OpenRaiser/Intern-Atlas huggingface dataset:
Decision Taking versus Promise Issuing
Venue: arXiv cs.SE 2013
Year: 2013

An alignment is developed between the terminology of outcome oriented decision taking and a terminology for promise issuing. Differences and correspondences are investigated between the concepts of decision and promise. For decision taking, two forms are distinguished: the external outcome delivering form and internalized decision taking. Internalized decision taking is brought in connection with Marc Slors' theory of self-programming. Examples are produced for decisions and promises in four different several settings each connected with software technology: instruction sequence effectuation, informational money transfer, budget announcement, and division by zero.
Altmetrics: New Indicators for Scientific Communication in Web 2.0
Venue: arXiv cs.DL 2013
Year: 2013

In this paper we review the socalled altmetrics or alternative metrics. This concept raises from the development of new indicators based on Web 2.0, for the evaluation of the research and academic activity. The basic assumption is that variables such as mentions in blogs, number of twits or of researchers bookmarking a research paper for instance, may be legitimate indicators for measuring the use and impact of scientific publications. In this sense, these indicators are currently the focus of the bibliometric community and are being discussed and debated. We describe the main platforms and indicators and we analyze as a sample the Spanish research output in Communication Studies. Comparing traditional indicators such as citations with these new indicators. The results show that the most cited papers are also the ones with a highest impact according to the altmetrics. We conclude pointing out the main shortcomings these metrics present and the role they may play when measuring the research impact through 2.0 platforms.
Digital Image Tamper Detection Techniques - A Comprehensive Study
Venue: arXiv cs.CR 2013
Year: 2013

Photographs are considered to be the most powerful and trustworthy media of expression. For a long time, those were accepted as proves of evidences in varied fields such as journalism, forensic investigations, military intelligence, scientific research and publications, crime detection and legal proceedings, investigation of insurance claims, medical imaging etc. Today, digital images have completely replaced the conventional photographs from every sphere of life but unfortunately, they seldom enjoy the credibility of their conventional counterparts, thanks to the rapid advancements in the field of digital image processing. The increasing availability of low cost and sometimes free of cost image editing software such as Photoshop, Corel Paint Shop, Photoscape, PhotoPlus, GIMP and Pixelmator have made the tampering of digital images even more easier and a common practice. Now it has become quite impossible to say whether a photograph is a genuine camera output or a manipulated version of it just by looking at it. As a result, photographs have almost lost their reliability and place as proves of evidences in all fields. This is why digital image tamper detection has emerged as an important research area to establish the authenticity of digital photographs by separating the tampered lots from the original ones. This paper gives a brief history of image tampering and a state-of-the-art review of the tamper detection techniques.
Enhanced Tiny Encryption Algorithm with Embedding (ETEA)
Venue: arXiv cs.MM 2013
Year: 2013

As computer systems become more pervasive and complex, security is increasingly important. Secure Transmission refers to the transfer of data such as confidential or proprietary information over a secure channel. Many secure transmission methods require a type of encryption. Secure transmissions are put in place to prevent attacks such as ARP spoofing and general data loss. Hence, in order to provide a better security mechanism, in this paper we propose Enhanced Tiny Encryption Algorithm with Embedding (ETEA), a data hiding technique called steganography along with the technique of encryption (Cryptography). The advantage of ETEA is that it incorporates cryptography and steganography. The advantage proposed algorithm is that it hides the messages.
Investigation of "Enhancing flexibility and robustness in multi-agent task scheduling"
Venue: arXiv cs.DS 2013
Year: 2013

Wilson et al. propose a measure of flexibility in project scheduling problems and propose several ways of distributing flexibility over tasks without overrunning the deadline. These schedules prove quite robust: delays of some tasks do not necessarily lead to delays of subsequent tasks. The number of tasks that finish late depends, among others, on the way of distributing flexibility. In this paper I study the different flexibility distributions proposed by Wilson et al. and the differences in number of violations (tasks that finish too late). I show one factor in the instances that causes differences in the number of violations, as well as two properties of the flexibility distribution that cause them to behave differently. Based on these findings, I propose three new flexibility distributions. Depending on the nature of the delays, these new flexibility distributions perform as good as or better than the distributions by Wilson et al.
Log Analysis Techniques using Clustering in Network Forensics
Venue: arXiv cs.CY 2013
Year: 2013

