OpenRaiser/Intern-Atlas huggingface dataset:
Modeling Coreference Relations in Visual Dialog
Venue: arXiv cs.CV 2022
Year: 2022

Visual dialog is a vision-language task where an agent needs to answer a series of questions grounded in an image based on the understanding of the dialog history and the image. The occurrences of coreference relations in the dialog makes it a more challenging task than visual question-answering. Most previous works have focused on learning better multi-modal representations or on exploring different ways of fusing visual and language features, while the coreferences in the dialog are mainly ignored. In this paper, based on linguistic knowledge and discourse features of human dialog we propose two soft constraints that can improve the model's ability of resolving coreferences in dialog in an unsupervised way. Experimental results on the VisDial v1.0 dataset shows that our model, which integrates two novel and linguistically inspired soft constraints in a deep transformer neural architecture, obtains new state-of-the-art performance in terms of recall at 1 and other evaluation metrics compared to current existing models and this without pretraining on other vision-language datasets. Our qualitative results also demonstrate the effectiveness of the method that we propose.
Election in Fully Anonymous Shared Memory Systems: Tight Space Bounds and Algorithms
Venue: arXiv cs.DC 2022
Year: 2022

This article addresses election in fully anonymous systems made up of $n$ asynchronous processes that communicate through atomic read-write registers or atomic read-modify-write registers. Given an integer $d\in\{1,\dots, n-1\}$, two elections problems are considered: $d$-election (at least one and at most $d$ processes are elected) and exact $d$-election (exactly $d$ processes are elected). Full anonymity means that both the processes and the shared registers are anonymous. Memory anonymity means that the processes may disagree on the names of the shared registers. That is, the same register name $A$ can denote different registers for different processes, and the register name $A$ used by a process and the register name $B$ used by another process can address the same shared register.
Interactive Disambiguation for Behavior Tree Execution
Venue: arXiv cs.RO 2022
Year: 2022

In recent years, robots are used in an increasing variety of tasks, especially by small- and medium- sized enterprises. These tasks are usually fast-changing, they have a collaborative scenario and happen in unpredictable environments with possible ambiguities. It is important to have methods capable of generating robot programs easily, that are made as general as possible by handling uncertainties. We present a system that integrates a method to learn Behavior Trees (BTs) from demonstration for pick and place tasks, with a framework that uses verbal interaction to ask follow-up clarification questions to resolve ambiguities. During the execution of a task, the system asks for user input when there is need to disambiguate an object in the scene, when the targets of the task are objects of a same type that are present in multiple instances. The integrated system is demonstrated on different scenarios of a pick and place task, with increasing level of ambiguities. The code used for this paper is made publicly available.
Coresets for Data Discretization and Sine Wave Fitting
Venue: arXiv cs.LG 2022
Year: 2022

In the \emph{monitoring} problem, the input is an unbounded stream $P={p_1,p_2\cdots}$ of integers in $[N]:=\{1,\cdots,N\}$, that are obtained from a sensor (such as GPS or heart beats of a human). The goal (e.g., for anomaly detection) is to approximate the $n$ points received so far in $P$ by a single frequency $\sin$, e.g. $\min_{c\in C}cost(P,c)+λ(c)$, where $cost(P,c)=\sum_{i=1}^n \sin^2(\frac{2π}{N} p_ic)$, $C\subseteq [N]$ is a feasible set of solutions, and $λ$ is a given regularization function. For any approximation error $\varepsilon>0$, we prove that \emph{every} set $P$ of $n$ integers has a weighted subset $S\subseteq P$ (sometimes called core-set) of cardinality $|S|\in O(\log(N)^{O(1)})$ that approximates $cost(P,c)$ (for every $c\in [N]$) up to a multiplicative factor of $1\pm\varepsilon$. Using known coreset techniques, this implies streaming algorithms using only $O((\log(N)\log(n))^{O(1)})$ memory. Our results hold for a large family of functions. Experimental results and open source code are provided.
Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors
Venue: arXiv cs.LG 2022
Year: 2022

