OpenRaiser/Intern-Atlas huggingface dataset:
Multi-criteria IoT Resource Discovery: A Comparative Analysis
Venue: arXiv cs.NI 2016
Year: 2016

The growth of real world objects with embedded and globally networked sensors allows to consolidate the Internet of Things paradigm and increase the number of applications in the domains of ubiquitous and context-aware computing. The merging between Cloud Computing and Internet of Things named Cloud of Things will be the key to handle thousands of sensors and their data. One of the main challenges in the Cloud of Things is context-aware sensor search and selection. Typically, sensors require to be searched using two or more conflicting context properties. Most of the existing work uses some kind of multi-criteria decision analysis to perform the sensor search and selection, but does not show any concern for the quality of the selection presented by these methods. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi-objective decision methods and their quality of selection comparing them with the Pareto-optimality solutions. The gathered results allow to analyse and compare these algorithms regarding their behaviour, the number of optimal solutions and redundancy.
Coalgebraic trace semantics via forgetful logics
Venue: arXiv cs.LO 2016
Year: 2016

We use modal logic as a framework for coalgebraic trace semantics, and show the flexibility of the approach with concrete examples such as the language semantics of weighted, alternating and tree automata, and the trace semantics of generative probabilistic systems. We provide a sufficient condition under which a logical semantics coincides with the trace semantics obtained via a given determinization construction. Finally, we consider a condition that guarantees the existence of a canonical determinization procedure that is correct with respect to a given logical semantics. That procedure is closely related to Brzozowski's minimization algorithm.
Longest Common Extensions with Recompression
Venue: arXiv cs.DS 2016
Year: 2016

Given two positions $i$ and $j$ in a string $T$ of length $N$, a longest common extension (LCE) query asks for the length of the longest common prefix between suffixes beginning at $i$ and $j$. A compressed LCE data structure is a data structure that stores $T$ in a compressed form while supporting fast LCE queries. In this article we show that the recompression technique is a powerful tool for compressed LCE data structures. We present a new compressed LCE data structure of size $O(z \lg (N/z))$ that supports LCE queries in $O(\lg N)$ time, where $z$ is the size of Lempel-Ziv 77 factorization without self-reference of $T$. Given $T$ as an uncompressed form, we show how to build our data structure in $O(N)$ time and space. Given $T$ as a grammar compressed form, i.e., an straight-line program of size n generating $T$, we show how to build our data structure in $O(n \lg (N/n))$ time and $O(n + z \lg (N/z))$ space. Our algorithms are deterministic and always return correct answers.
Neural Style Representations and the Large-Scale Classification of Artistic Style
Venue: arXiv cs.CV 2016
Year: 2016

The artistic style of a painting is a subtle aesthetic judgment used by art historians for grouping and classifying artwork. The recently introduced `neural-style' algorithm substantially succeeds in merging the perceived artistic style of one image or set of images with the perceived content of another. In light of this and other recent developments in image analysis via convolutional neural networks, we investigate the effectiveness of a `neural-style' representation for classifying the artistic style of paintings.
Spectral Convolution Networks
Venue: arXiv cs.LG 2016
Year: 2016

Previous research has shown that computation of convolution in the frequency domain provides a significant speedup versus traditional convolution network implementations. However, this performance increase comes at the expense of repeatedly computing the transform and its inverse in order to apply other network operations such as activation, pooling, and dropout. We show, mathematically, how convolution and activation can both be implemented in the frequency domain using either the Fourier or Laplace transformation. The main contributions are a description of spectral activation under the Fourier transform and a further description of an efficient algorithm for computing both convolution and activation under the Laplace transform. By computing both the convolution and activation functions in the frequency domain, we can reduce the number of transforms required, as well as reducing overall complexity. Our description of a spectral activation function, together with existing spectral analogs of other network functions may then be used to compose a fully spectral implementation of a convolution network.
ProjE: Embedding Projection for Knowledge Graph Completion
Venue: arXiv cs.AI 2016
Year: 2016

With the large volume of new information created every day, determining the validity of information in a knowledge graph and filling in its missing parts are crucial tasks for many researchers and practitioners. To address this challenge, a number of knowledge graph completion methods have been developed using low-dimensional graph embeddings. Although researchers continue to improve these models using an increasingly complex feature space, we show that simple changes in the architecture of the underlying model can outperform state-of-the-art models without the need for complex feature engineering. In this work, we present a shared variable neural network model called ProjE that fills-in missing information in a knowledge graph by learning joint embeddings of the knowledge graph's entities and edges, and through subtle, but important, changes to the standard loss function. In doing so, ProjE has a parameter size that is smaller than 11 out of 15 existing methods while performing $37\%$ better than the current-best method on standard datasets. We also show, via a new fact checking task, that ProjE is capable of accurately determining the veracity of many declarative statements.
New Hardness Results for Routing on Disjoint Paths
Venue: arXiv cs.DS 2016
Year: 2016

