Miracle-dz/UniFAR-FTUs huggingface dataset:
Inclusive Security Models To Building E-Government Trust

The low attention to security and privacy causes some problems on data and information that can lead to a lack of public trust in e-Gov service. Security threats are not only included in technical issues but also non-technical issues and therefore, it needs the implementation of inclusive security. The application of inclusive security to e-Gov needs to develop a model involving security and privacy requirements as a trusted security solution. The method used is the elicitation of security and privacy requirements in a security perspective. Identification is carried out on security and privacy properties, then security and privacy relationships are determined. The next step is developing the design of an inclusive security model on e-Gov. The last step is doing an analysis of e-Gov service activities and the role of inclusive security. The results of this study identified security and privacy requirements for building inclusive security. Identification of security requirements involves properties such as confidentiality (C), integrity (I), availability (A). Meanwhile, privacy requirement involves authentication (Au), authorization (Az), and Non-repudiation (Nr) properties. Furthermore, an inclusive security design model on e-Gov requires trust of internet (ToI) and trust of government (ToG) as an e-Gov service provider. Access control is needed to provide solutions to e-Gov service activities.
How Enterprise Architecture Creates Business Value: A Theoretical Model

Despite the growing interest in Enterprise Architecture (EA) over the last decade, many organizations still view EA as an abstract instrument that demands substantial investment, with hard to prove benefits. In fact, some organizations doubt the success of their EA initiatives due to the difficulties in justifying their EA-related expenditure. EA-benefits research often presents implicit perspectives of EA-benefits realization processes and there is no single thorough view of how EA might deliver value to organizations. Thus, this study endeavors to develop an EA-benefits realization process model using an established theoretical foundation. It uses the process theory of “how IT creates business value” to apprehend the focal elements of the formula for turning EA investment into organizational value. The contribution of this study is the development of a conceptual EA-benefits realization process model. The model conveys that EA investment results in an increased organizational performance through the realization of three interconnected processes. It connects and unpacks the EA-benefits realization process going from an early EA investment toward a preferred organizational performance using three processes: the conversion process, the use process, and the competitive process.
Ultra-Low Power Clock Generator for Self Powered IoT Sensor nodes

The power consumption of the existing self-powered IoT nodes is in watts or higher mW that has to be substantially reduced to $\mu \mathrm{W}$ or even nW such that these nodes can be powered through energy harvesting without batteries. To achieve this goal, we have to minimize the power consumption of each block, so that the overall power consumption can be reduced. In this paper, we have proposed an ultra-low power clock generator circuit for a self-powered IoT node. The new proposed clock generator consists of 44 transistors in place of 98 transistors used in earlier work. The power consumed by the proposed clock generator is $33.67 \mu \mathrm{W}$ which is far less than the traditional clock generator. It also occupies 0.2x times lower area as compared to the existing non-overlapping clock generator. The entire power management system simulation with the proposed clock generator is performed using $0.18 \mu \mathrm{m}$ CMOS technology PTM model with HSPICE. The circuit simulation demonstrates that the proposed clock generator is able to reduce the overall power consumption and increase the power efficiency of self-powered IoT node.
Triple-Band Microstrip Patch Antenna Made of Artificial Magnetic Conductors

This paper presents the design and characterization of triple-band microstrip patch antenna made of artificial magnetic conductor (AMC). The use of AMC is aimed to generate other resonant frequencies so as to get a triple-band frequency response. The AMC is structured by a square ring with two gaps arranged in 2×2 configuration. The proposed antenna has the dimension of 52mm × 52mm and is implemented on 2 layers of FR4 Epoxy dielectric substrate with the relative permittivity of 4.2 and the total thickness of 3.2mm. The results of characterization show that the proposed micorstrip patch antenna can produce a triple-band frequency response with the center frequency of 1.625GHz, 1.8GHz and 2.025GHz.
A Comprehensive Review on Enhancement of Voltage Stability by using Different FACTS Controllers Planning in Power Systems

In present time the power demand increases more rapidly in the power generation and transmission and distribution system sector. For maintaining the stability and steady state operation of power system causes from disturbance, faults and suddenly changing of the load, voltage instability, voltage sag, and change in frequency, stability is the most important factor for the power system. Due to instability, different problems occur in power system such as fluctuation in voltage and frequency, which may cause damage or failure of power system. This paper presents survey of various methods for enhancement of voltage stability by using different FACTS controllers. For voltage stability problem, the solutions with FACTS-controllers provide voltage support to the power system network. In this survey article will be very much helpful to the researchers for finding out the relevant references in the field of voltage stability analysis by different FACTS controllers in power system networks. Voltage stability is a one of the major focused areas in recent days due to difference between generation and demand. For voltage stability, maintenance is a challenging issue in planning and security assessment of power systems. Voltage instability problems mostly occur in heavily loaded systems where distance is large between the generator and load. Due to the application of reactive power compensation or load shedding may prevent this type of voltage instability.
Resilient System Engineering in a Multi-UAV System of Systems (SoS)

