Characterizing peers communities and dynamics in a P2P live streaming system

Despite the large number of works devoted to understand P2P live streaming applications, most of them put forth so far rely on characterizing the static view of these systems. In this work, we characterize the SopCast, one of the most important P2P live streaming applications. We focus on its dynamics behavior as well as on the community formation phenomena. Our results show that SopCast presents a low overlay topology diameter and low end-to-end shortest path. In fact, diameter is smaller than 6 hops in almost 90 % of the observation time. More than 96 % of peers’ end-to-end connections present only 3 hops. These values combined may lead to low latencies and a fast streaming diffusion. Second, we show that communities in SopCast are well defined by the streaming data exchange process. Moreover, the SopCast protocol does not group peers according to their Autonomous System. In fact, the probability that a community contains 50 % of its members belonging to the same AS (when we observe the largest AS of our experiments) is lower then 10 %. Peers exchange more data with partners belonging to the same community instead of peers inside the same AS. For the largest AS we have, less than 18 % of peer traffic has been exchanged with another AS partners. Finally, our analysis provides important information to support the future design of more efficient P2P live streaming systems and new protocols that exploit communities’ relationships.     

Our current understanding of lake ecosystem response to climate change: What have we really learned from the north temperate deep lakes?

Climatic change is recognized as an important factor capable of influencing the structural properties of aquatic ecosystems. Lake ecosystems are particularly sensitive to climate change. Several long time-series studies have shown close coupling between climate, lake thermal properties and individual organism physiology, population abundance, community structure, and food-web structure. Understanding the complex interplay between climate, hydrological variability, and ecosystem structure and functioning is essential to inform water resources risk assessment and fisheries management. The purpose of this paper is to present the current understanding of climate-induced changes on lake ecosystem phenology. We first review the ability of climate to modulate the interactions among lake hydrodynamics, chemical factors, and food-web structure in several north temperate deep lakes (e.g., Lake Washington, Lake Tahoe, Lake Constance, Lake Geneva, Lake Baikal, and Lake Zurich). Our aim is to assess long-term trends in the physical (e.g., temperature, timing of stratification, and duration of ice cover), chemical (e.g., nutrient concentrations), and biological (e.g., timing of the spring bloom, phytoplankton composition, and zooplankton abundance) characteristics of the lakes and to examine the signature of local weather conditions (e.g., air temperature and rainfall) and large-scale climatic variability (e.g., ENSO and PDO) on the lake physics, chemistry and biology. We also conducted modeling experiments to quantify the relative effect of climate change and nutrient loading on lake phenology. These modeling experiments focused on the relative changes to the major causal associations underlying plankton dynamics during the spring bloom and the summer stratified period. To further understand the importance of climate change on lakes, we propose two complementary directions of future research. First, additional research is needed to elucidate the wide array of in-lake processes that are likely to be affected by the climate change. Second, it is essential to examine the heterogeneity in responses among different water bodies. The rationale of this approach and its significance for dealing with the uncertainty that the climate signals cascade through lake ecosystems and shape abiotic variability and/or biotic responses have been recently advocated by several other synthesis papers.     

Data-Intensive Computing in the 21st Century

The deluge of data that future applications must process—in domains ranging from science to business informatics—creates a compelling argument for substantially increased R&D targeted at discovering scalable hardware and software solutions for data-intensive problems.     

Eigenfeature regularization and extraction in face recognition

This work proposes a subspace approach that regularizes and extracts eigenfeatures from the face image. Eigenspace of the within-class scatter matrix is decomposed into three subspaces: a reliable subspace spanned mainly by the facial variation, an unstable subspace due to noise and finite number of training samples and a null subspace. Eigenfeatures are regularized differently in these three subspaces based on an eigenspectrum model to alleviate problems of instability, over-fitting or poor generalization. This also enables the discriminant evaluation performed in the whole space. Feature extraction or dimensionality reduction occurs only at the final stage after the discriminant assessment. These efforts facilitate a discriminative and stable low-dimensional feature representation of the face image. Experiments comparing the proposed approach with some other popular subspace methods on the FERET, ORL, AR and GT databases show that our method consistently outperforms others.     

