OpenRaiser/Intern-Atlas huggingface dataset:
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation
Venue: arXiv cs.CV 2019
Year: 2019

Our understanding and ability to effectively monitor and manage coastal ecosystems are severely limited by observation methods. Automatic recognition of species in natural environment is a promising tool which would revolutionize video and image analysis for a wide range of applications in marine ecology. However, classifying fish from images captured by underwater cameras is in general very challenging due to noise and illumination variations in water. Previous classification methods in the literature relies on filtering the images to separate the fish from the background or sharpening the images by removing background noise. This pre-filtering process may negatively impact the classification accuracy. In this work, we propose a Convolutional Neural Network (CNN) using the Squeeze-and-Excitation (SE) architecture for classifying images of fish without pre-filtering. Different from conventional schemes, this scheme is divided into two steps. The first step is to train the fish classifier via a public data set, i.e., Fish4Knowledge, without using image augmentation, named as pre-training. The second step is to train the classifier based on a new data set consisting of species that we are interested in for classification, named as post-training. The weights obtained from pre-training are applied to post-training as a priori. This is also known as transfer learning. Our solution achieves the state-of-the-art accuracy of 99.27% accuracy on the pre-training. The accuracy on the post-training is 83.68%. Experiments on the post-training with image augmentation yields an accuracy of 87.74%, indicating that the solution is viable with a larger data set.
Generalized Lazy Search for Robot Motion Planning: Interleaving Search and Edge Evaluation via Event-based Toggles
Venue: arXiv cs.RO 2019
Year: 2019

Lazy search algorithms can efficiently solve problems where edge evaluation is the bottleneck in computation, as is the case for robotic motion planning. The optimal algorithm in this class, LazySP, lazily restricts edge evaluation to only the shortest path. Doing so comes at the expense of search effort, i.e., LazySP must recompute the search tree every time an edge is found to be invalid. This becomes prohibitively expensive when dealing with large graphs or highly cluttered environments. Our key insight is the need to balance both edge evaluation and search effort to minimize the total planning time. Our contribution is two-fold. First, we propose a framework, Generalized Lazy Search (GLS), that seamlessly toggles between search and evaluation to prevent wasted efforts. We show that for a choice of toggle, GLS is provably more efficient than LazySP. Second, we leverage prior experience of edge probabilities to derive GLS policies that minimize expected planning time. We show that GLS equipped with such priors significantly outperforms competitive baselines for many simulated environments in R2, SE(2) and 7-DoF manipulation.
Achieving Optimal Backlog in Multi-Processor Cup Games
Venue: arXiv cs.DS 2019
Year: 2019

The single- and multi- processor cup games can be used to model natural problems in areas such as processor scheduling, deamortization, and buffer management. At the beginning of the single-processor cup game, $n$ cups are initially empty. In each step of the game, a filler distributes $1$ unit of water among the cups, and then an emptier selects a cup and removes $1 + ε$ units from that cup. The goal of the emptier is to minimize the amount of water in the fullest cup, also known as the backlog. It is known that the greedy algorithm (i.e., empty the fullest cup) achieves backlog $O(\log n)$, and that no deterministic algorithm can do better. We show that the performance of the greedy algorithm can be greatly improved with a small amount of randomization: After any step $i$, and for any $k \ge Ω(\log ε^{-1})$, the emptier achieves backlog at most $O(k)$ with probability at least $1 -O(2^{-2^k})$. Whereas bounds for the single-processor cup game have been known for more than fifteen years, proving nontrivial bounds on backlog for the multi-processor extension has remained open. We present a simple analysis of the greedy algorithm for the multi-processor cup game, establishing a backlog of $O(ε^{-1} \log n)$, as long as $δ$, the game's other speed-augmentation constant, is at least $1/poly(n)$. Turning to randomized algorithms, we encounter an unexpected phenomenon: When the number of processors $p$ is large, the backlog after each step drops to \emph{constant} with large probability. Specifically, we show that if $δ$ and $ε$ satisfy reasonable constraints, then there exists an algorithm that bounds the backlog after a given step by three or less with probability at least $1 - O(\exp(-Ω(ε^2 p))$. We further extend the guarantees of our randomized algorithm to consider larger backlogs.
Snap and Find: Deep Discrete Cross-domain Garment Image Retrieval
Venue: arXiv cs.CV 2019
Year: 2019

