OpenRaiser/Intern-Atlas huggingface dataset:
TRk-CNN: Transferable Ranking-CNN for image classification of glaucoma, glaucoma suspect, and normal eyes
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we proposed Transferable Ranking Convolutional Neural Network (TRk-CNN) that can be effectively applied when the classes of images to be classified show a high correlation with each other. The multi-class classification method based on the softmax function, which is generally used, is not effective in this case because the inter-class relationship is ignored. Although there is a Ranking-CNN that takes into account the ordinal classes, it cannot reflect the inter-class relationship to the final prediction. TRk-CNN, on the other hand, combines the weights of the primitive classification model to reflect the inter-class information to the final classification phase. We evaluated TRk-CNN in glaucoma image dataset that was labeled into three classes: normal, glaucoma suspect, and glaucoma eyes. Based on the literature we surveyed, this study is the first to classify three status of glaucoma fundus image dataset into three different classes. We compared the evaluation results of TRk-CNN with Ranking-CNN (Rk-CNN) and multi-class CNN (MC-CNN) using the DenseNet as the backbone CNN model. As a result, TRk-CNN achieved an average accuracy of 92.96%, specificity of 93.33%, sensitivity for glaucoma suspect of 95.12% and sensitivity for glaucoma of 93.98%. Based on average accuracy, TRk-CNN is 8.04% and 9.54% higher than Rk-CNN and MC-CNN and surprisingly 26.83% higher for sensitivity for suspicious than multi-class CNN. Our TRk-CNN is expected to be effectively applied to the medical image classification problem where the disease state is continuous and increases in the positive class direction.
Joint Learning of Neural Networks via Iterative Reweighted Least Squares
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we introduce the problem of jointly learning feed-forward neural networks across a set of relevant but diverse datasets. Compared to learning a separate network from each dataset in isolation, joint learning enables us to extract correlated information across multiple datasets to significantly improve the quality of learned networks. We formulate this problem as joint learning of multiple copies of the same network architecture and enforce the network weights to be shared across these networks. Instead of hand-encoding the shared network layers, we solve an optimization problem to automatically determine how layers should be shared between each pair of datasets. Experimental results show that our approach outperforms baselines without joint learning and those using pretraining-and-fine-tuning. We show the effectiveness of our approach on three tasks: image classification, learning auto-encoders, and image generation.
Effects Without Monads: Non-determinism -- Back to the Meta Language
Venue: arXiv cs.PL 2019
Year: 2019

We reflect on programming with complicated effects, recalling an undeservingly forgotten alternative to monadic programming and checking to see how well it can actually work in modern functional languages. We adopt and argue the position of factoring an effectful program into a first-order effectful DSL with a rich, higher-order 'macro' system. Not all programs can be thus factored. Although the approach is not general-purpose, it does admit interesting programs. The effectful DSL is likewise rather problem-specific and lacks general-purpose monadic composition, or even functions. On the upside, it expresses the problem elegantly, is simple to implement and reason about, and lends itself to non-standard interpretations such as code generation (compilation) and abstract interpretation. A specialized DSL is liable to be frequently extended; the experience with the tagless-final style of DSL embedding shown that the DSL evolution can be made painless, with the maximum code reuse. We illustrate the argument on a simple but representative example of a rather complicated effect -- non-determinism, including committed choice. Unexpectedly, it turns out we can write interesting non-deterministic programs in an ML-like language just as naturally and elegantly as in the functional-logic language Curry -- and not only run them but also statically analyze, optimize and compile. The richness of the Meta Language does, in reality, compensate for the simplicity of the effectful DSL. The key idea goes back to the origins of ML as the Meta Language for the Edinburgh LCF theorem prover. Instead of using ML to build theorems, we now build (DSL) programs.
Recent Advances in Diversified Recommendation
Venue: arXiv cs.IR 2019
Year: 2019

With the rapid development of recommender systems, accuracy is no longer the only golden criterion for evaluating whether the recommendation results are satisfying or not. In recent years, diversity has gained tremendous attention in recommender systems research, which has been recognized to be an important factor for improving user satisfaction. On the one hand, diversified recommendation helps increase the chance of answering ephemeral user needs. On the other hand, diversifying recommendation results can help the business improve product visibility and explore potential user interests. In this paper, we are going to review the recent advances in diversified recommendation. Specifically, we first review the various definitions of diversity and generate a taxonomy to shed light on how diversity have been modeled or measured in recommender systems. After that, we summarize the major optimization approaches to diversified recommendation from a taxonomic view. Last but not the least, we project into the future and point out trending research directions on this topic.
A Simple Dual-decoder Model for Generating Response with Sentiment
Venue: arXiv cs.LG 2019
Year: 2019

