Miracle-dz/UniFAR-FTUs huggingface dataset:
Realtime DDoS Defense Using COTS SDN Switches via Adaptive Correlation Analysis

Distributed denial-of-service (DDoS) defense is still a difficult problem though it has been extensively studied. The existing approaches are not capable of detecting various types of DDoS attacks. In particular, new emerging sophisticated DDoS attacks (e.g., Crossfire) constructed by low-rate and short-lived benign traffic are even more challenging to capture. Moreover, it is difficult to enforce realtime defense to throttle these detected attacks since the attack traffic can be concealed in benign traffic. Software defined networking (SDN) opens a new door to address these issues. In this paper, we propose Reinforcing Anti-DDoS Actions in Realtime (RADAR) to detect and throttle DDoS attacks via adaptive correlation analysis built upon unmodified commercial off-the-shelf SDN switches. It is a practical system to defend against a wide range of flooding-based DDoS attacks, e.g., link flooding (including Crossfire), SYN flooding, and UDP-based amplification attacks, while requiring neither modifications in SDN switches/protocols nor extra appliances. It accurately detects attacks by identifying attack features in suspicious flows, and locates attackers (or victims) to throttle the attack traffic by adaptive correlation analysis. We implement RADAR prototype using open source Floodlight controller, and evaluate its performance under various DDoS attacks by real hardware testbed based experiments. We observe that our scheme can successfully detect and effectively defend against various DDoS attacks with acceptable overhead.
SOH Balancing Control Method for the MMC Battery Energy Storage System

The recycled batteries can be assumed for the cost-effective grid energy storage (ES) applications, where the state of health (SOH) of recycled batteries are hard to make equal when reassembling them in one ES system. Therefore, when implementing the traditional state-of-charge balancing control methods, the batteries integrated into the same ES system will demonstrate the SOH deviation characteristics during the long-term operation due to the different degradation they suffered, which will reduce the mean time to repair, increase the maintenance duty, and shorten the life time of the ES system. Alternatively, this paper proposes an SOH balancing control method for the modular multilevel-converter-based battery energy storage system (MMC BESS) by fully using the unique modular configuration. The relationship among SOH, depth of discharge, and life cycles is analyzed in the theory, which builds the criteria for power distribution among battery packs in the MMC BESS. In addition, instead of estimating the specific SOH value, a relative SOH evaluation method is presented for easier practical implementation. And then, multiple control loops are designed for achieving the expected operational performance. MATLAB simulation and experimental results are presented to verify the proposed control method.
Two-Stage Optimal Scheduling of Electric Vehicle Charging Based on Transactive Control

In this paper, a two-stage optimal charging scheme based on transactive control is proposed for the aggregator to manage day-ahead electricity procurement and real-time electric vehicles (EV) charging management in order to minimize its total operating cost. The day-ahead electricity procurement considers both the day-ahead energy cost and expected real-time operation cost. In the real-time charging management, the cost of employing the charging flexibility from the EV owners is explicitly modeled. The aggregator uses a transactive market to manage the real-time charging demand to provide the regulating power. A model predictive control-based method is proposed for the aggregator to clear the transactive market. The real-time charging decisions of the EVs are determined by the clearing of the proposed transactive market according to the real-time requests and preferences of the EV owners. As such, the aggregators decisions in the real-time EV charging management and regulating power markets can be optimized. At the same time, the charging requirements and response preferences of the EV owners are respected. Case studies using real world driving data from the Danish National Travel Surveys were conducted to verify the proposed framework.
Pneumocystis Jirovecii Pneumonia During Sirolimus Therapy for Kaposiform Hemangioendothelioma

