Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Rerouting Approach in Software Defined Networks

This paper illustrates an efficient traffic rerouting solution in Software-Defined Networks (SDN) by monitoring the network status periodically. The proposed approach provides a rerouting solution by first calculating the link utilization for available paths and then rerouting the flow to the least delay path among the available paths. The traffic rerouting solution is considering the network condition to prevent the switch overutilization and congestion while any new flow arrives. The proposed method is implemented by using ONOS controller and Mininet emulator. The proposed algorithm in the controller predicts the utilization and delay on the link to calculate how much load to be rerouted if the average link utilization exceeds the threshold level. Hence, this method will proactively avoid congestion by adding flows, monitoring the parameters and prevent the unbalanced distribution after rerouting as our experimental results show.
Compact Charge Modeling of Double-Gate MOSFETs Considering the Density-Gradient Equation

A compact charge model for double-gate metal–oxide–semiconductor field-effect transistors with the quantum confinement effect is presented. In addition to the Poisson equation, the density-gradient equation with a realistic boundary condition is considered to include the quantum confinement effect. The coupled governing equations are rigorously integrated. Contribution of the density-gradient equation is clearly identified. Based on the resultant integrated equation, a compact charge model is proposed. Expressions for model parameters are found. Numerical examples for various double-gate MOS structures are shown.
Stability Analysis of a ${\text{2}}\times {\text{2}}$ Linear Hyperbolic System With a Sampled-Data Controller via Backstepping Method and Looped-Functionals

This paper is concerned with the global exponential stability of a $2\times 2$ linear hyperbolic system with a sampled-data boundary feedback control designed by means of the backstepping method for a nominal continuous input. We show that there exists a sufficiently small intersampling time (that encompasses both periodic and aperiodic sampling) for which the global exponential stability of the closed-loop system is guaranteed. In addition, we provide easily tractable sufficient stability conditions that can be used to find an upper bound of the maximum intersampling time. The results rely on the combination of the Lyapunov method and looped-functionals. The effectiveness of the proposed results is illustrated with a numerical example.
SimNER – An Accurate and Faster Algorithm for Named Entity Recognition

The advancements in digital technologies have led to the generation of huge amounts of data. This data can be in the form of natural language text which is easy to interpret for human but not for machines. Extracting information from this data in a form which is understandable by machines plays an important role in facilitating the processing of the huge volume of data. In this paper, we present a computationally more efficient approach to extract and classify the named entities. This approach uses rule based learning in combination with regular expression and pattern matching to extract the entities from the given text. Once the extraction is completed the named entities are classified into three classes (person, place organization). In order to make this accurate and faster, we apply an enhanced version of the K-Means algorithm to the entities extracted in the previous stage. After the clusters have been formed we identify the cluster labels according to the properties of the cluster members. This clustering helps improve the speed and accuracy of the NER tagging.
Real-Time Vehicle and Pedestrian Detection Through SSD in Indian Traffic Conditions

Object detection is a standout amongst the most vital segment of self-driving cars. It is very challenging especially in the case of Indian roads which possess numerous issues including non-standard vehicles, unskilled drivers, lower visibility due to pollution and despicable path division. In this paper, we have to test our algorithm for Indian traffic conditions through Single Shot Multi-box Detection (SSD) method. For this, we have run our algorithm on the live feed video captured by the camera (30fps) for 25 km stretch and approximately 8000 image datasets on the Noida-Greater Noida Expressway. The implemented algorithm furnishes an accuracy of 96% with a high obvious positive rate of 94.23% and a minor false positive of 4%.
Performance Change With or Without ITEI Apps

