OpenRaiser/Intern-Atlas huggingface dataset:
Image Fusion With Cosparse Analysis Operator
Venue: arXiv cs.CV 2017
Year: 2017

The paper addresses the image fusion problem, where multiple images captured with different focus distances are to be combined into a higher quality all-in-focus image. Most current approaches for image fusion strongly rely on the unrealistic noise-free assumption used during the image acquisition, and then yield limited robustness in fusion processing. In our approach, we formulate the multi-focus image fusion problem in terms of an analysis sparse model, and simultaneously perform the restoration and fusion of multi-focus images. Based on this model, we propose an analysis operator learning, and define a novel fusion function to generate an all-in-focus image. Experimental evaluations confirm the effectiveness of the proposed fusion approach both visually and quantitatively, and show that our approach outperforms state-of-the-art fusion methods.
Semantic Similarity from Natural Language and Ontology Analysis
Venue: arXiv cs.AI 2017
Year: 2017

Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments -- most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli. In this context, this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language, e.g. words, sentences, or concepts and instances defined into knowledge bases. The aim of these measures is to assess the similarity or relatedness of such semantic entities by taking into account their semantics, i.e. their meaning -- intuitively, the words tea and coffee, which both refer to stimulating beverage, will be estimated to be more semantically similar than the words toffee (confection) and coffee, despite that the last pair has a higher syntactic similarity. The two state-of-the-art approaches for estimating and quantifying semantic similarities/relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based on Natural Language Processing techniques and semantic models while the second is based on more or less formal, computer-readable and workable forms of knowledge such as semantic networks, thesaurus or ontologies. (...) Beyond a simple inventory and categorization of existing measures, the aim of this monograph is to convey novices as well as researchers of these domains towards a better understanding of semantic similarity estimation and more generally semantic measures.
Understanding Negations in Information Processing: Learning from Replicating Human Behavior
Venue: arXiv cs.AI 2017
Year: 2017

Information systems experience an ever-growing volume of unstructured data, particularly in the form of textual materials. This represents a rich source of information from which one can create value for people, organizations and businesses. For instance, recommender systems can benefit from automatically understanding preferences based on user reviews or social media. However, it is difficult for computer programs to correctly infer meaning from narrative content. One major challenge is negations that invert the interpretation of words and sentences. As a remedy, this paper proposes a novel learning strategy to detect negations: we apply reinforcement learning to find a policy that replicates the human perception of negations based on an exogenous response, such as a user rating for reviews. Our method yields several benefits, as it eliminates the former need for expensive and subjective manual labeling in an intermediate stage. Moreover, the inferred policy can be used to derive statistical inferences and implications regarding how humans process and act on negations.
Mining Worse and Better Opinions. Unsupervised and Agnostic Aggregation of Online Reviews
Venue: arXiv cs.SI 2017
Year: 2017

In this paper, we propose a novel approach for aggregating online reviews, according to the opinions they express. Our methodology is unsupervised - due to the fact that it does not rely on pre-labeled reviews - and it is agnostic - since it does not make any assumption about the domain or the language of the review content. We measure the adherence of a review content to the domain terminology extracted from a review set. First, we demonstrate the informativeness of the adherence metric with respect to the score associated with a review. Then, we exploit the metric values to group reviews, according to the opinions they express. Our experimental campaign has been carried out on two large datasets collected from Booking and Amazon, respectively.
Ranking to Learn: Feature Ranking and Selection via Eigenvector Centrality
Venue: arXiv cs.CV 2017
Year: 2017

In an era where accumulating data is easy and storing it inexpensive, feature selection plays a central role in helping to reduce the high-dimensionality of huge amounts of otherwise meaningless data. In this paper, we propose a graph-based method for feature selection that ranks features by identifying the most important ones into arbitrary set of cues. Mapping the problem on an affinity graph-where features are the nodes-the solution is given by assessing the importance of nodes through some indicators of centrality, in particular, the Eigen-vector Centrality (EC). The gist of EC is to estimate the importance of a feature as a function of the importance of its neighbors. Ranking central nodes individuates candidate features, which turn out to be effective from a classification point of view, as proved by a thoroughly experimental section. Our approach has been tested on 7 diverse datasets from recent literature (e.g., biological data and object recognition, among others), and compared against filter, embedded and wrappers methods. The results are remarkable in terms of accuracy, stability and low execution time.
Reverse Engineering of Communications Networks: Evolution and Challenges
Venue: arXiv cs.IT 2017
Year: 2017