Internet crimes are now increasing. In a row with many crimes using information technology, in particular those using Internet, some crimes are often carried out in the form of attacks that occur within a particular agency or institution. To be able to find and identify the types of attacks, requires a long process that requires time, human resources and utilization of information technology to solve these problems. The process of identifying attacks that happened also needs the support of both hardware and software as well. The attack happened in the Internet network can generally be stored in a log file that has a specific data format. Clustering technique is one of methods that can be used to facilitate the identification process. Having grouped the data log file using K-means clustering technique, then the data is grouped into three categories of attack, and will be continued with the forensic process that can later be known to the source and target of attacks that exist in the network. It is concluded that the framework proposed can help the investigator in the trial process.
Concentration and Confidence for Discrete Bayesian Sequence Predictors
Venue: arXiv cs.LG 2013
Year: 2013

Bayesian sequence prediction is a simple technique for predicting future symbols sampled from an unknown measure on infinite sequences over a countable alphabet. While strong bounds on the expected cumulative error are known, there are only limited results on the distribution of this error. We prove tight high-probability bounds on the cumulative error, which is measured in terms of the Kullback-Leibler (KL) divergence. We also consider the problem of constructing upper confidence bounds on the KL and Hellinger errors similar to those constructed from Hoeffding-like bounds in the i.i.d. case. The new results are applied to show that Bayesian sequence prediction can be used in the Knows What It Knows (KWIK) framework with bounds that match the state-of-the-art.
Compression and Combining Based on Channel Shortening and Rank Reduction Techniques for Cooperative Wireless Sensor Networks
Venue: arXiv cs.IT 2013
Year: 2013

This paper investigates and compares the performance of wireless sensor networks where sensors operate on the principles of cooperative communications. We consider a scenario where the source transmits signals to the destination with the help of $L$ sensors. As the destination has the capacity of processing only $U$ out of these $L$ signals, the strongest $U$ signals are selected while the remaining $(L-U)$ signals are suppressed. A preprocessing block similar to channel-shortening is proposed in this contribution. However, this preprocessing block employs a rank-reduction technique instead of channel-shortening. By employing this preprocessing, we are able to decrease the computational complexity of the system without affecting the bit error rate (BER) performance. From our simulations, it can be shown that these schemes outperform the channel-shortening schemes in terms of computational complexity. In addition, the proposed schemes have a superior BER performance as compared to channel-shortening schemes when sensors employ fixed gain amplification. However, for sensors which employ variable gain amplification, a tradeoff exists in terms of BER performance between the channel-shortening and these schemes. These schemes outperform channel-shortening scheme for lower signal-to-noise ratio.
Dipole-Loaded Monopole Optimized Using VSO, v.3
Venue: arXiv cs.OH 2013
Year: 2013

A dipole-loaded monopole antenna is optimized for uniform hemispherical coverage using VSO, a new global search design and optimization algorithm. The antenna's performance is compared to genetic algorithm and hill-climber optimized loaded monopoles, and VSO is tested against two suites of benchmark functions and several other algorithms.
BigDataBench: a Big Data Benchmark Suite from Web Search Engines
Venue: arXiv cs.IR 2013
Year: 2013

This paper presents our joint research efforts on big data benchmarking with several industrial partners. Considering the complexity, diversity, workload churns, and rapid evolution of big data systems, we take an incremental approach in big data benchmarking. For the first step, we pay attention to search engines, which are the most important domain in Internet services in terms of the number of page views and daily visitors. However, search engine service providers treat data, applications, and web access logs as business confidentiality, which prevents us from building benchmarks. To overcome those difficulties, with several industry partners, we widely investigated the open source solutions in search engines, and obtained the permission of using anonymous Web access logs. Moreover, with two years' great efforts, we created a sematic search engine named ProfSearch (available from http://prof.ict.ac.cn). These efforts pave the path for our big data benchmark suite from search engines---BigDataBench, which is released on the web page (http://prof.ict.ac.cn/BigDataBench). We report our detailed analysis of search engine workloads, and present our benchmarking methodology. An innovative data generation methodology and tool are proposed to generate scalable volumes of big data from a small seed of real data, preserving semantics and locality of data. Also, we preliminarily report two case studies using BigDataBench for both system and architecture researches.
'Mutual Watch-dog Networking': Distributed Awareness of Faults and Critical Events in Petascale/Exascale systems
Venue: arXiv cs.DC 2013
Year: 2013

Many tile systems require techniques to be applied to increase components resilience and control the FIT (Failures In Time) rate. When scaling to peta- exa-scale systems the FIT rate may become unacceptable due to component numerosity, requiring more systemic countermeasures. Thus, the ability to be fault aware, i.e. to detect and collect information about fault and critical events, is a necessary feature that large scale distributed architectures must provide in order to apply systemic fault tolerance techniques. In this context, the LO|FA|MO approach is a way to obtain systemic fault awareness, by implementing a mutual watchdog mechanism and guaranteeing fault detection in a no-single-point-of-failure fashion. This document contains specification and implementation details about this approach, in the shape of a technical report.
An Experimental Comparison of Speed Scaling Algorithms with Deadline Feasibility Constraints
Venue: arXiv cs.DS 2013
Year: 2013

