Miracle-dz/UniFAR-FTUs huggingface dataset:
Preventing Distributed Denial of Service Attacks in Software Defined Mesh Networks

Mesh topology networks provide Network security in the form of redundancy of communication links. But redundancy also contributes to complexity in configuration and subsequent troubleshooting. Mesh topology deployed in Critical networks like Backbone Networks (used in Cloud Computing) deploy the Mesh topology provides additional security in terms of redundancy to ensure availability of services. One amongst most prominent attacks is Distributed Denial of Service attacks which cause an immense amount of loss of data as well as monetary losses to service providers. This paper proposes a method by which using SDN capabilities and sFlow-RT application, Distributed Denial of Service (DDoS) attacks is detected and consequently mitigated by using REST API to implement Policy Based Flow Management (PBFM) through the SDN Controller which will help in ensuring uninterrupted services in scenarios of such attacks and also further simply and enhance the management of Mesh architecture-based networks.
Gesture-SNN: Co-optimizing accuracy, latency and energy of SNNs for neuromorphic vision sensors

Spiking neural networks (SNNs) are recently gaining popularity due to their low-power, spatio-temporal computing paradigm as opposed to more conventional deep learning approaches that mainly focus on spatial characteristics of data. When paired with biologically-inspired asynchronous event sensors, they can create energy-efficient near-sensor systems that are ideal for mobile, resource-constrained and embedded-computing scenarios. Training deep SNNs, however, is challenging due to their discrete nature. The most successful method so far involves training deep artificial neural networks (ANNs) using Gradient-Descent and then converting them to SNNs. The ANN-to-SNN conversion technique has mostly been evaluated on standard static image datasets using rate-based encoding of spikes. In this work, we find that a direct application of the ANN-to-SNN conversion technique to process event data via SNNs leads to arbitrary accuracy losses. Through insights gained from theoretical analyses as well as empirical observations, we propose three novel techniques to restore the conversion accuracy on event data and show proof-of-concept results, comparable to the state-of-the-art, on the IBM DVS Gesture dataset. Further exploration of the SNN design space reveals additional insights to fine-tune the accuracy-latency-peak power trade-off. Finally, we evaluate our proposed schemes on an existing neuromorphic accelerator and show that our best-performing model is $\sim 38$% more accurate with $\sim 35$% lower energy and $\sim 55$% lower EDP compared to its traditional SNN counterpart.
Free Word Order in Sanskrit and Well-nestedness

The common wisdom about Sanskrit is that it is free word order language. This word order poses challenges such as handling non-projectivity in parsing. The earlier works on the word order of Sanskrit have shown that there are syntactic structures in Sanskrit which cannot be covered under even the non-planarity. In this paper, we study these structures further to investigate if they can fall under well-nestedness or not. A small manually tagged corpus of the verses of ´Sr ¯ ımad-Bhagavad-G ¯ ıt ¯ a (BhG) was considered for this study. It was noticed that there are as many well-nested trees as there are ill-nested ones. From the linguistic point of view, we could get a list of relations that are involved in the pla-narity violations. All these relations had one thing in common—that they have unilateral expectancy. It was this loose binding, as against the mutual expectancy with certain other relations, that allowed them to cross the phrasal boundaries.
Robust 3-D Plane Segmentation From Airborne Point Clouds Based on Quasi-A-Contrario Theory

Three-dimensional (3-D) plane segmentation has been and continues to be a challenge in 3-D point cloud processing. The current methods typically focus on the planar subsets separation but ignore the requirement of the precise plane fitting. We propose a quasi-a-contrario theory-based plane segmentation algorithm, which is capable of dealing with point clouds of severe noise level, low density, and high complexity robustly. The main proposition is that the final plane can be composed of basic planar subsets with high planar accuracy. We cast planar subset extraction from the point set as a geometric rigidity measuring problem. The meaningfulness of the planar subset is estimated by the number of false alarms (NFA), which can be used to eliminate false-positive effectively. Experiments were conducted to analyze both the planar subset extraction and the 3-D plane segmentation. The results show that the proposed algorithms perform well in terms of accuracy and robustness compared with state-of-art methods. Experimental datasets, results, and executable program of the proposed algorithm are available at https://skyearth.org/publication/project/QTPS.
Visibility improvement in turbid water by the fusion technology of Mueller matrix images

