Reinforcement learning (RL) provides an effective means to capture users' evolving preferences, leading to improved recommendation performance over time. However, existing RL approaches primarily rely on standard exploration strategies, which are less effective for a large item space with sparse reward signals given the limited interactions for most users. Therefore, they may not be able to learn the optimal policy that effectively captures user's evolving preferences and achieves the maximum expected reward over the long term. In this paper, we propose a novel evidential conservative Q-learning framework (ECQL) that learns an effective and conservative recommendation policy by integrating evidence-based uncertainty and conservative learning. ECQL conducts evidence-aware explorations to discover items that are located beyond current observations but reflect users' long-term interests. It offers an uncertainty-aware conservative view on policy evaluation to discourage deviating too much from users' current interests. Two central components of ECQL include a uniquely designed sequential state encoder and a novel conservative evidential-actor-critic (CEAC) module. The former generates the current state of the environment by aggregating historical information and a sliding window that contains the current user interactions as well as newly recommended items from RL exploration that may represent short and long-term interests respectively. The latter performs an evidence-based rating prediction by maximizing the conservative evidential Q-value and leverages an uncertainty-aware ranking score to explore the item space for a more diverse and valuable recommendation. Experiments on multiple real-world dynamic datasets demonstrate the state-of-the-art performance of ECQL and its capability to capture users' long-term interests.

This research full paper presents a study demonstrating the importance of empathy-building content in computing education to foster appreciation for accessible software. Past research identifies a lack of empathy among developers contributing to software inclusion issues. We propose that enhancing empathy can lead to more accessible and equitable software development. Despite empathy studies in other fields, integrating empathy into computing education lacks sufficient pedagogical insights and accessible resources. To address these gaps, we conducted an in-person study with 121 participants, using a randomized experimental design with two empathy-building intervention groups. We compared their responses to pre-/post-survey questions using statistical tests, examining experiential and expression-based interventions' impact on inclusiveness interest and awareness of non-inclusive software. Our research questions focused on whether these interventions increase the interest, awareness, and empathy of participants and if they are equally effective across demographics and intervention formats. Our findings demonstrate that empathy-building interventions effectively raise awareness and empathy among participants. Expression-based interventions significantly increase empathy and awareness across age and gender groups, with no significant differences in impact between experiential and expression-based methods. Furthermore, we provide an easily adoptable web-based educational lab to integrate empathy and inclusive development topics into diverse curricula formats, accessible via our project website: https://all.rit.edu

Few-shot open-set recognition (FSOSR) aims to detect instances from unseen classes by utilizing a small set of labeled instances from closed-set classes. Accurately rejecting instances from open-set classes in the few-shot setting is fundamentally more challenging due to the weaker supervised signals resulting from fewer labels. Transformer-based few-shot methods exploit attention mapping to achieve a consistent representation. However, the softmax-generated attention map normalizes all the instances that assign unnecessary high attentive weights to those instances not close to the closed-set classes that negatively impact the detection performance. In addition, open-set samples that are similar to a certain closed-set class also pose a significant challenge to most existing FSOSR models. To address these challenges, we propose a novel Meta Evidential Transformer (MET) based FSOSR model that uses an evidential open-set loss to learn more compact closed-set class representations by effectively leveraging similar closed-set classes. MET further integrates an evidence-to-variance ratio to detect fundamentally challenging tasks and uses an evidence-guided cross-attention mechanism to better separate the difficult open-set samples. Experiments on real-world datasets demonstrate consistent improvement over existing competitive methods in unseen class recognition without deteriorating closed-set performance.

Sequential recommender systems are designed to capture users' evolving interests over time. Existing methods typically assume a uniform time interval among consecutive user interactions and may not capture users' continuously evolving behavior in the short and long term. In reality, the actual time intervals of user interactions vary dramatically. Consequently, as the time interval between interactions increases, so does the uncertainty in user behavior. Intuitively, it is beneficial to establish a correlation between the interaction time interval and the model uncertainty to provide effective recommendations. To this end, we formulate a novel Evidential Neural Stochastic Differential Equation (*E-NSDE*) to seamlessly integrate NSDE and evidential learning for effective time-aware sequential recommendations. The NSDE enables the model to learn users' fine-grained time-evolving behavior by capturing continuous user representation while evidential learning quantifies both aleatoric and epistemic uncertainties considering interaction time interval to provide model confidence during prediction. Furthermore, we derive a mathematical relationship between the interaction time interval and model uncertainty to guide the learning process. Experiments on real-world data demonstrate the effectiveness of the proposed method compared to the SOTA methods.

