Miracle-dz/UniFAR-FTUs huggingface dataset:
J ul 2 01 5 Memory and burstiness in dynamic networks

It is advisable to refer to the publisher's version if you intend to cite from the work. A discrete-time random process is described which can generate bursty sequences of events. A Bernoulli process, where the probability of an event occurring at time t is given by a fixed probability x, is modified to include a memory effect where the event probability is increased proportionally to the number of events which occurred within a given amount of time preceding t. For small values of x the inter-event time distribution follows a power-law with exponent −2−x. We consider a dynamic network where each node forms, and breaks connections according to this process. The value of x for each node depends on the fitness distribution, ρ(x), from which it is drawn; we find exact solutions for the expectation of the degree distribution for a variety of possible fitness distributions, and for both cases where the memory effect either is, or is not present. This work can potentially lead to methods to uncover hidden fitness distributions from fast changing, temporal network data such as online social communications and fMRI scans. The mathematics of interactive complex systems has a vital role to play in the interpretation of large-scale social and biological data. Technology which facilitates the collection of vast amounts of information is increasingly becoming available for both academic and commercial purposes; however, in the absence of a detailed understanding of the underlying processes, there will always be a risk of deriving the wrong conclusion from the facts. Complexity science provides numerous models of social, biological, physical and economic systems which combine large numbers of individual components to reproduce the types of behaviour observed on the systemic level. The components in such systems are usually uninteresting in isolation, but when allowed to interact with each other they produce complex non-trivial patterns which in some cases agree very well with empirical results. This poses a challenge for data scientists: given information only about the system as a whole, with all its complex and interactive dynamics, how can one conclude anything about the individual components? To begin answering that question we need to understand, in mathematical terms, the form and extent of the biases that complexity creates. The purpose of the present work is to provide an understanding of how one very simple mechanism, a memory effect (brought about by interaction), will bias the statistical properties of …
Split diversity in constrained conservation prioritization using integer linear programming

Phylogenetic diversity (PD) is a measure of biodiversity based on the evolutionary history of species. Here, we discuss several optimization problems related to the use of PD, and the more general measure split diversity (SD), in conservation prioritization. Depending on the conservation goal and the information available about species, one can construct optimization routines that incorporate various conservation constraints. We demonstrate how this information can be used to select sets of species for conservation action. Specifically, we discuss the use of species' geographic distributions, the choice of candidates under economic pressure, and the use of predator–prey interactions between the species in a community to define viability constraints. Despite such optimization problems falling into the area of NP hard problems, it is possible to solve them in a reasonable amount of time using integer programming. We apply integer linear programming to a variety of models for conservation prioritization that incorporate the SD measure. We exemplarily show the results for two data sets: the Cape region of South Africa and a Caribbean coral reef community. Finally, we provide user‐friendly software at http://www.cibiv. at/software/pda.
Video sequence synchronization

[Truncated abstract] Video sequence synchronization is necessary for any computer vision application that integrates data from multiple simultaneously recorded video sequences. With the increased availability of video cameras as either dedicated devices, or as components within digital cameras or mobile phones, a large volume of video data is available as input for a growing range of computer vision applications that process multiple video sequences. To ensure that the output of these applications is correct, accurate video sequence synchronization is essential. Whilst hardware synchronization methods can embed timestamps into each sequence on-the-fly, they require specialized hardware and it is necessary to set up the camera network in advance. On the other hand, computer vision-based software synchronization algorithms can be used to post-process video sequences recorded by cameras that are not networked, such as common consumer hand-held video cameras or cameras embedded in mobile phones, or to synchronize historical videos for which hardware synchronization was not possible. The current state-of-the-art software algorithms vary in their input and output requirements and camera configuration assumptions. Next, I describe an approach that synchronizes two video sequences where an object exhibits ballistic motions. Given the epipolar geometry relating the two cameras and the imaged ballistic trajectory of an object, the algorithm uses a novel iterative approach that exploits object motion to rapidly determine pairs of temporally corresponding frames. This algorithm accurately synchronizes videos recorded at different frame rates and takes few iterations to converge to sub-frame accuracy. Whereas the method presented by the first algorithm integrates tracking data from all frames to synchronize the sequences as a whole, this algorithm recovers the synchronization by locating pairs of temporally corresponding frames in each sequence. Finally, I introduce an algorithm for synchronizing two video sequences recorded by stationary cameras with unknown epipolar geometry. This approach is unique in that it recovers both the frame rate ratio and the frame offset of the two sequences by finding matching space-time interest points that represent events in each sequence; the algorithm does not require object tracking. RANSAC-based approaches that take a set of putatively matching interest points and recover either a homography or a fundamental matrix relating a pair of still images are well known. This algorithm extends these techniques using space-time interest points in place of spatial features, and uses nested instances of RANSAC to also recover the frame rate ratio and frame offset of a pair of video sequences. In this thesis, it is demonstrated that each of the above algorithms can accurately recover the frame rate ratio and frame offset of a range of real video sequences. Each algorithm makes a contribution to the body of video sequence synchronization literature, and it is shown that the synchronization problem can…
Improve transmission reliability with multi-AP diversity in wireless networks: architecture and performance analysis

