OpenRaiser/Intern-Atlas huggingface dataset:
Evolution in complex objects
Venue: arXiv cs.SE 2018
Year: 2018

This paper describes work carried out on a model for the evolution of graph classes in complex objects. By defining evolution rules and propagation strategies on graph classes, we aim to define a user-definable means to manage data evolution model which tackles the complex nature of the classes managed, using the concepts defined in object systems. So, depending on their needs and on those of the targeted application, designers can choose the evolution mechanism they consider to suit them best. They can either create new evolutions or reuse predefined ones to respond to a given need.
Shared Services Center for E-Government Policy
Venue: arXiv cs.SE 2018
Year: 2018

It is a general opinion that applicative cooperation represents a useful vehicle for the development of e-government. At the architectural level, solutions for applicative cooperation are quite stable, but organizational and methodological problems prevent the expected and needed development of cooperation among different administrations. Moreover, the introduction of the digital government requires a considerable involvement of resources that can be unsustainable for small public administrations. This work shows how the above mentioned problems can be (partially) solved with the introduction of a Shared Services Center (SSC).
Towards an Understanding of Entity-Oriented Search Intents
Venue: arXiv cs.IR 2018
Year: 2018

Entity-oriented search deals with a wide variety of information needs, from displaying direct answers to interacting with services. In this work, we aim to understand what are prominent entity-oriented search intents and how they can be fulfilled. We develop a scheme of entity intent categories, and use them to annotate a sample of queries. Specifically, we annotate unique query refiners on the level of entity types. We observe that, on average, over half of those refiners seek to interact with a service, while over a quarter of the refiners search for information that may be looked up in a knowledge base.
Complex-valued Neural Networks with Non-parametric Activation Functions
Venue: arXiv cs.NE 2018
Year: 2018

Complex-valued neural networks (CVNNs) are a powerful modeling tool for domains where data can be naturally interpreted in terms of complex numbers. However, several analytical properties of the complex domain (e.g., holomorphicity) make the design of CVNNs a more challenging task than their real counterpart. In this paper, we consider the problem of flexible activation functions (AFs) in the complex domain, i.e., AFs endowed with sufficient degrees of freedom to adapt their shape given the training data. While this problem has received considerable attention in the real case, a very limited literature exists for CVNNs, where most activation functions are generally developed in a split fashion (i.e., by considering the real and imaginary parts of the activation separately) or with simple phase-amplitude techniques. Leveraging over the recently proposed kernel activation functions (KAFs), and related advances in the design of complex-valued kernels, we propose the first fully complex, non-parametric activation function for CVNNs, which is based on a kernel expansion with a fixed dictionary that can be implemented efficiently on vectorized hardware. Several experiments on common use cases, including prediction and channel equalization, validate our proposal when compared to real-valued neural networks and CVNNs with fixed activation functions.
Classification of Breast Cancer Histology using Deep Learning
Venue: arXiv cs.CV 2018
Year: 2018

Breast Cancer is a major cause of death worldwide among women. Hematoxylin and Eosin (H&E) stained breast tissue samples from biopsies are observed under microscopes for the primary diagnosis of breast cancer. In this paper, we propose a deep learning-based method for classification of H&E stained breast tissue images released for BACH challenge 2018 by fine-tuning Inception-v3 convolutional neural network (CNN) proposed by Szegedy et al. These images are to be classified into four classes namely, i) normal tissue, ii) benign tumor, iii) in-situ carcinoma and iv) invasive carcinoma. Our strategy is to extract patches based on nuclei density instead of random or grid sampling, along with rejection of patches that are not rich in nuclei (non-epithelial) regions for training and testing. Every patch (nuclei-dense region) in an image is classified in one of the four above mentioned categories. The class of the entire image is determined using majority voting over the nuclear classes. We obtained an average four class accuracy of 85% and an average two class (non-cancer vs. carcinoma) accuracy of 93%, which improves upon a previous benchmark by Araujo et al.
User Perceptions of Smart Home IoT Privacy
Venue: arXiv cs.HC 2018
Year: 2018

