Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-Efficient Service Chains with Network Function Virtualization

Service Function Chains (SFCs) are an ordered sequence of network functions, such as firewall. Using the new approaches of Software Defined Networks and of Network Function Virtualization (NFV), the network functions can be virtualized and executed on generic hardware. To optimize network management, it is thus crucial to place dynamically the network functions at the right positions in the network according to the network traffic. In this paper, we consider the problem of SFC placement with the goal of minimizing network energy consumption. We model the problem as an Integer Linear Program, which can be used to solve small instances. To solve larger instances, we propose GreenChains, a heuristic algorithm. We exhibit the benefit of dynamic routing and of NFV on the energy savings. We show that between 30 to 55% of energy can be saved for typical ISP networks, while respecting the SFC constraints.
The Politics of Punishment across Time and Space: A Pooled Time-Series Analysis of Imprisonment Rates

Despite considerable theoretical interest, little is known about the political determinants of punishment. This study uses a pooled time-series design to fill this gap by examining political and other determinants of state imprisonment rates. The presence of Republican elected officials is used to assess the strength of the law-and-order political party. Ethnic threat theories suggest that imprisonments will be more likely in jurisdictions with the most blacks or Hispanics, while economic threat theories suggest that the imprisoned population will be greater where economic stratification is most pronounced. After controlling for social disorganization, religious fundamentalism, political conservatism, and violent crimes, the results show that Republican strength and minority threat lead to higher imprisonment rates. Statistical interactions support predictions that these relationships became stronger after greater Republican stress on law and order. The latter findings confirm theoretical expectations that these relationships are historically contingent.
Variability of the Acoustic Emission Signals Generated by Partial Discharges in Mineral Oil

The main purpose of the presented research is to investigate the partial discharge (PD) phenomenon variability under long-term AC voltage with particular consideration of the selected physical quantities changes while measured and registered by acoustic emission method (AE). During the research a PD model source generating surface discharges is immersed in the brand new insulation mineral oil. Acoustic signals generated by the continuously occurred PDs within 168 hours are registered. Several qualitative and quantitative indicators are assigned to describe the PD variability in time. Furthermore some long-term characteristics of the applied PD model source in mineral oil, are also presented according to acoustic signals emitted by the PD. Finally various statistical tools are applied for the results analysis and presentation. Despite there are numerous contemporary research papers dealing with long-term PD analysis, such complementary and multiparametric approach has not been presented so far, regarding the presented research. According to the presented research from among all assigned indicators there are discriminated descriptors that could depend on PD long-term duration. On the grounds of the regression models analysis there are discovered trends that potentially allow to apply the results for modeling of the PD variability in time using acoustic emission method. Subsequently such an approach may potentially support the development and extend the abilities of the diagnostic tools and maintenance policy in electrical power industry.
AVALIAÇÃO DE MODELOS COMPUTACIONAIS DE APRENDIZADO DE MÁQUINA PARA DETECÇÃO REATIVA E PREVENTIVA DE BOTNETS

It is alarming the increase in the number of cyber attacks in the last years. For such, botnets, which are networks of remotely controlled infected machines, are generally used. In the detection of these botnets, there are reactive and preventive approaches, where the ﬁrst is mostly used. This approach entails greater risks, since the attack needs to start in order to be detected. The aim of this work is to evaluate supervised and unsupervised machine learning models for botnet detection both in reactive and preventive approches, by inspecting the control trafﬁc that rules the botnet. For that, attributes that best represent network activity were extracted in order to feed a selection of machine learning models. From the analysis of the results, we identiﬁed the features of network trafﬁc and algorithms that better performed for the experimental scenarios, comparing the efﬁcacy on preventive and reactive detections.
Introduction to the Special Issue on Interdisciplinary Conversations (Part 1): Models for Interdisciplinary Conversations

Abstract The two parts in this special issue address interdisciplinary conversations involving mathematics courses in the first two years of undergraduate work. The special issue was inspired by work funded by the National Science Foundation under a grant titled “SUMMIT-P.” The special issue includes papers written about projects both from SUMMIT-P and from outside of SUMMIT-P. Part I focuses on frameworks and models of interdisciplinary collaboration.
NUFFT for the Efficient Spectral Domain MoM Analysis of a Wide Variety of Multilayered Periodic Structures