With the increasing development of IEEE 802.11 based Wireless Local Area Network (WLAN) devices, large-scale WLANs with high dense deployment of user terminals and access points (APs) have emerged widely in various hotspots. Enhancing transmission reliability has been a primary challenge for scaling the WLANs because high dense deployment of user terminals and APs results in too many collisions. In this paper, we investigate the defects of single association mechanism defined in IEEE 802.11 on transmission reliability from network perspective. Then, we propose a multi-AP architecture, with which an AP Controller (AC) is employed to enable each user terminal to associate and cooperate with multiple APs. In this way, the user terminals can benefit from the diversity effect of multi-paths with independent collisions and transmission errors. This paper concentrates on the performance comparison between the proposed multi-AP architecture and that in IEEE 802.11 standard. Extensive simulation results show that the proposed mechanism can obtain much better performance in terms of the throughput per user and the total throughput, and the performance gain is position dependent. Moreover, the unfairness issue in traditional WLANs due to capture effect can be alleviated properly in the multi-AP framework.
Americas Conference on Information Systems ( AMCIS ) 1-1-2009 Responding to Technology-Enabled Organizational Transformation

As a result of the emergence of the Internet and net-enabled business processes, many industries have experienced a period of IT-enabled transformation in which organizations and business operations changed very rapidly. A natural question that arises is how can firms survive and even thrive during such transformations? In addressing this question, we show how a firm’s strategic change orientation—a meta-construct consisting of technological opportunism, market orientation, and entrepreneurial orientation—can influence the assimilation of IT and the resulting performance of business processes. We identify and examine two separate change enablers through which this influence occurs: (1) the development of IT capabilities; (2) the creation of a positive climate for IT use. These two change enablers influence the assimilation of technology within the organization and the resulting business process performance. We test the proposed model using a survey of 153 organizations in the retail auto industry, a compelling example of an industry that has undergone an IT-enabled transformation. Results explain 34% of the variance in process performance, and 34% of the variance in financial performance.
Localization and map building based on particle filter and unscented Kalman Filter for an AUV

Simultaneous localization and mapping (SLAM) is of prime importance for navigation problem of autonomous underwater vehicle. Currently EKF-based SLAM and particle filter-based SLAM are prevalent methods though they have their own deficiency respectively. In this paper a modified RBPF method is proposed to apply in navigation and localization for our underwater vehicle, C-RANGER. Unscented Kalman filter instead of extended Kalman filter is used to incorporate the current observations as well as the historical observations into the proposal distribution. The simulation results show that the improved algorithm is more accurate and reliable while it is used to estimate the pose of AUV and locations of features.
On the current agile assessment methods and approaches

Agile methods are widely used in software companies in recent years. Many software companies are replacing their traditional development methods with Agile methods. Nonetheless, measuring agility that they have achieved has be topic of debate. So far, only a few methods and tools have been proposed to measure the agility of software companies who are moving to agile. The main aim of this paper is reviewing the existing agile assessment methods and providing a brief discussion on drawbacks of these methods. This paper tries to elucidate the actual position of these methods in measuring agility degree of companies who are moving to agile approach.
A Survey on Botnet Architectures, Detection and Defences

Botnets are known to be one of the most serious Internet security threats. In this survey, we review botnet architectures and their controlling mechanisms. Botnet infection behavior is explained. Then, known botnet models are outlined to study botnet design. Furthermore, Fast-Flux Service Networks (FFSN) are discussed in great details as they play an important role in facilitating botnet traffic. We classify botnets based on their architecture. Our classification criterion relies on the underlying CC C&C traffic, bot generated traffic, and DNS traffic, (2) botmaster traceback detection, and (3) botnet detection using virtual machines. Finally, we summarize botnet defence measures that should be taken after detecting a botnet.
THE PROBLEM OF COMBINING INTEGRAL AND SEPARABLE DIMENSIONS

