OpenRaiser/Intern-Atlas huggingface dataset:
Breast mass classification in ultrasound based on Kendall's shape manifold
Venue: arXiv cs.CV 2019
Year: 2019

Morphological features play an important role in breast mass classification in sonography. While benign breast masses tend to have a well-defined ellipsoidal contour, shape of malignant breast masses is commonly ill-defined and highly variable. Various handcrafted morphological features have been developed over the years to assess this phenomenon and help the radiologists differentiate benign and malignant masses. In this paper we propose an automatic approach to morphology analysis, we express shapes of breast masses as points on the Kendall's shape manifold. Next, we use the full Procrustes distance to develop support vector machine classifiers for breast mass differentiation. The usefulness of our method is demonstrated using a dataset of B-mode images collected from 163 breast masses. Our method achieved area under the receiver operating characteristic curve of 0.81. The proposed method can be used to assess shapes of breast masses in ultrasound without any feature engineering.
Giant Panda Face Recognition Using Small Dataset
Venue: arXiv cs.CV 2019
Year: 2019

Giant panda (panda) is a highly endangered animal. Significant efforts and resources have been put on panda conservation. To measure effectiveness of conservation schemes, estimating its population size in wild is an important task. The current population estimation approaches, including capture-recapture, human visual identification and collection of DNA from hair or feces, are invasive, subjective, costly or even dangerous to the workers who perform these tasks in wild. Cameras have been widely installed in the regions where pandas live. It opens a new possibility for non-invasive image based panda recognition. Panda face recognition is naturally a small dataset problem, because of the number of pandas in the world and the number of qualified images captured by the cameras in each encounter. In this paper, a panda face recognition algorithm, which includes alignment, large feature set extraction and matching is proposed and evaluated on a dataset consisting of 163 images. The experimental results are encouraging.
Heterogeneous Multi-sensor Calibration based on Graph Optimization
Venue: arXiv cs.RO 2019
Year: 2019

Many robotics and mapping systems contain multiple sensors to perceive the environment. Extrinsic parameter calibration, the identification of the position and rotation transform between the frames of the different sensors, is critical to fuse data from different sensors. When obtaining multiple camera to camera, lidar to camera and lidar to lidar calibration results, inconsistencies are likely. We propose a graph-based method to refine the relative poses of the different sensors. We demonstrate our approach using our mapping robot platform, which features twelve sensors that are to be calibrated. The experimental results confirm that the proposed algorithm yields great performance.
The Chan-Vese Model with Elastica and Landmark Constraints for Image Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

In order to completely separate objects with large sections of occluded boundaries in an image, we devise a new variational level set model for image segmentation combining the Chan-Vese model with elastica and landmark constraints. For computational efficiency, we design its Augmented Lagrangian Method (ALM) or Alternating Direction Method of Multiplier (ADMM) method by introducing some auxiliary variables, Lagrange multipliers, and penalty parameters. In each loop of alternating iterative optimization, the sub-problems of minimization can be easily solved via the Gauss-Seidel iterative method and generalized soft thresholding formulas with projection, respectively. Numerical experiments show that the proposed model can not only recover larger broken boundaries but can also improve segmentation efficiency, as well as decrease the dependence of segmentation on parameter tuning and initialization.
Towards Automated Boundary Value Testing with Program Derivatives and Search
Venue: arXiv cs.SE 2019
Year: 2019

A natural and often used strategy when testing software is to use input values at boundaries, i.e. where behavior is expected to change the most, an approach often called boundary value testing or analysis (BVA). Even though this has been a key testing idea for long it has been hard to clearly define and formalize. Consequently, it has also been hard to automate. In this research note we propose one such formalization of BVA by, in a similar way as to how the derivative of a function is defined in mathematics, considering (software) program derivatives. Critical to our definition is the notion of distance between inputs and outputs which we can formalize and then quantify based on ideas from Information theory. However, for our (black-box) approach to be practical one must search for test inputs with specific properties. Coupling it with search-based software engineering is thus required and we discuss how program derivatives can be used as and within fitness functions. This brief note does not allow a deeper, empirical investigation but we use a simple illustrative example throughout to introduce the main ideas. By combining program derivatives with search, we thus propose a practical as well as theoretically interesting technique for automated boundary value (analysis and) testing.
Indicating Asynchronous Multipliers
Venue: arXiv cs.AR 2019
Year: 2019

