OpenRaiser/Intern-Atlas huggingface dataset:
GLB: Lifeline-based Global Load Balancing library in X10
Venue: arXiv cs.DC 2013
Year: 2013

We present GLB, a programming model and an associated implementation that can handle a wide range of irregular paral- lel programming problems running over large-scale distributed systems. GLB is applicable both to problems that are easily load-balanced via static scheduling and to problems that are hard to statically load balance. GLB hides the intricate syn- chronizations (e.g., inter-node communication, initialization and startup, load balancing, termination and result collection) from the users. GLB internally uses a version of the lifeline graph based work-stealing algorithm proposed by Saraswat et al. Users of GLB are simply required to write several pieces of sequential code that comply with the GLB interface. GLB then schedules and orchestrates the parallel execution of the code correctly and efficiently at scale. We have applied GLB to two representative benchmarks: Betweenness Centrality (BC) and Unbalanced Tree Search (UTS). Among them, BC can be statically load-balanced whereas UTS cannot. In either case, GLB scales well-- achieving nearly linear speedup on different computer architectures (Power, Blue Gene/Q, and K) -- up to 16K cores.
Identification of Employees Using RFID in IE-NTUA
Venue: arXiv cs.SY 2013
Year: 2013

During the last decade with the rapid increase in indoor wireless communications, location-aware services have received a great deal of attention for commercial, public-safety, and a military application, the greatest challenge associated with indoor positioning methods is moving object data and identification. Mobility tracking and localization are multifaceted problems, which have been studied for a long time in different contexts. Many potential applications in the domain of WSNs require such capabilities. The mobility tracking needs inherent in many surveillance, security and logistic applications. This paper presents the identification of employees in National Technical University in Athens (IE-NTUA), when the employees access to a certain area of the building (enters and leaves to/from the college), Radio Frequency Identification (RFID) applied for identification by offering special badges containing RFID-tags.
Random Basketball Routing for ZigBee based Sensor Networks
Venue: arXiv cs.NI 2013
Year: 2013

Random basketball routing (BR) \cite {Hwang} is a simple protocol that integrates MAC and multihop routing in a cross-layer optimized manner. Due to its lightness and performance, BR would be quite suitable for sensor networks, where communication nodes are usually simple devices. In this paper, we describe how we implemented BR in a ZigBee-based (IEEE 802.15.4) sensor network. In \cite{Hwang}, it is verified that BR takes advantages of dynamic environments (in particular, node mobility), however, here we focus on how BR works under static situations. For implementation purposes, we add some features such as destination RSSI measuring and loop-free procedure, to the original BR. With implemented testbed, we compare the performance of BR with that of the simplified AODV with CSMA/CA. The result is that BR has merits in terms of number of hops to traverse the network. Considering the simple structure of BR and its possible energy-efficiency, we can conclude that BR can be a good candidate for sensor networks both under dynamic- and static environments.
The Sparse Principal Component of a Constant-rank Matrix
Venue: arXiv cs.IT 2013
Year: 2013

The computation of the sparse principal component of a matrix is equivalent to the identification of its principal submatrix with the largest maximum eigenvalue. Finding this optimal submatrix is what renders the problem ${\mathcal{NP}}$-hard. In this work, we prove that, if the matrix is positive semidefinite and its rank is constant, then its sparse principal component is polynomially computable. Our proof utilizes the auxiliary unit vector technique that has been recently developed to identify problems that are polynomially solvable. Moreover, we use this technique to design an algorithm which, for any sparsity value, computes the sparse principal component with complexity ${\mathcal O}\left(N^{D+1}\right)$, where $N$ and $D$ are the matrix size and rank, respectively. Our algorithm is fully parallelizable and memory efficient.
Deep Separability of Ontological Constraints
Venue: arXiv cs.DB 2013
Year: 2013

When data schemata are enriched with expressive constraints that aim at representing the domain of interest, in order to answer queries one needs to consider the logical theory consisting of both the data and the constraints. Query answering in such a context is called ontological query answering. Commonly adopted database constraints in this field are tuple-generating dependencies (TGDs) and equality-generating dependencies (EGDs). It is well known that their interaction leads to intractability or undecidability of query answering even in the case of simple subclasses. Several conditions have been found to guarantee separability, that is lack of interaction, between TGDs and EGDs. Separability makes EGDs (mostly) irrelevant for query answering and therefore often guarantees tractability, as long as the theory is satisfiable. In this paper we review the two notions of separability found in the literature, as well as several syntactic conditions that are sufficient to prove them. We then shed light on the issue of satisfiability checking, showing that under a sufficient condition called deep separability it can be done by considering the TGDs only. We show that, fortunately, in the case of TGDs and EGDs, separability implies deep separability. This result generalizes several analogous ones, proved ad hoc for particular classes of constraints. Applications include the class of sticky TGDs and EGDs, for which we provide a syntactic separability condition which extends the analogous one for linear TGDs; preliminary experiments show the feasibility of query answering in this case.
Automated Verification of Quantum Protocols by Equivalence Checking
Venue: arXiv cs.LO 2013
Year: 2013

