OpenRaiser/Intern-Atlas huggingface dataset:
Classifying Mammographic Breast Density by Residual Learning
Venue: arXiv cs.CV 2018
Year: 2018

Mammographic breast density, a parameter used to describe the proportion of breast tissue fibrosis, is widely adopted as an evaluation characteristic of the likelihood of breast cancer incidence. In this study, we present a radiomics approach based on residual learning for the classification of mammographic breast densities. Our method possesses several encouraging properties such as being almost fully automatic, possessing big model capacity and flexibility. It can obtain outstanding classification results without the necessity of result compensation using mammographs taken from different views. The proposed method was instantiated with the INbreast dataset and classification accuracies of 92.6% and 96.8% were obtained for the four BI-RADS (Breast Imaging and Reporting Data System) category task and the two BI-RADS category task,respectively. The superior performances achieved compared to the existing state-of-the-art methods along with its encouraging properties indicate that our method has a great potential to be applied as a computer-aided diagnosis tool.
Supporting Answerers with Feedback in Social Q&A
Venue: arXiv cs.SI 2018
Year: 2018

Prior research has examined the use of Social Question and Answer (Q&A) websites for answer and help seeking. However, the potential for these websites to support domain learning has not yet been realized. Helping users write effective answers can be beneficial for subject area learning for both answerers and the recipients of answers. In this study, we examine the utility of crowdsourced, criteria-based feedback for answerers on a student-centered Q&A website, Brainly.com. In an experiment with 55 users, we compared perceptions of the current rating system against two feedback designs with explicit criteria (Appropriate, Understandable, and Generalizable). Contrary to our hypotheses, answerers disagreed with and rejected the criteria-based feedback. Although the criteria aligned with answerers' goals, and crowdsourced ratings were found to be objectively accurate, the norms and expectations for answers on Brainly conflicted with our design. We conclude with implications for the design of feedback in social Q&A.
Growing and Retaining AI Talent for the United States Government
Venue: arXiv cs.CY 2018
Year: 2018

Artificial Intelligence and Machine Learning have become transformative to a number of industries, and as such many industries need for AI talent is increasing the demand for individuals with these skills. This continues to exacerbate the difficulty of acquiring and retaining talent for the United States Federal Government, both for its direct employees as well as the companies that support it. We take the position that by focusing on growing and retaining current talent through a number of cultural changes, the government can work to remediate this problem today.
Vector Learning for Cross Domain Representations
Venue: arXiv cs.LG 2018
Year: 2018

Recently, generative adversarial networks have gained a lot of popularity for image generation tasks. However, such models are associated with complex learning mechanisms and demand very large relevant datasets. This work borrows concepts from image and video captioning models to form an image generative framework. The model is trained in a similar fashion as recurrent captioning model and uses the learned weights for image generation. This is done in an inverse direction, where the input is a caption and the output is an image. The vector representation of the sentence and frames are extracted from an encoder-decoder model which is initially trained on similar sentence and image pairs. Our model conditions image generation on a natural language caption. We leverage a sequence-to-sequence model to generate synthetic captions that have the same meaning for having a robust image generation. One key advantage of our method is that the traditional image captioning datasets can be used for synthetic sentence paraphrases. Results indicate that images generated through multiple captions are better at capturing the semantic meaning of the family of captions.
Smooth Inter-layer Propagation of Stabilized Neural Networks for Classification
Venue: arXiv cs.LG 2018
Year: 2018

Recent work has studied the reasons for the remarkable performance of deep neural networks in image classification. We examine batch normalization on the one hand and the dynamical systems view of residual networks on the other hand. Our goal is in understanding the notions of stability and smoothness of the inter-layer propagation of ResNets so as to explain when they contribute to significantly enhanced performance. We postulate that such stability is of importance for the trained ResNet to transfer.
Diagnostics in Semantic Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

Over the past years, computer vision community has contributed to enormous progress in semantic image segmentation, a per-pixel classification task, crucial for dense scene understanding and rapidly becoming vital in lots of real-world applications, including driverless cars and medical imaging. Most recent models are now reaching previously unthinkable numbers (e.g., 89% mean iou on PASCAL VOC, 83% on CityScapes), and, while intersection-over-union and a range of other metrics provide the general picture of model performance, in this paper we aim to extend them into other meaningful and important for applications characteristics, answering such questions as 'how accurate the model segmentation is on small objects in the general scene?', or 'what are the sources of uncertainty that cause the model to make an erroneous prediction?'. Besides establishing a methodology that covers the performance of a single model from different perspectives, we also showcase several extensions that can be worth pursuing in order to further improve current results in semantic segmentation.
Impact of Integrated Circuit Packaging on Synaptic Dynamics of Memristive Devices
Venue: arXiv cs.ET 2018
Year: 2018

