OpenRaiser/Intern-Atlas huggingface dataset:
A large annotated medical image dataset for the development and evaluation of segmentation algorithms
Venue: arXiv cs.CV 2019
Year: 2019

Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and Computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community.
Clustering-Based Codebook Design for MIMO Communication System
Venue: arXiv cs.IT 2019
Year: 2019

Codebook design is one of the core technologies in limited feedback multi-input multi-output (MIMO) communication systems. However, the conventional codebook designs usually assume MIMO vectors are uniformly distributed or isotropic. Motivated by the excellent classfication and analysis ability of clustering algorithms, we propose a K-means clustering based codebook design. First, large amounts of channel state information (CSI) is stored as the input data of the clustering, and finally divided into N clusters according to the minimal distance. The clustering centroids are used as the statistic channel information of the codebook construction which the sum distance is minimal to the real channel information. Simulation results consist with theoretical analysis in terms of the achievable rate, and demonstrate that the proposed codebook design outperforms conventional schemes, especially in the non-uniform distribution of channel scenarios.
Joint Multi-Domain Learning for Automatic Short Answer Grading
Venue: arXiv cs.CL 2019
Year: 2019

One of the fundamental challenges towards building any intelligent tutoring system is its ability to automatically grade short student answers. A typical automatic short answer grading system (ASAG) grades student answers across multiple domains (or subjects). Grading student answers requires building a supervised machine learning model that evaluates the similarity of the student answer with the reference answer(s). We observe that unlike typical textual similarity or entailment tasks, the notion of similarity is not universal here. On one hand, para-phrasal constructs of the language can indicate similarity independent of the domain. On the other hand, two words, or phrases, that are not strict synonyms of each other, might mean the same in certain domains. Building on this observation, we propose JMD-ASAG, the first joint multidomain deep learning architecture for automatic short answer grading that performs domain adaptation by learning generic and domain-specific aspects from the limited domain-wise training data. JMD-ASAG not only learns the domain-specific characteristics but also overcomes the dependence on a large corpus by learning the generic characteristics from the task-specific data itself. On a large-scale industry dataset and a benchmarking dataset, we show that our model performs significantly better than existing techniques which either learn domain-specific models or adapt a generic similarity scoring model from a large corpus. Further, on the benchmarking dataset, we report state-of-the-art results against all existing non-neural and neural models.
Towards Corner Case Detection for Autonomous Driving
Venue: arXiv cs.CV 2019
Year: 2019

The progress in autonomous driving is also due to the increased availability of vast amounts of training data for the underlying machine learning approaches. Machine learning systems are generally known to lack robustness, e.g., if the training data did rarely or not at all cover critical situations. The challenging task of corner case detection in video, which is also somehow related to unusual event or anomaly detection, aims at detecting these unusual situations, which could become critical, and to communicate this to the autonomous driving system (online use case). Such a system, however, could be also used in offline mode to screen vast amounts of data and select only the relevant situations for storing and (re)training machine learning algorithms. So far, the approaches for corner case detection have been limited to videos recorded from a fixed camera, mostly for security surveillance. In this paper, we provide a formal definition of a corner case and propose a system framework for both the online and the offline use case that can handle video signals from front cameras of a naturally moving vehicle and can output a corner case score.
Batch Virtual Adversarial Training for Graph Convolutional Networks
Venue: arXiv cs.LG 2019
Year: 2019

We present batch virtual adversarial training (BVAT), a novel regularization method for graph convolutional networks (GCNs). BVAT addresses the shortcoming of GCNs that do not consider the smoothness of the model's output distribution against local perturbations around the input. We propose two algorithms, sample-based BVAT and optimization-based BVAT, which are suitable to promote the smoothness of the model for graph-structured data by either finding virtual adversarial perturbations for a subset of nodes far from each other or generating virtual adversarial perturbations for all nodes with an optimization process. Extensive experiments on three citation network datasets Cora, Citeseer and Pubmed and a knowledge graph dataset Nell validate the effectiveness of the proposed method, which establishes state-of-the-art results in the semi-supervised node classification tasks.
GMC: Grid Based Motion Clustering in Dynamic Environment
Venue: arXiv cs.RO 2019
Year: 2019