Smart home Internet of Things (IoT) devices are rapidly increasing in popularity, with more households including Internet-connected devices that continuously monitor user activities. In this study, we conduct eleven semi-structured interviews with smart home owners, investigating their reasons for purchasing IoT devices, perceptions of smart home privacy risks, and actions taken to protect their privacy from those external to the home who create, manage, track, or regulate IoT devices and/or their data. We note several recurring themes. First, users' desires for convenience and connectedness dictate their privacy-related behaviors for dealing with external entities, such as device manufacturers, Internet Service Providers, governments, and advertisers. Second, user opinions about external entities collecting smart home data depend on perceived benefit from these entities. Third, users trust IoT device manufacturers to protect their privacy but do not verify that these protections are in place. Fourth, users are unaware of privacy risks from inference algorithms operating on data from non-audio/visual devices. These findings motivate several recommendations for device designers, researchers, and industry standards to better match device privacy features to the expectations and preferences of smart home owners.
Achievable Rate of Private Function Retrieval from MDS Coded Databases
Venue: arXiv cs.IT 2018
Year: 2018

We study the problem of private function retrieval (PFR) in a distributed storage system. In PFR the user wishes to retrieve a linear combination of $M$ messages stored in non-colluding $(N,K)$ MDS coded databases while revealing no information about the coefficients of the intended linear combination to any of the individual databases. We present an achievable scheme for MDS coded PFR with a rate that matches the capacity for coded private information retrieval derived recently, $R=(1+R_c+R_c^2+\dots+R_c^{M-1})^{-1}=\frac{1-R_c}{1-R_c^M}$, where $R_c=\frac{K}{N}$ is the rate of the MDS code. This achievable rate is tight in some special cases.
Vector Field Based Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

A novel Neural Network architecture is proposed using the mathematically and physically rich idea of vector fields as hidden layers to perform nonlinear transformations in the data. The data points are interpreted as particles moving along a flow defined by the vector field which intuitively represents the desired movement to enable classification. The architecture moves the data points from their original configuration to anew one following the streamlines of the vector field with the objective of achieving a final configuration where classes are separable. An optimization problem is solved through gradient descent to learn this vector field.
Proportional Volume Sampling and Approximation Algorithms for A-Optimal Design
Venue: arXiv cs.DS 2018
Year: 2018

We study the optimal design problems where the goal is to choose a set of linear measurements to obtain the most accurate estimate of an unknown vector in $d$ dimensions. We study the $A$-optimal design variant where the objective is to minimize the average variance of the error in the maximum likelihood estimate of the vector being measured. The problem also finds applications in sensor placement in wireless networks, sparse least squares regression, feature selection for $k$-means clustering, and matrix approximation. In this paper, we introduce proportional volume sampling to obtain improved approximation algorithms for $A$-optimal design. Our main result is to obtain improved approximation algorithms for the $A$-optimal design problem by introducing the proportional volume sampling algorithm. Our results nearly optimal bounds in the asymptotic regime when the number of measurements done, $k$, is significantly more than the dimension $d$. We also give first approximation algorithms when $k$ is small including when $k=d$. The proportional volume-sampling algorithm also gives approximation algorithms for other optimal design objectives such as $D$-optimal design and generalized ratio objective matching or improving previous best known results. Interestingly, we show that a similar guarantee cannot be obtained for the $E$-optimal design problem. We also show that the $A$-optimal design problem is NP-hard to approximate within a fixed constant when $k=d$.
Web-Based VR Experiments Powered by the Crowd
Venue: arXiv cs.HC 2018
Year: 2018

