178 research outputs found
Unified framework for spam detection and risk assessment in short message communication media / Adewole Kayode Sakariyah
No full textShort message communication media (SMCM), such as mobile and microblogging social networks, have become essential part of many people daily routine. Despite the benefits offered by these communication media, they have become the popular platforms for distributing spam contents. Research in spam message and spam account detection in SMCM has received growing interests in the recent years, mainly focusing on introducing separate frameworks that can identify spam message or spam account. There are hundreds of published works related to spam message and spam account detection that aim to identify effective detection methods. While spam message and spam account studies have recently advanced, there are still areas available to explore, mostly with respect to introduction of unified method that can detect spam message and spam account within a single framework as well as identifying risk levels of spam accounts. Existing content-based methods for spam detection degraded in performance due to many factors. For instance, unlike contents posted on social networks like Facebook and Renren, SMS and microblogging messages have limited size composed using many domain-specific words such as idioms and abbreviations. In addition, microblogging messages are unstructured and noisy. These distinguished characteristics posed challenges to existing approaches for spam message detection. The state-of-the-art solutions for spam accounts detection have faced different evasion tactics in the hands of intelligent spammers. Thus, the need to investigate features, which can be used to identify spam message and spam account in SMCM. This study is concerned with introduction of a unified framework that can detect spam message and spam account as well as assessing account risk level. To achieve this aim, this study proposed a novel framework, which combines three models: Spam Account Detection Model (SADM), Spam Message Detection Model (SMDM), and Spam Risk Assessment Model (SRAM). Sixty-nine (69) set of features were identified from five main categories to develop the SADM. Additionally, eighteen (18) features were introduced to build the SMDM. The performance of ten (10) machine learning algorithms were evaluated to select the best classifier for both SADM and SMDM. Bio-inspired evolutionary search method was studied to identify the discriminating features for spam account detection. A model to estimate the levels of risk of spam accounts is established using Fuzzy Analytic Hierarchy Process. Four levels of risk were employed with their corresponding response strategies used to map risk levels into different types of response. To assess the performance of the proposed framework, an evaluation study with four stages was undertaken. With promising results being gathered, a proof-of-concept study was conducted using an online assessment mode to demonstrate the applicability of the proposed framework. Based on the results gathered, this study has demonstrated that the proposed framework can be used to detect spam message and spam account as well as assess the risk level of spam accounts in SMCM
Students’ academic performance and dropout predictions: a review / Ahmed O. Ameen, Moshood A. Alarape and Kayode S. Adewole
No full textStudents’ Academic Performance (SAP) is an important metric in determining the status of students in any academic institution. It allows the instructors and other education managers to get an accurate evaluation of the students in different courses in a particular semester and also serve as an indicator to the students to review their strategies for better performance in the subsequent semesters. Predicting SAP is therefore important to help learners in obtaining the best from their studies. A number of researches in Educational Psychology (EP), Learning Analytics (LA) and Educational Data Mining (EDM) has been carried out to study and predict SAP, most especially in determining failures or dropouts with the goal of preventing the occurrence of the negative final outcome. This paper presents a comprehensive review of related studies that deal with SAP and dropout predictions. To group the studies, this review proposes taxonomy of the methods and features used in the literature for SAP and dropout prediction. The paper identifies some key issues and challenges for SAP and dropout predictions that require substantial research efforts. Limitations of the existing approaches for SAP and dropout prediction are identified. Finally, the paper exposes the current research directions in the area
Finite element failure analysis of wires for civil engineering applications with various crack-like laminations