We consider the first, and most well studied, speed scaling problem in the algorithmic literature: where the scheduling quality of service measure is a deadline feasibility constraint, and where the power objective is to minimize the total energy used. Four online algorithms for this problem have been proposed in the algorithmic literature. Based on the best upper bound that can be proved on the competitive ratio, the ranking of the online algorithms from best to worst is: $\qOA$, $\OA$, $\AVR$, $\BKP$. As a test case on the effectiveness of competitive analysis to predict the best online algorithm, we report on an experimental "horse race" between these algorithms using instances based on web server traces. Our main conclusion is that the ranking of our algorithms based on their performance in our experiments is identical to the order predicted by competitive analysis. This ranking holds over a large range of possible power functions, and even if the power objective is temperature.
A Novel Steganography Algorithm for Hiding Text in Image using Five Modulus Method
Venue: arXiv cs.MM 2013
Year: 2013

The needs for steganographic techniques for hiding secret message inside images have been arise. This paper is to create a practical steganographic implementation to hide text inside grey scale images. The secret message is hidden inside the cover image using Five Modulus Method. The novel algorithm is called (ST-FMM. FMM which consists of transforming all the pixels within the 5X5 window size into its corresponding multiples of 5. After that, the secret message is hidden inside the 5X5 window as a non-multiples of 5. Since the modulus of non-multiples of 5 are 1,2,3 and 4, therefore; if the reminder is one of these, then this pixel represents a secret character. The secret key that has to be sent is the window size. The main advantage of this novel algorithm is to keep the size of the cover image constant while the secret message increased in size. Peak signal-to-noise ratio is captured for each of the images tested. Based on the PSNR value of each images, the stego image has high PSNR value. Hence this new steganography algorithm is very efficient to hide the data inside the image.
Discovering the Markov network structure
Venue: arXiv cs.IT 2013
Year: 2013

In this paper a new proof is given for the supermodularity of information content. Using the decomposability of the information content an algorithm is given for discovering the Markov network graph structure endowed by the pairwise Markov property of a given probability distribution. A discrete probability distribution is given for which the equivalence of Hammersley-Clifford theorem is fulfilled although some of the possible vector realizations are taken on with zero probability. Our algorithm for discovering the pairwise Markov network is illustrated on this example, too.
From P100 to P100_: Conception and improvement of a new citation-rank approach in bibliometrics
Venue: arXiv cs.DL 2013
Year: 2013

Properties of a percentile-based rating scale needed in bibliometrics are formulated. Based on these properties, P100 was recently introduced as a new citation-rank approach (Bornmann, Leydesdorff, & Wang, in press). In this paper, we conceptualize P100 and propose an improvement which we call P100_. Advantages and disadvantages of citation-rank indicators are noted.
Security for Smart Mobile Networks: The NEMESYS Approach
Venue: arXiv cs.NI 2013
Year: 2013

The growing popularity of smart mobile devices such as smartphones and tablets has made them an attractive target for cyber-criminals, resulting in a rapidly growing and evolving mobile threat as attackers experiment with new business models by targeting mobile users. With the emergence of the first large-scale mobile botnets, the core network has also become vulnerable to distributed denial-of-service attacks such as the signaling attack. Furthermore, complementary access methods such as Wi-Fi and femtocells introduce additional vulnerabilities for the mobile users as well as the core network. In this paper, we present the NEMESYS approach to smart mobile network security. The goal of the NEMESYS project is to develop novel security technologies for seamless service provisioning in the smart mobile ecosystem, and to improve mobile network security through a better understanding of the threat landscape. To this purpose, NEMESYS will collect and analyze information about the nature of cyber-attacks targeting smart mobile devices and the core network so that appropriate counter-measures can be taken. We are developing a data collection infrastructure that incorporates virtualized mobile honeypots and honeyclients in order to gather, detect and provide early warning of mobile attacks and understand the modus operandi of cyber-criminals that target mobile devices. By correlating the extracted information with known attack patterns from wireline networks, we plan to reveal and identify the possible shift in the way that cyber-criminals launch attacks against smart mobile devices.
Distributed Online Big Data Classification Using Context Information
Venue: arXiv cs.LG 2013
Year: 2013