In this paper we introduce a technique and a tool for formal verification of various quantum information processing protocols. The tool uses stabilizer formalism and is capable of representing concurrent quantum protocol, thus is more expressive than quantum circuits. We also report on experimental results of using our Quantum Equivalence Checker (QEC) to analyse a range of quantum information processing protocols.
The return of AdaBoost.MH: multi-class Hamming trees
Venue: arXiv cs.LG 2013
Year: 2013

Within the framework of AdaBoost.MH, we propose to train vector-valued decision trees to optimize the multi-class edge without reducing the multi-class problem to $K$ binary one-against-all classifications. The key element of the method is a vector-valued decision stump, factorized into an input-independent vector of length $K$ and label-independent scalar classifier. At inner tree nodes, the label-dependent vector is discarded and the binary classifier can be used for partitioning the input space into two regions. The algorithm retains the conceptual elegance, power, and computational efficiency of binary AdaBoost. In experiments it is on par with support vector machines and with the best existing multi-class boosting algorithm AOSOLogitBoost, and it is significantly better than other known implementations of AdaBoost.MH.
Properties of Answer Set Programming with Convex Generalized Atoms
Venue: arXiv cs.AI 2013
Year: 2013

In recent years, Answer Set Programming (ASP), logic programming under the stable model or answer set semantics, has seen several extensions by generalizing the notion of an atom in these programs: be it aggregate atoms, HEX atoms, generalized quantifiers, or abstract constraints, the idea is to have more complicated satisfaction patterns in the lattice of Herbrand interpretations than traditional, simple atoms. In this paper we refer to any of these constructs as generalized atoms. Several semantics with differing characteristics have been proposed for these extensions, rendering the big picture somewhat blurry. In this paper, we analyze the class of programs that have convex generalized atoms (originally proposed by Liu and Truszczynski in [10]) in rule bodies and show that for this class many of the proposed semantics coincide. This is an interesting result, since recently it has been shown that this class is the precise complexity boundary for the FLP semantics. We investigate whether similar results also hold for other semantics, and discuss the implications of our findings.
A New Frequency Control Reserve Framework based on Energy-Constrained Units
Venue: arXiv cs.SY 2013
Year: 2013

Frequency control reserves are an essential ancillary service in any electric power system, guaranteeing that generation and demand of active power are balanced at all times. Traditionally, conventional power plants are used for frequency reserves. There are economical and technical benefits of instead using energy constrained units such as storage systems and demand response, but so far they have not been widely adopted as their energy constraints prevent them from following traditional regulation signals, which sometimes are biased over long time-spans. This paper proposes a frequency control framework that splits the control signals according to the frequency spectrum. This guarantees that all control signals are zero-mean over well-defined time-periods, which is a crucial requirement for the usage of energy-constraint units such as batteries. A case-study presents a possible implementation, and shows how different technologies with widely varying characteristics can all participate in frequency control reserve provision, while guaranteeing that their respective energy constraints are always fulfilled.
A Functional View of Strong Negation in Answer Set Programming
Venue: arXiv cs.AI 2013
Year: 2013

The distinction between strong negation and default negation has been useful in answer set programming. We present an alternative account of strong negation, which lets us view strong negation in terms of the functional stable model semantics by Bartholomew and Lee. More specifically, we show that, under complete interpretations, minimizing both positive and negative literals in the traditional answer set semantics is essentially the same as ensuring the uniqueness of Boolean function values under the functional stable model semantics. The same account lets us view Lifschitz's two-valued logic programs as a special case of the functional stable model semantics. In addition, we show how non-Boolean intensional functions can be eliminated in favor of Boolean intensional functions, and furthermore can be represented using strong negation, which provides a way to compute the functional stable model semantics using existing ASP solvers. We also note that similar results hold with the functional stable model semantics by Cabalar.
Query Answering in Object Oriented Knowledge Bases in Logic Programming: Description and Challenge for ASP
Venue: arXiv cs.AI 2013
Year: 2013

Research on developing efficient and scalable ASP solvers can substantially benefit by the availability of data sets to experiment with. KB_Bio_101 contains knowledge from a biology textbook, has been developed as part of Project Halo, and has recently become available for research use. KB_Bio_101 is one of the largest KBs available in ASP and the reasoning with it is undecidable in general. We give a description of this KB and ASP programs for a suite of queries that have been of practical interest. We explain why these queries pose significant practical challenges for the current ASP solvers.
Manifold regularized kernel logistic regression for web image annotation
Venue: arXiv cs.LG 2013
Year: 2013