In this paper the Method of Moments (MoM) in the spectral domain is used for the analysis of multilayered structures containing periodic arrays of either patches or apertures. The patches and apertures may have many different geometries including complex surfaces limited by two parallel lines and two arbitrary curves, circular and elliptic rings, circular and elliptic arcs, and circular and elliptic sectors. Basis functions accounting for edge singularities are used in the approximation of the electric/magnetic current density on the patches/apertures, which enables a fast convergence of MoM with respect to the number of basis functions. Since the 2-D Fourier transforms of the basis functions cannot be obtained in closed-form, these Fourier transforms are efficiently computed by means of the nonuniform fast Fourier transform (NUFFT) algorithm. Results have been obtained for frequency-selective surfaces (FSSs), and for the elements used in the design of both reflectarray and metasurface antennas. The results obtained indicate that the software based on the NUFFT is only 15% slower than the standard spectral domain MoM software used for structures in which the 2-D Fourier transform of the basis functions is analytical, and between 50 and 80 times faster than CST.
Strained-layer-superlattice-based compact thermal imager for the International Space Station.

The compact thermal imager (CTI) is a dual-band, strained-layer-superlattice (SLS) detector-based instrument that was installed on the exterior of the International Space Station (ISS) in conjunction with the third Robotic Refueling Mission 3 (RRM3) in 2018. The CTI serves as a pathfinder for future thermal infrared capability on Landsat. The CTI incorporates an SLS hybrid, a dual-band 3-5 and 8-10 μm, electrically switchable, 320×256 array with 30 μm2 pixels, bonded to an Indigo ISC0903 Readout Integrated Circuit (ROIC). The telescope was built around an integrated detector cryocooler assembly developed under a NASA Small Business Innovative Research award with QmagiQ, LLC. The cooler is a Ricor K508 and the front-end optics is a custom-designed, doublet lens telescope with a 150 mm focal length. The ground resolution is 80 meters/pixel from the ISS altitude of 400 km. A filter creates two spectral channels from the dual bands, 3.3-5.4 and 7.8-10.2 μm. The detector hybrid control electronics is a custom-developed system based on the Teledyne Imaging Systems SIDECAR Application-Specific Integrated Circuit. This module provides the electronic interface from the RRM3 SpaceCube on-board processor to the detector/ROIC assembly. The primary goal of this mission was to perform a technology demonstration of the SLS technology and the commercial cooler technology elevating the Technology Readiness Level (TRL) to TRL 9 on a bare-bones budget and relatively fast development cycle. Some science objectives include locating fires, approximating land surface temperatures, and monitoring evapotranspiration, sea ice, and glacier dynamics. In this paper, we will present the design of the focal plane, optics, electronics, and mechanical structure of the CTI. We will also describe the operation and qualification tests that were performed to bring the CTI to the NASA TRL 6 in preparation for the launch on a SpaceX Dragon from the Kennedy Space Center.
Robust multivariate functional discriminant coordinates

Abstract In this paper, we consider the discriminant coordinates for multivariate functional data and their application to classification. We present more general construction of the multivariate functional discriminant coordinates than that known in the literature. The construction is based on basis expansion of functional data. To overcome non-robustness of classical estimators, we also propose robust estimation methods of unknown parameters. The constructed classification rules for multivariate functional data based on the linear discriminant analysis and standard and robust discriminant coordinates are compared on simulation study and on real data set. The results indicate possible usefulness of proposed methods in practice.
Review of underwater image restoration algorithms

Underwater images are susceptible to various distortions compared to images taken on land, due to the nature of the water environment. These images often suffer from diffraction, polarisation, absorption, scattering, colour loss and attenuation of light. Each part of the ocean will have its own sources of distortions, due to flickers caused by direct sunlight, marine snow, the fluorescence of biological objects, the presence of macroscopical organisms, loss of stability in divers, loss of light, artificial lighting and floating dust particles present in the water. There are numerous techniques and algorithms that may be used to restore these underwater images. This study reviews different algorithms and methods, developed in the past two decades, to give clearer ideas on the techniques present in the image restoration process, specifically for underwater images.
A mutual-selecting market-based mechanism for dynamic coalition formation

This article presents a novel market-based mechanism for a dynamic coalition formation problem backgrounded under real-time task allocation. Specifically, we first analyze the main factors of the real-time task allocation problem, and formulate the problem based on the coalition game theory. Then, we employ a social network for communication among distributed agents in this problem, and propose a negotiation mechanism for agents forming coalitions on timely emerging tasks. In this mechanism, we utilize an auction algorithm for real-time agent assignment on coalitions, and then design a mutual-selecting method to acquire better performance on agent utilization rate and task completion rate. And finally, our experimental results demonstrate that our market-based mechanism has a comparable performance in task completion rate to a decentralized approach (within 25% better on average) and a centralized dynamic coalition formation method (within 10% less on average performance).
Scaling limit of a river delta to a continuum random tree : a Brownian web approach