We build on the increasing availability of Virtual Reality (VR) devices and Web technologies to conduct behavioral experiments in VR using crowdsourcing techniques. A new recruiting and validation method allows us to create a panel of eligible experiment participants recruited from Amazon Mechanical Turk. Using this panel, we ran three different crowdsourced VR experiments, each reproducing one of three VR illusions: place illusion, embodiment illusion, and plausibility illusion. Our experience and worker feedback on these experiments show that conducting Web-based VR experiments using crowdsourcing is already feasible, though some challenges---including scale---remain. Such crowdsourced VR experiments on the Web have the potential to finally support replicable VR experiments with diverse populations at a low cost.
Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network
Venue: arXiv cs.CV 2018
Year: 2018

Because of the internal malfunction of satellite sensors and poor atmospheric conditions such as thick cloud, the acquired remote sensing data often suffer from missing information, i.e., the data usability is greatly reduced. In this paper, a novel method of missing information reconstruction in remote sensing images is proposed. The unified spatial-temporal-spectral framework based on a deep convolutional neural network (STS-CNN) employs a unified deep convolutional neural network combined with spatial-temporal-spectral supplementary information. In addition, to address the fact that most methods can only deal with a single missing information reconstruction task, the proposed approach can solve three typical missing information reconstruction tasks: 1) dead lines in Aqua MODIS band 6; 2) the Landsat ETM+ Scan Line Corrector (SLC)-off problem; and 3) thick cloud removal. It should be noted that the proposed model can use multi-source data (spatial, spectral, and temporal) as the input of the unified framework. The results of both simulated and real-data experiments demonstrate that the proposed model exhibits high effectiveness in the three missing information reconstruction tasks listed above.
Enhanced PeerHunter: Detecting Peer-to-peer Botnets through Network-Flow Level Community Behavior Analysis
Venue: arXiv cs.CR 2018
Year: 2018

Peer-to-peer (P2P) botnets have become one of the major threats in network security for serving as the fundamental infrastructure for various cyber-crimes. More challenges are involved in the problem of detecting P2P botnets, despite a few work claimed to detect centralized botnets effectively. We propose Enhanced PeerHunter, a network-flow level community behavior analysis based system, to detect P2P botnets. Our system starts from a P2P network flow detection component. Then, it uses "mutual contacts" to cluster bots into communities. Finally, it uses network-flow level community behavior analysis to detect potential botnets. In the experimental evaluation, we propose two evasion attacks, where we assume the adversaries know our techniques in advance and attempt to evade our system by making the P2P bots mimic the behavior of legitimate P2P applications. Our results showed that Enhanced PeerHunter can obtain high detection rate with few false positives, and high robustness against the proposed attacks.
An Empirical Study on README contents for JavaScript Packages
Venue: arXiv cs.SE 2018
Year: 2018

Contemporary software projects often utilize a README.md to share crucial information such as installation and usage examples related to their software. Furthermore, these files serve as an important source of updated and useful documentation for developers and prospective users of the software. Nonetheless, both novice and seasoned developers are sometimes unsure of what is required for a good README file. To understand the contents of a README, we investigate the contents of 43,900 JavaScript packages. Results show that these packages contain common content themes (i.e., usage, install and license). Furthermore, we find that application-specific packages more frequently included content themes such as options, while library-based packages more frequently included other specific content themes (i.e., install and license).
Bounds on the Zero-Error List-Decoding Capacity of the $q/(q-1)$ Channel
Venue: arXiv cs.IT 2018
Year: 2018

