OpenRaiser/Intern-Atlas huggingface dataset:
A Systematic Evaluation of Recent Deep Learning Architectures for Fine-Grained Vehicle Classification
Venue: arXiv cs.CV 2018
Year: 2018

Fine-grained vehicle classification is the task of classifying make, model, and year of a vehicle. This is a very challenging task, because vehicles of different types but similar color and viewpoint can often look much more similar than vehicles of same type but differing color and viewpoint. Vehicle make, model, and year in com- bination with vehicle color - are of importance in several applications such as vehicle search, re-identification, tracking, and traffic analysis. In this work we investigate the suitability of several recent landmark convolutional neural network (CNN) architectures, which have shown top results on large scale image classification tasks, for the task of fine-grained classification of vehicles. We compare the performance of the networks VGG16, several ResNets, Inception architectures, the recent DenseNets, and MobileNet. For classification we use the Stanford Cars-196 dataset which features 196 different types of vehicles. We investigate several aspects of CNN training, such as data augmentation and training from scratch vs. fine-tuning. Importantly, we introduce no aspects in the architectures or training process which are specific to vehicle classification. Our final model achieves a state-of-the-art classification accuracy of 94.6% outperforming all related works, even approaches which are specifically tailored for the task, e.g. by including vehicle part detections.
A User Evaluation of Automated Process Discovery Algorithms
Venue: arXiv cs.SE 2018
Year: 2018

Process mining methods allow analysts to use logs of historical executions of business processes in order to gain knowledge about the actual behavior of these processes. One of the most widely studied process mining operations is automated process discovery. An event log is taken as input by an automated process discovery method and produces a business process model as output that captures the control-flow relations between tasks that are described by the event log. In this setting, this paper provides a systematic comparative evaluation of existing implementations of automated process discovery methods with domain experts by using a real-life event log extracted from an international software engineering company and four quality metrics. The evaluation results highlight gaps and unexplored trade-offs in the field and allow researchers to improve the lacks in the automated process discovery methods in terms of usability of process discovery techniques in industry.
A Generalized Matrix Splitting Algorithm
Venue: arXiv cs.NA 2018
Year: 2018

Composite function minimization captures a wide spectrum of applications in both computer vision and machine learning. It includes bound constrained optimization, $\ell_1$ norm regularized optimization, and $\ell_0$ norm regularized optimization as special cases. This paper proposes and analyzes a new Generalized Matrix Splitting Algorithm (GMSA) for minimizing composite functions. It can be viewed as a generalization of the classical Gauss-Seidel method and the Successive Over-Relaxation method for solving linear systems in the literature. Our algorithm is derived from a novel triangle operator mapping, which can be computed exactly using a new generalized Gaussian elimination procedure. We establish the global convergence, convergence rate, and iteration complexity of GMSA for convex problems. In addition, we also discuss several important extensions of GMSA. Finally, we validate the performance of our proposed method on three particular applications: nonnegative matrix factorization, $\ell_0$ norm regularized sparse coding, and $\ell_1$ norm regularized Dantzig selector problem. Extensive experiments show that our method achieves state-of-the-art performance in term of both efficiency and efficacy.
The landscape of NeuroImage-ing research
Venue: arXiv cs.SI 2018
Year: 2018

As the field of neuroimaging grows, it can be difficult for scientists within the field to gain and maintain a detailed understanding of its ever-changing landscape. While collaboration and citation networks highlight important contributions within the field, the roles of and relations among specific areas of study can remain quite opaque. Here, we apply techniques from network science to map the landscape of neuroimaging research documented in the journal NeuroImage over the past decade. We create a network in which nodes represent research topics, and edges give the degree to which these topics tend to be covered in tandem. The network displays small-world architecture, with communities characterized by common imaging modalities and medical applications, and with bridges that integrate these distinct subfields. Using node-level analysis, we quantify the structural roles of individual topics within the neuroimaging landscape, and find high levels of clustering within the structural MRI subfield as well as increasing participation among topics related to psychiatry. The overall prevalence of a topic is unrelated to the prevalence of its neighbors, but the degree to which a topic becomes more or less popular over time is strongly related to changes in the prevalence of its neighbors. Broadly, this work presents a cohesive model for understanding the landscape of neuroimaging research across the field, in broad subfields, and within specific topic areas.
Impact of End-User Behavior on User/Network Association in HetNets
Venue: arXiv cs.NI 2018
Year: 2018