Conventional SLAM algorithms takes a strong assumption of scene motionlessness, which limits the application in real environments. This paper tries to tackle the challenging visual SLAM issue of moving objects in dynamic environments. We present GMC, grid-based motion clustering approach, a lightweight dynamic object filtering method that is free from high-power and expensive processors. GMC encapsulates motion consistency as the statistical likelihood of detected key points within a certain region. Using this method can we provide real-time and robust correspondence algorithm that can differentiate dynamic objects with static backgrounds. We evaluate our system in public TUM dataset. To compare with the state-of-the-art methods, our system can provide more accurate results by detecting dynamic objects.
A stochastic approximation method for price-based assignment of autonomous EVs to Charging Stations
Venue: arXiv cs.GT 2019
Year: 2019

This paper proposes a method for the stochastic estimation of Charging Stations prices based on dual decomposition. Prices are determined so as to minimize the social cost of the EVs while satisfying their constraints in expectation
Homogenousness and Specificity
Venue: arXiv cs.LO 2019
Year: 2019

We interpret homogenousness as a second order property and base it on the same principle as nonmonotonic logic: there might be a small set of exceptions. We use this idea to analyse fundamental questions about defeasible inheritance systems. In an appendix, we discuss the concept of the core of a (model) set.
Robust Affordable 3D Haptic Sensation via Learning Deformation Patterns
Venue: arXiv cs.RO 2019
Year: 2019

Haptic sensation is an important modality for interacting with the real world. This paper proposes a general framework of inferring haptic forces on the surface of a 3D structure from internal deformations using a small number of physical sensors instead of employing dense sensor arrays. Using machine learning techniques, we optimize the sensor number and their placement and are able to obtain high-precision force inference for a robotic limb using as few as 9 sensors. For the optimal and sparse placement of the measurement units (strain gauges), we employ data-driven methods based on data obtained by finite element simulation. We compare data-driven approaches with model-based methods relying on geometric distance and information criteria such as Entropy and Mutual Information. We validate our approach on a modified limb of the Poppy robot and obtain 8 mm localization precision.
Incentive Compatibility, Scalability and Privacy in real time Demand Response
Venue: arXiv cs.GT 2019
Year: 2019

The high penetration of Renewable Energy Sources in modern smart grids necessitated the development of Demand Response (DR) mechanisms as well as corresponding innovative services for the emerging flexibility markets. From a game theoretic perspective, the basic key performance indicators (KPIs) for such DR mechanisms are efficiency in terms of social welfare, practical applicability, and incentive guarantees, in the sense of making it a dominant strategy for each user to act truthfully according to his/her preferences, leaving no room for cheating. In this paper, we propose a DR architecture, including a mechanism based on Ausubel clinching auction and a communication protocol, that provably guarantee both efficiency and truthful user participation. Practicality/easiness of participation is enhanced via simple queries, while user privacy issues are addressed via a distributed implementation. Simulation results confirm the desired properties, while also showing that the truthfulness property becomes even more important in markets where participants are not particularly flexible
Design of auction-based approach for market clearing in peer-to-peer market platform
Venue: arXiv cs.GT 2019
Year: 2019

This paper designs a market platform for Peer-to-Peer (P2P) energy trading in Transactive Energy (TE) systems, where prosumers and consumers actively participate in the market as seller or buyer to trade energy. An auction-based approach is used for market clearing in the proposed platform and a review of different types of auction is performed. The appropriate auction approach for market clearing in the proposed platform is designed. The proposed auction mechanism is implemented in three steps namely determination, allocation and payment. This paper identifies important P2P market clearing performance indices, which are used to compare and contrast the designed auction with different types of auction mechanisms. Comparative studies demonstrate the efficacy of the proposed auction mechanism for market clearing in the P2P platform.
PaRiS: Causally Consistent Transactions with Non-blocking Reads and Partial Replication
Venue: arXiv cs.DC 2019
Year: 2019

Geo-replicated data platforms are at the backbone of several large-scale online services. Transactional Causal Consistency (TCC) is an attractive consistency level for building such platforms. TCC avoids many anomalies of eventual consistency, eschews the synchronization costs of strong consistency, and supports interactive read-write transactions. Partial replication is another attractive design choice for building geo-replicated platforms, as it increases the storage capacity and reduces update propagation costs. This paper presents PaRiS, the first TCC system that supports partial replication and implements non-blocking parallel read operations, whose latency is paramount for the performance of read-intensive applications. PaRiS relies on a novel protocol to track dependencies, called Universal Stable Time (UST). By means of a lightweight background gossip process, UST identifies a snapshot of the data that has been installed by every DC in the system. Hence, transactions can consistently read from such a snapshot on any server in any replication site without having to block. Moreover, PaRiS requires only one timestamp to track dependencies and define transactional snapshots, thereby achieving resource efficiency and scalability. We evaluate PaRiS on a large-scale AWS deployment composed of up to 10 replication sites. We show that PaRiS scales well with the number of DCs and partitions, while being able to handle larger data-sets than existing solutions that assume full replication. We also demonstrate a performance gain of non-blocking reads vs. a blocking alternative (up to 1.47x higher throughput with 5.91x lower latency for read-dominated workloads and up to 1.46x higher throughput with 20.56x lower latency for write-heavy workloads).
A New Method To Find The Nash Equilibrium Point in Financial Transmission Rights Bidding Problem
Venue: arXiv cs.GT 2019
Year: 2019

