Miracle-dz/UniFAR-FTUs huggingface dataset:
Task Placement on Fog Computing Made Efficient for IoT Application Provision

Fog computing is one of the promising technologies for realizing global-scale Internet of Things (IoT) applications as it allows moving compute and storage resources closer to IoT devices, where data is generated, in order to solve the limitations in cloud-based technologies such as communication delay, network load, energy consumption, and operational cost. However, this technology is still in its infancy stage containing essential research challenges. For instance, what is a suitable fog computing scheme where effective service provision models can be deployed is still an open question. This paper proposes a novel multitier fog computing architecture that supports IoT service provisioning. Concretely, a solid service placement mechanism that optimizes service decentralization on fog landscape leveraging context-aware information such as location, response time, and resource consumption of services has been devised. The proposed approach optimally utilizes virtual resources available on the network edges to improve the performance of IoT services in terms of response time, energy, and cost reduction. The experimental results from both simulated data and use cases from service deployments in real-world applications, namely, the intelligent transportation system (ITS) in Ho Chi Minh City, show the effectiveness of the proposed solution in terms of maximizing fog device utilization while reducing latency, energy consumption, network load, and operational cost. The results confirm the robustness of the proposed scheme revealing its capability to maximize the IoT potential.
Multi-Target Position and Velocity Estimation Using OFDM Communication Signals

In this paper, we consider a passive radar system that estimates the positions and velocities of multiple moving targets by using OFDM signals transmitted by a totally un-coordinated and un-synchronizated illuminator and multiple receivers. It is assumed that data demodulation is performed separately based on the direct-path signal, and the error-prone estimated data symbols are made available to the passive radar receivers, which estimate the positions and velocities of the targets in two stages. First, we formulate a problem of joint estimation of the delay-Doppler of reflectors and the demodulation errors, by exploiting two types of sparsities of the system, namely, the numbers of reflectors (i.e., targets and clutters) and demodulation errors are both small. This problem is non-convex and a conjugate gradient descent method is proposed to solve it. Then in the second stage we determine the positions and velocities of targets based on the estimated delay-Doppler in the first stage. For the second stage, two methods are proposed: the first is based on numerically solving a set of nonlinear equations, while the second is based on the neural network, which is more efficient. The performance of the proposed algorithms is evaluated through extensive simulations.
Facile synthesis of model 1,4‐dihydro‐1H‐1,3,4‐benzotriazepin‐5‐ones

2-Aminobenzoic acid reacts readily, in the presence of triethylamine, with hydrazonoyl chlorides (5a-c) (precursors of the reactive nitrile imine 1,3-dipolar species) to afford high yields of the corresponding acyclic amidrazone adducts (6a-c). The latter adducts undergo, in THF in presence of 1,1-carbonyldiimida-zole, smooth intramolecular cyclization involving the activated carboxyl and the NH- termini to deliver unequivocally the respective dihydro-1,3,4-benzotriazepin-5-ones (7a-c).
Descending Kernel Track of Static and Dynamic Topic Models in Topic Tracking

Topic tracking is a task in research on identifying,mining and self-organizing relevant information to news topics.Its key issue is to establish statistical models that adapt the kind of news topic.This includes two aspects: one is topical structure;the other is topic evolution.This paper focuses on comparing and analyzing the features of three main kinds of topic models including words bag,hierarchical tree and chain.Different performances of static and dynamic topic models are deeply discussed,and a term overlapping rate based evaluation method,namely descending kernel track,is proposed to evaluate the abilities of static and dynamic topic models on tracking the trend of topic development.On this basis,this paper respectively proposes two methods of burst based incremental learning and temporal event chain to improve the performance of capturing topic kernels of dynamic topic models.Experiments adopt the international-standard corpus TDT4 and minimum detection error tradeoff evaluation method proposed by NIST(National Institute of Standards and Technology),along with descending kernel track method to evaluate the main topic models.The results show that structural dynamic models have the best tracking performance,and the burst based incremental learning algorithm and temporal event chain achieve 0.4% and 3.3% improvement respectively.
Multi-passive-sensor fusion tracking based on unscented Kalman filter

