OpenRaiser/Intern-Atlas huggingface dataset:
Leveraging Transitive Relations for Crowdsourced Joins
Venue: arXiv cs.DB 2014
Year: 2014

The development of crowdsourced query processing systems has recently attracted a significant attention in the database community. A variety of crowdsourced queries have been investigated. In this paper, we focus on the crowdsourced join query which aims to utilize humans to find all pairs of matching objects from two collections. As a human-only solution is expensive, we adopt a hybrid human-machine approach which first uses machines to generate a candidate set of matching pairs, and then asks humans to label the pairs in the candidate set as either matching or non-matching. Given the candidate pairs, existing approaches will publish all pairs for verification to a crowdsourcing platform. However, they neglect the fact that the pairs satisfy transitive relations. As an example, if $o_1$ matches with $o_2$, and $o_2$ matches with $o_3$, then we can deduce that $o_1$ matches with $o_3$ without needing to crowdsource $(o_1, o_3)$. To this end, we study how to leverage transitive relations for crowdsourced joins. We propose a hybrid transitive-relations and crowdsourcing labeling framework which aims to crowdsource the minimum number of pairs to label all the candidate pairs. We prove the optimal labeling order in an ideal setting and propose a heuristic labeling order in practice. We devise a parallel labeling algorithm to efficiently crowdsource the pairs following the order. We evaluate our approaches in both simulated environment and a real crowdsourcing platform. Experimental results show that our approaches with transitive relations can save much more money and time than existing methods, with a little loss in the result quality.
An Information Retrieval Approach to Short Text Conversation
Venue: arXiv cs.IR 2014
Year: 2014

Human computer conversation is regarded as one of the most difficult problems in artificial intelligence. In this paper, we address one of its key sub-problems, referred to as short text conversation, in which given a message from human, the computer returns a reasonable response to the message. We leverage the vast amount of short conversation data available on social media to study the issue. We propose formalizing short text conversation as a search problem at the first step, and employing state-of-the-art information retrieval (IR) techniques to carry out the task. We investigate the significance as well as the limitation of the IR approach. Our experiments demonstrate that the retrieval-based model can make the system behave rather "intelligently", when combined with a huge repository of conversation data from social media.
Mutualistic Relationships in Service-Oriented Communities and Fractal Social Organizations
Venue: arXiv cs.CY 2014
Year: 2014

In this paper we consider two social organizations -- service-oriented communities and fractal organizations -- and discuss how their main characteristics provide an answer to several shortcomings of traditional organizations. In particular, we highlight their ability to tap into the vast basins of "social energy" of our societies. This is done through the establishing of mutualistic relationships among the organizational components. The paper also introduces a mathematical model of said mutualistic processes as well as its translation in terms of semantic service description and matching. Preliminary investigations of the resilience of fractal social organizations are reported. Simulations show that fractal organizations outperform non-fractal organizations and are able to quickly recover from disruptions and changes characterizing dynamic environments.
Temporal Extension of Scale Pyramid and Spatial Pyramid Matching for Action Recognition
Venue: arXiv cs.CV 2014
Year: 2014

Historically, researchers in the field have spent a great deal of effort to create image representations that have scale invariance and retain spatial location information. This paper proposes to encode equivalent temporal characteristics in video representations for action recognition. To achieve temporal scale invariance, we develop a method called temporal scale pyramid (TSP). To encode temporal information, we present and compare two methods called temporal extension descriptor (TED) and temporal division pyramid (TDP) . Our purpose is to suggest solutions for matching complex actions that have large variation in velocity and appearance, which is missing from most current action representations. The experimental results on four benchmark datasets, UCF50, HMDB51, Hollywood2 and Olympic Sports, support our approach and significantly outperform state-of-the-art methods. Most noticeably, we achieve 65.0% mean accuracy and 68.2% mean average precision on the challenging HMDB51 and Hollywood2 datasets which constitutes an absolute improvement over the state-of-the-art by 7.8% and 3.9%, respectively.
Challenges in Bridging Social Semantics and Formal Semantics on the Web
Venue: arXiv cs.AI 2014
Year: 2014

This paper describes several results of Wimmics, a research lab which names stands for: web-instrumented man-machine interactions, communities, and semantics. The approaches introduced here rely on graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The re-search results are applied to support and foster interactions in online communities and manage their resources.
Personalization of Itineraries search using Ontology and Rules to Avoid Congestion in Urban Areas
Venue: arXiv cs.AI 2014
Year: 2014

