Miracle-dz/UniFAR-FTUs huggingface dataset:
DoS Attack Detection Using Machine Learning and Neural Network

The current digital world is using the internet almost everywhere. The usage of internet has been increasing, however, threats are also increasing in numbers. One such threat is DoS attack which uses reasonable service requests to gain excessive computing and network resources and results in an inability to access them by legitimate users. The DoS attack can happen at different layers of OSI model such as network, transport and application layers. The aim of this paper is to detect DoS attack effectively using Machine learning (ML) and Neural Network (NN) algorithms. The detection is specifically focused on application layer DoS attack detection rather than at transport and network DoS attack detection. The latest DoS attack dataset CIC IDS 2017 dataset is used in the experiment. The experimentation has divided the dataset into different splits and the best split is found for each algorithm i.e. RF and MLP. Results of RF and MLP are compared and it is shown that RF provides better results than MLP.
Generating Digital Signature using Facial Landmlark Detection

Information security has developed rapidly over the recent years with a key being the emergence of social media. To standardize this discipline, security of an individual becomes an urgent concern. In 2019, it is estimated that there will be over 2.5 billion social media users around the globe. Unfortunately, anonymous identity has become a major concern for the security advisors. Due to the technological advancements, the phishers are able to access the confidential information. To resolve these issues numerous solutions have been proposed, such as biometric identification, facial and audio recognition etc prior access to any highly secure forum on the web. Generating digital signatures is the recent trend being incorporated in the field of digital security. We have designed an algorithm that after generating 68 point facial landmark, converts the image to a highly compressed and secure digital signature. The proposed algorithm generates a unique signature for an individual which when stored in the user account information database will limit the creation of fake or multiple accounts. At the same time the algorithm reduces the database storage overhead as it stores the facial identity of an individual in the form of a compressed textual signature rather than the traditional method where the image file was being stored, occupying lesser amount of space and making it more efficient in terms of searching, fetching and manipulation. A unique new analysis of the features produced at intermediate layers has been applied. Here, we opt to use the normal and two opposites’ angular measures of the triangle as the invariance. It simply acts as the real-time optimized encryption procedure to achieve the reliable security goals explained in detail in the later sections.
Magnetic and Thermal Analysis of Switched Reluctance Machines Integrated Battery Chargers

In this study, magnetic and thermal analysis of switched reluctance machine (SRM) 8/6, 4 kw in integrated battery chargers with electrical vehicle application is presented, this analysis done in charging mode with three level of battery’s current. Zero torque control in this machine calculated with consideration of mutual inductance in multi-phase mode. Because of participate the SRM’s winding in charging operation, so thermal management is very important, overheating causes the winding insulation to deteriorate and reduces its lifetime.
Inequalities in Crowdfunding for Transgender Health Care

Abstract Purpose: An emerging body of research analyzes the scope, ethics, and inequalities of web-based crowdfunding to raise money for medical expenses related to illness or injury. To date, little research has investigated how transgender communities utilize crowdfunding expenses related to gender affirming medical care. Methods: Using GoFundMe.com, we created a data set of 391 crowdfunding campaigns for gender-affirming care created from 2012 to 2016. In addition to descriptive statistics of recipient demographics and campaign financials, we conducted hierarchical multiple regression analyses to determine the factors associated with financial outcomes of fundraising for medical expenses. Results: Findings indicate that the majority of campaigns were used to fund chest surgeries among young, white, binary-identified trans men in the United States. Few campaigns met their fundraising goal. Being a trans man whose Facebook community shares the crowdfunding campaign is predictive of meeting a higher percentage of the fundraising goal, whereas being a trans woman whose Facebook community shares the campaign is predictive of raising more money. Conclusion: The use of crowdfunding for gender affirming highlights the difficulties that transgender persons face with using private and public health care programs to fund medically necessary care. Health care providers should exercise caution in recommending crowdfunding as a viable strategy to raise money for out-of-pocket costs.
An Improved Three-Input DC-DC Boost Converter for Hybrid PV/FC/Battery and Bidirectional Load as Backup System for Smart Home