Performance of teachers is largely determined by how the teacher performs all tasks and responsibilities in the form of professional behavior. Because the teacher is an adult and as a professional who cannot be handled by just anyone, then teachers should be able to assess and improve their own performance to achieve the best. To be able to assess and improve their own performance at any time, it is the necessary application of early detection of teacher performance which is named "Indonesian Teacher Engagement Index (ITEI) apps". This study aims to present the design of ITEI Apps that is able to assist teachers in controlling their performance condition, thereby impacting on performance improvement. The research uses neuroresearch method and enriches the application design as one of the decision support system which has four main parts namely integration, implementation, intelligence, and innovation. Results of ITEI Apps can recommend how a person should act, so as to improve the performance of individuals and organizations.
Marine Oil Slicks Quantification From L-band Dual-Polarization SAR Imagery

We show, using simple physical models, that a quantitative estimation of the volume fraction of marine oil slicks can be achieved from dual-polarization synthetic aperture radar (SAR) imagery. Volume fraction, which quantifies the proportion of seawater in oil in the case of a mixture, depends primarily on volume scattering mechanisms and is inferred from the polarization ratio in the L-band. A quantification algorithm is derived, namely, the volume fraction estimation algorithm that is applied to two experimental data sets acquired in the Mediterranean Sea during the POLLUPROOF’2015 exercise and in the North Sea during the NOFO’2015 experiment using the Office National d’Études et de Recherches Aérospatiales airborne L-band SETHI system. The resulting volume fraction maps of the quantification method are presented and discussed, opening new perspectives for marine oil slicks monitoring by means of dual-polarization SAR imagery.
Joint-Sparse-Blocks and Low-Rank Representation for Hyperspectral Unmixing

Hyperspectral unmixing has attracted much attention in recent years. Single sparse unmixing assumes that a pixel in a hyperspectral image consists of a relatively small number of spectral signatures from large, ever-growing, and available spectral libraries. Joint-sparsity (or row-sparsity) model typically enforces all pixels in a neighborhood to share the same set of spectral signatures. The two sparse models are widely used in the literature. In this paper, we propose a joint-sparsity-blocks model for abundance estimation problem. Namely, the abundance matrix of size <inline-formula> <tex-math notation="LaTeX">$m\times n$ </tex-math></inline-formula> is partitioned to have one row block and <inline-formula> <tex-math notation="LaTeX">$s$ </tex-math></inline-formula> column blocks and each column block itself is joint-sparse. It generalizes both the single (i.e., <inline-formula> <tex-math notation="LaTeX">$s=n$ </tex-math></inline-formula>) and the joint (i.e., <inline-formula> <tex-math notation="LaTeX">$s=1$ </tex-math></inline-formula>) sparsities. Moreover, concatenating the proposed joint-sparsity-blocks structure and low rankness assumption on the abundance coefficients, we develop a new algorithm called <italic>joint-sparse-blocks and low-rank unmixing</italic>. In particular, for the joint-sparse-blocks regression problem, we develop a two-level reweighting strategy to enhance the sparsity along the rows within each block. Simulated and real-data experiments demonstrate the effectiveness of the proposed algorithm.
Baseline Design of a 16 T $\cos \theta$ Bending Dipole for the Future Circular Collider

The EuroCirCol project is part of the Future Circular Collider (FCC) study under the European Union leadership in the framework of a H2020 project. In particular, the Italian Institute for Nuclear Physics, in collaboration with CERN and other European laboratories, is developing the design of a cos theta <inline-formula><tex-math notation="LaTeX">${{\rm Nb}_{3}{\rm Sn}}$</tex-math></inline-formula> dipole magnet which will be part of the Conceptual Design Report of the FCC studies in 2019. The magnet, with an aperture diameter of 50 mm and a bore field of 16 T, will be able to bend the beams at final energies and within collider size constraints. Here we present an update of the electromagnetic design of the <inline-formula><tex-math notation="LaTeX">${{\rm Nb}_{3}{\rm Sn}}$</tex-math></inline-formula> cos theta dipole in double-aperture configuration, the 2-D mechanical analysis, and also the 3-D coil-ends study.
Challenges of Multi-Factor Authentication for Securing Advanced IoT Applications