An infant with KHE with KMP developed PCP while on sirolimus. Pneumocystis prophylaxis should be considered for patients receiving sirolimus. Sirolimus is an effective therapy for children with kaposiform hemangioendothelioma with or without the Kasabach-Merritt phenomenon. We report the case of a child with kaposiform hemangioendothelioma and the Kasabach-Merritt phenomenon who developed Pneumocystis carinii pneumonia (PCP) while on sirolimus and a prednisolone taper, after lack of adequate response to prednisolone, propranolol, and vincristine. He had a prompt positive clinical and laboratory response to sirolimus, but 4 weeks after starting it, at the age of 4 months, he developed PCP. This led to respiratory failure, which required extracorporeal membrane oxygenation. Sirolimus was temporarily discontinued, and he was successfully treated for PCP with sulfamethoxazole-trimethoprim and methylprednisolone. He was restarted on sirolimus 3 weeks after discharge and given sulfamethoxazole-trimethoprim prophylaxis. At the age of 22 months, while still on sirolimus, the lesion continued to improve with test results revealing stable hemoglobin and platelet counts. PCP is a rare but life-threatening side effect of sirolimus therapy, especially in the setting of concurrent steroid treatment. Pneumocystis prophylaxis should be considered for patients receiving sirolimus.
Improved Circulant Structure with Kernel Tracker using Correlation Kernel based on Wavelet Feature

Visual object tracking has many applications related to computer vision. Recently, correlation filter based trackers have been ranked as the highest performers in this field. However, handling chronic problems such as occlusion, deformation, and scale variations is difficult with such trackers. These problems are solved by many other researches that employ other features and improve an appearance update. In this paper, we propose an improved CSK (Circulant Structure with Kernel) tracker using object feature decomposition in the wavelet domain. Specifically, a newly created correlation kernel is generated from different filter-banks reflecting the visual properties of a given object, and it is stable and robust to the environmental variations. Experimental results demonstrate that the proposed scheme outperforms the conventional CSK tracker in terms of center location error by 59% on an average for 100 sequences. Therefore, we believe that the proposed tracker can be useful for robust object tracking in occlusion.
Personalization Services in CADAL

CADAL is a part of universal digital library project supported by the Chinese government, 16 Chinese and American first-class universities that have taken part in the collection of resources and the construction of technical environment. Finding useful information and knowledge in the digital library is a time consuming process. Personalization Services help individuals and communities address the challenges of information overload. This paper shows the architecture of the entire Personalization Services for CADAL, and techniques exploited by us to construct it, such as query expansion, relevance feedback, user modeling, collaborative-based and content-based filtering methods. The system keeps track of user interests in different domains by automatically analyzing users’ query in our search engine and browsing behaviors in the website. Questionnaires are presented to users to explicitly give out the ratings about specific items, based on which the system predicts the potential interested items for the individuals.
A hybridized RA-APSO approach for humanoid navigation

With an increasing demand in the field of industrial automation, robotics research has occupied a significant attention of people dealing with automation technology. In this paper, a hybridization technique is proposed combining regression analysis with adaptive particle swarm optimization for navigation of humanoids. In context of humanoid navigation, sensory information regarding obstacle distances are fed as input parameters to a basic regression controller and the output of the regression controller is again fed as input to the adaptive particle swarm optimization controller to obtain the final output. The final output of the hybridized controller acts as the controlling factor for humanoid navigation in a complex environment. The logic of the proposed hybridized controller is tested in both simulated and experimental environments and the results obtained from both the environments are compared against each other with a good agreement between them. Finally, the proposed controller is also tested against other existing navigational techniques to validate the efficiency.
Disaster Management Using D2D Communication With Power Transfer and Clustering Techniques

Device-to-device (D2D) communications as an underlay to cellular networks can not only increase the system capacity and energy efficiency but also enable national security and public safety services. A key requirement for these services is to provide alternative access to cellular networks when they are partially or fully damaged due to a natural disaster event. In this paper, we employ energy harvesting (EH) at the relay with simultaneous wireless information and power transfer to prolong the lifetime of energy constrained network. In particular, we consider a user equipment relay that harvests energy from radio frequency signal via base station and use harvested energy for D2D communications. We integrate clustering technique with D2D communications into cellular networks such that communication services can be maintained when the cellular infrastructure becomes partially dysfunctional. Simulation results show that our proposed EH-based D2D clustering model performs efficiently in terms of coverage, energy efficiency, and cluster formation to extend the communication area. Moreover, a novel concept of power transfer in D2D clustering with user equipment relay and cluster head is proposed to provide a new framework to handle critical and emergency situations. The proposed approach is shown to provide significant energy saving for both mobile users and clustering heads to survive in emergency and disaster situations.
Using Closed-Set Speaker Identification Score Confidence to Enhance Audio-Based Collaborative Filtering for Multiple Users