Reverse engineering of a communications network is the process of identifying the communications protocol used in the network. This problem arises in various situations such as eavesdropping, intelligent jamming, cognitive radio, and adaptive coding and modulation (ACM). According to the Open Systems Interconnection (OSI) reference model, the first step in reverse engineering of communications networks is recognition of physical layer which consists of recognition of digital modulations and identification of physical layer transmission techniques. The next step is recognition of data link layer (consisting of frame synchronization, recognition of channel codes, reconstruction of interleavers, reconstruction of scramblers, etc.) and also recognition of network and transport layers. The final step in reverse engineering of communications networks is recognition of upper layers which essentially can be seen as identification of source encoders. The objective of this paper is to provide a comprehensive overview on the current methods for reverse engineering of communications networks. Furthermore, challenges and open research issues in this field are introduced.
25 Tweets to Know You: A New Model to Predict Personality with Social Media
Venue: arXiv cs.SI 2017
Year: 2017

Predicting personality is essential for social applications supporting human-centered activities, yet prior modeling methods with users written text require too much input data to be realistically used in the context of social media. In this work, we aim to drastically reduce the data requirement for personality modeling and develop a model that is applicable to most users on Twitter. Our model integrates Word Embedding features with Gaussian Processes regression. Based on the evaluation of over 1.3K users on Twitter, we find that our model achieves comparable or better accuracy than state of the art techniques with 8 times fewer data.
The Impact of Antenna Height Difference on the Performance of Downlink Cellular Networks
Venue: arXiv cs.IT 2017
Year: 2017

Capable of significantly reducing cell size and enhancing spatial reuse, network densification is shown to be one of the most dominant approaches to expand network capacity. Due to the scarcity of available spectrum resources, nevertheless, the over-deployment of network infrastructures, e.g., cellular base stations (BSs), would strengthen the inter-cell interference as well, thus in turn deteriorating the system performance. On this account, we investigate the performance of downlink cellular networks in terms of user coverage probability (CP) and network spatial throughput (ST), aiming to shed light on the limitation of network densification. Notably, it is shown that both CP and ST would be degraded and even diminish to be zero when BS density is sufficiently large, provided that practical antenna height difference (AHD) between BSs and users is involved to characterize pathloss. Moreover, the results also reveal that the increase of network ST is at the expense of the degradation of CP. Therefore, to balance the tradeoff between user and network performance, we further study the critical density, under which ST could be maximized under the CP constraint. Through a special case study, it follows that the critical density is inversely proportional to the square of AHD. The results in this work could provide helpful guideline towards the application of network densification in the next-generation wireless networks.
Illuminant Spectra-based Source Separation Using Flash Photography
Venue: arXiv cs.CV 2017
Year: 2017

Real-world lighting often consists of multiple illuminants with different spectra. Separating and manipulating these illuminants in post-process is a challenging problem that requires either significant manual input or calibrated scene geometry and lighting. In this work, we leverage a flash/no-flash image pair to analyze and edit scene illuminants based on their spectral differences. We derive a novel physics-based relationship between color variations in the observed flash/no-flash intensities and the spectra and surface shading corresponding to individual scene illuminants. Our technique uses this constraint to automatically separate an image into constituent images lit by each illuminant. This separation can be used to support applications like white balancing, lighting editing, and RGB photometric stereo, where we demonstrate results that outperform state-of-the-art techniques on a wide range of images.
Automated Sized-Type Inference and Complexity Analysis
Venue: arXiv cs.LO 2017
Year: 2017

This paper introduces a new methodology for the complexity analysis of higher-order functional programs, which is based on three components: a powerful type system for size analysis and a sound type inference procedure for it, a ticking monadic transformation and a concrete tool for constraint solving. Noticeably, the presented methodology can be fully automated, and is able to analyse a series of examples which cannot be handled by most competitor methodologies. This is possible due to various key ingredients, and in particular an abstract index language and index polymorphism at higher ranks. A prototype implementation is available.
FSITM: A Feature Similarity Index For Tone-Mapped Images
Venue: arXiv cs.CV 2017
Year: 2017

In this work, based on the local phase information of images, an objective index, called the feature similarity index for tone-mapped images (FSITM), is proposed. To evaluate a tone mapping operator (TMO), the proposed index compares the locally weighted mean phase angle map of an original high dynamic range (HDR) to that of its associated tone-mapped image calculated using the output of the TMO method. In experiments on two standard databases, it is shown that the proposed FSITM method outperforms the state-of-the-art index, the tone mapped quality index (TMQI). In addition, a higher performance is obtained by combining the FSITM and TMQI indices. The MATLAB source code of the proposed metric(s) is available at https://www.mathworks.com/matlabcentral/fileexchange/59814.
Stabilization of slow-fast systems at fold points
Venue: arXiv cs.SY 2017
Year: 2017