For geometrical models of cognition, the notion of distance rules - or metrics - is fundamental. Within psychology, it is well established that pairs of dimensions that are processed holistically - integral dimensions - normally combine so as they are best described with a Euclidean metric, whereas pairs of dimensions that are processed analytically - separable dimensions - most often combine with a city-block metric. The experimental tradition studying information integration has typically been limited to two-dimensional stimuli. A next step is to study information integration when dealing with more complex stimuli. This step give rise to several interesting questions regarding information integration behaviour, especially when both integral and separable pairs are included. For example: How do we integrate information when both integral and separable pairs are involved? This paper extends earlier research regarding information integration in that it deals with stimuli with more than two dimensions, and with complex stimuli consisting of both dimensional pairs previously identified as holistic, and dimensional pairs previously identified as analytical. The general pattern identified is that information integration can be more accurately described with a rule taking aspects of stimuli into consideration compared to a traditional rule. For example, it appears that combinations of analytical and holistic stimuli, are better described by treating the different subspaces individually and then combining these with addition, compared to any single Minkowskian rule, and much better compared to any of the Minkowskian rules traditionally used (i.e. the city-block-, the Euclidean or the dominance-metrics). For stimuli that are subject to confusion (e.g. when stimuli are too similar) single Minkowskian rules appears to describe the data best - but with more substantial violations against the assumptions of correspondence and interdimensional additivity.
Image segmentation based thresholding technique: Application to DNA sequence scalograms

Image segmentation is a key step in the image analysis and understanding. The images on which we are focusing are DNA sequence scalograms. DNA is formed of a linear chain composed of a sequence of ATCG. This chain is converted by using the PNUC coding technique followed by a wavelet analysis to scalograms. The scalograms enhanced regular patterns representing the periodicities existing in the C. Elegans organism. These particular patterns characterize an area of biological interest. The segmentation is the most used method to extract regular patterns. In this paper, we will investigate the Image Segmentation by using the Thresholding Technique. This method allows focusing on the repeating patterns related to particular DNA regions especially in repetitive sequences. The final purpose consists in associating elementary shapes with the scalogram image to particular DNA base succession.
Automatic prosodic and syntactic analysis from speech in cognitive infocommunication

Speech prosody and speech syntax are closely related, and this correspondence - syntax to prosody mapping - is exploited in text-to-speech infocommunication applications. However, in automatic speech recognition and understanding based inter-cognitive infocommunication, the use of prosody to syntax mapping is mostly restricted to minimal pair disambiguation purposes, although a partial recovery of the syntactic structure can be feasible based on speech prosody. This paper presents an experiment for the automatic analysis of the prosodic and syntactic structure of speech based on automatic alignment of prosodic units - so called phonological phrases. Results show that a basic skeleton of the underlying syntactic structure and also some layering (hierarchical) information can be reconstructed based on speech prosody.
Are the Most Plastic Species the Most Abundant Ones? An Assessment Using a Fish Assemblage

Few studies have evaluated phenotypic plasticity at the community level, considering, for example, plastic responses in an entire species assemblage. In addition, none of these studies have addressed the relationship between phenotypic plasticity and community structure. Within this context, here we assessed the magnitude of seasonal changes in digestive traits (seasonal flexibility), and of changes during short-term fasting (flexibility during fasting), occurring in an entire fish assemblage, comprising ten species, four trophic levels, and a 37-fold range in body mass. In addition, we analyzed the relationship between estimates of digestive flexibility and three basic assemblage structure attributes, i.e., species trophic position, body size, and relative abundance. We found that: (1) Seasonal digestive flexibility was not related with species trophic position or with body size; (2) Digestive flexibility during fasting tended to be inversely correlated with body size, as expected from scaling relationships; (3) Digestive flexibility, both seasonal and during fasting, was positively correlated with species relative abundance. In conclusion, the present study identified two trends in digestive flexibility in relation to assemblage structure, which represents an encouraging departure point in the search of general patterns in phenotypic plasticity at the local community scale.
Formal Certification of Randomized Algorithms