In the classical Node-Disjoint Paths (NDP) problem, the input consists of an undirected $n$-vertex graph $G$, and a collection $\mathcal{M}=\{(s_1,t_1),\ldots,(s_k,t_k)\}$ of pairs of its vertices, called source-destination, or demand, pairs. The goal is to route the largest possible number of the demand pairs via node-disjoint paths. The best current approximation for the problem is achieved by a simple greedy algorithm, whose approximation factor is $O(\sqrt n)$, while the best current negative result is an $Ω(\log^{1/2-δ}n)$-hardness of approximation for any constant $δ$, under standard complexity assumptions. Even seemingly simple special cases of the problem are still poorly understood: when the input graph is a grid, the best current algorithm achieves an $\tilde O(n^{1/4})$-approximation, and when it is a general planar graph, the best current approximation ratio of an efficient algorithm is $\tilde O(n^{9/19})$. The best currently known lower bound on the approximability of both these versions of the problem is APX-hardness. In this paper we prove that NDP is $2^{Ω(\sqrt{\log n})}$-hard to approximate, unless all problems in NP have algorithms with running time $n^{O(\log n)}$. Our result holds even when the underlying graph is a planar graph with maximum vertex degree $3$, and all source vertices lie on the boundary of a single face (but the destination vertices may lie anywhere in the graph). We extend this result to the closely related Edge-Disjoint Paths problem, showing the same hardness of approximation ratio even for sub-cubic planar graphs with all sources lying on the boundary of a single face.
Unmatched Perturbation Accommodation for an Aerospace Launch Vehicle Autopilot Using Dynamic Sliding Manifolds
Venue: arXiv cs.SY 2016
Year: 2016

Sliding mode control of a launch vehicle during its atmospheric flight phase is studied in the presence of unmatched disturbances. Linear time-varying dynamics of the aerospace vehicle is converted into a systematic formula and then dynamic sliding manifold as an advanced method is used in order to overcome the limited capability of conventional sliding manifolds in minimizing the undesired effects of unmatched perturbations on the control system. At the end, simulation results are evaluated and the performance of two approaches are compared in terms of stability and robustness of the autopilot.
Deep Action- and Context-Aware Sequence Learning for Activity Recognition and Anticipation
Venue: arXiv cs.CV 2016
Year: 2016

Action recognition and anticipation are key to the success of many computer vision applications. Existing methods can roughly be grouped into those that extract global, context-aware representations of the entire image or sequence, and those that aim at focusing on the regions where the action occurs. While the former may suffer from the fact that context is not always reliable, the latter completely ignore this source of information, which can nonetheless be helpful in many situations. In this paper, we aim at making the best of both worlds by developing an approach that leverages both context-aware and action-aware features. At the core of our method lies a novel multi-stage recurrent architecture that allows us to effectively combine these two sources of information throughout a video. This architecture first exploits the global, context-aware features, and merges the resulting representation with the localized, action-aware ones. Our experiments on standard datasets evidence the benefits of our approach over methods that use each information type separately. We outperform the state-of-the-art methods that, as us, rely only on RGB frames as input for both action recognition and anticipation.
Robust Hashing for Multi-View Data: Jointly Learning Low-Rank Kernelized Similarity Consensus and Hash Functions
Venue: arXiv cs.LG 2016
Year: 2016

Learning hash functions/codes for similarity search over multi-view data is attracting increasing attention, where similar hash codes are assigned to the data objects characterizing consistently neighborhood relationship across views. Traditional methods in this category inherently suffer three limitations: 1) they commonly adopt a two-stage scheme where similarity matrix is first constructed, followed by a subsequent hash function learning; 2) these methods are commonly developed on the assumption that data samples with multiple representations are noise-free,which is not practical in real-life applications; 3) they often incur cumbersome training model caused by the neighborhood graph construction using all $N$ points in the database ($O(N)$). In this paper, we motivate the problem of jointly and efficiently training the robust hash functions over data objects with multi-feature representations which may be noise corrupted. To achieve both the robustness and training efficiency, we propose an approach to effectively and efficiently learning low-rank kernelized \footnote{We use kernelized similarity rather than kernel, as it is not a squared symmetric matrix for data-landmark affinity matrix.} hash functions shared across views. Specifically, we utilize landmark graphs to construct tractable similarity matrices in multi-views to automatically discover neighborhood structure in the data. To learn robust hash functions, a latent low-rank kernel function is used to construct hash functions in order to accommodate linearly inseparable data. In particular, a latent kernelized similarity matrix is recovered by rank minimization on multiple kernel-based similarity matrices. Extensive experiments on real-world multi-view datasets validate the efficacy of our method in the presence of error corruptions.
Throughput of TCP over Cognitive Radio Channels
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we study the performance of a TCP connection over cognitive radio networks. In these networks, the network may not always be available for transmission. Also, the packets can be lost due to wireless channel impairments. We evaluate the throughput and packet retransmission timeout probability of a secondary TCP connection over an ON/OFF channel. We first assume that the ON and OFF time durations are exponential and later extend it to more general distributions. We then consider multiple TCP connections over the ON/OFF channel. We validate our theoretical models and the approximations made therein via ns2 simulations.
Bus Stops Location and Bus Route Planning Using Mean Shift Clustering and Ant Colony in West Jakarta
Venue: arXiv cs.CY 2016
Year: 2016

