OpenRaiser/Intern-Atlas huggingface dataset:
On the privacy-utility trade-off in differentially private hierarchical text classification
Venue: arXiv cs.CR 2021
Year: 2021

Hierarchical text classification consists in classifying text documents into a hierarchy of classes and sub-classes. Although artificial neural networks have proved useful to perform this task, unfortunately they can leak training data information to adversaries due to training data memorization. Using differential privacy during model training can mitigate leakage attacks against trained models, enabling the models to be shared safely at the cost of reduced model accuracy. This work investigates the privacy-utility trade-off in hierarchical text classification with differential privacy guarantees, and identifies neural network architectures that offer superior trade-offs. To this end, we use a white-box membership inference attack to empirically assess the information leakage of three widely used neural network architectures. We show that large differential privacy parameters already suffice to completely mitigate membership inference attacks, thus resulting only in a moderate decrease in model utility. More specifically, for large datasets with long texts we observed Transformer-based models to achieve an overall favorable privacy-utility trade-off, while for smaller datasets with shorter texts convolutional neural networks are preferable.
Semi-supervised Left Atrium Segmentation with Mutual Consistency Training
Venue: arXiv cs.CV 2021
Year: 2021

Semi-supervised learning has attracted great attention in the field of machine learning, especially for medical image segmentation tasks, since it alleviates the heavy burden of collecting abundant densely annotated data for training. However, most of existing methods underestimate the importance of challenging regions (e.g. small branches or blurred edges) during training. We believe that these unlabeled regions may contain more crucial information to minimize the uncertainty prediction for the model and should be emphasized in the training process. Therefore, in this paper, we propose a novel Mutual Consistency Network (MC-Net) for semi-supervised left atrium segmentation from 3D MR images. Particularly, our MC-Net consists of one encoder and two slightly different decoders, and the prediction discrepancies of two decoders are transformed as an unsupervised loss by our designed cycled pseudo label scheme to encourage mutual consistency. Such mutual consistency encourages the two decoders to have consistent and low-entropy predictions and enables the model to gradually capture generalized features from these unlabeled challenging regions. We evaluate our MC-Net on the public Left Atrium (LA) database and it obtains impressive performance gains by exploiting the unlabeled data effectively. Our MC-Net outperforms six recent semi-supervised methods for left atrium segmentation, and sets the new state-of-the-art performance on the LA database.
MP Twitter Engagement and Abuse Post-first COVID-19 Lockdown in the UK: White Paper
Venue: arXiv cs.CY 2021
Year: 2021

The UK has had a volatile political environment for some years now, with Brexit and leadership crises marking the past five years. With this work, we wanted to understand more about how the global health emergency, COVID-19, influences the amount, type or topics of abuse that UK politicians receive when engaging with the public. With this work, we wanted to understand more about how the global health emergency, COVID-19, influences the amount, type or topics of abuse that UK politicians receive when engaging with the public. This work covers the period of June to December 2020 and analyses Twitter abuse in replies to UK MPs. This work is a follow-up from our analysis of online abuse during the first four months of the COVID-19 pandemic in the UK. The paper examines overall abuse levels during this new seven month period, analyses reactions to members of different political parties and the UK government, and the relationship between online abuse and topics such as Brexit, government's COVID-19 response and policies, and social issues. In addition, we have also examined the presence of conspiracy theories posted in abusive replies to MPs during the period. We have found that abuse levels toward UK MPs were at an all-time high in December 2020 (5.4% of all reply tweets sent to MPs). This is almost 1% higher that the two months preceding the General Election. In a departure from the trend seen in the first four months of the pandemic, MPs from the Tory party received the highest percentage of abusive replies from July 2020 onward, which stays above 5% starting from September 2020 onward, as the COVID-19 crisis deepened and the Brexit negotiations with the EU started nearing completion.
Understanding WeChat User Preferences and "Wow" Diffusion
Venue: arXiv cs.SI 2021
Year: 2021

WeChat is the largest social instant messaging platform in China, with 1.1 billion monthly active users. "Top Stories" is a novel friend-enhanced recommendation engine in WeChat, in which users can read articles based on preferences of both their own and their friends. Specifically, when a user reads an article by opening it, the "click" behavior is private. Moreover, if the user clicks the "wow" button, (only) her/his direct connections will be aware of this action/preference. Based on the unique WeChat data, we aim to understand user preferences and "wow" diffusion in Top Stories at different levels. We have made some interesting discoveries. For instance, the "wow" probability of one user is negatively correlated with the number of connected components that are formed by her/his active friends, but the click probability is the opposite. We further study to what extent users' "wow" and click behavior can be predicted from their social connections. To address this problem, we present a hierarchical graph representation learning based model DiffuseGNN, which is capable of capturing the structure-based social observations discovered above. Our experiments show that the proposed method can significantly improve the prediction performance compared with alternative methods.
On subgraph complementation to H-free graphs
Venue: arXiv cs.DS 2021
Year: 2021