Randomized algorithms have broad applications throughout computer science. They also pose a challenge for formal verification: even intuitive properties of simple programs can have elaborate proofs, mixing program verification with probabilistic reasoning. We present Ellora, a tool-assisted framework for the interactive verification of general properties of randomized algorithms. The central component of Ellora is a new and expressive program logic for imperative programs with adversarial code. In particular, the logic supports fine-grained reasoning about probabilistic loops by offering different reasoning principles according to the termination behavior, and assertions can model key notions from probability theory, such as probabilistic independence and expected values. We show soundness for the logic and develop an implementation on top of the EasyCrypt proof assistant. We demonstrate the strength of Ellora in two ways. First, we embed several specialized logics into Ellora: an adaptation of greatest pre-expectation calculus from Kozen [26], Morgan et al. [31] (restricted to loop-free programs without nondeterminism), the union bound logic from Barthe et al. [6], and a novel Hoare logic for reasoning about distribution laws and probabilistic independence. Second, we formally verify several classical randomized algorithms.
Detecting Socialization Events in Ageing People: The Experience of the DOREMI Project

The detection of socialization events is useful to build indicators about social isolation of people, which is an important indicator in e-health applications. On the other hand, it is rather difficult to achieve with non-invasive solutions. This paper reports about the currently work-in-progress on the technological solution for the detection of socialization events adopted in the DOREMI project.
Improving Emotion Recognition with a Learning Multi-agent system

In Human-Computer Interaction Machine recognition of human emotional state is very important. Individual emotions play a crucial role during the interaction. Identifying person’s emotion state and providing personalized feedback is very difficult. We propose a novel and yet effective system to recognize human emotional state using learning Multi-agent system. In our system, the feature vectors are extracted and used to classify the emotion of the human. A Multi – agent system consists of a group of distributed agents to recognize the emotional state of a human. This paper provides an empirical study that how human’s emotions may be predicted and based upon their mood. Index terms – Emotional Intelligence, Human Computer Interaction (HCI), Self Reporting System, Facial Expression Recognition, Emotion recognition, Human action, Group behaviour, Human-centered computing.
Not All that Glitters is Gold: Gold Boom, Child Labor and Schooling in Colombia

This paper estimates the impact of the boom in international gold prices on child labor and schooling in Colombia. I first set up a simple agricultural household model of child labor and commodity prices hocks which guides the empirical analysis. Then, I use individual level information from the censuses of 1985, 1993 (when prices where stable) and 2005 (when prices surged) merged with regional data on gold production capabilities. I define Gold Boom as an interaction between regional gold production capabilities and the international price of gold. I find that child labor is increasing (0.3 standard deviations) and school attendance is decreasing (0.9 standard deviations) in the measure of gold boom. Accordingly, the gold boom decreases school attainment (0.2 standard deviations). This is consistent with the model when initial child labor is low and substitution effects dominate income effects. Finally, I find that the years of education of the head of the head of the household but not her ownership of assets mitigate the collateral effects of the gold boom.
A Review Paper on Oculus Rift-A Virtual Reality Headset

Oculus rift: Virtual reality (VR) is a burgeoning field that has the inherent potential of manipulating peoples mind with a superlative 3D experience. Oculus rift is one such application that assists in achieving the same. With the fleeting enhancements in VR it now seems very feasible to provide the user with experiences that were earlier thought to be merely a dream or a nightmare.
Investigating the Relationship Between Aid and Trade Flows

This paper demonstrates that an empirical link between aid and trade exist (for some donor-recipient pairs), but that the nature of this linkage is complex and can take a variety of forms. By identifying this complexity (and variability) we challenge the assertion, often made in debates regarding tied aid, that aid creates or promotes trade. The argument that aid leads to trade is usually based on anecdotal or microeconomic (project or firm-level data), but this cannot be generalised to the claim that aggregate aid flows are linked in a causal manner with aggregate trade flows. It is the latter claim we investigate. We examine data on aid and trade flows for a sample of four European donors and 26 African recipients over 1969-95. Three broad findings emerge. First, a statistical link between aid and trade, of whatever form, is the exception rather than the norm. Second, there is very little evidence that aid creates trade; this argument for tied aid is unproven on our analysis of aggregate bilateral flows. Third, France, unlike the other donors examined, does appear more likely to use trade links as a criterion in determining aid allocations. Outline
Towards better veracity for breast cancer detection using Gabor analysis and statistical learning

Breast Cancer is by far the most prevalent cancer diagnosed in women worldwide. Early diagnosis and detection is now possible through modern technology like mammography. In this paper, we present a method to augment the detection process by efficiently recognizing the carcinogenic tissue or cells. To address this issue, we propose an algorithm using Discrete Gabor Wavelet Transforms based on Hidden Markov Model for classification. We test our proposed method on the Mini Mammographie Image Analysis Society (MIAS) database. The proposed method yields about 90% recognition accuracy. This increase in accuracy is due to the statistical classification of benign and malignant cells.