In this paper, we utilize a closed-set speaker-identification approach to convey the ratings needed for collaborative filtering-based recommendation. Instead of explicitly providing a rating for a given program, users use a speech interface to dictate the desired rating after watching a movie. Due to the inaccuracies that may be imposed by a state-of-the-art speaker identification system, it is possible to mistake a user for another user in the household, especially when the users exhibit similar or identical age and gender demographics. This leads to the undesirable effect of injecting unwanted ratings into the collaborative rating matrix, and when the users have different tastes, can result in the recommendation of undesirable items. We therefore propose a simple confidence-based heuristic that utilizes the log-likelihood scores from the speaker identification front-end. The algorithm limits the degree to which unwanted ratings negatively affect the integrity of the ratings information. Using real-speaker utterances over a range of age and gender demographics, we compare our approach against upper and lower-bound (nonspeaker-identification-based) baseline systems. Results show that by taking the confidence into account of users that we were able to improve upon the lower-bound that unconditionally accepts ratings by a relative 6.9%.
Lithium chloride reduces orthodontically induced root resorption and affects tooth root movement in rats.

OBJECTIVE To investigate the influence of lithium chloride (LiCl) on orthodontic tooth movement (OTM), orthodontically induced root resorption (OIRR), and bone morphometry. MATERIALS AND METHODS Ten-week-old female Sprague Dawley rats (n = 32) were divided into four groups based on the concentration of LiCl administered daily per kilogram body weight: 0 (control group), 0.32, 0.64, and 1.28 mM/kg body weight. The maxillary left first molars were moved mesially by a 10 cN coil spring for 14 days. Micro-computed tomography, scanning electron microscope, and scanning laser microscope images were taken to measure the amount of OTM, the volume of OIRR, and bone morphometry. RESULTS OIRR clearly decreased depending on the amount of LiCl administered, although OTM moderately decreased. The tooth inclined mesially and the root apex moved distally in the control and 0.32 mM groups. On the other hand, the tooth inclination angle became smaller and the root apex moved mesially in the 0.64 and 1.28 mM groups. In bone morphometry, the cortical bone mineral content and bone volume increased because of LiCl administration, and the trabecular bone measurements decreased. OIRR negatively correlated to the cortical bone measurements, and the amount of OTM significantly correlated to the cortical bone morphometry. CONCLUSIONS In rats, LiCl reduced OIRR, which induced mesial movement of the tooth root apex. OIRR positively correlated to cortical bone morphometry.
Potentials of machine learning in electric drives production using the example of contacting processes and selective magnet assembly

Machine learning (ML) is a key technology in data driven industries. In general, ML algorithms offer insight in complex processes by analyzing measured data without acquiring in-depth domain knowledge. In contrast to common physical simulations they do not require excessive computational time and are well suited for real time analysis. This study focuses on transferring the potential of ML to the production of electric drives. Three major issues are identified: the preprocessing of the data, dealing with small data sets and the selection of an appropriate machine learning algorithm. Depending on the specific application in production, different algorithms, for example support vector machines, neural networks, random forests or boosted algorithms come into consideration. The potential of ML in electric drives production is demonstrated using two concrete applications: In the case of contacting technologies, such as thermo and ultrasonic crimping, ML algorithms for predictive maintenance, quality management and process control are considered. The second use case covers the selective magnet assembly. Here, a ML-based concept is proposed that predicts the cogging torque by analyzing magnet properties, as well as process parameters.
Commissioning of a PTW 34070 large‐area plane‐parallel ionization chamber for small field megavoltage photon dosimetry

