OpenRaiser/Intern-Atlas huggingface dataset:
Software-Defined Network (SDN) Data Plane Security: Issues, Solutions and Future Directions
Venue: arXiv cs.CR 2018
Year: 2018

Software-Defined Network (SDN) radically changes the network architecture by decoupling the network logic from the underlying forwarding devices. This architectural change rejuvenates the network-layer granting centralized management and re-programmability of the networks. From a security perspective, SDN separates security concerns into control and data plane, and this architectural recomposition brings up exciting opportunities and challenges. The overall perception is that SDN capabilities will ultimately result in improved security. However, in its raw form, SDN could potentially make networks more vulnerable to attacks and harder to protect. In this paper, we focus on identifying challenges faced in securing the data plane of SDN - one of the least explored but most critical components of this technology. We formalize this problem space, identify potential attack scenarios while highlighting possible vulnerabilities and establish a set of requirements and challenges to protect the data plane of SDNs. Moreover, we undertake a survey of existing solutions with respect to the identified threats, identifying their limitations and offer future research directions.
Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using deep convolutional neural networks
Venue: arXiv cs.CV 2018
Year: 2018

Computerized Tomography Angiography (CTA) based follow-up of Abdominal Aortic Aneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essential to evaluate the progress of the patient and detect complications. In this context, accurate quantification of post-operative thrombus volume is required. However, a proper evaluation is hindered by the lack of automatic, robust and reproducible thrombus segmentation algorithms. We propose a new fully automatic approach based on Deep Convolutional Neural Networks (DCNN) for robust and reproducible thrombus region of interest detection and subsequent fine thrombus segmentation. The DetecNet detection network is adapted to perform region of interest extraction from a complete CTA and a new segmentation network architecture, based on Fully Convolutional Networks and a Holistically-Nested Edge Detection Network, is presented. These networks are trained, validated and tested in 13 post-operative CTA volumes of different patients using a 4-fold cross-validation approach to provide more robustness to the results. Our pipeline achieves a Dice score of more than 82% for post-operative thrombus segmentation and provides a mean relative volume difference between ground truth and automatic segmentation that lays within the experienced human observer variance without the need of human intervention in most common cases.
Input Voltage and Current Sensorless Control of a Single Phase AC-DC Boost Converter
Venue: arXiv cs.SY 2018
Year: 2018

It is well-known that measuring the input voltage and current, as the feedforward and feedback terms, are vital for the controller design in the problem of power factor compensation of an AC-DC boost converter. Traditional adaptive scenarios correspond to the simultaneous estimation of these variables are failed because the system dynamics is not in the classical adaptive form. In this paper, the system dynamics is immersed to a proper form by a new filtered transformation to overcome the obstacle. The phase and amplitude of the input voltage along with the input current is estimated from the output voltage with global convergent. An application of the proposed estimator is presented in conjunction with a well-known dynamic controller. The estimator and controller performances are evaluated by some realistic simulations.
Differentially Private Hierarchical Count-of-Counts Histograms
Venue: arXiv cs.DB 2018
Year: 2018

We consider the problem of privately releasing a class of queries that we call hierarchical count-of-counts histograms. Count-of-counts histograms partition the rows of an input table into groups (e.g., group of people in the same household), and for every integer j report the number of groups of size j. Hierarchical count-of-counts queries report count-of-counts histograms at different granularities as per hierarchy defined on an attribute in the input data (e.g., geographical location of a household at the national, state and county levels). In this paper, we introduce this problem, along with appropriate error metrics and propose a differentially private solution that generates count-of-counts histograms that are consistent across all levels of the hierarchy.
Bridging the Gap Between 2D and 3D Organ Segmentation with Volumetric Fusion Net
Venue: arXiv cs.CV 2018
Year: 2018

There has been a debate on whether to use 2D or 3D deep neural networks for volumetric organ segmentation. Both 2D and 3D models have their advantages and disadvantages. In this paper, we present an alternative framework, which trains 2D networks on different viewpoints for segmentation, and builds a 3D Volumetric Fusion Net (VFN) to fuse the 2D segmentation results. VFN is relatively shallow and contains much fewer parameters than most 3D networks, making our framework more efficient at integrating 3D information for segmentation. We train and test the segmentation and fusion modules individually, and propose a novel strategy, named cross-cross-augmentation, to make full use of the limited training data. We evaluate our framework on several challenging abdominal organs, and verify its superiority in segmentation accuracy and stability over existing 2D and 3D approaches.
An End-to-end Neural Natural Language Interface for Databases
Venue: arXiv cs.DB 2018
Year: 2018