Multiplication is a basic arithmetic operation that is encountered in almost all general-purpose microprocessing and digital signal processing applications, and multiplication is physically realized using a multiplier. This paper discusses the physical implementation of indicating asynchronous multipliers, which are inherently elastic and are robust to timing, process, and parametric variations, and are modular. We consider the physical implementation of many weak-indication asynchronous multipliers using a 32/28-nm CMOS technology by adopting the array multiplier architecture. The multipliers are synthesized in a semi-custom ASIC-design style. The 4-phase return-to-zero (RTZ) and the 4-phase return-to-one (RTO) handshake protocols are considered for the data communication. The multipliers are realized using strong-indication or weak-indication full adders. Strong-indication 2-input AND function is used to generate the partial products in the case of both RTZ and RTO handshaking. The full adders considered are derived from different indicating asynchronous logic design methods. Among the multipliers considered, a weak-indication asynchronous multiplier utilizing the biased weak-indication full adder is found to be efficient in terms of the cycle time and the power-cycle time product with respect to both RTZ and RTO handshaking. Also, the 4-phase RTO handshake protocol is found to be preferable than the 4-phase RTZ handshake protocol for achieving enhanced optimizations in the design metrics.
On approximating dropout noise injection
Venue: arXiv cs.LG 2019
Year: 2019

This paper examines the assumptions of the derived equivalence between dropout noise injection and $L_2$ regularisation for logistic regression with negative log loss. We show that the approximation method is based on a divergent Taylor expansion, making, subsequent work using this approximation to compare the dropout trained logistic regression model with standard regularisers unfortunately ill-founded to date. Moreover, the approximation approach is shown to be invalid using any robust constraints. We show how this finding extends to general neural network topologies that use a cross-entropy prediction layer.
Invariants and Inequivalence of Linear Rank-Metric Codes
Venue: arXiv cs.IT 2019
Year: 2019

We show that the sequence of dimensions of the linear spaces, generated by a given rank-metric code together with itself under several applications of a field automorphism, is an invariant for the whole equivalence class of the code. These invariants give rise to an easily computable criterion to check if two codes are inequivalent. With this criterion we then derive bounds on the number of equivalence classes of classical and twisted Gabidulin codes.
Straight to Shapes++: Real-time Instance Segmentation Made More Accurate
Venue: arXiv cs.CV 2019
Year: 2019

Instance segmentation is an important problem in computer vision, with applications in autonomous driving, drone navigation and robotic manipulation. However, most existing methods are not real-time, complicating their deployment in time-sensitive contexts. In this work, we extend an existing approach to real-time instance segmentation, called `Straight to Shapes' (STS), which makes use of low-dimensional shape embedding spaces to directly regress to object shape masks. The STS model can run at 35 FPS on a high-end desktop, but its accuracy is significantly worse than that of offline state-of-the-art methods. We leverage recent advances in the design and training of deep instance segmentation models to improve the performance accuracy of the STS model whilst keeping its real-time capabilities intact. In particular, we find that parameter sharing, more aggressive data augmentation and the use of structured loss for shape mask prediction all provide a useful boost to the network performance. Our proposed approach, `Straight to Shapes++', achieves a remarkable 19.7 point improvement in mAP (at IOU of 0.5) over the original method as evaluated on the PASCAL VOC dataset, thus redefining the accuracy frontier at real-time speeds. Since the accuracy of instance segmentation is closely tied to that of object bounding box prediction, we also study the error profile of the latter and examine the failure modes of our method for future improvements.
Context-Aware Middleware: A Review
Venue: arXiv cs.SE 2019
Year: 2019