For a class $\mathcal{G}$ of graphs, the problem SUBGRAPH COMPLEMENT TO $\mathcal{G}$ asks whether one can find a subset $S$ of vertices of the input graph $G$ such that complementing the subgraph induced by $S$ in $G$ results in a graph in $\mathcal{G}$. We investigate the complexity of the problem when $\mathcal{G}$ is $H$-free for $H$ being a complete graph, a star, a path, or a cycle. We obtain the following results: - When $H$ is a $K_t$ (a complete graph on $t$ vertices) for any fixed $t\geq 1$, the problem is solvable in polynomial-time. This applies even when $\mathcal{G}$ is a subclass of $K_t$-free graphs recognizable in polynomial-time, for example, the class of $(t-2)$-degenerate graphs. - When $H$ is a $K_{1,t}$ (a star graph on $t+1$ vertices), we obtain that the problem is NP-complete for every $t\geq 5$. This, along with known results, leaves only two unresolved cases - $K_{1,3}$ and $K_{1,4}$. - When $H$ is a $P_t$ (a path on $t$ vertices), we obtain that the problem is NP-complete for every $t\geq 7$, leaving behind only two unresolved cases - $P_5$ and $P_6$. - When $H$ is a $C_t$ (a cycle on $t$ vertices), we obtain that the problem is NP-complete for every $t\geq 8$, leaving behind four unresolved cases - $C_4, C_5, C_6,$ and $C_7$. Further, we prove that these hard problems do not admit subexponential-time algorithms (algorithms running in time $2^{o(|V(G)|)}$), assuming the Exponential Time Hypothesis. A simple complementation argument implies that results for $\mathcal{G}$ are applicable for $\overline{\mathcal{G}}$, thereby obtaining similar results for $H$ being the complement of a complete graph, a star, a path, or a cycle. Our results generalize two main results and resolve one open question by Fomin et al. (Algorithmica, 2020).
FootApp: an AI-Powered System for Football Match Annotation
Venue: arXiv cs.HC 2021
Year: 2021

In the last years, scientific and industrial research has experienced a growing interest in acquiring large annotated data sets to train artificial intelligence algorithms for tackling problems in different domains. In this context, we have observed that even the market for football data has substantially grown. The analysis of football matches relies on the annotation of both individual players' and team actions, as well as the athletic performance of players. Consequently, annotating football events at a fine-grained level is a very expensive and error-prone task. Most existing semi-automatic tools for football match annotation rely on cameras and computer vision. However, those tools fall short in capturing team dynamics, and in extracting data of players who are not visible in the camera frame. To address these issues, in this manuscript we present FootApp, an AI-based system for football match annotation. First, our system relies on an advanced and mixed user interface that exploits both vocal and touch interaction. Second, the motor performance of players is captured and processed by applying machine learning algorithms to data collected from inertial sensors worn by players. Artificial intelligence techniques are then used to check the consistency of generated labels, including those regarding the physical activity of players, to automatically recognize annotation errors. Notably, we implemented a full prototype of the proposed system, performing experiments to show its effectiveness in a real-world adoption scenario.
The Dota 2 Bot Competition
Venue: arXiv cs.AI 2021
Year: 2021

Multiplayer Online Battle Area (MOBA) games are a recent huge success both in the video game industry and the international eSports scene. These games encourage team coordination and cooperation, short and long-term planning, within a real-time combined action and strategy gameplay. Artificial Intelligence and Computational Intelligence in Games research competitions offer a wide variety of challenges regarding the study and application of AI techniques to different game genres. These events are widely accepted by the AI/CI community as a sort of AI benchmarking that strongly influences many other research areas in the field. This paper presents and describes in detail the Dota 2 Bot competition and the Dota 2 AI framework that supports it. This challenge aims to join both, MOBAs and AI/CI game competitions, inviting participants to submit AI controllers for the successful MOBA \textit{Defense of the Ancients 2} (Dota 2) to play in 1v1 matches, which aims for fostering research on AI techniques for real-time games. The Dota 2 AI framework makes use of the actual Dota 2 game modding capabilities to enable to connect external AI controllers to actual Dota 2 game matches using the original Free-to-Play game.se of the actual Dota 2 game modding capabilities to enable to connect external AI controllers to actual Dota 2 game matches using the original Free-to-Play game.
A framework for power line inspection tasks with multi-robot systems from signal temporal logic specifications
Venue: arXiv cs.RO 2021
Year: 2021