The underwater visibility is limited because of the particle scattering and absorption. As a promising technology, Mueller matrix imaging can effectively improve image quality and reveal additional information about the target. In this study, a method using the fusion of Mueller matrix images to enhance underwater degraded images is presented. Every Mueller matrix element image is given a weight and then fused to create a new image. The optimal weight is obtained by searching values that maximize the quality of the image. It is proved that our method is effective by comparing with the Mueller matrix images and state-of-the-art methods employing subjective and objective analysis. Under the same condition, we only need to search the weight once and then images of other objects can be enhanced by the same weight. At the same time, one can use simulated weight to enhance the image. The proposed method can let the enhanced image have some information that the normal method does not have because of the property of the Mueller matrix. The underwater viewing distance and image quality can be improved.
Prediction of Parkinson's Disease using Machine Learning and Deep Transfer Learning from different Feature Sets

Precursors that appear diminutive hold a world of significance with regard to indicating and diagnosing premature stages of degenerative diseases, especially neurologically concerned ailments like Parkinson's disease (PD). PD usually shows symptoms like spasm in the limbs, jaw or head, rigidness of the limbs and trunk, sluggish movement, etc. and it is crucial to detect Parkinson Disease in early stages by keeping an eye out for these preliminary symptoms. In this paper, various datasets were researched, analyzed and run through certain algorithms to detect several symptoms. The Freezing of Gait dataset was used to predict if there were symptoms related to legs and trunk by analyzing the patient's gait, the Parkinson Clinical speech dataset to detect deviation in audio frequency and lastly the Parkinson Disease wave and spiral drawing dataset which can help find out impairment in writing due to a tremor in hand or arm. The detection of impairment in handwriting seems to be the most convenient method and Convolutional Neural Network using Transfer Learning is implemented on this image dataset.
Analysis and Reduction of DC Winding Induced Voltage Pulsation in Five-Phase Non-Overlapped Stator Wound Field Flux Switching Machine

DC winding induced voltage pulsation in Wound field flux switching (WFFS) machine is the main cause of the current ripples in DC winding. It may also deteriorates the DC power source and enhances the challenging issues to the control performances of the machine. Hence, reduction of this pulsation is very considerable parameter to analyze in WFFS machine’s analysis. In this paper, DC winding induced voltage for five phase non overlapping stator WFFS machine is analyzed and reduced through various techniques. Three techniques i.e. rotor pole arc optimization, chamfering and rotor step skewing are analyzed and optimized to reduce the DC winding induced voltage. For optimization, krigging method based on genetic algorithm (GA) is performed due to compatibility with non-linear data and then results are verified by finite element (FE) analysis. By applying the chamfering and rotor pole arc optimization, 49.58% and 28.45% reduction in the peak to peak value of DC winding induced voltage pulsation is achieved and electromagnetic torque is maintained at 97.82% and 100% respectively while by applying the rotor skewing, 33.29% reduction in DC winding induced voltage pulsation is achieved and the torque is maintained at 98.37% for the five phase 10S-11P WFFS machine.
Efficient Online Shopping Platforms in Southeast Asia