During previous years several studies have introduced the concept of "context-aware middleware" and also proposed solutions under this title; however, these systems are different in functionality. In this chapter, context-aware middleware is investigated from the standpoint of functional and non-functional requirements. Afterward, some well-known middleware systems are reviewed and, finally, open research directions as well as conclusion remarks are presented.
A Self-Attention Joint Model for Spoken Language Understanding in Situational Dialog Applications
Venue: arXiv cs.CL 2019
Year: 2019

Spoken language understanding (SLU) acts as a critical component in goal-oriented dialog systems. It typically involves identifying the speakers intent and extracting semantic slots from user utterances, which are known as intent detection (ID) and slot filling (SF). SLU problem has been intensively investigated in recent years. However, these methods just constrain SF results grammatically, solve ID and SF independently, or do not fully utilize the mutual impact of the two tasks. This paper proposes a multi-head self-attention joint model with a conditional random field (CRF) layer and a prior mask. The experiments show the effectiveness of our model, as compared with state-of-the-art models. Meanwhile, online education in China has made great progress in the last few years. But there are few intelligent educational dialog applications for students to learn foreign languages. Hence, we design an intelligent dialog robot equipped with different scenario settings to help students learn communication skills.
Capsule Routing via Variational Bayes
Venue: arXiv cs.LG 2019
Year: 2019

Capsule networks are a recently proposed type of neural network shown to outperform alternatives in challenging shape recognition tasks. In capsule networks, scalar neurons are replaced with capsule vectors or matrices, whose entries represent different properties of objects. The relationships between objects and their parts are learned via trainable viewpoint-invariant transformation matrices, and the presence of a given object is decided by the level of agreement among votes from its parts. This interaction occurs between capsule layers and is a process called routing-by-agreement. In this paper, we propose a new capsule routing algorithm derived from Variational Bayes for fitting a mixture of transforming gaussians, and show it is possible transform our capsule network into a Capsule-VAE. Our Bayesian approach addresses some of the inherent weaknesses of MLE based models such as the variance-collapse by modelling uncertainty over capsule pose parameters. We outperform the state-of-the-art on smallNORB using 50% fewer capsules than previously reported, achieve competitive performances on CIFAR-10, Fashion-MNIST, SVHN, and demonstrate significant improvement in MNIST to affNIST generalisation over previous works.
Forecasting Stock Market with Support Vector Regression and Butterfly Optimization Algorithm
Venue: arXiv cs.LG 2019
Year: 2019

Support Vector Regression (SVR) has achieved high performance on forecasting future behavior of random systems. However, the performance of SVR models highly depends upon the appropriate choice of SVR parameters. In this study, a novel BOA-SVR model based on Butterfly Optimization Algorithm (BOA) is presented. The performance of the proposed model is compared with eleven other meta-heuristic algorithms on a number of stocks from NASDAQ. The results indicate that the presented model here is capable to optimize the SVR parameters very well and indeed is one of the best models judged by both prediction performance accuracy and time consumption.
Single-Net Continual Learning with Progressive Segmented Training (PST)
Venue: arXiv cs.LG 2019
Year: 2019

There is an increasing need of continual learning in dynamic systems, such as the self-driving vehicle, the surveillance drone, and the robotic system. Such a system requires learning from the data stream, training the model to preserve previous information and adapt to a new task, and generating a single-headed vector for future inference. Different from previous approaches with dynamic structures, this work focuses on a single network and model segmentation to prevent catastrophic forgetting. Leveraging the redundant capacity of a single network, model parameters for each task are separated into two groups: one important group which is frozen to preserve current knowledge, and secondary group to be saved (not pruned) for a future learning. A fixed-size memory containing a small amount of previously seen data is further adopted to assist the training. Without additional regularization, the simple yet effective approach of PST successfully incorporates multiple tasks and achieves the state-of-the-art accuracy in the single-head evaluation on CIFAR-10 and CIFAR-100 datasets. Moreover, the segmented training significantly improves computation efficiency in continual learning.
Adversarially Robust Learning Could Leverage Computational Hardness
Venue: arXiv cs.LG 2019
Year: 2019