Inspection of power line infrastructures must be periodically conducted by electric companies in order to ensure reliable electric power distribution. Research efforts are focused on automating the power line inspection process by looking for strategies that satisfy different requirements expressed in terms of potential damage and faults detection. This problem comes up with the need of safe planning and control techniques for autonomous robots to perform visual inspection tasks. Such an application becomes even more interesting and of critical importance when considering a multi-robot extension. In this paper, we propose to compute feasible and constrained trajectories for a fleet of quad-rotors leveraging on Signal Temporal Logic (STL) specifications. The planner allows to formulate rather complex missions avoiding obstacles and forbidden areas along the path. Simulations results achieved in MATLAB show the effectiveness of the proposed approach leading the way to experimental tests on the hardware.
Reinforcement Learning Trajectory Generation and Control for Aggressive Perching on Vertical Walls with Quadrotors
Venue: arXiv cs.RO 2021
Year: 2021

Micro aerial vehicles are widely being researched and employed due to their relative low operation costs and high flexibility in various applications. We study the under-actuated quadrotor perching problem, designing a trajectory planner and controller which generates feasible trajectories and drives quadrotors to desired state in state space. This paper proposes a trajectory generating and tracking method for quadrotor perching that takes the advantages of reinforcement learning controller and traditional controller. The trained low-level reinforcement learning controller would manipulate quadrotor toward the perching point in simulation environment. Once the simulated quadrotor has successfully perched, the relative trajectory information in simulation will be sent to tracking controller on real quadrotor and start the actual perching task. Generating feasible trajectories via the trained reinforcement learning controller requires less time, and the traditional trajectory tracking controller could easily be modified to control the quadrotor and mathematically analysis its stability and robustness. We show that this approach permits the control structure of trajectories and controllers enabling such aggressive maneuvers perching on vertical surfaces with high precision.
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy
Venue: arXiv cs.LG 2021
Year: 2021

Neural network pruning is a popular technique used to reduce the inference costs of modern, potentially overparameterized, networks. Starting from a pre-trained network, the process is as follows: remove redundant parameters, retrain, and repeat while maintaining the same test accuracy. The result is a model that is a fraction of the size of the original with comparable predictive performance (test accuracy). Here, we reassess and evaluate whether the use of test accuracy alone in the terminating condition is sufficient to ensure that the resulting model performs well across a wide spectrum of "harder" metrics such as generalization to out-of-distribution data and resilience to noise. Across evaluations on varying architectures and data sets, we find that pruned networks effectively approximate the unpruned model, however, the prune ratio at which pruned networks achieve commensurate performance varies significantly across tasks. These results call into question the extent of \emph{genuine} overparameterization in deep learning and raise concerns about the practicability of deploying pruned networks, specifically in the context of safety-critical systems, unless they are widely evaluated beyond test accuracy to reliably predict their performance. Our code is available at https://github.com/lucaslie/torchprune.
FAtiMA Toolkit -- Toward an effective and accessible tool for the development of intelligent virtual agents and social robots
Venue: arXiv cs.MA 2021
Year: 2021

More than a decade has passed since the development of FearNot!, an application designed to help children deal with bullying through role-playing with virtual characters. It was also the application that led to the creation of FAtiMA, an affective agent architecture for creating autonomous characters that can evoke empathic responses. In this paper, we describe FAtiMA Toolkit, a collection of open-source tools that is designed to help researchers, game developers and roboticists incorporate a computational model of emotion and decision-making in their work. The toolkit was developed with the goal of making FAtiMA more accessible, easier to incorporate into different projects and more flexible in its capabilities for human-agent interaction, based upon the experience gathered over the years across different virtual environments and human-robot interaction scenarios. As a result, this work makes several different contributions to the field of Agent-Based Architectures. More precisely, FAtiMA Toolkit's library based design allows developers to easily integrate it with other frameworks, its meta-cognitive model affords different internal reasoners and affective components and its explicit dialogue structure gives control to the author even within highly complex scenarios. To demonstrate the use of FAtiMA Toolkit, several different use cases where the toolkit was successfully applied are described and discussed.
Neuromechanics-based Deep Reinforcement Learning of Neurostimulation Control in FES cycling
Venue: arXiv cs.LG 2021
Year: 2021