There is a relatively small amount of research covering urban freight movements. Most research dealing with the subject of urban mobility focuses on passenger vehicles, not commercial vehicles hauling freight. However, in many ways, urban freight transport contributes to congestion, air pollution, noise, accident and more fuel consumption which raises logistic costs, and hence the price of products. The main focus of this paper is to propose a new solution for congestion in order to improve the distribution process of goods in urban areas and optimize transportation cost, time of delivery, fuel consumption, and environmental impact, while guaranteeing the safety of goods and passengers. A novel technique for personalization in itinerary search based on city logistics ontology and rules is proposed to overcome this problem. The integration of personalization plays a key role in capturing or inferring the needs of each stakeholder (user), and then satisfying these needs in a given context. The proposed approach is implemented to an itinerary search problem for freight transportation in urban areas to demonstrate its ability in facilitating intelligent decision support by retrieving the best itinerary that satisfies the most users preferences (stakeholders).
On minimal realisations of dynamical structure functions
Venue: arXiv cs.SY 2014
Year: 2014

Motivated by the fact that transfer functions do not contain structural information about networks, dynamical structure functions were introduced to capture causal relationships between measured nodes in networks. From the dynamical structure functions, a) we show that the actual number of hidden states can be larger than the number of hidden states estimated from the corresponding transfer function; b) we can obtain partial information about the true state-space equation, which cannot in general be obtained from the transfer function. Based on these properties, this paper proposes algorithms to find minimal realisations for a given dynamical structure function. This helps to estimate the minimal number of hidden states, to better understand the complexity of the network, and to identify potential targets for new measurements.
Designing Path Planning Algorithms for Mobile Anchor towards Range-Free Localization
Venue: arXiv cs.DC 2014
Year: 2014

Localization is one of the most important factor in wireless sensor networks as many applications demand position information of sensors. Recently there is an increasing interest on the use of mobile anchors for localizing sensors. Most of the works available in the literature either looks into the aspect of reducing path length of mobile anchor or tries to increase localization accuracy. The challenge is to design a movement strategy for a mobile anchor that reduces path length while meeting the requirements of a good range-free localization technique. In this paper we propose two cost-effective movement strategies i.e., path planning for a mobile anchor so that localization can be done using the localization scheme \cite{Lee2009}. In one strategy we use a hexagonal movement pattern for the mobile anchor to localize all sensors inside a bounded rectangular region with lesser movement compared to the existing works in literature. In other strategy we consider a connected network in an unbounded region where the mobile anchor moves in the hexagonal pattern to localize the sensors. In this approach, we guarantee localization of all sensors within $r/2$ error-bound where $r$ is the communication range of the mobile anchor and sensors. Our simulation results support theoretical results along with localization accuracy.
Costly Collaborations: The Impact of Scientific Fraud on Co-authors' Careers
Venue: arXiv cs.DL 2014
Year: 2014

Over the last few years, several major scientific fraud cases have shocked the scientific community. The number of retractions each year has also increased tremendously, especially in the biomedical field, and scientific misconduct accounts for approximately more than half of those retractions. It is assumed that co-authors of retracted papers are affected by their colleagues' misconduct, and the aim of this study is to provide empirical evidence of the effect of retractions in biomedical research on co-authors' research careers. Using data from the Web of Science (WOS), we measured the productivity, impact and collaboration of 1,123 co-authors of 293 retracted articles for a period of five years before and after the retraction. We found clear evidence that collaborators do suffer consequences of their colleagues' misconduct, and that a retraction for fraud has higher consequences than a retraction for error. Our results also suggest that the extent of these consequences is closely linked with the ranking of co-authors on the retracted paper, being felt most strongly by first authors, followed by the last authors, while the impact is less important for middle authors.
Approximations and Optimal Control for State-dependent Limited Processor Sharing Queues
Venue: arXiv cs.SY 2014
Year: 2014

The paper studies approximations and control of a processor sharing (PS) server where the service rate depends on the number of jobs occupying the server. The control of such a system is implemented by imposing a limit on the number of jobs that can share the server concurrently, with the rest of the jobs waiting in a first-in-first-out (FIFO) buffer. A desirable control scheme should strike the right balance between efficiency (operating at a high service rate) and parallelism (preventing small jobs from getting stuck behind large ones). We employ the framework of heavy-traffic diffusion analysis to devise near optimal control heuristics for such a queueing system. However, while the literature on diffusion control of state-dependent queueing systems begins with a sequence of systems and an exogenously defined drift function, we begin with a finite discrete PS server and propose an axiomatic recipe to explicitly construct a sequence of state-dependent PS servers which then yields a drift function. We establish diffusion approximations and use them to obtain insightful and closed-form approximations for the original system under a static concurrency limit control policy. We extend our study to control policies that dynamically adjust the concurrency limit. We provide two novel numerical algorithms to solve the associated diffusion control problem. Our algorithms can be viewed as "average cost" iteration: The first algorithm uses binary-search on the average cost and can find an $ε$-optimal policy in time $O\left( \log^2 \frac{1}ε \right)$; the second algorithm uses the Newton-Raphson method for root-finding and requires $O\left( \log \frac{1}ε \log\log \frac{1}ε\right)$ time. Numerical experiments demonstrate the accuracy of our approximation for choosing optimal or near-optimal static and dynamic concurrency control heuristics.
Computers Should Be Uniters Not Dividers: A Vision of Computer-Enhanced Happy Future
Venue: arXiv cs.CY 2014
Year: 2014