Abstract Purpose This study investigates a large‐area plane‐parallel ionization chamber (LAC) for measurements of dose‐area product in water (DAP w) in megavoltage (MV) photon fields. Methods Uniformity of electrode separation of the LAC (PTW34070 Bragg Peak Chamber, sensitive volume diameter: 8.16 cm) was measured using high‐resolution microCT. Signal dependence on angle α of beam incidence for square 6 MV fields of side length s = 20 cm and 1 cm was measured in air. Polarity and recombination effects were characterized in 6, 10, and 18 MV photons fields. To assess the lateral setup tolerance, scanned LAC profiles of a 1 × 1 cm2 field were acquired. A 6 MV calibration coefficient, ND ,w, LAC, was determined in a field collimated by a 5 cm diameter stereotactic cone with known DAP w. Additional calibrations in 10 × 10 cm2 fields at 6, 10, and 18 MV were performed. Results Electrode separation is uniform and agrees with specifications. Volume‐averaging leads to a signal increase proportional to ~1/cos(α) in small fields. Correction factors for polarity and recombination range between 0.9986 to 0.9996 and 1.0007 to 1.0024, respectively. Off‐axis displacement by up to 0.5 cm did not change the measured signal in a 1 × 1 cm2 field. ND ,w, LAC was 163.7 mGy cm−2 nC −1 and differs by +3.0% from the coefficient derived in the 10 × 10 cm2 6 MV field. Response in 10 and 18 MV fields increased by 1.0% and 2.7% compared to 6 MV. Conclusions The LAC requires only small correction factors for DAP w measurements and shows little energy dependence. Lateral setup errors of 0.5 cm are tolerated in 1 × 1 cm2 fields, but beam incidence must be kept as close to normal as possible. Calibration in 10 × 10 fields is not recommended because of the LAC's over‐response. The accuracy of relative point‐dose measurements in the field's periphery is an important limiting factor for the accuracy of DAP w measurements.
Provably Robust Learning-Based Approach for High-Accuracy Tracking Control of Lagrangian Systems

Lagrangian systems represent a wide range of robotic systems, including manipulators, wheeled and legged robots, and quadrotors. Inverse dynamics control and feedforward linearization are typically used to convert the complex nonlinear dynamics of Lagrangian systems to a set of decoupled double integrators, and then a standard, outer-loop controller can be used to calculate the commanded acceleration for the linearized system. However, these methods typically depend on having a very accurate system model, which is often not available in practice. While this challenge has been addressed in the literature using different learning approaches, most of these approaches do not provide safety guarantees in terms of stability of the learning-based control system. In this letter, we provide a novel, learning-based control approach based on Gaussian processes (GPs) that ensures both stability of the closed-loop system and high-accuracy tracking. We use GPs to approximate the error between the commanded and the actual acceleration of the system, and then use the predicted mean and variance of the GP to calculate an upper bound on the uncertainty of the linearized model. This uncertainty bound is then used in a robust, outer-loop controller to ensure stability of the overall system. Moreover, we show that the tracking error converges to a ball with a radius that can be made arbitrarily small. Finally, we verify the effectiveness of our approach via simulations on a 2 degree-of-freedom (DOF) planar manipulator and experimentally on a 6 DOF industrial manipulator.
Application of Spectroscopic and Chromatographic Methods for Chemical Characterization of an Ayurvedic Herbo-Mineral Preparation: Maha Yograja Guggulu

Rasa Shastra is an exclusive branch of ayurveda that uses processed metals and minerals in various combinations. Though the formulations are time tested, safety and quality concerns are being raised since the past two decades. In view of this, it becomes mandatory to generate quality control profiles of such formulations by following available parameters. Considering this, we attempted to develop standard manufacturing procedures of Maha Yogaraja Guggulu and generate preliminary physicochemical profiles using inductively coupled plasma mass spectrometry, X-ray diffraction, and high-performance thin-layer chromatography. The results from high-performance thin-layer chromatography revealed presence of organic constituents from plant material. X-ray diffraction indicated that the prepared drug contained cinnabar (mercury sulfide; Rasa sindhura), cassiterite (tin oxide; Vanga bhasma), litharge (lead oxide; Naga bhasma), and iron dioxide and magnetite (di-iron oxide; Loha and Mandura bhasma). The observations of the present study are preliminary and first of its kind that may be considered as baseline data for future studies.
Dual band dual polarized cavity backed cross slot half mode substrate integrated waveguide antenna