The ability to extract insights from new data sets is critical for decision making. Visual interactive tools play an important role in data exploration since they provide non-technical users with an effective way to visually compose queries and comprehend the results. Natural language has recently gained traction as an alternative query interface to databases with the potential to enable non-expert users to formulate complex questions and information needs efficiently and effectively. However, understanding natural language questions and translating them accurately to SQL is a challenging task, and thus Natural Language Interfaces for Databases (NLIDBs) have not yet made their way into practical tools and commercial products. In this paper, we present DBPal, a novel data exploration tool with a natural language interface. DBPal leverages recent advances in deep models to make query understanding more robust in the following ways: First, DBPal uses a deep model to translate natural language statements to SQL, making the translation process more robust to paraphrasing and other linguistic variations. Second, to support the users in phrasing questions without knowing the database schema and the query features, DBPal provides a learned auto-completion model that suggests partial query extensions to users during query formulation and thus helps to write complex queries.
A Study of Student Learning Skills Using Fuzzy Relation Equations
Venue: arXiv cs.AI 2018
Year: 2018

Fuzzy relation equations (FRE)are associated with the composition of binary fuzzy relations. In the present work FRE are used as a tool for studying the process of learning a new subject matter by a student class. A classroom application and other csuitable examples connected to the student learning of the derivative are also presented illustrating our results and useful conclusions are obtained.
A Note on the Hardness of the Critical Tuple Problem
Venue: arXiv cs.DB 2018
Year: 2018

The notion of critical tuple was introduced by Miklau and Suciu (Gerome Miklau and Dan Suciu. A formal analysis of information disclosure in data exchange. J. Comput. Syst. Sci., 73(3):507-534, 2007), who also claimed that the problem of checking whether a tuple is non-critical is complete for the second level of the polynomial hierarchy. Kostylev identified an error in the 12-page-long hardness proof. It turns out that the issue is rather fundamental: the proof can be adapted to show hardness of a relative variant of tuple-non-criticality, but we have neither been able to prove the original claim nor found an algorithm for it of lower complexity. In this note we state formally the relative variant and present an alternative, simplified proof of its hardness; we also give an NP-hardness proof for the original problem, the best lower bound we have been able to show. Hence, the precise complexity of the original critical tuple problem remains open.
A Review on Image Texture Analysis Methods
Venue: arXiv cs.CV 2018
Year: 2018

Texture classification is an active topic in image processing which plays an important role in many applications such as image retrieval, inspection systems, face recognition, medical image processing, etc. There are many approaches extracting texture features in gray-level images such as local binary patterns, gray level co-occurrence matrices, statistical features, skeleton, scale invariant feature transform, etc. The texture analysis methods can be categorized in 4 groups titles: statistical methods, structural methods, filter-based and model based approaches. In many related researches, authors have tried to extract color and texture features jointly. In this respect, combined methods are considered as efficient image analysis descriptors. Mostly important challenges in image texture analysis are rotation sensitivity, gray scale variations, noise sensitivity, illumination and brightness conditions, etc. In this paper, we review most efficient and state-of-the-art image texture analysis methods. Also, some texture classification approaches are survived.
Decoding Superposed LoRa Signals
Venue: arXiv cs.NI 2018
Year: 2018

Long-range low-power wireless communications, such as LoRa, are used in many IoT and environmental monitoring applications. They typically increase the communication range to several kilometers, at the cost of reducing the bitrate to a few bits per seconds. Collisions further reduce the performance of these communications. In this paper, we propose two algorithms to decode colliding signals: one algorithm requires the transmitters to be slightly desynchronized, and the other requires the transmitters to be synchronized. To do so, we use the timing information to match the correct symbols to the correct transmitters. We show that our algorithms are able to significantly improve the overall throughput of LoRa.
Towards Highly Accurate Coral Texture Images Classification Using Deep Convolutional Neural Networks and Data Augmentation
Venue: arXiv cs.CV 2018
Year: 2018

The recognition of coral species based on underwater texture images pose a significant difficulty for machine learning algorithms, due to the three following challenges embedded in the nature of this data: 1) datasets do not include information about the global structure of the coral; 2) several species of coral have very similar characteristics; and 3) defining the spatial borders between classes is difficult as many corals tend to appear together in groups. For this reason, the classification of coral species has always required an aid from a domain expert. The objective of this paper is to develop an accurate classification model for coral texture images. Current datasets contain a large number of imbalanced classes, while the images are subject to inter-class variation. We have analyzed 1) several Convolutional Neural Network (CNN) architectures, 2) data augmentation techniques and 3) transfer learning. We have achieved the state-of-the art accuracies using different variations of ResNet on the two current coral texture datasets, EILAT and RSMAS.
A Compressed Sensing Approach for Distribution Matching
Venue: arXiv cs.IT 2018
Year: 2018