According to the nonlinear problem for the passive-sensor system,unscented transformation is introduced into Kalman filter based on passive target tracking algorithm.Then,multi-passive-sensor fusion scheme is adopted to deal with the difficulty of observability problem for single passive sensor system.Thus,multi-passive-sensor fusion tracking algorithm based on unscented Kalman filter(UKF) is proposed.Simulation results show that the proposed algorithm perform better than extended Kalman filter(EKF) in target tracking for a three-passive-sensor system.
Biotechnology for Beginners

Biotechnology for Beginners is a gloriously unorthodox textbook. Orthodox textbooks concentrate on “the facts” without giving much thought about making the facts interesting. Students should just learn them. Biotechnology for Beginners takes a different approach. By making the material exciting and engaging, the students will want to understand the “facts. ” Graphics are the cornerstone to this approach. The graphics (mostly color) are luxurious and used liberally (typically 3–5 per page). Line drawings, three dimensional figures, and illustrations that reach the level of art draw students into the material and aid in its comprehension. The illustrations of “bacteria and fungi” and the “tree of life” (among others) could be framed and displayed in any office or laboratory. Biochemical pathways are illustrated by line drawings and “stick-and-ball” representations; corresponding enzymes appear as three dimensional structures. Pictures of the scientists behind the concepts and their stories humanize the subject for students and generate enthusiasm for the scientific process. Visual learners will be ecstatic. The chapters (see below) are supplemented by boxed material and end with eight questions that help students hone their critical thinking skills.
The Fermentation Time Model and the Optimization Fermentation Temperature Model Based on Neural Networks

Using BP neural networks and the Fourier neural networks which was proposed in this paper,the fermentation time model and the optimization fermentation temperature model is proposed.On the base of two models,this paper proposed a new method by which different fermentation batch should adopt different optimization fermentation temperature.The practice showed that this method can improve average product proportion,5%.
Improved algorithm on image haze removal using dark channel prior

Images captured in fog suffer from poor contrast and visibility.It is important to remove weather effects from the degraded image in order to make vision systems more reliable and more robust.An improved method is proposed to overcome the weakness of original algorithm.By including tolerance mechanism and adaptive efforts to fog,the improved algorithm can effectively deal with the sky,white object,and so bright area that do not meet the assumptions of dark channel,to overcome color distortion which is arise by using the original algorithm dealing with these areas.Experimental results show that such changes is feasible,to eliminate color distortion of the image,visibility can be significantly enhanced.
MEASUREMENT OF ANTI-STRIPPING AGENT CONTENT IN ASPHALT MIXTURE WITH COLORIMETRIC TEST

Stripping can cause premature damage to asphalt pavement. The content of anti-stripping agents in the asphalt mixture needs to control, but the detection and quantification of the content of amine-based antistripping agent in asphalt mixture remain a practical problem, therefore research was conducted on how to determine the content of anti-stripping agent contained in the asphalt mixture. Marshall Tests used in this study to evaluate the use of an anti-stripping agent in asphalt mixture through Retained Stability, and Colorimetric Test was held to measure the content of anti-stripping agent contained in asphalt mixture. The Colorimetric Test was developed as a three-step process consisting of amine extraction and trapping through a steam distillation process, sample preparation process and absorbance measurement used spectrophotometer UV/Vis. The anti-stripping agent used in this study was Wetfix-Be with the addition content of 0,2%, 0,3%, 0,4%, and 0,5% of the asphalt weight. The result of the Marshall Tests indicated that the presence of an anti-stripping agent in an asphalt mixture characterized by an increase of the value of the Retained Stability. The addition of 0,4% Wetfix-Be gave the highest increase in Retained Stability, which is equal to 9,25%. The Colorimetric Test result indicated that this test could be used to measure the Wetfix-Be anti-stripping content contained in the asphalt mixture, which in addition to Wetfix-Be anti-stripping 0,4% obtained measured anti-stripping content of 0,365%. This study is critical because the accuracy of the content of anti-stripping agent added to the asphalt mixture will reduce the problem of stripping.
Research and Implementation of the 3D Radar Coverage Visualization Accelerated by Hardware