Traffic Jam has been a daily problem for people in Jakarta which is one of the busiest city in Indonesia up until now. Even though the official government has tried to reduce the impact of traffic issues by developing a new public transportation which takes up a lot of resources and time, it failed to diminish the problem. The actual concern to this problem actually lies in how people move between places in Jakarta where they always using their own vehicle like cars, and motorcycles that fill most of the street in Jakarta. Among much other public transportations that roams the street of Jakarta, Buses is believed to be an efficient transportation that can move many people at once. However, the location of the bus stop is now have moved to the middle of the main road, and it is too far for the nearby residence to access to it. This paper proposes an optimal location of optimal bus stops in West Jakarta that is experimentally proven to have a maximal distance of 350 m. The optimal location is estimated by means of mean shift clustering method while the optimal routes are calculated using Ant Colony algorithm. The bus stops locations rate of error is 0.07% with overall route area of 32 km. Based on our experiments, we believe our proposed bus stop plan can be an interesting alternative to reduce traffic congestion in West Jakarta.
Multimodal Memory Modelling for Video Captioning
Venue: arXiv cs.CV 2016
Year: 2016

Video captioning which automatically translates video clips into natural language sentences is a very important task in computer vision. By virtue of recent deep learning technologies, e.g., convolutional neural networks (CNNs) and recurrent neural networks (RNNs), video captioning has made great progress. However, learning an effective mapping from visual sequence space to language space is still a challenging problem. In this paper, we propose a Multimodal Memory Model (M3) to describe videos, which builds a visual and textual shared memory to model the long-term visual-textual dependency and further guide global visual attention on described targets. Specifically, the proposed M3 attaches an external memory to store and retrieve both visual and textual contents by interacting with video and sentence with multiple read and write operations. First, text representation in the Long Short-Term Memory (LSTM) based text decoder is written into the memory, and the memory contents will be read out to guide an attention to select related visual targets. Then, the selected visual information is written into the memory, which will be further read out to the text decoder. To evaluate the proposed model, we perform experiments on two publicly benchmark datasets: MSVD and MSR-VTT. The experimental results demonstrate that our method outperforms the state-of-theart methods in terms of BLEU and METEOR.
Stream Packing for Asynchronous Multi-Context Systems using ASP
Venue: arXiv cs.LO 2016
Year: 2016

When a processing unit relies on data from external streams, we may face the problem that the stream data needs to be rearranged in a way that allows the unit to perform its task(s). On arrival of new data, we must decide whether there is sufficient information available to start processing or whether to wait for more data. Furthermore, we need to ensure that the data meets the input specification of the processing step. In the case of multiple input streams it is also necessary to coordinate which data from which incoming stream should form the input of the next process instantiation. In this work, we propose a declarative approach as an interface between multiple streams and a processing unit. The idea is to specify via answer-set programming how to arrange incoming data in packages that are suitable as input for subsequent processing. Our approach is intended for use in asynchronous multi-context systems (aMCSs), a recently proposed framework for loose coupling of knowledge representation formalisms that allows for online reasoning in a dynamic environment. Contexts in aMCSs process data streams from external sources and other contexts.
A Theory of Available-by-Design Communicating Systems
Venue: arXiv cs.PL 2016
Year: 2016

