Reviewerly/exHarmony huggingface dataset:
Extracting and Analyzing Cybersecurity Named Entity and its Relationship with Noncontextual IOCs from Unstructured Text of CTI Sources

The increasing frequency and sophistication of cyberattacks makes it essential to keep up-to-date with threat information by using cyber threat intelligence (CTI). Structured CTI such as Structured Threat Information eXpression (STIX) is particularly useful because it can automate security operations such as updating FW/IDS rules and analyzing attack trends. However, as most CTIs are written in natural language, manual analysis with domain knowledge is required, which becomes quite time-consuming. In this work, we prose CyNER, a method for automatically structuring CTIs and converting them into STIX format. CyNER extracts named entities in the context of CTI and then extracts the relations between named entities and IOCs in order to convert them into STIX. In addition, by using key phrase extraction, CyNER can extract relations between IOCs that lack contextual information such as those listed at the bottom of a CTI, and named entities. We describe our design and implementation of CyNER and demonstrate that it can extract named entities with the F-measure of 0.80 and extract relations between named entities and IOCs with a maximum accuracy of 81.6%. Our analysis of structured CTI showed that CyNER can extract IOCs that are not included in existing reputation sites, and that it can automatically extract IOCs that have been exploited for a long time and across multiple attack groups. CyNER will therefore make CTI analysis more efficient.
Revisiting The Offensive Text Detection Problem with a Chain-of-Reasoning Approach

We introduce the task of implicit offensive-text detection (OTD) in dialogues, where a statement may have either an offensive or nonoffensive interpretation depending on the listener and context. We argue that reasoning is crucial for understanding this broader class of offensive utterances, and release SLIGHT, a test dataset to support research on this topic. Experiments using the data show that state-of-the-art methods for offense detection perform poorly when tasked with detecting implicitly offensive statements, achieving only ∼11% accuracy. In contrast to the existing OTD datasets, SLIGHT features human-annotated chains of reasoning that describe the mental process through which an offensive interpretation can be reached from an ambiguous statement. We explore the potential of a multihop reasoning approach, by utilizing the existing entailment models to evaluate the probabilities of these chains. Our results demonstrate that reasoning through chains can yield performances better than that of a baseline entailment setting without chains. Furthermore, the analysis of the chains provides insights into the human interpretation process and emphasizes the importance of incorporating additional commonsense knowledge.
Broadening the Scope: Evaluating the Potential of Recommender Systems beyond prioritizing Accuracy

Although beyond-accuracy metrics have gained attention in the last decade, the accuracy of recommendations is still considered the gold standard to evaluate Recommender Systems (RSs). This approach prioritizes the accuracy of recommendations, neglecting the quality of suggestions to enhance user needs, such as diversity and novelty, as well as trustworthiness regulations in RSs for user and provider fairness. As a result, single metrics determine the success of RSs, but this approach fails to consider other criteria simultaneously. A downside of this method is that the most accurate model configuration may not excel in addressing the remaining criteria. This study seeks to broaden RS evaluation by introducing a multi-objective evaluation that considers all model configurations simultaneously under several perspectives. To achieve this, several hyper-parameter configurations of an RS model are trained, and the Pareto-optimal ones are retrieved. The Quality Indicators (QI) of Pareto frontiers, which are gaining interest in Multi-Objective Optimization research, are adapted to RSs. QI enables evaluating the model’s performance by considering various configurations and giving the same importance to each metric. The experiments show that this multi-objective evaluation overturns the ranking of performance among RSs, paving the way to revisit the evaluation approaches of the RecSys research community. We release codes and datasets in the following GitHub repository: https://github.com/sisinflab/RecMOE.
Personalised Recommendations for the BBC iPlayer: Initial approach and current challenges

BBC iPlayer is one of the most important digital products of the BBC, offering live and on-demand television for audiences in the UK with over 10 million weekly active users. The BBC’s role as a public service broadcaster, broadcasting over traditional linear channels as well as online presents a number of challenges for a recommender system. In addition to having substantially different objectives to a commercial service, we show that the diverse content offered by the BBC including news and sport, factual, drama and live events lead to a catalogue with a diversity of consumption patterns, depending on genre. Our research shows that simple models represent strong baselines in this system. We discuss our initial attempts to improve upon these baselines, and conclude with our current challenges.
Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM

