Miracle-dz/UniFAR-FTUs huggingface dataset:
Phenolic Content and Antioxidant Activities of Commercial Pomegranate and Date Concentrates

The aim of this study was to determine the polyphenol content and antioxidant activities in commercialized date and pomegranate concentrates that are available in local market. A total of seven samples comprised of two brands of date concentrates (D1 and D2) and five brands of pomegranate concentrates (P1, P2, P3, P4, and P5) have been analyzed for their polyphenol content and antioxidant activity. The Folin-Ciocalteu assay was used to determine the polyphenol content (TPC) of the samples while 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH) and Ferric Reducing Antioxidant Power (FRAP) assays were used to determine the antioxidant activity. The results showed the commercialized date concentrate D2 contained the highest TPC ( 1243.00 ± 67.00 mg GAE/100 ml) and antioxidant activity using FRAP assay (2.67 ± 0.01 mmol TE/100 ml). Date concentrate D1 showed the highest antioxidant activity using DPPH assay ( 0.39 ± 0.02 mmol TE/100 ml). The pomegranate concentrate P5 showed the highest reading for TPC ( 3260.10 ± 428.70 mg GAE/100 ml), DPPH (1.61 ± 0.05 mmol TE/100 ml) and FRAP assays (20.98 ± 0.42 mmol TE/100 ml) . As a conclusion, the studied commercial date and pomegranate concentrates available in the local market contained high polyphenol and antioxidant contents although the content levels were different from one brand to another. Further study is needed to identify the causes of this distinction. The results of this study could help the consumers to make a better selection of commercialized date and pomegranate concentrates that are available in local market in term of polyphenol content and antioxidant activity. DOI : http://dx.doi. org. /10.17576/JSKM-2019-1702-15
ICN "Begin-End" Hop by Hop Fragmentation

This document describes a simple hop-by-hop fragmentation scheme for ICN and mappings to the CCNx 1.0 and NDN packet formats, called "begin-end fragmentation". This scheme may be used at Layer 3 when ICN packets are used natively over a Layer 2 media which does not reorder packets.
Sensorless force estimation of end-effect upper limb rehabilitation robot system with friction compensation

In human robot interaction systems, human intent detection plays an important role to improve the interactive performances and then the rehabilitation effects. A study is proposed to estimate the interactive forces that indirectly detect the human motion intent. A disturbance observer is designed to estimate interactive torques and friction forces without force sensors, and then a friction force model is constructed to estimate the friction force in the robot system. To detect the human–robot interaction force, we subtract the friction force from disturbance observer estimation result. Several experiments were performed to test the performances of the proposed methods. Those methods were applied in an end-effect upper limb rehabilitation robot system. The results show that the precision of the estimated sensor force can increase 5% than the force sensor. The senseless force estimation method we proposed in this article can be an alternative option in force control tasks when force sensors are not suitable.
Ubiquitäre Systeme (Seminar) WS 2012/13. Mobile und Verteilte Systeme. Ubiquitous Computing. Teil VIII

The update process of cyber-physical systems (CPS) and wireless sensor networks (WSN) is accompanied by various challenges. This article provides a list of challenges to be solved and motivates them with two exemplary CPS use cases: the smart grid and automotive electronics with vehicle to vehicle communication. Afterwards the software update mechanisms RemoWare, Deluge and Dynamic TinyOS, HERMES and QARI are compared, each presenting possible solutions for various challenges.
Inventory Control under Possible Delivery Perturbations in Physical Internet Supply Chain Network

Nowadays, the urgent need for sustainable development is imposing radical changes in how to design, produce and distribute the right product, in the right place, at the right time and at the right price. A new paradigm named "Physical internet" has been presented as worldwide open interconnected logistics system to make the current logistical systems more flexible and sustainable. This paper addresses the inventory management in the classical supply chain and the Physical Internet supply chain networks while considering possible perturbations during the delivery of goods between the hubs. We consider a supply chain network configuration from the literature. Two simulation models are proposed to test both the classical supply chain network and the Physical Internet supply chain under perturbations. Three scenarios are considered by varying delivery delays, retailers' demands and the number of trucks for the two simulation models. Several key performance indicators (KPIs) are considered, such as resources usage, transportation cost, storage cost and delivery delays.
Speeding up Deep Neural Networks on the Jetson TX1

In recent years, Deep Learning (DL) showed new top performances in almost all computer vision tasks that are important for automotive and robotic applications. In these applications both space and power are limited resources. Therefore, there is a need to apply DL approaches on a small and power ecient device, like the NVIDIA Jetson TX1 with a powerful GPU onboard. In this paper, we analyze the Jetson's suitability by benchmarking the run-time of DL operations in comparison to a high performance GPU. Exemplary, we port a topperforming DL-based person detector to this platform. We explain the steps necessary to signicantly speed up this approach on the device.
Performance Analysis of Two-Way Relaying in Cooperative Power Line Communications