How to generate human like response is one of the most challenging tasks for artificial intelligence. In a real application, after reading the same post different people might write responses with positive or negative sentiment according to their own experiences and attitudes. To simulate this procedure, we propose a simple but effective dual-decoder model to generate response with a particular sentiment, by connecting two sentiment decoders to one encoder. To support this model training, we construct a new conversation dataset with the form of (post, resp1, resp2) where two responses contain opposite sentiment. Experiment results show that our dual-decoder model can generate diverse responses with target sentiment, which obtains significant performance gain in sentiment accuracy and word diversity over the traditional single-decoder model. We will make our data and code publicly available for further study.
Bisimulation Invariant Monadic-Second Order Logic in the Finite
Venue: arXiv cs.LO 2019
Year: 2019

We consider bisimulation-invariant monadic second-order logic over various classes of finite transition systems. We present several combinatorial characterisations of when the expressive power of this fragment coincides with that of the modal mu-calculus. Using these characterisations we prove for some simple classes of transition systems that this is indeed the case. In particular, we show that, over the class of all finite transition systems with Cantor-Bendixson rank at most k, bisimulation-invariant MSO coincides with L_mu.
Towards a Security Baseline for IaaS-Cloud Back-Ends in Industry 4.0
Venue: arXiv cs.CR 2019
Year: 2019

The popularity of cloud based Infrastructure-as-a- Service (IaaS) solutions is becoming increasingly popular. However, since IaaS providers and customers interact in a flexible and scalable environment, security remains a serious concern. To handle such security issues, defining a set of security parameters in the service level agreements (SLA) between both, IaaS provider and customer, is of utmost importance. In this paper, the European Network and Information Security Agency (ENISA) guidelines are evaluated to extract a set of security parameters for IaaS. Furthermore, the level of applicability and implementation of this set is used to assess popular industrial and open-source IaaS cloud platforms, respectively VMware and OpenStack. Both platforms provide private clouds, used as backend infrastructures in Industry 4.0 application scenarios. The results serve as initial work to identify a security baseline and research needs for creating secure cloud environments for Industry 4.0.
Multi Web Audio Sequencer: Collaborative Music Making
Venue: arXiv cs.SD 2019
Year: 2019

Recent advancements in web-based audio systems have enabled sufficiently accurate timing control and real-time sound processing capabilities. Numerous specialized music tools, as well as digital audio workstations, are now accessible from browsers. Features such as the large accessibility of data and real-time communication between clients make the web attractive for collaborative data manipulation. However, this innovative field has yet to produce effective tools for multiple-user coordination on specialized music creation tasks. The Multi Web Audio Sequencer is a prototype of an application for segment-based sequencing of Freesound sound clips, with an emphasis on seamless remote collaboration. In this work we consider a fixed-grid step sequencer as a probe for understanding the necessary features of crowd-shared music creation sessions. This manuscript describes the sequencer and the functionalities and types of interactions required for effective and attractive collaboration of remote people during creative music creation activities.
Visual Analytics of Anomalous User Behaviors: A Survey
Venue: arXiv cs.HC 2019
Year: 2019

The increasing accessibility of data provides substantial opportunities for understanding user behaviors. Unearthing anomalies in user behaviors is of particular importance as it helps signal harmful incidents such as network intrusions, terrorist activities, and financial frauds. Many visual analytics methods have been proposed to help understand user behavior-related data in various application domains. In this work, we survey the state of art in visual analytics of anomalous user behaviors and classify them into four categories including social interaction, travel, network communication, and transaction. We further examine the research works in each category in terms of data types, anomaly detection techniques, and visualization techniques, and interaction methods. Finally, we discuss the findings and potential research directions.
Deep Compressed Sensing
Venue: arXiv cs.LG 2019
Year: 2019