We study the impact of end-user behavior on user/network association in a HetNet with multiple service providers (SPs). Specifically, we consider the uncertainty in the service guarantees offered by SPs in a HetNet, and use Prospect Theory (PT) to model end-user decision making. We formulate user association with SPs as a multiple leader Stackelberg game where each SP offers a data rate to each user with a certain service guarantee and at a certain price, while the user chooses the best offer among multiple such bids. Using the specific example of a HetNet with one cellular base station and one WiFi access point, we show that when the end users underestimate the advertised service guarantees, then some of the Nash Equilibrium strategies under the Expected Utility Theory (EUT) model become infeasible under PT, and for those Nash Equilibria that are feasible under both EUT and PT, the resulting user utilities are less under PT. We propose resource allocation and bidding mechanisms for the SPs to mitigate these effects.
Reconfigurable Ubiquitous Computing Infrastructure for Load Balancing
Venue: arXiv cs.CY 2018
Year: 2018

Ubiquitous computing helps make data and services available to users anytime and anywhere. This makes the cooperation of devices a crucial need. In return, such cooperation causes an overload of the devices and/or networks, resulting in network malfunction and suspension of its activities. Our goal in this paper is to propose an approach of devices reconfiguration in order to help to reduce the energy consumption in ubiquitous environments. The idea is that when high-energy consumption is detected, we proceed to a change in component distribution on the devices to reduce and/or balance the energy consumption. We also investigate the possibility to detect high-energy consumption of devices/network based on devices abilities. As a result, our idea realizes a reconfiguration of devices aimed at reducing the consumption of energy and/or load balancing in ubiquitous environments.
Does supplier evaluation impact process improvement?
Venue: arXiv cs.CY 2018
Year: 2018

The research explores and examines factors for supplier evaluation and its impact on process improvement particularly aiming on a steel pipe manufacturing firm in Gujarat, India. Data was collected using in-depth interview. The questionnaire primarily involves the perception of evaluation of supplier. Factors influencing supplier evaluation and its influence on process improvement is also examined in this study. The model testing and validation were done using partial least square method. Outcomes signified that the factors that influence the evaluation of the supplier are quality, cost, delivery and supplier relationship management. The study depicted that quality and cost factors for supplier evaluation are insignificant. The delivery and supplier relationship management have the significant influence on the evaluation of the supplier. The research also depicted that supplier evaluation has a significant influence on process improvement. Many researchers have considered quality, cost and delivery as the factors for evaluating the suppliers. But for a company, it is quintessential to have a good relationship with the supplier. Hence, the factor, supplier relationship management is considered for the study. Also, the case study company focused more on quality and cost factors for the supplier evaluation of the firm. However, delivery and supplier relationship management are also equally important for a firm in evaluating the supplier.
EU General Data Protection Regulation: A Gentle Introduction
Venue: arXiv cs.CY 2018
Year: 2018

The GDPR, or the Datenschutz Grundverordnung (DSGVO) in German, is an EU Law which addresses the subject of safeguarding privacy of personal data of the citizens of the EU and EEA. It also specifies how data the collected data might be transported out of the EU/EEA. It is the first genuine effort to unify the plethora of disparate privacy regulations put forward by different regulatory bodies. The GDPR aims to not only give more control over their personal data to the citizens, but also make conformance for businesses easier by defining unified guidelines. It also presses businesses, especially those dealing with sensitive personal data, to build their information systems in a way that confirms with Privacy by Design. These regulations aim to ensure a more transparent handling and processing of personal data, and create an environment of trust and awareness on both sides, i.e., the data owner as well as the controllers/processors.
Algorithms and Training for Weighted Multiset Automata and Regular Expressions
Venue: arXiv cs.FL 2018
Year: 2018

