OpenRaiser/Intern-Atlas huggingface dataset:
More Than Words: Towards Better Quality Interpretations of Text Classifiers
Venue: arXiv cs.CL 2021
Year: 2021

The large size and complex decision mechanisms of state-of-the-art text classifiers make it difficult for humans to understand their predictions, leading to a potential lack of trust by the users. These issues have led to the adoption of methods like SHAP and Integrated Gradients to explain classification decisions by assigning importance scores to input tokens. However, prior work, using different randomization tests, has shown that interpretations generated by these methods may not be robust. For instance, models making the same predictions on the test set may still lead to different feature importance rankings. In order to address the lack of robustness of token-based interpretability, we explore explanations at higher semantic levels like sentences. We use computational metrics and human subject studies to compare the quality of sentence-based interpretations against token-based ones. Our experiments show that higher-level feature attributions offer several advantages: 1) they are more robust as measured by the randomization tests, 2) they lead to lower variability when using approximation-based methods like SHAP, and 3) they are more intelligible to humans in situations where the linguistic coherence resides at a higher granularity level. Based on these findings, we show that token-based interpretability, while being a convenient first choice given the input interfaces of the ML models, is not the most effective one in all situations.
Heuristic Random Designs for Exact Identification of Defectives Using Single Round Non-adaptive Group Testing and Compressed Sensing
Venue: arXiv cs.IT 2021
Year: 2021

Among the challenges that the COVID-19 pandemic outbreak revealed is the problem to reduce the number of tests required for identifying the virus carriers in order to contain the viral spread while preserving the tests reliability. To cope with this issue, a prevalence testing paradigm based on group testing and compressive sensing approach or GTCS was examined. In these settings, a non-adaptive group testing algorithm is designed to rule out sure-negative samples. Then, on the reduced problem, a compressive sensing algorithm is applied to decode the positives without requiring any further testing besides the initial test matrix designed for the group testing phase. The result is a single-round non-adaptive group testing - compressive sensing algorithm to identify the positive samples. In this paper, we propose a heuristic random method to construct the test design called $α-$random row design or $α-$RRD. In the $α-$RRD, a random test matrix is constructed such that each test aggregates at most $α$ samples in one group test or pool. The pooled tests are heuristically selected one by one such that samples that were previously selected in the same test are less likely to be aggregated together in a new test. We examined the performance of the $α-$RRD design within the GTCS paradigm for several values of $α$. The experiments were conducted on synthetic data. Our results show that, for some values of $α$, a reduction of up to 10 fold in the tests number can be achieved when $α-$RRD design is applied in the GTCS paradigm.
Attentive Multi-View Deep Subspace Clustering Net
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we propose a novel Attentive Multi-View Deep Subspace Nets (AMVDSN), which deeply explores underlying consistent and view-specific information from multiple views and fuse them by considering each view's dynamic contribution obtained by attention mechanism. Unlike most multi-view subspace learning methods that they directly reconstruct data points on raw data or only consider consistency or complementarity when learning representation in deep or shallow space, our proposed method seeks to find a joint latent representation that explicitly considers both consensus and view-specific information among multiple views, and then performs subspace clustering on learned joint latent representation.Besides, different views contribute differently to representation learning, we therefore introduce attention mechanism to derive dynamic weight for each view, which performs much better than previous fusion methods in the field of multi-view subspace clustering. The proposed algorithm is intuitive and can be easily optimized just by using Stochastic Gradient Descent (SGD) because of the neural network framework, which also provides strong non-linear characterization capability compared with traditional subspace clustering approaches. The experimental results on seven real-world data sets have demonstrated the effectiveness of our proposed algorithm against some state-of-the-art subspace learning approaches.
Dependability Analysis of Data Storage Systems in Presence of Soft Errors
Venue: arXiv cs.PF 2021
Year: 2021

