OpenRaiser/Intern-Atlas huggingface dataset:
Geometry-Aware Self-Training for Unsupervised Domain Adaptationon Object Point Clouds
Venue: arXiv cs.CV 2021
Year: 2021

The point cloud representation of an object can have a large geometric variation in view of inconsistent data acquisition procedure, which thus leads to domain discrepancy due to diverse and uncontrollable shape representation cross datasets. To improve discrimination on unseen distribution of point-based geometries in a practical and feasible perspective, this paper proposes a new method of geometry-aware self-training (GAST) for unsupervised domain adaptation of object point cloud classification. Specifically, this paper aims to learn a domain-shared representation of semantic categories, via two novel self-supervised geometric learning tasks as feature regularization. On one hand, the representation learning is empowered by a linear mixup of point cloud samples with their self-generated rotation labels, to capture a global topological configuration of local geometries. On the other hand, a diverse point distribution across datasets can be normalized with a novel curvature-aware distortion localization. Experiments on the PointDA-10 dataset show that our GAST method can significantly outperform the state-of-the-art methods.
Readability and Understandability Scores for Snippet Assessment: an Exploratory Study
Venue: arXiv cs.SE 2021
Year: 2021

Code search engines usually use readability feature to rank code snippets. There are several metrics to calculate this feature, but developers may have different perceptions about readability. Correlation between readability and understandability features has already been proposed, i.e., developers need to read and comprehend the code snippet syntax, but also understand the semantics. This work investigate scores for understandability and readability features, under the perspective of the possible subjective perception of code snippet comprehension. We find that code snippets with higher readability score has better comprehension than lower ones. The understandability score presents better comprehension in specific situations, e.g. nested loops or if-else chains. The developers also mentioned writability aspects as the principal characteristic to evaluate code snippets comprehension. These results provide insights for future works in code comprehension score optimization.
GUDIE: a flexible, user-defined method to extract subgraphs of interest from large graphs
Venue: arXiv cs.SI 2021
Year: 2021

Large, dense, small-world networks often emerge from social phenomena, including financial networks, social media, or epidemiology. As networks grow in importance, it is often necessary to partition them into meaningful units of analysis. In this work, we propose GUDIE, a message-passing algorithm that extracts relevant context around seed nodes based on user-defined criteria. We design GUDIE for rich, labeled graphs, and expansions consider node and edge attributes. Preliminary results indicate that GUDIE expands to insightful areas while avoiding unimportant connections. The resulting subgraphs contain the relevant context for a seed node and can accelerate and extend analysis capabilities in finance and other critical networks.
Fex: Assisted Identification of Domain Features from C Programs
Venue: arXiv cs.SE 2021
Year: 2021

Modern software typically performs more than one functionality. These functionalities or features are not always organized in a way for modules representing these features to be used individually. Many software engineering approaches like programming language constructs, or product line visualization techniques have been proposed to organize projects as modules. Unfortunately, much legacy software suffer from years or decades of improper coding practices that leave the modules in the code almost undetectable. In such scenarios, a desirable requirement is to identify modules representing different features to be extracted. In this paper, we propose a novel approach that combines information retrieval and program analysis approaches to allow domain experts to identify slices of the program that represent modules using natural language search terms. We evaluate our approach by building a proof of concept tool in C, and extract modules from open source projects.
The n-ary Initial Literal and Literal Shuffle
Venue: arXiv cs.FL 2021
Year: 2021

The literal and the initial literal shuffle have been introduced to model the behavior of two synchronized processes. However, it is not possible to describe the synchronization of multiple processes. Furthermore, both restricted forms of shuffling are not associative. Here, we extend the literal shuffle and the initial literal shuffle to multiple arguments. We also introduce iterated versions, much different from the iterated ones previously introduced for the binary literal and initial literal shuffle. We investigate formal properties, and show that in terms of expressive power, in a full trio, they coincide with the general shuffle. Furthermore, we look at closure properties with respect to the regular, context-free, context-sensitive, recursive and recursively enumerable languages for all operations introduced. Then, we investigate various decision problems motivated by analogous problems for the (ordinary) shuffle operation. Most problems we look at are tractable, but we also identify one intractable decision problem.
Early-exit deep neural networks for distorted images: providing an efficient edge offloading
Venue: arXiv cs.CV 2021
Year: 2021

