OpenRaiser/Intern-Atlas huggingface dataset:
Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation
Venue: arXiv cs.IR 2021
Year: 2021

Social recommendation based on social network has achieved great success in improving the performance of recommendation system. Since social network (user-user relations) and user-item interactions are both naturally represented as graph-structured data, Graph Neural Networks (GNNs) have thus been widely applied for social recommendation. In this work, we propose an end-to-end heterogeneous global graph learning framework, namely Graph Learning Augmented Heterogeneous Graph Neural Network (GL-HGNN) for social recommendation. GL-HGNN aims to learn a heterogeneous global graph that makes full use of user-user relations, user-item interactions and item-item similarities in a unified perspective. To this end, we design a Graph Learner (GL) method to learn and optimize user-user and item-item connections separately. Moreover, we employ a Heterogeneous Graph Neural Network (HGNN) to capture the high-order complex semantic relations from our learned heterogeneous global graph. To scale up the computation of graph learning, we further present the Anchor-based Graph Learner (AGL) to reduce computational complexity. Extensive experiments on four real-world datasets demonstrate the effectiveness of our model.
Multi-behavior Graph Contextual Aware Network for Session-based Recommendation
Venue: arXiv cs.IR 2021
Year: 2021

Predicting the next interaction of a short-term sequence is a challenging task in session-based recommendation (SBR).Multi-behavior session recommendation considers session sequence with multiple interaction types, such as click and purchase, to capture more effective user intention representation sufficiently.Despite the superior performance of existing multi-behavior based methods for SBR, there are still several severe limitations:(i) Almost all existing works concentrate on single target type of next behavior and fail to model multiplex behavior sessions uniformly.(ii) Previous methods also ignore the semantic relations between various next behavior and historical behavior sequence, which are significant signals to obtain current latent intention for SBR.(iii) The global cross-session item-item graph established by some existing models may incorporate semantics and context level noise for multi-behavior session-based recommendation. To overcome the limitations (i) and (ii), we propose two novel tasks for SBR, which require the incorporation of both historical behaviors and next behaviors into unified multi-behavior recommendation modeling. To this end, we design a Multi-behavior Graph Contextual Aware Network (MGCNet) for multi-behavior session-based recommendation for the two proposed tasks. Specifically, we build a multi-behavior global item transition graph based on all sessions involving all interaction types. Based on the global graph, MGCNet attaches the global interest representation to final item representation based on local contextual intention to address the limitation (iii). In the end, we utilize the next behavior information explicitly to guide the learning of general interest and current intention for SBR. Experiments on three public benchmark datasets show that MGCNet can outperform state-of-the-art models for multi-behavior session-based recommendation.
Can We Trust Tests To Automate Dependency Updates? A Case Study of Java Projects
Venue: arXiv cs.SE 2021
Year: 2021

Developers are increasingly using services such as Dependabot to automate dependency updates. However, recent research has shown that developers perceive such services as unreliable, as they heavily rely on test coverage to detect conflicts in updates. To understand the prevalence of tests exercising dependencies, we calculate the test coverage of direct and indirect uses of dependencies in 521 well-tested Java projects. We find that tests only cover 58% of direct and 20% of transitive dependency calls. By creating 1,122,420 artificial updates with simple faults covering all dependency usages in 262 projects, we measure the effectiveness of test suites in detecting semantic faults in dependencies; we find that tests can only detect 47% of direct and 35% of indirect artificial faults on average. To increase reliability, we investigate the use of change impact analysis as a means of reducing false negatives; on average, our tool can uncover 74% of injected faults in direct dependencies and 64% for transitive dependencies, nearly two times more than test suites. We then apply our tool in 22 real-world dependency updates, where it identifies three semantically conflicting cases and five cases of unused dependencies. Our findings indicate that the combination of static and dynamic analysis should be a requirement for future dependency updating systems.
How to write a coequation
Venue: arXiv cs.LO 2021
Year: 2021

There is a large amount of literature on the topic of covarieties, coequations and coequational specifications, dating back to the early seventies. Nevertheless, coequations have not (yet) emerged as an everyday practical specification formalism for computer scientists. In this review paper, we argue that this is partly due to the multitude of syntaxes for writing down coequations, which seems to have led to some confusion about what coequations are and what they are for. By surveying the literature, we identify four types of syntaxes: coequations-as-corelations, coequations-as-predicates, coequations-as-equations, and coequations-as-modal-formulas. We present each of these in a tutorial fashion, relate them to each other, and discuss their respective uses.
Rethinking Crowd Sourcing for Semantic Similarity
Venue: arXiv cs.CL 2021
Year: 2021

