OpenRaiser/Intern-Atlas huggingface dataset:
The Next Best Underwater View
Venue: arXiv cs.CV 2015
Year: 2015

To image in high resolution large and occlusion-prone scenes, a camera must move above and around. Degradation of visibility due to geometric occlusions and distances is exacerbated by scattering, when the scene is in a participating medium. Moreover, underwater and in other media, artificial lighting is needed. Overall, data quality depends on the observed surface, medium and the time-varying poses of the camera and light source. This work proposes to optimize camera/light poses as they move, so that the surface is scanned efficiently and the descattered recovery has the highest quality. The work generalizes the next best view concept of robot vision to scattering media and cooperative movable lighting. It also extends descattering to platforms that move optimally. The optimization criterion is information gain, taken from information theory. We exploit the existence of a prior rough 3D model, since underwater such a model is routinely obtained using sonar. We demonstrate this principle in a scaled-down setup.
Entropy bounds for conjunctive queries with functional dependencies
Venue: arXiv cs.DB 2015
Year: 2015

We study the problem of finding the worst-case bound for the size of the result $Q(\mathbb{ D})$ of a fixed conjunctive query $Q$ applied to a database $\mathbb{ D}$ satisfying given functional dependencies. We provide a precise characterization of this bound in terms of entropy vectors, and in terms of finite groups. In particular, we show that an upper bound provided by Gottlob, Lee, Valiant and Valiant is tight, answering a question from their paper. Our result generalizes the bound due to Atserias, Grohe and Marx, who consider the case without functional dependencies. Our result shows that the problem of computing the worst-case size bound, in the general case, is closely related to difficult problems from information theory.
High quality voice conversion using prosodic and high-resolution spectral features
Venue: arXiv cs.SD 2015
Year: 2015

Voice conversion methods have advanced rapidly over the last decade. Studies have shown that speaker characteristics are captured by spectral feature as well as various prosodic features. Most existing conversion methods focus on the spectral feature as it directly represents the timbre characteristics, while some conversion methods have focused only on the prosodic feature represented by the fundamental frequency. In this paper, a comprehensive framework using deep neural networks to convert both timbre and prosodic features is proposed. The timbre feature is represented by a high-resolution spectral feature. The prosodic features include F0, intensity and duration. It is well known that DNN is useful as a tool to model high-dimensional features. In this work, we show that DNN initialized by our proposed autoencoder pretraining yields good quality DNN conversion models. This pretraining is tailor-made for voice conversion and leverages on autoencoder to capture the generic spectral shape of source speech. Additionally, our framework uses segmental DNN models to capture the evolution of the prosodic features over time. To reconstruct the converted speech, the spectral feature produced by the DNN model is combined with the three prosodic features produced by the DNN segmental models. Our experimental results show that the application of both prosodic and high-resolution spectral features leads to quality converted speech as measured by objective evaluation and subjective listening tests.
Reconstruction of Sparse and Nonsparse Signals from a Reduced Set of Samples
Venue: arXiv cs.IT 2015
Year: 2015

Signals sparse in a transformation domain can be recovered from a reduced set of randomly positioned samples by using compressive sensing algorithms. Simple re- construction algorithms are presented in the first part of the paper. The missing samples manifest themselves as a noise in this reconstruction. Once the reconstruction conditions for a sparse signal are met and the reconstruction is achieved, the noise due to missing samples does not influence the results in a direct way. It influences the possibility to recover a signal only. Additive input noise will remain in the resulting reconstructed signal. The accuracy of the recovery results is related to the additive input noise. Simple derivation of this relation is presented. If a reconstruction algorithm for a sparse signal is used in the reconstruction of a nonsparse signal then the noise due to missing samples will remain and behave as an additive input noise. An exact relation for the mean square error of this error is derived for the partial DFT matrix case in this paper and presented in form of a theorem. It takes into account very important fact that if all samples are available then the error will be zero, for both sparse and nonsparse recovered signals. Theory is illustrated and checked on statistical examples.
Building exploration with leeches Hirudo verbana
Venue: arXiv cs.ET 2015
Year: 2015