This paper studies the performance of a half-duplex (HD) two-way relay (TWR) in power line communication (PLC) over a log-normal fading channel with impulsive noise. We consider the two common relaying protocols: amplify-and-forward (AF) and decode-and-forward (DF). For the DF relaying, we apply physical-layer network coding (PNC) and analog-network coding (ANC) to the PLC TWR. We derive analytic expressions for the average sum capacity and the outage probability of the system. The Monte Carlo simulations are provided throughout to validate our analysis. The analytical results show a tight approximation to the simulation results. We compare the one-way relay (OWR) to the TWR and show that the HD spectral efficiency loss incurred by the OWR can be sufficiently mitigated in PLC. However, the outage probability of the TWR is inferior to that of the OWR. To enhance the outage performance of the PLC TWR, we implement a hybrid PLC/wireless (HPW) system, where all nodes are equipped with the PLC and wireless capabilities. Data transmission occurs over the two parallel links. The diversity in the transmission allows the TWR to improve its outage performance in the AF and DF protocols. The impact of the impulsive noise, inherent to the PLC channels, is also highlighted in the simulation results. It is shown that the impulsive noise severely impairs system performance.
Microbial Biodegradation of Lignocellulose for Ethanol Production

Bioethanol can be produced from a range of substrates and it is considered to be renewable. Lignocellulose is one of the substrates that can be used for bioethanol production. Genetic engineering of a microorganism that can completely convert all the sugars of cellulosic material into ethanol is one of the important steps for the production process. This study has two parts that aim to produce bioethanol by using genetically modified microorganisms. The first part of the study deals with bioethanol production from lignocellulosic material by microbial fermentation in two steps. The first step is the degradation of the lignocellulosic material by Bacilli to produce cellobiose, and the second step is to convert cellobiose into glucose by a B-glucosidase expressing recombinant yeast strain. As lignocellulosic material, wood powder from industrial waste was used and the degradation of the wood powder into glucose was confirmed by following the growth curve of the microorganisms and through appropriate enzymatic assays. The second part of the study utilizes two novel technologies, Yeast Artificial Chromosome (YAC) and Bridge-Induced Translocation (BIT) to introduce new, multi-factorial genetic traits into a yeast strain, a process that would otherwise take several time-consuming and labor-intensive rounds of genetic engineering. After cloning exogenous cellulases onto a YAC vector, this was transformed into a PEP4 mutant yeast upon which BIT technology was applied. This technology allowed the research to gain two advantages; one of them was to stabilize the YAC within the yeast genome and the other one was to have increased gene expression level consequent to the translocation event. In conclusion, this work defines a successful microbial system that is able to efficiently utilize lignocellulosic material as a carbon source and a translocant yeast strain that has high level of cellulase activity.
Learning a Compact Vein Discrimination Model With GANerated Samples

Despite the great success achieved by convolutional neural networks (CNNs) in various image understanding tasks, it is still difficult for CNNs to be applied to vein recognition tasks due to the problems of insufficient training datasets, intra-class variations, and inter-class similarities. Besides, due to the essential requirement on the storage of millions of parameters for CNN, it is challenging to use a CNN for designing a vein-based embedded person identification system. In this paper, these two problems are addressed by learning a discriminative and compact vein recognition model. For the first problem, a hierarchical generative adversarial network (HGAN) consisting of a constrained CNN and a CycleGAN is proposed for data augmentation. Two similarity losses are defined for estimating the self-similarity and inter-class dissimilarity, and a CycleGAN model is properly trained with these two losses for better task-specific training sample generation. After obtaining a baseline vein recognition model fine-tuned on the augmented datasets, the existence of parameter redundancy in the over-parameterized network motivates the proposal of model compression by way of filter pruning and low rank approximation, thus making the compressed model more suitable for deployment on embedded systems. Through the vein recognition experiments with two different datasets and an additional palmprint recognition experiment, the proposed algorithms are shown to yield a highly compact model while keeping the accuracy acceptable for application.
Machine Learning for Connectomics

In this thesis, automated methods for the reconstruction of neural networks from 3D electron microscopy data are presented. A novel method for synapse detection is developed using a Machine learning approach. The method is further extended by using artificial neural networks for automated feature extraction and to improve the underlying cellular process segmentation. The developed methods are used to study all synapses onto a spiny stellate neuron in mouse primary somatosensory cortex.
Authentication Layer for IEC 61113-3 Applications