We consider the problem of determining the zero-error list-decoding capacity of the $q/(q-1)$ channel studied by Elias (1988). The $q/(q-1)$ channel has input and output alphabet consisting of $q$ symbols, say, $Q = \{x_1,x_2,\ldots, x_q\}$; when the channel receives an input $x \in Q$, it outputs a symbol other than $x$ itself. Let $n(m,q,\ell)$ be the smallest $n$ for which there is a code $C \subseteq Q^n$ of $m$ elements such that for every list $w_1, w_2, \ldots, w_{\ell+1}$ of distinct code-words from $C$, there is a coordinate $j \in [n]$ that satisfies $\{w_1[j], w_2[j], \ldots, w_{\ell+1}[j]\} = Q$. We show that for $ε<1/6$, for all large $q$ and large enough $m$, $n(m,q, εq\ln{q}) \geq Ω(\exp{(q^{1-6ε}/8)}\log_2{m})$. The lower bound obtained by Fredman and Komlós (1984) for perfect hashing implies that $n(m,q,q-1) = \exp(Ω(q)) \log_2 m$; similarly, the lower bound obtained by Körner (1986) for nearly-perfect hashing implies that $n(m,q,q) = \exp(Ω(q)) \log_2 m$. These results show that the zero-error list-decoding capacity of the $q/(q-1)$ channel with lists of size at most $q$ is exponentially small. Extending these bounds, Chakraborty et al. (2006) showed that the capacity remains exponentially small even if the list size is allowed to be as large as $1.58q$. Our result implies that the zero-error list-decoding capacity of the $q/(q-1)$ channel with list size $εq$ for $ε<1/6$ is $\exp{(Ω(q^{1-6ε}))}$. This resolves the conjecture raised by Chakraborty et al. (2006) about the zero-error list-decoding capcity of the $q/(q-1)$ channel at larger list sizes.
Adaptive specular reflection detection and inpainting in colonoscopy video frames
Venue: arXiv cs.CV 2018
Year: 2018

Colonoscopy video frames might be contaminated by bright spots with unsaturated values known as specular reflection. Detection and removal of such reflections could enhance the quality of colonoscopy images and facilitate diagnosis procedure. In this paper we propose a novel two-phase method for this purpose, consisting of detection and removal phases. In the detection phase, we employ both HSV and RGB color space information for segmentation of specular reflections. We first train a non-linear SVM for selecting a color space based on image statistical features extracted from each channel of the color spaces. Then, a cost function for detection of specular reflections is introduced. In the removal phase, we propose a two-step inpainting method which consists of appropriate replacement patch selection and removal of the blockiness effects. The proposed method is evaluated by testing on an available colonoscopy image database where accuracy and Dice score of 99.68% and 71.79% are achieved respectively.
Resilience against Misbehaving Nodes in Asynchronous Networks
Venue: arXiv cs.SY 2018
Year: 2018

Network systems are one of the most active research areas in the engineering community as they feature a paradigm shift from centralized to distributed control and computation. When dealing with network systems, a fundamental challenge is to ensure their functioning even when some of the network nodes do not operate as intended due to faults or attacks. The objective of this paper is to address the problem of resilient consensus in a context where the nodes have their own clocks, possibly operating in an asynchronous way, and can make updates at arbitrary time instants. The results represent a first step towards the development of resilient event-triggered and self-triggered coordination protocols.
Business Rules in e-Government Applications
Venue: arXiv cs.SE 2018
Year: 2018

The introduction of Information and Communication Technologies (ICT) into public administrations has been radically changing the way organizations cooperate and, more generally, the way to think about business processes over organizational boundaries. In this paper we describe our approach to combining business processes with business rules in order to integrate effectively single units in an inter- or intra-organizational cooperation. Business rules represent the knowledge that an administration has about its business; with regard to this, they can express strategies, contracts and can influence not only staff relations, but, finally, citizen relations, as well. In other words, business rules are the core of an administration and affect either the business processes or the behaviours of the system participants. They are typically expressed implicitly in business contracts and they are embedded within the source code of many application modules. So a concise and declarative statement of business behaviour is converted into a set of programming instructions, which are spread widely throughout the whole information system. In this way, business rules are difficult to change and keep consistent over the time. For this reason, it is necessary to reengineer the system in order to logically and perhaps physically externalize rules from the application code. In our proposed approach, we describe a cooperation as a collection of tasks combined in certain ways according to the organization logic specified by business rules. Our rule-driven methodology has the goal to make the business process design more adaptable to the changes of internal or external environment.
Deep Unsupervised Learning of Visual Similarities
Venue: arXiv cs.CV 2018
Year: 2018