The use of Unmanned Aerial Vehicles (UAVs) in multi-mission Unmanned Aerial Systems (UASs) has grown exponentially in recent years. Multi-UAV System of Systems (SoS) have proliferated due to their abilities to work autonomously, to decentralize mission workload and to exhibit resiliency in the ability to retain mission functionality even if a UAV node or network connectivity is lost. This resiliency is not inherent, and must be architected into both the SoS and UAV node/payload, especially in the areas of communications, control, ad-hoc networking, and path planning. As an exemplar, the Resilient System Engineering (RSE) process will be used to derive and identify resilience attributes for a Multi-UAV SoS architecture. For this paper, the Multi-UAV SoS will be tasked for Search and Rescue (SAR) and use a Time/Frequency Difference of Arrival (T/FDOA) based location algorithm to locate agents in distress. A simulation of the SAR SoS, using Contract-Based Design (CBD) invariant contracts are used to quantify the intended resilience/emergence attributes of the Multi-UAV SAR SoS and the T/FDOA location approach.
Reduction of Total Harmonic Distortion and Effective Source Current in Circuits with Nonsinusoidal Waveforms

In linear resistor-inductor circuits, minimization of source rms current according to S. Fryze causes increase of total harmonic distortion (THD) and increase of source active powers resulting from higher harmonics of voltage and current. The authors of the present paper have proposed a method of the reduction of THD and rms value of the source current on the basis of the components of instantaneous power of the source. It has been demonstrated that the even component of that power secures a supply of the given active power from the source to the load. The odd component may be excluded from the instantaneous power of the source. Components of instantaneous power are the sums of the products of the even and odd components of source voltage and current. On the basis of these sums the voltage and current components have been determined, the compensation of which enables excluding the odd component of source instantaneous power, and in the same way– reduction of THD and rms value of the source current. The authors of the paper have presented the way of making use of the method in the situation when supply voltage is only odd or even function. It has been found out that it is possible to reduce THD of the source current several times in relation to THD of the source current while minimizing the source rms current according to Fryze, with negligible increase in the effective value of the source current in relation to the minimum value. Decrease of THD also causes significant decrease of active power of the source caused by higher harmonics of voltage and current.
Review of Exoskeleton: History, Design and Control

An Exoskeleton is wearable device that consists of mechanical structure, sensors, actuators and controller. This is widely used in various engineering applications such as to enhance endurance and strength of soldiers and assisting the patient with walking disorder. In view of this, paper brings out brief history of the Exoskeleton and their design. Further, study of different control aspects used in major augmented type Exoskeleton is also carried out. Finally, future challenges have also been discussed.
Hybrid Beamforming Channel Estimation With Reduced Phase shifter numbers for Massive MIMO Systems

A hybrid analog and digital beamforming structure has recently emerged as a promising technique to make low-cost large scale multiple-input-multiple-output (MIMO) communication systems. To decrease the power consumption and system complexity of hybrid beamforming structure, two subconnected analog beamforming methods are used: 1) with phase shifter, 2) and with fully-connected switch network. In the first method, a subconnected structure with phase shifter is used to reduce the computational complexity of fully connected hybrid beam-forming systems. Then, in the second method, to minimize power consumption of analog beamformer, a subconnected phase shifter structure is combined with a fully-connected switch network which reduces phase shifter upto 50%. In this paper, the impact of the channel estimation error (CEE) for the mentioned two hybrid beamforming structures on the effective spectral efficiency for a multi-user MIMO system with zero-forcing detection technique is investigated. Later, the variation in spectral efficiency of subconnected structure with phase shifter, and with fully-connected switch network is compared in presence of both perfect and imperfect channel state information (CSI). All the results are verified using computer simulations.
Incorporating one M2M Platform to Pharmaceutical Industry

Manufacturing industries, such as pharmaceutical, use machines from many vendors to manufacture products. Different machines use different protocols and have different tools to configure and to monitor it. However, in spite of having machine-specific tools, factory managers demand generic tools that covers all machines. An IoT standard body, that is oneM2M, has defined a high-level IoT system architecture that can cater different use cases, including the one in pharmaceutical industry. This paper proves the concept of oneM2M architecture by implementing it into the industry. The factory managers can rely on one dashboard to monitor all machines, one alarm application to give alert from any machine, and one system to give official reports for energy usage of all machines.
PAS-SNP: iOS App with GWAS SNP-Disease Database for Personalized Genomics Research: PAS-SNP for GWAS SNP-Disease