Distributed, online data mining systems have emerged as a result of applications requiring analysis of large amounts of correlated and high-dimensional data produced by multiple distributed data sources. We propose a distributed online data classification framework where data is gathered by distributed data sources and processed by a heterogeneous set of distributed learners which learn online, at run-time, how to classify the different data streams either by using their locally available classification functions or by helping each other by classifying each other's data. Importantly, since the data is gathered at different locations, sending the data to another learner to process incurs additional costs such as delays, and hence this will be only beneficial if the benefits obtained from a better classification will exceed the costs. We model the problem of joint classification by the distributed and heterogeneous learners from multiple data sources as a distributed contextual bandit problem where each data is characterized by a specific context. We develop a distributed online learning algorithm for which we can prove sublinear regret. Compared to prior work in distributed online data mining, our work is the first to provide analytic regret results characterizing the performance of the proposed algorithm.
A survey on Human Mobility and its applications
Venue: arXiv cs.SI 2013
Year: 2013

Human Mobility has attracted attentions from different fields of studies such as epidemic modeling, traffic engineering, traffic prediction and urban planning. In this survey we review major characteristics of human mobility studies including from trajectory-based studies to studies using graph and network theory. In trajectory-based studies statistical measures such as jump length distribution and radius of gyration are analyzed in order to investigate how people move in their daily life, and if it is possible to model this individual movements and make prediction based on them. Using graph in mobility studies, helps to investigate the dynamic behavior of the system, such as diffusion and flow in the network and makes it easier to estimate how much one part of the network influences another by using metrics like centrality measures. We aim to study population flow in transportation networks using mobility data to derive models and patterns, and to develop new applications in predicting phenomena such as congestion. Human Mobility studies with the new generation of mobility data provided by cellular phone networks, arise new challenges such as data storing, data representation, data analysis and computation complexity. A comparative review of different data types used in current tools and applications of Human Mobility studies leads us to new approaches for dealing with mentioned challenges.
Modelling Reactive and Proactive Behaviour in Simulation: A Case Study in a University Organisation
Venue: arXiv cs.CE 2013
Year: 2013

Simulation is a well established what-if scenario analysis tool in Operational Research (OR). While traditionally Discrete Event Simulation (DES) and System Dynamics Simulation (SDS) are the predominant simulation techniques in OR, a new simulation technique, namely Agent-Based Simulation (ABS), has emerged and is gaining more attention. In our research we focus on discrete simulation methods (i.e. DES and ABS). The contribution made by this paper is the comparison of DES and combined DES/ABS for modelling human reactive and different level of detail of human proactive behaviour in service systems. The results of our experiments show that the level of proactiveness considered in the model has a big impact on the simulation output. However, there is not a big difference between the results from the DES and the combined DES/ABS simulation models. Therefore, for service systems of the type we investigated we would suggest to use DES as the preferred analysis tool.
Application of a clustering framework to UK domestic electricity data
Venue: arXiv cs.CE 2013
Year: 2013

This paper takes an approach to clustering domestic electricity load profiles that has been successfully used with data from Portugal and applies it to UK data. Clustering techniques are applied and it is found that the preferred technique in the Portuguese work (a two stage process combining Self Organised Maps and Kmeans) is not appropriate for the UK data. The work shows that up to nine clusters of households can be identified with the differences in usage profiles being visually striking. This demonstrates the appropriateness of breaking the electricity usage patterns down to more detail than the two load profiles currently published by the electricity industry. The paper details initial results using data collected in Milton Keynes around 1990. Further work is described and will concentrate on building accurate and meaningful clusters of similar electricity users in order to better direct demand side management initiatives to the most relevant target customers.
Quality indicators for scientific journals based on experts opinion
Venue: arXiv cs.DL 2013
Year: 2013

This paper presents the results and further development of a survey sent to 11,799 Spanish faculty members and researchers from various fields of the social sciences and the humanities, obtaining a total of 45.6% (5,368 responses) usable answers. Respondents were asked (a) to indicate the three most important journals in their field and (b) to rate them on a 0-10 scale according to their quality. The information obtained has been synthesized in two indicators which reflect the perceived quality of journals. Once the values were obtained, the journals were categorized according to each indicator and the ordinal positions were compared. Different profiles of journals are analyzed in connection with experts opinion, such as regional orientation, and the consensus among researchers is studied. Finally, the possibilities of extending the research and indicators to sets of international journals are explored.
Submodularity of a Set Label Disagreement Function
Venue: arXiv cs.CV 2013
Year: 2013

A set label disagreement function is defined over the number of variables that deviates from the dominant label. The dominant label is the value assumed by the largest number of variables within a set of binary variables. The submodularity of a certain family of set label disagreement function is discussed in this manuscript. Such disagreement function could be utilized as a cost function in combinatorial optimization approaches for problems defined over hypergraphs.