Argument mining automatically identifies and extracts the structure of inference and reasoning conveyed in natural language arguments. To the best of our knowledge, most of the state-of-the-art works in this field have focused on using tree-like structures and linguistic modeling. But, these approaches are not able to model more complex structures which are often found in online forums and real world argumentation structures. In this paper, a novel methodology for argument mining is proposed which employs attention-based embeddings for link prediction to model the causational hierarchies in typical argument structures prevalent in online discourse.

In real dialogue scenarios, the existing slot filling model, which tends to memorize entity patterns, has a significantly reduced generalization facing Out-of-Vocabulary (OOV) problems. To address this issue, we propose an OOV robust slot filling model based on multi-level data augmentations to solve the OOV problem from both word and slot perspectives. We present a unified contrastive learning framework, which pull representations of the origin sample and augmentation samples together, to make the model resistant to OOV problems. We evaluate the performance of the model from some specific slots and carefully design test data with OOV word perturbation to further demonstrate the effectiveness of OOV words. Experiments on two datasets show that our approach outperforms the previous sota methods in terms of both OOV slots and words.

With the widespread use of knowledge graphs (KG) in various automated AI systems and applications, it is very important to ensure that information retrieval algorithms leveraging them are free from societal biases. Previous works have depicted biases that persist in KGs, as well as employed several metrics for measuring the biases. However, such studies lack the systematic exploration of the sensitivity of the bias measurements, through varying sources of data, or the embedding algorithms used. To address this research gap, in this work, we present a holistic analysis of bias measurement on the knowledge graph. First, we attempt to reveal data biases that surface in Wikidata for thirteen different demographics selected from seven continents. Next, we attempt to unfold the variance in the detection of biases by two different knowledge graph embedding algorithms - TransE and ComplEx. We conduct our extensive experiments on a large number of occupations sampled from the thirteen demographics with respect to the sensitive attribute, i.e., gender. Our results show that the inherent data bias that persists in KG can be altered by specific algorithm bias as incorporated by KG embedding learning algorithms. Further, we show that the choice of the state-of-the-art KG embedding algorithm has a strong impact on the ranking of biased occupations irrespective of gender. We observe that the similarity of the biased occupations across demographics is minimal which reflects the socio-cultural differences around the globe. We believe that this full-scale audit of the bias measurement pipeline will raise awareness among the community while deriving insights related to design choices of data and algorithms both and refrain from the popular dogma of ``one-size-fits-all''.

We present a novel graph neural network we call AgentNet, which is designed specifically for graph-level tasks. AgentNet is inspired by sublinear algorithms, featuring a computational complexity that is independent of the graph size. The architecture of AgentNet differs fundamentally from the architectures of traditional graph neural networks. In AgentNet, some trained \textit{neural agents} intelligently walk the graph, and then collectively decide on the output. We provide an extensive theoretical analysis of AgentNet: We show that the agents can learn to systematically explore their neighborhood and that AgentNet can distinguish some structures that are even indistinguishable by 2-WL. Moreover, AgentNet is able to separate any two graphs which are sufficiently different in terms of subgraphs. We confirm these theoretical results with synthetic experiments on hard-to-distinguish graphs and real-world graph classification tasks. In both cases, we compare favorably not only to standard GNNs but also to computationally more expensive GNN extensions.

ChatGPT has gained huge attention and discussion worldwide, with responsible AI being a crucial topic of discussion. One key question is how we can ensure that AI systems, like ChatGPT, are developed and adopted in a responsible way? Responsible AI is the practice of developing, deploying, and maintaining AI systems in a way that benefits the humans, society, and environment, while minimising the risk of negative consequences. To solve the challenge of responsible AI, many AI ethics principles have been released recently by governments, organisations, and enterprises [1]. A principle-based approach provides technology-neutral and context-independent guidance while allowing contextspecific interpretations for implementing responsible AI. However, those principles are too abstract and high-level for practitioners to use in practice. For example, it is a very challenging and complex task to operationalise the the human-centered value principle regarding how it can be designed for, implemented and monitored throughout the entire lifecycle of AI systems. In addition, the existing work mainly focuses on algorithm-level solutions for a subset of mathematics-amenable AI ethics principles (such as privacy and fairness). However, responsible AI issues can happen at any stage of the development lifecycle crosscutting various AI and non-AI components of systems beyond AI algorithms and models.