In recent years, high availability and reliability of Data Storage Systems (DSS) have been significantly threatened by soft errors occurring in storage controllers. Due to their specific functionality and hardware-software stack, error propagation and manifestation in DSS is quite different from general-purpose computing architectures. To our knowledge, no previous study has examined the system-level effects of soft errors on the availability and reliability of data storage systems. In this paper, we first analyze the effects of soft errors occurring in the server processors of storage controllers on the entire storage system dependability. To this end, we implemented the major functions of a typical data storage system controller, running on a full stack of storage system operating system, and developed a framework to perform fault injection experiments using a full system simulator. We then propose a new metric, Storage System Vulnerability Factor (SSVF), to accurately capture the impact of soft errors in storage systems. By conducting extensive experiments, it is revealed that depending on the controller configuration, up to 40% of cache memory contains end-user data where any unrecoverable soft errors in this part will result in Data Loss (DL) in an irreversible manner. However, soft errors in the rest of cache memory filled by Operating System (OS) and storage applications will result in Data Unavailability (DU) at the storage system level. Our analysis also shows that Detectable Unrecoverable Errors (DUEs) on the cache data field are the major cause of DU in storage systems, while Silent Data Corruptions (SDCs) in the cache tag and data field are mainly the cause of DL in storage systems.
A Modeling Framework for Reliability of Erasure Codes in SSD Arrays
Venue: arXiv cs.PF 2021
Year: 2021

To help reliability of SSD arrays, Redundant Array of Independent Disks (RAID) are commonly employed. However, the conventional reliability models of HDD RAID cannot be applied to SSD arrays, as the nature of failures in SSDs are different from HDDs. Previous studies on the reliability of SSD arrays are based on the deprecated SSD failure data, and only focus on limited failure types, device failures, and page failures caused by the bit errors, while recent field studies have reported other failure types including bad blocks and bad chips, and a high correlation between failures. In this paper, we explore the reliability of SSD arrays using field storage traces and real-system implementation of conventional and emerging erasure codes. The reliability is evaluated by statistical fault injections that post-process the usage logs from the real-system implementation, while the fault/failure attributes are obtained from field data. As a case study, we examine conventional and emerging erasure codes in terms of both reliability and performance using Linux MD RAID and commercial SSDs. Our analysis shows that a) emerging erasure codes fail to replace RAID6 in terms of reliability, b) row-wise erasure codes are the most efficient choices for contemporary SSD devices, and c) previous models overestimate the SSD array reliability by up to six orders of magnitude, as they focus on the coincidence of bad pages and bad chips that roots the minority of Data Loss (DL) in SSD arrays. Our experiments show that the combination of bad chips with bad blocks is the major source of DL in RAID5 and emerging codes (contributing more than 54% and 90% of DL in RAID5 and emerging codes, respectively), while RAID6 remains robust under these failure combinations. Finally, the fault injection results show that SSD array reliability, as well as the failure breakdown is significantly correlated with SSD type.
Attention Based Communication and Control for Multi-UAV Path Planning
Venue: arXiv cs.LG 2021
Year: 2021

Inspired by the multi-head attention (MHA) mechanism in natural language processing, this letter proposes an iterative single-head attention (ISHA) mechanism for multi-UAV path planning. The ISHA mechanism is run by a communication helper collecting the state embeddings of UAVs and distributing an attention score vector to each UAV. The attention scores computed by ISHA identify how many interactions with other UAVs should be considered in each UAV's control decision-making. Simulation results corroborate that the ISHA-based communication and control framework achieves faster travel with lower inter-UAV collision risks than an MHA-aided baseline, particularly under limited communication resources.
Towards Universal GAN Image Detection
Venue: arXiv cs.CV 2021
Year: 2021