Over recent years, devising classification algorithms that are robust to adversarial perturbations has emerged as a challenging problem. In particular, deep neural nets (DNNs) seem to be susceptible to small imperceptible changes over test instances. However, the line of work in provable robustness, so far, has been focused on information-theoretic robustness, ruling out even the existence of any adversarial examples. In this work, we study whether there is a hope to benefit from algorithmic nature of an attacker that searches for adversarial examples, and ask whether there is any learning task for which it is possible to design classifiers that are only robust against polynomial-time adversaries. Indeed, numerous cryptographic tasks can only be secure against computationally bounded adversaries, and are indeed impossible for computationally unbounded attackers. Thus, it is natural to ask if the same strategy could help robust learning. We show that computational limitation of attackers can indeed be useful in robust learning by demonstrating the possibility of a classifier for some learning task for which computational and information theoretic adversaries of bounded perturbations have very different power. Namely, while computationally unbounded adversaries can attack successfully and find adversarial examples with small perturbation, polynomial time adversaries are unable to do so unless they can break standard cryptographic hardness assumptions. Our results, therefore, indicate that perhaps a similar approach to cryptography (relying on computational hardness) holds promise for achieving computationally robust machine learning. On the reverse directions, we also show that the existence of such learning task in which computational robustness beats information theoretic robustness requires computational hardness by implying (average-case) hardness of NP.
Case-Based Histopathological Malignancy Diagnosis using Convolutional Neural Networks
Venue: arXiv cs.CV 2019
Year: 2019

In practice, histopathological diagnosis of tumor malignancy often requires a human expert to scan through histopathological images at multiple magnification levels, after which a final diagnosis can be accurately determined. However, previous research on such classification tasks using convolutional neural networks primarily determine a diagnosis for a single magnification level. In this paper, we propose a case-based approach using deep residual neural networks for histopathological malignancy diagnosis, where a case is defined as a sequence of images from the patient at all available levels of magnification. Effectively, through mimicking what a human expert would actually do, our approach makes a diagnosis decision based on features learned in combination at multiple magnification levels. Our results show that the case-based approach achieves better performance than the state-of-the-art methods when evaluated on BreaKHis, a histopathological image dataset for breast tumors.
On the Complexity of Distributed Splitting Problems
Venue: arXiv cs.DC 2019
Year: 2019

One of the fundamental open problems in the area of distributed graph algorithms is the question of whether randomization is needed for efficient symmetry breaking. While there are fast, $\text{poly}\log n$-time randomized distributed algorithms for all of the classic symmetry breaking problems, for many of them, the best deterministic algorithms are almost exponentially slower. The following basic local splitting problem, which is known as the \emph{weak splitting} problem takes a central role in this context: Each node of a graph $G=(V,E)$ has to be colored red or blue such that each node of sufficiently large degree has at least one node of each color among its neighbors. Ghaffari, Kuhn, and Maus [STOC '17] showed that this seemingly simple problem is complete w.r.t. the above fundamental open question in the following sense: If there is an efficient $\text{poly}\log n$-time determinstic distributed algorithm for weak splitting, then there is such an algorithm for all locally checkable graph problems for which an efficient randomized algorithm exists. In this paper, we investigate the distributed complexity of weak splitting and some closely related problems. E.g., we obtain efficient algorithms for special cases of weak splitting, where the graph is nearly regular. In particular, we show that if $δ$ and $Δ$ are the minimum and maximum degrees of $G$ and if $δ=Ω(\log n)$, weak splitting can be solved deterministically in time $O\big(\fracΔδ\cdot\text{poly}(\log n)\big)$. Further, if $δ= Ω(\log\log n)$ and $Δ\leq 2^{\varepsilonδ}$, there is a randomized algorithm with time complexity $O\big(\fracΔδ\cdot\text{poly}(\log\log n)\big)$.
EncryptGAN: Image Steganography with Domain Transform
Venue: arXiv cs.MM 2019
Year: 2019