Website fingerprinting attack is an extensively studied technique used in a web browser to analyze traffic patterns and thus infer confidential information about users. Several website fingerprinting attacks based on machine learning and deep learning tend to use the most typical features to achieve a satisfactory performance of attacking rate. However, these attacks suffer from several practical implementation factors, such as a skillfully pre-processing step or a clean dataset. To defend against such attacks, random packet defense (RPD) with a high cost of excessive network overhead is usually applied. In this work, we first propose a practical filter-assisted attack against RPD, which can filter out the injected noises using the statistical characteristics of TCP/IP traffic. Then, we propose a list-assisted defensive mechanism to defend the proposed attack method. To achieve a configurable trade-off between the defense and the network overhead, we further improve the list-based defense by a traffic splitting mechanism, which can combat the mentioned attacks as well as save a considerable amount of network overhead. In the experiments, we collect real-life traffic patterns using three mainstream browsers, i.e., Microsoft Edge, Google Chrome, and Mozilla Firefox, and extensive results conducted on the closed and open-world datasets show the effectiveness of the proposed algorithms in terms of defense accuracy and network efficiency.

Offline reinforcement learning (RL) struggles in environments with rich and noisy inputs, where the agent only has access to a fixed dataset without environment interactions. Past works have proposed common workarounds based on the pre-training of state representations, followed by policy training. In this work, we introduce a simple, yet effective approach for learning state representations. Our method, Behavior Prior Representation (BPR), learns state representations with an easy-to-integrate objective based on behavior cloning of the dataset: we first learn a state representation by mimicking actions from the dataset, and then train a policy on top of the fixed representation, using any off-the-shelf Offline RL algorithm. Theoretically, we prove that BPR carries out performance guarantees when integrated into algorithms that have either policy improvement guarantees (conservative algorithms) or produce lower bounds of the policy values (pessimistic algorithms). Empirically, we show that BPR combined with existing state-of-the-art Offline RL algorithms leads to significant improvements across several offline control benchmarks. The code is available at \url{https://github.com/bit1029public/offline_bpr}.

Robotic shepherding is a bio-inspired approach to autonomously guiding a swarm of agents towards a desired location. The research area has earned increasing research interest recently due to the efficacy of controlling a large number of agents in a swarm (sheep) using a smaller number of actuators (sheepdogs). However, shepherding a highly dispersed swarm in an obstacle-cluttered environment remains challenging for existing methods. To improve the efficacy of shepherding in complex environments with obstacles and dispersed sheep, this paper proposes a planning-assisted context-sensitive autonomous shepherding framework with collision avoidance abilities. The proposed approach models the swarm shepherding problem as a single Travelling Salesperson Problem (TSP), with two sheepdogs\textquoteright\ modes: no-interaction and interaction. An adaptive switching approach is integrated into the framework to guide real-time path planning for avoiding collisions with static and dynamic obstacles; the latter representing moving sheep swarms. We then propose an overarching hierarchical mission planning system, which is made of three sub-systems: a clustering approach to group and distinguish sheep sub-swarms, an Ant Colony Optimisation algorithm as a TSP solver for determining the optimal herding sequence of the sub-swarms, and an online path planner for calculating optimal paths for both sheepdogs and sheep. The experiments on various environments, both with and without obstacles, objectively demonstrate the effectiveness of the proposed shepherding framework and planning approaches.

Federated Learning (FL) under distributed concept drift is a largely unexplored area. Although concept drift is itself a well-studied phenomenon, it poses particular challenges for FL, because drifts arise staggered in time and space (across clients). To the best of our knowledge, this work is the first to explicitly study data heterogeneity in both dimensions. We first demonstrate that prior solutions to drift adaptation that use a single global model are ill-suited to staggered drifts, necessitating multiple-model solutions. We identify the problem of drift adaptation as a time-varying clustering problem, and we propose two new clustering algorithms for reacting to drifts based on local drift detection and hierarchical clustering. Empirical evaluation shows that our solutions achieve significantly higher accuracy than existing baselines, and are comparable to an idealized algorithm with oracle knowledge of the ground-truth clustering of clients to concepts at each time step.

The importance of social media is again exposed in the recent tragedy of the 2023 Turkey and Syria earthquake. Many victims who were trapped under the rubble called for help by posting messages in Twitter. We present an interactive tool to provide situational awareness for missing and trapped people, and disaster relief for rescue and donation efforts. The system (i) collects tweets, (ii) classifies the ones calling for help, (iii) extracts important entity tags, and (iv) visualizes them in an interactive map screen. Our initial experiments show that the performance in terms of the F1 score is up to 98.30 for tweet classification, and 84.32 for entity extraction. The demonstration, dataset, and other related files can be accessed at https://github.com/avaapm/deprem