The memristor can be used as non volatile memory (NVM) and for emulating neuron behavior. It has the ability to switch between low resistance $R_{on}$ and high resistance values $R_{off}$, and exhibit the synaptic dynamic behaviour such as potentiation and depression. This paper presents a study on potentiation and depression of memristors in Quad Flat Pack. A comparison is drawn between the memristors with and without the impact of parasitics of packaging, using measured data and equivalent circuit models. The parameters in memristor and packaging models for the SPICE simulations were determined using measured data to reflect the memristor parasitics in Quad Flat Packs.
Point Location in Incremental Planar Subdivisions
Venue: arXiv cs.CG 2018
Year: 2018

We study the point location problem in incremental (possibly disconnected) planar subdivisions, that is, dynamic subdivisions allowing insertions of edges and vertices only. Specifically, we present an $O(n\log n)$-space data structure for this problem that supports queries in $O(\log^2 n)$ time and updates in $O(\log n\log\log n)$ amortized time. This is the first result that achieves polylogarithmic query and update times simultaneously in incremental (possibly disconnected) planar subdivisions. Its update time is significantly faster than the update time of the best known data structure for fully-dynamic (possibly disconnected) planar subdivisions.
Equilibria in Quantitative Concurrent Games
Venue: arXiv cs.GT 2018
Year: 2018

Synthesis of finite-state controllers from high-level specifications in multi-agent systems can be reduced to solving multi-player concurrent games over finite graphs. The complexity of solving such games with qualitative objectives for agents, such as reaching a target set, is well understood resulting in tools with applications in robotics. In this paper, we introduce quantitative concurrent graph games, where transitions have separate costs for different agents, and each agent attempts to reach its target set while minimizing its own cost along the path. In this model, a solution to the game corresponds to a set of strategies, one per agent, that forms a Nash equilibrium. We study the problem of computing the set of all Pareto-optimal Nash equilibria, and give a comprehensive analysis of its complexity and related problems such as the price of stability and the price of anarchy. In particular, while checking the existence of a Nash equilibrium is NP-complete in general, with multiple parameters contributing to the computational hardness separately, two-player games with bounded costs on individual transitions admit a polynomial-time solution.
Kernel based low-rank sparse model for single image super-resolution
Venue: arXiv cs.CV 2018
Year: 2018

Self-similarity learning has been recognized as a promising method for single image super-resolution (SR) to produce high-resolution (HR) image in recent years. The performance of learning based SR reconstruction, however, highly depends on learned representation coeffcients. Due to the degradation of input image, conventional sparse coding is prone to produce unfaithful representation coeffcients. To this end, we propose a novel kernel based low-rank sparse model with self-similarity learning for single image SR which incorporates nonlocalsimilarity prior to enforce similar patches having similar representation weights. We perform a gradual magnification scheme, using self-examples extracted from the degraded input image and up-scaled versions. To exploit nonlocal-similarity, we concatenate the vectorized input patch and its nonlocal neighbors at different locations into a data matrix which consists of similar components. Then we map the nonlocal data matrix into a high-dimensional feature space by kernel method to capture their nonlinear structures. Under the assumption that the sparse coeffcients for the nonlocal data in the kernel space should be low-rank, we impose low-rank constraint on sparse coding to share similarities among representation coeffcients and remove outliers in order that stable weights for SR reconstruction can be obtained. Experimental results demonstrate the advantage of our proposed method in both visual quality and reconstruction error.
Weakly-Supervised Localization and Classification of Proximal Femur Fractures
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we target the problem of fracture classification from clinical X-Ray images towards an automated Computer Aided Diagnosis (CAD) system. Although primarily dealing with an image classification problem, we argue that localizing the fracture in the image is crucial to make good class predictions. Therefore, we propose and thoroughly analyze several schemes for simultaneous fracture localization and classification. We show that using an auxiliary localization task, in general, improves the classification performance. Moreover, it is possible to avoid the need for additional localization annotations thanks to recent advancements in weakly-supervised deep learning approaches. Among such approaches, we investigate and adapt Spatial Transformers (ST), Self-Transfer Learning (STL), and localization from global pooling layers. We provide a detailed quantitative and qualitative validation on a dataset of 1347 femur fractures images and report high accuracy with regard to inter-expert correlation values reported in the literature. Our investigations show that i) lesion localization improves the classification outcome, ii) weakly-supervised methods improve baseline classification without any additional cost, iii) STL guides feature activations and boost performance. We plan to make both the dataset and code available.
AutoBotCatcher: Blockchain-based P2P Botnet Detection for the Internet of Things
Venue: arXiv cs.CR 2018
Year: 2018