Choreographic programming is a programming-language design approach that drives error-safe protocol development in distributed systems. Starting from a global specification (choreography) one can generate distributed implementations. The advantages of this top-down approach lie in the correctness-by-design principle, where implementations (endpoints) generated from a choreography behave according to the strict control flow described in the choreography, and do not deadlock. Motivated by challenging scenarios in Cyber-Physical Systems (CPS), we study how choreographic programming can cater for dynamic infrastructures where not all endpoints are always available. We introduce the Global Quality Calculus ($GC_q$), a variant of choreographic programming for the description of communication systems where some of the components involved in a communication might fail. GCq features novel operators for multiparty, partial and collective communications. This paper studies the nature of failure-aware communication: First, we introduce $GC_q$ syntax, semantics and examples of its use. The interplay between failures and collective communications in a choreography can lead to choreographies that cannot progress due to absence of resources. In our second contribution, we provide a type system that ensures that choreographies can be realized despite changing availability conditions. A specification in $GC_q$ guides the implementation of distributed endpoints when paired with global (session) types. Our third contribution provides an endpoint-projection based methodology for the generation of failure-aware distributed processes. We show the correctness of the projection, and that well-typed choreographies with availability considerations enjoy progress.
Learning to detect and localize many objects from few examples
Venue: arXiv cs.CV 2016
Year: 2016

The current trend in object detection and localization is to learn predictions with high capacity deep neural networks trained on a very large amount of annotated data and using a high amount of processing power. In this work, we propose a new neural model which directly predicts bounding box coordinates. The particularity of our contribution lies in the local computations of predictions with a new form of local parameter sharing which keeps the overall amount of trainable parameters low. Key components of the model are spatial 2D-LSTM recurrent layers which convey contextual information between the regions of the image. We show that this model is more powerful than the state of the art in applications where training data is not as abundant as in the classical configuration of natural images and Imagenet/Pascal VOC tasks. We particularly target the detection of text in document images, but our method is not limited to this setting. The proposed model also facilitates the detection of many objects in a single image and can deal with inputs of variable sizes without resizing.
Compensating for Large In-Plane Rotations in Natural Images
Venue: arXiv cs.CV 2016
Year: 2016

Rotation invariance has been studied in the computer vision community primarily in the context of small in-plane rotations. This is usually achieved by building invariant image features. However, the problem of achieving invariance for large rotation angles remains largely unexplored. In this work, we tackle this problem by directly compensating for large rotations, as opposed to building invariant features. This is inspired by the neuro-scientific concept of mental rotation, which humans use to compare pairs of rotated objects. Our contributions here are three-fold. First, we train a Convolutional Neural Network (CNN) to detect image rotations. We find that generic CNN architectures are not suitable for this purpose. To this end, we introduce a convolutional template layer, which learns representations for canonical 'unrotated' images. Second, we use Bayesian Optimization to quickly sift through a large number of candidate images to find the canonical 'unrotated' image. Third, we use this method to achieve robustness to large angles in an image retrieval scenario. Our method is task-agnostic, and can be used as a pre-processing step in any computer vision system.
DeeperBind: Enhancing Prediction of Sequence Specificities of DNA Binding Proteins
Venue: arXiv cs.CV 2016
Year: 2016

Transcription factors (TFs) are macromolecules that bind to \textit{cis}-regulatory specific sub-regions of DNA promoters and initiate transcription. Finding the exact location of these binding sites (aka motifs) is important in a variety of domains such as drug design and development. To address this need, several \textit{in vivo} and \textit{in vitro} techniques have been developed so far that try to characterize and predict the binding specificity of a protein to different DNA loci. The major problem with these techniques is that they are not accurate enough in prediction of the binding affinity and characterization of the corresponding motifs. As a result, downstream analysis is required to uncover the locations where proteins of interest bind. Here, we propose DeeperBind, a long short term recurrent convolutional network for prediction of protein binding specificities with respect to DNA probes. DeeperBind can model the positional dynamics of probe sequences and hence reckons with the contributions made by individual sub-regions in DNA sequences, in an effective way. Moreover, it can be trained and tested on datasets containing varying-length sequences. We apply our pipeline to the datasets derived from protein binding microarrays (PBMs), an in-vitro high-throughput technology for quantification of protein-DNA binding preferences, and present promising results. To the best of our knowledge, this is the most accurate pipeline that can predict binding specificities of DNA sequences from the data produced by high-throughput technologies through utilization of the power of deep learning for feature generation and positional dynamics modeling.
Towards the Modeling of Behavioral Trajectories of Users in Online Social Media
Venue: arXiv cs.CY 2016
Year: 2016

In this paper, we introduce a methodology that allows to model behavioral trajectories of users in online social media. First, we illustrate how to leverage the probabilistic framework provided by Hidden Markov Models (HMMs) to represent users by embedding the temporal sequences of actions they performed online. We then derive a model-based distance between trained HMMs, and we use spectral clustering to find homogeneous clusters of users showing similar behavioral trajectories. To provide platform-agnostic results, we apply the proposed approach to two different online social media --- i.e. Facebook and YouTube. We conclude discussing merits and limitations of our approach as well as future and promising research directions.