OpenRaiser/Intern-Atlas huggingface dataset:
Description Languages for Consistency Management Scenarios Based on Examples from the Industry Automation Domain
Venue: arXiv cs.PL 2018
Year: 2018

To cope with the increasing complexity of developing and maintaining modern (software) systems, multiple abstractions (models) of the same system can be established and used to allow different domain experts to collaborate and contribute their respective expertise. This divide-and-conquer, model-based approach requires, however, support for a concurrent engineering process, i.e., providing a means of checking, restoring, and ensuring the consistency of all involved and concurrently maintained models. The task of providing such support is often referred to as consistency management. Although there exist various approaches to consistency management and numerous (industrial) case studies described in the literature on bidirectional transformations (bx), there is currently no uniform description of diverse but related industrial applications of model synchronisation and other forms of consistency management. This makes it challenging to detect similarities and differences related to requirements, constraints, applied techniques and tools. It is thus difficult to compare and transfer knowledge gained from (successful) projects to other bx approaches or even other bx tools for the same general approach. In this paper, therefore, we propose a description language for envisioned scenarios in the problem domain of consistency management, as well as a complementary description language for solution strategies in terms of method fragments and method patterns in the solution domain of Model-Driven Engineering (MDE). Our work is inspired by previous research in the bx and MDE communities, and is also based on our collective experience from over ten years of investigating a series of application scenarios in the industry automation section together with Siemens AG as an industrial partner. [Abridged due to arXiv]
A Hybrid Approach for Efficient Wireless Information and Power Transfer in Green C-RAN
Venue: arXiv cs.NI 2018
Year: 2018

In this paper, we consider a green cloud radio access network (C-RAN) with simultaneous wireless and power transfer ability. In order to reduce the energy consumed for updating the chanel state information (CSI), energy users are divided into two different groups, including the free charge group and the MIMO group. Then a semi-definite programming problem is formulated under the constraints of energy and information transmission requirements. To minimize the total energy consumption, two algorithms are developed to authorize the energy users into two group divisions in single time slot. Then the algorithms are extended to long term scenarios consisting training and long term stages, the CSI of free charge energy users are not required during the long term stage. Simulation and numerical results are presented to demonstrate the efficiency of the proposed algorithms in significantly reducing the energy consumption of C-RAN systems.
Congestion Pricing in a World of Self-driving vehicles: an Analysis of Different Strategies in Alternative Future Scenarios
Venue: arXiv cs.GT 2018
Year: 2018

The introduction of autonomous (self-driving) and shared autonomous vehicles (AVs and SAVs) will affect travel destinations and distances, mode choice, and congestion. From a traffic perspective, although some congestion reduction may be achieved (thanks to fewer crashes and tighter headways), car-trip frequencies and vehicle miles traveled (VMT) are likely to rise significantly, reducing the benefits of driverless vehicles. Congestion pricing (CP) and road tolls are key tools for moderating demand and incentivizing more socially and environmentally optimal travel choices. This work develops multiple CP and tolling strategies in alternative future scenarios, and investigates their effects on the Austin, Texas network conditions and traveler welfare, using the agent-based simulation model MATSim. Results suggest that, while all pricing strategies reduce congestion, their social welfare impacts differ in meaningful ways. More complex and advanced strategies perform better in terms of traffic conditions and traveler welfare, depending on the development of the mobility landscape of autonomous driving. The possibility to refund users by reinvesting toll revenues as traveler budgets plays a salient role in the overall efficiency of each CP strategy as well as in the public acceptability.
Effective Capacity Analysis in Ultra-Dense Wireless Networks with Random Interference
Venue: arXiv cs.IT 2018
Year: 2018

Ultra-dense networks (UDNs) provide a promising paradigm to cope with exponentially increasing mobile traffic. However, little work has to date considered unsaturated traffic with quality-of-service (QoS) requirements. This paper presents a new cross-layer analytical model to capture the unsaturated traffic of a UDN in the presence of QoS requirements. The effective capacity (EC) of the UDN is derived, taking into account small-scale channel fading and possible interference. Key properties of the EC are revealed. The amount of traffic impacts effective capacity of the UDN due to the sophisticated interactions among small base stations operating in the same frequency. The maximization of total effective capacity is formulated as a non-cooperative game in the paper. The best-response function is derived, iteratively searching the Nash equilibrium point. System simulation results indicate that our proposed model is accurate. The simulations also show the maximum allowed arrival rate with the QoS guarantee, compared with the full interference model.
Distributed Path Reconfiguration and Data Forwarding in Industrial IoT Networks
Venue: arXiv cs.NI 2018
Year: 2018