The ever higher quality and wide diffusion of fake images have spawn a quest for reliable forensic tools. Many GAN image detectors have been proposed, recently. In real world scenarios, however, most of them show limited robustness and generalization ability. Moreover, they often rely on side information not available at test time, that is, they are not universal. We investigate these problems and propose a new GAN image detector based on a limited sub-sampling architecture and a suitable contrastive learning paradigm. Experiments carried out in challenging conditions prove the proposed method to be a first step towards universal GAN image detection, ensuring also good robustness to common image impairments, and good generalization to unseen architectures.
PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving
Venue: arXiv cs.CV 2021
Year: 2021

The accelerating development of autonomous driving technology has placed greater demands on obtaining large amounts of high-quality data. Representative, labeled, real world data serves as the fuel for training deep learning networks, critical for improving self-driving perception algorithms. In this paper, we introduce PandaSet, the first dataset produced by a complete, high-precision autonomous vehicle sensor kit with a no-cost commercial license. The dataset was collected using one 360° mechanical spinning LiDAR, one forward-facing, long-range LiDAR, and 6 cameras. The dataset contains more than 100 scenes, each of which is 8 seconds long, and provides 28 types of labels for object classification and 37 types of labels for semantic segmentation. We provide baselines for LiDAR-only 3D object detection, LiDAR-camera fusion 3D object detection and LiDAR point cloud segmentation. For more details about PandaSet and the development kit, see https://scale.com/open-datasets/pandaset.
3D Skeleton-based Few-shot Action Recognition with JEANIE is not so Naïve
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we propose a Few-shot Learning pipeline for 3D skeleton-based action recognition by Joint tEmporal and cAmera viewpoiNt alIgnmEnt (JEANIE). To factor out misalignment between query and support sequences of 3D body joints, we propose an advanced variant of Dynamic Time Warping which jointly models each smooth path between the query and support frames to achieve simultaneously the best alignment in the temporal and simulated camera viewpoint spaces for end-to-end learning under the limited few-shot training data. Sequences are encoded with a temporal block encoder based on Simple Spectral Graph Convolution, a lightweight linear Graph Neural Network backbone (we also include a setting with a transformer). Finally, we propose a similarity-based loss which encourages the alignment of sequences of the same class while preventing the alignment of unrelated sequences. We demonstrate state-of-the-art results on NTU-60, NTU-120, Kinetics-skeleton and UWA3D Multiview Activity II.
Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations
Venue: arXiv cs.LG 2021
Year: 2021

Neural ordinary differential equations (NODE) have been proposed as a continuous depth generalization to popular deep learning models such as Residual networks (ResNets). They provide parameter efficiency and automate the model selection process in deep learning models to some extent. However, they lack the much-required uncertainty modelling and robustness capabilities which are crucial for their use in several real-world applications such as autonomous driving and healthcare. We propose a novel and unique approach to model uncertainty in NODE by considering a distribution over the end-time $T$ of the ODE solver. The proposed approach, latent time NODE (LT-NODE), treats $T$ as a latent variable and apply Bayesian learning to obtain a posterior distribution over $T$ from the data. In particular, we use variational inference to learn an approximate posterior and the model parameters. Prediction is done by considering the NODE representations from different samples of the posterior and can be done efficiently using a single forward pass. As $T$ implicitly defines the depth of a NODE, posterior distribution over $T$ would also help in model selection in NODE. We also propose, adaptive latent time NODE (ALT-NODE), which allow each data point to have a distinct posterior distribution over end-times. ALT-NODE uses amortized variational inference to learn an approximate posterior using inference networks. We demonstrate the effectiveness of the proposed approaches in modelling uncertainty and robustness through experiments on synthetic and several real-world image classification data.
Assessing the Impact of Attention and Self-Attention Mechanisms on the Classification of Skin Lesions
Venue: arXiv cs.CV 2021
Year: 2021