We propose an image steganographic algorithm called EncryptGAN, which disguises private image communication in an open communication channel. The insight is that content transform between two very different domains (e.g., face to flower) allows one to hide image messages in one domain (face) and communicate using its counterpart in another domain (flower). The key ingredient in our method, unlike related approaches, is a specially trained network to extract transformed images from both domains and use them as the public and private keys. We ensure the image communication remain secret except for the intended recipient even when the content transformation networks are exposed. To communicate, one directly pastes the `message' image onto a larger public key image (face). Depending on the location and content of the message image, the `disguise' image (flower) alters its appearance and shape while maintaining its overall objectiveness (flower). The recipient decodes the alternated image to uncover the original image message using its message image key. We implement the entire procedure as a constrained Cycle-GAN, where the public and the private key generating network is used as an additional constraint to the cycle consistency. Comprehensive experimental results show our EncryptGAN outperforms the state-of-arts in terms of both encryption and security measures.
Learning Efficient and Effective Exploration Policies with Counterfactual Meta Policy
Venue: arXiv cs.LG 2019
Year: 2019

A fundamental issue in reinforcement learning algorithms is the balance between exploration of the environment and exploitation of information already obtained by the agent. Especially, exploration has played a critical role for both efficiency and efficacy of the learning process. However, Existing works for exploration involve task-agnostic design, that is performing well in one environment, but be ill-suited to another. To the purpose of learning an effective and efficient exploration policy in an automated manner. We formalized a feasible metric for measuring the utility of exploration based on counterfactual ideology. Based on that, We proposed an end-to-end algorithm to learn exploration policy by meta-learning. We demonstrate that our method achieves good results compared to previous works in the high-dimensional control tasks in MuJoCo simulator.
iNavFIter: Next-Generation Inertial Navigation Computation Based on Functional Iteration
Venue: arXiv cs.RO 2019
Year: 2019

Inertial navigation computation is to acquire the attitude, velocity and position information of a moving body by integrating inertial measurements from gyroscopes and accelerometers. Over half a century has witnessed great efforts in coping with the motion non-commutativity errors to accurately compute the navigation information as far as possible, so as not to compromise the quality measurements of inertial sensors. Highly dynamic applications and the forthcoming cold-atom precision inertial navigation systems demand for even more accurate inertial navigation computation. The paper gives birth to an inertial navigation algorithm to fulfill that demand, named the iNavFIter, which is based on a brand-new framework of functional iterative integration and Chebyshev polynomials. Remarkably, the proposed iNavFIter reduces the non-commutativity errors to almost machine precision, namely, the coning/sculling/scrolling errors that have perplexed the navigation community for long. Numerical results are provided to demonstrate its accuracy superiority over the state-of-the-art inertial navigation algorithms at affordable computation cost.
Cheat-Proof Communication through Cluster Head (C3H) in Mobile Ad Hoc Network
Venue: arXiv cs.NI 2019
Year: 2019

The mobile ad hoc network (MANET) is a wireless network based on a group of mobile nodes without any centralised infrastructure. In civilian data communication, all nodes cannot be homogeneous-type and not do a specific data communication. Therefore, node co-operation and cheat-proof are essential characteristics for successfully running MANETs in civilian data communication. Denial of service and malicious behaviour of the node are the main concerns in securing successful communication in MANETs. This scheme proposed a generic solution to preventing malicious behaviour of the node by the cluster head through the single hop node clustering strategy.