With the increasing number of online stores, there is a pressing need for intelligent search systems to understand the item photos snapped by customers and search against large-scale product databases to find their desired items. However, it is challenging for conventional retrieval systems to match up the item photos captured by customers and the ones officially released by stores, especially for garment images. To bridge the customer- and store- provided garment photos, existing studies have been widely exploiting the clothing attributes (\textit{e.g.,} black) and landmarks (\textit{e.g.,} collar) to learn a common embedding space for garment representations. Unfortunately they omit the sequential correlation of attributes and consume large quantity of human labors to label the landmarks. In this paper, we propose a deep multi-task cross-domain hashing termed \textit{DMCH}, in which cross-domain embedding and sequential attribute learning are modeled simultaneously. Sequential attribute learning not only provides the semantic guidance for embedding, but also generates rich attention on discriminative local details (\textit{e.g.,} black buttons) of clothing items without requiring extra landmark labels. This leads to promising performance and 306$\times$ boost on efficiency when compared with the state-of-the-art models, which is demonstrated through rigorous experiments on two public fashion datasets.
Security Landscape for Robotics
Venue: arXiv cs.CR 2019
Year: 2019

In this paper, the current state of security in robotics is described to be in need of review. When we consider safety mechanisms implemented in an Internet-connected robot, the requirement of safety becomes a crucial security requirement. Upon review of the current state of security in the field of robotics, four key requirements are in need of addressing: the supply chain for calibration, integrity and authenticity of commands (i.e. in teleoperation), physical-plane security and finally, secure, controlled logging and auditing.
A Conceptual Architecture for Contractual Data Sharing in a Decentralised Environment
Venue: arXiv cs.CY 2019
Year: 2019

Machine Learning systems rely on data for training, input and ongoing feedback and validation. Data in the field can come from varied sources, often anonymous or unknown to the ultimate users of the data. Whenever data is sourced and used, its consumers need assurance that the data accuracy is as described, that the data has been obtained legitimately, and they need to understand the terms under which the data is made available so that they can honour them. Similarly, suppliers of data require assurances that their data is being used legitimately by authorised parties, in accordance with their terms, and that usage is appropriately recompensed. Furthermore, both parties may want to agree on a specific set of quality of service (QoS) metrics, which can be used to negotiate service quality based on cost, and then receive affirmation that data is being supplied within those agreed QoS levels. Here we present a conceptual architecture which enables data sharing agreements to be encoded and computationally enforced, remuneration to be made when required, and a trusted audit trail to be produced for later analysis or reproduction of the environment. Our architecture uses blockchain-based distributed ledger technology, which can facilitate transactions in situations where parties do not have an established trust relationship or centralised command and control structures. We explore techniques to promote faith in the accuracy of the supplied data, and to let data users determine trade-offs between data quality and cost. Our system is exemplified through consideration of a case study using multiple data sources from different parties to monitor traffic levels in urban locations.
A Literature Study of Embeddings on Source Code
Venue: arXiv cs.LG 2019
Year: 2019

Natural language processing has improved tremendously after the success of word embedding techniques such as word2vec. Recently, the same idea has been applied on source code with encouraging results. In this survey, we aim to collect and discuss the usage of word embedding techniques on programs and source code. The articles in this survey have been collected by asking authors of related work and with an extensive search on Google Scholar. Each article is categorized into five categories: 1. embedding of tokens 2. embedding of functions or methods 3. embedding of sequences or sets of method calls 4. embedding of binary code 5. other embeddings. We also provide links to experimental data and show some remarkable visualization of code embeddings. In summary, word embedding has been successfully applied on different granularities of source code. With access to countless open-source repositories, we see a great potential of applying other data-driven natural language processing techniques on source code in the future.
Efficient attack countermeasure selection accounting for recovery and action costs
Venue: arXiv cs.CR 2019
Year: 2019