Exemplar learning of visual similarities in an unsupervised manner is a problem of paramount importance to Computer Vision. In this context, however, the recent breakthrough in deep learning could not yet unfold its full potential. With only a single positive sample, a great imbalance between one positive and many negatives, and unreliable relationships between most samples, training of Convolutional Neural networks is impaired. In this paper we use weak estimates of local similarities and propose a single optimization problem to extract batches of samples with mutually consistent relations. Conflicting relations are distributed over different batches and similar samples are grouped into compact groups. Learning visual similarities is then framed as a sequence of categorization tasks. The CNN then consolidates transitivity relations within and between groups and learns a single representation for all samples without the need for labels. The proposed unsupervised approach has shown competitive performance on detailed posture analysis and object classification.
On Streaming Algorithms for the Steiner Cycle and Path Cover Problem on Interval Graphs and Falling Platforms in Video Games
Venue: arXiv cs.DS 2018
Year: 2018

We introduce a simplified model for platform game levels with falling platforms based on interval graphs and show that solvability of such levels corresponds to finding Steiner cycles or Steiner paths in the corresponding graphs. Linear time algorithms are obtained for both of these problems. We also study these algorithms as streaming algorithms and analyze the necessary memory with respect to the maximum number of intervals contained in another interval. This corresponds to understanding which parts of a level have to be visible at each point to allow the player to make optimal deterministic decisions.
Limiting gaming opportunities on incentive-based demand response programs
Venue: arXiv cs.GT 2018
Year: 2018

Demand Response (DR) is a program designed to match supply and demand by modifying consumption profile. Some of these programs are based on economic incentives, in which, a user is paid to reduce his energy requirements according to an estimated baseline. Literature review and practice have shown that the counter-factual models of employing baselines are vulnerable for gaming. Classical solutions of mechanism design require that agents communicate their full types which result in greater difficulties for its practical implementation. In this paper, a novel contract is developed to induce individual rationality (voluntary participation) and asymptotic incentive-compatibility (truthfulness) through probability of call, where an agent does not require to report the marginal utility. In this approach, a consumer only announces the baseline and reduction capacity, given a payment scheme that includes cost of electricity, incentive price, and penalty caused by any deviation between self-reported and actual energy consumption. The aggregator decides randomly what users are called to perform the energy reduction. As result, asymptotic truth-telling behavior in incentive-based DR is managed by the aggregator through the probability of call for each agent. Mathematical proofs and numerical studies are provided to demonstrate the properties and advantages of this contract in limiting gaming opportunities and in terms of its implementation.
Visualizing the Flow of Discourse with a Concept Ontology
Venue: arXiv cs.CL 2018
Year: 2018

Understanding and visualizing human discourse has long being a challenging task. Although recent work on argument mining have shown success in classifying the role of various sentences, the task of recognizing concepts and understanding the ways in which they are discussed remains challenging. Given an email thread or a transcript of a group discussion, our task is to extract the relevant concepts and understand how they are referenced and re-referenced throughout the discussion. In the present work, we present a preliminary approach for extracting and visualizing group discourse by adapting Wikipedia's category hierarchy to be an external concept ontology. From a user study, we found that our method achieved better results than 4 strong alternative approaches, and we illustrate our visualization method based on the extracted discourse flows.
Loss-aware Weight Quantization of Deep Networks
Venue: arXiv cs.LG 2018
Year: 2018

The huge size of deep networks hinders their use in small computing devices. In this paper, we consider compressing the network by weight quantization. We extend a recently proposed loss-aware weight binarization scheme to ternarization, with possibly different scaling parameters for the positive and negative weights, and m-bit (where m > 2) quantization. Experiments on feedforward and recurrent neural networks show that the proposed scheme outperforms state-of-the-art weight quantization algorithms, and is as accurate (or even more accurate) than the full-precision network.