The visualization of electromagnetic information,especially the visualization of radar coverage,is an important issue in virtual battlefield environment.But now most of the research is just for 2D and the research in 3D is limited in speed.So a new hardware-accelerated 3D visualization method is put forth in this paper.Based on the 3D data field of radar Electromagnetic(EM) loss,a hardware-accelerated algorithm is designed to extract the isosurface for an isovalue,which corresponds to the radar coverage for a given target of one RCS.Then this isosurface is represented into the digital battlefield.In the experiment on popular PCs specific to typical radars,the 3D radar coverage is vividly visualized in time,which overcomes the flaws of the former non-intuitive 2D and slow 3D methods,and provides an excellent decision-making support for users.
Circulating fluidized bed boiler control system

The commonly used control method can not effectively control the circulating fluidized bed boiler which needs a coordinated control system with distribution parameters, non-linearity, time- dependence, and long time lags. The dynamic properties of the circulating fluidized bed boiler were analyzed to develop a new control system based on fuzzy control theory and generalized forecast control theory. A fuzzy controller is used in the control loop. The boiler bed temperature is coordinated with the main steam pressure using the forecast guidance system which can decouple coordination. This new control system which can effectively control circulating fluidized bed boiler has been applied in practice.
Voltage-clock scaling and scheduling for energy-constrained real-time systems

Nowadays, exponentially increasing demands for portable devices and more sophisticated and intelligent functionalities in embedded applications make processors much power-hungrier. Due to a limited energy budget, low-power computing at a relatively low level of power consumption or power-aware computing with the knowledge of power source plays important role in extending systems' lifetime. In the dissertation, we study how to reduce power consumption using a low power voltage-clock scaling scheme in real-time systems that require strict time and energy constraints. The property of power-delay tradeoff has significant impact on the schedulability as well as on the performance of the systems. First, we focus on dynamic reclaiming of early released resources in earliest deadline first (EDF) scheduling using voltage-clock scaling. In addition to a static voltage assignment, we propose a new dynamic-mode assignment, which has a flexible voltage mode setting at run-time, enabling much larger energy savings. Using simulation results and exploiting the interplay among power supply voltage, frequency, and circuit delay in CMOS technology, we find the optimal two-level voltage settings that minimize energy consumption. Next, we study battery-driven real-time systems that jointly schedule hard periodic tasks and aperiodic (sporadic) tasks, whose power sources are bounded in a feasible range decided by a set of tasks. Therefore, reducing power consumption is not the only objective of task scheduling in this case. To make the most of the available energy budget, an effective energy-sharing scheme is proposed using two-mode voltage-clock scaling-EDF scheduling. Based on the proposed energy allocation model for energy-constrained real-time systems, a dual-policy dynamic scheduling method is proposed not only for faster responsiveness but also for reducing power consumption. The feature of the approach is an intermixing of two schedules; the one consumes minimum energy and the other the worst-case energy, respectively, leading to much longer lifetime under a bounded energy budget. Lastly, we build a scaling mechanism that optimizes energy saving and responsiveness to the aim of the system's performance. Fully utilizing the property in sharing constrained-energy and processor's capacity in total bandwidth server, we adjust total power consumption and average response time of aperiodic tasks by simply controlling the utilization of periodic tasks.
Extensions to Path Computation Element Communication Protocol (PCEP) for Hierarchical Path Computation Elements (PCE)

The Hierarchical Path Computation Element (H-PCE) architecture is defined in RFC 6805. It provides a mechanism to derive an optimum end- to-end path in a multi-domain environment by using a hierarchical relationship between domains to select the optimum sequence of domains and optimum paths across those domains. This document defines extensions to the Path Computation Element Protocol (PCEP) to support Hierarchical PCE procedures.
The lexical knowledge and semantic representation of E-HowNet