The analysis and mining of user heterogeneous behavior are of paramount importance in recommendation systems. However, the conventional approach of incorporating various types of heterogeneous behavior into recommendation models leads to feature sparsity and knowledge fragmentation issues. To address this challenge, we propose a novel approach for personalized recommendation via Large Language Model (LLM), by extracting and fusing heterogeneous knowledge from user heterogeneous behavior information. In addition, by combining heterogeneous knowledge and recommendation tasks, instruction tuning is performed on LLM for personalized recommendations. The experimental results demonstrate that our method can effectively integrate user heterogeneous behavior and significantly improve recommendation performance.
A Trial to Embed RAM Encryption Scheme in Cryptographic Programs

The RAM encryption encrypts the data on memory to prevent data leakage from an adversary to eavesdrop the memory space of the target program. The well-known implementation is Intel SGX, whose RAM encryption mechanism is definitely hardware dependent. In contrast, Watanabe et al. proposed a fully software-based RAM encryption scheme (SBRES). In this paper, we developed the tools for embedding the SBRES in C source codes for its practical application. We applied the tools to the source codes of some cryptographic implementations in Mbed TLS and confirmed that the tools successfully embedded the SBRES functionality in the cryptographic implementations.
Refinement Types for Call-by-name Programs

A refinement type system can be used as a basis of a fully automated verification tool of higher-order functional programs. Most of existing refinement type systems are, however, designed for call-by-value programs, which are unsound for call-by-name programs. In this paper, we introduce a refinement type system for a call-by-name functional language. The most important difference between call-by-value and call-by-name for constructing a refinement type system is whether a variable can be treated as a value or not. The soundness of a typical refinement type systems depends on this fact. On the other hand, in a call-by-name program, since a variable is bound to a term, we cannot treat a variable as a value. To overcome this problem, we track when the term bound to a variable will be evaluated. If we found that a variable is already evaluated in some context, we can treat the variable as a value at the context. We also introduce a type inference algorithm and report on a prototype implementation and preliminary experiments.
Continual Collaborative Filtering Through Gradient Alignment

A recommender system operates in a dynamic environment where new items emerge and new users join the system, resulting in ever-growing user-item interactions over time. Existing works either assume a model trained offline on a static dataset (requiring periodic re-training with ever larger datasets); or an online learning setup that favors recency over history. As privacy-aware users could hide their histories, the loss of older information means that periodic retraining may not always be feasible, while online learning may lose sight of users’ long-term preferences. In this work, we adopt a continual learning perspective to collaborative filtering, by compartmentalizing users and items over time into a notion of tasks. Of particular concern is to mitigate catastrophic forgetting that occurs when the model would reduce performance for older users and items in prior tasks even as it tries to fit the newer users and items in the current task. To alleviate this, we propose a method that leverages gradient alignment to deliver a model that is more compatible across tasks and maximizes user agreement for better user representations to improve long-term recommendations.
A Multi-view Graph Contrastive Learning Framework for Cross-Domain Sequential Recommendation

Sequential recommendation methods play an irreplaceable role in recommender systems which can capture the users' dynamic preferences from the behavior sequences. Despite their success, these works usually suffer from the sparsity problem commonly existed in real applications. Cross-domain sequential recommendation aims to alleviate this problem by introducing relatively richer source-domain data. However, most existing methods capture the users' preferences independently of each domain, which may neglect the item transition patterns across sequences from different domains, i.e., a user's interaction in one domain may influence his/her next interaction in other domains. Moreover, the data sparsity problem still exists since some items in the target and source domains are interacted with only a limited number of times. To address these issues, in this paper we propose a generic framework named multi-view graph contrastive learning (MGCL). Specifically, we adopt the contrastive mechanism in an intra-domain item representation view and an inter-domain user preference view. The former is to jointly learn the dynamic sequential information in the user sequence graph and the static collaborative information in the cross-domain global graph, while the latter is to capture the complementary information of the user's preferences from different domains. Extensive empirical studies on three real-world datasets demonstrate that our MGCL significantly outperforms the state-of-the-art methods.
Integrating Offline Reinforcement Learning with Transformers for Sequential Recommendation

We consider the problem of sequential recommendation, where the current recommendation is made based on past interactions. This recommendation task requires efficient processing of the sequential data and aims to provide recommendations that maximize the long-term reward. To this end, we train a farsighted recommender by using an offline RL algorithm with the policy network in our model architecture that has been initialized from a pre-trained transformer model. The pre-trained model leverages the superb ability of the transformer to process sequential information. Compared to prior works that rely on online interaction via simulation, we focus on implementing a fully offline RL framework that is able to converge in a fast and stable way. Through extensive experiments on public datasets, we show that our method is robust across various recommendation regimes, including e-commerce and movie suggestions. Compared to state-of-the-art supervised learning algorithms, our algorithm yields recommendations of higher quality, demonstrating the clear advantage of combining RL and transformers.
Exploring Unlearning Methods to Ensure the Privacy, Security, and Usability of Recommender Systems