Mid 2010, the Stuxnet ICS attack targeted the Siemens automation products, and after this attack the ICS security was thrust into spotlight, automation products suppliers started to re-examine their business approach to cyber security. The OPC Foundation made also significant changes and improvements on its new design OPC-UA to increase security of automation applications but, what is still missing and seems to be not resolved any time soon is having security in depth for industrial automation applications. In this paper, we propose a simple but strong security control solution, what we will call a logic application level security particularly for SCADA and DCS. This proposed method is based on message integrity and should not be viewed as the main, nor the only level of protection that an industrial automation system is expected to have, but can be a low-level security procedure that avoids intelligent attacks such as Stuxnet.
On enhancing trust in cryptographic solutions: student research abstract

Public key cryptography and its applications can be found in almost every aspect of online communication nowadays. Connecting to the website of your bank or to your email server by establishing a TLS connection are just two popular examples. Integral to achieving a secure connection to these services is, among others, authenticating their identity, i.e., that a certain public key belongs to the person or service that claims it. That is, a trust network is necessary in order to enable secure communication in any kind of scalable and dynamic network. Such networks are expected to be more and more common as the Internet of Things paradigm gets widely adopted. Existing approaches, namely the Web PKI and the various Webs of Trust are inherently vulnerable to attacks and cannot solve the problem in the long run.
Vulnerability analysis of cyber-behavioral biometric authentication

Research on cyber-behavioral biometric authentication has traditionally as­ sumed naive (or zero-effort) impostors who make no attem pt to generate sophisticat­ ed forgeries of biometric samples. Given the plethora of adversarial technologies on the Internet, it is questionable as to whether the zero-effort threat model provides a realistic estimate of how these authentication systems would perform in the wake of adversity. To better evaluate the efficiency of these authentication systems, there is need for research on algorithmic attacks which simulate the state-of-the-art threats. To tackle this problem, we took the case of keystroke and touch-based authenti­ cation and developed a new family of algorithmic attacks which leverage the intrinsic instability and variability exhibited by users’ behavioral biometric patterns. For both fixed-text (or password-based) keystroke and continuous touch-based authentication, we: 1) Used a wide range of pattern analysis and statistical techniques to examine large repositories of biometrics data for weaknesses that could be exploited by ad­ versaries to break these systems, 2) Designed algorithmic attacks whose mechanisms hinge around the discovered weaknesses, and 3) Rigorously analyzed the impact of the attacks on the best verification algorithms in the respective research domains. When launched against three high performance password-based keystroke ver­ ification systems, our attacks increased the mean Equal Error Rates (EERs) of the systems by between 28.6% and 84.4% relative to the traditional zero-effort attack. For the touch-based authentication system, the attacks performed even better, as they increased the system’s mean EER by between 338.8% and 1535.6% depending on parameters such as the failure-to-enroll threshold and the type of touch gesture subjected to attack. For both keystroke and touch-based authentication, we found that there was a small proportion of users who saw considerably greater performance degradation than others as a result of the attack. There was also a sub-set of users who were completely immune to the attacks. Our work exposes a previously unexplored weakness of keystroke and touchbased authentication and opens the door to the design of behavioral biometric systems which are resistant to statistical attacks. APPROVAL FOR SCHOLARLY DISSEMINATION The author grants to the Prescott Memorial Library o f Louisiana Tech University the right to reproduce, by appropriate methods, upon request, any or all portions o f this Dissertation. It is understood that “proper request” consists o f the agreement, on the part o f the requesting party, that said reproduction is for his personal use and that subsequent reproduction will not occur without written approval o f the author o f this Dissertation. Further, any portions o f the Dissertation used in books, papers, and other works must be appropriately referenced to this Dissertation. Finally, the author o f this Dissertation reserves the right to publish freely, in the literature, at any time, any or all portions o f this Dissertation.
An Approach of 3-Dimendional Modeling for Aircraft Recognition

A algorithm for adaptive data sampling of freeform surface based on chord approximate error is proposed to optimize the uneven spacing of data points acquisition.Furthermore,a modeling method applying this algorithm to construct complex surface objects is addressed.First,range data of control sections are extracted with measuring method in revere engineering.Then non-uniform cubic B-Spline curve is applied to fit the discrete data.Finally,using skinning method,the mode is made and object contour surface is reconstructed.
JSON Data Management in RDBMS

Being a simple semi-structured data model, JSON has been widely accepted as a simple way to store, query, modify, and exchange data among applications. In comparison with schema-oriented relational data model to store, query, and update application data, JSON data model has the advantage of being self-contained, free from schema evolution issues, and flexible enough to enable agile style development paradigm. Therefore, during the last 5 years, SQL/JSON standard has been established as foundation for managing JSON data in SQL standard and there have been JSON functionalities added into RDBMS products to support SQL/JSON standard to various degree. In this chapter, the authors will analyze the strength and weakness of using JSON as the data model to manage data for applications. For use cases where JSON data model is ideal, they present the design approaches to store, index, query, and update JSON in the kernel of RDBMS to support SQL/JSON standard defined operations effectively and efficiently.
Seed and Seedling Detection Using Unmanned Aerial Vehicles and Automated Image Classification in the Monitoring of Ecological Recovery

