OpenRaiser/Intern-Atlas huggingface dataset:
Computational Techniques in Multispectral Image Processing: Application to the Syriac Galen Palimpsest
Venue: arXiv cs.CV 2017
Year: 2017

Multispectral and hyperspectral image analysis has experienced much development in the last decade. The application of these methods to palimpsests has produced significant results, enabling researchers to recover texts that would be otherwise lost under the visible overtext, by improving the contrast between the undertext and the overtext. In this paper we explore an extended number of multispectral and hyperspectral image analysis methods, consisting of supervised and unsupervised dimensionality reduction techniques, on a part of the Syriac Galen Palimpsest dataset (www.digitalgalen.net). Of this extended set of methods, eight methods gave good results: three were supervised methods Generalized Discriminant Analysis (GDA), Linear Discriminant Analysis (LDA), and Neighborhood Component Analysis (NCA); and the other five methods were unsupervised methods (but still used in a supervised way) Gaussian Process Latent Variable Model (GPLVM), Isomap, Landmark Isomap, Principal Component Analysis (PCA), and Probabilistic Principal Component Analysis (PPCA). The relative success of these methods was determined visually, using color pictures, on the basis of whether the undertext was distinguishable from the overtext, resulting in the following ranking of the methods: LDA, NCA, GDA, Isomap, Landmark Isomap, PPCA, PCA, and GPLVM. These results were compared with those obtained using the Canonical Variates Analysis (CVA) method on the same dataset, which showed remarkably accuracy (LDA is a particular case of CVA where the objects are classified to two classes).
Construction of Unrestricted Rate Parallel Random Input Output Code
Venue: arXiv cs.IT 2017
Year: 2017

A coding scheme for two-page unrestricted-rate PRIO code that each page may have different code rates is proposed. In the second page, the code for each messages consists of two complementary codewords with code length n. There are a total of 2n-1 codes which are disjoint to guarantees uniquely decodable for 2n-1 messages. In the first page, the code for each message consists of all weight-u vectors with their non-zero elements restricted to (2u-1) same positions, where non-negative integer u is less than or equal to half of code length. Finding codes to be disjoint in first page is equivalent to construction of constant-weight codes, and the numbers of disjoint codes are the best-known numbers of codewords in constant-weight codes. Our coding scheme is constructive, and the code length is arbitrary.The sum rates of our proposed codes are higher than those of previous work.
Effective face landmark localization via single deep network
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we propose a novel face alignment method using single deep network (SDN) on existing limited training data. Rather than using a max-pooling layer followed one convolutional layer in typical convolutional neural networks (CNN), SDN adopts a stack of 3 layer groups instead. Each group layer contains two convolutional layers and a max-pooling layer, which can extract the features hierarchically. Moreover, an effective data augmentation strategy and corresponding training skills are also proposed to over-come the lack of training images on COFW and 300-W da-tasets. The experiment results show that our method outper-forms state-of-the-art methods in both detection accuracy and speed.
Memetic search for overlapping topics based on a local evaluation of link communities
Venue: arXiv cs.SI 2017
Year: 2017

In spite of recent advances in field delineation methods, bibliometricians still don't know the extent to which their topic detection algorithms reconstruct `ground truths', i.e. thematic structures in the scientific literature. In this paper, we demonstrate a new approach to the delineation of thematic structures that attempts to match the algorithm to theoretically derived and empirically observed properties all thematic structures have in common. We cluster citation links rather than publication nodes, use predominantly local information and search for communities of links starting from seed subgraphs in order to allow for pervasive overlaps of topics. We evaluate sets of links with a new cost function and assume that local minima in the cost landscape correspond to link communities. Because this cost landscape has many local minima we define a valid community as the community with the lowest minimum within a certain range. Since finding all valid communities is impossible for large networks, we designed a memetic algorithm that combines probabilistic evolutionary strategies with deterministic local searches. We apply our approach to a network of about 15,000 Astronomy & Astrophysics papers published 2010 and their cited sources, and to a network of about 100,000 Astronomy & Astrophysics papers (published 2003--2010) which are linked through direct citations.
Tor is not enough: Coercion in Remote Electronic Voting Systems
Venue: arXiv cs.CR 2017
Year: 2017