No full textThis paper presents the finite element (FE) failure predictions and analyses of a typical wire for civil engineering applications with various crack-like lamination types (Single and double), geometries (straight-end and inclined-end) and orientations (longitudinal, lateral and transverse). FE prediction and analysis of the failure of notched pre-cracked wires with a surface across-the-thickness crack-like lamination validated with experimental results are also presented. The FE predicted fracture shape for the notched pre-cracked wires that consists of a cup and cone fracture shape below the bottom tip of the surface across-the-thickness crack-like lamination agrees with the experimental fracture shape. Wires with the double straight-end and double inclined-end crack-like/line-type laminations exhibit a “slant-middle W” and a “zigzag” fractures respectively. Above and below the lateral mid-width across-the-thickness lamination, the wires with the lateral mid-width across-the-thickness lamination exhibit a combination of a transverse mid-thickness flat fracture that is perpendicular to the lateral mid-width across-the-thickness lamination and negatively inclined slant fractures on each side of the mid-thickness flat fracture at the remaining outer edges of the wire's thickness. On both the front and back sides of the transverse mid-thickness across-the-width lamination, the wires with the transverse mid-thickness across-the-width lamination exhibit a combination of transverse flat fractures parallel to the transverse mid-thickness across-the-width lamination and positively inclined slant fractures at the outer edges of the wire's thickness. FE failure analysis reveals that fracture initiations do not always begin at the termini of every longitudinal crack-like/line-type lamination as reported in a published fractographic failure analysis report of wires with longitudinal crack-like laminations. Fracture initiation only begins at the termini/tip of longitudinal inclined-end crack-like laminations and at the termini/tip of transverse and lateral laminations. FE failure analysis also reveals that wires with single straight-end, double straight-end and double inclined-end longitudinal crack-like/line-type laminations do not exhibit cup and cone fractures as reported. This work further demonstrates the need to employ FE failure analysis as a complimentary or alternative failure analysis approach where the destruction/alteration of the fracture markings by corrosion could affect the accuracy of fractographic failure analysis.Fil: Adewole, Kazeem Kayode. University Of Newcastle; Reino Unido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnologica; ArgentinaFil: Bull, Steve J.. University Of Newcastle; Reino Unid
The role of big data in smart city
No full textThe expansion of big data and the evolution of Internet of Things (IoT) technologies have played an important role in the feasibility of smart city initiatives. Big data offer the potential for cities to obtain valuable insights from a large amount of data collected through various sources, and the IoT allows the integration of sensors, radio-frequency identification, and Bluetooth in the real-world environment using highly networked services. The combination of the IoT and big data is an unexplored research area that has brought new and interesting challenges for achieving the goal of future smart cities. These new challenges focus primarily on problems related to business and technology that enable cities to actualize the vision, principles, and requirements of the applications of smart cities by realizing the main smart environment characteristics. In this paper, we describe the existing communication technologies and smart-based applications used within the context of smart cities. The visions of big data analytics to support smart cities are discussed by focusing on how big data can fundamentally change urban populations at different levels. Moreover, a future business model that can manage big data for smart cities is proposed, and the business and technological research challenges are identified. This study can serve as a benchmark for researchers and industries for the future progress and development of smart cities in the context of big data
Privacy Protection of Synthetic Smart Grid Data Simulated via Generative Adversarial Networks
No full textThe development in smart meter technology has made grid operations more efficient based on fine-grained electricity usage data generated at different levels of time granularity. Consequently, machine learning algorithms have benefited from these data to produce useful models for important grid operations. Although machine learning algorithms need historical data to improve predictive performance, these data are not readily available for public utilization due to privacy issues. The existing smart grid data simulation frameworks generate grid data with implicit privacy concerns since the data are simulated from a few real energy consumptions that are publicly available. This paper addresses two issues in smart grid. First, it assesses the level of privacy violation with the individual household appliances based on synthetic household aggregate loads consumption. Second, based on the findings, it proposes two privacy-preserving mechanisms to reduce this risk. Three inference attacks are simulated and the results obtained confirm the efficacy of the proposed privacy-preserving mechanisms
Energy disaggregation risk resilience through microaggregation and discrete Fourier transform