In today's typical industrial environments, the computation of the data distribution schedules is highly centralised. Typically, a central entity configures the data forwarding paths so as to guarantee low delivery delays between data producers and consumers. However, these requirements might become impossible to meet later on, due to link or node failures, or excessive degradation of their performance. In this paper, we focus on maintaining the network functionality required by the applications after such events. We avoid continuously recomputing the configuration centrally, by designing an energy efficient local and distributed path reconfiguration method. Specifically, given the operational parameters required by the applications, we provide several algorithmic functions which locally reconfigure the data distribution paths, when a communication link or a network node fails. We compare our method through simulations to other state of the art methods and we demonstrate performance gains in terms of energy consumption and data delivery success rate as well as some emerging key insights which can lead to further performance gains.
Migrating SGX Enclaves with Persistent State
Venue: arXiv cs.CR 2018
Year: 2018

Hardware-supported security mechanisms like Intel Software Guard Extensions (SGX) provide strong security guarantees, which are particularly relevant in cloud settings. However, their reliance on physical hardware conflicts with cloud practices, like migration of VMs between physical platforms. For instance, the SGX trusted execution environment (enclave) is bound to a single physical CPU. Although prior work has proposed an effective mechanism to migrate an enclave's data memory, it overlooks the migration of persistent state, including sealed data and monotonic counters; the former risks data loss whilst the latter undermines the SGX security guarantees. We show how this can be exploited to mount attacks, and then propose an improved enclave migration approach guaranteeing the consistency of persistent state. Our software-only approach enables migratable sealed data and monotonic counters, maintains all SGX security guarantees, minimizes developer effort, and incurs negligible performance overhead.
Exploiting Weak Supermodularity for Coalition-Proof Mechanisms
Venue: arXiv cs.GT 2018
Year: 2018

Under the incentive-compatible Vickrey-Clarke-Groves mechanism, coalitions of participants can influence the auction outcome to obtain higher collective profit. These manipulations were proven to be eliminated if and only if the market objective is supermodular. Nevertheless, several auctions do not satisfy the stringent conditions for supermodularity. These auctions include electricity markets, which are the main motivation of our study. To characterize nonsupermodular functions, we introduce the supermodularity ratio and the weak supermodularity. We show that these concepts provide us with tight bounds on the profitability of collusion and shill bidding. We then derive an analytical lower bound on the supermodularity ratio. Our results are verified with case studies based on the IEEE test systems.
Automatic Generation of Optimal Reductions of Distributions
Venue: arXiv cs.SY 2018
Year: 2018

A reduction of a source distribution is a collection of smaller sized distributions that are collectively equivalent to the source distribution with respect to the property of decomposability. That is, an arbitrary language is decomposable with respect to the source distribution if and only if it is decomposable with respect to each smaller sized distribution (in the reduction). The notion of reduction of distributions has previously been proposed to improve the complexity of decomposability verification. In this work, we address the problem of generating (optimal) reductions of distributions automatically. A (partial) solution to this problem is provided, which consists of 1) an incremental algorithm for the production of candidate reductions and 2) a reduction validation procedure. In the incremental production stage, backtracking is applied whenever a candidate reduction that cannot be validated is produced. A strengthened substitution-based proof technique is used for reduction validation, while a fixed template of candidate counter examples is used for reduction refutation; put together, they constitute our (partial) solution to the reduction verification problem. In addition, we show that a recursive approach for the generation of (small) reductions is easily supported.
Regain Sliding super point from distributed edge routers by GPU
Venue: arXiv cs.DC 2018
Year: 2018

Sliding super point is a special host defined under sliding time window with which there are huge other hosts contact. It plays important roles in network security and management. But how to detect them in real time from nowadays high-speed network which contains several distributed routers is a hard task. Distributed sliding super point detection requires an algorithm that can estimate the number of contacting hosts incrementally, scan packets faster than their flowing speed and reconstruct sliding super point at the end of a time period. But no existing algorithm satisfies these three requirements simultaneously. To solve this problem, this paper firstly proposed a distributed sliding super point detection algorithm running on GPU. The advantage of this algorithm comes from a novel sliding estimator, which can estimate contacting host number incrementally under a sliding window, and a set of reversible hash functions, by which sliding super points could be regained without storing additional data such as IP list. There are two main procedures in this algorithm: packets scanning and sliding super points reconstruction. Both could run parallel without any data reading conflict. When deployed on a low cost GPU, this algorithm could deal with traffic with bandwidth as high as 680 Gb/s. A real world core network traffic is used to evaluate the performance of this sliding super point detection algorithm on a cheap GPU, Nvidia GTX950 with 4 GB graphic memory. Experiments comparing with other algorithms under discrete time window show that this algorithm has the highest accuracy. Under sliding time widow, this algorithm has the same performance as in discrete time window, where no other algorithms can work.
COBRAS: Fast, Iterative, Active Clustering with Pairwise Constraints
Venue: arXiv cs.LG 2018
Year: 2018