Current electronic voting systems require an anonymous channel during the voting phase to prevent coercion. Typically, low-latency anonymization-networks like Tor are used for this purpose. In this paper we devise a monitoring attack that allows an attacker to monitor whether participants of an election voted, despite the use of a low-latency network during the voting phase, thereby breaking an important part of coercion-freeness. We implement a simulation carrying out our attack and measure its success rates.
Artificial Intelligence as an Enabler for Cognitive Self-Organizing Future Networks
Venue: arXiv cs.NI 2017
Year: 2017

The explosive increase in number of smart devices hosting sophisticated applications is rapidly affecting the landscape of information communication technology industry. Mobile subscriptions, expected to reach 8.9 billion by 2022, would drastically increase the demand of extra capacity with aggregate throughput anticipated to be enhanced by a factor of 1000. In an already crowded radio spectrum, it becomes increasingly difficult to meet ever growing application demands of wireless bandwidth. It has been shown that the allocated spectrum is seldom utilized by the primary users and hence contains spectrum holes that may be exploited by the unlicensed users for their communication. As we enter the Internet Of Things (IoT) era in which appliances of common use will become smart digital devices with rigid performance requirements (such as low latency, energy efficiency, etc.), current networks face the vexing problem of how to create sufficient capacity for such applications. The fifth generation of cellular networks (5G) envisioned to address these challenges are thus required to incorporate cognition and intelligence to resolve the aforementioned issues.
Answer Set Solving with Bounded Treewidth Revisited
Venue: arXiv cs.LO 2017
Year: 2017

Parameterized algorithms are a way to solve hard problems more efficiently, given that a specific parameter of the input is small. In this paper, we apply this idea to the field of answer set programming (ASP). To this end, we propose two kinds of graph representations of programs to exploit their treewidth as a parameter. Treewidth roughly measures to which extent the internal structure of a program resembles a tree. Our main contribution is the design of parameterized dynamic programming algorithms, which run in linear time if the treewidth and weights of the given program are bounded. Compared to previous work, our algorithms handle the full syntax of ASP. Finally, we report on an empirical evaluation that shows good runtime behaviour for benchmark instances of low treewidth, especially for counting answer sets.
Multi-agent Reinforcement Learning in Sequential Social Dilemmas
Venue: arXiv cs.MA 2017
Year: 2017

Matrix games like Prisoner's Dilemma have guided research on social dilemmas for decades. However, they necessarily treat the choice to cooperate or defect as an atomic action. In real-world social dilemmas these choices are temporally extended. Cooperativeness is a property that applies to policies, not elementary actions. We introduce sequential social dilemmas that share the mixed incentive structure of matrix game social dilemmas but also require agents to learn policies that implement their strategic intentions. We analyze the dynamics of policies learned by multiple self-interested independent learning agents, each using its own deep Q-network, on two Markov games we introduce here: 1. a fruit Gathering game and 2. a Wolfpack hunting game. We characterize how learned behavior in each domain changes as a function of environmental factors including resource abundance. Our experiments show how conflict can emerge from competition over shared resources and shed light on how the sequential nature of real world social dilemmas affects cooperation.
Density Functional Estimators with k-Nearest Neighbor Bandwidths
Venue: arXiv cs.IT 2017
Year: 2017

