KISS: Stochastic Packet Inspection Classifier for UDP Traffic

This paper proposes KISS, a novel Internet classifica- tion engine. Motivated by the expected raise of UDP traffic, which stems from the momentum of Peer-to-Peer (P2P) streaming appli- cations, we propose a novel classification framework that leverages on statistical characterization of payload. Statistical signatures are derived by the means of a Chi-Square-like test, which extracts the protocol "format," but ignores the protocol "semantic" and "synchronization" rules. The signatures feed a decision process based either on the geometric distance among samples, or on Sup- port Vector Machines. KISS is very accurate, and its signatures are intrinsically robust to packet sampling, reordering, and flow asym- metry, so that it can be used on almost any network. KISS is tested in different scenarios, considering traditional client-server proto- cols, VoIP, and both traditional and new P2P Internet applications. Results are astonishing. The average True Positive percentage is 99.6%, with the worst case equal to 98.1,% while results are al- most perfect when dealing with new P2P streaming applications

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Trace elements in stream bed sediments from agricultural catchments (Gascogne region, S-W France): Where do they come from?

The Gascogne region (SW of France) is cultivated for more than 75% of the area. 83 samples of stream bed sediments were collected in three main Gascogne river basins (Gers, Save and Touch, left tributaries of the Garonne river) to evaluate the impact of agricultural practices on trace elements behavior. Eight potential harmful elements (PHE) (Cr, Co, Ni, Cu, Zn, As, Cd and Pb), four reference elements for normalization (Sc, Cs, Al and Fe) and four major elements (Mn, Ca, Mg and P) were considered. The average trace element concentrations in the fine fractions (b63 μm) are in the decreasing order: ZnNCrNNiNPbNCuNCoNAsNScNCsNCd. Geochemical investigations and an original approach combining regression analysis and chemical sequential extraction allowed to select the most adequate reference material (regional molasse)and reference element (Cs) for normalization procedure. The enrichment factor (EF) is generally lower than 2.5, particularly for Cr, Ni, Cu, As, Zn; however, 23% of the sampling stations are more contaminated (2.5bEFb4.5), particularly for Cd, Pb and Co. The PHE in the Gascogne river sediments are mainly originated from natural weathering processes; nevertheless, anthropogenic contribution could represent up to 34% of the total sediment content. For lead, geochemical and isotopic methods gave very similar anthropogenic contributions (24% and 22%, respectively). The enrichment of Cu, Pb, Zn, Co, As, Ni, Cr was mainly related to global and local atmospheric deposition of industrial emissions and gasoline combustion, and was associated to forested catchments. All PHE's are controlled by clay and oxi-hydroxides minerals. Cdwas the only PHE enriched downstreamcultivated catchments and this enrichmentwas linked to Ca and P. This indicates a major origin of Cd fromfertilizer inputs and a main control by carbonate mineral

Your click decides your fate: Inferring Information Processing and Attrition Behavior from MOOC Video Clickstream Interactions

In this work, we explore video lecture interaction in Massive Open Online Courses (MOOCs), which is central to student learning experience on these educational platforms. As a research contribution, we operationalize video lecture clickstreams of students into cognitively plausible higher level behaviors, and construct a quantitative information processing index, which can aid instructors to better understand MOOC hurdles and reason about unsatisfactory learning outcomes. Our results illustrate how such a metric inspired by cognitive psychology can help answer critical questions regarding students' engagement, their future click interactions and participation trajectories that lead to in-video & course dropouts. Implications for research and practice are discusse

Extracting text from PostScript

We show how to extract plain text from PostScript files. A textual scan is inadequate because PostScript interpreters can generate characters on the page that do not appear in the source file. Furthermore, word and line breaks are implicit in the graphical rendition, and must be inferred from the positioning of word fragments. We present a robust technique for extracting text and recognizing words and paragraphs. The method uses a standard PostScript interpreter but redefines several PostScript operators, and simple heuristics are employed to locate word and line breaks. The scheme has been used to create a full-text index, and plain-text versions, of 40,000 technical reports (34 Gbyte of PostScript). Other text-extraction systems are reviewed: none offer the same combination of robustness and simplicity