With the rapid advance of Internet technology and smart devices, users often need to manage large amounts of multimedia information using smart devices, such as personal image and video accessing and browsing. These requirements heavily rely on the success of image (video) annotation, and thus large scale image annotation through innovative machine learning methods has attracted intensive attention in recent years. One representative work is support vector machine (SVM). Although it works well in binary classification, SVM has a non-smooth loss function and can not naturally cover multi-class case. In this paper, we propose manifold regularized kernel logistic regression (KLR) for web image annotation. Compared to SVM, KLR has the following advantages: (1) the KLR has a smooth loss function; (2) the KLR produces an explicit estimate of the probability instead of class label; and (3) the KLR can naturally be generalized to the multi-class case. We carefully conduct experiments on MIR FLICKR dataset and demonstrate the effectiveness of manifold regularized kernel logistic regression for image annotation.
Socially-Aware Networking: A Survey
Venue: arXiv cs.SI 2013
Year: 2013

The widespread proliferation of handheld devices enables mobile carriers to be connected at anytime and anywhere. Meanwhile, the mobility patterns of mobile devices strongly depend on the users' movements, which are closely related to their social relationships and behaviors. Consequently, today's mobile networks are becoming increasingly human centric. This leads to the emergence of a new field which we call socially-aware networking (SAN). One of the major features of SAN is that social awareness becomes indispensable information for the design of networking solutions. This emerging paradigm is applicable to various types of networks (e.g. opportunistic networks, mobile social networks, delay tolerant networks, ad hoc networks, etc) where the users have social relationships and interactions. By exploiting social properties of nodes, SAN can provide better networking support to innovative applications and services. In addition, it facilitates the convergence of human society and cyber physical systems. In this paper, for the first time, to the best of our knowledge, we present a survey of this emerging field. Basic concepts of SAN are introduced. We intend to generalize the widely-used social properties in this regard. The state-of-the-art research on SAN is reviewed with focus on three aspects: routing and forwarding, incentive mechanisms and data dissemination. Some important open issues with respect to mobile social sensing and learning, privacy, node selfishness and scalability are discussed.
Predictive User Modeling with Actionable Attributes
Venue: arXiv cs.AI 2013
Year: 2013

Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target variable for unseen instances. For example, for marketing purposes a company consider profiling a new user based on her observed web browsing behavior, referral keywords or other relevant information. In many real world applications the values of some attributes are not only observable, but can be actively decided by a decision maker. Furthermore, in some of such applications the decision maker is interested not only to generate accurate predictions, but to maximize the probability of the desired outcome. For example, a direct marketing manager can choose which type of a special offer to send to a client (actionable attribute), hoping that the right choice will result in a positive response with a higher probability. We study how to learn to choose the value of an actionable attribute in order to maximize the probability of a desired outcome in predictive modeling. We emphasize that not all instances are equally sensitive to changes in actions. Accurate choice of an action is critical for those instances, which are on the borderline (e.g. users who do not have a strong opinion one way or the other). We formulate three supervised learning approaches for learning to select the value of an actionable attribute at an instance level. We also introduce a focused training procedure which puts more emphasis on the situations where varying the action is the most likely to take the effect. The proof of concept experimental validation on two real-world case studies in web analytics and e-learning domains highlights the potential of the proposed approaches.
Topic and Sentiment Analysis on OSNs: a Case Study of Advertising Strategies on Twitter
Venue: arXiv cs.SI 2013
Year: 2013

Social media have substantially altered the way brands and businesses advertise: Online Social Networks provide brands with more versatile and dynamic channels for advertisement than traditional media (e.g., TV and radio). Levels of engagement in such media are usually measured in terms of content adoption (e.g., likes and retweets) and sentiment, around a given topic. However, sentiment analysis and topic identification are both non-trivial tasks. In this paper, using data collected from Twitter as a case study, we analyze how engagement and sentiment in promoted content spread over a 10-day period. We find that promoted tweets lead to higher positive sentiment than promoted trends; although promoted trends pay off in response volume. We observe that levels of engagement for the brand and promoted content are highest on the first day of the campaign, and fall considerably thereafter. However, we show that these insights depend on the use of robust machine learning and natural language processing techniques to gather focused, relevant datasets, and to accurately gauge sentiment, rather than relying on the simple keyword- or frequency-based metrics sometimes used in social media research.
Sensor Discovery and Configuration Framework for The Internet of Things Paradigm
Venue: arXiv cs.NI 2013
Year: 2013