Safe evacuation of people from building and outdoor environments, and search and rescue operations, always will remain actual in course of all socio-technological developments. Modern facilities offer a range of automated systems to guide residents towards emergency exists. The systems are assumed to be infallible. But what if they fail? How occupants not familiar with a building layout will be looking for exits in case of very limited visibility where tactile sensing is the only way to assess the environment? Analogous models of human behaviour, and socio-dynamics in general, are provided to be fruitful ways to explore alternative, or would-be scenarios. Crowd, or a single person, dynamics could be imitated using particle systems, reaction-diffusion chemical medium, electro-magnetic fields, or social insects. Each type of analogous model offer unique insights on behavioural patterns of natural systems in constrained geometries. In this particular paper we have chosen leeches to analyse patterns of exploration. Reasons are two-fold. First, when deprived from other stimuli leeches change their behavioural modes in an automated regime in response to mechanical stimulation. Therefore leeches can give us invaluable information on how human beings might behave under stress and limited visibility. Second, leeches are ideal blueprints of future soft-bodied rescue robots. Leeches have modular nervous circuitry with a rich behavioral spectrum. Leeches are multi-functional, fault-tolerant with autonomous inter-segment coordination and adaptive decision-making. We aim to answer the question: how efficiently a real building can be explored and whether there any dependencies on the pathways of exploration and geometrical complexity of the building. In our case studies we use templates made on the floor plan of real building.
In the Age of Web: Typed Functional-First Programming Revisited
Venue: arXiv cs.PL 2015
Year: 2015

Most programming languages were designed before the age of web. This matters because the web changes many assumptions that typed functional language designers take for granted. For example, programs do not run in a closed world, but must instead interact with (changing and likely unreliable) services and data sources, communication is often asynchronous or event-driven, and programs need to interoperate with untyped environments. In this paper, we present how the F# language and libraries face the challenges posed by the web. Technically, this comprises using type providers for integration with external information sources and for integration with untyped programming environments, using lightweight meta-programming for targeting JavaScript and computation expressions for writing asynchronous code. In this inquiry, the holistic perspective is more important than each of the features in isolation. We use a practical case study as a starting point and look at how F# language and libraries approach the challenges posed by the web. The specific lessons learned are perhaps less interesting than our attempt to uncover hidden assumptions that no longer hold in the age of web.
Scalable domain adaptation of convolutional neural networks
Venue: arXiv cs.CV 2015
Year: 2015

Convolutional neural networks (CNNs) tend to become a standard approach to solve a wide array of computer vision problems. Besides important theoretical and practical advances in their design, their success is built on the existence of manually labeled visual resources, such as ImageNet. The creation of such datasets is cumbersome and here we focus on alternatives to manual labeling. We hypothesize that new resources are of uttermost importance in domains which are not or weakly covered by ImageNet, such as tourism photographs. We first collect noisy Flickr images for tourist points of interest and apply automatic or weakly-supervised reranking techniques to reduce noise. Then, we learn domain adapted models with a standard CNN architecture and compare them to a generic model obtained from ImageNet. Experimental validation is conducted with publicly available datasets, including Oxford5k, INRIA Holidays and Div150Cred. Results show that low-cost domain adaptation improves results compared to the use of generic models but also compared to strong non-CNN baselines such as triangulation embedding.
From rules to runs: A dynamic epistemic take on imperfect information games
Venue: arXiv cs.AI 2015
Year: 2015

In the literature of game theory, the information sets of extensive form games have different interpretations, which may lead to confusions and paradoxical cases. We argue that the problem lies in the mix-up of two interpretations of the extensive form game structures: game rules or game runs which do not always coincide. In this paper, we try to separate and connect these two views by proposing a dynamic epistemic framework in which we can compute the runs step by step from the game rules plus the given assumptions of the players. We propose a modal logic to describe players' knowledge and its change during the plays, and provide a complete axiomatization. We also show that, under certain conditions, the mix-up of the rules and the runs is not harmful due to the structural similarity of the two.
Proof Driven Development
Venue: arXiv cs.SE 2015
Year: 2015

A new workflow for software development (proof-driven development) is presented. An extension of test-driven development, the new workflow utilizes the paradigm of dependently typed programming. The differences in design, complexity and provability of software are discussed, based on the technique used to create the system. Furthermore, the difference in what properties can be expressed in a proof-driven development workflow versus a traditional test-driven development workflow or using test-last development.
Joint Time-Frequency Scattering for Audio Classification
Venue: arXiv cs.SD 2015
Year: 2015

We introduce the joint time-frequency scattering transform, a time shift invariant descriptor of time-frequency structure for audio classification. It is obtained by applying a two-dimensional wavelet transform in time and log-frequency to a time-frequency wavelet scalogram. We show that this descriptor successfully characterizes complex time-frequency phenomena such as time-varying filters and frequency modulated excitations. State-of-the-art results are achieved for signal reconstruction and phone segment classification on the TIMIT dataset.
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
Venue: arXiv cs.CV 2015
Year: 2015