No full textProgress in the field of Non-Intrusive Load Monitoring (NILM) has been attributed to the rise in the application of artificial intelligence. Nevertheless, the ability of energy disaggregation algorithms to disaggregate different appliance signatures from aggregated smart grid data poses some privacy issues. This paper introduces a new notion of disclosure risk termed energy disaggregation risk. The performance of Sequence-to-Sequence (Seq2Seq) NILM deep learning algorithm along with three activation extraction methods are studied using two publicly available datasets. To understand the extent of disclosure, we study three inference attacks on aggregated data. The results show that Variance Sensitive Thresholding (VST) event detection method outperformed the other two methods in revealing households' lifestyles based on the signature of the appliances. To reduce energy disaggregation risk, we investigate the performance of two privacy-preserving mechanisms based on microaggregation and Discrete Fourier Transform (DFT). Empirically, for the first scenario of inference attack on UK-DALE, VST produces disaggregation risks of 99%, 100%, 89% and 99% for fridge, dish washer, microwave, and kettle respectively. For washing machine, Activation Time Extraction (ATE) method produces a disaggregation risk of 87%. We obtain similar results for other inference attack scenarios and the risk reduces using the two privacy-protection mechanisms
High Pressure Co2 Separation Using Membranes Membrane Selection and Process Modeling
No full textPemisahan CO2 daripada gas asli (NG) telah menarik minat penyelidikan kerana
permintaan tenaga yang semakin meningkat dan keperluan teknik penulenan gas yang lebih
cekap dan mesra alam. Kebanyakan NG dihasilkan bersama CO2 yang perlu disingkirkan
demi untuk meningkatkan nilai kalorinya. Teknologi membran merupakan salah satu
teknologi yang digunakan secara meluas untuk penyingkiran CO2. Walau bagaimanapun,
pasarannya masih kecil berbanding proses-proses pemisahan gas yang lain. Ini adalah kerana
penggunaan bahan-bahan membran dengan prestasi pemisahan yang rendah dan keadaan
pengoperasian modul yang tidak optimum. Pengoptimuman bersistematik bagi setiap
peringkat penyediaan membran dan operasi modul bertekanan tinggi telah dicadangkan untuk
menyelesaikan masalah tersebut.Salah satu cabaran utama operasi bertekanan tinggi adalah
fenomena kesan penusukan pemplastikan yang disebabkan oleh peningkatan tekanan suapan.
Polimer komersil polisulfona telah diubahsuai untuk mengoptimumkan prestasi
pemisahannya. Kajian bertekanan tinggi dan pemodelan matematik telah dijalankan untuk
menilai prestasi pemisahan membran. Bagi mewujudkan tekanan suapan yang tertinggi
semasa penyingkiran CO2 tanpa pemplastikan, ciri-ciri pemisahan membran telah dikaji
menggunakan ujian penelapan pada tekanan mencecah 57 bar. Kajian dinamik bagi prestasi
membran juga dilakukan menggunakan ujikaji penelapan bagi tempoh masa antara 5 hingga
1080 jam (45 hari) dengan pelbagai tekanan antara 6 hingga 57 bar. Model matematik telah
dibangunkan berdasarkan teori “dual-sorption” dan model keseluruhan tidak bergerak. Proses
pengoptimuman untuk pemilihan membran telah dicapai dengan menggunakan kaedah
pengoptimuman pelbagai objektif, manakala keadaan operasi modul dicapai menggunakan
model pengaturcaraan pengoptimuman kekangan non-linear dan algoritma “Golden search”
yang dilaksanakan menggunakan MATLAB.
Tekanan pemplastikan bagi membran yang disediakan adalah 41.07 bar manakala
kebolehtelapan dan kememilihan pada tekanan ini adalah masing-masing 5.99 Barrer, dan
44.19. Ini merupakan peningkatan sebanyak 17.65% bagi tekanan pemplastikan dan 66.39%
bagi kebolehtelapan. Walau bagaimanapun, membran tersebut kehilangan kira-kira 79.65%
kebolehtelapannya pada tekanan ini manakala kememilihannya meningkat sebanyak 91.13%
jika dibanding dengan nilai pada 5 bar. Ujian kebolehtelapan yang bergantung kepada masa
mendedahkan bahawa tekanan pemplastikan sebagai titik keseimbangan boleh digunakan
sebagai kekangan dalam pengoptimuman proses pemisahan gas membran. Model matematik
yang dibangunkan menunjukkan keupayaan ramalan yang sangat baik untuk tekanan
pemplastikan. Pemilihan bahan membran juga didapati mampu dioptimumkan dengan cekap
dengan menggunakan kaedah pengoptimuman multi-objektif.
________________________________________________________________________________________________________________________
Separation of CO2 from natural gas (NG) has attracted research interest due to
increasing demand for energy and the need for more energy efficient and environmental
friendly gas purification techniques. Most of the NG is coproduced with CO2 which need to
be removed in order to increase its calorific value. Membrane separation is one of the widely
used technologies for CO2 removal. However, its market share is still very small as compared
to other gas separation processes. This is due to the use of membrane materials with poor
separation performance and the use of non-optimum module operating conditions.
Systematic optimization of every stage of membrane preparation and high pressure module
operation was proposed to solve this problem. One major challenge of high pressure
operation is penetrant-induced plasticization phenomenon which is caused by increasing the
feed pressure.