To efficiently fathom the wealth of genomics and clinical data, there is a crucial need to generate appropriate gene-disease annotation repositories accessed through modern resources. Our focus was to create a comprehensive database with mobile access to authentic Single Nucleotide Polymorphisms (SNPs) and classified diseases worldwide, considered as the foundation for clinical and genomics research, epidemiology and precision medicine. In this manuscript, we present non-profit, academic and publicly available iOS application, PAS-SNP, which invites global users to freely download it on iPhone & iPad devices, effortlessly adopt it's easy to use interface and search for SNPs, genes and related diseases. PAS-SNP is developed with Swift multi-paradigm programming language, XCODE integrated development environment for MacOS, and PHP scripting that uses a MySQL server database, which includes over 67,000 SNPs reported for over 19,000 genes, from patients located in over 1000 regions, published in over 3000 articles in over 415 journals available at the PubMed, and over 100,000 classified SNP-disease combinations. It includes SNPs released and organized by the Genome-wide Association Study (GWAS). PAS-SNP is founded on the clinical and scientific premise to support and promote healthcare and genomics data sharing with technological advancements.
Task-oriented Function Detection Based on Operational Tasks

We propose novel representations for functions of an object, namely Task-oriented Function, which is improved upon the idea of Afforadance in the field of Robotics Vision. We also propose a convolutional neural network to detect task-oriented functions. This network takes as input an operational task as well as an RGB image and assign each pixel an appropriate label for every task. Task-oriented funciton makes it possible to descibe various ways to use an object because the outputs from the network differ depending on operational tasks. We introduce a new dataset for task-oriented function detection, which contains about 1200 RGB images and 6000 pixel-level annotations assuming five tasks. Our proposed method reached 0.80 mean IOU in our dataset.
Millimeter-Wave Bandpass Filter By Open Loop Elliptical Ring Resonators

This paper presents a bandpass filter using two elliptical open loop ring resonators(EOLRR) at the resonant frequency of 28 GHz. It is well suited for the next generation 5G application. Coupled resonators were developed with the expectation of pairing parasitic resonance of various degenerate modes operating close to the fundamental mode in the suggested structure of filter. The frequency response of the design is approximated using CST software. The frequency response of the filter characterizes with low insertion loss and good rejection performance. The fabrication of the EOLRRs is also easy as there is no use of via or defected ground structure. The proposed structure offers a simple and compact design while exhibiting wide stop-band characteristics Additionally, the geometrical parameters of the proposed filter design can simply tune the bands to the chosen level. The proposed EOLRRs demonstrate worthy broadband characteristics.
Human brain cell type-specific gene co-expression associated with autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neuropsychiatric disorder with substantial phenotypic and genetic heterogeneity. Until now, about a thousand diverse genes have been associated with ASD, while it remains elusive that how disruptions in these different genes can lead to a common clinical phenotype. Therefore, it is essential to understand how ASD candidate genes relate to each other and identify potential shared molecular pathways. Human brain is a highly heterogeneous organ involving multiple cell types. Different functions in different types of cells may be dysregulated in ASD; investigating functional interactions between ASD candidate genes in normal human brain cells may shed new light on the genetic heterogeneity of ASD. To this end, we construct cell type-associated gene co-expression networks based on human brain cell gene expression data. Then we identify seven cell type-specific gene modules and analyze the specific gene functions in each cell type. We also identify six ASD-associated gene modules and study the dysregulated functions in ASD. Lastly, we obtain two ASD-associated cell type-specific gene modules for studying the cell type-specific aberrant functions in ASD. It is found that ASD-associated astrocytes-specific gene modules are relevant to endocytosis, neuron differentiation and cell projection organization, while ASD-associated neurons-specific gene modules are relevant to presynapse, glutamatergic synapse and neuron projection morphogenesis. Our method has been proven to be effective in discovering ASD-associated cell type-specific gene expression pattern. Our findings can promote the study of the heterogeneity of ASD in gene expression between different cell types, providing new insights into the molecular mechanisms underlying the pathogenesis of ASD.
Network Intrusion Detection: A comparative study using state-of-the-art machine learning methods