The losses arising from a system being hit by cyber attacks can be staggeringly high, but defending against such attacks can also be costly. This work proposes an attack countermeasure selection approach based on cost impact analysis that takes into account the impacts of actions by both the attacker and the defender. We consider a networked system providing services whose provision depends on other components in the network. We model the costs and losses to service availability from compromises and defensive actions to the components, and show that while containment of the attack can be an effective defensive strategy, it can be more cost-efficient to allow parts of the attack to continue further whilst focusing on recovering services to a functional state. Based on this insight, we build a countermeasure selection method that chooses the most cost-effective action based on its impact on expected losses and costs over a given time horizon. Our method is evaluated using simulations in synthetic graphs representing network dependencies and vulnerabilities, and found to perform well in comparison to alternatives.
Radiotherapy Target Contouring with Convolutional Gated Graph Neural Network
Venue: arXiv cs.CV 2019
Year: 2019

Tomography medical imaging is essential in the clinical workflow of modern cancer radiotherapy. Radiation oncologists identify cancerous tissues, applying delineation on treatment regions throughout all image slices. This kind of task is often formulated as a volumetric segmentation task by means of 3D convolutional networks with considerable computational cost. Instead, inspired by the treating methodology of considering meaningful information across slices, we used Gated Graph Neural Network to frame this problem more efficiently. More specifically, we propose convolutional recurrent Gated Graph Propagator (GGP) to propagate high-level information through image slices, with learnable adjacency weighted matrix. Furthermore, as physicians often investigate a few specific slices to refine their decision, we model this slice-wise interaction procedure to further improve our segmentation result. This can be set by editing any slice effortlessly as updating predictions of other slices using GGP. To evaluate our method, we collect an Esophageal Cancer Radiotherapy Target Treatment Contouring dataset of 81 patients which includes tomography images with radiotherapy target. On this dataset, our convolutional graph network produces state-of-the-art results and outperforms the baselines. With the addition of interactive setting, performance is improved even further. Our method has the potential to be easily applied to diverse kinds of medical tasks with volumetric images. Incorporating both the ability to make a feasible prediction and to consider the human interactive input, the proposed method is suitable for clinical scenarios.
A Surface Integral Formulation for Scattering Modeling by 2D Penetrable Objects
Venue: arXiv cs.CE 2019
Year: 2019

In this paper, we proposed a single-source surface integral formulation to accurately solve the scattering problems by 2D penetrable objects. In this method, the objects are replaced by their surrounding medium through enforcing a surface equivalent electric current to ensure fields exactly the same as those in the original scattering problem. The equivalent electric current is obtained through emitting the equivalent magnetic current by enforcing that the electric fields in the original and equivalent problems equal to each other. Through solving the Helmholtz equation inside objects by the scalar second Green theorem, we could accurately model arbitrarily shaped objects. Then, we solve the exterior scattering problems through the combined integral equation (CFIE) with the equivalent electric current. The proposed formulation only requires a single electric current source to model penetrable objects. At last, two numerical experiments are carried out to validate its accuracy, stability and capability of handling non-smoothing objects.
Phase Analysis of MIMO LTI Systems
Venue: arXiv cs.SY 2019
Year: 2019

In this paper, we introduce a definition of phase response for a class of multi-input multi-output (MIMO) linear time-invariant (LTI) systems, the frequency responses of which are cramped at all frequencies. This phase concept generalizes the notions of positive realness and negative imaginariness. We also define the half-cramped systems and provide a time-domain interpretation. As a starting point in an endeavour to develop a comprehensive phase theory for MIMO systems, we establish a small phase theorem for feedback stability, which complements the well-known small gain theorem. In addition, we derive a sectored real lemma for phase-bounded systems as a natural counterpart of the bounded real lemma.
Spectral analysis of matrix scaling and operator scaling
Venue: arXiv cs.DS 2019
Year: 2019

