Miracle-dz/UniFAR-FTUs huggingface dataset:
Water storage on forest foliage: A general model

A model for the prediction of water accumulation on forest foliage is presented. It is general enough to include linear, exponential, or logarithmic forms for the buildup of accumulated water and to go from any one of these three limiting forms to any other in a continuous manner by varying a single dimensionless parameter. This equation unifies into a single form many other models used to predict water accumulation (from either rain or fog) on forest foliage. An exploration of the underlying assumptions relating drip rate to interception intensity shows how these other models differ from one another. Evaporation is included in the model by using another dimensionless parameter which may be very useful in regions where only an approximate estimate of the evaporation rate is available. However, to include evaporation correctly into the model it is necessary to define the water storage capacity first in order to properly interpret the model predictions.
Efficacy of Neural Prediction-Based NAS for Zero-Shot NAS Paradigm

—In prediction-based Neural Architecture Search (NAS), performance indicators derived from graph convolutional networks have shown significant success. These indicators, achieved by representing feed-forward structures as component graphs through one-hot encoding, face a limitation: their inability to evaluate architecture performance across varying search spaces. In contrast, handcrafted performance indicators (zero-shot NAS), which use the same architecture with random initialization, can generalize across multiple search spaces. Addressing this limitation, we propose a novel approach for zero-shot NAS using deep learning. Our method employs Fourier sum of sines encoding for convolutional kernels, enabling the construction of a computational feed-forward graph with a structure similar to the architecture under evaluation. These encodings are learnable and offer a comprehensive view of the architecture’s topological information. An accompanying multi-layer perceptron (MLP) then ranks these architectures based on their encodings. Experimental results show that our approach surpasses previous methods using graph convolutional networks in terms of correlation on the NAS-Bench-201 dataset and exhibits a higher convergence rate. Moreover, our extracted feature representation trained on each NAS-Benchmark is transferable to other NAS-Benchmarks, showing promising generalizability across multiple search spaces. The code is available at: https://github.com/minh1409/DFT-NPZS-NAS
A method for business sequential data prediction

The paper describes the process and new methodology of a hybrid prediction system for business sequential data i.e. debt portfolio appraisal. Conducting the local area competence data mining approach, repayment values are predicted by means of combination of various data mining techniques. The methods include clustering of references, model selection and enrichment of input variables with prediction outputs from preceding periods. Experimental studies concern the method’s configuration influence on its general performance such as number of distinct predictors and number of competence areas.
Temperature Aware Workload Managementin Geo-Distributed Data Centers

Lately, for geo-distributed data centers, a workload management approach that routes user requests to locations with cheaper and cleaner electricity has been developed to reduce energy consumption and cost. We consider two key aspects that have not been explored in this approach. First, through empirical studies, we find that the energy efficiency of cooling systems depends critically on the ambient temperature, which exhibits significant geographical diversity. Temperature diversity can be used to reduce the cooling energy overhead. Second, energy consumption comes from not only interactive workloads driven by user requests, but also delay tolerant batch workloads that run at the back-end. The elastic nature of batch workloads can be exploited to further reduce the energy cost. In this paper, we propose to make workload management temperature aware. We formulate the problem as a joint optimization of request routing for interactive workloads and capacity allocation for batch workloads. We develop a distributed algorithm based on an m-block alternating direction method of multipliers (ADMM) algorithm that extends the classical two-block algorithm. We prove the convergence and rate of convergence results under general assumptions. Through trace-driven simulations, we find that our approach consistently provides 15-20 percent cooling energy reduction, and 5-20 percent overall cost reduction over existing methods.
Poisoning of Online Learning Filters: DDoS Attacks and Countermeasures