We present a simple and effective approach for posterior uncertainty quantification in deep operator networks (DeepONets); an emerging paradigm for supervised learning in function spaces. We adopt a frequentist approach based on randomized prior ensembles, and put forth an efficient vectorized implementation for fast parallel inference on accelerated hardware. Through a collection of representative examples in computational mechanics and climate modeling, we show that the merits of the proposed approach are fourfold. (1) It can provide more robust and accurate predictions when compared against deterministic DeepONets. (2) It shows great capability in providing reliable uncertainty estimates on scarce data-sets with multi-scale function pairs. (3) It can effectively detect out-of-distribution and adversarial examples. (4) It can seamlessly quantify uncertainty due to model bias, as well as noise corruption in the data. Finally, we provide an optimized JAX library called {\em UQDeepONet} that can accommodate large model architectures, large ensemble sizes, as well as large data-sets with excellent parallel performance on accelerated hardware, thereby enabling uncertainty quantification for DeepONets in realistic large-scale applications.
HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data
Venue: arXiv cs.CV 2022
Year: 2022

The problem of human activity recognition from mobile sensor data applies to multiple domains, such as health monitoring, personal fitness, daily life logging, and senior care. A critical challenge for training human activity recognition models is data quality. Acquiring balanced datasets containing accurate activity labels requires humans to correctly annotate and potentially interfere with the subjects' normal activities in real-time. Despite the likelihood of incorrect annotation or lack thereof, there is often an inherent chronology to human behavior. For example, we take a shower after we exercise. This implicit chronology can be used to learn unknown labels and classify future activities. In this work, we propose HAR-GCCN, a deep graph CNN model that leverages the correlation between chronologically adjacent sensor measurements to predict the correct labels for unclassified activities that have at least one activity label. We propose a new training strategy enforcing that the model predicts the missing activity labels by leveraging the known ones. HAR-GCCN shows superior performance relative to previously used baseline methods, improving classification accuracy by about 25% and up to 68% on different datasets. Code is available at \url{https://github.com/abduallahmohamed/HAR-GCNN}.
Privacy Leakage in Proactive VR Streaming: Modeling and Tradeoff
Venue: arXiv cs.MM 2022
Year: 2022

Proactive tile-based virtual reality (VR) video streaming employs the viewpoint of a user to predict the tiles to be requested, renders and delivers the predicted tiles before playback. Recently, it has been found that the identity and preference of the user can be inferred from the trace of viewpoint uploaded for proactive streaming, which indicates that viewpoint leakage incurs privacy leakage. In this paper, we strive to answer the following questions regarding viewpoint leakage during proactive VR video streaming. When is the viewpoint leaked? Can privacy-preserving approaches (e.g., federated or individual training, using predictors with no need for training, or predicting locally) avoid viewpoint leakage? We find that if the prediction error or the quality of experience (QoE) metric is uploaded for adaptive streaming, the real viewpoint can be inferred even with the privacy-preserving approaches. Then, we define viewpoint leakage probability to characterize the accuracy of the inferred viewpoint, and respectively derive the probability when uploading prediction error and QoE metric. We find that the viewpoint leakage probability can be reduced by sacrificing QoE or increasing resources. Simulation with the state-of-the-art predictor over a real dataset shows that such a tradeoff does not exist only in rare cases.
Combining Individual and Joint Networking Behavior for Intelligent IoT Analytics
Venue: arXiv cs.LG 2022
Year: 2022