Multiset automata are a class of automata for which the symbols can be read in any order and obtain the same result. We investigate weighted multiset automata and show how to construct them from weighted regular expressions. We present training methods to learn the weights for weighted regular expressions and for general multiset automata from data. Finally, we examine situations in which inside weights can be computed more efficiently.
ReHand: Secure Region-based Fast Handover with User Anonymity for Small Cell Networks in 5G
Venue: arXiv cs.CR 2018
Year: 2018

Due to the expectedly higher density of mobile devices and exhaust of radio resources, the fifth generation (5G) mobile networks introduce small cell concept in the radio access technologies, so-called Small Cell Networks (SCNs), to improve radio spectrum utilization. However, this increases the chance of handover due to smaller coverage of a micro base station, i.e., home eNodeB (HeNB) in 5G. Subsequently, the latency will increase as the costs of authenticated key exchange protocol, which ensures entity authentication and communication confidentiality for secure handover, also increase totally. Thus, this work presents a secure region-based handover scheme (ReHand) with user anonymity and fast revocation for SCNs in 5G. ReHand greatly reduces the communication costs when UEs roam between small cells within the region of a macro base station, i.e., eNB in 5G, and the computation costs due to the employment of symmetry-based cryptographic operations. Compared to the three elaborated related works, ReHand dramatically reduces the costs from 82.92% to 99.99%. Nevertheless, this work demonstrates the security of ReHand by theoretically formal proofs.
An Optimization Approach to the Langberg-Médard Multiple Unicast Conjecture
Venue: arXiv cs.IT 2018
Year: 2018

The Langberg-Médard multiple unicast conjecture claims that for any strongly reachable $k$-pair network, there exists a multi-flow with rate $(1,1,\dots,1)$. In a previous work, through combining and concatenating the so-called elementary flows, we have constructed a multi-flow with rate at least $(\frac{8}{9}, \frac{8}{9}, \dots, \frac{8}{9})$ for any $k$. In this paper, we examine an optimization problem arising from this construction framework. We first show that our previous construction yields a sequence of asymptotically optimal solutions to the aforementioned optimization problem. And furthermore, based on this solution sequence, we propose a perturbation framework, which not only promises a better solution for any $k \mod 4 \neq 2$ but also solves the optimization problem for the cases $k=3, 4, \dots, 10$, accordingly yielding multi-flows with the largest rate to date.
GHTraffic: A Dataset for Reproducible Research in Service-Oriented Computing
Venue: arXiv cs.SE 2018
Year: 2018

We present GHTraffic, a dataset of significant size comprising HTTP transactions extracted from GitHub data and augmented with synthetic transaction data. The dataset facilitates reproducible research on many aspects of service-oriented computing. This paper discusses use cases for such a dataset and extracts a set of requirements from these use cases. We then discuss the design of GHTraffic, and the methods and tool used to construct it. We conclude our contribution with some selective metrics that characterise GHTraffic.
Learning Oriented Cross-Entropy Approach to User Association in Load-Balanced HetNet
Venue: arXiv cs.IT 2018
Year: 2018

This letter considers optimizing user association in a heterogeneous network via utility maximization, which is a combinatorial optimization problem due to integer constraints. Different from existing solutions based on convex optimization, we alternatively propose a cross-entropy (CE)-based algorithm inspired by a sampling approach developed in machine learning. Adopting a probabilistic model, we first reformulate the original problem as a CE minimization problem which aims to learn the probability distribution of variables in the optimal association. An efficient solution by stochastic sampling is introduced to solve the learning problem. The integer constraint is directly handled by the proposed algorithm, which is robust to network deployment and algorithm parameter choices. Simulations verify that the proposed CE approach achieves near-optimal performance quite efficiently.
A Preliminary Exploration of Floating Point Grammatical Evolution
Venue: arXiv cs.AI 2018
Year: 2018

