OpenRaiser/Intern-Atlas huggingface dataset:
Optimization and Evaluation of a Multimedia Streaming Service on Hybrid Telco cloud
Venue: arXiv cs.DC 2011
Year: 2011

With recent developments in cloud computing, a paradigm shift from rather static deployment of resources to more dynamic, on-demand practices means more flexibility and better utilization of resources. This demands new ways to efficiently configure networks. In this paper, we will characterize a class of competitive cloud services that telecom operators could provide based on the characteristics of telecom infrastructure through an applicable streaming service architecture. Then, we will model this architecture as a cost-based mathematic model. This model provides a tool to evaluate and compare the cost of software services for different telecom network topologies and deployment strategies. Additionally, with each topology it acts as a means to characterize the deployment solution that yields the lowest resource usage over the entire network. These applications are illustrated through numerical analysis. Finally, a proof-of-concept prototype is deployed to shows dynamic properties of the service in the architecture and the model above.
BRIDGE: A Model for Modern Software Development Process to Cater the Present Software Crisis
Venue: arXiv cs.SE 2011
Year: 2011

As hardware components are becoming cheaper and powerful day by day, the expected services from modern software are increasing like any thing. Developing such software has become extremely challenging. Not only the complexity, but also the developing of such software within the time constraints and budget has become the real challenge. Quality concern and maintainability are added flavour to the challenge. On stream, the requirements of the clients are changing so frequently that it has become extremely tough to manage these changes. More often, the clients are unhappy with the end product. Large, complex software projects are notoriously late to market, often exhibit quality problems, and don't always deliver on promised functionality. None of the existing models are helpful to cater the modern software crisis. Hence, a better modern software development process model to handle with the present software crisis is badly needed. This paper suggests a new software development process model, BRIDGE, to tackle present software crisis.
Efficient Implementation of Elliptic Curve Cryptography Using Low-power Digital Signal Processor
Venue: arXiv cs.CR 2011
Year: 2011

RSA(Rivest, Shamir and Adleman)is being used as a public key exchange and key agreement tool for many years. Due to large numbers involved in RSA, there is need for more efficient methods in implementation for public key cryptosystems. Elliptic Curve Cryptography(ECC) is based on elliptic curves defined over a finite field. Elliptic curve cryptosystems(ECC) were discovered by Victor Miller and Neal Koblitz in 1985.This paper comprises of five sections. Section I is introduction to ECC and its components. Section II describes advantages of ECC schemes and its comparison with RSA. Section III is about some of the applications of ECC. Section IV gives some embedded implementations of ECC. Section V contains ECC implementation on fixed point Digital Signal Processor(TMS320VC5416). ECC was implemented using general purpose microcontrollers and Field Programmable Gate Arrays (FPGA) before this work. DSP is more powerful than microcontrollers and much economical than FPGA. So this implementation can be efficiently utilized in low-power applications.
A Real-time Localization System Using RFID for Visually Impaired
Venue: arXiv cs.MA 2011
Year: 2011

Gadgets helping the disabled, especially blind that are in least accessibility of information, use acoustic methods that can cause stress to ear and infringe user's privacy. Even if some project uses embedded Radio Frequency Identification (RFID) into the sidewalk for blind's free walking, the tag memory design is not specified for buildings and road conditions. This paper suggested allocation scheme of RFID tag referring to EPCglobal SGLN, tactile method for conveying information, and use of lithium battery as power source with solar cells as an alternative. Results have shown independent mobility, accidents prevention, stress relief and satisfied factors in terms of cost and human usability.
An Expressive Language and Efficient Execution System for Software Agents
Venue: arXiv cs.AI 2011
Year: 2011

Software agents can be used to automate many of the tedious, time-consuming information processing tasks that humans currently have to complete manually. However, to do so, agent plans must be capable of representing the myriad of actions and control flows required to perform those tasks. In addition, since these tasks can require integrating multiple sources of remote information ? typically, a slow, I/O-bound process ? it is desirable to make execution as efficient as possible. To address both of these needs, we present a flexible software agent plan language and a highly parallel execution system that enable the efficient execution of expressive agent plans. The plan language allows complex tasks to be more easily expressed by providing a variety of operators for flexibly processing the data as well as supporting subplans (for modularity) and recursion (for indeterminate looping). The executor is based on a streaming dataflow model of execution to maximize the amount of operator and data parallelism possible at runtime. We have implemented both the language and executor in a system called THESEUS. Our results from testing THESEUS show that streaming dataflow execution can yield significant speedups over both traditional serial (von Neumann) as well as non-streaming dataflow-style execution that existing software and robot agent execution systems currently support. In addition, we show how plans written in the language we present can represent certain types of subtasks that cannot be accomplished using the languages supported by network query engines. Finally, we demonstrate that the increased expressivity of our plan language does not hamper performance; specifically, we show how data can be integrated from multiple remote sources just as efficiently using our architecture as is possible with a state-of-the-art streaming-dataflow network query engine.
Formal Analysis of UMTS Privacy
Venue: arXiv cs.CR 2011
Year: 2011