An improved non-isolated three-input DC-DC boost converter is proposed in this paper. The proposed topology has the capability of optimizing the energy conversion within the smart homes. The recommended structure is designed for flexible energy systems such as smart homes, smart cities, and electric vehicles. The suggested structure has employed a boost type converter as well as multi-input and single output (MISO) converter along with renewable sources. The proposed converter consists of two unidirectional input power ports and two bidirectional ports for a storage element in an integrated structure. The proposed converter has several input sources with different voltage levels which are fixed at mid voltage ranges. Also, it includes a step-up gain to boost voltage to higher ranges by a gain cell. In order to optimally supply the output load and charge or discharge the battery storage, photovoltaic (PV) and fuel cell (FC) along with bidirectional loads could be exclusively or simultaneously employed as power resources. The main feature of the proposed converter is presented as follows: flexible bidirectional load can be charged at home during the daytime, while it could be discharged at nights when the demand is the maximum in which the system is usually faced with overload. To verify the performance of the proposed structure, simulations are carried out using PSCAD/EMTDC software and the results are presented to prove the practicality of proposed algorithm in covering renewable resources and dynamically managing energy consumption.
A Radar Target Simulator Concept for Close-Range Targets with Micro-Doppler Signatures

In this publication a concept of a radar target simulator is presented, designed to simulate micro-Doppler signatures at short distances. The progress of the project is still at the beginning of the development process, but nevertheless the motivation and first partial results will be presented. In the first part, the requirements of the radar target simulator are derived from measurements of vulnerable road users, which will demonstrate the possibilities and advantages of such a system. The second part deals with the system design and the first measurements of the system.
Comparative Analysis of Three Single Trait Biometric Authentication Models

In traditional authentication model, user identification is based on Password, PIN or Signature, but sometimes these can be lost, stolen, or subjected to spoofing attack. In biometric authentication system, a user is identified through physical or behavioural features. These features include fingerprint, palmprint, face, iris, signature, speech, and so on. The main challenging task in biometric is about storage problem, and transmission of huge data. By replacing PINs, use of biometric traits can certainly deny unauthorized access or fraudulent use of ATMs, mobile phones, smart cards, data centres, personal computers, and computer networks. The performance can be measured by evaluating performance metrics i.e., FAR, FRR, FTC, and EER. This paper proposes three different uni-modal biometric authentication systems using fingerprint, face, and voice signal as sources. A new technique is designed and applied to each biometric authentication system.
LeMeNo: Personalised News Using Machine Learning

In today's information era, people are keen to know about various topics & events but are unable to keep up because of lack of time & information overload. Inspired by the need to say “Let me know”, ‘LeMeNo’ aims to be an application that provides personalized news & event updates to its users without losing out on time or having to go through irrelevant content. This paper presents the event-based approach used by LeMeNo for News Recommendation based on user interest. The Recommendation System is based on both news & users interests. News articles are recommended based on machine learning techniques like clustering of similar articles, predicting their category, content similarity & keyword extraction. The system learns user interests based on time spent on reading an article, whether user likes/dislikes the article as well as user specified levels of interest in various topics. The proposed system provides a unique Event Timeline feature, which filters out multiple news articles related to an event, displays them in chronological order & appends new upcoming related articles, thus providing a journey of that event.
Cross-System Adaptation and Combination The Influence of Phoneme Set a

Cross-system adaptation and system combination methods, such as ROVER and confusion network combination, are known to lower the word error rate of speech recognition systems. They require the training of systems that are reasonably close in performance but at the same time produce output that differs in its errors. This provides complementary information which leads to performance improvements. In this paper we demonstrate the gains we have seen with cross-system adaptation and system combination on the English EPPS and RT0-05S lecture meeting task. We obtained the necessary varying systems by using different acoustic front-ends and phoneme sets on which our models are based. In a set of contrastive experiments we show the influence that the exchange of the components has on adaptation and system combination.
Antenna selection and power allocation design for 5G massive MIMO uplink networks

Massive MIMO is one of the key technologies in future 5G communications which can satisfy the requirement of high speed and large capacity. This paper considers antenna selection and power allocation design to promote energy conservation then provide good quality of service (QoS) for the whole massive MIMO uplink network. Unlike previous related works, hardware impairment, transmission efficiency, and energy consumption at the circuit and antennas are involved in massive MIMO networks. In order to ensure the QoS, we consider the minimum rate constraint for each user and the system, which increases the complexity of power allocation problem for maximizing energy and spectral efficiency in massive MIMO system. To this end, a quantum-inspired social emotional optimization (QSEO) algorithm is proposed to obtain the optimal power control strategy in massive MIMO uplink networks. Simulation results assess the great advantages of QSEO which previous strategies do not have.
On Building Detection Using the Class Activation Map: Case Study on a Landsat8 Image