—The recent advancements in machine learning have led to a wave of interest in adopting online learning-based approaches for long-standing attack mitigation issues. In particular, DDoS attacks remain a signiﬁcant threat to network service availability even after more than two decades. These attacks have been well studied under the assumption that malicious trafﬁc originates from a single attack proﬁle. Based on this premise, malicious trafﬁc characteristics are assumed to be considerably different from legitimate trafﬁc. Consequently, online ﬁltering methods are designed to learn network trafﬁc distributions adaptively and rank requests according to their attack likelihood. During an attack, requests rated as malicious are precipitously dropped by the ﬁlters. In this paper, we conduct the ﬁrst systematic study on the effects of data poisoning attacks on online DDoS ﬁltering; introduce one such attack method, and propose practical protective countermeasures for these attacks. We investigate an adverse scenario where the attacker is “crafty”, switching proﬁles during attacks and generating erratic attack trafﬁc that is ever-shifting. This elusive attacker generates malicious requests by manipulating and shifting trafﬁc distribution to poison the training data and corrupt the ﬁlters. To this end, we present a generative model M IMIC S HIFT , capable of controlling trafﬁc generation while retaining the originating regular trafﬁc’s intrinsic properties. Comprehensive experiments show that online learning ﬁlters are highly susceptible to poisoning attacks, sometimes performing much worse than a random ﬁltering strategy in this attack scenario. At the same time, our proposed protective countermeasure effectively minimizes the attack impact.
On the use of SAR interferometry to aid navigation of UAV

This study is aimed at exploring the potentials of SAR Interferometry (InSAR) to aid Unmanned Aerial Vehicles (UAV) navigation. The basic idea is to infer both position and attitude of an aerial platform by inspecting the InSAR phase derived by a real time SAR interferometer mounted onboard the platform. Thanks to the expected favorable conditions in terms of geometrical sensitivity as well as signal coherence, the InSAR phase field can be used to derive the terrain elevation. By using both approximated position and attitude values of the platform as well as a reference Digital Terrain Model (DTM) from a mission database available onboard, it is possible to generate a synthetic InSAR phase model to be compared w.r. t. that derived by SAR observations. The geometrical transformation needed to match these two terrain models depends on the difference between position and attitude values derived by the instruments available on board and their actual values. Hence, this matching provides a feedback to be used for adjusting position and attitude. In order to assess the reliability of the proposed approach, we evaluated the interferometric sensitivity to changes in position and attitude. This analysis defines the limits of applicability of the InSAR-based approach and provides indications and requirements on geometric and radiometric parameters.
Turbulent aerosol fluxes over the Arctic Ocean: 2. Wind‐driven sources from the sea

An eddy-covariance flux system was successfully applied over open sea, leads and ice floes during the Arctic Ocean Expedition in July-August 1996. Wind-driven upward aerosol number fluxes were observed over open sea and leads in the pack ice. These particles must originate from droplets ejected into the air at the bursting of small air bubbles at the water surface. The source flux F (in 106 m−2 s−1) had a strong dependency on wind speed, log(F)=0.20U¯-1.71 and 0.11U¯-1.93, over the open sea and leads, respectively (where U¯ is the local wind speed at about 10 m height). Over the open sea the wind-driven aerosol source flux consisted of a film drop mode centered at ∼100 nm diameter and a jet drop mode centered at ∼1 μm diameter. Over the leads in the pack ice, a jet drop mode at ∼2 μm diameter dominated. The jet drop mode consisted of sea-salt, but oxalate indicated an organic contribution, and bacterias and other biogenic particles were identified by single particle analysis. Particles with diameters less than −100 nm appear to have contributed to the flux, but their chemical composition is unknown. Whitecaps were probably the bubble source at open sea and on the leads at high wind speed, but a different bubble source is needed in the leads owing to their small fetch. Melting of ice in the leads is probably the best candidate. The flux over the open sea was of such a magnitude that it could give a significant contribution to the condensation nuclei (CCN) population. Although the flux from the leads were roughly an order of magnitude smaller and the leads cover only a small fraction of the pack ice, the local source may till be important for the CCN population in Arctic fogs. The primary marine aerosol source will increase both with increased wind speed and with decreased ice fraction and extent. The local CCN production may therefore increase and influence cloud or fog albedo and lifetime in response to greenhouse warming in the Arctic Ocean region.
Paleoearthquakes of the past 30,000 years along the North Tehran Fault (Iran)

