the misjudged seller for a period of time, which allows the seller to recover his utility loss caused by the misreported ratings. Extensive realistic simulation based experiments demonstrate the necessity and effectiveness of the proposed mechanism.

Reputation models depend on the ratings provided by buyers togauge the reliability of sellers in multi-agent based e-commerce environment. However, there is no prevention forthe cases in which a buyer misjudges a seller, and provides a negative rating to an original satisfactory transaction. In this case,how should the seller get his reputation repaired andutility loss recovered? In this work, we propose a mechanism to mitigate the negativeeffect of the misreported ratings. It temporarily inflates the reputation of thevictim seller with a certain value for a period of time. This allows the seller to recover hisutility loss due to lost opportunities caused by the misreported ratings. Experiments demonstrate the necessity and effectiveness of the proposed mechanism.

With the world population aging rapidly, improving the quality of life for senior citizens has become an important societal issue. Parkinson's Disease (PD) is one of the most debilitating neuro-degenerative disorders that seriously affect the seniors' quality of life. In recent years, video games have been shown to be a viable way through which partial rehabilitation for PD can be carried out in a fun and low cost manner. Earlier research has shown that both patients' physical and mental conditions can be improved by playing video games. However, so far, the available games developed for PD are mostly intended for rehabilitation purposes. PD diagnosis still depends on the traditional neurological exams and experience of doctors, which require the patients to become self-aware of the symptoms and are usually too late for the patients to delay the progression of PD. To support the early detection of PD symptoms, we propose a fuzzy logic based PD risk predictor that has been implemented in a tablet game platform. The player's behavior data in the game environment are captured unobtrusively and analyzed in real-time. The player's current risk of developing PD is estimated using the proposed fuzzy logic based approach, which will help the player to be aware of high risk of having PD at an earlier stage. A pilot evaluation has been conducted to demonstrate the effectiveness of the proposed approach.

Reputation systems have contributed much to the success of electronic marketplaces. However, the problem of unfair testimonies has to be addressed effectively to improve the robustness of reputation systems. Until now, most of the existing approaches focus only on reputation systems using binary testimonies, and thus have limited applicability and effectiveness. In this paper, We propose an integrated CLUstering‐Based approach called iCLUB to filter unfair testimonies for reputation systems using multinominal testimonies, in an example application of multiagent‐based e‐commerce. It adopts clustering techniques and considers buyer agents’ local as well as global knowledge about seller agents. Experimental evaluation demonstrates the promising results of our approach in filtering various types of unfair testimonies, its robustness against collusion attacks, and better performance compared to competing models.

With the rapid development of reputation systems in various online social networks, manipulations against such systems are evolving quickly. In this paper, we propose scheme TATA, the abbreviation of joint Temporal And Trust Analysis, which protects reputation systems from a new angle: the combination of time domain anomaly detection and Dempster–Shafer theory-based trust computation. Real user attack data collected from a cyber competition is used to construct the testing data set. Compared with two representative reputation schemes and our previous scheme, TATA achieves a significantly better performance in terms of identifying items under attack, detecting malicious users who insert dishonest ratings, and recovering reputation scores.

Reputation systems have contributed much to resisting against the threats from malicious seller agents in electronic marketplaces. Buyer agents can benefit from modeling the reputation of seller agents to make a decision on which seller to have transaction with. However, the existence of unfair ratings decreases the accuracy of the seller agents' reputation evaluation, which will lead to inappropriate sellers to be selected and hence harm buyers' profits. In this paper, to address the problem of unfair ratings, we propose a witness trustworthiness model based on Dempster-Shafer theory to evaluate the trustworthiness of a witness's ratings regarding a particular seller. The proposed approach uses local and global ratings together to model a witness's trustworthiness which is specific for different sellers. The experimental results demonstrate that the proposed approach can effectively address the problem of unfair ratings and outperform the comparative approaches, especially when collusion attacks exist.