Recent work has shown that optical flow estimation can be formulated as a supervised learning task and can be successfully solved with convolutional networks. Training of the so-called FlowNet was enabled by a large synthetically generated dataset. The present paper extends the concept of optical flow estimation via convolutional networks to disparity and scene flow estimation. To this end, we propose three synthetic stereo video datasets with sufficient realism, variation, and size to successfully train large networks. Our datasets are the first large-scale datasets to enable training and evaluating scene flow methods. Besides the datasets, we present a convolutional network for real-time disparity estimation that provides state-of-the-art results. By combining a flow and disparity estimation network and training it jointly, we demonstrate the first scene flow estimation with a convolutional network.
Faster Information Gathering in Ad-Hoc Radio Tree Networks
Venue: arXiv cs.DS 2015
Year: 2015

We study information gathering in ad-hoc radio networks. Initially, each node of the network has a piece of information called a rumor, and the overall objective is to gather all these rumors in the designated target node. The ad-hoc property refers to the fact that the topology of the network is unknown when the computation starts. Aggregation of rumors is not allowed, which means that each node may transmit at most one rumor in one step. We focus on networks with tree topologies, that is we assume that the network is a tree with all edges directed towards the root, but, being ad-hoc, its actual topology is not known. We provide two deterministic algorithms for this problem. For the model that does not assume any collision detection nor acknowledgement mechanisms, we give an $O(n\log\log n)$-time algorithm, improving the previous upper bound of $O(n\log n)$. We also show that this running time can be further reduced to $O(n)$ if the model allows for acknowledgements of successful transmissions.
Securing Cloud - The Quantum Way
Venue: arXiv cs.CR 2015
Year: 2015

Confidentiality, Integrity, and Availability are basic goals of security architecture. To ensure CIA, many authentication scheme has been introduced in several years. Currently deployment of Public Key Infrastructure (PKI) is a most significant solution. PKI involving exchange key using certificates via a public channel to a authenticate users in the cloud infrastructure. It is exposed to widespread security threats such as eavesdropping, the man in the middle attack, masquerade et al. Quantum cryptography is of the most prominent fields in the modern world of information security. Quantum cryptography is considered to be a future replica of classical cryptography along with a vital stance to break existing classical cryptography. This paper aims to look into basic security architecture in place currently and further it tries to introduce a new proposed security architecture for cloud computing environment, which makes use of the knowledge of Quantum Mechanics and current advances in research in Quantum Computing, to provide a more secure architecture.
Predicting Popularity of Twitter Accounts through the Discovery of Link-Propagating Early Adopters
Venue: arXiv cs.SI 2015
Year: 2015

In this paper, we propose a method of ranking recently created Twitter accounts according to their prospective popularity. Early detection of new promising accounts is useful for trend prediction, viral marketing, user recommendation, and so on. New accounts are, however, difficult to evaluate because they have not established their reputations, and we cannot apply existing link-based or other popularity-based account evaluation methods. Our method first finds "early adopters", i.e., users who often find new good information sources earlier than others. Our method then regards new accounts followed by good early adopters as promising, even if they do not have many followers now. In order to find good early adopters, we estimate the frequency of link propagation from each account, i.e., how many times the follow links from the account have been copied by its followers. If its followers have copied many of its follow links in the past, the account must be an early adopter, who find good information sources earlier than its followers. We develop a method of inferring which links are created by copying which links. One advantage of our method is that our method only uses information that can be easily obtained only by crawling neighbors of the target accounts in the current Twitter graph. We evaluated our method by an experiment on Twitter data. We chose then-new accounts from an old snapshot of Twitter, compute their ranking by our method, and compare it with the number of followers the accounts currently have. The result shows that our method produces better rankings than various baseline methods, especially for new accounts that have only a few followers.
The 3D virtual environment online for real shopping
Venue: arXiv cs.CY 2015
Year: 2015

The development of information technology and Internet has led to rapidly progressed in e-commerce and online shopping, due to the convenience that they provide consumers. E-commerce and online shopping are still not able to fully replace onsite shopping. In contrast, conventional online shopping websites often cannot provide enough information about a product for the customer to make an informed decision before checkout. 3D virtual shopping environment show great potential for enhancing e-commerce systems and provide customers information about a product and real shopping environment. This paper presents a new type of e-commerce system, which obviously brings virtual environment online with an active 3D model that allows consumers to access products into real physical environments for user interaction. Such system with easy process can helps customers make better purchasing decisions that allows users to manipulate 3D virtual models online. The stores participate in the 3D virtual mall by communicating with a mall management. The 3D virtual mall allows shoppers to perform actions across multiple stores simultaneously such as viewing product availability. The mall management can authenticate clients on all stores participating in the 3D virtual mall while only requiring clients to provide authentication information once. 3D virtual shopping online mall convenient and easy process allow consumers directly buy goods or services from a seller in real-time, without an intermediary service, over the Internet. The virtual mall with an active 3D model is implemented by using 3D Language (VRML) and asp.net as the script language for shopping online pages
A randomized polynomial kernel for Subset Feedback Vertex Set
Venue: arXiv cs.DS 2015
Year: 2015