The ubiquitous presence of mobile communication devices and the continuous development of mo- bile data applications, which results in high level of mobile devices' activity and exchanged data, often transparent to the user, makes privacy preservation an important feature of mobile telephony systems. We present a formal analysis of the UMTS Authentication and Key Agreement protocol, using the applied pi-calculus and the ProVerif tool. We formally verify the model with respect to privacy properties. We show a linkability attack which makes it possible, for individuals with low-cost equipment, to trace UMTS subscribers. The attack exploits information leaked by poorly designed error messages.
LexRank: Graph-based Lexical Centrality as Salience in Text Summarization
Venue: arXiv cs.CL 2011
Year: 2011

We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. We test the technique on the problem of Text Summarization (TS). Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence. We consider a new approach, LexRank, for computing sentence importance based on the concept of eigenvector centrality in a graph representation of sentences. In this model, a connectivity matrix based on intra-sentence cosine similarity is used as the adjacency matrix of the graph representation of sentences. Our system, based on LexRank ranked in first place in more than one task in the recent DUC 2004 evaluation. In this paper we present a detailed analysis of our approach and apply it to a larger data set including data from earlier DUC evaluations. We discuss several methods to compute centrality using the similarity graph. The results show that degree-based methods (including LexRank) outperform both centroid-based methods and other systems participating in DUC in most of the cases. Furthermore, the LexRank with threshold method outperforms the other degree-based techniques including continuous LexRank. We also show that our approach is quite insensitive to the noise in the data that may result from an imperfect topical clustering of documents.
Extremal Behaviour in Multiagent Contract Negotiation
Venue: arXiv cs.MA 2011
Year: 2011

We examine properties of a model of resource allocation in which several agents exchange resources in order to optimise their individual holdings. The schemes discussed relate to well-known negotiation protocols proposed in earlier work and we consider a number of alternative notions of rationality covering both quantitative measures, e.g. cooperative and individual rationality and more qualitative forms, e.g. Pigou-Dalton transfers. While it is known that imposing particular rationality and structural restrictions may result in some reallocations of the resource set becoming unrealisable, in this paper we address the issue of the number of restricted rational deals that may be required to implement a particular reallocation when it is possible to do so. We construct examples showing that this number may be exponential (in the number of resources m), even when all of the agent utility functions are monotonic. We further show that k agents may achieve in a single deal a reallocation requiring exponentially many rational deals if at most k-1 agents can participate, this same reallocation being unrealisable by any sequences of rational deals in which at most k-2 agents are involved.
A Framework for Sequential Planning in Multi-Agent Settings
Venue: arXiv cs.AI 2011
Year: 2011

This paper extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the notion of agent models into the state space. Agents maintain beliefs over physical states of the environment and over models of other agents, and they use Bayesian updates to maintain their beliefs over time. The solutions map belief states to actions. Models of other agents may include their belief states and are related to agent types considered in games of incomplete information. We express the agents autonomy by postulating that their models are not directly manipulable or observable by other agents. We show that important properties of POMDPs, such as convergence of value iteration, the rate of convergence, and piece-wise linearity and convexity of the value functions carry over to our framework. Our approach complements a more traditional approach to interactive settings which uses Nash equilibria as a solution paradigm. We seek to avoid some of the drawbacks of equilibria which may be non-unique and do not capture off-equilibrium behaviors. We do so at the cost of having to represent, process and continuously revise models of other agents. Since the agents beliefs may be arbitrarily nested, the optimal solutions to decision making problems are only asymptotically computable. However, approximate belief updates and approximately optimal plans are computable. We illustrate our framework using a simple application domain, and we show examples of belief updates and value functions.
Solving Set Constraint Satisfaction Problems using ROBDDs
Venue: arXiv cs.AI 2011
Year: 2011