Constraint-based clustering algorithms exploit background knowledge to construct clusterings that are aligned with the interests of a particular user. This background knowledge is often obtained by allowing the clustering system to pose pairwise queries to the user: should these two elements be in the same cluster or not? Active clustering methods aim to minimize the number of queries needed to obtain a good clustering by querying the most informative pairs first. Ideally, a user should be able to answer a couple of these queries, inspect the resulting clustering, and repeat these two steps until a satisfactory result is obtained. We present COBRAS, an approach to active clustering with pairwise constraints that is suited for such an interactive clustering process. A core concept in COBRAS is that of a super-instance: a local region in the data in which all instances are assumed to belong to the same cluster. COBRAS constructs such super-instances in a top-down manner to produce high-quality results early on in the clustering process, and keeps refining these super-instances as more pairwise queries are given to get more detailed clusterings later on. We experimentally demonstrate that COBRAS produces good clusterings at fast run times, making it an excellent candidate for the iterative clustering scenario outlined above.
The Fine Structure of Preferential Attachment Graphs I: Somewhere-Denseness
Venue: arXiv cs.DM 2018
Year: 2018

Preferential attachment graphs are random graphs designed to mimic properties of typical real world networks. They are constructed by a random process that iteratively adds vertices and attaches them preferentially to vertices that already have high degree. We use improved concentration bounds for vertex degrees to show that preferential attachment graphs contain asymptotically almost surely (a.a.s.) a one-subdivided clique of size at least $(\log n)^{1/4}$. Therefore, preferential attachment graphs are a.a.s somewhere-dense. This implies that algorithmic techniques developed for sparse graphs are not directly applicable to them. The concentration bounds state: Assuming that the exact degree $d$ of a fixed vertex (or set of vertices) at some early time $t$ of the random process is known, the probability distribution of $d$ is sharply concentrated as the random process evolves if and only if $d$ is large at time $t$.
MIMO Over-the-Air Computation for High-Mobility Multi-Modal Sensing
Venue: arXiv cs.IT 2018
Year: 2018

In future Internet-of-Things networks, sensors or even access points can be mounted on ground/aerial vehicles for smart-city surveillance or environment monitoring. To support the high-mobility sensing with low network latency, a technique called over-the-air-computation (AirComp) was recently developed which enables an access-point to receive a desired function of sensing-data from concurrent-transmissions by exploiting the superposition property of a multi-access-channel. This work aims at further developing AirComp for next-generation multi-antenna multi-modal sensor networks. Specifically, we design beamforming and channel-feedback techniques for multi-function AirComp. Given the objective of minimizing sum-mean-squared-error of computed functions, the optimization of receive-beamforming for multi-function AirComp is a NP-hard problem. The approximate problem based on tightening transmission-power constraints, however, is shown to be solvable using differential-geometry. The solution is proved to be the weighted-centroid of points on a Grassmann-manifold, where each point represents the subspace spanned by the channel matrix of a sensor. As a by-product, the beamforming problem is found to have the same form as the classic problem of multicast-beamforming, establishing the AirComp-multicasting-duality. Its significance lies in making the said Grassmannian-centroid solution transferable to the latter problem which otherwise is solved using the computation-intensive semidefinite-relaxation-technique. Last, building on the AirComp-beamforming solution, an efficient channel-feedback technique is designed for an access-point to receive the beamformer from distributed sensor transmissions of designed signals that are functions of local channel-state-information.
Proof-of-Concept Examples of Performance-Transparent Programming Models
Venue: arXiv cs.PL 2018
Year: 2018

Machine-specific optimizations command the machine to behave in a specific way. As current programming models largely leave machine details unexposed, they cannot accommodate direct encoding of such commands. In previous work we have proposed the design of performance-transparent programming models to facilitate this use-case; this report contains proof-of-concept examples of such programming models. We demonstrate how programming model abstractions may reveal the memory footprint, vector unit utilization and data reuse of an application, with prediction accuracy ranging from 0 to 25 \%.
Deep Recurrent Neural Networks for Product Attribute Extraction in eCommerce
Venue: arXiv cs.CL 2018
Year: 2018

