OpenRaiser/Intern-Atlas huggingface dataset:
Mining and Analyzing the Italian Parliament: Party Structure and Evolution
Venue: arXiv cs.SI 2015
Year: 2015

The roll calls of the Italian Parliament in the XVI legislature are studied by employing multidimensional scaling, hierarchical clustering, and network analysis. In order to detect changes in voting behavior, the roll calls have been divided in seven periods of six months each. All the methods employed pointed out an increasing fragmentation of the political parties endorsing the previous government that culminated in its downfall. By using the concept of modularity at different resolution levels, we identify the community structure of Parliament and its evolution in each of the considered time periods. The analysis performed revealed as a valuable tool in detecting trends and drifts of Parliamentarians. It showed its effectiveness at identifying political parties and at providing insights on the temporal evolution of groups and their cohesiveness, without having at disposal any knowledge about political membership of Representatives.
Variable-length Splittable Codes with Multiple Delimiters
Venue: arXiv cs.IT 2015
Year: 2015

Variable-length splittable codes are derived from encoding sequences of ordered integer pairs, where one of the pair's components is upper bounded by some constant, and the other one is any positive integer. Each pair is encoded by the concatenation of two fixed independent prefix encoding functions applied to the corresponding components of a pair. The codeword of such a sequence of pairs consists of the sequential concatenation of corresponding pair's encodings. We call such codes splittable. We show that Fibonacci codes of higher orders and codes with multiple delimiters of the form 011...10 are splittable. Completeness and universality of multi-delimiter codes are proved. Encoding of integers by multi-delimiter codes is considered in detail. For these codes, a fast byte aligned decoding algorithm is constructed. The comparative compression performance of Fibonacci codes and different multi-delimiter codes is presented. By many useful properties, multi-delimiter codes are superior to Fibonacci codes.
Privacy-Preserving Multi-Document Summarization
Venue: arXiv cs.IR 2015
Year: 2015

State-of-the-art extractive multi-document summarization systems are usually designed without any concern about privacy issues, meaning that all documents are open to third parties. In this paper we propose a privacy-preserving approach to multi-document summarization. Our approach enables other parties to obtain summaries without learning anything else about the original documents' content. We use a hashing scheme known as Secure Binary Embeddings to convert documents representation containing key phrases and bag-of-words into bit strings, allowing the computation of approximate distances, instead of exact ones. Our experiments indicate that our system yields similar results to its non-private counterpart on standard multi-document evaluation datasets.
Nonlinear Metric Learning for kNN and SVMs through Geometric Transformations
Venue: arXiv cs.LG 2015
Year: 2015

In recent years, research efforts to extend linear metric learning models to handle nonlinear structures have attracted great interests. In this paper, we propose a novel nonlinear solution through the utilization of deformable geometric models to learn spatially varying metrics, and apply the strategy to boost the performance of both kNN and SVM classifiers. Thin-plate splines (TPS) are chosen as the geometric model due to their remarkable versatility and representation power in accounting for high-order deformations. By transforming the input space through TPS, we can pull same-class neighbors closer while pushing different-class points farther away in kNN, as well as make the input data points more linearly separable in SVMs. Improvements in the performance of kNN classification are demonstrated through experiments on synthetic and real world datasets, with comparisons made with several state-of-the-art metric learning solutions. Our SVM-based models also achieve significant improvements over traditional linear and kernel SVMs with the same datasets.
A Mood-based Genre Classification of Television Content
Venue: arXiv cs.IR 2015
Year: 2015

The classification of television content helps users organise and navigate through the large list of channels and programs now available. In this paper, we address the problem of television content classification by exploiting text information extracted from program transcriptions. We present an analysis which adapts a model for sentiment that has been widely and successfully applied in other fields such as music or blog posts. We use a real-world dataset obtained from the Boxfish API to compare the performance of classifiers trained on a number of different feature sets. Our experiments show that, over a large collection of television content, program genres can be represented in a three-dimensional space of valence, arousal and dominance, and that promising classification results can be achieved using features based on this representation. This finding supports the use of the proposed representation of television content as a feature space for similarity computation and recommendation generation.
Requirements Engineering Aspects of a Geographically Distributed Architecture
Venue: arXiv cs.SE 2015
Year: 2015