The North Tehran Fault (NTF) is located at the southernmost piedmont of Central Alborz and crosses the northern suburbs of the Tehran metropolis and adjacent cities, where ∼15 million people live. Extending over a length of about 110 km, the NTF stands out as a major active fault and represents an important seismic hazard for the Iranian capital after historical seismicity. In order to characterize the activity of the NTF in terms of kinematics, magnitude and recurrence intervals of earthquakes, we carried out a first paleoseismological study of the fault within its central part between Tehran and Karaj cities. We opened a trench across a 3 m-high fault scarp affecting Quaternary deposits. Our study shows that the scarp is the result of repeated events along a main N115°E trending shallow dipping thrust fault, associated with secondary ruptures. From the trench analysis and Infrared Stimulated Luminescence (IRSL) dating of fault-related sediments, we interpreted between 6 and 7 surface-rupturing events that occurred during the past 30 kyrs. Their magnitudes (estimated from the displacements along the faults) are comprised between 6.1 and 7.2. The two last events - the largest - occurred during the past 7.9 ± 1.2 ka, which yields a Holocene slip rate of ∼0.3 mm/yr. The 7 earthquakes scenario suggests a regular periodicity with a mean recurrence interval of ∼3.8 kyrs. However, the two most recent events could correspond to the two largest historical earthquakes recorded in the area (in 312-280 B.C. and 1177 A.D.), and therefore suggest that the NTF activity is not regular.
Infiltration pattern in a regolith–fractured bedrock profile: field observation of a dye stain pattern

We examined the infiltration pattern of water in a regolith–bedrock profile consisting of two overburdens (OB1 and OB2), a buried rice paddy soil (PS), two texturally distinctive weathered materials (WM1 and WM2) and a fractured sedimentary rock (BR), using a Brilliant Blue FCF dye tracer. A black‐coloured coating in conducting fractures in WM1, WM2 and BR was analysed by X‐ray diffraction and scanning electron microscopy. The dye tracer penetrated to greater than 2 m depth in the profile. The macropore flow and saturated interflow were the major infiltration patterns in the profile. Macropore flow and saturated interflow were observed along fractures in WM1, WM2 and BR and at the dipping interfaces of PS–WM1, PS–WM2 and PS–BR respectively. Heterogeneous matrix flow occurred in upper overburden (OB1) and PS. Compared with OB1, the coarser textured OB2 acted as a physical barrier for vertical flow of water. The PS with low bulk density and many fine roots was another major conducting route of water in the profile. Manganese oxide and iron oxide were positively identified in the black coating material and had low crystallinity and high surface area, indicating their high reactivity with conducting contaminants. Copyright © 2006 John Wiley & Sons, Ltd.
Achieving digital literacy through game development: an authentic learning experience

Purpose – This paper aims to argue that the process of making an original game develops digital literacy skills and provides an authentic learning experience as students create, publish and deploy interactive games. Teaching students to create computer games has become common in both K-12 and tertiary education to introducing programming concepts, increase student engagement and recruit majors and minors in technology fields. This study describes a project where first-year college students in an introductory technology concepts course use a visual game creation tool to develop original games to play on their computers and mobile devices. Design/methodology/approach – The author created a game development exercise which was implemented in three different sections of an introductory technology course. Students who participated were surveyed about their experiences. In addition, the author considers information technology (IT) skills and aspects of authentic learning which are achieved through this assignmen.
WavePipe: Parallel transient simulation of analog and digital circuits on multi-core shared-memory machines