Internet of Things (IoT) will comprise billions of devices that can sense, communicate, compute and potentially actuate. The data generated by the Internet of Things are valuable and have the potential to drive innovative and novel applications. The data streams coming from these devices will challenge the traditional approaches to data management and contribute to the emerging paradigm of big data. One of the most challenging tasks before collecting and processing data from these devices (e.g. sensors) is discovering and configuring the sensors and the associated data streams. In this paper, we propose a tool called SmartLink that can be used to discover and configure sensors. Specifically, SmartLink, is capable of discovering sensors deployed in a particular location despite their heterogeneity (e.g. different communication protocols, communication sequences, capabilities). SmartLink establishes the direct communication between the sensor hardware and cloud-based IoT middleware. We address the challenge of heterogeneity using a plugin architecture. Our prototype tool is developed on the Android platform. We evaluate the significance of our approach by discovering and configuring 52 different types of Libelium sensors.
Bounded Rational Decision-Making in Changing Environments
Venue: arXiv cs.AI 2013
Year: 2013

A perfectly rational decision-maker chooses the best action with the highest utility gain from a set of possible actions. The optimality principles that describe such decision processes do not take into account the computational costs of finding the optimal action. Bounded rational decision-making addresses this problem by specifically trading off information-processing costs and expected utility. Interestingly, a similar trade-off between energy and entropy arises when describing changes in thermodynamic systems. This similarity has been recently used to describe bounded rational agents. Crucially, this framework assumes that the environment does not change while the decision-maker is computing the optimal policy. When this requirement is not fulfilled, the decision-maker will suffer inefficiencies in utility, that arise because the current policy is optimal for an environment in the past. Here we borrow concepts from non-equilibrium thermodynamics to quantify these inefficiencies and illustrate with simulations its relationship with computational resources.
Socially-Aware Venue Recommendation for Conference Participants
Venue: arXiv cs.IR 2013
Year: 2013

Current research environments are witnessing high enormities of presentations occurring in different sessions at academic conferences. This situation makes it difficult for researchers (especially juniors) to attend the right presentation session(s) for effective collaboration. In this paper, we propose an innovative venue recommendation algorithm to enhance smart conference participation. Our proposed algorithm, Social Aware Recommendation of Venues and Environments (SARVE), computes the Pearson Correlation and social characteristic information of conference participants. SARVE further incorporates the current context of both the smart conference community and participants in order to model a recommendation process using distributed community detection. Through the integration of the above computations and techniques, we are able to recommend presentation sessions of active participant presenters that may be of high interest to a particular participant. We evaluate SARVE using a real world dataset. Our experimental results demonstrate that SARVE outperforms other state-of-the-art methods.
An Optimal ODAM-Based Broadcast Algorithm for Vehicular Ad-Hoc Networks
Venue: arXiv cs.NI 2013
Year: 2013

Broadcast routing has become an important research field for vehicular ad-hoc networks (VANETs) recently. However, the packet delivery rate is generally low in existing VANET broadcast routing protocols. Therefore, the design of an appropriate broadcast protocol based on the features of VANET has become a crucial part of the development of VANET. This paper analyzes the disadvantage of existing broadcast routing protocols in VANETs, and proposes an improved algorithm (namely ODAM-C) based on the ODAM (Optimized Dissemination of Alarm Messages) protocol. The ODAM-C algorithm improves the packet delivery rate by two mechanisms based on the forwarding features of ODAM. The first distance-based mechanism reduces the possibility of packet loss by considering the angles between source nodes, forwarding nodes and receiving nodes. The second mechanism increases the redundancy of forwarding nodes to guarantee the packet success delivery ratio. We show by analysis and simulations that the proposed algorithm can improve packet delivery rate for vehicular networks compared against two widely-used existing protocols.
A Localization Strategy Based on N-times Trilateral Centroid with Weight
Venue: arXiv cs.NI 2013
Year: 2013

Localization based on received signal strength indication (RSSI) is a low cost and low complexity technology, and it is widely applied in distance-based localization of wireless sensor networks (WSNs). Error of existed localization technologies is significant. This paper presents the N-times trilateral centroid weighted localization algorithm (NTCWLA), which can reduce the error considerably. Considering the instability of RSSI, we use the weighted average of many RSSIs as current RSSI. To improve the accuracy we select a number of (no less than three) reliable beacon nodes to increase the localization times. Then we calculate the distances between reliable beacon nodes and the mobile node using an empirical formula. The mobile node is located N times using the trilateral centroid algorithm. Finally, we take the weighted average of the filtered reference coordinates as the mobile node's coordinates. We conduct experiments with the STM32W108 chip which supports IEEE 802.15.4. The results show that the proposed algorithm performs better than the trilateral centroid algorithm.