We present a spectral analysis for matrix scaling and operator scaling. We prove that if the input matrix or operator has a spectral gap, then a natural gradient flow has linear convergence. This implies that a simple gradient descent algorithm also has linear convergence under the same assumption. The spectral gap condition for operator scaling is closely related to the notion of quantum expander studied in quantum information theory. The spectral analysis also provides bounds on some important quantities of the scaling problems, such as the condition number of the scaling solution and the capacity of the matrix and operator. These bounds can be used in various applications of scaling problems, including matrix scaling on expander graphs, permanent lower bounds on random matrices, the Paulsen problem on random frames, and Brascamp-Lieb constants on random operators. In some applications, the inputs of interest satisfy the spectral condition and we prove significantly stronger bounds than the worst case bounds.
The complexity of 3-colouring $H$-colourable graphs
Venue: arXiv cs.CC 2019
Year: 2019

We study the complexity of approximation on satisfiable instances for graph homomorphism problems. For a fixed graph $H$, the $H$-colouring problem is to decide whether a given graph has a homomorphism to $H$. By a result of Hell and Nešetřil, this problem is NP-hard for any non-bipartite graph $H$. In the context of promise constraint satisfaction problems, Brakensiek and Guruswami conjectured that this hardness result extends to promise graph homomorphism as follows: fix any non-bipartite graph $H$ and another graph $G$ with a homomorphism from $H$ to $G$, it is NP-hard to find a homomorphism to $G$ from a given $H$-colourable graph. Arguably, the two most important special cases of this conjecture are when $H$ is fixed to be the complete graph on 3 vertices (and $G$ is any graph with a triangle) and when $G$ is the complete graph on 3 vertices (and $H$ is any 3-colourable graph). The former case is equivalent to the notoriously difficult approximate graph colouring problem. In this paper, we confirm the Brakensiek-Guruswami conjecture for the latter case. Our proofs rely on a novel combination of the universal-algebraic approach to promise constraint satisfaction, that was recently developed by Barto, Bulín and the authors, with some ideas from algebraic topology.
HOList: An Environment for Machine Learning of Higher-Order Theorem Proving
Venue: arXiv cs.LO 2019
Year: 2019

We present an environment, benchmark, and deep learning driven automated theorem prover for higher-order logic. Higher-order interactive theorem provers enable the formalization of arbitrary mathematical theories and thereby present an interesting, open-ended challenge for deep learning. We provide an open-source framework based on the HOL Light theorem prover that can be used as a reinforcement learning environment. HOL Light comes with a broad coverage of basic mathematical theorems on calculus and the formal proof of the Kepler conjecture, from which we derive a challenging benchmark for automated reasoning. We also present a deep reinforcement learning driven automated theorem prover, DeepHOL, with strong initial results on this benchmark.
Single-Carrier Index Modulation for IoT Uplink
Venue: arXiv cs.IT 2019
Year: 2019

For the Internet of Things (IoT), there might be a large number of devices to be connected to the Internet through wireless technologies. In general, IoT devices would have various constraints due to limited processing capability, memory, energy source, and so on, and it is desirable to employ efficient wireless transmission schemes, especially for uplink transmissions. For example, orthogonal frequency division multiplexing (OFDM) with index modulation (IM) or OFDM-IM can be considered for IoT devices due to its energy efficiency. In this paper, we study a different IM scheme for a single-carrier (SC) system, which is referred to as SCIM. While SCIM is similar to OFDM-IM in terms of energy efficiency, SCIM may be better suited for IoT uplink because it has a low peak-to-average power ratio (PAPR) and does not require inverse fast Fourier transform (FFT) at devices compared to OFDM-IM. We also consider precoding for SCIM and generalize it to multiple access channel so that multiple IoT devices can share the same radio resource block. To detect precoded SCIM signals, low-complexity detectors are derived. For a better performance, based on variational inference that is widely used in machine learning, we consider a detector that provides an approximate solution to an optimal detection.
ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples
Venue: arXiv cs.CL 2019
Year: 2019