We present our ongoing work on requirements specification and analysis for the geographically distributed software and systems. Developing software and systems within/for different countries or states or even within/for different organisations means that the requirements to them can differ in each particular case. These aspects naturally impact on the software architecture and on the development process as a whole. The challenge is to deal with this diversity in a systematic way, avoiding contradictions and non-compliance. In this paper, we present a formal framework for the analysis of the requirements diversity, which comes from the differences in the regulations, laws and cultural aspects for different countries or organisations. The framework also provides the corresponding architectural view and the methods for requirements structuring and optimisation.
SCION Five Years Later: Revisiting Scalability, Control, and Isolation on Next-Generation Networks
Venue: arXiv cs.NI 2015
Year: 2015

The SCION (Scalability, Control, and Isolation on Next-generation Networks) inter-domain network architecture was proposed to address the availability, scalability, and security shortcomings of the current Internet. This paper presents a retrospective of the SCION goals and design decisions, its attacker model and limitations, and research highlights of work conducted in the 5 years following SCION's initial publication.
Addictive Facebook Use among University Students
Venue: arXiv cs.CY 2015
Year: 2015

The Facebook has become an essential part of almost every university students daily life.
Your Proof Fails? Testing Helps to Find the Reason
Venue: arXiv cs.SE 2015
Year: 2015

Applying deductive verification to formally prove that a program respects its formal specification is a very complex and time-consuming task due in particular to the lack of feedback in case of proof failures. Along with a non-compliance between the code and its specification (due to an error in at least one of them), possible reasons of a proof failure include a missing or too weak specification for a called function or a loop, and lack of time or simply incapacity of the prover to finish a particular proof. This work proposes a new methodology where test generation helps to identify the reason of a proof failure and to exhibit a counter-example clearly illustrating the issue. We describe how to transform an annotated C program into C code suitable for testing and illustrate the benefits of the method on comprehensive examples. The method has been implemented in STADY, a plugin of the software analysis platform FRAMA-C. Initial experiments show that detecting non-compliances and contract weaknesses allows to precisely diagnose most proof failures.
A Reliable User Authentication and Data Protection Model in Cloud Computing Environments
Venue: arXiv cs.DC 2015
Year: 2015

Security issues are the most challenging problems in cloud computing environments as an emerging technology. Regarding to this importance, an efficient and reliable user authentication and data protection model has been presented in this paper to increase the rate of reliability cloud-based environments. Accordingly, two encryption procedures have been established in an independent middleware (Agent) to perform the process of user authentication, access control, and data protection in cloud servers. AES has been used as a symmetric cryptography algorithm in cloud servers and RSA has been used as an asymmetric cryptography algorithm in Agent servers. The theoretical evaluation of the proposed model shows that the ability of resistance in face with possible attacks and unpredictable events has been enhanced considerably in comparison with similar models because of using dual encryption and an independent middleware during user authentication and data protection procedures.
Big Data Analytics on Traditional HPC Infrastructure Using Two-Level Storage
Venue: arXiv cs.DC 2015
Year: 2015

Data-intensive computing has become one of the major workloads on traditional high-performance computing (HPC) clusters. Currently, deploying data-intensive computing software framework on HPC clusters still faces performance and scalability issues. In this paper, we develop a new two-level storage system by integrating Tachyon, an in-memory file system with OrangeFS, a parallel file system. We model the I/O throughputs of four storage structures: HDFS, OrangeFS, Tachyon and two-level storage. We conduct computational experiments to characterize I/O throughput behavior of two-level storage and compare its performance to that of HDFS and OrangeFS, using TeraSort benchmark. Theoretical models and experimental tests both show that the two-level storage system can increase the aggregate I/O throughputs. This work lays a solid foundation for future work in designing and building HPC systems that can provide a better support on I/O intensive workloads with preserving existing computing resources.
On environments as systemic exoskeletons: Crosscutting optimizers and antifragility enablers
Venue: arXiv cs.OH 2015
Year: 2015

Classic approaches to General Systems Theory often adopt an individual perspective and a limited number of systemic classes. As a result, those classes include a wide number and variety of systems that result equivalent to each other. This paper introduces a different approach: First, systems belonging to a same class are further differentiated according to five major general characteristics. This introduces a "horizontal dimension" to system classification. A second component of our approach considers systems as nested compositional hierarchies of other sub-systems. The resulting "vertical dimension" further specializes the systemic classes and makes it easier to assess similarities and differences regarding properties such as resilience, performance, and quality-of-experience. Our approach is exemplified by considering a telemonitoring system designed in the framework of Flemish project "Little Sister". We show how our approach makes it possible to design intelligent environments able to closely follow a system's horizontal and vertical organization and to artificially augment its features by serving as crosscutting optimizers and as enablers of antifragile behaviors.
Combining Text and Formula Queries in Math Information Retrieval: Evaluation of Query Results Merging Strategies
Venue: arXiv cs.IR 2015
Year: 2015