The unprecedented proliferation of smart devices together with novel communication, computing, and control technologies have paved the way for A-IoT. This development involves new categories of capable devices, such as high-end wearables, smart vehicles, and consumer drones aiming to enable efficient and collaborative utilization within the smart city paradigm. While massive deployments of these objects may enrich people's lives, unauthorized access to said equipment is potentially dangerous. Hence, highly secure human authentication mechanisms have to be designed. At the same time, human beings desire comfortable interaction with the devices they own on a daily basis, thus demanding authentication procedures to be seamless and user-friendly, mindful of contemporary urban dynamics. In response to these unique challenges, this work advocates for the adoption of multi-factor authentication for A-IoT, such that multiple heterogeneous methods -- both well established and emerging -- are combined intelligently to grant or deny access reliably. We thus discuss the pros and cons of various solutions as well as introduce tools to combine the authentication factors, with an emphasis on challenging smart city environments. We finally outline the open questions to shape future research efforts in this emerging field.
A Low-Power 8-GS/s Comparator for High-Speed Analog-to-Digital Conversion in  $0.13\mu$ m CMOS Technology

This brief presents a novel comparator circuit design for high-speed analog-to-digital converters in data communication systems. The circuit achieves higher bandwidth compared to existing architectures, without the use of bandwidth enhancing inductors. Implemented in <inline-formula> <tex-math notation="LaTeX">$0.13 {\mu }\text{m}$ </tex-math></inline-formula> CMOS technology, the proposed comparator operates from a 1.2V supply and consumes a total power of 2.4mW, with sampling speeds up to 8-GS/s.
Stability Analysis of Third-Order Parametric Uncertain Nonlinear System

This paper proposes a stability analysis method for a third-order parametric uncertain system having separable but uncertain nonlinearity. The analysis makes use of Kharitonov theorem and describing function concept to test the stability. The case study is conducted for the load frequency control problem in which the power system model has non-reheated turbine with generation rate constraint as a nonlinearity. The model also exhibits parametric uncertainty.
Frequency Compensation Analysis for Two Stage Amplifier

In this work, a comparative analysis of compensation techniques is presented. Various compensation techniques are employed in proposed two stage amplifier and importance of compensation is shown using Phase Margin as parameter. The focus of this work is to raise the phase margin nearest to its ideal value so that proposed amplifier is stable. While improving phase margin, other parameters like gain, power dissipation, bandwidth, etc have not degraded. The proposed op-amp works at low voltage and has been designed in on 0.18 u CMOS technology. Cadence virtuoso simulation results validate the proposed concept.
Fast Start-Up RF Energy Harvester Design for GSM-900 Uplink Band

In this brief, a co-design and integration of rectifier, antenna, and matching network are introduced in order to obtain a fast harvester with sufficient bandwidth to capture energy of the activity-dependent GSM-900 uplink signals. A tunable two-state matching network is proposed to improve the transient response of the harvester. A complete harvester comprising of a Schottky diode rectifier, small patch antenna and the proposed two-state matching network is designed and fabricated. Measurements show that the proposed matching network compared to the traditional fixed matching can result in about a 20% reduction in the charging time of the harvester for input available power of −10 dBm.
Identification of Seismic Zones of India using DBSCAN

Technology today has made available plenty of seismic data. Their study regarding associated risks and hazards is very important for sustainable development. The use of density based clustering algorithm enables the identification of the seismic cluster formation and its evolution. The current work applies a density based clustering algorithm (DBSCAN) to the earthquake point data of India and thereby divides the region into the various seismic zones depending on the analysis of the various clusters formed. The obtained results are in conformance with seismic map of India as per Bureau of Indian Standards IS 1893:2002.
First Line Defense Against Spreading New Malware in the Network