Cyber threats are not only increasing with the years, but are also becoming harder to recognize and evolving with time so that they can easily bypass normal antivirus. There have been numerous cyber crimes that have attacked confidentiality and privacy of data. To ensure network security, an effective intrusion detection system is required. Several ensemble methods like XG-Boost and LGBM have been developed in the past 4-5 years. These have not been exploited in the previous researches on anomaly detection. This study makes use of these novel Gradient Boosting Decision Tree algorithms. XG-Boost and LGBM have proved to be the most productive techniques for several supervised and unsupervised learning tasks. This research studies several machine learning and deep learning classifiers and compare their performances. To predict the probability of occurrence of 21 different classes of attacks on a network the NSL KDD dataset has been used. We studied three different categories of models-Linear Models including Logistic Regression and Stochastic Gradient Descent (SGD) classifier; Gradient Boosting Decision Tree ensembles including Light GBM (LGBM) and XG-Boost; and a Deep Neural Network (DNN) classifier and also trained a stacked model consisting of all these models as base learners. This study compares the performances of all the models for Network Intrusion Detection and useful conclusions are drawn. The simulation results show that ensemble methods are more effective for detecting network intrusion.
High-speed Classification of AER Data Based on a Low-cost Hardware System

This paper proposes a low-cost hardware system for rapid classification of visual objects encoded in the address-event representation (AER) format. A lightweight statistical inference algorithm is implemented in the proposed hardware to process the asynchronous pixel address streams from an AER visual sensor. The algorithm involves only a few simple operations for patch-based binary feature extraction and Random Ferns inference, which are highly parallelized and accelerated by our dedicated hardware architecture. Simulation results suggested that our hardware system was able to process as many as 50M address events per second and achieved 75% classification accuracy for the popular MNIST-DVS dataset. Compared to other AER classification systems, our low-cost FPGA implementation is more plausible for embedded applications because of its similar accuracy and higher system throughput.
Mapping of deflection caused due to hydrostatic pressure using Differential SAR Interferometry (DInSAR) on Bhakhra dam

Microwave remote sensing is truly the most rapidly emerging field of satellite remote sensing today. In a very short span of time, it has found variety of applications in almost every field which had seen optical remote sensing dominance. This shows that its both state of the art and a versatile non-evasive remote sensing tool. It has added advantage of all-weather availability and penetration abilities which the optical remote sensing lacks. Hence can be easily used for areas and months with dense cloud cover. Of the many applications of SAR (Synthetic Aperture RADAR) or Microwave remote sensing is its ability to map and monitor land surface deformations shown by both natural and man-made features over a period, which helps as an early warning for many possible disastrous events like structural failures in dams. The technique involved is Differential SAR Interferometry or DInSAR. DInSAR involves a repeat pass configuration of SAR Interferometry with minimum baseline. The presence of any random motion on the surface or surface features on earth, tends to decorrelate the backscatter signals and add noise to phase difference. Small change due to surface movements, causes change in phase difference which is measured precisely and accurately by this technique. This study aims to measure the deflection that has come over a period of one year on the walls of Bhakhra Dam in Himachal Pradesh in India, due to the change in water levels and pressure exerted by it. Using DInSAR, the deflection was measured to be around 0.952cm from August 2017 to October 2017.
Wideband HMSIW Band Pass Filter design using H-shaped Periodic Structures

Design of half mode substrate integrated waveguide (HMSIW) wideband band pass filter planer structure is presented in this paper. Three H-shaped periodic electronic band gap structures are engraved over the top conducting layer in order to achieve attenuation of higher frequencies. Several major parameters have been studied and presented in detail. This technique is used to achieve insertion loss < 0.9dB over the entire pass band. Further more significant attenuation at the stop band frequencies (≈ 30dB) is observed. The filter is designed covering microwave C-band and X-band radar and satellite systems.
FPGA Deployable Fire Detection Model for Real-Time Video Surveillance Systems Using Convolutional Neural Networks

Unchecked Fires can cause massive disasters leading to loss of human and animal life, property, forests, and other areas of vegetation and causing serious environmental damage. Moreover, fire has a tendency to spread rapidly over a small duration of time. Hence early detection is a very crucial factor when handling fire disasters. Recent studies have proposed the use of Convolutional Neural Network (CNN) architectures for the purpose of fire detection. In deep learning, a CNN is a class of deep neural networks, most commonly applied to analyzing visual imagery. CNNs have the ability to automatically learn features from raw fire data which eliminates the tedious and time consuming task of feature engineering. However, popular CNNs (like Alexnet, GoogleNet, VGGNet etc) are computationally expensive and bulky in size. Our research aims at exploring other lightweight alternatives without compromising the performance. This would make the CNNs easily deployable on Field Programmable Gate Arrays(FPGAs) and other mobile devices with limited resources. Deploying the CNN model onto FPGAs would make it portable and hence, it can be conveniently used as an efficient stand-alone fire detection system. In our research we have trained MobileNet, MobileNetV2 and DenseNet like architectures on our dataset and compared their performance on several metrics. Based on our experiments, we have developed a system that successfully detects fire in real-time scenarios and raises an alarm.