Edge offloading for deep neural networks (DNNs) can be adaptive to the input's complexity by using early-exit DNNs. These DNNs have side branches throughout their architecture, allowing the inference to end earlier in the edge. The branches estimate the accuracy for a given input. If this estimated accuracy reaches a threshold, the inference ends on the edge. Otherwise, the edge offloads the inference to the cloud to process the remaining DNN layers. However, DNNs for image classification deals with distorted images, which negatively impact the branches' estimated accuracy. Consequently, the edge offloads more inferences to the cloud. This work introduces expert side branches trained on a particular distortion type to improve robustness against image distortion. The edge detects the distortion type and selects appropriate expert branches to perform the inference. This approach increases the estimated accuracy on the edge, improving the offloading decisions. We validate our proposal in a realistic scenario, in which the edge offloads DNN inference to Amazon EC2 instances.
Detecting and Segmenting Adversarial Graphics Patterns from Images
Venue: arXiv cs.CV 2021
Year: 2021

Adversarial attacks pose a substantial threat to computer vision system security, but the social media industry constantly faces another form of "adversarial attack" in which the hackers attempt to upload inappropriate images and fool the automated screening systems by adding artificial graphics patterns. In this paper, we formulate the defense against such attacks as an artificial graphics pattern segmentation problem. We evaluate the efficacy of several segmentation algorithms and, based on observation of their performance, propose a new method tailored to this specific problem. Extensive experiments show that the proposed method outperforms the baselines and has a promising generalization capability, which is the most crucial aspect in segmenting artificial graphics patterns.
Deadlock and Noise in Self-Organized Aggregation Without Computation
Venue: arXiv cs.RO 2021
Year: 2021

Aggregation is a fundamental behavior for swarm robotics that requires a system to gather together in a compact, connected cluster. In 2014, Gauci et al. proposed a surprising algorithm that reliably achieves swarm aggregation using only a binary line-of-sight sensor and no arithmetic computation or persistent memory. It has been rigorously proven that this algorithm will aggregate one robot to another, but it remained open whether it would always aggregate a system of $n > 2$ robots as was observed in experiments and simulations. We prove that there exist deadlocked configurations from which this algorithm cannot achieve aggregation for $n > 3$ robots when the robots' motion is uniform and deterministic. On the positive side, we show that the algorithm (i) is robust to small amounts of error, enabling deadlock avoidance, and (ii) provably achieves a linear runtime speedup for the $n = 2$ case when using a cone-of-sight sensor. Finally, we introduce a noisy, discrete adaptation of this algorithm that is more amenable to rigorous analysis of noise and whose simulation results align qualitatively with the original, continuous algorithm.
Multi-scale Edge-based U-shape Network for Salient Object Detection
Venue: arXiv cs.CV 2021
Year: 2021

Deep-learning based salient object detection methods achieve great improvements. However, there are still problems existing in the predictions, such as blurry boundary and inaccurate location, which is mainly caused by inadequate feature extraction and integration. In this paper, we propose a Multi-scale Edge-based U-shape Network (MEUN) to integrate various features at different scales to achieve better performance. To extract more useful information for boundary prediction, U-shape Edge Network modules are embedded in each decoder units. Besides, the additional down-sampling module alleviates the location inaccuracy. Experimental results on four benchmark datasets demonstrate the validity and reliability of the proposed method. Multi-scale Edge based U-shape Network also shows its superiority when compared with 15 state-of-the-art salient object detection methods.
BoundaryNet: An Attentive Deep Network with Fast Marching Distance Maps for Semi-automatic Layout Annotation
Venue: arXiv cs.CV 2021
Year: 2021

Precise boundary annotations of image regions can be crucial for downstream applications which rely on region-class semantics. Some document collections contain densely laid out, highly irregular and overlapping multi-class region instances with large range in aspect ratio. Fully automatic boundary estimation approaches tend to be data intensive, cannot handle variable-sized images and produce sub-optimal results for aforementioned images. To address these issues, we propose BoundaryNet, a novel resizing-free approach for high-precision semi-automatic layout annotation. The variable-sized user selected region of interest is first processed by an attention-guided skip network. The network optimization is guided via Fast Marching distance maps to obtain a good quality initial boundary estimate and an associated feature representation. These outputs are processed by a Residual Graph Convolution Network optimized using Hausdorff loss to obtain the final region boundary. Results on a challenging image manuscript dataset demonstrate that BoundaryNet outperforms strong baselines and produces high-quality semantic region boundaries. Qualitatively, our approach generalizes across multiple document image datasets containing different script systems and layouts, all without additional fine-tuning. We integrate BoundaryNet into a document annotation system and show that it provides high annotation throughput compared to manual and fully automatic alternatives.
Towards Personalized and Human-in-the-Loop Document Summarization
Venue: arXiv cs.AI 2021
Year: 2021

