OpenRaiser/Intern-Atlas huggingface dataset:
Scenario and Sensitivity Analysis for Flooding Vulnerability using Genetic Algorithms
Venue: arXiv cs.CY 2017
Year: 2017

We try to answer the question: "can we 'modify' our neighborhoods to make them less vulnerable to flooding?" We minimize flooding vulnerability for a city in the central plain of Luzon, by modeling the city as a biological organism with 'traits', and try to 'breed' a 'champion' city (with a low flooding vulnerability) via a genetic algorithm. The result is a description of the traits the barangays (neighborhoods) should have (the 'design' of the city). As far as we can tell, this kind of modeling has not been attempted before. The different components of flooding vulnerability were investigated, and each was given a weight, which allows us to express vulnerability as a weighted sum; this serves as the fitness function for the genetic algorithm. We also allowed non-linear interactions among related but independent components, viz, poverty and mortality rate, and literacy and radio/TV penetration. The two-table system we used to prioritize the components of vulnerability is prone to subjectivity, a common problem in analyses of vulnerability. Thus, a sensitivity analysis was done, which gave a design with a 24% decrease in vulnerability alongside a 14% percent decrease in cost, a significant improvement over this initial scenario analysis, where the proposed design had a 12% decrease in vulnerability with a one percent increase in cost.
RARD: The Related-Article Recommendation Dataset
Venue: arXiv cs.IR 2017
Year: 2017

Recommender-system datasets are used for recommender-system evaluations, training machine-learning algorithms, and exploring user behavior. While there are many datasets for recommender systems in the domains of movies, books, and music, there are rather few datasets from research-paper recommender systems. In this paper, we introduce RARD, the Related-Article Recommendation Dataset, from the digital library Sowiport and the recommendation-as-a-service provider Mr. DLib. The dataset contains information about 57.4 million recommendations that were displayed to the users of Sowiport. Information includes details on which recommendation approaches were used (e.g. content-based filtering, stereotype, most popular), what types of features were used in content based filtering (simple terms vs. keyphrases), where the features were extracted from (title or abstract), and the time when recommendations were delivered and clicked. In addition, the dataset contains an implicit item-item rating matrix that was created based on the recommendation click logs. RARD enables researchers to train machine learning algorithms for research-paper recommendations, perform offline evaluations, and do research on data from Mr. DLib's recommender system, without implementing a recommender system themselves. In the field of scientific recommender systems, our dataset is unique. To the best of our knowledge, there is no dataset with more (implicit) ratings available, and that many variations of recommendation algorithms. The dataset is available at http://data.mr-dlib.org, and published under the Creative Commons Attribution 3.0 Unported (CC-BY) license.
Secret-Key-Aided Scheme for Securing Untrusted DF Relaying Networks
Venue: arXiv cs.IT 2017
Year: 2017

This paper proposes a new scheme to secure the transmissions in an untrusted decode-and-forward (DF) relaying network. A legitimate source node, Alice, sends her data to a legitimate destination node, Bob, with the aid of an untrusted DF relay node, Charlie. To secure the transmissions from Charlie during relaying time slots, each data codeword is secured using a secret-key codeword that has been previously shared between Alice and Bob during the perfectly secured time slots (i.e., when the channel secrecy rate is positive). The secret-key bits exchanged between Alice and Bob are stored in a finite-length buffer and are used to secure data transmission whenever needed. We model the secret-key buffer as a queueing system and analyze its Markov chain. Our numerical results show the gains of our proposed scheme relative to benchmarks. Moreover, the proposed scheme achieves an upper bound on the secure throughput.
A filter based approach for inbetweening
Venue: arXiv cs.CV 2017
Year: 2017

We present a filter based approach for inbetweening. We train a convolutional neural network to generate intermediate frames. This network aim to generate smooth animation of line drawings. Our method can process scanned images directly. Our method does not need to compute correspondence of lines and topological changes explicitly. We experiment our method with real animation production data. The results show that our method can generate intermediate frames partially.
Resilient Work Stealing
Venue: arXiv cs.DC 2017
Year: 2017

Future generations of processors will exhibit an increase of faults over their lifetime, and it becomes increasingly expensive to solve the resulting reliability issues purely at the hardware level. We propose to model computations in terms of restartable task graphs in order to improve reliability at the software level. As a proof of concept, we present Cobra, a novel design for a shared-memory work-stealing scheduler that realizes this notion of restartable task graphs, and enables computations to survive hardware failures due to soft errors. A comparison with the work-stealing scheduler of Threading Building Blocks on the PARSEC benchmark suite shows that Cobra incurs no performance overhead in the absence of failures, and low performance overheads in the presence of single and multiple failures.
Post-processing partitions to identify domains of modularity optimization
Venue: arXiv cs.SI 2017
Year: 2017