In this work, we formulate the fixed-length distribution matching as a Bayesian inference problem. Our proposed solution is inspired from the compressed sensing paradigm and the sparse superposition (SS) codes. First, we introduce sparsity in the binary source via position modulation (PM). We then present a simple and exact matcher based on Gaussian signal quantization. At the receiver, the dematcher exploits the sparsity in the source and performs low-complexity dematching based on generalized approximate message-passing (GAMP). We show that GAMP dematcher and spatial coupling lead to asymptotically optimal performance, in the sense that the rate tends to the entropy of the target distribution with vanishing reconstruction error in a proper limit. Furthermore, we assess the performance of the dematcher on practical Hadamard-based operators. A remarkable feature of our proposed solution is the possibility to: i) perform matching at the symbol level (nonbinary); ii) perform joint channel coding and matching.
mQAPViz: A divide-and-conquer multi-objective optimization algorithm to compute large data visualizations
Venue: arXiv cs.HC 2018
Year: 2018

Algorithms for data visualizations are essential tools for transforming data into useful narratives. Unfortunately, very few visualization algorithms can handle the large datasets of many real-world scenarios. In this study, we address the visualization of these datasets as a Multi-Objective Optimization Problem. We propose mQAPViz, a divide-and-conquer multi-objective optimization algorithm to compute large-scale data visualizations. Our method employs the Multi-Objective Quadratic Assignment Problem (mQAP) as the mathematical foundation to solve the visualization task at hand. The algorithm applies advanced sampling techniques originating from the field of machine learning and efficient data structures to scale to millions of data objects. The algorithm allocates objects onto a 2D grid layout. Experimental results on real-world and large datasets demonstrate that mQAPViz is a competitive alternative to existing techniques.
Sparse Matrix-Matrix Multiplication on Multilevel Memory Architectures : Algorithms and Experiments
Venue: arXiv cs.DC 2018
Year: 2018

Architectures with multiple classes of memory media are becoming a common part of mainstream supercomputer deployments. So called multi-level memories offer differing characteristics for each memory component including variation in bandwidth, latency and capacity. This paper investigates the performance of sparse matrix multiplication kernels on two leading high-performance computing architectures -- Intel's Knights Landing processor and NVIDIA's Pascal GPU. We describe a data placement method and a chunking-based algorithm for our kernels that exploits the existence of the multiple memory spaces in each hardware platform. We evaluate the performance of these methods w.r.t. standard algorithms using the auto-caching mechanisms. Our results show that standard algorithms that exploit cache reuse performed as well as multi-memory-aware algorithms for architectures such as KNLs where the memory subsystems have similar latencies. However, for architectures such as GPUs where memory subsystems differ significantly in both bandwidth and latency, multi-memory-aware methods are crucial for good performance. In addition, our new approaches permit the user to run problems that require larger capacities than the fastest memory of each compute node without depending on the software-managed cache mechanisms.
ROLP: Runtime Object Lifetime Profiling for Big Data Memory Management
Venue: arXiv cs.DC 2018
Year: 2018

Low latency services such as credit-card fraud detection and website targeted advertisement rely on Big Data platforms (e.g., Lucene, Graphchi, Cassandra) which run on top of memory managed runtimes, such as the JVM. These platforms, however, suffer from unpredictable and unacceptably high pause times due to inadequate memory management decisions (e.g., allocating objects with very different lifetimes next to each other, resulting in memory fragmentation). This leads to long and frequent application pause times, breaking Service Level Agreements (SLAs). This problem has been previously identified and results show that current memory management techniques are ill-suited for applications that hold in memory massive amounts of middle to long-lived objects (which is the case for a wide spectrum of Big Data applications). Previous works try to reduce such application pauses by allocating objects off-heap or in special allocation regions/generations, thus alleviating the pressure on memory management. However, all these solutions require a combination of programmer effort and knowledge, source code access, or off-line profiling, with clear negative impact on programmer productivity and/or application performance. This paper presents ROLP, a runtime object lifetime profiling system. ROLP profiles application code at runtime in order to identify which allocation contexts create objects with middle to long lifetimes, given that such objects need to be handled differently (regarding short-lived ones). This profiling information greatly improves memory management decisions, leading to long tail latencies reduction of up to 51% for Lucene, 85% for GraphChi, and 60% for Cassandra, with negligible throughput and memory overhead. ROLP is implemented for the OpenJDK 8 HotSpot JVM and it does not require any programmer effort or source code access.
5G and Beyond: Smart Devices as part of the Network Fabric
Venue: arXiv cs.NI 2018
Year: 2018