Traditionally, the land cover mapping process needs a ground data to be collected with high precision in both class labeling and spatial locations. To collect enough, high precise ground data require resources. As a result, we proposed an approach for building an image classification based on the class activation map (CAM) where the goal is not to identify the relationship between each pixel and a class label, but to identify whether each sub-images contain the class of interest or not. The output of the class activation map is the filter responds where pixels with high respond are likely to belong to the class of interest. We examined the performance on a LAND-SAT 8 and found. The result of CAM showed that the proposed method achieves high accuracy in identifying whether a sub-image contains the class of interest or not. However, the precision in localizing the class is relatively moderate.
A Riemannian Modification of Artifact Subspace Reconstruction for EEG Artifact Handling

Artifact Subspace Reconstruction (ASR) is an adaptive method for the online or offline correction of artifacts comprising multichannel electroencephalography (EEG) recordings. It repeatedly computes a principal component analysis (PCA) on covariance matrices to detect artifacts based on their statistical properties in the component subspace. We adapted the existing ASR implementation by using Riemannian geometry for covariance matrix processing. EEG data that were recorded on smartphone in both outdoors and indoors conditions were used for evaluation (N = 27). A direct comparison between the original ASR and Riemannian ASR (rASR) was conducted for three performance measures: reduction of eye-blinks (sensitivity), improvement of visual-evoked potentials (VEPs) (specificity), and computation time (efficiency). Compared to ASR, our rASR algorithm performed favorably on all three measures. We conclude that rASR is suitable for the offline and online correction of multichannel EEG data acquired in laboratory and in field conditions.
An Improved Differential Privacy K-Means Algorithm Based on MapReduce

In order to solve the low clustering accuracy and the local optimum problem of the traditional differential privacy k-means algorithm, this paper proposed an improved differential privacy K-means algorithm based on MapReduce. The proposed algorithm uses Canopy to select the initial center point, and uses Laplace mechanism to realize the differential privacy protection. The simulation shows that the clustering results of the proposed algorithm outperform the traditional DP K-means in usability and convergence speed.
AIS Electronic Library (AISeL) AIS Electronic Library (AISeL)

Platform ecosystems are complex ecologies of firms with individual competencies and collective objectives. The sustainable evolution of platform ecosystems is thereby contingent on taking advantage of the individual competencies of the ecosystem’s actors toward obtaining collective objectives. To learn more about platform ecosystem evolution and dynamics, we study Salesforce, a leading and thriving B2B platform ecosystem. We find that the ecosystem’s evolution was closely defined not only by the platform owner’s orchestrating initiatives, but also by its complementors’ and customers’ competencies and particularities. Specifically, we derive three distinct dimensions of evolution, namely the extension of the platform core technology, the extension of the platform’s functional scope, and the industry-specific specialization of the platform. We further identify three cross-dimension levers, namely proprietary developments, acquisitions, as well as partnerships and alliances, which were employed by the platform owner to drive its platform ecosystem’s evolution.
Predictionand Diagnosis of Heart Disease Patients using Data Mining Technique

we are living in a post modern era and there are tremendous changes happening to our daily routines which make an impact on our health positively and negatively. As a result of these changes various kind of diseases are enormously increased. Especially, heart disease has become more common these days. The life of people is at a risk. Variation in Blood pressure, sugar, pulse rate etc. can lead to cardiovascular diseases that include narrowed or blocked blood vessels. It may causes Heart failure, Aneurysm, Peripheral artery disease, Heart attack, Stroke and even sudden cardiac arrest. Many forms of heart disease can be detected or diagnosed with different medical tests by considering family medical history and other factors. But, the prediction of heart diseases without doing any medical tests is quite difficult. The aim of this project is to diagnose different heart diseases and to make all possible precautions to prevent at early stage itself with affordable rate. We follow ‘Data mining’ technique in which attributes are fed in to SVM, Random forest, KNN, and ANN classification Algorithms for the prediction of heart diseases. The preliminary readings and studies obtained from this technique is used to know the possibility of detecting heart diseases at early stage and can be completely cured by proper diagnosis.
Evaluation of Advantageous LTE MIMO Nefer Antennas in Real Test Drives and in a New Reproducible Half Virtual Test Drive

A reproducible virtual test drive for LTE MIMO automotive application is investigated in comparison with real field test measurements by way of examples for different LTE-antennas. Both test drives, the virtual and the real one cover the same test route in Munich city for 2x2-MIMO data transmission. Special focus is given on a flat Nefer antenna, showing a high performance in laboratory measurements as well as via its throughput values in the test drives. The antenna covers all LTE-frequency bands as well as the WLAN frequency up to 6 GHz.
Geometrically Consistent Pedestrian Trajectory Extraction for Gait Recognition