Compressed sensing (CS) provides an elegant framework for recovering sparse signals from compressed measurements. For example, CS can exploit the structure of natural images and recover an image from only a few random measurements. CS is flexible and data efficient, but its application has been restricted by the strong assumption of sparsity and costly reconstruction process. A recent approach that combines CS with neural network generators has removed the constraint of sparsity, but reconstruction remains slow. Here we propose a novel framework that significantly improves both the performance and speed of signal recovery by jointly training a generator and the optimisation process for reconstruction via meta-learning. We explore training the measurements with different objectives, and derive a family of models based on minimising measurement errors. We show that Generative Adversarial Nets (GANs) can be viewed as a special case in this family of models. Borrowing insights from the CS perspective, we develop a novel way of improving GANs using gradient information from the discriminator.
Generating Pairwise Combinatorial Interaction Test Suites Using Single Objective Dragonfly Optimisation Algorithm
Venue: arXiv cs.SE 2019
Year: 2019

Combinatorial interaction testing has been addressed as an effective software testing technique recently. It shows its ability to reduce the number of test cases that have to be considered for software-under-test by taking the combinations of parameters as an interaction of input. This combination could be considered as input-configuration of different software families. Pairwise combinatorial test suite takes the interaction of two input parameters into consideration instead of many parameter interactions. Evidence showed that this test suite could detect most of the faults in the software-under-test as compared to higher interactions. This paper presents a new technique to generate pairwise combinatorial test suites. Also, Dragon Fly (DF), a new swarm intelligent optimization algorithm, is assessed. The design and adaptation of the algorithm are addresses in the paper in detail. The algorithm is evaluated extensively through different experiments and benchmarks. The evaluation shows the efficiency of the proposed technique for test suite generation and the usefulness of DF optimization algorithm for future investigations.
Harvesting Information from Captions for Weakly Supervised Semantic Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

Since acquiring pixel-wise annotations for training convolutional neural networks for semantic image segmentation is time-consuming, weakly supervised approaches that only require class tags have been proposed. In this work, we propose another form of supervision, namely image captions as they can be found on the Internet. These captions have two advantages. They do not require additional curation as it is the case for the clean class tags used by current weakly supervised approaches and they provide textual context for the classes present in an image. To leverage such textual context, we deploy a multi-modal network that learns a joint embedding of the visual representation of the image and the textual representation of the caption. The network estimates text activation maps (TAMs) for class names as well as compound concepts, i.e. combinations of nouns and their attributes. The TAMs of compound concepts describing classes of interest substantially improve the quality of the estimated class activation maps which are then used to train a network for semantic segmentation. We evaluate our method on the COCO dataset where it achieves state of the art results for weakly supervised image segmentation.
Statistical Analysis and Modeling of the Geometry and Topology of Plant Roots
Venue: arXiv cs.GR 2019
Year: 2019

The root is an important organ of a plant since it is responsible for water and nutrient uptake. Analyzing and modelling variabilities in the geometry and topology of roots can help in assessing the plant's health, understanding its growth patterns, and modeling relations between plant species and between plants and their environment. In this article, we develop a framework for the statistical analysis and modeling of the geometry and topology of plant roots. We represent root structures as points in a tree-shape space equipped with a metric that quantifies geometric and topological differences between pairs of roots. We then use these building blocks to compute geodesics, i.e., optimal deformations under the metric between root structures, and to perform statistical analysis on root populations. We demonstrate the utility of the proposed framework through an application to a dataset of wheat roots grown in different environmental conditions. We also show that the framework can be used in various applications including classification and regression.
Artificial intelligence technology in oncology: a new technological paradigm
Venue: arXiv cs.CY 2019
Year: 2019

Artificial Intelligence (AI) technology is based on theory and development of computer systems able to perform tasks that normally require human intelligence. In this context, deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior. Application of these methods to medical imaging can assist pathologists in the detection of cancer subtype, gene mutations and/or metastases for applying appropriate therapies. The purpose of this study is to show the emerging application of AI in medical imaging to detect lung and breast cancer. Moreover, this study shows the comparative evolutionary pathways of this emerging technology for three critical cancers: lung, breast and thyroid. A main finding of this study is the recognition that, since the late 1990, the sharp increase of technological trajectories of AI technology applied in cancer imaging seems to be driven by high rates of mortality of some types of cancer (e.g., lung and breast) in order to find new techniques for a more accurate detection, characterization and monitoring as well as to apply efficiently anticancer therapies that increase the progression-free survival of patients: the so-called mortality-driven AI technological trajectories. Results also suggest that this new technology can generate a technological paradigm shift for diagnostic assessment of any cancer type. However, application of these methods to medical imaging requires further assessment and validation to assist pathologists to increase the efficiency of their workflow in both routine tasks and critical cases of diagnostics.
A Clinical Approach to Training Effective Data Scientists
Venue: arXiv cs.CY 2019
Year: 2019