We introduce the Convex Hull of Admissible Modularity Partitions (CHAMP) algorithm to prune and prioritize different network community structures identified across multiple runs of possibly various computational heuristics. Given a set of partitions, CHAMP identifies the domain of modularity optimization for each partition ---i.e., the parameter-space domain where it has the largest modularity relative to the input set---discarding partitions with empty domains to obtain the subset of partitions that are "admissible" candidate community structures that remain potentially optimal over indicated parameter domains. Importantly, CHAMP can be used for multi-dimensional parameter spaces, such as those for multilayer networks where one includes a resolution parameter and interlayer coupling. Using the results from CHAMP, a user can more appropriately select robust community structures by observing the sizes of domains of optimization and the pairwise comparisons between partitions in the admissible subset. We demonstrate the utility of CHAMP with several example networks. In these examples, CHAMP focuses attention onto pruned subsets of admissible partitions that are 20-to-1785 times smaller than the sets of unique partitions obtained by community detection heuristics that were input into CHAMP.
Dynamic Networks of Finite State Machines
Venue: arXiv cs.DC 2017
Year: 2017

Like distributed systems, biological multicellular processes are subject to dynamic changes and a biological system will not pass the survival-of-the-fittest test unless it exhibits certain features that enable fast recovery from these changes. In particular, a question that is crucial in the context of biological cellular networks, is whether the system can keep the changing components \emph{confined} so that only nodes in their vicinity may be affected by the changes, but nodes sufficiently far away from any changing component remain unaffected. Based on this notion of confinement, we propose a new metric for measuring the dynamic changes recovery performance in distributed network algorithms operating under the \emph{Stone Age} model (Emek \& Wattenhofer, PODC 2013), where the class of dynamic topology changes we consider includes inserting/deleting an edge, deleting a node together with its incident edges, and inserting a new isolated node. Our main technical contribution is a distributed algorithm for maximal independent set (MIS) in synchronous networks subject to these topology changes that performs well in terms of the aforementioned new metric. Specifically, our algorithm guarantees that nodes which do not experience a topology change in their immediate vicinity are not affected and that all surviving nodes (including the affected ones) perform $\mathcal{O}((C + 1) \log^{2} n)$ computationally-meaningful steps, where $C$ is the number of topology changes; in other words, each surviving node performs $\mathcal{O}(\log^{2} n)$ steps when amortized over the number of topology changes. This is accompanied by a simple example demonstrating that the linear dependency on $C$ cannot be avoided.
Large-Scale Plant Classification with Deep Neural Networks
Venue: arXiv cs.LG 2017
Year: 2017

This paper discusses the potential of applying deep learning techniques for plant classification and its usage for citizen science in large-scale biodiversity monitoring. We show that plant classification using near state-of-the-art convolutional network architectures like ResNet50 achieves significant improvements in accuracy compared to the most widespread plant classification application in test sets composed of thousands of different species labels. We find that the predictions can be confidently used as a baseline classification in citizen science communities like iNaturalist (or its Spanish fork, Natusfera) which in turn can share their data with biodiversity portals like GBIF.
Contracting Bipartite Graphs to Paths and Cycles
Venue: arXiv cs.DM 2017
Year: 2017

Testing if a given graph $G$ contains the $k$-vertex path $P_k$ as a minor or as an induced minor is trivial for every fixed integer $k\geq 1$. However, the situation changes for the problem of checking if a graph can be modified into $P_k$ by using only edge contractions. In this case the problem is known to be NP-complete even if $k=4$. This led to an intensive investigation for testing contractibility on restricted graph classes. We focus on bipartite graphs. Heggernes, van 't Hof, Lévêque and Paul proved that the problem stays NP-complete for bipartite graphs if $k=6$. We strengthen their result from $k=6$ to $k=5$. We also show that the problem of contracting a bipartite graph to the $6$-vertex cycle $C_6$ is NP-complete. The cyclicity of a graph is the length of the longest cycle the graph can be contracted to. As a consequence of our second result, determining the cyclicity of a bipartite graph is NP-hard.
Encoding of phonology in a recurrent neural model of grounded speech
Venue: arXiv cs.CL 2017
Year: 2017

We study the representation and encoding of phonemes in a recurrent neural network model of grounded speech. We use a model which processes images and their spoken descriptions, and projects the visual and auditory representations into the same semantic space. We perform a number of analyses on how information about individual phonemes is encoded in the MFCC features extracted from the speech signal, and the activations of the layers of the model. Via experiments with phoneme decoding and phoneme discrimination we show that phoneme representations are most salient in the lower layers of the model, where low-level signals are processed at a fine-grained level, although a large amount of phonological information is retain at the top recurrent layer. We further find out that the attention mechanism following the top recurrent layer significantly attenuates encoding of phonology and makes the utterance embeddings much more invariant to synonymy. Moreover, a hierarchical clustering of phoneme representations learned by the network shows an organizational structure of phonemes similar to those proposed in linguistics.
Approximate Structure Construction Using Large Statistical Swarms
Venue: arXiv cs.RO 2017
Year: 2017