Machine learning-driven human behavior analysis is gaining attention in behavioral/mental healthcare, due to its potential to identify behavioral patterns that cannot be recognized by traditional assessments. Real-life applications, such as digital behavioral biomarker identification, often require the discovery of complex spatiotemporal patterns in multimodal data, which is largely under-explored. To fill this gap, we propose a novel model that integrates uniquely designed Deep Temporal Sets (DTS) with Evidential Reinforced Attentions (ERA). DTS captures complex temporal relationships in the input and generates a set-based representation, while ERA captures the policy network’s uncertainty and conducts evidence-aware exploration to locate attentive regions in behavioral data. Using child-computer interaction data as a testing platform, we demonstrate the effectiveness of DTS-ERA in differentiating children with Autism Spectrum Disorder and typically developing children based on sequential multimodal visual and touch behaviors. Comparisons with baseline methods show that our model achieves superior performance and has the potential to provide objective, quantitative, and precise analysis of complex human behaviors.

We present a novel dynamic recommendation model that focuses on users who have interactions in the past but turn relatively inactive recently. Making effective recommendations to these time-sensitive cold-start users is critical to maintain the user base of a recommender system. Due to the sparse recent interactions, it is challenging to capture these users' current preferences precisely. Solely relying on their historical interactions may also lead to outdated recommendations misaligned with their recent interests. The proposed model leverages historical and current user-item interactions and dynamically factorizes a user's (latent) preference into time-specific and time-evolving representations that jointly affect user behaviors. These latent factors further interact with an optimized item embedding to achieve accurate and timely recommendations. Experiments over real-world data help demonstrate the effectiveness of the proposed time-sensitive cold-start recommendation model.

Recommender systems have been widely used to predict users’ interests and filter information from a large number of candidate items. However, accurately capturing the interests of users having limited interactions with a system remains a long-lasting challenge. Furthermore, existing recommender systems primarily focus on predicting user preferences without quantifying the prediction uncertainty. Uncertainty can help to quantify the model confidence when making a recommendation where low model confidence could serve as a more accurate indicator of a user’s cold-start level than simply using the number of interactions. We present a novel recommendation model that seamlessly integrates a meta-learning module with an evidential learning approach. The former module generalizes meta knowledge to tackle cold-start recommendations by exploiting fast adaptation. The latter quantifies both aleatoric and epistemic uncertainty without performing expensive posterior inference. Evidential learning achieves this by placing evidential priors and treating the output of the meta-learning module as evidence-based pseudo counts and learns a function to directly predict the evidence of a target interaction. Experiments on four benchmark datasets justify that our proposed model captures the uncertainty of users and demonstrates its superior performance over the state-of-the-art recommendation models.

Programmability in the data plane has become increasingly important as virtualization is introduced into networking and software-defined networking becomes more prevalent. Yet, the performance of programmable data planes on commodity hardware is a major concern, in light of ever-increasing network speed and routing table size. This paper focuses on IP lookup, specifically the longest prefix matching for IPv6 addresses, which is a major performance bottleneck in programmable switches. As a solution, the paper presents CuVPP, a programmable switch that uses packet batch processing and cache locality for both instructions and data by leveraging Vector Packet Processing (VPP). We thoroughly evaluate CuVPP with both real network traffic and file-based lookup on a commodity hardware server connected via 80 Gbps network links and compare its performance with the other popular approaches. Our evaluation shows that CuVPP can achieve up to 4.5 million lookups per second with real traffic, higher than the other trie- or filter-based lookup approaches, and scales well even when the routing table size grows to 2 million prefixes.

Emotions are an integral part of human nature. Emotion awareness is critical to any form of interpersonal communication and collaboration, including these in the software development process. Recently, the SE community starts having growing interests in emotion awareness in software development. While researchers have accomplished many valuable results, most extant research ignores the dynamic nature of emotion. To investigate the emotion dynamics, SE community needs an effective approach to capture and model emotion dynamics rather than focuses on extracting isolated emotion states. In this paper, we proposed such an approach-EmoD. EmoD is able to automatically collect project teams' communication records, identify the emotions and their intensities in them, model the emotion dynamics into time series, and provide efficient data management. We developed a prototype tool that instantiates the EmoD approach by assembling state-of-the-art NLP, SE, and time series techniques. We demonstrate the utility of the tool using the IPython's project data on GitHub and a visualization solution built on EmoD. Thus, we demonstrate that EmoD can provide end-to-end support for various emotion awareness research and practices through automated data collection, modeling, storage, analysis, and presentation.

Emotion awareness is critical to interpersonal communication, including that in software development. The SE community has studied emotion in software development using isolated emotion states but it has not considered the dynamic nature of emotion. To investigate the emotion dynamics, SE community needs an effective approach. In this paper, we propose such an approach which can automatically collect project teams' communication records, identify the emotions and their intensities in them, model the emotion dynamics into time series, and provide efficient data management. We demonstrate that this approach can provide end-to-end support for various emotion awareness research and practices through automated data collection, modeling, storage, analysis, and presentation using the IPython's project data on GitHub.