Attention mechanisms have raised significant interest in the research community, since they promise significant improvements in the performance of neural network architectures. However, in any specific problem, we still lack a principled way to choose specific mechanisms and hyper-parameters that lead to guaranteed improvements. More recently, self-attention has been proposed and widely used in transformer-like architectures, leading to significant breakthroughs in some applications. In this work we focus on two forms of attention mechanisms: attention modules and self-attention. Attention modules are used to reweight the features of each layer input tensor. Different modules have different ways to perform this reweighting in fully connected or convolutional layers. The attention models studied are completely modular and in this work they will be used with the popular ResNet architecture. Self-Attention, originally proposed in the area of Natural Language Processing makes it possible to relate all the items in an input sequence. Self-Attention is becoming increasingly popular in Computer Vision, where it is sometimes combined with convolutional layers, although some recent architectures do away entirely with convolutions. In this work, we study and perform an objective comparison of a number of different attention mechanisms in a specific computer vision task, the classification of samples in the widely used Skin Cancer MNIST dataset. The results show that attention modules do sometimes improve the performance of convolutional neural network architectures, but also that this improvement, although noticeable and statistically significant, is not consistent in different settings. The results obtained with self-attention mechanisms, on the other hand, show consistent and significant improvements, leading to the best results even in architectures with a reduced number of parameters.
Customising Ranking Models for Enterprise Search on Bilingual Click-Through Dataset
Venue: arXiv cs.IR 2021
Year: 2021

In this work, we provide the details about the process of establishing an end-to-end system for enterprise search on bilingual click-through dataset. The first part of the paper will be about the high-level workflow of the system. Then, in the second part we will elaborately mention about the ranking models to improve the search results in the vertical search of the technical documents in enterprise domain. Throughout the paper, we will mention the way which we combine the methods in IR literature. Finally, in the last part of the paper we will report our results using different ranking algorithms with $NDCG@k$ where k is the cut-off value.
Multi-Camera Sensor Fusion for Visual Odometry using Deep Uncertainty Estimation
Venue: arXiv cs.CV 2021
Year: 2021

Visual Odometry (VO) estimation is an important source of information for vehicle state estimation and autonomous driving. Recently, deep learning based approaches have begun to appear in the literature. However, in the context of driving, single sensor based approaches are often prone to failure because of degraded image quality due to environmental factors, camera placement, etc. To address this issue, we propose a deep sensor fusion framework which estimates vehicle motion using both pose and uncertainty estimations from multiple on-board cameras. We extract spatio-temporal feature representations from a set of consecutive images using a hybrid CNN - RNN model. We then utilise a Mixture Density Network (MDN) to estimate the 6-DoF pose as a mixture of distributions and a fusion module to estimate the final pose using MDN outputs from multi-cameras. We evaluate our approach on the publicly available, large scale autonomous vehicle dataset, nuScenes. The results show that the proposed fusion approach surpasses the state-of-the-art, and provides robust estimates and accurate trajectories compared to individual camera-based estimations.
A Rationale-Based Classification of MISRA C Guidelines
Venue: arXiv cs.PL 2021
Year: 2021

MISRA C is the most authoritative language subset for the C programming language that is a de facto standard in several industry sectors where safety and security are of paramount importance. While MISRA C is currently encoded in 175 guidelines (coding rules and directives), it does not coincide with them: proper adoption of MISRA C requires embracing its preventive approach (as opposed to the "bug finding" approach) and a documented development process where justifiable non-compliances are authorized and recorded as deviations. MISRA C guidelines are classified along several axes in the official MISRA documents. In this paper, we add to these an orthogonal classification that associates guidelines with their main rationale. The advantages of this new classification are illustrated for different kinds of projects, including those not (yet) having MISRA compliance among their objectives.
Software Engineering Education Knowledge Versus Industrial Needs
Venue: arXiv cs.SE 2021
Year: 2021