Estimating expected polynomials of density functions from samples is a basic problem with numerous applications in statistics and information theory. Although kernel density estimators are widely used in practice for such functional estimation problems, practitioners are left on their own to choose an appropriate bandwidth for each application in hand. Further, kernel density estimators suffer from boundary biases, which are prevalent in real world data with lower dimensional structures. We propose using the fixed-k nearest neighbor distances for the bandwidth, which adaptively adjusts to local geometry. Further, we propose a novel estimator based on local likelihood density estimators, that mitigates the boundary biases. Although such a choice of fixed-k nearest neighbor distances to bandwidths results in inconsistent estimators, we provide a simple debiasing scheme that precomputes the asymptotic bias and divides off this term. With this novel correction, we show consistency of this debiased estimator. We provide numerical experiments suggesting that it improves upon competing state-of-the-art methods.
Quantitative aspects of linear and affine closed lambda terms
Venue: arXiv cs.DM 2017
Year: 2017

Affine $λ$-terms are $λ$-terms in which each bound variable occurs at most once and linear $λ$-terms are $λ$-terms in which each bound variables occurs once. and only once. In this paper we count the number of closed affine $λ$-terms of size $n$, closed linear $λ$-terms of size $n$, affine $β$-normal forms of size $n$ and linear $β$-normal forms of ise $n$, for different ways of measuring the size of $λ$-terms. From these formulas, we show how we can derive programs for generating all the terms of size $n$ for each class. For this we use a specific data structure, which are contexts taking into account all the holes at levels of abstractions.
A Las Vegas approximation algorithm for metric $1$-median selection
Venue: arXiv cs.DS 2017
Year: 2017

Given an $n$-point metric space, consider the problem of finding a point with the minimum sum of distances to all points. We show that this problem has a randomized algorithm that {\em always} outputs a $(2+ε)$-approximate solution in an expected $O(n/ε^2)$ time for each constant $ε>0$. Inheriting Indyk's algorithm, our algorithm outputs a $(1+ε)$-approximate $1$-median in $O(n/ε^2)$ time with probability $Ω(1)$.
A Study on the Vulnerabilities of Mobile Apps associated with Software Modules
Venue: arXiv cs.CR 2017
Year: 2017

This paper reports a large-scale study that aims to understand how mobile application (app) vulnerabilities are associated with software libraries. We analyze both free and paid apps. Studying paid apps was quite meaningful because it helped us understand how differences in app development/maintenance affect the vulnerabilities associated with libraries. We analyzed 30k free and paid apps collected from the official Android marketplace. Our extensive analyses revealed that approximately 70%/50% of vulnerabilities of free/paid apps stem from software libraries, particularly from third-party libraries. Somewhat paradoxically, we found that more expensive/popular paid apps tend to have more vulnerabilities. This comes from the fact that more expensive/popular paid apps tend to have more functionality, i.e., more code and libraries, which increases the probability of vulnerabilities. Based on our findings, we provide suggestions to stakeholders of mobile app distribution ecosystems.
A clustering approach to heterogeneous change detection
Venue: arXiv cs.CV 2017
Year: 2017

Change detection in heterogeneous multitemporal satellite images is a challenging and still not much studied topic in remote sensing and earth observation. This paper focuses on comparison of image pairs covering the same geographical area and acquired by two different sensors, one optical radiometer and one synthetic aperture radar, at two different times. We propose a clustering-based technique to detect changes, identified as clusters that split or merge in the different images. To evaluate potentials and limitations of our method, we perform experiments on real data. Preliminary results confirm the relationship between splits and merges of clusters and the occurrence of changes. However, it becomes evident that it is necessary to incorporate prior, ancillary, or application-specific information to improve the interpretation of clustering results and to identify unambiguously the areas of change.
Performance of Cell-Free Massive MIMO Systems with MMSE and LSFD Receivers
Venue: arXiv cs.IT 2017
Year: 2017