Estimation of semantic similarity is crucial for a variety of natural language processing (NLP) tasks. In the absence of a general theory of semantic information, many papers rely on human annotators as the source of ground truth for semantic similarity estimation. This paper investigates the ambiguities inherent in crowd-sourced semantic labeling. It shows that annotators that treat semantic similarity as a binary category (two sentences are either similar or not similar and there is no middle ground) play the most important role in the labeling. The paper offers heuristics to filter out unreliable annotators and stimulates further discussions on human perception of semantic similarity.
Making opportunistic networks in IoT environments CCN-ready: A performance evaluation of the MobCCN protocol
Venue: arXiv cs.NI 2021
Year: 2021

In future IoT environments it is expected that the role of personal devices of mobile users in the physical area where IoT devices are deployed will become more and more important. In particular, due to the push towards decentralisation of services towards the edge, it is likely that a significant share of data generated by IoT devices will be needed by other (mobile) nodes nearby, while global Internet access will be limited only to a small fraction of data. In this context, opportunistic networking schemes can be adopted to build efficient content-centric protocols, through which data generated by IoT devices (or by mobile nodes themselves) can be accessed by the other nodes nearby. In this paper, we propose MobCCN, which is an ICN-compliant protocol for this heterogeneous environment. MobCCN is designed to implement the routing and forwarding mechanisms of the main ICN realisations, such as CCN. The original aspect of MobCCN is to implement an efficient opportunistic networking routing scheme to populate the Forwarding Interest Base (FIB) tables of the nodes, in order to guide the propagation of Interest packets towards nodes that store the required data. Specifically, MobCCN defines the utility of each node as a forwarder of Interest packets for a certain type of content, such that Interest packets can be propagated along a positive utility gradient, until reaching some node storing the data. We evaluate MobCCN against protocols representing two possible endpoints of the spectrum, respectively in terms of minimising the data delivery delay and the resource consumption. Performance results show that MobCCN is very effective and efficient, as it guarantees very high delivery rates and low delays, while keeping the total generated traffic at a reasonable level and also saving local resources.
Indirectly Supervised English Sentence Break Prediction Using Paragraph Break Probability Estimates
Venue: arXiv cs.CL 2021
Year: 2021

This report explores the use of paragraph break probability estimates to help predict the location of sentence breaks in English natural language text. We show that a sentence break predictor based almost solely on paragraph break probability estimates can achieve high accuracy on this task. This sentence break predictor is trained almost entirely on a large amount of naturally occurring text without sentence break annotations, with only a small amount of annotated data needed to tune two hyperparameters. We also show that even better results can be achieved across in-domain and out-of-domain test data, if paragraph break probability signals are combined with a support vector machine classifier trained on a somewhat larger amount of sentence-break-annotated data. Numerous related issues are addressed along the way.
Optimized Power Normalized Cepstral Coefficients towards Robust Deep Speaker Verification
Venue: arXiv cs.SD 2021
Year: 2021

After their introduction to robust speech recognition, power normalized cepstral coefficient (PNCC) features were successfully adopted to other tasks, including speaker verification. However, as a feature extractor with long-term operations on the power spectrogram, its temporal processing and amplitude scaling steps dedicated on environmental compensation may be redundant. Further, they might suppress intrinsic speaker variations that are useful for speaker verification based on deep neural networks (DNN). Therefore, in this study, we revisit and optimize PNCCs by ablating its medium-time processor and by introducing channel energy normalization. Experimental results with a DNN-based speaker verification system indicate substantial improvement over baseline PNCCs on both in-domain and cross-domain scenarios, reflected by relatively 5.8% and 61.2% maximum lower equal error rate on VoxCeleb1 and VoxMovies, respectively.
Reduced-Lead ECG Classifier Model Trained with DivideMix and Model Ensemble
Venue: arXiv cs.LG 2021
Year: 2021

Automatic diagnosis of multiple cardiac abnormalities from reduced-lead electrocardiogram (ECG) data is challenging. One of the reasons for this is the difficulty of defining labels from standard 12-lead data. Reduced-lead ECG data usually do not have identical characteristics of cardiac abnormalities because of the noisy label problem. Thus, there is an inconsistency in the annotated labels between the reduced-lead and 12-lead ECG data. To solve this, we propose deep neural network (DNN)-based ECG classifier models that incorporate DivideMix and stochastic weight averaging (SWA). DivideMix was used to refine the noisy label by using two separate models. Besides DivideMix, we used a model ensemble technique, SWA, which also focuses on the noisy label problem, to enhance the effect of the models generated by DivideMix. Our classifiers (ami_kagoshima) received scores of 0.49, 0.47, 0.48, 0.47, and 0.47 (ranked 9th, 10th, 10th, 11th, and 10th, respectively, out of 39 teams) for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions, respectively, of the hidden test set with the challenge evaluation metric. We obtained the scores of 0.701, 0.686, 0.693, 0.693, and 0.685 on the 10-fold cross validation, and 0.623, 0.593, 0.606, 0.612, and 0.601 on the hidden validation set for each lead combination.
CLIPort: What and Where Pathways for Robotic Manipulation
Venue: arXiv cs.RO 2021
Year: 2021