Machine learning algorithms have proven highly effective in analyzing large amounts of data and identifying complex patterns and relationships. One application of machine learning that has received significant attention in recent years is recommender systems, which are algorithms that analyze user behavior and other data to suggest items or content that a user may be interested in. However useful, these systems may unintentionally retain sensitive, outdated, or faulty information. Posing a risk to user privacy, system security, and limiting a system’s usability. In this research proposal, we aim to address these challenges by investigating methods for machine “unlearning”, which would allow information to be efficiently “forgotten” or “unlearned” from machine learning models. The main objective of this proposal is to develop the foundation for future machine unlearning methods. We first evaluate current unlearning methods and explore novel adversarial attacks on these methods’ verifiability, efficiency, and accuracy to gain new insights and further develop the theory of machine unlearning. Using our gathered insights, we seek to create novel unlearning methods that are verifiable, efficient, and limit unnecessary accuracy degradation. Through this research, we seek to make significant contributions to the theoretical foundations of machine unlearning while also developing unlearning methods that can be applied to real-world problems.
Multilayer Action Representation based on MITRE ATT&amp;CK for Automated Penetration Testing

Penetration testing is among the most efficient techniques to improve network system defense and search for potential weaknesses. Applying penetration testing with reinforcement learning can enhance automation and accuracy and reduce dependence on human labor. However, this approach still encounters obstacles in intricate network systems, such as large ones, where compromising is challenging. The lack of modeling derived from a specific common cybersecurity knowledge base also complicates effective applications in practice. Therefore, based on MITRE ATT&CK knowledge, we propose a multilayer action representation to improve the performance, accuracy, and applicability of penetration testing on complex networks. The multilayer action representation's goal is to embody actions in penetration testing as n-dimensional vectors while faithfully capturing their characteristics and relationships. Therefore, it directly improves the performance of reinforcement learning agents in large and complicated network scenarios. For faster training, we also use an epsilon-Wolpertinger architecture. We conducted experiments on four difficulty levels with three network configurations and 119 system scenarios and compared our approach with four different reinforcement learning techniques. Our approach not only represents and models actions with high accuracy but also improves the ability of reinforcement learning agents in a variety of difficult levels of network systems.
Complementary Product Recommendation for Long-tail Products

Identifying complementary relations between products plays a key role in e-commerce Recommender Systems (RS). Existing methods in Complementary Product Recommendation (CPR), however, focus only on identifying complementary relations in huge and data-rich catalogs, while none of them considers real-world scenarios of small and medium e-commerce platforms with limited number of interactions. In this paper, we discuss our research proposal that addresses the problem of identifying complementary relations in such sparse settings. To overcome the data sparsity problem, we propose to first learn complementary relations in large and data-rich catalogs and then transfer learned knowledge to small and scarce ones. To be able to map individual products across different catalogs and thus transfer learned relations between them, we propose to create Product Universal Embedding Space (PUES) using textual and visual product meta-data, which serves as a common ground for the products from arbitrary catalog.
Uncertainty-adjusted Inductive Matrix Completion with Graph Neural Networks

We propose a robust recommender systems model which performs matrix completion and a ratings-wise uncertainty estimation jointly. Whilst the prediction module is purely based on an implicit low-rank assumption imposed via nuclear norm regularization, our loss function is augmented by an uncertainty estimation module which learns an anomaly score for each individual rating via a Graph Neural Network: data points deemed more anomalous by the GNN are downregulated in the loss function used to train the low-rank module. The whole model is trained in an end-to-end fashion, allowing the anomaly detection module to tap on the supervised information available in the form of ratings. Thus, our model’s predictors enjoy the favourable generalization properties that come with being chosen from small function space (i.e., low-rank matrices), whilst exhibiting the robustness to outliers and flexibility that comes with deep learning methods. Furthermore, the anomaly scores themselves contain valuable qualitative information. Experiments on various real-life datasets demonstrate that our model outperforms standard matrix completion and other baselines, confirming the usefulness of the anomaly detection module.
Workshop on Learning and Evaluating Recommendations with Impressions (LERI)