Like medicine, psychology, or education, data science is fundamentally an applied discipline, with most students who receive advanced degrees in the field going on to work on practical problems. Unlike these disciplines, however, data science education remains heavily focused on theory and methods, and practical coursework typically revolves around cleaned or simplified data sets that have little analog in professional applications. We believe that the environment in which new data scientists are trained should more accurately reflect that in which they will eventually practice and propose here a data science master's degree program that takes inspiration from the residency model used in medicine. Students in the suggested program would spend three years working on a practical problem with an industry, government, or nonprofit partner, supplemented with coursework in data science methods and theory. We also discuss how this program can also be implemented in shorter formats to augment existing professional masters programs in different disciplines. This approach to learning by doing is designed to fill gaps in our current approach to data science education and ensure that students develop the skills they need to practice data science in a professional context and under the many constraints imposed by that context.
TraceWalk: Semantic-based Process Graph Embedding for Consistency Checking
Venue: arXiv cs.CL 2019
Year: 2019

Process consistency checking (PCC), an interdiscipline of natural language processing (NLP) and business process management (BPM), aims to quantify the degree of (in)consistencies between graphical and textual descriptions of a process. However, previous studies heavily depend on a great deal of complex expert-defined knowledge such as alignment rules and assessment metrics, thus suffer from the problems of low accuracy and poor adaptability when applied in open-domain scenarios. To address the above issues, this paper makes the first attempt that uses deep learning to perform PCC. Specifically, we proposed TraceWalk, using semantic information of process graphs to learn latent node representations, and integrates it into a convolutional neural network (CNN) based model called TraceNet to predict consistencies. The theoretical proof formally provides the PCC's lower limit and experimental results demonstrate that our approach performs more accurately than state-of-the-art baselines.
Leveraging exploration in off-policy algorithms via normalizing flows
Venue: arXiv cs.LG 2019
Year: 2019

The ability to discover approximately optimal policies in domains with sparse rewards is crucial to applying reinforcement learning (RL) in many real-world scenarios. Approaches such as neural density models and continuous exploration (e.g., Go-Explore) have been proposed to maintain the high exploration rate necessary to find high performing and generalizable policies. Soft actor-critic(SAC) is another method for improving exploration that aims to combine efficient learning via off-policy updates while maximizing the policy entropy. In this work, we extend SAC to a richer class of probability distributions (e.g., multimodal) through normalizing flows (NF) and show that this significantly improves performance by accelerating the discovery of good policies while using much smaller policy representations. Our approach, which we call SAC-NF, is a simple, efficient,easy-to-implement modification and improvement to SAC on continuous control baselines such as MuJoCo and PyBullet Roboschool domains. Finally, SAC-NF does this while being significantly parameter efficient, using as few as 5.5% the parameters for an equivalent SAC model.
Harvey: A Greybox Fuzzer for Smart Contracts
Venue: arXiv cs.SE 2019
Year: 2019

We present Harvey, an industrial greybox fuzzer for smart contracts, which are programs managing accounts on a blockchain. Greybox fuzzing is a lightweight test-generation approach that effectively detects bugs and security vulnerabilities. However, greybox fuzzers randomly mutate program inputs to exercise new paths; this makes it challenging to cover code that is guarded by narrow checks, which are satisfied by no more than a few input values. Moreover, most real-world smart contracts transition through many different states during their lifetime, e.g., for every bid in an auction. To explore these states and thereby detect deep vulnerabilities, a greybox fuzzer would need to generate sequences of contract transactions, e.g., by creating bids from multiple users, while at the same time keeping the search space and test suite tractable. In this experience paper, we explain how Harvey alleviates both challenges with two key fuzzing techniques and distill the main lessons learned. First, Harvey extends standard greybox fuzzing with a method for predicting new inputs that are more likely to cover new paths or reveal vulnerabilities in smart contracts. Second, it fuzzes transaction sequences in a targeted and demand-driven way. We have evaluated our approach on 27 real-world contracts. Our experiments show that the underlying techniques significantly increase Harvey's effectiveness in achieving high coverage and detecting vulnerabilities, in most cases orders-of-magnitude faster; they also reveal new insights about contract code.