E-HowNet,extended from HowNet,is a frame-based entity-relation model aimed for the goal of natural language understanding. We adopt the taxonomy structure of sememes of HowNet and modify its lexical semantic representation to suit for automatic semantic composition and decomposition. In E-HowNet, all lexical senses, including function words and content words,are expressed by either semantic primitives ( called sememes in HowNet) or basic concepts and their semantic relations in a uniform way. Therefore senses of phrases or sentences can be derived by unifying the lexical sense representations of their words. The consistency of E-HowNet is examined by checking the taxonomy of synonym classes which is automatically constructed according to E-HowNet lexical expressions. Difficulties in lexical semantic representation are also encountered and solutions to these problems are offered. E-HowNet systematizes the complex nature of language semantics and hopefully may shed light on the goal of natural language understanding.
Identification of vegetable leaf-eating pests based on image analysis

To achieve the computer recognition of vegetable leaf-eating pests and the scientific evaluation of pest level's,the paper proposed a reverse identification method through the eaten leaves to determine the pests.After image pre-processing of the eaten leaves by pests,seven spherical shape feature values of eaten leaves could be automatically extracted: roundness degree,complexity degree,and spherical degree,etc.,and then the BP neural network model for recognition could be built up.The results show that the method is good at recognizing the pest species by the geometric feature of eaten leaves by pests,and give a scientific evaluation for the leaf's harm degree.
Design of a Multi-Route Optimal Simulation System for Urban Traffic Controls

In order to optimize urban traffic control to ease urban traffic,a multi-line coordination control optimization and simulation model of urban traffic is proposed.And the algorithm of space-time-phase evolution and the improved genetic algorithm(GA) based on the model are given also.After summing up the system architecture,data structure and technical course,a multi-line coordination control optimization and simulation system of urban traffic were completed.The simulation system and its modeling are simple,and it can be applied to any optimization of urban traffic control,in order to solve the problem of multi-line coordination of urban traffic control(multi-line green wave). By the actual modeling and simulation on a road network of Foshan city,the system achieves good results on urban traffic control optimization.
Regioselective Enolization and Alkylation of 4-Oxo-N-(9-phenylfluoren-9-yl)proline: Synthesis of Enantiopure Proline−Valine and Hydroxyproline−Valine Chimeras

The regioselective enolization of 4-oxo-N-(9-phenylfluoren-9-yl)proline benzyl ester (5) followed by alkylation with different alkyl halides has been used to synthesize a variety of β-alkylproline derivatives. In particular, enolization of 5 with 400 mol % of KN(SiMe3)2 and alkylation with iodomethane provided 3,3-dimethyl-4-oxo-N-(9-phenylfluoren-9-yl)proline benzyl ester (7a) in excellent yield. Subsequent hydride reduction of ketone 7a and protecting group exchange by hydrogenation in the presence of di-tert-butyl dicarbonate provided enantiopure (2S,4R)- and (2S,4S)-3,3-dimethyl-4-hydroxy-N-(BOC)prolines 2. Hydroxyproline−valine chimeras (2S,4R)- and (2S,4S)-2 are each synthesized from hydroxyproline in six steps and 27% respective overall yield. Deoxygenation of 3,3-dimethyl-4-hydroxy-N-(9-phenylfluoren-9-yl)proline benzyl esters 9 via their conversion to xanthates 10 followed by tributylstannane-mediated reduction provided 3,3-dimethyl-N-(9-phenylfluoren-9-yl)proline benzyl ester (11) in excellent y.
Unsupervised shape and motion analysis of 3822 cardiac 4D MRIs of UK Biobank

We perform unsupervised analysis of image-derived shape and motion features extracted from 3822 cardiac 4D MRIs of the UK Biobank. First, with a feature extraction method previously published based on deep learning models, we extract from each case 9 feature values characterizing both the cardiac shape and motion. Second, a feature selection is performed to remove highly correlated feature pairs. Third, clustering is carried out using a Gaussian mixture model on the selected features. After analysis, we identify two small clusters which probably correspond to two pathological categories. Further confirmation using a trained classification model and dimensionality reduction tools is carried out to support this discovery. Moreover, we examine the differences between the other large clusters and compare our measures with the ground-truth.
Analysis and design of authenticated ciphers