Recommender systems typically rely on past user interactions as the primary source of information for making predictions. However, although highly informative, past user interactions are strongly biased. Impressions, on the other hand, are a new source of information that indicate the items displayed on screen when the user interacted (or not) with them, and have the potential to impact the field of recommender systems in several ways. Early research on impressions was constrained by the limited availability of public datasets, but this is rapidly changing and, as a consequence, interest in impressions has increased. Impressions present new research questions and opportunities, but also bring new challenges. Several works propose to use impressions as part of recommender models in various ways and discuss their information content. Others explore their potential in off-policy-estimation and reinforcement learning. Overall, the interest of the community is growing, but efforts in this direction remain disconnected. Therefore, we believe that a workshop would be useful in bringing the community together.
Climbing crags repetitive choices and recommendations

Outdoor sport climbing in Northern Italy attracts climbers from around the world. While this country has many rock formations, it offers enormous possibilities for adventurous people to explore the mountains. Unfortunately, this great potential causes a problem in finding suitable destinations (crags) to visit for climbing activity. Existing recommender systems in this domain address this issue and suggest potentially interesting items to climbers utilizing a content-based approach. These systems understand users’ preferences from past logs recorded in an electronic training diary. At the same time, some sports people have a behavioral tendency to revisit the same place for subjective reasons. It might be related to weather and seasonality (for instance, some crags are suitable for climbing in winter/summer only), the users’ preferences (when climbers like specific destinations more than others), or personal goals to be achieved in sport (when climbers plan to try some routes again). Unfortunately, current climbing crags recommendations do not adapt when users demonstrate these repetitive behavior patterns. Sequential recommender systems can capture such users’ habits since their architectures were designed to model users’ next item choice by learning from their previous decision manners. To understand to which extent these sequential recommendations can predict the following crags choices in sport climbing, we analyzed a scenario when climbers show repetitious decisions. Further, we present a data set from collected climbers’ e-logs in the Arco region (Italy) and applied several sequential recommender systems architectures for predicting climbers’ following crags’ visits from their past logs. We evaluated these recommender systems offline and compared ranking metrics with the other reported results on the different data sets. The work concludes that sequential models obtain comparably accurate results as in the studies conducted in the field of sequential recommender systems. Hence, it has the prospect for outdoor sport climbers’ subsequent visits prediction and recommendations.
Reciprocal Sequential Recommendation

Reciprocal recommender system (RRS), considering a two-way matching between two parties, has been widely applied in online platforms like online dating and recruitment. Existing RRS models mainly capture static user preferences, which have neglected the evolving user tastes and the dynamic matching relation between the two parties. Although dynamic user modeling has been well-studied in sequential recommender systems, existing solutions are developed in a user-oriented manner. Therefore, it is non-trivial to adapt sequential recommendation algorithms to reciprocal recommendation. In this paper, we formulate RRS as a distinctive sequence matching task, and further propose a new approach ReSeq for RRS, which is short for Reciprocal Sequential recommendation. To capture dual-perspective matching, we propose to learn fine-grained sequence similarities by co-attention mechanism across different time steps. Further, to improve the inference efficiency, we introduce the self-distillation technique to distill knowledge from the fine-grained matching module into the more efficient student module. In the deployment stage, only the efficient student module is used, greatly speeding up the similarity computation. Extensive experiments on five real-world datasets from two scenarios demonstrate the effectiveness and efficiency of the proposed method. Our code is available at https://github.com/RUCAIBox/ReSeq/.
Contrastive Learning with Frequency-Domain Interest Trends for Sequential Recommendation

Recently, contrastive learning for sequential recommendation has demonstrated its powerful ability to learn high-quality user representations. However, constructing augmented samples in the time domain poses challenges due to various reasons, such as fast-evolving trends, interest shifts, and system factors. Furthermore, the F-principle indicates that deep learning preferentially fits the low-frequency part, resulting in poor performance on high-frequency tasks. The complexity of time series and the low-frequency preference limit the utility of sequence encoders. To address these challenges, we need to construct augmented samples from the frequency domain, thus improving the ability to accommodate events of different frequency sizes. To this end, we propose a novel Contrastive Learning with Frequency-Domain Interest Trends for Sequential Recommendation (CFIT4SRec). We treat the embedding representations of historical interactions as "images" and introduce the second-order Fourier transform to construct augmented samples. The components of different frequency sizes reflect the interest trends between attributes and their surroundings in the hidden space. We introduce three data augmentation operations to accommodate events of different frequency sizes: low-pass augmentation, high-pass augmentation, and band-stop augmentation. Extensive experiments on four public benchmark datasets demonstrate the superiority of CFIT4SRec over the state-of-the-art baselines. The implementation code is available at https://github.com/zhangyichi1Z/CFIT4SRec.