Contribution: Determine and analyze the gap between software practitioners' education outlined in the 2014IEEE/ACM Software Engineering Education Knowledge (SEEK) and industrial needs pointed by Wikipedia articles referenced in Stack Overflow (SO) posts. Background: Previous work has uncovered deficiencies in the coverage of computer fundamentals, people skills, software processes, and human-computer interaction, suggesting rebalancing. Research Questions: 1) To what extent are developers' needs, in terms of Wikipedia articles referenced in SO posts, covered by the SEEK knowledge units? 2) How does the popularity of Wikipedia articles relate to their SEEK coverage? 3) What areas of computing knowledge can be better covered by the SEEK knowledge units? 4) Why are Wikipedia articles covered by the SEEK knowledge units cited on SO? Methodology: Wikipedia articles were systematically collected from SO posts. The most cited were manually mapped to the SEEK knowledge units, assessed according to their degree of coverage. Articles insufficiently covered by the SEEK were classified by hand using the 2012 ACM Computing Classification System. A sample of posts referencing sufficiently covered articles was manually analyzed. A survey was conducted on software practitioners to validate the study findings. Findings: SEEK appears to cover sufficiently computer science fundamentals, software design and mathematical concepts, but less so areas like the World Wide Web, software engineering components, and computer graphics. Developers seek advice, best practices and explanations about software topics, and code review assistance. Future SEEK models and the computing education could dive deeper in information systems, design, testing, security, and soft skills.
nvBench: A Large-Scale Synthesized Dataset for Cross-Domain Natural Language to Visualization Task
Venue: arXiv cs.HC 2021
Year: 2021

NL2VIS - which translates natural language (NL) queries to corresponding visualizations (VIS) - has attracted more and more attention both in commercial visualization vendors and academic researchers. In the last few years, the advanced deep learning-based models have achieved human-like abilities in many natural language processing (NLP) tasks, which clearly tells us that the deep learning-based technique is a good choice to push the field of NL2VIS. However, a big balk is the lack of benchmarks with lots of (NL, VIS) pairs. We present nvBench, the first large-scale NL2VIS benchmark, containing 25,750 (NL, VIS) pairs from 750 tables over 105 domains, synthesized from (NL, SQL) benchmarks to support cross-domain NL2VIS task. The quality of nvBench has been extensively validated by 23 experts and 300+ crowd workers. Deep learning-based models training using nvBench demonstrate that nvBench can push the field of NL2VIS.
Realtime Global Attention Network for Semantic Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we proposed an end-to-end realtime global attention neural network (RGANet) for the challenging task of semantic segmentation. Different from the encoding strategy deployed by self-attention paradigms, the proposed global attention module encodes global attention via depth-wise convolution and affine transformations. The integration of these global attention modules into a hierarchy architecture maintains high inferential performance. In addition, an improved evaluation metric, namely MGRID, is proposed to alleviate the negative effect of non-convex, widely scattered ground-truth areas. Results from extensive experiments on state-of-the-art architectures for semantic segmentation manifest the leading performance of proposed approaches for robotic monocular visual perception.
A matheuristic algorithm for the single-source capacitated facility location problem and its variants
Venue: arXiv cs.DM 2021
Year: 2021

This article presents a matheuristic algorithm for the single-source capacitated facility location problem (SSCFLP) and its variants: SSCFLP with K facilities (SSCKFLP), SSCFLP with contiguous service areas (CFLSAP), and SSCFLP with K facilities and contiguous service areas (CKFLSAP). The algorithm starts from an initial solution, and iteratively improves the solution by exactly solving large neighborhood-based sub-problems. The performance of the algorithm is tested on 5 sets of SSCFLP benchmark instances. Among the 272 instances, 191 optimal solutions are found, and 35 best-known solutions are updated. For the largest set of instances with 300-1000 facilities and 300-1500 customers (Avella and Boccia 2009), the proposed algorithm outperforms existing methods in terms of the solution quality and the computational time. Furthermore, based on two geographic areas, two sets of instances are generated to test the algorithm for solving SSCFLP and its variants. The solutions found by the proposed algorithm approximate optimal solutions or the lower bounds with average gaps of 0.07% for SSCFLP, 0.22% for CFLSAP, 0.04% for SSCKFLP, and 0.13% for CKFLSAP.