Specific to Math Information Retrieval is combining text with mathematical formulae both in documents and in queries. Rigorous evaluation of query expansion and merging strategies combining math and standard textual keyword terms in a query are given. It is shown that techniques similar to those known from textual query processing may be applied in math information retrieval as well, and lead to a cutting edge performance. Striping and merging partial results from subqueries is one technique that improves results measured by information retrieval evaluation metrics like Bpref.
Minimizing the Net Present Cost of Deploying and Operating Wireless Sensor Networks
Venue: arXiv cs.NI 2015
Year: 2015

Minimizing the cost of deploying and operating a Wireless Sensor Network (WSN) involves deciding how to partition a budget between competing expenses such as node hardware, energy, and labor. Most commercial network operators account for interest rates in their budgeting exercises, providing a financial incentive to defer some costs until a later time. In this paper, we propose a net present cost (NPC) model for WSN capital and operating expenses that accounts for interest rates. Our model optimizes the number, size, and spacing between expenditures in order to minimize the NPC required for the network to achieve a desired operational lifetime. In general this optimization problem is non-convex, but if the spacing between expenditures is linearly proportional to the size of the expenditures, and the number of maintenance cycles is known in advance, the problem becomes convex and can be solved to global optimality. If non-deferrable recurring costs are low, then evenly spacing the expenditures can provide near-optimal results. With the provided models and methods, network operators can now derive a payment schedule to minimize NPC while accounting for various operational parameters. The numerical examples show substantial cost benefits under practical assumptions.
Bootstrapping Real-world Deployment of Future Internet Architectures
Venue: arXiv cs.NI 2015
Year: 2015

The past decade has seen many proposals for future Internet architectures. Most of these proposals require substantial changes to the current networking infrastructure and end-user devices, resulting in a failure to move from theory to real-world deployment. This paper describes one possible strategy for bootstrapping the initial deployment of future Internet architectures by focusing on providing high availability as an incentive for early adopters. Through large-scale simulation and real-world implementation, we show that with only a small number of adopting ISPs, customers can obtain high availability guarantees. We discuss design, implementation, and evaluation of an availability device that allows customers to bridge into the future Internet architecture without modifications to their existing infrastructure.
RF-Based Charger Placement for Duty Cycle Guarantee in Battery-Free Sensor Networks
Venue: arXiv cs.NI 2015
Year: 2015

Battery-free sensor networks have emerged as a promising solution to conquer the lifetime limitation of battery-powered systems. In this paper, we study a sensor network built from battery-free sensor nodes which harvest energy from radio frequency (RF) signals transmitted by RF-based chargers, e.g., radio frequency identification (RFID) readers. Due to the insufficiency of harvested energy, the sensor nodes have to work in duty cycles to harvest enough energy before turning active and performing tasks. One fundamental issue in this kind of network design is how to deploy the chargers to ensure that the battery-free nodes can maintain a designated duty cycle for continuous operation. Based on a new wireless recharge model, we formulate the charger placement problem for node's duty cycle guarantee as a constrained optimization problem. We develop both greedy and efficient heuristics for solving the problem and validate our solutions through extensive simulations. The simulation results show that the proposed particle swarm optimization (PSO)-based divide-and-conquer approach can effectively reduce the number of chargers compared with the greedy approach.
Adaptive Sampling of RF Fingerprints for Fine-grained Indoor Localization
Venue: arXiv cs.IT 2015
Year: 2015

Indoor localization is a supporting technology for a broadening range of pervasive wireless applications. One promis- ing approach is to locate users with radio frequency fingerprints. However, its wide adoption in real-world systems is challenged by the time- and manpower-consuming site survey process, which builds a fingerprint database a priori for localization. To address this problem, we visualize the 3-D RF fingerprint data as a function of locations (x-y) and indices of access points (fingerprint), as a tensor and use tensor algebraic methods for an adaptive tubal-sampling of this fingerprint space. In particular using a recently proposed tensor algebraic framework in [1] we capture the complexity of the fingerprint space as a low-dimensional tensor-column space. In this formulation the proposed scheme exploits adaptivity to identify reference points which are highly informative for learning this low-dimensional space. Further, under certain incoherency conditions we prove that the proposed scheme achieves bounded recovery error and near-optimal sampling complexity. In contrast to several existing work that rely on random sampling, this paper shows that adaptivity in sampling can lead to significant improvements in localization accuracy. The approach is validated on both data generated by the ray-tracing indoor model which accounts for the floor plan and the impact of walls and the real world data. Simulation results show that, while maintaining the same localization accuracy of existing approaches, the amount of samples can be cut down by 71% for the high SNR case and 55% for the low SNR case.
Modeling and Validation of Electrical Load Profiling in Residential Buildings in Singapore
Venue: arXiv cs.SY 2015
Year: 2015