While the emergence of multi-core shared-memory machines offers a promising computing solution to ever complex chip design problems, new parallel CAD methodologies must be developed to gain the full benefit of these increasingly parallel computing systems. We present a parallel transient simulation methodology and its multi-threaded implementation for general analog and digital ICs. Our new approach, Waveform Pipelining (abbreviated as WavePipe), exploits coarsegrained application-level parallelism by simultaneously computing circuit solutions at multiple adjacent time points in a way resembling hardware pipelining. There are two embodiments in WavePipe: backward and forward pipelining schemes. While the former creates independent computing tasks that contribute to a larger future time step by moving backwards in time, the latter performs predictive computing along the forward direction of the time axis. Unlike existing relaxation methods, WavePipe facilitates parallel circuit simulation without jeopardying convergence and accuracy. As a coarse-grained parallel approach, WavePipe not only requires low parallel programming effort, more importantly, it creates new avenues to fully utilize increasingly parallel hardware by going beyond conventional finer grained parallel device model evaluation and matrix solutions.
Data An Introduction To Data Mining 2nd Edition Discovering Knowledge In Data An Introduction To Data Mining 2nd Edition |

Advanced Methods for Knowledge Discovery from Complex DataData Mining and Knowledge Discovery for Process Monitoring and ControlKnowledge Discovery from Data StreamsText MiningData Mining and Knowledge Discovery with Evolutionary AlgorithmsKnowledge Discovery Practices and Emerging Applications of Data Mining: Trends and New DomainsThe Essentials of Data Science: Knowledge Discovery Using RKnowledge Discovery and Data MiningStatistical Data Mining and Knowledge DiscoveryMining the WebBiomedical InformaticsData MiningGeographic Data Mining and Knowledge DiscoveryMobility, Data Mining and PrivacyDatabase Support for Data Mining ApplicationsInformation Visualization in Data Mining and Knowledge DiscoveryDecomposition Methodology for Knowledge Discovery and Data MiningDiscovering Knowledge in DataData Mining and Knowledge Discovery HandbookDiscovering Knowledge in DataMedical Data Mining and Knowledge DiscoveryKnowledge Discovery in the Social SciencesData Clustering in C++Mathematical Methods for Knowledge Discovery and Data MiningScientific Data Mining and Knowledge DiscoveryKnowledge Discovery in Big Data from Astronomy and Earth ObservationAdvances in Knowledge Discovery and Data MiningData Analysis, Machine Learning and Knowledge DiscoveryKnowledge Discovery and Data Mining: Challenges and RealitiesFeature Selection for Knowledge Discovery and Data MiningAdvanced Techniques in Knowledge Discovery and Data MiningData Mining with Rattle and RKnowledge Discovery in the Social SciencesData Mining Methods and ModelsData Mining and Knowledge Discovery for GeoscientistsSoft Computing for Knowledge Discovery and Data MiningHandbook of Software Engineering & Knowledge EngineeringDiscovering Knowledge in DataData Mining and Knowledge Discovery for Big DataDiscovering Knowledge in Data Knowledge Discovery Practices and Emerging Applications of Data Mining: Trends and New Domains introduces the reader to recent research activities in the field of data mining. This book covers association mining, classification, mobile marketing, opinion mining, microarray data mining, internet mining and applications of data mining on biological data, telecommunication and distributed databases, among others, while promoting understanding and implementation of data mining techniques in emerging domains. Knowledge Discovery in the Social Sciences helps readers find valid, meaningful
Dynamically Evolving, Large-scale Information Systems