With the emergence of current developments and their devices that allow correspondence between organizations with each other, just as it is between organizations and service providers, just as with their customers, for some reasons, including advertising and a return to what is acceptable to them with the organization. Items, deals, etc., therefore, this examination provides an internal and external investigation of the current web-based business phase with a focus on promising conditions, risks, qualities, and shortcomings of FashionValet's electronic stage, whose administrations offer more than the Internet. Also, FashionValet is a stage organizer for online shopping and style in Southeast Asia other than Poplook, Zalora, and others, and the bulk of them are important competitors to FashionValet and they implement their own systems. The FashionValet experience, for example, is the history of the creator just as the other company's foundation has been pointed out to characterize how organizations, especially FashionValet, have moved from their primary stage to where they are today as the most aggressive design project. In addition, the technologies and innovations used by FashionValet are shown to provide a comprehensive detailed blueprint. To understand the improvement in style organizations in Malaysia, it was examined further. The paper outlines the critical elements to FashionValet's thriving as an end.
When Climate Meets Machine Learning: Edge to Cloud ML Energy Efficiency

A large portion of current cloud and edge workloads feature Machine Learning (ML) tasks, thereby requiring a deep understanding of their energy efficiency. While the holy grail for judging the quality of a ML model has largely been testing accuracy, and only recently its resource usage, neither of these metrics translate directly to energy efficiency, runtime, or mobile device battery lifetime. This work uncovers the need for building accurate, platform-specific power and latency models for ML and efficient hardware-aware ML design methodologies, thus allowing machine learners and hardware designers to identify not just the best accuracy ML model configuration, but also those that satisfy given hardware constraints.
A Trusted Approach for Decentralised and Privacy-Preserving Identity Management

Identity Management (IdM) systems have traditionally relied on a centralized model prone to privacy, trust, and security problems, like potential massive data breaches or identity spoofing. Identity providers accumulate excessive power that might allow them to become a big brother, analyzing and storing as much data as possible. Users should be able to trust identity providers and manage their personal information straightforwardly without compromising their privacy. The European OLYMPUS project introduces a distributed approach for IdM based on enhanced Attribute-Based Credentials (ABC) that splits the role of Identity Provider to limit their influence and chances to become a unique point of failure. However, the trust relationship between service providers, users, and identity providers is still a gap in those kinds of privacy-preserving ABC systems. Decentralized technologies are an opportunity to break away from the centralized model and propose systems that respect privacy while increasing users’ trust. This paper presents an evolution of the OLYMPUS architecture, maintaining all the privacy features and incorporating distributed ledger technologies to enhance trust and security in online transactions and IdM systems. The proposed system has been implemented, tested, and validated, showing its performance and feasibility to manage user’s identity in a fully privacy-preserving, distributed and reliable way.
Tradeoffs in Time-to-Digital Converter Architectures for Harsh Radiation Environments

Performance degradation of standard time-to-digital converters (TDCs) is inevitable due to the effects of ionizing radiation. In this article, the tradeoffs of different mitigation techniques for TDC architectures are presented. As the effect of radiation is challenging for such high-performance circuits in the state-of-the-art technologies, architectures that can compensate for radiation effects have become a necessity for TDCs to operate in these harsh radiation environments. By looking into the functionality of different architectures focusing on improving radiation tolerance, the main weak and strong points of different TDC architectures, including the compensation circuitry, are described. Possible solutions to those weak points are presented.
Spatial Selected Spin Filtering Effect in Z-Shaped MoS2 Nanoribbon

Due to the gapless between any two successive subbands with the same spin, the spin polarization in uniform MoS2 zigzag nanoribbon (MoS2-ZR) is weak. Here, through configuring Z-shaped structure, we realize 100% spin polarization in MoS2-ZR with only exchange field. The filtered spin electrons transmit along a specified edge of the device. This phenomenon is the result of the combined effect of the localized edge states and structure induced inversion of the Mo-edge and S-edge. In our calculation the fully spin polarization appears in several energy regions, so this Z-shaped MoS2-ZRs can be used in making spintronic devices.
Compliant Floating-Base Control of Space Robots