The Subset Feedback Vertex Set problem generalizes the classical Feedback Vertex Set problem and asks, for a given undirected graph $G=(V,E)$, a set $S \subseteq V$, and an integer $k$, whether there exists a set $X$ of at most $k$ vertices such that no cycle in $G-X$ contains a vertex of $S$. It was independently shown by Cygan et al. (ICALP '11, SIDMA '13) and Kawarabayashi and Kobayashi (JCTB '12) that Subset Feedback Vertex Set is fixed-parameter tractable for parameter $k$. Cygan et al. asked whether the problem also admits a polynomial kernelization. We answer the question of Cygan et al. positively by giving a randomized polynomial kernelization for the equivalent version where $S$ is a set of edges. In a first step we show that Edge Subset Feedback Vertex Set has a randomized polynomial kernel parameterized by $|S|+k$ with $O(|S|^2k)$ vertices. For this we use the matroid-based tools of Kratsch and Wahlström (FOCS '12) that for example were used to obtain a polynomial kernel for $s$-Multiway Cut. Next we present a preprocessing that reduces the given instance $(G,S,k)$ to an equivalent instance $(G',S',k')$ where the size of $S'$ is bounded by $O(k^4)$. These two results lead to a polynomial kernel for Subset Feedback Vertex Set with $O(k^9)$ vertices.
Modelling Decoding Errors in HARQ
Venue: arXiv cs.IT 2015
Year: 2015

In this work we address the issues of probabilistic modelling of the decoding errors in hybrid ARQ (HARQ) rounds. In particular we i) claim that the assumption of independence of decoding errors, used implicitly in various works on this subject, is an approximation, and ii) propose equally simple but much more accurate method to calculate the probability of the sequence of decoding errors. The model we propose is useful from the point of view of performance evaluation, system-level simulation, and/or link adaptation. Its simplicity leads also to closed form expression for the outage probability and for the average number of transmissions in block-fading channel.
Contextuality and the Weak Axiom in the Theory of Choice
Venue: arXiv cs.GT 2015
Year: 2015

Recent work on the logical structure of non-locality has constructed scenarios where observations of multi-partite systems cannot be adequately described by compositions of non-signaling subsystems. In this paper we apply these frameworks to economics. First we construct a empirical model of choice, where choices are understood as observable outcomes in a certain sense. An analysis of contextuality within this framework allows us to characterize which scenarios allow for the possible construction of an adequate global choice rule. In essence, we mathematically characterize when it makes sense to consider the choices of a group as composed of individual choices. We then map out the logical space of some relevant empirical principles, relating properties of these contextual choice scenarios to no-signalling theories and to the weak axiom of revealed preference.
Topology Optimization for Galvanic Coupled Wireless Intra-body Communication
Venue: arXiv cs.NI 2015
Year: 2015

Implanted sensors and actuators in the human body promise in-situ health monitoring and rapid advancements in personalized medicine. We propose a new paradigm where such implants may communicate wirelessly through a technique called as galvanic coupling, which uses weak electrical signals and the conduction properties of body tissues. While galvanic coupling overcomes the problem of massive absorption of RF waves in the body, the unique intra-body channel raises several questions on the topology of the implants and the external (i.e., on skin) data collection nodes. This paper makes the first contributions towards (i) building an energy-efficient topology through optimal placement of data collection points/relays using measurement-driven tissue channel models, and (ii) balancing the energy consumption over the entire implant network so that the application needs are met. We achieve this via a two-phase iterative clustering algorithm for the implants and formulate an optimization problem that decides the position of external data-gathering points. Our theoretical results are validated via simulations and experimental studies on real tissues, with demonstrated increase in the network lifetime.
Model-based Hazard and Impact Analysis
Venue: arXiv cs.SE 2015
Year: 2015

Hazard and impact analysis is an indispensable task during the specification and development of safety-critical technical systems, and particularly of their software-intensive control parts. There is a lack of methods supporting an effective (reusable, automated) and integrated (cross-disciplinary) way to carry out such analyses. This report was motivated by an industrial project whose goal was to survey and propose methods and models for documentation and analysis of a system and its environment to support hazard and impact analysis as an important task of safety engineering and system development. We present and investigate three perspectives of how to properly encode safety-relevant domain knowledge for better reuse and automation, identify and assess all relevant hazards, as well as pre-process this information and make it easily accessible for reuse in other safety and systems engineering activities and, moreover, in similar engineering projects.