This paper describes our system, Joint Encoders for Stable Suggestion Inference (JESSI), for the SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums. JESSI is a combination of two sentence encoders: (a) one using multiple pre-trained word embeddings learned from log-bilinear regression (GloVe) and translation (CoVe) models, and (b) one on top of word encodings from a pre-trained deep bidirectional transformer (BERT). We include a domain adversarial training module when training for out-of-domain samples. Our experiments show that while BERT performs exceptionally well for in-domain samples, several runs of the model show that it is unstable for out-of-domain samples. The problem is mitigated tremendously by (1) combining BERT with a non-BERT encoder, and (2) using an RNN-based classifier on top of BERT. Our final models obtained second place with 77.78\% F-Score on Subtask A (i.e. in-domain) and achieved an F-Score of 79.59\% on Subtask B (i.e. out-of-domain), even without using any additional external data.
Optimal Sampling of Water Distribution Network Dynamics using Graph Fourier Transform
Venue: arXiv cs.IT 2019
Year: 2019

Water Distribution Networks (WDNs) are critical infrastructures that ensure safe drinking water. One of the major threats is the accidental or intentional injection of pollutants. Data collection remains challenging in underground WDNs and in order to quantify its threat to end users, modeling pollutant spread with minimal sensor data is can important open challenge. Existing approaches using numerical optimisation suffer from scalability issues and lack detailed insight and performance guarantees. Applying general data-driven approaches such as compressed sensing (CS) offer limited improvements in sample node reduction. Graph theoretic approaches link topology (e.g. Laplacian spectra) to optimal sensing locations, it neglects the complex dynamics. In this work, we introduce a novel Graph Fourier Transform (GFT) that exploits the low-rank property to optimally sample junction nodes in WDNs. The proposed GFT allows us to fully recover the full network dynamics using a subset of data sampled at the identified nodes. The proposed GFT technique offers attractive improvements over existing numerical optimisation, compressed sensing, and graph theoretic approaches. Our results show that, on average, with nearly 30-40\% of the junctions monitored, we are able to fully recover the dynamics of the whole network. The framework is useful beyond the application of WDNs and can be applied to a variety of infrastructure sensing for digital twin modeling.
Usability in the Larger Reality: A Contrarian Argument for the Importance of Social and Political Considerations
Venue: arXiv cs.HC 2019
Year: 2019

Usability engineering is situated in a much larger social and institutional context than is usually acknowledged by usability professionals in the way that they define their field. The definitions and processes used in the improvement of user interfaces are subordinate to interests that often have narrow goals, having adverse effects on user awareness and autonomy that have further adverse effects on society as a whole. These effects are brought about by the way that knowledge about systems is limited by the design of the interface that limits the user to tasks and goals defined by organizations, often commercial ones. It is the point at which the structures of the user interface are defined by usability professionals that sources of the limitation of knowledge can be identified. These sources are defined by their reliance on a construction of the user's wants and needs that cyclically reinforce, through the actual use of the interface, the user's own construction of her wants and needs. To alleviate this, it is necessary to come up with new processes of user interface design that do not make assumptions about the user that tend to subordinate the user, and it is also necessary to reconstruct the user as a participant in the interface. NOTE: This article was written in 2003 as a final project for a Master's course in human-computer interaction at the University of Ottawa. The author attempted to publish it at the time, but did not really understand the process. The ideas stand up fairly well, however, and he would like to contribute it as a comment on the current state of affairs. It is only lightly edited for format.