Cell-Free Massive MIMO comprises a large number of distributed single-antenna access points (APs) serving a much smaller number of users. There is no partitioning into cells and each user is served by all APs. In this paper, the uplink performance of cell-free systems with minimum mean squared error (MMSE) and large scale fading decoding (LSFD) receivers is investigated. The main idea of LSFD receiver is to maximize achievable throughput using only large scale fading coefficients between APs and users. Capacity lower bounds for MMSE and LSFD receivers are derived. An asymptotic approximation for signal-to-interference-plus-noise ratio (SINR) of MMSE receiver is derived as a function of large scale fading coefficients only. The obtained approximation is accurate even for a small number of antennas. MMSE and LSFD receivers demonstrate five-fold and two-fold gains respectively over matched filter (MF) receiver in terms of 5%-outage rate.
Spatial Oversampling in LOS MIMO Systems with 1-Bit Quantization at the Receiver
Venue: arXiv cs.IT 2017
Year: 2017

In this paper we investigate the achievable rate of LOS MIMO systems that use 1-bit quantization and spatial oversampling at the receiver. We propose that by using additional antennas at the receiver, the loss incurred due to the strong limitation of the quantization can be decreased. Mutual information results show that considerable rate gains can be achieved depending on the number and arrangement of the antennas. In some of the cases, even the full available rate from the transmitter can be attained. Furthermore, the results also reveal that two-dimensional antenna arrays can benefit more from spatial oversampling than one-dimensional arrays, when using 1-bit quantization in the LOS MIMO scenario.
Multidimensional Index Modulation in Wireless Communications
Venue: arXiv cs.IT 2017
Year: 2017

In index modulation schemes, information bits are conveyed through indexing of transmission entities such as antennas, subcarriers, times slots, precoders, subarrays, and radio frequency (RF) mirrors. Index modulation schemes are attractive for their advantages such as good performance, high rates, and hardware simplicity. This paper focuses on index modulation schemes in which multiple transmission entities, namely, {\em antennas}, {\em time slots}, and {\em RF mirrors}, are indexed {\em simultaneously}. Recognizing that such multidimensional index modulation schemes encourage sparsity in their transmit signal vectors, we propose efficient signal detection schemes that use compressive sensing based reconstruction algorithms. Results show that, for a given rate, improved performance is achieved when the number of indexed transmission entities is increased. We also explore indexing opportunities in {\em load modulation}, which is a modulation scheme that offers power efficiency and reduced RF hardware complexity advantages in multiantenna systems. Results show that indexing space and time in load modulated multiantenna systems can achieve improved performance.
Two Optimization Problems for Unit Disks
Venue: arXiv cs.CG 2017
Year: 2017

We present an implementation of a recent algorithm to compute shortest-path trees in unit disk graphs in $O(n\log n)$ worst-case time, where $n$ is the number of disks. In the minimum-separation problem, we are given $n$ unit disks and two points $s$ and $t$, not contained in any of the disks, and we want to compute the minimum number of disks one needs to retain so that any curve connecting $s$ to $t$ intersects some of the retained disks. We present a new algorithm solving this problem in $O(n^2\log^3 n)$ worst-case time and its implementation.
Efficient Resource Allocation in Mass Customization based on Service Oriented Architecture
Venue: arXiv cs.DC 2017
Year: 2017

Mass customization explains the phenomenon to provide a unique designed products and services to all customer by achieving a high process integration and flexibility. It has been used as a competitive approach by many companies. Adequate resource implementation in mass customization-particularly in terms of resource usage, it is therefore important to meet customer's requirement in terms effective responsiveness and meeting deadlines, at the same time offering high scalability. An architecture for solving the resource allocation issue in mass customized flexible manufacturing system was illustrated, by putting in place a couple of advance reservation systems and scheduling algorithms for effective usage of the product.
On the Improvement of Quality and Reliability of Trust Cues in Micro-task Crowdsourcing (Position paper)
Venue: arXiv cs.SI 2017
Year: 2017