In this document, we deal with the stabilization problem of slow-fast systems (or singularly perturbed Ordinary Differential Equations) at a non-hyperbolic point. The class of systems studied here have the following properties: 1) they have one fast variable and an arbitrary number of slow variables, 2) they have a non-hyperbolic singularity of the fold type at the origin. The presence of the aforementioned singularity complicates the analysis and the controller design of such systems. In particular, the classical theory of singular perturbations cannot be used. We show a novel design process based on geometric desingularization, which allows the stabilization of a fold point of singularly perturbed control systems. Our results are exemplified on an electric circuit.
C-RAN with Hybrid RF/FSO Fronthaul Links: Joint Optimization of RF Time Allocation and Fronthaul Compression
Venue: arXiv cs.IT 2017
Year: 2017

This paper considers the uplink of a cloud radio access network (C-RAN) comprised of several multi-antenna remote radio units (RUs) which send the data that they received from multiple mobile users (MUs) to a central unit (CU) via a wireless fronthaul link. One of the fundamental challenges in implementing C-RAN is the huge data rate required for fronthauling. To address this issue, we employ hybrid radio frequency (RF)/free space optical (FSO) systems for the fronthaul links as they benefit from both the large data rates of FSO links and the reliability of RF links. To efficiently exploit the fronthaul capacity, the RUs employ vector quantization to jointly compress the signals received at their antennas. Moreover, due to the limited available RF spectrum, we assume that the RF multiple-access and fronthaul links employ the same RF resources. Thereby, we propose an adaptive protocol which allocates transmission time to the RF multiple-access and fronthaul links in a time division duplex (TDD) manner and optimizes the quantization noise covariance matrix at each RU such that the sum rate is maximized. Our simulation results reveal that a considerable gain in terms of sum rate can be achieved by the proposed protocol in comparison with benchmark schemes from the literature, especially when the FSO links experience unfavorable atmospheric conditions.
On Measuring Bias in Online Information
Venue: arXiv cs.DB 2017
Year: 2017

Bias in online information has recently become a pressing issue, with search engines, social networks and recommendation services being accused of exhibiting some form of bias. In this vision paper, we make the case for a systematic approach towards measuring bias. To this end, we discuss formal measures for quantifying the various types of bias, we outline the system components necessary for realizing them, and we highlight the related research challenges and open problems.
Collaborative Filtering with Social Local Models
Venue: arXiv cs.SI 2017
Year: 2017

Matrix Factorization (MF) is a very popular method for recommendation systems. It assumes that the underneath rating matrix is low-rank. However, this assumption can be too restrictive to capture complex relationships and interactions among users and items. Recently, Local LOw-Rank Matrix Approximation (LLORMA) has been shown to be very successful in addressing this issue. It just assumes the rating matrix is composed of a number of low-rank submatrices constructed from subsets of similar users and items. Although LLORMA outperforms MF, how to construct such submatrices remains a big problem. Motivated by the availability of rich social connections in today's recommendation systems, we propose a novel framework, i.e., Social LOcal low-rank Matrix Approximation (SLOMA), to address this problem. To the best of our knowledge, SLOMA is the first work to incorporate social connections into the local low-rank framework. Furthermore, we enhance SLOMA by applying social regularization to submatrices factorization, denoted as SLOMA++. Therefore, the proposed model can benefit from both social recommendation and the local low-rank assumption. Experimental results from two real-world datasets, Yelp and Douban, demonstrate the superiority of the proposed models over LLORMA and MF.
Reliable Probabilistic Gossip over Large-Scale Random Topologies
Venue: arXiv cs.NI 2017
Year: 2017

This paper studies reliability of probabilistic neighbor-aware gossip algorithms over three well- known large-scale random topologies, namely Bernoulli (or Erdős-Rényi) graph, the random geometric graph, and the scale-free graph. We propose a new and simple algorithm which ensures higher reliability at lower message complexity than the three families of gossip algorithms over every topology in our study. We also present a uniform approach to model the reliability of probabilistic gossip algorithms in the different random graphs, whose properties, in fact, are quite different. In our model a forwarding probability is derived with consideration of parameters in gossip algorithms and graph properties. Our simulations show that our model gives a reasonable prediction of the trade-off between reliability and message complexity for all probabilistic neighbor-aware gossip algorithms in various random networks. Therefore, it allows to fine-tune the input parameters in the gossip protocols to achieve a desirable reliability with tolerable message complexity.
Beating 1-1/e for Ordered Prophets
Venue: arXiv cs.DS 2017
Year: 2017

Hill and Kertz studied the prophet inequality on iid distributions [The Annals of Probability 1982]. They proved a theoretical bound of $1-\frac{1}{e}$ on the approximation factor of their algorithm. They conjectured that the best approximation factor for arbitrarily large n is $\frac{1}{1+1/e} \approx 0.731$. This conjecture remained open prior to this paper for over 30 years. In this paper we present a threshold-based algorithm for the prophet inequality with n iid distributions. Using a nontrivial and novel approach we show that our algorithm is a 0.738-approximation algorithm. By beating the bound of $\frac{1}{1+1/e}$, this refutes the conjecture of Hill and Kertz. Moreover, we generalize our results to non-iid distributions and discuss its applications in mechanism design.
The Magic Barrier Revisited: Accessing Natural Limitations of Recommender Assessment
Venue: arXiv cs.HC 2017
Year: 2017