In general, a botnet is a collection of compromised internet computers, controlled by attackers for malicious purposes. To increase attacks' success chance and resilience against defence mechanisms, modern botnets have often a decentralized P2P structure. Here, IoT devices are playing a critical role, becoming one of the major tools for malicious parties to perform attacks. Notable examples are DDoS attacks on Krebs on Security and DYN, which have been performed by IoT devices part of botnets. We take a first step towards detecting P2P botnets in IoT, by proposing AutoBotCatcher, whose design is driven by the consideration that bots of the same botnet frequently communicate with each other and form communities. As such, the purpose of AutoBotCatcher is to dynamically analyze communities of IoT devices, formed according to their network traffic flows, to detect botnets. AutoBotCatcher exploits a permissioned Byzantine Fault Tolerant (BFT) blockchain, as a state transition machine that allows collaboration of a set of pre-identified parties without trust, in order to perform collaborative and dynamic botnet detection by collecting and auditing IoT devices' network traffic flows as blockchain transactions. In this paper, we focus on the design of the AutoBotCatcher by first defining the blockchain structure underlying AutoBotCatcher, then discussing its components.
Learning and Acting in Peripersonal Space: Moving, Reaching, and Grasping
Venue: arXiv cs.RO 2018
Year: 2018

The young infant explores its body, its sensorimotor system, and the immediately accessible parts of its environment, over the course of a few months creating a model of peripersonal space useful for reaching and grasping objects around it. Drawing on constraints from the empirical literature on infant behavior, we present a preliminary computational model of this learning process, implemented and evaluated on a physical robot. The learning agent explores the relationship between the configuration space of the arm, sensing joint angles through proprioception, and its visual perceptions of the hand and grippers. The resulting knowledge is represented as the peripersonal space (PPS) graph, where nodes represent states of the arm, edges represent safe movements, and paths represent safe trajectories from one pose to another. In our model, the learning process is driven by intrinsic motivation. When repeatedly performing an action, the agent learns the typical result, but also detects unusual outcomes, and is motivated to learn how to make those unusual results reliable. Arm motions typically leave the static background unchanged, but occasionally bump an object, changing its static position. The reach action is learned as a reliable way to bump and move an object in the environment. Similarly, once a reliable reach action is learned, it typically makes a quasi-static change in the environment, moving an object from one static position to another. The unusual outcome is that the object is accidentally grasped (thanks to the innate Palmar reflex), and thereafter moves dynamically with the hand. Learning to make grasps reliable is more complex than for reaches, but we demonstrate significant progress. Our current results are steps toward autonomous sensorimotor learning of motion, reaching, and grasping in peripersonal space, based on unguided exploration and intrinsic motivation.
An Empirical Comparison of Syllabuses for Curriculum Learning
Venue: arXiv cs.LG 2018
Year: 2018

Syllabuses for curriculum learning have been developed on an ad-hoc, per task basis and little is known about the relative performance of different syllabuses. We identify a number of syllabuses used in the literature. We compare the identified syllabuses based on their effect on the speed of learning and generalization ability of a LSTM network on three sequential learning tasks. We find that the choice of syllabus has limited effect on the generalization ability of a trained network. In terms of speed of learning our results demonstrate that the best syllabus is task dependent but that a recently proposed automated curriculum learning approach - Predictive Gain, performs very competitively against all identified hand-crafted syllabuses. The best performing hand-crafted syllabus which we term Look Back and Forward combines a syllabus which steps through tasks in the order of their difficulty with a uniform distribution over all tasks. Our experimental results provide an empirical basis for the choice of syllabus on a new problem that could benefit from curriculum learning. Additionally, insights derived from our results shed light on how to successfully design new syllabuses.
Embedded-State Latent Conditional Random Fields for Sequence Labeling
Venue: arXiv cs.LG 2018
Year: 2018