Monitoring is a crucial component of ecological recovery projects, yet it can be challenging to achieve at scale and during the formative stages of plant establishment. The monitoring of seeds and seedlings, which represent extremely vulnerable stages in the plant life cycle, is particularly challenging due to their diminutive size and lack of distinctive morphological characteristics. Counting and classifying seedlings to species level can be time-consuming and extremely difficult, and there is a need for technological approaches offering restoration practitioners with fine-resolution, rapid and scalable plant-based monitoring solutions. Unmanned aerial vehicles (UAVs) offer a novel approach to seed and seedling monitoring, as the combination of high-resolution sensors and low flight altitudes allow for the detection and monitoring of small objects, even in challenging terrain and in remote areas. This study utilized low-altitude UAV imagery and an automated object-based image analysis software to detect and count target seeds and seedlings from a matrix of non-target grasses across a variety of substrates reflective of local restoration substrates. Automated classification of target seeds and target seedlings was achieved at accuracies exceeding 90% and 80%, respectively, although the classification accuracy decreased with increasing flight altitude (i.e., decreasing image resolution) and increasing background surface complexity (increasing percentage cover of non-target grasses and substrate surface texture). Results represent the first empirical evidence that small objects such as seeds and seedlings can be classified from complex ecological backgrounds using automated processes from UAV-imagery with high levels of accuracy. We suggest that this novel application of UAV use in ecological monitoring offers restoration practitioners an excellent tool for rapid, reliable and non-destructive early restoration trajectory assessment.
Design and Implementation of Off-Line Handwritten Document Recognition System of Manchu Manuscript

Based on an off-line handwritten Manchu manuscript recognition system, a corresponding system model is established. Firstly, the digital image processing method is used to pre-process and extract the words from the identified targets. Next, the processed words are decomposed into the stroke units . The statistics pattern recognition method is employed to identify them and obtain the stroke sequence. Then the stroke sequence is converted into the root sequence. The fuzzy identification method is used to achieve the output of Manchu-Roman characters. Hidden Markov Model method is also involved to post-process the recognition results of every single word and enhance the recognition rate. The experimental results show that the recognition rate and the self-adaptability of the system are increased substantially on the basis of the single font stroke learning and probability statistics of great corpus of two-word simultaneity.
Comparison of Evolutionary Development of Cellular Automata Using Various Representations

A comparative study is presented regarding the evolutionary design of complex multi-state cellular automata. In particular, two-dimensional cellular automata will be considered in combination with pattern development problem as a~case study. Two techniques for the representation of transition functions for the cellular automata are proposed: a conventional table-based method and an advanced concept utilising conditionally matching rules. It will be shown that using a proper settings of Evolution Strategy, various working solutions can be obtained using both representations. Some observations from an analysis of resulting cellular automata will be presented which indicate that the behavior of the automata is totally different and depends on the representation applied. Specifically, the table representation exhibit a chaotic development during which a target pattern emerges at a moment. On the other hand, the conditional rules are able to achieve behavior that progressively constructs the target pattern which, in addition, represents a stable final state. Moreover, the latter method also exhibits significantly higher success rate which represents one of its advantages and proves an importance of systematic research in this area.
THE STUDY OF THE ROLE OF BIOLOGICAL IRON SULFIDE COMPOSITES ON COPPER REMOVAL BY CHANGING GEOCHEMICAL FORMS

The growth of biological iron sulfide composites and their efficacy for changing the geochemical forms of copper in sediment have been investigated. The role of bacteria in copper removal was identified using copper concentrations in three forms extracted via three sequential extraction methods. Furthermore, bacterial community diversity was investigated between different treatments. Copper was transformed from the weak acidic extraction state to the reduction state and the oxidation state under the action of biological iron sulfide composites. The conversion rates of copper from the weak acidic extraction state to other states was up to 76.66% and the increasing rates of the reduction state was up to 177.85%. Higher dissolved oxygen does harm sulfate reducing bacteria and results in low removal efficiency of copper. It can be inferred that differences in the bacterial community structure resulted in differences between conversion rates and biological efficacy through the correlation between copper concentration and alpha diversity or beta diversity. Furthermore, bacterial 16S rRNA gene analysis showed that Proteobacteria, which sulfate reducing bacteria belong to, were predominant in the group with higher removal efficiency. The biological iron sulfide composites affected the removal efficiency of copper by increasing the sulfate reducing bacteria activity inside the sediments