Consider the problem of discovering (or verifying) the edges and non-edges of a network, modeled as a connected undirected graph, using a minimum number of queries. A query at a vertex v discovers (or veriﬁes) all edges and non-edges whose endpoints have diﬀerent distance from v . In the network discovery problem, the edges and non-edges are initially unknown, and the algorithm must select the next query based only on the results of previous queries. We study the problem using competitive analysis and give a randomized on-line algorithm with competitive ratio O ( √ n log n ) for graphs with n vertices. We also show that no deterministic algorithm can have competitive ratio better than 3 . In the network veriﬁcation problem, the graph is known in advance and the goal is to compute a minimum number of queries that verify all edges and non-edges. This problem has previously been studied as the problem of placing landmarks in a graph or determining the metric dimension of a graph. We show that there is no approximation algorithm for this problem with ratio o (log n ) unless P = NP .
Radar target scattering matrix measurement through orthogonal signals

A method recently proposed for the simultaneous measurement of all four elements of the target scattering matrix in polarimetric radars is considered and analysed. The method makes use of pulse signal coding and is theoretically based on the simultaneous independent transmission of two orthogonal signals on two orthogonally polarised channels feeding the same antenna. The elements of the target scattering matrix are retrieved by correlating the received signal on each orthogonally polarised channel with both the transmitted waveforms. The potential advantages offered by this measurement method are discussed, with particular reference to the accuracy of the scattering matrix measurement in the presence of point targets. Related radar system implications and improvements are discussed, and applications of different types of transmission waveforms are considered.
Subantarctic Mode Water Formation, Destruction, and Export in the Eddy-Permitting Southern Ocean State Estimate

Subantarctic Mode Water (SAMW) is examined using the data-assimilating, eddy-permitting Southern Ocean State Estimate, for 2005 and 2006. Surface formation due to air-sea buoyancy flux is estimated using Walin analysis, and diapycnal mixing is diagnosed as the difference between surface formation and transport across 30°S, accounting for volume change with time. Water in the density range 26.5 < σθ < 27.1 kg m−3 that includes SAMW is exported northward in all three ocean sectors, with a net transport of (18.2, 17.1) Sv (1 Sv ≡ 106 m3 s−1; for years 2005, 2006); air-sea buoyancy fluxes form (13.2, 6.8) Sv, diapycnal mixing removes (−14.5, −12.6) Sv, and there is a volume loss of (−19.3, −22.9) Sv mostly occurring in the strongest SAMW formation locations. The most vigorous SAMW formation is in the Indian Ocean by air-sea buoyancy flux (9.4, 10.9) Sv, where it is partially destroyed by diapycnal mixing (−6.6, −3.1) Sv. There is strong export to the Pacific, where SAMW is destroyed both by air-sea buoyancy flux (−1.1, −4.6) Sv and diapycnal mixing (−5.6, −8.4) Sv. In the South Atlantic, SAMW is formed by air-sea buoyancy flux (5.0, 0.5) Sv and is destroyed by diapycnal mixing (−2.3, −1.1) Sv. Peaks in air-sea flux formation occur at the Southeast Indian and Southeast Pacific SAMWs (SEISAMWs, SEPSAMWs) densities. Formation over the broad SAMW circumpolar outcrop windows is largely from denser water, driven by differential freshwater gain, augmented or decreased by heating or cooling. In the SEISAMW and SEPSAMW source regions, however, formation is from lighter water, driven by differential heat loss.
Alarm Reduction and Correlation in Intrusion Detection Systems

Large Critical Complex Infrastructures are increasingly dependent on IP networks. Reliability by redundancy and tolerance are an imperative for such dependable networks. In order to achieve the desired reliability, the detection of faults, misuse, and attacks is essential. This can be achieved by applying methods of intrusion detection. However, in large systems, these methods produce an uncontrollable vast amount of data which overwhelms human operators. This paper studies the role of alarm reduction and correlation in existing networks for building more intelligent safeguards that support and complement the decisions by the operator. We present an architecture that incorporates Intrusion Detection Systems as sensors, and provides quantitatively and qualitatively improved alarms to the human operator. Alarm reduction via static and adaptive filtering, aggregation, and correlation is demonstrated using realistic data from sensors such as Snort, Samhain, and Syslog.