How can we imbue robots with the ability to manipulate objects precisely but also to reason about them in terms of abstract concepts? Recent works in manipulation have shown that end-to-end networks can learn dexterous skills that require precise spatial reasoning, but these methods often fail to generalize to new goals or quickly learn transferable concepts across tasks. In parallel, there has been great progress in learning generalizable semantic representations for vision and language by training on large-scale internet data, however these representations lack the spatial understanding necessary for fine-grained manipulation. To this end, we propose a framework that combines the best of both worlds: a two-stream architecture with semantic and spatial pathways for vision-based manipulation. Specifically, we present CLIPort, a language-conditioned imitation-learning agent that combines the broad semantic understanding (what) of CLIP [1] with the spatial precision (where) of Transporter [2]. Our end-to-end framework is capable of solving a variety of language-specified tabletop tasks from packing unseen objects to folding cloths, all without any explicit representations of object poses, instance segmentations, memory, symbolic states, or syntactic structures. Experiments in simulated and real-world settings show that our approach is data efficient in few-shot settings and generalizes effectively to seen and unseen semantic concepts. We even learn one multi-task policy for 10 simulated and 9 real-world tasks that is better or comparable to single-task policies.
From Bench to Bedside: The First Live Robotic Surgery on the dVRK to Enable Remote Telesurgery with Motion Scaling
Venue: arXiv cs.RO 2021
Year: 2021

Innovations from surgical robotic research rarely translates to live surgery due to the significant difference between the lab and a live environment. Live environments require considerations that are often overlooked during early stages of research such as surgical staff, surgical procedure, and the challenges of working with live tissue. One such example is the da Vinci Research Kit (dVRK) which is used by over 40 robotics research groups and represents an open-sourced version of the da Vinci Surgical System. Despite dVRK being available for nearly a decade and the ideal candidate for translating research to practice on over 5,000 da Vinci Systems used in hospitals around the world, not one live surgery has been conducted with it. In this paper, we address the challenges, considerations, and solutions for translating surgical robotic research from bench-to-bedside. This is explained from the perspective of a remote telesurgery scenario where motion scaling solutions previously experimented in a lab setting are translated to a live pig surgery. This study presents results from the first ever use of a dVRK in a live animal and discusses how the surgical robotics community can approach translating their research to practice.
An animated picture says at least a thousand words: Selecting Gif-based Replies in Multimodal Dialog
Venue: arXiv cs.CL 2021
Year: 2021

Online conversations include more than just text. Increasingly, image-based responses such as memes and animated gifs serve as culturally recognized and often humorous responses in conversation. However, while NLP has broadened to multimodal models, conversational dialog systems have largely focused only on generating text replies. Here, we introduce a new dataset of 1.56M text-gif conversation turns and introduce a new multimodal conversational model Pepe the King Prawn for selecting gif-based replies. We demonstrate that our model produces relevant and high-quality gif responses and, in a large randomized control trial of multiple models replying to real users, we show that our model replies with gifs that are significantly better received by the community.
Logical Credal Networks
Venue: arXiv cs.AI 2021
Year: 2021

This paper introduces Logical Credal Networks, an expressive probabilistic logic that generalizes many prior models that combine logic and probability. Given imprecise information represented by probability bounds and conditional probability bounds of logic formulas, this logic specifies a set of probability distributions over all interpretations. On the one hand, our approach allows propositional and first-order logic formulas with few restrictions, e.g., without requiring acyclicity. On the other hand, it has a Markov condition similar to Bayesian networks and Markov random fields that is critical in real-world applications. Having both these properties makes this logic unique, and we investigate its performance on maximum a posteriori inference tasks, including solving Mastermind games with uncertainty and detecting credit card fraud. The results show that the proposed method outperforms existing approaches, and its advantage lies in aggregating multiple sources of imprecise information.
Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning Algorithms
Venue: arXiv cs.LG 2021
Year: 2021

The hierarchical interaction between the actor and critic in actor-critic based reinforcement learning algorithms naturally lends itself to a game-theoretic interpretation. We adopt this viewpoint and model the actor and critic interaction as a two-player general-sum game with a leader-follower structure known as a Stackelberg game. Given this abstraction, we propose a meta-framework for Stackelberg actor-critic algorithms where the leader player follows the total derivative of its objective instead of the usual individual gradient. From a theoretical standpoint, we develop a policy gradient theorem for the refined update and provide a local convergence guarantee for the Stackelberg actor-critic algorithms to a local Stackelberg equilibrium. From an empirical standpoint, we demonstrate via simple examples that the learning dynamics we study mitigate cycling and accelerate convergence compared to the usual gradient dynamics given cost structures induced by actor-critic formulations. Finally, extensive experiments on OpenAI gym environments show that Stackelberg actor-critic algorithms always perform at least as well and often significantly outperform the standard actor-critic algorithm counterparts.
Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems
Venue: arXiv cs.RO 2021
Year: 2021