Consider the graph with vertex set $\mathbb{Z}^2_{\text{even}} := \{(x,t) : x + t \text{ even}\}$ and a random edge set $E$ which, for each $(x,t) \in \mathbb{Z}^2_{\text{even}}$, contains either the edge $\langle (x,t), (x+1, t+1)\rangle$ or $\langle (x,t), (x-1, t+1)\rangle$ with equal probability and independent of other vertices in $\mathbb{Z}^2_{\text{even}}$. This graph may be viewed as a graphical representation of a system of $1$-dimensional coalescing simple symmetric random walks starting at every space time point $(x,t) \in \mathbb{Z}^2_{\text{even}}$. Let $T(0,0)$ be the subtree obtained from the vertices $\{(x,t)\in \mathbb{Z}^2_{\text{even}} : t \leq 0\}$ and their associated edges lying in the connected component of the origin $(0,0)$ of this random graph. With the usual graph distance, this gives a tree-like metric space. For this subtree $T(0,0)$, Aldous conjectured that conditioned on the event that the farthest leaf of this tree is at a graph distance $n$, the scaled tree obtained by scaling the distances by $1/n$ converges in distribution to a continuum random tree (CRT) in Gromov-Hausdorff topology. We prove the conjecture conditioned on the event that the distance of the farthest leaf is at least $n$. The limiting continuum random tree is slightly different than that was surmised. For each $n\geq 1$, we define a scaled `dual' tree and prove that the sequence of pair of scaled trees converges to a pair of continuum random trees and both these limiting continuum trees appear to be new. We expect that the same limit holds for other drainage network models also in the basin of attraction of the Brownian web.
Marine Boundary Layer Clouds Associated with Coastally Trapped Disturbances: Observations and Model Simulations

Modeling marine low clouds and fog in coastal environments remains an outstanding challenge due to the inherently complex ocean–land–atmosphere system. This is especially important in the context of global circulation models due to the profound radiative impact of these clouds. This study utilizes aircraft and satellite measurements, in addition to numerical simulations using the Weather Research and Forecasting (WRF) Model, to examine three well-observed coastally trapped disturbance (CTD) events from June 2006, July 2011, and July 2015. Cloud water-soluble ionic and elemental composition analyses conducted for two of the CTD cases indicate that anthropogenic aerosol sources may impact CTD cloud decks due to synoptic-scale patterns associated with CTD initiation. In general, the dynamics and thermodynamics of the CTD systems are well represented and are relatively insensitive to the choice of physics parameterizations; however, a set of WRF simulations suggests that the treatment of model physics strongly influences CTD cloud field evolution. Specifically, cloud liquid water path (LWP) is highly sensitive to the choice of the planetary boundary layer (PBL) scheme; in many instances, the PBL scheme affects cloud extent and LWP values as much as or more than the microphysics scheme. Results suggest that differences in the treatment of entrainment and vertical mixing in the Yonsei University (nonlocal) and Mellor–Yamada–Janjić (local) PBL schemes may play a significant role. The impact of using different driving models—namely, the North American Mesoscale Forecast System (NAM) 12-km analysis and the NCEP North American Regional Reanalysis (NARR) 32-km products—is also investigated.
Performance Improvement of Object Recognition System in Broadcast Media Using Hierarchical CNN

This paper is a smartphone object recognition system using hierarchical convolutional neural network. The overall configuration is a method of communicating object information to the smartphone by matching the collected data by connecting the smartphone and the server and recognizing the object to the convergence neural network in the server. It is also compared to a hierarchical convolutional neural network and a fractional convolutional neural network. Hierarchical convolutional neural networks have 88% accuracy, fractional convolutional neural networks have 73% accuracy and 15%p performance improvement. Based on this, it shows possibility of expansion of T-Commerce market connected with smartphone and broadcasting media.
Time-dependent recommender systems for the prediction of appropriate learning objects