In this paper we present a new approach to modeling finite set domain constraint problems using Reduced Ordered Binary Decision Diagrams (ROBDDs). We show that it is possible to construct an efficient set domain propagator which compactly represents many set domains and set constraints using ROBDDs. We demonstrate that the ROBDD-based approach provides unprecedented flexibility in modeling constraint satisfaction problems, leading to performance improvements. We also show that the ROBDD-based modeling approach can be extended to the modeling of integer and multiset constraint problems in a straightforward manner. Since domain propagation is not always practical, we also show how to incorporate less strict consistency notions into the ROBDD framework, such as set bounds, cardinality bounds and lexicographic bounds consistency. Finally, we present experimental results that demonstrate the ROBDD-based solver performs better than various more conventional constraint solvers on several standard set constraint problems.
Ignorability in Statistical and Probabilistic Inference
Venue: arXiv cs.AI 2011
Year: 2011

When dealing with incomplete data in statistical learning, or incomplete observations in probabilistic inference, one needs to distinguish the fact that a certain event is observed from the fact that the observed event has happened. Since the modeling and computational complexities entailed by maintaining this proper distinction are often prohibitive, one asks for conditions under which it can be safely ignored. Such conditions are given by the missing at random (mar) and coarsened at random (car) assumptions. In this paper we provide an in-depth analysis of several questions relating to mar/car assumptions. Main purpose of our study is to provide criteria by which one may evaluate whether a car assumption is reasonable for a particular data collecting or observational process. This question is complicated by the fact that several distinct versions of mar/car assumptions exist. We therefore first provide an overview over these different versions, in which we highlight the distinction between distributional and coarsening variable induced versions. We show that distributional versions are less restrictive and sufficient for most applications. We then address from two different perspectives the question of when the mar/car assumption is warranted. First we provide a static analysis that characterizes the admissibility of the car assumption in terms of the support structure of the joint probability distribution of complete data and incomplete observations. Here we obtain an equivalence characterization that improves and extends a recent result by Grunwald and Halpern. We then turn to a procedural analysis that characterizes the admissibility of the car assumption in terms of procedural models for the actual data (or observation) generating process. The main result of this analysis is that the stronger coarsened completely at random (ccar) condition is arguably the most reasonable assumption, as it alone corresponds to data coarsening procedures that satisfy a natural robustness property.
Feature-Based Matrix Factorization
Venue: arXiv cs.AI 2011
Year: 2011

Recommender system has been more and more popular and widely used in many applications recently. The increasing information available, not only in quantities but also in types, leads to a big challenge for recommender system that how to leverage these rich information to get a better performance. Most traditional approaches try to design a specific model for each scenario, which demands great efforts in developing and modifying models. In this technical report, we describe our implementation of feature-based matrix factorization. This model is an abstract of many variants of matrix factorization models, and new types of information can be utilized by simply defining new features, without modifying any lines of code. Using the toolkit, we built the best single model reported on track 1 of KDDCup'11.
Case study and analysis of WAN Optimization pre-requirements
Venue: arXiv cs.NI 2011
Year: 2011

This paper deals with HOW to analyze the requirements for setting up the WAN Optimizer. The criteria's that needs to be taken into account, the steps involved in the analysis of WAN optimization requirement. These entire analyses will give a complete framework for setting up a WAN optimizer within an organization and the organization will have a clear record on the analysis made before setting up this WAN Optimizer.
Efficient management of IT Infrastructure implementation and support at enterprise level
Venue: arXiv cs.CY 2011
Year: 2011

This paper deals with how to manage effectively in the design, implementation and support of an IT infrastructure at an enterprise level. This particular management is lacking in today's IT infrastructure scenario. Just implementation is not sufficient for an NON-IT industry, they need a proper support in the infrastructure like documentation, support work flow, ticketing systems (used for IT related issue either hardware or software) etc... Many organizations spend a lot of money for this support and they expect a lot from the provider. Many providers sign in the SLA that they will provide them with an excellent support, but 80-90% it doesn't happen. Many times they don't meet the expectations of their client. So how to make these expectations being met 100% for the client? That is what is going to be discussed in this paper with respect to ITIL framework and other technical terminologies.
Breaking Instance-Independent Symmetries In Exact Graph Coloring
Venue: arXiv cs.AI 2011
Year: 2011