Financial transmission right (FTR) is an important tool and an especially feature for stopping congestion charges in restructured electricity markets. Participants in the transmission market as players are assumed to be a generation company (Gencos) which also take part in an energy market and able to buy their require FTRs. In this regard, there are two types of FTR: obligation or option. There are three main questions which immediately arise for each player who is placed in the market. First, which type of FTR is the best choice second, how much power is needed to generate by each player and third, how bid prices should be offered. Deciding on these trade-offs is difficult and requires definition of special matrices to measure risk in each possible condition in the transmission market. These matrices include: possibility of flow direction alteration, probable forward and reverse power flow on each line, maximum and minimum offering FTRs and the worst condition of load variation which influence on each players decision. Based on these matrices, players try to maximize their expected payoffs by taking into account the associated risks. Supposing these matrices are known to respective players, the FTR bidding problem is modeled as a bi-level optimization based on the Nash equilibrium game theory with the upper sub-problem representing player profit maximization and the lower sub-problem representing the optimal solution to the market clearing. An eight-bus system with six players is simulated to verify the proposed method and the obtained results are illustrated the complex interaction between FTR obligation and FTR option bidding strategies. Furthermore, the results are demonstrated to be consistent between the impacts of FTR type, forecast bid offer of the other players and players preferred risk levels on FTR bidding strategies.
A Systematic Impact Study for Fuzzer-Found Compiler Bugs
Venue: arXiv cs.SE 2019
Year: 2019

Despite much recent interest in compiler randomized testing (fuzzing), the practical impact of fuzzer-found compiler bugs on real-world applications has barely been assessed. We present the first quantitative and qualitative study of the tangible impact of miscompilation bugs in a mature compiler. We follow a rigorous methodology where the bug impact over the compiled application is evaluated based on (1) whether the bug appears to trigger during compilation; (2) the extent to which generated assembly code changes syntactically due to triggering of the bug; and (3) how much such changes do cause regression test suite failures and could be used to manually trigger divergences during execution. The study is conducted with respect to the compilation of more than 10 million lines of C/C++ code from 309 Debian packages, using 12% of the historical and now fixed miscompilation bugs found by four state-of-the-art fuzzers in the Clang/LLVM compiler, as well as 18 bugs found by human users compiling real code or by formal verification. The results show that almost half of the fuzzer-found bugs propagate to the generated binaries for some packages, but rarely affect their syntax and cause two failures in total when running their test suites. User-reported and formal verification bugs do not exhibit a higher impact, with less frequently triggered bugs and one test failure. Our manual analysis of a selection of bugs, either fuzzer-found or not, suggests that none can easily trigger a runtime divergence on the packages considered in the analysis, and that in general they affect only corner cases.
CFM-BD: a distributed rule induction algorithm for building Compact Fuzzy Models in Big Data classification problems
Venue: arXiv cs.LG 2019
Year: 2019

Interpretability has always been a major concern for fuzzy rule-based classifiers. The usage of human-readable models allows them to explain the reasoning behind their predictions and decisions. However, when it comes to Big Data classification problems, fuzzy rule-based classifiers have not been able to maintain the good trade-off between accuracy and interpretability that has characterized these techniques in non-Big Data environments. The most accurate methods build too complex models composed of a large number of rules and fuzzy sets, while those approaches focusing on interpretability do not provide state-of-the-art discrimination capabilities. In this paper, we propose a new distributed learning algorithm named CFM-BD to construct accurate and compact fuzzy rule-based classification systems for Big Data. This method has been specifically designed from scratch for Big Data problems and does not adapt or extend any existing algorithm. The proposed learning process consists of three stages: 1) pre-processing based on the probability integral transform theorem; 2) rule induction inspired by CHI-BD and Apriori algorithms; 3) rule selection by means of a global evolutionary optimization. We conducted a complete empirical study to test the performance of our approach in terms of accuracy, complexity, and runtime. The results obtained were compared and contrasted with four state-of-the-art fuzzy classifiers for Big Data (FBDT, FMDT, Chi-Spark-RS, and CHI-BD). According to this study, CFM-BD is able to provide competitive discrimination capabilities using significantly simpler models composed of a few rules of less than 3 antecedents, employing 5 linguistic labels for all variables.
Optimal Distributed Covering Algorithms
Venue: arXiv cs.DC 2019
Year: 2019