The IoT vision of a trillion connected devices over the next decade requires reliable end-to-end connectivity and automated device management platforms. While we have seen successful efforts for maintaining small IoT testbeds, there are multiple challenges for the efficient management of large-scale device deployments. With Industrial IoT, incorporating millions of devices, traditional management methods do not scale well. In this work, we address these challenges by designing a set of novel machine learning techniques, which form a foundation of a new tool, it IoTelligent, for IoT device management, using traffic characteristics obtained at the network level. The design of our tool is driven by the analysis of 1-year long networking data, collected from 350 companies with IoT deployments. The exploratory analysis of this data reveals that IoT environments follow the famous Pareto principle, such as: (i) 10% of the companies in the dataset contribute to 90% of the entire traffic; (ii) 7% of all the companies in the set own 90% of all the devices. We designed and evaluated CNN, LSTM, and Convolutional LSTM models for demand forecasting, with a conclusion of the Convolutional LSTM model being the best. However, maintaining and updating individual company models is expensive. In this work, we design a novel, scalable approach, where a general demand forecasting model is built using the combined data of all the companies with a normalization factor. Moreover, we introduce a novel technique for device management, based on autoencoders. They automatically extract relevant device features to identify device groups with similar behavior to flag anomalous devices.
Find a Way Forward: a Language-Guided Semantic Map Navigator
Venue: arXiv cs.AI 2022
Year: 2022

In this paper, we introduce the map-language navigation task where an agent executes natural language instructions and moves to the target position based only on a given 3D semantic map. To tackle the task, we design the instruction-aware Path Proposal and Discrimination model (iPPD). Our approach leverages map information to provide instruction-aware path proposals, i.e., it selects all potential instruction-aligned candidate paths to reduce the solution space. Next, to represent the map observations along a path for a better modality alignment, a novel Path Feature Encoding scheme tailored for semantic maps is proposed. An attention-based Language Driven Discriminator is designed to evaluate path candidates and determine the best path as the final result. Our method can naturally avoid error accumulation compared with single-step greedy decision methods. Comparing to a single-step imitation learning approach, iPPD has performance gains above 17% on navigation success and 0.18 on path matching measurement nDTW in challenging unseen environments.
Distributed distance domination in graphs with no $K_{2,t}$-minor
Venue: arXiv cs.DS 2022
Year: 2022

We prove that a simple distributed algorithm finds a constant approximation of an optimal distance-$k$ dominating set in graphs with no $K_{2,t}$-minor. The algorithm runs in a constant number of rounds. We further show how this procedure can be used to give a distributed algorithm which given $ε>0$ and $k,t\in \mathbb{Z}^+$ finds in a graph $G=(V,E)$ with no $K_{2,t}$-minor a distance-$k$ dominating set of size at most $(1+ε)$ of the optimum. The algorithm runs in $O(\log^*{|V|})$ rounds in the Local model. In particular, both algorithms work in outerplanar graphs.
$μ$BERT: Mutation Testing using Pre-Trained Language Models
Venue: arXiv cs.SE 2022
Year: 2022

We introduce $μ$BERT, a mutation testing tool that uses a pre-trained language model (CodeBERT) to generate mutants. This is done by masking a token from the expression given as input and using CodeBERT to predict it. Thus, the mutants are generated by replacing the masked tokens with the predicted ones. We evaluate $μ$BERT on 40 real faults from Defects4J and show that it can detect 27 out of the 40 faults, while the baseline (PiTest) detects 26 of them. We also show that $μ$BERT can be 2 times more cost-effective than PiTest, when the same number of mutants are analysed. Additionally, we evaluate the impact of $μ$BERT's mutants when used by program assertion inference techniques, and show that they can help in producing better specifications. Finally, we discuss about the quality and naturalness of some interesting mutants produced by $μ$BERT during our experimental evaluation.
Whole-Body Control for Velocity-Controlled Mobile Collaborative Robots Using Coupling Dynamic Movement Primitives
Venue: arXiv cs.RO 2022
Year: 2022

In this paper, we propose a unified whole-body control framework for velocity-controlled mobile collaborative robots which can distribute task motion into the arm and mobile base according to specific task requirements by adjusting weighting factors. Our framework focuses on addressing two challenging issues in whole-body coordination: 1) different dynamic characteristics of the mobile base and the arm; 2) avoidance of violating both safety and configuration constraints. In addition, our controller involves Coupling Dynamic Movement Primitives to enable the essential capabilities for collaboration and interaction applications, such as obstacle avoidance, human teaching, and compliance control. Based on these, we design an adaptive motion mode for intuitive physical human-robot interaction through adjusting the weighting factors. The proposed controller is in closed-form and thus quite computationally efficient. Several typical experiments carried out on a real mobile collaborative robot validate the effectiveness of the proposed controller.
Machine Learning based Anomaly Detection for Smart Shirt: A Systematic Review
Venue: arXiv cs.LG 2022
Year: 2022