The ubiquitous availability of computing devices and the widespread use of the internet have generated a large amount of data continuously. Therefore, the amount of available information on any given topic is far beyond humans' processing capacity to properly process, causing what is known as information overload. To efficiently cope with large amounts of information and generate content with significant value to users, we require identifying, merging and summarising information. Data summaries can help gather related information and collect it into a shorter format that enables answering complicated questions, gaining new insight and discovering conceptual boundaries. This thesis focuses on three main challenges to alleviate information overload using novel summarisation techniques. It further intends to facilitate the analysis of documents to support personalised information extraction. This thesis separates the research issues into four areas, covering (i) feature engineering in document summarisation, (ii) traditional static and inflexible summaries, (iii) traditional generic summarisation approaches, and (iv) the need for reference summaries. We propose novel approaches to tackle these challenges, by: i)enabling automatic intelligent feature engineering, ii) enabling flexible and interactive summarisation, iii) utilising intelligent and personalised summarisation approaches. The experimental results prove the efficiency of the proposed approaches compared to other state-of-the-art models. We further propose solutions to the information overload problem in different domains through summarisation, covering network traffic data, health data and business process data.
A computational study on imputation methods for missing environmental data
Venue: arXiv cs.DB 2021
Year: 2021

Data acquisition and recording in the form of databases are routine operations. The process of collecting data, however, may experience irregularities, resulting in databases with missing data. Missing entries might alter analysis efficiency and, consequently, the associated decision-making process. This paper focuses on databases collecting information related to the natural environment. Given the broad spectrum of recorded activities, these databases typically are of mixed nature. It is therefore relevant to evaluate the performance of missing data processing methods considering this characteristic. In this paper we investigate the performances of several missing data imputation methods and their application to the problem of missing data in environment. A computational study was performed to compare the method missForest (MF) with two other imputation methods, namely Multivariate Imputation by Chained Equations (MICE) and K-Nearest Neighbors (KNN). Tests were made on 10 pretreated datasets of various types. Results revealed that MF generally outperformed MICE and KNN in terms of imputation errors, with a more pronounced performance gap for mixed typed databases where MF reduced the imputation error up to 150%, when compared to the other methods. KNN was usually the fastest method. MF was then successfully applied to a case study on Quebec wastewater treatment plants performance monitoring. We believe that the present study demonstrates the pertinence of using MF as imputation method when dealing with missing environmental data.
Networks of Twin Peaks: the Dale Cooper Effect
Venue: arXiv cs.SI 2021
Year: 2021

In this work, we analyse character networks in the cult TV show Twin Peaks. In the small-scale community network of Twin Peaks we discovered a new storytelling network phenomenon we called the Dale Cooper Effect, a phase transition in network structure. It is a sharp demarcation between the two statistically and topologically distinct networks of characters, where the point of demarcation is the protagonist himself (Special Agent Dale Cooper) introduced as a median character.
Reliable Firmware Updates for the Information-Centric Internet of Things
Venue: arXiv cs.NI 2021
Year: 2021

Security in the Internet of Things (IoT) requires ways to regularly update firmware in the field. These demands ever increase with new, agile concepts such as security as code and should be considered a regular operation. Hosting massive firmware roll-outs present a crucial challenge for the constrained wireless environment. In this paper, we explore how information-centric networking can ease reliable firmware updates. We start from the recent standards developed by the IETF SUIT working group and contribute a system that allows for a timely discovery of new firmware versions by using cryptographically protected manifest files. Our design enables a cascading firmware roll-out from a gateway towards leaf nodes in a low-power multi-hop network. While a chunking mechanism prepares firmware images for typically low-sized maximum transmission units (MTUs), an early Denial-of-Service (DoS) detection prevents the distribution of tampered or malformed chunks. In experimental evaluations on a real-world IoT testbed, we demonstrate feasible strategies with adaptive bandwidth consumption and a high resilience to connectivity loss when replicating firmware images into the IoT edge.
Heterogeneous Graph Attention Networks for Learning Diverse Communication
Venue: arXiv cs.MA 2021
Year: 2021

Multi-agent teaming achieves better performance when there is communication among participating agents allowing them to coordinate their actions for maximizing shared utility. However, when collaborating a team of agents with different action and observation spaces, information sharing is not straightforward and requires customized communication protocols, depending on sender and receiver types. Without properly modeling such heterogeneity in agents, communication becomes less helpful and could even deteriorate the multi-agent cooperation performance. We propose heterogeneous graph attention networks, called HetNet, to learn efficient and diverse communication models for coordinating heterogeneous agents towards accomplishing tasks that are of collaborative nature. We propose a Multi-Agent Heterogeneous Actor-Critic (MAHAC) learning paradigm to obtain collaborative per-class policies and effective communication protocols for composite robot teams. Our proposed framework is evaluated against multiple baselines in a complex environment in which agents of different types must communicate and cooperate to satisfy the objectives. Experimental results show that HetNet outperforms the baselines in learning sophisticated multi-agent communication protocols by achieving $\sim$10\% improvements in performance metrics.
DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network
Venue: arXiv cs.AI 2021
Year: 2021