This letter presents a compliant feedback controller of an arm-equipped spacecraft, which does not enforce requirements on the spacecraft position and attitude. The controller is applicable to the pre-contact, contact, and post-contact phases of a robotic operation. In contrast to conventional floating-base strategies, the controller eliminates the instability of the system during a steady contact, and thus can be used in realistic applications. The controller uses an external-internal transposed-Jacobian control for compliant regulation of the end-effector, together with regulation of the whole-body Center-of-Mass (CoM) and angular momentum for achieving post-contact stability, and force feedback for achieving a stable contact phase. The method is validated experimentally using a hardware-in-the-loop simulator composed of a seven degrees-of-freedom (DOF) arm mounted on a 6 DOF simulated spacecraft. Numerical simulations further validate the method considering a realistic thrusters system, measurement noise, and time delay.
Two Parallel Sorting Algorithms for Massive Data

As a basic operation, sorting has a wide range of applications. Early research provides a variety of serial sorting algorithms to speed up sorting. However, these serial algorithms waste the parallel computing capacity of current computers. On the other hand, it is of vital importance to promote the speed of sorting large-scale data because of the boom in data science, while traditional sorting algorithms are no longer suitable due to its relatively long complexity. Consequently, we propose two parallel sorting algorithms, Parallel Quick Sort Algorithm (PQSA) and Merging Subarrays from Quick Sort Algorithm (MSQSA) to solve this problem. In the meantime, we carry out simulation experiments to evaluate their performance. The experiment results show that two sorting algorithms run faster than traditional serial sorting algorithms. Furthermore, with the number of total threads increasing, PQSA and MSQSA have shorter running time and higher parallel speedup. Most importantly, when data size grows, MSQSA becomes slightly faster than PQSA, which shows its stronger practicability in sorting massive data.
Oops! It's Too Late. Your Autonomous Driving System Needs a Faster Middleware

Autonomous Driving (AD) has entered a period of rapid development in recent years. With the amount of sensors and control logics installed increasing tremendously to guarantee robustness, a big challenge is posed for AD middleware. Both the academia and the industry are eager for an investigation of the performance of middlewares in Autonomous Driving Vehicles (AVs). To fill this gap, we summarize typical communication scenarios of AVs and evaluate different communication mechanisms of three popular open-source middlewares comprehensively. Besides, we construct a benchmark pack named ComP which consists of a perception communication scenario and a group of real AD applications for researchers to assess middleware performance. Our findings provide useful guidelines for researchers and insightful optimization advice for designing middlewares.
A Switchable Frequency Selective Rasorber With Wide Passband

Frequency selective rasorbers with wide passband have been investigated for the stealth of wideband antennas over the past few years. However, the wide passband degrades its in-band stealth performance. Therefore, in this work, a switchable wide-passband rasorber, whose passband can be switched into absorption band, is proposed utilizing a frequency-selective surface (FSS) and two resistive layers. First, a switchable second-order FSS is presented and the design of switchable resistive layer with wide passband is analyzed. Second, the reactance variation of the low-$Q$ resonator in switchable resistive layer during the switching process is investigated to elaborate its effects on the out-of-band absorption performance of rasorber. Third, a nonswitchable resistive layer is designed to improve out-of-band absorption. Simulated results indicate that in rasorbing state the proposed single-polarized device exhibits a passband from 3.5 to 4.8 GHz (31%) with insertion loss less than $-$1 dB and a low-reflection band from 0.93 to 7.16 GHz (154%). In absorbing state, the passband is eliminated and the device exhibits a wide low-reflection band from 0.96 to 6.85 GHz (150.8%).
Research on Visual Tracking Algorithm Based on Peak Sidelobe Ratio

Aiming at the problems of illumination changes, target deformation and background clutter in the target tracking field, a visual tracking algorithm based on peak sidelobe ratio is proposed. The object-interference model is used to represent the target appearance model, and context information is added to the relevant filtering framework. Training the filter internally to enhance the ability of filter discrimination. In the model update process, it is easier to introduce samples that cannot characterize the target, and use peak sidelobe comparison to update the tracking parameters, which can enhance the generalization ability of the model. Tested with some classic and recently algorithms in the OTB50, OTB100, UAV123, TC128 experimental video data set, the experiment’ results show that the visual tracking algorithm that is proposed in the article can track the target more accurately. It has important research on the development of intelligent video surveillance value.
Design and crumpling analysis of a wearable antenna for WiMAX applications