We present a time-optimal deterministic distributed algorithm for approximating a minimum weight vertex cover in hypergraphs of rank $f$. This problem is equivalent to the Minimum Weight Set Cover problem in which the frequency of every element is bounded by $f$. The approximation factor of our algorithm is $(f+ε)$. Our algorithm runs in the CONGEST model and requires $O(\logΔ/ \log\logΔ)$ rounds, for constants $ε\in(0,1]$ and $f\in N^+$. This is the first distributed algorithm for this problem whose running time does not depend on the vertex weights nor the number of vertices. For constant values of $f$ and $ε$, our algorithm improves over the $(f+ε)$-approximation algorithm of KMW06 whose running time is $O(\log Δ+ \log W)$, where $W$ is the ratio between the largest and smallest vertex weights in the graph. Our algorithm also achieves an $f$-approximation for the problem in $O(f\log n)$ rounds, improving over the classical result of KVY94 that achieves a running time of $O(f\log^2 n)$. Finally, for weighted vertex cover ($f=2$) our algorithm achieves a \emph{deterministic} running time of $O(\log n)$, matching the \emph{randomized} previously best result of KY11. We also show that integer covering-programs can be reduced to the Minimum Weight Set Cover problem in the distributed setting. This allows us to achieve an $(f+ε)$-approximate integral solution in $O(\frac{\logΔ}{\log\logΔ}+(f\cdot\log M)^{1.01}\logε^{-1}(\logΔ)^{0.01})$ rounds, where $f$ bounds the number of variables in a constraint, $Δ$ bounds the number of constraints a variable appears in, and $M=\max \{1, 1/a_{\min}\}$, where $a_{min}$ is the smallest normalized constraint coefficient. This improves over the results of KMW06 for the integral case, which runs in $O(ε^{-4}\cdot f^4\cdot \log f\cdot\log(M\cdotΔ))$ rounds.
Dynamic Content Updates in Heterogeneous Wireless Networks
Venue: arXiv cs.NI 2019
Year: 2019

Content storage at the network edge is a promising solution to mitigate the excessive traffic load due to on-demand streaming applications as well as to reduce the streaming delay. To this end, cache-enabled cellular architectures can be utilized to increase the provided quality-of-service (QoS) and to reduce the network cost. However, there are certain issues to be considered in the design of the content storage strategy such that the contents should be refreshed in order to responds user`s expectations. Using a frequent cache refreshment strategy the ratio of satisfied users can be increased at an increasing network cost. In this paper, we introduce a cache refreshment strategy via leveraging learning techniques so that users' tolerance to the age of content is learned and the content is refreshed accordingly.
Slotted ALOHA on LoRaWAN - Design, Analysis, and Deployment
Venue: arXiv cs.NI 2019
Year: 2019

LoRaWAN is one of the most promising standards for long-range sensing applications. However, the high number of end devices expected in at-scale deployment, combined with the absence of an effective synchronization scheme, challenge the scalability of this standard. In this paper, we present an approach to increase network throughput through a Slotted-ALOHA overlay on LoRaWAN networks. To increase the single channel capacity, we propose to regulate the communication of LoRaWAN networks using a Slotted-ALOHA variant on the top of the Pure-ALOHA approach used by the standard; thus, no modification in pre-existing libraries is necessary. Our method is based on an innovative synchronization service that is suitable for low-cost wireless sensor nodes. We modelled the LoRaWAN channel with extensive measurement on hardware platforms, and we quantified the impact of tuning parameters on physical and medium access control layers, as well as the packet collision rate. Results show that Slotted-ALOHA supported by our synchronization service significantly improves the performance of traditional LoRaWAN networks regarding packet loss rate and network throughput.
Condition-Invariant Multi-View Place Recognition
Venue: arXiv cs.CV 2019
Year: 2019

Visual place recognition is particularly challenging when places suffer changes in its appearance. Such changes are indeed common, e.g., due to weather, night/day or seasons. In this paper we leverage on recent research using deep networks, and explore how they can be improved by exploiting the temporal sequence information. Specifically, we propose 3 different alternatives (Descriptor Grouping, Fusion and Recurrent Descriptors) for deep networks to use several frames of a sequence. We show that our approaches produce more compact and best performing descriptors than single- and multi-view baselines in the literature in two public databases.