Extracting accurate attribute qualities from product titles is a vital component in delivering eCommerce customers with a rewarding online shopping experience via an enriched faceted search. We demonstrate the potential of Deep Recurrent Networks in this domain, primarily models such as Bidirectional LSTMs and Bidirectional LSTM-CRF with or without an attention mechanism. These have improved overall F1 scores, as compared to the previous benchmarks (More et al.) by at least 0.0391, showcasing an overall precision of 97.94%, recall of 94.12% and the F1 score of 0.9599. This has made us achieve a significant coverage of important facets or attributes of products which not only shows the efficacy of deep recurrent models over previous machine learning benchmarks but also greatly enhances the overall customer experience while shopping online.
Pancreas Segmentation in CT and MRI Images via Domain Specific Network Designing and Recurrent Neural Contextual Learning
Venue: arXiv cs.CV 2018
Year: 2018

Automatic pancreas segmentation in radiology images, eg., computed tomography (CT) and magnetic resonance imaging (MRI), is frequently required by computer-aided screening, diagnosis, and quantitative assessment. Yet pancreas is a challenging abdominal organ to segment due to the high inter-patient anatomical variability in both shape and volume metrics. Recently, convolutional neural networks (CNNs) have demonstrated promising performance on accurate segmentation of pancreas. However, the CNN-based method often suffers from segmentation discontinuity for reasons such as noisy image quality and blurry pancreatic boundary. From this point, we propose to introduce recurrent neural networks (RNNs) to address the problem of spatial non-smoothness of inter-slice pancreas segmentation across adjacent image slices. To inference initial segmentation, we first train a 2D CNN sub-network, where we modify its network architecture with deep-supervision and multi-scale feature map aggregation so that it can be trained from scratch with small-sized training data and presents superior performance than transferred models. Thereafter, the successive CNN outputs are processed by another RNN sub-network, which refines the consistency of segmented shapes. More specifically, the RNN sub-network consists convolutional long short-term memory (CLSTM) units in both top-down and bottom-up directions, which regularizes the segmentation of an image by integrating predictions of its neighboring slices. We train the stacked CNN-RNN model end-to-end and perform quantitative evaluations on both CT and MRI images.
Learning to generate classifiers
Venue: arXiv cs.LG 2018
Year: 2018

We train a network to generate mappings between training sets and classification policies (a 'classifier generator') by conditioning on the entire training set via an attentional mechanism. The network is directly optimized for test set performance on an training set of related tasks, which is then transferred to unseen 'test' tasks. We use this to optimize for performance in the low-data and unsupervised learning regimes, and obtain significantly better performance in the 10-50 datapoint regime than support vector classifiers, random forests, XGBoost, and k-nearest neighbors on a range of small datasets.
Contrast-Oriented Deep Neural Networks for Salient Object Detection
Venue: arXiv cs.CV 2018
Year: 2018

Deep convolutional neural networks have become a key element in the recent breakthrough of salient object detection. However, existing CNN-based methods are based on either patch-wise (region-wise) training and inference or fully convolutional networks. Methods in the former category are generally time-consuming due to severe storage and computational redundancies among overlapping patches. To overcome this deficiency, methods in the second category attempt to directly map a raw input image to a predicted dense saliency map in a single network forward pass. Though being very efficient, it is arduous for these methods to detect salient objects of different scales or salient regions with weak semantic information. In this paper, we develop hybrid contrast-oriented deep neural networks to overcome the aforementioned limitations. Each of our deep networks is composed of two complementary components, including a fully convolutional stream for dense prediction and a segment-level spatial pooling stream for sparse saliency inference. We further propose an attentional module that learns weight maps for fusing the two saliency predictions from these two streams. A tailored alternate scheme is designed to train these deep networks by fine-tuning pre-trained baseline models. Finally, a customized fully connected CRF model incorporating a salient contour feature embedding can be optionally applied as a post-processing step to improve spatial coherence and contour positioning in the fused result from these two streams. Extensive experiments on six benchmark datasets demonstrate that our proposed model can significantly outperform the state of the art in terms of all popular evaluation metrics.
Delaunay Triangulations of Points on Circles
Venue: arXiv cs.CG 2018
Year: 2018

Delaunay triangulations of a point set in the Euclidean plane are ubiquitous in a number of computational sciences, including computational geometry. Delaunay triangulations are not well defined as soon as 4 or more points are concyclic but since it is not a generic situation, this difficulty is usually handled by using a (symbolic or explicit) perturbation. As an alternative, we propose to define a canonical triangulation for a set of concyclic points by using a max-min angle characterization of Delaunay triangulations. This point of view leads to a well defined and unique triangulation as long as there are no symmetric quadruples of points. This unique triangulation can be computed in quasi-linear time by a very simple algorithm.