This dissertation deals with adaptive learning technologies which aim to optimize Technology Enhanced Learning (TEL) offerings to fit the individual learner’s needs. Thereby, Recommender Systems play a key role in supporting the user’s decision process for items of interest. This works very well for e-commerce and Video on Demand services. However, it is found to be the case that these traditional Recommender Systems cannot be directly transferred to TEL as the recommendation of course items follows a particular educational paradigm. The special conditions of this paradigm are first investigated and then taken into account for the realization of new algorithms. In order to allow a broad interoperability of a Recommender System with other technical components, a set of open standards and specifications results in a reference architecture for such an adaptive learning environment. Based on the realized architecture, activity data have been collected from students using course materials available online – the courses themselves comprising face-to-face lectures backed up by digital representations of the presented contents, blended learning settings as well as online-only courses. The courses provided access to the course materials via a novel Learning Companion Application. This app also presents learning recommendations to make the content selection more efficient and effective. Thereby, this work indicates that an educational Recommender System should not be evaluated using standard evaluation frameworks that utilize, for instance, a classical n-fold cross-validation. For this reason, a time-dependent evaluation framework is defined to investigate the precision of the Top-N learning recommendations at various points in time. Moreover, a new measure is introduced to determine the Mean Absolute Timeliness Deviation between an item recommendation and the time when it is actually accessed by the user. Subsequently, four major techniques for Recommender Systems are realized and applied to the collected data, evaluated with the time-dependent evaluation framework and successively optimized. As a reference implementation, a traditional Collaborative Filtering algorithm is developed and extended to incorporate time information. The results are then compared to the results of other time sensitive algorithms: an Item-based Collaborative Filtering approach which has previously been applied to TEL and a new learning path generator which incorporates a set of contextual information. Finally, a novel time-weighted Knowledge-based Filtering algorithm is presented and exhaustively analyzed. The evaluation results indicate that time-dependent filtering which incorporates multi-contextual activity data can produce the most precise recommendations.
Instrument predevelopment activities for FLEX mission

Abstract. The fluorescence imaging spectrometer (FLORIS) is the payload of the fluorescence explorer mission (FLEX) of the European Space Agency. The mission objective is to perform quantitative measurements of the solar-induced vegetation fluorescence aiming at monitoring photosynthetic activity. FLORIS works in a push-broom configuration, and it is designed to acquire data in the 500 to 780 nm spectral range with a sampling of 0.1 nm in the oxygen bands (759 to 769 nm and 686 to 697 nm) and 0.5 to 2.0 nm in the red edge, chlorophyll absorption, and photochemical reflectance index bands. FLEX will fly in formation with Sentinel-3 to benefit from the measurements made by Sentinel-3 instruments, OLCI, and SLSTR, particularly concerning the cloud screening, the proper characterization of the atmospheric state, and the determination of the surface temperature. The instrument concept is based on a common telescope and two modified Offner spectrometers with reflective concave gratings both for the high resolution (HR) and low resolution (LR) spectrometers. In the frame of the instrument predevelopment, Leonardo Company (Italy) has built and tested an elegant breadboard of the instrument consisting of the telescope and the HR spectrometer. OHB System AG (Germany) is in charge of the development of the LR spectrometer. The main objectives of the activity are to anticipate the development of the instrument and provide early risk retirement of the critical components; evaluate the system performances such as imaging quality parameters, straylight, ghost, polarization sensitivity, and environmental influences; verify the adequacy of critical tests such as spectral characterization and straylight; and define and optimize instrument alignment procedures. Following a brief overview of the FLEX mission, we will cover the design and the development of the optics breadboard with emphasis on the results obtained during the tests and the lessons learned for the flight unit.
Resonator-Loaded Multi-Band Microstrip Slot Antennas With Bidirectional Radiation Patterns

In this communication, an approach for designing multi-band microstrip slot antennas is proposed. Multiple resonators are loaded on a conventional microstrip slot antenna to introduce multiple resonances. The number of the operational bands of the antenna can be easily controlled by varying the number of the loading resonators. The locations of each resonance can be adjusted by varying the positions and lengths of the loading resonators together with the geometry of the slot if necessary. For validation, prototypes with dual-, tri-, and quad-band operations are designed, fabricated, and measured. Since no modified slot is used in this design method, all the operating bands show good bidirectional radiation patterns. The measured and simulated results agree well. The proposed antenna features low profile, small radiating slot, and easy fabrication, showing promising application prospect for modern communication system.
On time‐varying formation feasibility and reference function of time‐delayed linear multiagent systems with switching digraphs

Time‐varying formation feasibility and formation reference function of linear multiagent systems with both time‐varying delays and switching directed topologies are studied. For a given linear multiagent system, not all the time‐varying formations can be realized due to the dynamic restriction of each agent. The formation feasibility constraint reveals the requirement on the desired time‐varying formation to be compatible with the agent dynamics. Formation reference is a representation for the macroscopic movement of the whole multiagent system. Novel features of the formation feasibility constraint and the formation reference are the main focus of this paper. Firstly, a time‐delayed formation control protocol with switching directed topologies is constructed using local neighboring information. Then, a time‐varying formation feasibility constraint is derived based on nonsingular transformations. It is proven that the time‐varying formation feasibility constraint is independent of the time‐varying delays and the switching directed topologies. Moreover, an explicit expression of the formation reference function is proposed. It is shown that neither the time‐varying delays nor the switching directed topologies has influence on the obtained formation reference function. Finally, comparative examples are provided to demonstrate the obtained results.