Complex textual information extraction tasks are often posed as sequence labeling or \emph{shallow parsing}, where fields are extracted using local labels made consistent through probabilistic inference in a graphical model with constrained transitions. Recently, it has become common to locally parametrize these models using rich features extracted by recurrent neural networks (such as LSTM), while enforcing consistent outputs through a simple linear-chain model, representing Markovian dependencies between successive labels. However, the simple graphical model structure belies the often complex non-local constraints between output labels. For example, many fields, such as a first name, can only occur a fixed number of times, or in the presence of other fields. While RNNs have provided increasingly powerful context-aware local features for sequence tagging, they have yet to be integrated with a global graphical model of similar expressivity in the output distribution. Our model goes beyond the linear chain CRF to incorporate multiple hidden states per output label, but parametrizes their transitions parsimoniously with low-rank log-potential scoring matrices, effectively learning an embedding space for hidden states. This augmented latent space of inference variables complements the rich feature representation of the RNN, and allows exact global inference obeying complex, learned non-local output constraints. We experiment with several datasets and show that the model outperforms baseline CRF+RNN models when global output constraints are necessary at inference-time, and explore the interpretable latent structure.
UVM Based Reusable Verification IP for Wishbone Compliant SPI Master Core
Venue: arXiv cs.OH 2018
Year: 2018

The System on Chip design industry relies heavily on functional verification to ensure that the designs are bug-free. As design engineers are coming up with increasingly dense chips with much functionality, the functional verification field has advanced to provide modern verification techniques. In this paper, we present verification of a wishbone compliant Serial Peripheral Interface (SPI) Master core using a System Verilog based standard verification methodology, the Universal Verification Methodology (UVM). The reason for using UVM factory pattern with parameterized classes is to develop a robust and reusable verification IP. SPI is a full duplex communication protocol used to interface components most likely in embedded systems. We have verified an SPI Master IP core design that is wishbone compliant and compatible with SPI protocol and bus and furnished the results of our verification. We have used QuestaSim for simulation and analysis of waveforms, Integrated Metrics Center, Cadence for coverage analysis. We also propose interesting future directions for this work in developing reliable systems.
New Thread Migration Strategies for NUMA Systems
Venue: arXiv cs.DC 2018
Year: 2018

Multicore systems present on-board memory hierarchies and communication networks that influence performance when executing shared memory parallel codes. Characterising this influence is complex, and understanding the effect of particular hardware configurations on different codes is of paramount importance. In previous works, monitoring information extracted from hardware counters at runtime has been used to characterise the behaviour of each thread in the parallel code in terms of the number of floating point operations per second, operational intensity, and latency of memory access. We propose to use this information to guide thread migration strategies that improve execution efficiency by increasing locality and affinity. Different configurations of NAS Parallel OpenMP benchmarks on multicores were used to validate the benefits of the proposed thread migration strategies. Our proposed strategies produce up to 70% improvement in scenarios where locality and affinity are low, there being a small degradation in performance for codes with high locality and affinity.
Are Children Well-Supported by Their Parents Concerning Online Privacy Risks, and Who Supports the Parents?
Venue: arXiv cs.HC 2018
Year: 2018

Tablet computers are becoming ubiquitously available at home or school for young children to complement education or entertainment. However, parents of children aged 6-11 often believe that children are too young to face or comprehend online privacy issues, and often take a protective approach to restrict or monitor what children can access online, instead of discussing privacy issues with children. Parents work hard to protect their children's online safety. However, little is known how much parents are aware of the risks associated with the implicit personal data collection by the first- or third-party companies behind the mobile `apps' used by their children, and hence how well parents can safeguard their children from this kind of risks. Parents have always been playing a pivotal role in mitigating children's interactions with digital technologies --- from TV to game consoles, to personal computers --- but the rapidly changing technologies are posing challenges for parents to keep up with. There is a pressing need to understand how much parents are aware of privacy risks concerning the use of tablets and how they are managing them for their primary school-aged young children. At the same time, we must also reach out to the children themselves, who are on the frontline of these technologies, to learn how capable they are to recognise risks and how well they are supported by their parents to cope with these risks. Therefore, in the summer of 2017, we conducted face-to-face interviews with 12 families in Oxfordshire and an online survey with 250 parents. This report summarises our key findings of these two studies.
Humanoid TeenSize Open Platform NimbRo-OP
Venue: arXiv cs.RO 2018
Year: 2018

In recent years, the introduction of affordable platforms in the KidSize class of the Humanoid League has had a positive impact on the performance of soccer robots. The lack of readily available larger robots, however, severely affects the number of participants in Teen- and AdultSize and consequently the progress of research that focuses on the challenges arising with robots of larger weight and size. This paper presents the first hardware release of a low cost Humanoid TeenSize open platform for research, the first software release, and the current state of ROS-based software development. The NimbRo-OP robot was designed to be easily manufactured, assembled, repaired, and modified. It is equipped with a wide-angle camera, ample computing power, and enough torque to enable full-body motions, such as dynamic bipedal locomotion, kicking, and getting up.