The demand of electricity keeps increasing in this modern society and the behavior of customers vary greatly from time to time, city to city, type to type, etc. Generally, buildings are classified into residential, commercial and industrial. This study is aimed to distinguish the types of residential buildings in Singapore and establish a mathematical model to represent and model the load profile of each type. Modeling household energy consumption is the first step in exploring the possible demand response and load reduction opportunities under the smart grid initiative. Residential electricity load profiling includes the details on the electrical appliances, its energy requirement, and consumption pattern. The model is generated with a bottom-up load model. Simulation is performed for daily load profiles of 1 or 2 rooms, 3 rooms, 4 rooms and 5 rooms public housing. The simulated load profile is successfully validated against the measured electricity consumption data, using a web-based Customer Energy Portal (CEP) at the campus housings of Nanyang Technological University, Singapore.
Approximation-Aware Dependency Parsing by Belief Propagation
Venue: arXiv cs.CL 2015
Year: 2015

We show how to train the fast dependency parser of Smith and Eisner (2008) for improved accuracy. This parser can consider higher-order interactions among edges while retaining O(n^3) runtime. It outputs the parse with maximum expected recall -- but for speed, this expectation is taken under a posterior distribution that is constructed only approximately, using loopy belief propagation through structured factors. We show how to adjust the model parameters to compensate for the errors introduced by this approximation, by following the gradient of the actual loss on training data. We find this gradient by back-propagation. That is, we treat the entire parser (approximations and all) as a differentiable circuit, as Stoyanov et al. (2011) and Domke (2010) did for loopy CRFs. The resulting trained parser obtains higher accuracy with fewer iterations of belief propagation than one trained by conditional log-likelihood.
Gait Assessment for Multiple Sclerosis Patients Using Microsoft Kinect
Venue: arXiv cs.CV 2015
Year: 2015

Gait analysis of patients with neurological disorders, including multiple sclerosis (MS), is important for rehabilitation and treatment. The Mircrosoft Kinect sensor, which was developed for motion recognition in gaming applications, is an ideal candidate for an inexpensive system providing the capability for human gait analysis. In this research, we develop a framework to quantify the gait abnormality of MS patients using a Kinect for Windows camera. In addition to the previously introduced gait indices, a novel set of MS gait indices based on the concept of dynamic time warping is introduced. The newly introduced indices can characterize a patient's gait pattern as a whole and quantify a subject's gait distance from the healthy population. We will investigate the correlation of gait indices with the multiple sclerosis walking scale (MSWS) and the clinical ambulation score. This work establishes the feasibility of using the Kinect sensor for clinical gait assessment for MS patients.
Guaranteed Cost Dynamic Coherent Control for Uncertain Linear Quantum Systems
Venue: arXiv cs.SY 2015
Year: 2015

This paper concerns a class of uncertain linear quantum systems subject to quadratic perturbations in the system Hamiltonian. A small gain approach is used to evaluate the performance of the given quantum system. In order to get improved control performance, we propose two methods to design a coherent controller for the system. One is to formulate a static quantum controller by adding a controller Hamiltonian to the given system, and the other is to build a dynamic quantum controller which is directly coupled to the given system. Both controller design methods are given in terms of LMIs and a non-convex equality. Hence, a rank constrained LMI method is used as a numerical procedure. An illustrative example is given to demonstrate the proposed methods and also to make a performance comparison with different controller design methods. Results show that for the same uncertain quantum system, the dynamic quantum controller can offer an improvement in performance over the static quantum controller.
Minority population in the one-dimensional Schelling model of segregation
Venue: arXiv cs.DM 2015
Year: 2015

The Schelling model of segregation looks to explain the way in which a population of agents or particles of two types may come to organise itself into large homogeneous clusters, and can be seen as a variant of the Ising model in which the system is subjected to rapid cooling. While the model has been very extensively studied, the unperturbed (noiseless) version has largely resisted rigorous analysis, with most results in the literature pertaining to versions of the model in which noise is introduced into the dynamics so as to make it amenable to standard techniques from statistical mechanics or stochastic evolutionary game theory. We rigorously analyse the one-dimensional version of the model in which one of the two types is in the minority, and establish various forms of threshold behaviour. Our results are in sharp contrast with the case when the distribution of the two types is uniform (i.e. each agent has equal chance of being of each type in the initial configuration), which was studied by Brandt, Immorlica, Kamath, and Kleinberg.