This manifesto provides a vision of how computers can be used to bring people together, to enhance people's use of their natural creativity, and thus, make them happier.
Towards Wire-speed Platform-agnostic Control of OpenFlow Switches
Venue: arXiv cs.NI 2014
Year: 2014

The possibility to offload, via a platform-agnostic specification, the execution of (some/part of the) control functions down to the switch and operate them at wire speed based on packet level events, would yield significant benefits in terms of control latency and reaction times, meanwhile retaining the SDN-type ability to program and instantiate a desired network operation from a central controller. While programmability inside the switches, at wire speed and using platform-independent abstractions, of "any possible'" control function seems well beyond the OpenFlow capabilities, in this paper we argue that a non trivial sub-class of stateful control functions, namely those that can be abstracted in terms of Mealy (Finite State) Machines, is already compatible with off-the-shelf OpenFlow version 1.1+ Hardware with marginal architectural modifications. With minimal additional hardware circuitry, the above sub-class can be extended to include support for bidirectional/cross-flow state handling. We demonstrate the viability of our proposed approach via two proof-of-concept implementations (hardware and software), and we show how some stateful control functionalities frequently exploited in network protocols are readily deployed using our application programming interface.
Cyber-Physical Systems -- eine Herausforderung an die Automatisierungstechnik?
Venue: arXiv cs.SE 2014
Year: 2014

We discuss challenges to control systems engineering arising from the advent of cyber-physical systems (CPS). After discussing the terminology, general, IT-related issues are treated which need cooperation with computer science, in particular software engineering. Then we study those challenges that require specific core competencies from control systems engineering. We sketch solution approaches for the exemplary problem of dealing with changes in the physical environment of a CPS. ---- Der Beitrag befasst sich mit den methodischen Herausforderungen, die durch die Verbreitung der Cyber-Physical Systems (CPS) in der Automatisierungstechnik entstehen, und stellt Lösungsansätze vor. Nach einer Behandlung des Begriffs CPS werden zunächst die allgemeinen, IT-bezogenen Fragestellungen angesprochen, die gemeinsam mit der Informatik gelöst werden müssen. Danach gehen wir auf die Herausforderungen ein, deren Behandlung spezifisch automatisierungstechnische Kernkompetenzen erfordern und skizzieren für eine beispielhafte Problemstellung, den Umgang mit Änderungen in der physikalischen Umgebung, wie entsprechende Lösungen aussehen können.
Dynamic Component Composition
Venue: arXiv cs.SE 2014
Year: 2014

This paper presents an approach to dynamic component composition that facilitates creating new composed components using existing ones at runtime and without any code generation. The dynamic abilities are supported by extended type notion and implementation based on additional superstructure provided with its Java API and corresponding JavaBeans components. The new component composition is performed by building the composed prototype object that can be dynamically transformed into the new instantiable type (component). That approach demonstrates interrelations between prototype-based and class-based component-oriented programming. The component model proposed can be used when implementing user-defined types in declarative languages for event-driven applications programming.
Data-Oblivious Graph Algorithms in Outsourced External Memory
Venue: arXiv cs.DS 2014
Year: 2014

Motivated by privacy preservation for outsourced data, data-oblivious external memory is a computational framework where a client performs computations on data stored at a semi-trusted server in a way that does not reveal her data to the server. This approach facilitates collaboration and reliability over traditional frameworks, and it provides privacy protection, even though the server has full access to the data and he can monitor how it is accessed by the client. The challenge is that even if data is encrypted, the server can learn information based on the client data access pattern; hence, access patterns must also be obfuscated. We investigate privacy-preserving algorithms for outsourced external memory that are based on the use of data-oblivious algorithms, that is, algorithms where each possible sequence of data accesses is independent of the data values. We give new efficient data-oblivious algorithms in the outsourced external memory model for a number of fundamental graph problems. Our results include new data-oblivious external-memory methods for constructing minimum spanning trees, performing various traversals on rooted trees, answering least common ancestor queries on trees, computing biconnected components, and forming open ear decompositions. None of our algorithms make use of constant-time random oracles.
Performance Portability Study of Linear Algebra Kernels in OpenCL
Venue: arXiv cs.MS 2014
Year: 2014