A novel design of dual band dual Polarized (DBBP) cavity backed cross slot (CBCS) half-mode substrate integrated waveguide (HMSIW) antenna is described. The proposed antenna is verse of triangular shape HMSIW cavity and backed by unequal length cross slot. The unequal length cross slot is responsible to radiate linear polarized and left hand circularly polarized (LHCP) wave. LHCP is achieved at lower resonating mode with impedance band width 2.1% (8.17–8.35) GHz over 10 dB return loss and 3 dB axial ratio (AR) bandwidth 1.70% (8.18–8.32) GHz. The LP-wave achieved at upper resonating mode (9.42–9.54) GHz and gain is more than 5 dB and efficiency is more than 96% in mid frequency of both bands.
A Scalable Fair Heterogeneous Resource Allocation Scheme in Distributed Systems

Fair and efficient multiple resource allocation is often considered in a data center with a large scale of heterogeneous nodes and requests. Previous work proposed to maximize the minimum resource share received by any user and developed a fast Best-Fit algorithm to allocate jobs to servers. However, Best-Fit algorithm does not always make the optimal assignment. We first apply the subgradient technique to the max-min fairness allocation problem, and further develop two fast online algorithms, the heuristic algorithm and the randomized algorithm, respectively. The latter is completely decentralized and with low complexity. Experiments show that the two algorithms achieve better fairness compared with Best-Fit algorithm.
Enrichment and Identification of the Most Abundant Zinc Binding Proteins in Developing Barley Grains by Zinc-IMAC Capture and Nano LC-MS/MS

Background: Zinc accumulates in the embryo, aleurone, and subaleurone layers at different amounts in cereal grains. Our hypothesis is that zinc could be stored bound, not only to low MW metabolites/proteins, but also to high MW proteins as well. Methods: In order to identify the most abundant zinc binding proteins in different grain tissues, we microdissected barley grains into (1) seed coats; (2) aleurone/subaleurone; (3) embryo; and (4) endosperm. Initial screening for putative zinc binding proteins from the different tissue types was performed by fractionating proteins according to solubility (Osborne fractionation), and resolving those via Sodium Dodecyl Sulfate Polyacrylamide Gel Electrophoresis (SDS-PAGE) followed by polyvinylidene fluoride (PVDF) membrane blotting and dithizone staining. Selected protein fractions were subjected to Zn2+-immobilized metal ion affinity chromatography, and the captured proteins were identified using nanoscale liquid chromatography coupled to tandem mass spectrometry (nanoLC-MS/MS). Results: In the endosperm, the most abundant zinc binding proteins were the storage protein B-hordeins, gamma-, and D-hordeins, while in the embryo, 7S globulins storage proteins exhibited zinc binding. In the aleurone/subaleurone, zinc affinity captured proteins were late abundant embryogenesis proteins, dehydrins, many isoforms of non-specific lipid transfer proteins, and alpha amylase trypsin inhibitor. Conclusions: We have shown evidence that abundant barley grain proteins have been captured by Zn-IMAC, and their zinc binding properties in relationship to the possibility of zinc storage is discussed.
On the conceptual equivalence of reflectarray and conductor-backed artificial lens

This paper reports a preliminary examination on whether the operation of a reflectarray is conceptually equivalent to a flat conducting sheet-backed artificial lens. This examination is done by comparing the estimated reflection phase of a reflectarray with the simulated reflection phase of a reflector-lens and with the transmission phase of a simple, transmitting dielectric lens at X-band. To facilitate simulations, the dielectric lenses are divided into segments, in correspondence with the elements of the reflectarray, and S11/S21 comparisons made between appropriate segments/elements. A reasonable match is noted among these observations.