An authenticated cipher is a symmetric key cryptographic primitive which protects the confidentiality, integrity and authenticity of the data. It is an integration of the existing symmetric key primitives such as block ciphers, stream ciphers and hash functions, and attracts a lot of research interests in recent years, especially after the announcement of the CAESAR competition. In this thesis, we study the analysis and designs of the authenticated ciphers. We begin with an introduction to symmetric key cryptography and authenticated ciphers followed by discussing on the typical methods used in the cryptanalysis and design of authenticated ciphers. Then, several concrete case studies in analyzing the authenticated ciphers are presented. We apply differential-linear cryptanalysis to recover the internal state of ICEPOLE. Differential IV cryptanalysis is used to attack the initialization of the 128-EEA3/128-EIA3 stream cipher ZUC. By exploiting the leaked state from the keystreams, we present a forgery attack on ALE. By exploiting the parameter settings, we present distinguishing and forgery attacks against the authenticated encryption scheme COFFE. We provide a collision attack to break the authentication claim for the authenticated encryption mode IOC. For the design of authenticated ciphers, we propose two schemes, JAMBU and MORUS fulfilling various features. JAMBU is a lightweight authenticated encryption mode which provides an intermediate level of nonce misuse resistance. MORUS is a nonce-based authenticated cipher which is targeted for high performance in both software and hardware.
Accelerating convergence of backpropagation for multilayer perceptron neural networks: a case study on character bit-mapped pixel image to ASCII conversion

Abstract In this paper, we introduced a modified version of backpropagation algorithm which is able to converge faster than the traditional backpropagation for multilayer perceptron neural networks. We also proposed a dynamic technique for specifying the number of hidden layers and number of neuron in each hidden layer along with an initialization technique to set start values of the synaptic weights. A novel two activation functions have been proposed, which are proved experimentally that they accelerate convergence of neural networks while maintaining the accuracy of the trained network. Experimental results showed that the average number of epochs required to reach approximation using exponential hyperbolic tangent is less than the half of average number using traditional sigmoid function. Finally, a case study is performed on converting alphanumeric character bit-mapped pixel image to its ASCII equivalent code.
A Simple Sparse Channel Estimation Method for Single-Carrier Frequency Domain Equalization Based on Noise Space

It is widely accepted that SC-FDE (Single-Carrier Frequency Domain Equalization) is an excellent candidate for broadband wireless systems. Channel estimation is one of the key challenges in SC-FDE, since accurate channel estimation can significantly improve the equalization at the receiver and consequently enhance the communication performances. In this paper, we proposed a simple sparse channel estimation method for SC-FDE system based on noise space, through sorting the results of the Least-squares (LS) channel estimation, channel tap locations and value are evaluated. The proposed system can realize channel estimation at a very low complexity, and simulation result shows that it can achieve significantly improved performance in frequency selective fading sparse channel.
Securing BGP Networks using Consistent Check Algorithm

Abstract— The Border Gateway Protocol (BGP) is the critical routing protocol in the Internet infrastructure. However, there is no security concern in the original design of BGP, which suffers from various kinds of threats for attacks. To secure the BGP operation, this paper proposes an algorithm called consistent check. The algorithm is to verify the correctness of AS path in an incoming BGP update message by consulting the knowledge of other autonomous systems in the network. Unlike existing solution, this proposed algorithm does not require the need of cryptography calculation. I. I NTRODUCTION A. BGP basic The Border Gateway Protocol (BGP) [1][2] is a path vector routing protocol. The basic idea is that each router exchanges network reachability information with its directly connected neighbors. BGP treats each autonomous system as a single point on the path to any given destination. In BGP, a set of address (or an IP clock) (e.g., 1.2.3.0/24) that is being routed is called a