Functional Electrical Stimulation (FES) can restore motion to a paralysed person's muscles. Yet, control stimulating many muscles to restore the practical function of entire limbs is an unsolved problem. Current neurostimulation engineering still relies on 20th Century control approaches and correspondingly shows only modest results that require daily tinkering to operate at all. Here, we present our state of the art Deep Reinforcement Learning (RL) developed for real time adaptive neurostimulation of paralysed legs for FES cycling. Core to our approach is the integration of a personalised neuromechanical component into our reinforcement learning framework that allows us to train the model efficiently without demanding extended training sessions with the patient and working out of the box. Our neuromechanical component includes merges musculoskeletal models of muscle and or tendon function and a multistate model of muscle fatigue, to render the neurostimulation responsive to a paraplegic's cyclist instantaneous muscle capacity. Our RL approach outperforms PID and Fuzzy Logic controllers in accuracy and performance. Crucially, our system learned to stimulate a cyclist's legs from ramping up speed at the start to maintaining a high cadence in steady state racing as the muscles fatigue. A part of our RL neurostimulation system has been successfully deployed at the Cybathlon 2020 bionic Olympics in the FES discipline with our paraplegic cyclist winning the Silver medal among 9 competing teams.
Gaze-contingent decoding of human navigation intention on an autonomous wheelchair platform
Venue: arXiv cs.HC 2021
Year: 2021

We have pioneered the Where-You-Look-Is Where-You-Go approach to controlling mobility platforms by decoding how the user looks at the environment to understand where they want to navigate their mobility device. However, many natural eye-movements are not relevant for action intention decoding, only some are, which places a challenge on decoding, the so-called Midas Touch Problem. Here, we present a new solution, consisting of 1. deep computer vision to understand what object a user is looking at in their field of view, with 2. an analysis of where on the object's bounding box the user is looking, to 3. use a simple machine learning classifier to determine whether the overt visual attention on the object is predictive of a navigation intention to that object. Our decoding system ultimately determines whether the user wants to drive to e.g., a door or just looks at it. Crucially, we find that when users look at an object and imagine they were moving towards it, the resulting eye-movements from this motor imagery (akin to neural interfaces) remain decodable. Once a driving intention and thus also the location is detected our system instructs our autonomous wheelchair platform, the A.Eye-Drive, to navigate to the desired object while avoiding static and moving obstacles. Thus, for navigation purposes, we have realised a cognitive-level human interface, as it requires the user only to cognitively interact with the desired goal, not to continuously steer their wheelchair to the target (low-level human interfacing).
Advances in Multi-turn Dialogue Comprehension: A Survey
Venue: arXiv cs.CL 2021
Year: 2021

Training machines to understand natural language and interact with humans is an elusive and essential task of artificial intelligence. A diversity of dialogue systems has been designed with the rapid development of deep learning techniques, especially the recent pre-trained language models (PrLMs). Among these studies, the fundamental yet challenging type of task is dialogue comprehension whose role is to teach the machines to read and comprehend the dialogue context before responding. In this paper, we review the previous methods from the technical perspective of dialogue modeling for the dialogue comprehension task. We summarize the characteristics and challenges of dialogue comprehension in contrast to plain-text reading comprehension. Then, we discuss three typical patterns of dialogue modeling. In addition, we categorize dialogue-related pre-training techniques which are employed to enhance PrLMs in dialogue scenarios. Finally, we highlight the technical advances in recent years and point out the lessons from the empirical analysis and the prospects towards a new frontier of researches.
Analytic Modeling of Idle Waves in Parallel Programs: Communication, Cluster Topology, and Noise Impact
Venue: arXiv cs.DC 2021
Year: 2021

Most distributed-memory bulk-synchronous parallel programs in HPC assume that compute resources are available continuously and homogeneously across the allocated set of compute nodes. However, long one-off delays on individual processes can cause global disturbances, so-called idle waves, by rippling through the system. This process is mainly governed by the communication topology of the underlying parallel code. This paper makes significant contributions to the understanding of idle wave dynamics. We study the propagation mechanisms of idle waves across the ranks of MPI-parallel programs. We present a validated analytic model for their propagation velocity with respect to communication parameters and topology, with a special emphasis on sparse communication patterns. We study the interaction of idle waves with MPI collectives and show that, depending on the implementation, a collective may be transparent to the wave. Finally we analyze two mechanisms of idle wave decay: topological decay, which is rooted in differences in communication characteristics among parts of the system, and noise-induced decay, which is caused by system or application noise. We show that noise-induced decay is largely independent of noise characteristics but depends only on the overall noise power. An analytic expression for idle wave decay rate with respect to noise power is derived. For model validation we use microbenchmarks and stencil algorithms on three different supercomputing platforms.