Recommender systems nowadays have many applications and are of great economic benefit. Hence, it is imperative for success-oriented companies to compare different of such systems and select the better one for their purposes. To this end, various metrics of predictive accuracy are commonly used, such as the Root Mean Square Error (RMSE), or precision and recall. All these metrics more or less measure how well a recommender system can predict human behaviour. Unfortunately, human behaviour is always associated with some degree of uncertainty, making the evaluation difficult, since it is not clear whether a deviation is system-induced or just originates from the natural variability of human decision making. At this point, some authors speculated that we may be reaching some Magic Barrier where this variability prevents us from getting much more accurate. In this article, we will extend the existing theory of the Magic Barrier into a new probabilistic but a yet pragmatic model. In particular, we will use methods from metrology and physics to develop easy-to-handle quantities for computation to describe the Magic Barrier for different accuracy metrics and provide suggestions for common application. This discussion is substantiated by comprehensive experiments with real users and large-scale simulations on a high-performance cluster.
On fast bounded locality sensitive hashing
Venue: arXiv cs.DS 2017
Year: 2017

In this paper, we examine the hash functions expressed as scalar products, i.e., $f(x)=<v,x>$, for some bounded random vector $v$. Such hash functions have numerous applications, but often there is a need to optimize the choice of the distribution of $v$. In the present work, we focus on so-called anti-concentration bounds, i.e. the upper bounds of $\mathbb{P}\left[|<v,x>| < α\right]$. In many applications, $v$ is a vector of independent random variables with standard normal distribution. In such case, the distribution of $<v,x>$ is also normal and it is easy to approximate $\mathbb{P}\left[|<v,x>| < α\right]$. Here, we consider two bounded distributions in the context of the anti-concentration bounds. Particularly, we analyze $v$ being a random vector from the unit ball in $l_{\infty}$ and $v$ being a random vector from the unit sphere in $l_{2}$. We show optimal up to a constant anti-concentration measures for functions $f(x)=<v,x>$. As a consequence of our research, we obtain new best results for \newline \textit{$c$-approximate nearest neighbors without false negatives} for $l_p$ in high dimensional space for all $p\in[1,\infty]$, for $c=Ω(\max\{\sqrt{d},d^{1/p}\})$. These results improve over those presented in [16]. Finally, our paper reports progress on answering the open problem by Pagh~[17], who considered the nearest neighbor search without false negatives for the Hamming distance.
User-driven Intelligent Interface on the Basis of Multimodal Augmented Reality and Brain-Computer Interaction for People with Functional Disabilities
Venue: arXiv cs.HC 2017
Year: 2017

The analysis of the current integration attempts of some modes and use cases of user-machine interaction is presented. The new concept of the user-driven intelligent interface is proposed on the basis of multimodal augmented reality and brain-computer interaction for various applications: in disabilities studies, education, home care, health care, etc. The several use cases of multimodal augmentation are presented. The perspectives of the better human comprehension by the immediate feedback through neurophysical channels by means of brain-computer interaction are outlined. It is shown that brain-computer interface (BCI) technology provides new strategies to overcome limits of the currently available user interfaces, especially for people with functional disabilities. The results of the previous studies of the low end consumer and open-source BCI-devices allow us to conclude that combination of machine learning (ML), multimodal interactions (visual, sound, tactile) with BCI will profit from the immediate feedback from the actual neurophysical reactions classified by ML methods. In general, BCI in combination with other modes of AR interaction can deliver much more information than these types of interaction themselves. Even in the current state the combined AR-BCI interfaces could provide the highly adaptable and personal services, especially for people with functional disabilities.
Effects of virtual acoustics on dynamic auditory distance perception
Venue: arXiv cs.SD 2017
Year: 2017

Sound propagation encompasses various acoustic phenomena including reverberation. Current virtual acoustic methods, ranging from parametric filters to physically-accurate solvers, can simulate reverberation with varying degrees of fidelity. We investigate the effects of reverberant sounds generated using different propagation algorithms on acoustic distance perception, i.e., how faraway humans perceive a sound source. In particular, we evaluate two classes of methods for real-time sound propagation in dynamic scenes based on parametric filters and ray tracing. Our study shows that the more accurate method shows less distance compression as compared to the approximate, filter-based method. This suggests that accurate reverberation in VR results in a better reproduction of acoustic distances. We also quantify the levels of distance compression introduced by different propagation methods in a virtual environment.