In this paper we describe a novel local algorithm for large statistical swarms using "harmonic attractor dynamics", by means of which a swarm can construct harmonics of the environment. This in turn allows the swarm to approximately reconstruct desired structures in the environment. The robots navigate in a discrete environment, completely free of localization, being able to communicate with other robots in its own discrete cell only, and being able to sense or take reliable action within a disk of radius $r$ around itself. We present the mathematics that underlie such dynamics and present initial results demonstrating the proposed algorithm.
Google Cardboard Dates Augmented Reality : Issues, Challenges and Future Opportunities
Venue: arXiv cs.HC 2017
Year: 2017

The Google's frugal Cardboard solution for immersive Virtual Reality experiences has come a long way in the VR market. The Google Cardboard VR applications will support us in the fields such as education, virtual tourism, entertainment, gaming, design etc. Recently, Qualcomm's Vuforia SDK has introduced support for developing mixed reality applications for Google Cardboard which can combine Virtual and Augmented Reality to develop exciting and immersive experiences. In this work, we present a comprehensive review of Google Cardboard for AR and also highlight its technical and subjective limitations by conducting a feasibility study through the inspection of a Desktop computer use-case. Additionally, we recommend the future avenues for the Google Cardboard in AR. This work also serves as a guide for Android/iOS developers as there are no published scholarly articles or well documented studies exclusively on Google Cardboard with both user and developer's experience captured at one place.
Revisiting Definitional Foundations of Oblivious RAM for Secure Processor Implementations
Venue: arXiv cs.CR 2017
Year: 2017

Oblivious RAM (ORAM) is a renowned technique to hide the access patterns of an application to an untrusted memory. According to the standard ORAM definition presented by Goldreich and Ostrovsky, two ORAM access sequences must be computationally indistinguishable if the lengths of these sequences are identically distributed. An artifact of this definition is that it does not apply to modern ORAM implementations adapted in current secure processors technology because of their arbitrary lengths of memory access sequences depending on programs' behaviors (their termination times). As a result, the ORAM definition does not directly apply; the theoretical foundations of ORAM do not clearly argue about the timing and termination channels. This paper conducts a first rigorous study of the standard Goldreich-Ostrovsky ORAM definition in view of modern practical ORAMs (e.g., Path ORAM) and demonstrates the gap between theoretical foundations and real implementations. A new ORAM formulation which clearly separates out termination channel leakage is proposed. It is shown how this definition implies the standard ORAM definition (for finite length input access sequences) and better fits the modern practical ORAM implementations. The proposed definition relaxes the constraints around the stash size and overflow probability for Path ORAM, and essentially transforms its security argument into a performance consideration problem. Finally, a `strong' ORAM formulation which clearly includes obfuscation of termination leakage is shown to imply our new ORAM formulation and applies to ORAM for outsourced disk storage. In this strong formulation constraints are not relaxed and the security argument for Path ORAM remains complex as one needs to prove that the stash overflows with negligible probability.
Wiretap Channels: Nonasymptotic Fundamental Limits
Venue: arXiv cs.IT 2017
Year: 2017

This paper investigates the maximal secret communication rate over a wiretap channel subject to reliability and secrecy constraints at a given blocklength. New achievability and converse bounds are derived, which are uniformly tighter than existing bounds, and lead to the tightest bounds on the second-order coding rate for discrete memoryless and Gaussian wiretap channels. The exact second-order coding rate is established for semi-deterministic wiretap channels, which characterizes the optimal tradeoff between reliability and secrecy in the finite-blocklength regime. Underlying our achievability bounds are two new privacy amplification results, which not only refine the existing results, but also achieve stronger notions of secrecy.
Exact Learning from an Honest Teacher That Answers Membership Queries
Venue: arXiv cs.LG 2017
Year: 2017

Given a teacher that holds a function $f:X\to R$ from some class of functions $C$. The teacher can receive from the learner an element~$d$ in the domain $X$ (a query) and returns the value of the function in $d$, $f(d)\in R$. The learner goal is to find $f$ with a minimum number of queries, optimal time complexity, and optimal resources. In this survey, we present some of the results known from the literature, different techniques used, some new problems, and open problems.
Asynchronous Graph Pattern Matching on Multiprocessor Systems
Venue: arXiv cs.DB 2017
Year: 2017