Code optimization and high level synthesis can be posed as constraint satisfaction and optimization problems, such as graph coloring used in register allocation. Graph coloring is also used to model more traditional CSPs relevant to AI, such as planning, time-tabling and scheduling. Provably optimal solutions may be desirable for commercial and defense applications. Additionally, for applications such as register allocation and code optimization, naturally-occurring instances of graph coloring are often small and can be solved optimally. A recent wave of improvements in algorithms for Boolean satisfiability (SAT) and 0-1 Integer Linear Programming (ILP) suggests generic problem-reduction methods, rather than problem-specific heuristics, because (1) heuristics may be upset by new constraints, (2) heuristics tend to ignore structure, and (3) many relevant problems are provably inapproximable. Problem reductions often lead to highly symmetric SAT instances, and symmetries are known to slow down SAT solvers. In this work, we compare several avenues for symmetry breaking, in particular when certain kinds of symmetry are present in all generated instances. Our focus on reducing CSPs to SAT allows us to leverage recent dramatic improvement in SAT solvers and automatically benefit from future progress. We can use a variety of black-box SAT solvers without modifying their source code because our symmetry-breaking techniques are static, i.e., we detect symmetries and add symmetry breaking predicates (SBPs) during pre-processing. An important result of our work is that among the types of instance-independent SBPs we studied and their combinations, the simplest and least complete constructions are the most effective. Our experiments also clearly indicate that instance-independent symmetries should mostly be processed together with instance-specific symmetries rather than at the specification level, contrary to what has been suggested in the literature.
MIS-Boost: Multiple Instance Selection Boosting
Venue: arXiv cs.LG 2011
Year: 2011

In this paper, we present a new multiple instance learning (MIL) method, called MIS-Boost, which learns discriminative instance prototypes by explicit instance selection in a boosting framework. Unlike previous instance selection based MIL methods, we do not restrict the prototypes to a discrete set of training instances but allow them to take arbitrary values in the instance feature space. We also do not restrict the total number of prototypes and the number of selected-instances per bag; these quantities are completely data-driven. We show that MIS-Boost outperforms state-of-the-art MIL methods on a number of benchmark datasets. We also apply MIS-Boost to large-scale image classification, where we show that the automatically selected prototypes map to visually meaningful image regions.
Facebook and political communication -- Macedonian case
Venue: arXiv cs.SI 2011
Year: 2011

Analysis how to use Internet influence to the process of political communication, marketing and the management of public relations, what kind of online communication methods are used by political parties, and to assess satisfaction, means of communication and the services they provide to their partys voters (people) and other interest groups and whether social networks can affect the political and economic changes in the state, and the political power of one party.
Query processing in distributed, taxonomy-based information sources
Venue: arXiv cs.DC 2011
Year: 2011

We address the problem of answering queries over a distributed information system, storing objects indexed by terms organized in a taxonomy. The taxonomy consists of subsumption relationships between negation-free DNF formulas on terms and negation-free conjunctions of terms. In the first part of the paper, we consider the centralized case, deriving a hypergraph-based algorithm that is efficient in data complexity. In the second part of the paper, we consider the distributed case, presenting alternative ways implementing the centralized algorithm. These ways descend from two basic criteria: direct vs. query re-writing evaluation, and centralized vs. distributed data or taxonomy allocation. Combinations of these criteria allow to cover a wide spectrum of architectures, ranging from client-server to peer-to-peer. We evaluate the performance of the various architectures by simulation on a network with O(10^4) nodes, and derive final results. An extensive review of the relevant literature is finally included.
Maximal frequent itemset generation using segmentation approach
Venue: arXiv cs.DB 2011
Year: 2011

Finding frequent itemsets in a data source is a fundamental operation behind Association Rule Mining. Generally, many algorithms use either the bottom-up or top-down approaches for finding these frequent itemsets. When the length of frequent itemsets to be found is large, the traditional algorithms find all the frequent itemsets from 1-length to n-length, which is a difficult process. This problem can be solved by mining only the Maximal Frequent Itemsets (MFS). Maximal Frequent Itemsets are frequent itemsets which have no proper frequent superset. Thus, the generation of only maximal frequent itemsets reduces the number of itemsets and also time needed for the generation of all frequent itemsets as each maximal itemset of length m implies the presence of 2m-2 frequent itemsets. Furthermore, mining only maximal frequent itemset is sufficient in many data mining applications like minimal key discovery and theory extraction. In this paper, we suggest a novel method for finding the maximal frequent itemset from huge data sources using the concept of segmentation of data source and prioritization of segments. Empirical evaluation shows that this method outperforms various other known methods.