Mutable malware including metamorphic malware evades detection by mutating and altering its code structure in each infection. To provide a first line of defense against malware, this paper proposes a strategy to detect the malware contents at the network level as the first line of defense to protect systems connected to the network from being infected. This detection strategy is a combination of machine learning classification and malware sub-signature. This allows the detection of mutated malware from packet payload. To detect previously unseen or mutated malware, the frequency distribution of informative inherited mutated (n-gram) malware features are extracted. These extracted features are then classified using Support Vector Machine classifier. The proposed technique has been tested and verified using (DARPA, and metamorphic malware dataset) for the packet level and flow level. Experimental results showed that the proposed technique has been detected and dropped more than 97% of malware packets as well as metamorphic malware packets in the network level with low FPR around 3⊠10-3.
An Improved Computational Linguistic Approach for Fine-Grained Sentiment Analysis of Textual Reviews

Nowadays, online shopping is becoming popular and customers are able to share their opinions online about the products they purchased. However, information overloading problem occurs because everyday millions of reviews are generated. Mining valuable information from textual reviews is important because we can understand the preferences of the user and also recommend the correct item to the user. In this paper, we propose a method to calculate the sentiment score of each review by considering all the opinion words and also identifying the noun product features that imply opinions. Special words and phrases in the review are handled separately by applying some of the linguistic rules.
Link prediction in complex dynamic networks using multiple interdependent time series

Link Prediction is an important problem in complex network analysis. The link prediction problem in dynamic network is a more difficult problem than its static counterpart, as dynamic networks allow links to be created or deleted over a period of time. We model the link prediction problem in dynamic network as a classification problem and use vector auto-regression based time series models and supervised learning approaches to predict the link status at future. The proposed method shows a significant improvement over the state-of-the-art.
Full-Fault-Tolerant Single-Phase 13-Level Cascaded Multilevel Inverter with Modified H-Bridge Modules

This paper presents a 13-level fully fault-tolerant single phase cascaded multilevel inverter based on modified H-bridge modules. The inverter can operate normally with the failure of any one switch. The failure mode could either be short-circuit or open-circuit. To make the system fully fault-tolerant, an additional redundant module is used, thiscan be bypassed during normal operation. A modified level shifted PWM scheme is used to generate the gating signals of the switches so that during normal operation all the modules are equally used. A fault detection algorithm is presentedthat requires only the output voltage measurement. The fault detection works on the basis, that when a fault occurs, the output voltage deviates from the expected voltage. Post faultdetection, a fast and reliable method is proposed, that modifies the gating signals to continue with the normal operation of the inverter. Simulations are carried out to verify the proposed fault detection and correction algorithm.
Improving the competitiveness of dairy production via value chain approach: The case of Lemu-Bilbilo district in Arsi highlands of Ethiopia

This study was initiated with the purpose to assess the dairy value chain in Lemu-Bilbilo district of Arsi zone, Ethiopia. This study made use of both qualitative and quantitative data collected from primary and secondary sources. Different Participatory Rural Appraisal (PRA) techniques were utilized to collect primary data in July 2012. The dairy value chain in Lemu-Bilbilo district involves six distinct value adding activities including input supply, production, gathering, processing, transportation and retail trading. The proportion of milk off-take rate by dairy producers was only 15%. Some of the challenges for dairy production at input, production, processing and marketing stage include low quality and poor timeliness of Artificial Insemination (AI) service, information gap on credit services, feed shortage, lack of awareness and knowledge regarding improved feed formulation, unavailability of government and private ranches and multiplication centers for the supply of improved dairy animals. Therefore, to create knowledge based commercial dairy development in the area, there is an urgent need to encourage the introduction and development of improved forage seed, organize recurrent training for dairy producers on improved forages, feed conservation, formulation and feed preservation techniques. Key words: Dairy; market channel; gross margin; forage; artificial insemination.
OBSERVATION OF PARAMETRIC X-RAY RADIATION EXCITED BY 50 GeV PROTONS AND IDENTIFICATION OF BACKGROUND RADIATION ORIGIN