In recent years, the popularity and use of Artificial Intelligence (AI) and large investments on theInternet of Medical Things (IoMT) will be common to use products such as smart socks, smartpants, and smart shirts. These products are known as Smart Textile or E-textile, which has theability to monitor and collect signals that our body emits. These signals make it possible to extractanomalous components using Machine Learning (ML) techniques that play an essential role in thisarea. This study presents a Systematic Review of the Literature (SLR) on Anomaly Detection usingML techniques in Smart Shirt. The objectives of the SLR are: (i) to identify what type of anomalythe smart shirt; (ii) what ML techniques are being used; (iii) which datasets are being used; (iv)identify smart shirt or signal acquisition devices; (v) list the performance metrics used to evaluatethe ML model; (vi) the results of the techniques in general; (vii) types of ML algorithms are beingapplied.The SLR selected 11 primary studies published between 2017-2021. The results showed that6 types of anomalies were identified, with the Fall anomaly being the most cited. The Support VectorMachines (SVM) algorithm is most used. Most of the primary studies used public or private datasets.The Hexoskin smart shirt was most cited. The most used metric performance was Accuracy. Onaverage, almost all primary studies presented a result above 90%, and all primary studies used theSupervisioned type of ML.
PAC-Bayesian Lifelong Learning For Multi-Armed Bandits
Venue: arXiv cs.LG 2022
Year: 2022

We present a PAC-Bayesian analysis of lifelong learning. In the lifelong learning problem, a sequence of learning tasks is observed one-at-a-time, and the goal is to transfer information acquired from previous tasks to new learning tasks. We consider the case when each learning task is a multi-armed bandit problem. We derive lower bounds on the expected average reward that would be obtained if a given multi-armed bandit algorithm was run in a new task with a particular prior and for a set number of steps. We propose lifelong learning algorithms that use our new bounds as learning objectives. Our proposed algorithms are evaluated in several lifelong multi-armed bandit problems and are found to perform better than a baseline method that does not use generalisation bounds.
SkillNet-NLU: A Sparsely Activated Model for General-Purpose Natural Language Understanding
Venue: arXiv cs.CL 2022
Year: 2022

Prevailing deep models are single-purpose and overspecialize at individual tasks. However, when being extended to new tasks, they typically forget previously learned skills and learn from scratch. We address this issue by introducing SkillNet-NLU, a general-purpose model that stitches together existing skills to learn new tasks more effectively. The key feature of our approach is that it is sparsely activated guided by predefined skills. Different from traditional dense models that always activate all the model parameters, SkillNet-NLU only activates parts of the model parameters whose skills are relevant to the target task. When learning for a new task, our approach precisely activates required skills and also provides an option to add new skills. We evaluate on natural language understandings tasks and have the following findings. First, with only one model checkpoint, SkillNet-NLU performs better than task-specific fine-tuning and two multi-task learning baselines (i.e., dense model and Mixture-of-Experts model) on six tasks. Second, sparsely activated pre-training further improves the overall performance. Third, SkillNet-NLU significantly outperforms baseline systems when being extended to new tasks.
Simulating Authenticated Broadcast in Networks of Bounded Degree
Venue: arXiv cs.DC 2022
Year: 2022

The authenticated broadcast is simulated in the bounded-degree networks to provide efficient broadcast primitives for building efficient higher-layer Byzantine protocols. A general abstraction of the relay-based broadcast system is introduced, in which the properties of the relay-based broadcast primitives are generalized. With this, fault-tolerant propagation is proposed as a building block of the broadcast primitives. Meanwhile, complementary systems are proposed in complementing fault-tolerant propagation and localized communication. Analysis shows that efficient fault-tolerant propagation can be built with sufficient initiation areas. Meanwhile, by integrating fault-tolerant propagation and localized communication, efficient broadcast primitives can be built in bounded-degree networks.
Deep Reinforcement Learning for Entity Alignment
Venue: arXiv cs.AI 2022
Year: 2022