The performance portability of OpenCL kernel implementations for common memory bandwidth limited linear algebra operations across different hardware generations of the same vendor as well as across vendors is studied. Certain combinations of kernel implementations and work sizes are found to exhibit good performance across compute kernels, hardware generations, and, to a lesser degree, vendors. As a consequence, it is demonstrated that the optimization of a single kernel is often sufficient to obtain good performance for a large class of more complicated operations.
Efficient Scheme for Active Particle Selection in N-body Simulations
Venue: arXiv cs.DS 2014
Year: 2014

We propose an efficient method for active particle selection, working with Hermite Individual Time Steps (HITS) scheme in direct N-body simulation code $φ$GRAPE. For a simulation with $N$ particles, this method can reduce the computation complexity of active particle selection, from $O(N\cdot N_{step})$ to $O(\overline{N_{act}}\cdot N_{step})$, where $\overline{N_{act}}$ is the average active particle number in every time step which is much smaller than $N$ and $N_{step}$ is the total time steps integrated during the simulation. Thus can save a lot of time spent on active particle selection part, especially in the case of low $\overline{N_{act}}$.
Comparison of algorithms that detect drug side effects using electronic healthcare databases
Venue: arXiv cs.LG 2014
Year: 2014

The electronic healthcare databases are starting to become more readily available and are thought to have excellent potential for generating adverse drug reaction signals. The Health Improvement Network (THIN) database is an electronic healthcare database containing medical information on over 11 million patients that has excellent potential for detecting ADRs. In this paper we apply four existing electronic healthcare database signal detecting algorithms (MUTARA, HUNT, Temporal Pattern Discovery and modified ROR) on the THIN database for a selection of drugs from six chosen drug families. This is the first comparison of ADR signalling algorithms that includes MUTARA and HUNT and enabled us to set a benchmark for the adverse drug reaction signalling ability of the THIN database. The drugs were selectively chosen to enable a comparison with previous work and for variety. It was found that no algorithm was generally superior and the algorithms' natural thresholds act at variable stringencies. Furthermore, none of the algorithms perform well at detecting rare ADRs.
Image Retrieval And Classification Using Local Feature Vectors
Venue: arXiv cs.IR 2014
Year: 2014

Content Based Image Retrieval(CBIR) is one of the important subfield in the field of Information Retrieval. The goal of a CBIR algorithm is to retrieve semantically similar images in response to a query image submitted by the end user. CBIR is a hard problem because of the phenomenon known as $\textit {semantic gap}$. In this thesis, we aim at analyzing the performance of a CBIR system build using local feature vectors and Intermediate Matching Kernel. We also propose a Two-Step Matching process for reducing the response time of the CBIR systems. Further, we develop a Meta-Learning framework for improving the retrieval performance of these systems. Our results show that the Two-Step Matching process significantly reduces response time and the Meta-Learning Framework improves the retrieval performance by more than two fold. We also analyze the performance of various image classification systems that use different image representations constructed from the local feature vectors.
Feature selection in detection of adverse drug reactions from the Health Improvement Network (THIN) database
Venue: arXiv cs.LG 2014
Year: 2014

Adverse drug reaction (ADR) is widely concerned for public health issue. ADRs are one of most common causes to withdraw some drugs from market. Prescription event monitoring (PEM) is an important approach to detect the adverse drug reactions. The main problem to deal with this method is how to automatically extract the medical events or side effects from high-throughput medical events, which are collected from day to day clinical practice. In this study we propose a novel concept of feature matrix to detect the ADRs. Feature matrix, which is extracted from big medical data from The Health Improvement Network (THIN) database, is created to characterize the medical events for the patients who take drugs. Feature matrix builds the foundation for the irregular and big medical data. Then feature selection methods are performed on feature matrix to detect the significant features. Finally the ADRs can be located based on the significant features. The experiments are carried out on three drugs: Atorvastatin, Alendronate, and Metoclopramide. Major side effects for each drug are detected and better performance is achieved compared to other computerized methods. The detected ADRs are based on computerized methods, further investigation is needed.
DataHub: Collaborative Data Science & Dataset Version Management at Scale
Venue: arXiv cs.DB 2014
Year: 2014

Relational databases have limited support for data collaboration, where teams collaboratively curate and analyze large datasets. Inspired by software version control systems like git, we propose (a) a dataset version control system, giving users the ability to create, branch, merge, difference and search large, divergent collections of datasets, and (b) a platform, DataHub, that gives users the ability to perform collaborative data analysis building on this version control system. We outline the challenges in providing dataset version control at scale.
Polynomial-time algorithms for minimum weighted colorings of ($P_5, \bar{P}_5$)-free graphs and related graph classes
Venue: arXiv cs.DM 2014
Year: 2014

We design an $O(n^3)$ algorithm to find a minimum weighted coloring of a ($P_5, \bar{P}_5$)-free graph. Furthermore, the same technique can be used to solve the same problem for several classes of graphs, defined by forbidden induced subgraphs, such as (diamond, co-diamond)-free graphs.