5G networks mainly rely on infrastructure-centric cellular solutions to address data traffic and service demands. Continuously scaling infrastructure-centric cellular networks is not exempt of challenges, and beyond 5G networks should consider the active coexistence and coordination of infrastructure-centric and device-centric wireless networks. Device-centric wireless networks will build from D2D and MCNs. Device-centric wireless networks can push the limits of edge computing and networking to smart devices (including smartphones, vehicles, machines and robots), and exploit their mobile computing, storage and connectivity capabilities. These capabilities can be more efficiently utilized using demand-driven opportunistic networking that establishes the connections between devices and nodes based on their capacity to support the requested demand and services. This paper presents results from experimental field tests that demonstrate the cellular spectral efficiency gains that can be achieved from the combined use of device-centric wireless communications and demand-driven opportunistic networking. The field trials demonstrate that these technologies can improve the cellular spectral efficiency of conventional cellular communications by up to a factor of 4.7 and 12 in outdoor pedestrian and vehicular scenarios, respectively. These gains have been obtained using 4G and IEEE 802.11 technologies. However, the potential of device-centric wireless networks is not constrained to any particular radio interface. The results presented in this paper provide empirical evidences that further motivate progressing towards a new paradigm where edge networking capabilities are moved to smart mobile devices that become part of the network fabric, and can opportunistically and locally integrate network management functions to ensure that sufficient resources are placed where the demand arises.
Predicting Electric Vehicle Charging Station Usage: Using Machine Learning to Estimate Individual Station Statistics from Physical Configurations of Charging Station Networks
Venue: arXiv cs.LG 2018
Year: 2018

Electric vehicles (EVs) have been gaining popularity due to their environmental friendliness and efficiency. EV charging station networks are scalable solutions for supporting increasing numbers of EVs within modern electric grid constraints, yet few tools exist to aid the physical configuration design of new networks. We use neural networks to predict individual charging station usage statistics from the station's physical location within a network. We have shown this quickly gives accurate estimates of average usage statistics given a proposed configuration, without the need for running many computationally expensive simulations. The trained neural network can help EV charging network designers rapidly test various placements of charging stations under additional individual constraints in order to find an optimal configuration given their design objectives.
Virtualized Application Function Chaining: Maximizing the Wearable System Lifetime
Venue: arXiv cs.NI 2018
Year: 2018

The number of smart devices wear and carry by users is growing rapidly which is driven by innovative new smart wearables and interesting service o erings. This has led to applications that utilize multiple devices around the body to provide immersive environments such as mixed reality. These applications rely on a number of di erent types of functions such as sensing, communication and various types of processing, that require considerable resources. Thus one of the major challenges in supporting of these applications is dependent on the battery lifetime of the devices that provide the necessary functionality. The battery lifetime can be extended by either incorporating a battery with larger capacity and/or by utilizing the available resources e ciently. However, the increases in battery capacity are not keeping up with the demand and larger batteries add to both the weight and size of the device. Thus, the focus of this paper is to improve the battery e ciency through intelligent resources utilization. We show that, when the same resource is available on multiple devices that form part of the wearable system, and or is in close proximity, it is possible consider them as a resource pool and further utilize them intelligently to improve the system lifetime. Speci cally, we formulate the function allocation algorithm as a Mixed Integer Linear Programming (MILP) optimization problem and propose an e cient heuristic solution. The experimental data driven simulation results show that approximately 40-50% system battery life improvement can be achieved with proper function allocation and orchestration.
A Bi-population Particle Swarm Optimizer for Learning Automata based Slow Intelligent System
Venue: arXiv cs.NE 2018
Year: 2018

Particle Swarm Optimization (PSO) is an Evolutionary Algorithm (EA) that utilizes a swarm of particles to solve an optimization problem. Slow Intelligence System (SIS) is a learning framework which slowly learns the solution to a problem performing a series of operations. Moreover, Learning Automata (LA) are minuscule but effective decision making entities which are best suited to act as a controller component. In this paper, we combine two isolate populations of PSO to forge the Adaptive Intelligence Optimizer (AIO) which harnesses the advantages of a bi-population PSO to escape from the local minimum and avoid premature convergence. Furthermore, using the rich framework of SIS and the nifty control theory that LA derived from, we find the perfect matching between SIS and LA where acting slowly is the pillar of both of them. Both SIS and LA need time to converge to the optimal decision where this enables AIO to outperform standard PSO having an incomparable performance on evolutionary optimization benchmark functions.