Commercial polysulfone polymer was modified to optimize its separation
performance. High pressure experimental studies and mathematical modeling were
performed to evaluate the separation performance of the membrane. To establish the highest
possible feed pressure which can be attained during CO2 removal without plasticization,
transport properties of the membrane were evaluated using permeation tests at pressure up to
57 bar. Also, dynamic evaluation of membrane performance was performed using timedependent
permeation experiments over a period ranging from 5 hours to 1080 hours (45
days) at various pressures between 6 and 57 bar. Mathematical model was developed based on the theory of dual-sorption and the total immobilization models. The optimization for
membrane selection was achieved using a multi-objective optimization method while that of
module operating conditions was achieved using non-linear programming constraint
optimization model and a Golden search algorithm which was implemented using MATLAB.
The plasticization pressure of the prepared membrane is 41.07 bar while the
permeability and selectivity at this pressure are 5.99 Barrer, and 44.19 respectively. This is
equivalent to a 17.65% and 66.39% increase in plasticization pressure and permeability,
respectively. However, the membrane lost about 79.65% of its permeability at this pressure
while its selectivity increased by 91.13% as compared to the value at 5 bar. The timedependent
permeability tests revealed plasticization pressure as possible equilibrium point
which can be used as constraint during membrane gas separation process optimization. The
mathematical model developed showed an excellent predictive capability for plasticization
pressure. It was also shown that membrane materials selection can be efficiently optimized
using the multi – objective optimization approach
HOMEFUS : A Privacy and Security-Aware Model for IoT Data Fusion in Smart Connected Homes
No full textThe benefit associated with the deployment of Internet of Things (IoT) technology is increasing daily. IoT has revolutionized our ways of life, especially when we consider its applications in smart connected homes. Smart devices at home enable the collection of data from multiple sensors for a range of applications and services. Nevertheless, the security and privacy issues associated with aggregating multiple sensors’ data in smart connected homes have not yet been sufficiently prioritized. Along this development, this paper proposes HOMEFUS, a privacy and security-aware model that leverages information theoretic correlation analysis and gradient boosting to fuse multiple sensors’ data at the edge nodes of smart connected homes. HOMEFUS employs federated learning, edge and cloud computing to reduce privacy leakage of sensitive data. To demonstrate its applicability, we show that the proposed model meets the requirements for efficient data fusion pipelines. The model guides practitio ners and researchers on how to setup secure smart connected homes that comply with privacy laws, regulations, and standards.
Finite Element Analysis of Double-Bolt Shear-Out Fracture Failure
No full textThis paper presents the finite element (FE) analysis of double-bolt shear-out (DBSO) fracture failure. The DBSO fracture shape consists of two oppositely: inclined outer main shear fractures, inner main shear fracture, outer shear lips, and curved inner curved fractures. The DBSO begins with two outer main shear fracture initiations under shear, vertical compressive bending, and sideways bending deformations/stresses followed by the two inner main shear fracture initiations under shear and vertical compressive bending deformations. The outer shear lips occurred under vertical compression bending, shear, and sideways tensile bending stresses/deformations while the two inner curved fractures occur under rotational deformation
Privacy Issues in Smart Grid Data: From Energy Disaggregation to Disclosure Risk
No full textThe advancement in artificial intelligence (AI) techniques has given rise to the success rate recorded in the field of Non-Intrusive Load Monitoring (NILM). The development of robust AI and machine learning algorithms based on deep learning architecture has enabled accurate extraction of individual appliance load signature from aggregated energy data. However, the success rate of NILM algorithm in disaggregating individual appliance load signature in smart grid data violates the privacy of the individual household lifestyle. This paper investigates the performance of Sequence-to-Sequence (Seq2Seq) deep learning NILM algorithm in predicting the load signature of appliances. Furthermore, we define a new notion of disclosure risk to understand the risk associated with individual appliances in aggregated signals. Two publicly available energy disaggregation datasets have been considered. We simulate three inference attack scenarios to better ascertain the risk of publishing raw energy data. In addition, we investigate three activation extraction methods for appliance event detection. The results show that the disclosure risk associated with releasing smart grid data in their original form is on the high side. Therefore, future privacy protection mechanisms should devise efficient methods to reduce this risk.</p
- …