Current GP frameworks are highly effective on a range of real and simulated benchmarks. However, due to the high dimensionality of the genotypes for GP, the task of visualising the fitness landscape for GP search can be difficult. This paper describes a new framework: Floating Point Grammatical Evolution (FP-GE) which uses a single floating point genotype to encode an individual program. This encoding permits easier visualisation of the fitness landscape arbitrary problems by providing a way to map fitness against a single dimension. The new framework also makes it trivially easy to apply continuous search algorithms, such as Differential Evolution, to the search problem. In this work, the FP-GE framework is tested against several regression problems, visualising the search landscape for these and comparing different search meta-heuristics.
Functional Pearl: Theorem Proving for All (Equational Reasoning in Liquid Haskell)
Venue: arXiv cs.PL 2018
Year: 2018

Equational reasoning is one of the key features of pure functional languages such as Haskell. To date, however, such reasoning always took place externally to Haskell, either manually on paper, or mechanised in a theorem prover. This article shows how equational reasoning can be performed directly and seamlessly within Haskell itself, and be checked using Liquid Haskell. In particular, language learners --- to whom external theorem provers are out of reach --- can benefit from having their proofs mechanically checked. Concretely, we show how the equational proofs and derivations from Graham's textbook can be recast as proofs in Haskell (spoiler: they look essentially the same).
Location Privacy Preservation in Database-Driven Wireless Cognitive Networks Through Encrypted Probabilistic Data Structures
Venue: arXiv cs.NI 2018
Year: 2018

In this paper, we propose new location privacy preserving schemes for database-driven cognitive radio networks that protect secondary users' (SUs) location privacy while allowing them to learn spectrum availability in their vicinity. Our schemes harness probabilistic set membership data structures to exploit the structured nature of spectrum databases (DBs) and SUs' queries. This enables us to create a compact representation of DB that could be queried by SUs without having to share their location with DB, thus guaranteeing their location privacy. Our proposed schemes offer different cost-performance characteristics. Our first scheme relies on a simple yet powerful two-party protocol that achieves unconditional security with a plausible communication overhead by making DB send a compacted version of its content to SU which needs only to query this data structure to learn spectrum availability. Our second scheme achieves significantly lower communication and computation overhead for SUs, but requires an additional architectural entity which receives the compacted version of the database and fetches the spectrum availability information in lieu of SUs to alleviate the overhead on the latter. We show that our schemes are secure, and also demonstrate that they offer significant advantages over existing alternatives for various performance and/or security metrics.
Accurate Spectral Super-resolution from Single RGB Image Using Multi-scale CNN
Venue: arXiv cs.CV 2018
Year: 2018

Different from traditional hyperspectral super-resolution approaches that focus on improving the spatial resolution, spectral super-resolution aims at producing a high-resolution hyperspectral image from the RGB observation with super-resolution in spectral domain. However, it is challenging to accurately reconstruct a high-dimensional continuous spectrum from three discrete intensity values at each pixel, since too much information is lost during the procedure where the latent hyperspectral image is downsampled (e.g., with x10 scaling factor) in spectral domain to produce an RGB observation. To address this problem, we present a multi-scale deep convolutional neural network (CNN) to explicitly map the input RGB image into a hyperspectral image. Through symmetrically downsampling and upsampling the intermediate feature maps in a cascading paradigm, the local and non-local image information can be jointly encoded for spectral representation, ultimately improving the spectral reconstruction accuracy. Extensive experiments on a large hyperspectral dataset demonstrate the effectiveness of the proposed method.
Generative Adversarial Nets for Information Retrieval: Fundamentals and Advances
Venue: arXiv cs.IR 2018
Year: 2018