315 OBSERVATION OF PARAMETRIC X-RAY RADIATION EXCITED BY 50 GeV PROTONS AND IDENTIFICATION OF BACKGROUND RADIATION ORIGIN A.G. Afonin, G.I. Britvich, Yu.A. Chesnokov, P.N. Chirkov, A.A. Durum, M.Yu. Kostin, A.V. Lutchev, V.A. Maisheev, A.A. Yanovich, A.V. Shchagin*, V.I. Truten’, V.B. Ganenko, I.V. Kirillin, N.F. Shul’ga, A.S. Kubankin, N.N. Nasonov, A.P. Potylitsyn, A.S. Gogolev, S.R. Uglov, Yu.M. Cherepennikov, P. Karataev Institute of High Energy Physics, Moscow Region, Protvino, Russia; National Science Center “Kharkov Institute of Physics and Technology”, Kharkov, Ukraine; Belgorod State University, Belgorod, Russia; Tomsk Polytechnic University, Tomsk, Russia; John Adams Institute at Royal Holloway College, University of London, Egham, UK; N.V. Karazin Kharkov National University, Kharkov, Ukraine *E-mail: shchagin@kipt.kharkov. ua
Comparison of Antioxidant System and Anaerobic Metabolism in Seedlings of Contrasting Maize Genotypes under Short Term Waterlogging

Aim: The aim of the study was to understand the biochemical mechanism of tolerance against waterlogging stress in two contrasting maize genotypes viz . Parkash (waterlogging tolerant) and Paras (waterlogging sensitive). Methodology: Both the genotypes were subjected to short term waterlogging stress treatment (18 h) after fifteen days of germination. Two major biochemical defense systems under hypoxia conditions, namely antioxidant and anaerobic metabolism, were compared in leaf and root tissues of tolerant and sensitive maize genotypes. Results: Both the genotypes efficiently mitigate the oxidative stress generated due to waterlogging, as shown by increased activities of SOD (superoxide dismutase), POX (peroxidase), CAT (catalase) and APX (ascorbate peroxidase) and constant level of H 2 O 2 (hydrogen peroxide) and MDA (malondialdehyde) in plant tissues. ADH activity was also significantly enhanced in the roots of both the genotypes but ALDH activity was significantly induced in tolerant genotype only, whereas activity remained unchanged in sensitive genotype. Conclusion: The present results suggest that under sudden short term waterlogging shocks, plant survival depends upon the simultaneous activation of ADH and ALDH activity for continuous energy supply and removal of toxic end products of anaerobic respiration.
Regulation of Gastrulation Through Dynamic Patterning in the Drosophila Embryo

Gene patterning delineates an embryo into precise domains of differential gene expression. However, throughout gastrulation, these patterns are spatiotemporally dynamic due to the changing environment inherent in development and the contribution of multiple inputs. We investigated how the spatiotemporal dynamics of gene expressions influence two processes in the early Drosophila embryo: the establishment of the dorsal-ventral axis and the subsequent mesoderm migration. We found that genes are able to integrate many forms of regulation over space and time in order to refine their expression boundaries and guide gastrulation. Live imaging of the Dorsal transcription factor morphogen gradient revealed spatiotemporal dynamics that never reached steady state. Computational simulations correlated these changes with shifts in the boundaries of downstream target genes. For early mesoderm development, we conducted a screen to ectopically express proteins in specific domains to identify factors involved in migration. We showed that modulation of fibroblast growth factor (FGF) signaling switches between two proteoglycans to transition cells from migration to differentiation. In addition, multiple contributions regulate the complementary expression of cadherins, which is required to provide the proper balance of cell-cell interactions during mesoderm migration. We conclude that the changing environment of the embryo is an important factor during gastrulation and give examples of its impact in defining gene expression domains, supporting specificity of signaling pathways, and regulating adhesion during collective movements.