Robustness is key to engineering, automation, and science as a whole. However, the property of robustness is often underpinned by costly requirements such as over-provisioning, known uncertainty and predictive models, and known adversaries. These conditions are idealistic, and often not satisfiable. Resilience on the other hand is the capability to endure unexpected disruptions, to recover swiftly from negative events, and bounce back to normality. In this survey article, we analyze how resilience is achieved in networks of agents and multi-robot systems that are able to overcome adversity by leveraging system-wide complementarity, diversity, and redundancy - often involving a reconfiguration of robotic capabilities to provide some key ability that was not present in the system a priori. As society increasingly depends on connected automated systems to provide key infrastructure services (e.g., logistics, transport, and precision agriculture), providing the means to achieving resilient multi-robot systems is paramount. By enumerating the consequences of a system that is not resilient (fragile), we argue that resilience must become a central engineering design consideration. Towards this goal, the community needs to gain clarity on how it is defined, measured, and maintained. We address these questions across foundational robotics domains, spanning perception, control, planning, and learning. One of our key contributions is a formal taxonomy of approaches, which also helps us discuss the defining factors and stressors for a resilient system. Finally, this survey article gives insight as to how resilience may be achieved. Importantly, we highlight open problems that remain to be tackled in order to reap the benefits of resilient robotic systems.
Graph Reasoning with Context-Aware Linearization for Interpretable Fact Extraction and Verification
Venue: arXiv cs.CL 2021
Year: 2021

This paper presents an end-to-end system for fact extraction and verification using textual and tabular evidence, the performance of which we demonstrate on the FEVEROUS dataset. We experiment with both a multi-task learning paradigm to jointly train a graph attention network for both the task of evidence extraction and veracity prediction, as well as a single objective graph model for solely learning veracity prediction and separate evidence extraction. In both instances, we employ a framework for per-cell linearization of tabular evidence, thus allowing us to treat evidence from tables as sequences. The templates we employ for linearizing tables capture the context as well as the content of table data. We furthermore provide a case study to show the interpretability our approach. Our best performing system achieves a FEVEROUS score of 0.23 and 53% label accuracy on the blind test data.
A Multi-Agent System for Autonomous Mobile Robot Coordination
Venue: arXiv cs.RO 2021
Year: 2021

The automation of internal logistics and inventory-related tasks is one of the main challenges of modern-day manufacturing corporations since it allows a more effective application of their human resources. Nowadays, Autonomous Mobile Robots (AMR) are state of the art technologies for such applications due to their great adaptability in dynamic environments, replacing more traditional solutions such as Automated Guided Vehicles (AGV), which are quite limited in terms of flexibility and require expensive facility updates for their installation. The application of Artificial Intelligence (AI) to increase AMRs capabilities has been contributing for the development of more sophisticated and efficient robots. Nevertheless, multi-robot coordination and cooperation for solving complex tasks is still a hot research line with increasing interest. This work proposes a Multi-Agent System for coordinating multiple TIAGo robots in tasks related to the manufacturing ecosystem such as the transportation and dispatching of raw materials, finished products and tools. Furthermore, the system is showcased in a realistic simulation using both Gazebo and Robot Operating System (ROS).
Integrating Unsupervised Clustering and Label-specific Oversampling to Tackle Imbalanced Multi-label Data
Venue: arXiv cs.LG 2021
Year: 2021

There is often a mixture of very frequent labels and very infrequent labels in multi-label datatsets. This variation in label frequency, a type class imbalance, creates a significant challenge for building efficient multi-label classification algorithms. In this paper, we tackle this problem by proposing a minority class oversampling scheme, UCLSO, which integrates Unsupervised Clustering and Label-Specific data Oversampling. Clustering is performed to find out the key distinct and locally connected regions of a multi-label dataset (irrespective of the label information). Next, for each label, we explore the distributions of minority points in the cluster sets. Only the minority points within a cluster are used to generate the synthetic minority points that are used for oversampling. Even though the cluster set is the same across all labels, the distributions of the synthetic minority points will vary across the labels. The training dataset is augmented with the set of label-specific synthetic minority points, and classifiers are trained to predict the relevance of each label independently. Experiments using 12 multi-label datasets and several multi-label algorithms show that the proposed method performed very well compared to the other competing algorithms.