Micro-task crowdsourcing has become a successful mean to obtain high-quality data from a large crowd of diverse people. In this context, trust between all the involved actors (i.e. requesters, workers, and platform owners) is a critical factor for acceptance and long-term success. As actors have no expectation for "real life" meetings, thus trust can only be attributed through computer-mediated trust cues like workers qualifications and requester ratings. Such cues are often the result of technical or social assessments that are performed in isolation, considering only a subset of relevant properties, and with asynchronous and asymmetrical interactions. In this paper, we advocate for a new generation of micro-task crowdsourcing systems that pursue an holistic understanding of trust, by offering an open, transparent, privacy-friendly, and socially-aware view on the all the actors of a micro-task crowdsourcing environment.
Whale swarm algorithm for function optimization
Venue: arXiv cs.NE 2017
Year: 2017

Increasing nature-inspired metaheuristic algorithms are applied to solving the real-world optimization problems, as they have some advantages over the classical methods of numerical optimization. This paper has proposed a new nature-inspired metaheuristic called Whale Swarm Algorithm for function optimization, which is inspired by the whales behavior of communicating with each other via ultrasound for hunting. The proposed Whale Swarm Algorithm has been compared with several popular metaheuristic algorithms on comprehensive performance metrics. According to the experimental results, Whale Swarm Algorithm has a quite competitive performance when compared with other algorithms.
Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth
Venue: arXiv cs.CV 2017
Year: 2017

When integrating computational tools such as automatic segmentation into clinical practice, it is of utmost importance to be able to assess the level of accuracy on new data, and in particular, to detect when an automatic method fails. However, this is difficult to achieve due to absence of ground truth. Segmentation accuracy on clinical data might be different from what is found through cross-validation because validation data is often used during incremental method development, which can lead to overfitting and unrealistic performance expectations. Before deployment, performance is quantified using different metrics, for which the predicted segmentation is compared to a reference segmentation, often obtained manually by an expert. But little is known about the real performance after deployment when a reference is unavailable. In this paper, we introduce the concept of reverse classification accuracy (RCA) as a framework for predicting the performance of a segmentation method on new data. In RCA we take the predicted segmentation from a new image to train a reverse classifier which is evaluated on a set of reference images with available ground truth. The hypothesis is that if the predicted segmentation is of good quality, then the reverse classifier will perform well on at least some of the reference images. We validate our approach on multi-organ segmentation with different classifiers and segmentation methods. Our results indicate that it is indeed possible to predict the quality of individual segmentations, in the absence of ground truth. Thus, RCA is ideal for integration into automatic processing pipelines in clinical routine and as part of large-scale image analysis studies.
Classical-Quantum Arbitrarily Varying Wiretap Channel: Secret Message Transmission under Jamming Attacks
Venue: arXiv cs.IT 2017
Year: 2017

We analyze arbitrarily varying classical-quantum wiretap channels.These channels are subject to two attacks at the same time: one passive (eavesdropping), and one active (jamming). We progress on previous works by introducing a reduced class of allowed codes that fulfills a more stringent secrecy requirement than earlier definitions. In addition, we prove that non-symmetrizability of the legal link is suficient for equality of the deterministic and the common randomness assisted secrecy capacities. At last, we focus on analytic properties of both secrecy capacities: We completely characterize their discontinuity points, and their super-activation properties.
MapSQ: A MapReduce-based Framework for SPARQL Queries on GPU
Venue: arXiv cs.DB 2017
Year: 2017

In this paper, we present a MapReduce-based framework for evaluating SPARQL queries on GPU (named MapSQ) to large-scale RDF datesets efficiently by applying both high performance. Firstly, we develop a MapReduce-based Join algorithm to handle SPARQL queries in a parallel way. Secondly, we present a coprocessing strategy to manage the process of evaluating queries where CPU is used to assigns subqueries and GPU is used to compute the join of subqueries. Finally, we implement our proposed framework and evaluate our proposal by comparing with two popular and latest SPARQL query engines gStore and gStoreD on the LUBM benchmark. The experiments demonstrate that our proposal MapSQ is highly efficient and effective (up to 50% speedup).