Knowledge graph completion (KGC) has become a focus of attention across deep learning community owing to its excellent contribution to numerous downstream tasks. Although recently have witnessed a surge of work on KGC, they are still insufficient to accurately capture complex relations, since they adopt the single and static representations. In this work, we propose a novel Disentangled Knowledge Graph Attention Network (DisenKGAT) for KGC, which leverages both micro-disentanglement and macro-disentanglement to exploit representations behind Knowledge graphs (KGs). To achieve micro-disentanglement, we put forward a novel relation-aware aggregation to learn diverse component representation. For macro-disentanglement, we leverage mutual information as a regularization to enhance independence. With the assistance of disentanglement, our model is able to generate adaptive representations in terms of the given scenario. Besides, our work has strong robustness and flexibility to adapt to various score functions. Extensive experiments on public benchmark datasets have been conducted to validate the superiority of DisenKGAT over existing methods in terms of both accuracy and explainability.
StarVQA: Space-Time Attention for Video Quality Assessment
Venue: arXiv cs.CV 2021
Year: 2021

The attention mechanism is blooming in computer vision nowadays. However, its application to video quality assessment (VQA) has not been reported. Evaluating the quality of in-the-wild videos is challenging due to the unknown of pristine reference and shooting distortion. This paper presents a novel \underline{s}pace-\underline{t}ime \underline{a}ttention network fo\underline{r} the \underline{VQA} problem, named StarVQA. StarVQA builds a Transformer by alternately concatenating the divided space-time attention. To adapt the Transformer architecture for training, StarVQA designs a vectorized regression loss by encoding the mean opinion score (MOS) to the probability vector and embedding a special vectorized label token as the learnable variable. To capture the long-range spatiotemporal dependencies of a video sequence, StarVQA encodes the space-time position information of each patch to the input of the Transformer. Various experiments are conducted on the de-facto in-the-wild video datasets, including LIVE-VQC, KoNViD-1k, LSVQ, and LSVQ-1080p. Experimental results demonstrate the superiority of the proposed StarVQA over the state-of-the-art. Code and model will be available at: https://github.com/DVL/StarVQA.
Classifying In-Place Gestures with End-to-End Point Cloud Learning
Venue: arXiv cs.HC 2021
Year: 2021

Walking in place for moving through virtual environments has attracted noticeable attention recently. Recent attempts focused on training a classifier to recognize certain patterns of gestures (e.g., standing, walking, etc) with the use of neural networks like CNN or LSTM. Nevertheless, they often consider very few types of gestures and/or induce less desired latency in virtual environments. In this paper, we propose a novel framework for accurate and efficient classification of in-place gestures. Our key idea is to treat several consecutive frames as a "point cloud". The HMD and two VIVE trackers provide three points in each frame, with each point consisting of 12-dimensional features (i.e., three-dimensional position coordinates, velocity, rotation, angular velocity). We create a dataset consisting of 9 gesture classes for virtual in-place locomotion. In addition to the supervised point-based network, we also take unsupervised domain adaptation into account due to inter-person variations. To this end, we develop an end-to-end joint framework involving both a supervised loss for supervised point learning and an unsupervised loss for unsupervised domain adaptation. Experiments demonstrate that our approach generates very promising outcomes, in terms of high overall classification accuracy (95.0%) and real-time performance (192ms latency). Our code will be publicly available at: https://github.com/ZhaoLizz/PCT-MCD.
Domain Adaptation for Underwater Image Enhancement
Venue: arXiv cs.CV 2021
Year: 2021

Recently, learning-based algorithms have shown impressive performance in underwater image enhancement. Most of them resort to training on synthetic data and achieve outstanding performance. However, these methods ignore the significant domain gap between the synthetic and real data (i.e., interdomain gap), and thus the models trained on synthetic data often fail to generalize well to real underwater scenarios. Furthermore, the complex and changeable underwater environment also causes a great distribution gap among the real data itself (i.e., intra-domain gap). However, almost no research focuses on this problem and thus their techniques often produce visually unpleasing artifacts and color distortions on various real images. Motivated by these observations, we propose a novel Two-phase Underwater Domain Adaptation network (TUDA) to simultaneously minimize the inter-domain and intra-domain gap. Concretely, a new dual-alignment network is designed in the first phase, including a translation part for enhancing realism of input images, followed by an enhancement part. With performing image-level and feature-level adaptation in two parts by jointly adversarial learning, the network can better build invariance across domains and thus bridge the inter-domain gap. In the second phase, we perform an easy-hard classification of real data according to the assessed quality of enhanced images, where a rank-based underwater quality assessment method is embedded. By leveraging implicit quality information learned from rankings, this method can more accurately assess the perceptual quality of enhanced images. Using pseudo labels from the easy part, an easy-hard adaptation technique is then conducted to effectively decrease the intra-domain gap between easy and hard samples.