Embedding-based methods have attracted increasing attention in recent entity alignment (EA) studies. Although great promise they can offer, there are still several limitations. The most notable is that they identify the aligned entities based on cosine similarity, ignoring the semantics underlying the embeddings themselves. Furthermore, these methods are shortsighted, heuristically selecting the closest entity as the target and allowing multiple entities to match the same candidate. To address these limitations, we model entity alignment as a sequential decision-making task, in which an agent sequentially decides whether two entities are matched or mismatched based on their representation vectors. The proposed reinforcement learning (RL)-based entity alignment framework can be flexibly adapted to most embedding-based EA methods. The experimental results demonstrate that it consistently advances the performance of several state-of-the-art methods, with a maximum improvement of 31.1% on Hits@1.
Automated Few-Shot Time Series Forecasting based on Bi-level Programming
Venue: arXiv cs.LG 2022
Year: 2022

New micro-grid design with renewable energy sources and battery storage systems can help improve greenhouse gas emissions and reduce the operational cost. To provide an effective short-/long-term forecasting of both energy generation and load demand, time series predictive modeling has been one of the key tools to guide the optimal decision-making for planning and operation. One of the critical challenges of time series renewable energy forecasting is the lack of historical data to train an adequate predictive model. Moreover, the performance of a machine learning model is sensitive to the choice of its corresponding hyperparameters. Bearing these considerations in mind, this paper develops a BiLO-Auto-TSF/ML framework that automates the optimal design of a few-shot learning pipeline from a bi-level programming perspective. Specifically, the lower-level meta-learning helps boost the base-learner to mitigate the small data challenge while the hyperparameter optimization at the upper level proactively searches for the optimal hyperparameter configurations for both base- and meta-learners. Note that the proposed framework is so general that any off-the-shelf machine learning method can be used in a plug-in manner. Comprehensive experiments fully demonstrate the effectiveness of our proposed BiLO-Auto-TSF/ML framework to search for a high-performance few-shot learning pipeline for various energy sources.
L2CS-Net: Fine-Grained Gaze Estimation in Unconstrained Environments
Venue: arXiv cs.CV 2022
Year: 2022

Human gaze is a crucial cue used in various applications such as human-robot interaction and virtual reality. Recently, convolution neural network (CNN) approaches have made notable progress in predicting gaze direction. However, estimating gaze in-the-wild is still a challenging problem due to the uniqueness of eye appearance, lightning conditions, and the diversity of head pose and gaze directions. In this paper, we propose a robust CNN-based model for predicting gaze in unconstrained settings. We propose to regress each gaze angle separately to improve the per-angel prediction accuracy, which will enhance the overall gaze performance. In addition, we use two identical losses, one for each angle, to improve network learning and increase its generalization. We evaluate our model with two popular datasets collected with unconstrained settings. Our proposed model achieves state-of-the-art accuracy of 3.92° and 10.41° on MPIIGaze and Gaze360 datasets, respectively. We make our code open source at https://github.com/Ahmednull/L2CS-Net.
Reliably Re-Acting to Partner's Actions with the Social Intrinsic Motivation of Transfer Empowerment
Venue: arXiv cs.AI 2022
Year: 2022

We consider multi-agent reinforcement learning (MARL) for cooperative communication and coordination tasks. MARL agents can be brittle because they can overfit their training partners' policies. This overfitting can produce agents that adopt policies that act under the expectation that other agents will act in a certain way rather than react to their actions. Our objective is to bias the learning process towards finding reactive strategies towards other agents' behaviors. Our method, transfer empowerment, measures the potential influence between agents' actions. Results from three simulated cooperation scenarios support our hypothesis that transfer empowerment improves MARL performance. We discuss how transfer empowerment could be a useful principle to guide multi-agent coordination by ensuring reactiveness to one's partner.