In the gait recognition community, silhouette-based gait representations such as gait energy image have been widely employed for the last decade. In order to obtain good quality of gait features, it is essential to get a well aligned silhouette sequence, which is, however, not necessarily easy with imperfect silhouettes and also with scale and position changes due to perspective projection. We therefore propose a gait recognition-oriented approach to pedestrian trajectory extraction, i. e. , bounding box sequence for each pedestrian. More specifically, we firstly developed an inter-active tool to get camera calibration parameters for a target scene geometry without on-site workload. We then introduce a geometric constraint to better keep the consistency of bounding boxes among frames w.r. t. the pedestrian ’s height and foot bottom points on the ground plane. Sub-sequently, we apply analytical dynamic programing (DP) repeatedly to find multiple pedestrian ’s trajectories on the ground plane, where data and transition scores are computed based on semantic segmentation results and color histogram similarity. Moreover, since DP just considers the smoothness between adjacent frames, we approximate the trajectory by a piece-wise linear trajectory to make it more globally smooth. Experimental results show that the proposed method enables us to make better aligned gait features and consequently improves gait recognition accuracy.
Inverter-Based Subthreshold Amplifier Techniques and Their Application in 0.3-V $\Delta\Sigma$ -Modulators

Subthreshold amplifiers suffer from the limited voltage headroom which leaves little space for conventional analog techniques to enhance performance and efficiency. This paper presents an evolution process of implementing conventional structures with inverters, allowing ultralow-voltage operation with increased flexibility in adopting traditional circuit techniques. Based on the proposed inverter-based elementary structure and CMFB, both the Miller-compensated (MC) operational transconductance amplifier (OTA) and the feedforward-compensated (FFC) OTA achieve significantly improved performance as compared to previous works. The proposed amplifier techniques are verified in <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula> modulator (DSM) design, with MC-OTA for a DT-DSM and FFC-OTA for a CT-DSM, both fabricated in a 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS. The 0.3-V DT-DSM achieves 74.1-dB SNDR, 83.4-dB SFDR and 20-kHz bandwidth with 79.3-<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> power, resulting in a Schreier figure of merit (FoM) of 158 dB. The 0.3-V CT-DSM achieves 68.5-dB SNDR, 82.6-dB SFDR, and 50-kHz bandwidth with 26.3-<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> power, leading to a Schreier FoM of 161 dB. Both DSMs exhibit highly competitive performance among sub-0.5-V designs, validating the proposed subthreshold amplifier techniques.
Differential Evolution Improved with Intelligent Mutation Operator Based on Proximity and Ranking

Differential evolution (DE) is the perfect optimization algorithm to solve real-parameter optimization problems. The most important of DE operators is the differential mutation operator. Motivated by the selection strategies of the proximity-based mutation operator and the ranking-based one, a novel intelligent selection for mutation operator is proposed, in which the selection of some individuals for mutation is based on proximity between individuals and ranking of fitness successively. Since the proposed intelligent selection affects only the mutation step of DE algorithms, it could be directly applied to other DE mutation strategies. Through extensive experimental studies on the CEC 2017 benchmark suite, the results show that it remarkably enhances the performance of most mutation strategies for all benchmark problems in the DE/best/1 and DE/current-to-best/1 evolution strategies and has good characteristics about computational complexity
TM-Miner: TFS-Based Algorithm for Mining Temporal Motifs in Large Temporal Network

Temporal network is a basic tool for representing complex systems, such as communication networks and social networks; besides the temporal motif (TM) plays an important role in the analysis of temporal networks. Without considering the temporal information, most existing motif mining methods focus on static networks and are not suitable for mining temporal motifs. In this paper, we study the problem of temporal motif mining for the temporal network. To formulate the problem, we define the temporal motif as a frequently connected subgraph that has a similar sequence of information flows. Moreover, an efficient algorithm called TM-Miner is proposed. Based on the time first search (TFS) algorithm, the TM-Miner builds a canonical labeling system that uses a new lexicographic order and maps the temporal graph to the unique minimum TFS code. By utilizing the canonical labeling system, the computational cost of temporal graph isomorphism is reduced and the efficiency of the algorithm is improved. Finally, we evaluate the performance of the TM-Miner algorithm in real datasets and extensive experiments demonstrate that it is faster than the existing algorithms.