Pattern matching on large graphs is the foundation for a variety of application domains. Strict latency requirements and continuously increasing graph sizes demand the usage of highly parallel in-memory graph processing engines that need to consider non-uniform memory access (NUMA) and concurrency issues to scale up on modern multiprocessor systems. To tackle these aspects, graph partitioning becomes increasingly important. Hence, we present a technique to process graph pattern matching on NUMA systems in this paper. As a scalable pattern matching processing infrastructure, we leverage a data-oriented architecture that preserves data locality and minimizes concurrency-related bottlenecks on NUMA systems. We show in detail, how graph pattern matching can be asynchronously processed on a multiprocessor system.
Automatic Recognition of Public Transport Trips from Mobile Device Sensor Data and Transport Infrastructure Information
Venue: arXiv cs.CY 2017
Year: 2017

Automatic detection of public transport (PT) usage has important applications for intelligent transport systems. It is crucial for understanding the commuting habits of passengers at large and over longer periods of time. It also enables compilation of door-to-door trip chains, which in turn can assist public transport providers in improved optimisation of their transport networks. In addition, predictions of future trips based on past activities can be used to assist passengers with targeted information. This article documents a dataset compiled from a day of active commuting by a small group of people using different means of PT in the Helsinki region. Mobility data was collected by two means: (a) manually written details of each PT trip during the day, and (b) measurements using sensors of travellers' mobile devices. The manual log is used to cross-check and verify the results derived from automatic measurements. The mobile client application used for our data collection provides a fully automated measurement service and implements a set of algorithms for decreasing battery consumption. The live locations of some of the public transport vehicles in the region were made available by the local transport provider and sampled with a 30-second interval. The stopping times of local trains at stations during the day were retrieved from the railway operator. The static timetable information of all the PT vehicles operating in the area is made available by the transport provider, and linked to our dataset. The challenge is to correctly detect as many manually logged trips as possible by using the automatically collected data. This paper includes an analysis of challenges due to missing or partially sampled information in the data, and initial results from automatic recognition using a set of algorithms. Improvement of correct recognitions is left as an ongoing challenge.
Live Service Migration in Mobile Edge Clouds
Venue: arXiv cs.DC 2017
Year: 2017

Mobile edge clouds (MECs) bring the benefits of the cloud closer to the user, by installing small cloud infrastructures at the network edge. This enables a new breed of real-time applications, such as instantaneous object recognition and safety assistance in intelligent transportation systems, that require very low latency. One key issue that comes with proximity is how to ensure that users always receive good performance as they move across different locations. Migrating services between MECs is seen as the means to achieve this. This article presents a layered framework for migrating active service applications that are encapsulated either in virtual machines (VMs) or containers. This layering approach allows a substantial reduction in service downtime. The framework is easy to implement using readily available technologies, and one of its key advantages is that it supports containers, which is a promising emerging technology that offers tangible benefits over VMs. The migration performance of various real applications is evaluated by experiments under the presented framework. Insights drawn from the experimentation results are discussed.
Investigating the Parameter Space of Evolutionary Algorithms
Venue: arXiv cs.NE 2017
Year: 2017

The practice of evolutionary algorithms involves the tuning of many parameters. How big should the population be? How many generations should the algorithm run? What is the (tournament selection) tournament size? What probabilities should one assign to crossover and mutation? Through an extensive series of experiments over multiple evolutionary algorithm implementations and problems we show that parameter space tends to be rife with viable parameters, at least for 25 the problems studied herein. We discuss the implications of this finding in practice.
Video Imagination from a Single Image with Transformation Generation
Venue: arXiv cs.CV 2017
Year: 2017

In this work, we focus on a challenging task: synthesizing multiple imaginary videos given a single image. Major problems come from high dimensionality of pixel space and the ambiguity of potential motions. To overcome those problems, we propose a new framework that produce imaginary videos by transformation generation. The generated transformations are applied to the original image in a novel volumetric merge network to reconstruct frames in imaginary video. Through sampling different latent variables, our method can output different imaginary video samples. The framework is trained in an adversarial way with unsupervised learning. For evaluation, we propose a new assessment metric $RIQA$. In experiments, we test on 3 datasets varying from synthetic data to natural scene. Our framework achieves promising performance in image quality assessment. The visual inspection indicates that it can successfully generate diverse five-frame videos in acceptable perceptual quality.
Predicting Research that will be Cited in Policy Documents
Venue: arXiv cs.DL 2017
Year: 2017

Scientific publications and other genres of research output are increasingly being cited in policy documents. Citations in documents of this nature could be considered a critical indicator of the significance and societal impact of the research output. In this study, we built classification models that predict whether a particular research work is likely to be cited in a public policy document based on the attention it received online, primarily on social media platforms. We evaluated the classifiers based on their accuracy, precision, and recall values. We found that Random Forest and Multinomial Naive Bayes classifiers performed better overall.