A two-stage hardware scheduler combining greedy and optimal scheduling

Greedy scheduling heuristics provide a low complexity and scalable albeit particularly sub-optimal strategy for hardware-based crossbar schedulers. In contrast, the maximum matching algorithm for Bipartite graphs can be used to provide optimal scheduling for crossbar-based interconnection networks with a significant complexity and scalability cost. In this paper, we show how maximum matching can be reformulated in terms of Boolean operations rather than the more traditional formulations. By leveraging the inherent parallelism available in custom hardware design, we reformulate maximum matching in terms of Boolean operations rather than matrix computations and introduce three maximum matching implementations in hardware. Specifically, we examine a Pure Logic Scheduler with three dimensions of parallelism, a Matrix Scheduler with two dimensions of parallelism and a Vector Scheduler with one dimension of parallelism. These designs reduce the algorithmic complexity for an N×N$N\times N$ network from O(N³)$O\left(N^3\right)$ to O(1)$O\left(1\right)$, O(K)$O\left(K\right)$, and O(KN)$O\left(KN\right)$, respectively, where K$K$ is the number of optimization steps. While an optimal scheduling algorithm requires K=2N−1$K=2N-1$ steps, by starting with our hardware-based greedy strategy to generate an initial schedule, our simulation results show that the maximum matching scheduler can achieve 99% of the optimal schedule when K=9$K=9$. We examine hardware and time complexity of these architectures for crossbar sizes of up to N=1024$N=1024$. Using FPGA synthesis results, we show that a greedy schedule for crossbars, ranging from 8×8 to 256×256, can be optimized in less than 20 ns per optimization step. For crossbars reaching 1024×1024 the scheduling can be completed in approximately 10 μs with current technology and could reach under 90 ns with future technologies.     

Incremental application of knowledge to continuously arriving time-oriented data

In our previous work, we introduced a computational architecture that effectively supports the tasks of continuous monitoring and of aggregation querying of complex domain meaningful time-oriented concepts and patterns (temporal abstractions), in environments featuring large volumes of continuously arriving and accumulating time-oriented raw data. Examples include provision of decision support in clinical medicine, making financial decisions, detecting anomalies and potential threats in communication networks, integrating intelligence information from multiple sources, etc. In this paper, we describe the general, domain-independent but task-specific problem-solving method underling our computational architecture, which we refer to as incremental knowledge-based temporal abstraction (IKBTA). The IKBTA method incrementally computes temporal abstractions by maintaining persistence and validity of continuously computed temporal abstractions from arriving time-stamped data. We focus on the computational framework underlying our reasoning method, provide well-defined semantic and knowledge requirements for incremental inference, which utilizes a logical model of time, data, and high-level abstract concepts, and provide a detailed analysis of the computational complexity of our approach.     

Scan Cell Positioning for Boosting the Compression of Fan-Out Networks

Ensuring a high manufacturing test quality of an integrated electronic circuit mandates the application of a large volume test set. Even if the test data can be fit into the memory of an external tester, the consequent increase in test application time reflects into elevated production costs. Test data compression solutions have been proposed to address the test time and data volume problem by storing and delivering the test data in a compressed format, and subsequently by expanding the data on-chip. In this paper, we propose a scan cell positioning methodology that accompanies a compression technique in order to boost the compression ratio, and squash the test data even further. While we present the application of the proposed approach in conjunction with the fan-out based decompression architecture, this approach can be extended for application along with other compression solutions as well. The experimental results also confirm the compression enhancement of the proposed methodology.     

Perceived interactivity leading to e-loyalty: Development of a model for cognitive–affective user responses

Novel applications of website interactivity are important to attract and retain online users. In this empirical study five designs for interactivity are examined using different web-poll interfaces. The goal of the investigation is to examine perceived interactivity in a model which includes most commonly tested cognitive elements such as efficiency and effectiveness, but augments this model with the inclusion of a cognitive–affective element for trust, and an affective element of enjoyment. More specifically, a model is created to validate the relationship of perceived interactivity (comprised of user control, user connectedness, and responsiveness of the web-poll application) to efficiency, effectiveness, trust and enjoyment, of the website. In turn, efficiency, effectiveness, trust, and enjoyment are tested for their influence on user behavioral intentions for e-loyalty. All relationships in the model are supported. In addition, exploratory evaluation of qualitative comments is conducted to investigate additional insights between the five web-poll treatments in this investigation. The research confirms the complexity of a model in which cognitive, cognitive–affective and affective elements are present, and advances knowledge on the consequences of perceived interactivity. In additional to theoretical advancements, the research has merit for web designers and online marketers regarding how to enhance interactive online web applications.     

Delay- and Disruption-Tolerant Networking

Delay- and disruption-tolerant networking (DTN) grew out of attempts to develop an Interplanetary Internet but has evolved into an active area of networking research, with applications in space networking, military tactical networking, and networking for various challenged communities. The DTN Research Group provides an open forum in which DTN researchers and developers can collaborate to further develop this experimental technology.