In this work, a wearable antenna with 5.5 GHz resonant frequency is designed and its crumpled version is analyzed and presented. The dimensions of the proposed antenna are optimized using Finite Difference Time Domain technique. The FDTD scheme has been further used to perform the crumpling analysis of the designed antenna. The usage of FDTD implementation provided the improved understanding of the antenna assembly and its functioning during crumpling. The proposed wearable antenna has shown an impedance bandwidth of 180 MHz. The reflection coefficient of the antenna under crumpling is compared with the uncrumpled designed antenna and results are presented.
An Allreduce Algorithm and Network Co-design for Large-Scale Training of Distributed Deep Learning

Distributed training of Deep Neural Networks (DNNs) on High-Performance Computing (HPC) systems is becoming increasingly common. HPC systems dedicated entirely or mainly to Deep Learning (DL) workloads are becoming a reality. The collective communication overhead for calculating the average of weight gradients, e.g., an Allreduce operations, is one of the main factors limiting the scaling of data parallelism training. Several active efforts across different layers of the training stack including the training algorithms, parallelism strategy, communication algorithms, and system design have been proposed to cope with this communication challenge when scaling distributed training of DNNs. However, even with those methods, communication still becomes a bottleneck with the steady increase in model sizes, e.g.,100-10,000s MB, and the number of compute nodes, e.g., 1,000-10,000s of GPUs.In this work, we investigate the benefits of co-design of Allreduce algorithms and the network system. We propose to replace the Fat-tree network topology with a variant of Distributed Loop Network topology that guarantees a fixed routing paths length between any pairs of computing nodes for the communication pattern of halving-doubling Allreduce algorithm. We also propose a technique to eliminate/mitigate the network contention.
Cyano terminated tolane compounds for polymer dispersed liquid crystal application: relationship between cyano terminated tolane based molecular structures and electro-optical properties

ABSTRACT The structures of the liquid crystal (LC) molecules have a key role in impacting the electro-optical performance of a polymer dispersed liquid crystal (PDLC) film. In this paper, the relationship between the LC molecular structures and the electro-optical properties of PDLC films is investigated based on an unexplored cyano-terminated tolane compounds (CTTCs) doped E8 LCs/UV polymers system. Due to the high polarity of CTTCs, LCs doped with the cyano-terminated tolane (CTT) molecules exhibit high birefringence and large positive dielectric anisotropy. On the whole, PDLC films doped with the CTT molecules exhibit a lower driving voltage than that doped with the pure E8. More excitingly, PDLC films based on CTT molecules with larger length-to-width ratio and longer conjugated system show higher contrast ratio (CR) and faster response time. Eventually, the mechanism of the effects of CTT-based molecular structures and the relationship between the electro-optical performance of PDLC films and CTT molecules are illustrated. This work paves a new way for optimising the electro-optical properties of PDLC films. Graphical Abstract
Speech Activity Detection under Adverse Noisy Conditions at Low SNRs

Speech originating from the noisy environments degrades the speech quality and intelligibility, thus reducing the human perceived Quality of Experience (QoE). For example, surveillance using drone during natural catastrophe needs an efficient speech recognition device to recognise the speech of the frozen human in presence of drone noise to save their life. Therefore, it often requires to pre-process the noisy speech in order to reduce the noise artifacts and enhance the speech. This paper detects the speech activity using Voice Activity Detection (VAD). The VAD distinguishes speech activity (speech presence) and speech inactivity (silence/noise) by extracting the speech features and comparing to a threshold. The energy and spectral centroid features are deployed to design VADs. Noisy dataset consisting of urban noise, for example, drone, helicopter, airplane and station noise, is created at different signal-to-noise ratios (SNRs). F-score and Euclidean distance are used to measure the performance of VADs. Results demonstrate that the spectral centroid VAD performs outstanding with various noise degradations tested.