Generative adversarial nets (GANs) have been widely studied during the recent development of deep learning and unsupervised learning. With an adversarial training mechanism, GAN manages to train a generative model to fit the underlying unknown real data distribution under the guidance of the discriminative model estimating whether a data instance is real or generated. Such a framework is originally proposed for fitting continuous data distribution such as images, thus it is not straightforward to be directly applied to information retrieval scenarios where the data is mostly discrete, such as IDs, text and graphs. In this tutorial, we focus on discussing the GAN techniques and the variants on discrete data fitting in various information retrieval scenarios. (i) We introduce the fundamentals of GAN framework and its theoretic properties; (ii) we carefully study the promising solutions to extend GAN onto discrete data generation; (iii) we introduce IRGAN, the fundamental GAN framework of fitting single ID data distribution and the direct application on information retrieval; (iv) we further discuss the task of sequential discrete data generation tasks, e.g., text generation, and the corresponding GAN solutions; (v) we present the most recent work on graph/network data fitting with node embedding techniques by GANs. Meanwhile, we also introduce the relevant open-source platforms such as IRGAN and Texygen to help audience conduct research experiments on GANs in information retrieval. Finally, we conclude this tutorial with a comprehensive summarization and a prospect of further research directions for GANs in information retrieval.
Deep Curiosity Loops in Social Environments
Venue: arXiv cs.NE 2018
Year: 2018

Inspired by infants' intrinsic motivation to learn, which values informative sensory channels contingent on their immediate social environment, we developed a deep curiosity loop (DCL) architecture. The DCL is composed of a learner, which attempts to learn a forward model of the agent's state-action transition, and a novel reinforcement-learning (RL) component, namely, an Action-Convolution Deep Q-Network, which uses the learner's prediction error as reward. The environment for our agent is composed of visual social scenes, composed of sitcom video streams, thereby both the learner and the RL are constructed as deep convolutional neural networks. The agent's learner learns to predict the zero-th order of the dynamics of visual scenes, resulting in intrinsic rewards proportional to changes within its social environment. The sources of these socially informative changes within the sitcom are predominantly motions of faces and hands, leading to the unsupervised curiosity-based learning of social interaction features. The face and hand detection is represented by the value function and the social interaction optical-flow is represented by the policy. Our results suggest that face and hand detection are emergent properties of curiosity-based learning embedded in social environments.
Segmentation of Arterial Walls in Intravascular Ultrasound Cross-Sectional Images Using Extremal Region Selection
Venue: arXiv cs.CV 2018
Year: 2018

Intravascular Ultrasound (IVUS) is an intra-operative imaging modality that facilitates observing and appraising the vessel wall structure of the human coronary arteries. Segmentation of arterial wall boundaries from the IVUS images is not only crucial for quantitative analysis of the vessel walls and plaque characteristics, but is also necessary for generating 3D reconstructed models of the artery. The aim of this study is twofold. Firstly, we investigate the feasibility of using a recently proposed region detector, namely Extremal Region of Extremum Level (EREL) to delineate the luminal and media-adventitia borders in IVUS frames acquired by 20 MHz probes. Secondly, we propose a region selection strategy to label two ERELs as lumen and media based on the stability of their textural information. We extensively evaluated our selection strategy on the test set of a standard publicly available dataset containing 326 IVUS B-mode images. We showed that in the best case, the average Hausdorff Distances (HD) between the extracted ERELs and the actual lumen and media were $0.22$ mm and $0.45$ mm, respectively. The results of our experiments revealed that our selection strategy was able to segment the lumen with $\le 0.3$ mm HD to the gold standard even though the images contained major artifacts such as bifurcations, shadows, and side branches. Moreover, when there was no artifact, our proposed method was able to delineate media-adventitia boundaries with $0.31$ mm HD to the gold standard. Furthermore, our proposed segmentation method runs in time that is linear in the number of pixels in each frame. Based on the results of this work, by using a 20 MHz IVUS probe with controlled pullback, not only can we now analyze the internal structure of human arteries more accurately, but also segment each frame during the pullback procedure because of the low run time of our proposed segmentation method.