Miracle-dz/UniFAR-FTUs huggingface dataset:
Tree-Based Methods for Loan Approval

Loan approval is one of the most important processes that any banking organization owns. The acceptance or rejection of any loan application has a direct impact on the bank revenue and the profitability in quarterly issued financial statements. Though loan approval is a critical process, the actual decision made is not a straightforward procedure and comes with a lot of uncertainties. Recently, statisticians and data scientists have tried to automate this process to minimize risk and increase profitability by applying different statistical learning methods. In this work we explore a framework with an application by applying tree-based methods on publicly available dataset. This work aimed at developing a high performance predictive model for loan approval prediction using decision trees. Experiments were made in different varieties of tree methods ranging from the most simplified and comprehensible decision tree reaching up to the most complex random forests. Results yielded inadequate performance with respect to simplified decision trees due to the highlight correlated and complex feature space, majority of critical parameters affecting loan approval was not reflected upon and yielded an impractically over-simplified tree. However, boosting came in superior in terms of performance, relevance and interpretation via the importance chart scoring accuracy on testing dataset [98.75%] specificity [100%], Minority class prediction accuracy [92.85%], and classification efficiency of [97.0%]. Therefore, boosting-based decision-tree predictive model was recommended to facilitate decision making regarding the eligibility of loan applicants based on their characteristics.
Total Quality Management Practices and Adoption in Construction Industry Organizations: A Review

Total Quality Management (TQM) practices are well-known to encourage the performance of organizations in terms of procedures, services, and products to meet customers’ expectations. TQM plays a critical role in developing products as well as other developments in the arena of construction industry organizations. This paper reviews the concept of TQM, benefits and connection between TQM and project performance in construction organizations. The reviewed studies focused on practices and adoption of quality through implementing TQM in the construction industry and its suitable applications in the various phases of the construction project. The concept of TQM mostly facilitates attaining continual performance improvements in order to sustain business excellence. This study contributes to the science and knowledge on TQM and project performance by delivering theoretical significance on TQM ability to improve the performance of the construction industry. Numerous studies have recommended the practices and adoption of TQM are likely to enhance a company’s performance. This paper has further identified some benefits of TQM such as improvement in product quality, increased customer satisfaction, and continues improvement towards world-class organizations and achieve the customers’ needs and expectations successfully.
Cloud-based Internet of Things Approach for Smart Irrigation System: Design and Implementation

Water plays a significant role in the economic development of countries. The agriculture sector is the most water-consuming; this sector consumes 69% of the freshwater. However, farmers often use traditional irrigation systems to water their crops. These systems are ineffective and consume a IoT of time and effort, mainly when several fields are distributed in different geographical regions. Therefore, employing smart irrigation techniques will significantly overcome these problems. This paper proposes an intelligent irrigation framework based on Wireless Sensor Network (WSN) and Internet of Things (IoT) cloud services. The proposed system consists of three main components; the WSN, the control unit, and cloud services. Arduino Uno and XBee ZigBee modules are combined to gather sensors data and send them wirelessly to the control unit. YL-69 sensor is used to monitor the soil moisture. Raspberry Pi is utilized to gather data, process them, provide the proper decision, and transfer them to ThingSpeak IoT cloud. In the cloud, the data collected from the system is stored to create instance visualization of live data and send alerts. This allows farmers to monitor the status of crops and make the required decisions. After inspecting the prototype, many challenges are posed for future work.
Slice Merging/Spliting Operations and Tenant Profit Optimization Across 5G Base Stations

Network slicing is a promising technology that can tailor the network to support diverse types of services over a common physical network infrastructure, and tenants can lease the customized slices from network providers to provide customized services for their users. This paper focuses on tenant profit optimization in 5G Radio Access Network (RAN) slicing and analyzes slice resource reservation and reconfiguration operation. A slice cost model is proposed, in which a base cost is included to compensate reconfiguration management overhead for the network providers. Increasing the accuracy of tenant resource requirement prediction can decrease tenant cost while guaranteeing QoS of its users. Further, we analyze the price strategy based on user traffic, and propose several alternative price scheme. In slicing across multiple Base Stations (BSs) scenario, we study two slice management operations, called slice mergence/split, similar but not identical to the breathing behavior of conventional cellular BS management. The tenant profit optimization problem across adjacent BSs is formulated, which can be used in slice mergence/splilt decision making. Taking VR (Virtual Reality) application as an example, simulation in allusion to the typical user arrival process is performed. The simulation indicates how these two operations can increase tenant profits.
The Ability to Recognize Microexpression and Detect Deception in the Elderly

The ability to read other people’s emotions among older people decline with age, which may result in weakened ability to detect deception. Microexpression is closely related to deception; and microexpression can be a valid cue to deception. Therefore, we hypothesized that there existed a positive correlation between microexpression recognition and deception detection in the older adults. To test the hypothesis, twenty-eight older adults (14 females, mean age=70 years, SD=6.2 years) were recruited for this study. They completed the tasks of the microexpression recognition and deception detection. The results showed that: (1) there was a positive correlation between fear macroexpression recognition and deception detection in the elderly (r=0.416, p =.028); (2) the ability of the elderly to detect truth (i.e., truth detection, mean accuracy=0.53) was significantly higher than the ability to detect deception (lie detection), t$(27)$=2.856, p=0.008; (3) there was significant main effect of type of emotional expressions$(F(2,25)$=11.721, p<. 001), specifically the accuracy of recognizing happiness was superior to every other emotions’ accuracies of recognition; and (4) there were also significant differences between macroexpressions and microexpressions recognitions among the elderly $(F(1,26)$=113.918, p<. 001). The results suggested that, to some degree, there was a positive correlation between the ability to recognize expressions and detecting deception in the elderly, and some factors will modulate the relationship.
Improving Wind Turbine Pitch Control by Effective Wind Neuro-Estimators

Data about wind are usually available from different databases, for different locations. In general, this information is the average of the wind speed over time. The wind reports are crucial for designing wind turbine controllers. But when working with floating offshore wind turbines (FOWT), two problems arise regarding the wind measurement. On the one hand, there are no buoys at deep sea, but near the coast where the wind is not so strong neither so stable; so the measurements do not fully correspond to reality. On the other hand, these floating devices are subjected to extreme environmental conditions (waves, currents, $\ldots $ ) that produce disturbances and thus may distort wind measurements. To address this problem, this work presents a novel pitch neuro-control architecture based on neuro-estimators of the effective wind. The control system is composed of a proportional-integral-derivative (PID) controller, a lookup table, a neuro-estimator, and a virtual sensor. The neuro-estimator is used to estimate the effective wind in the FOWT and to forecast its future value. Both current and future wind signals are combined and power the controller. The virtual sensor also provides a measure of the effective wind based on other available signals related to the wind turbine, such as the pitch angle and the angular velocity of the generator. Neural networks are trained online to adapt to changes in the environment. Intensive simulations are carried out to validate the effectiveness of this neuro control approach. Controller performance is compared to a PID, obtaining better results. Indeed, an improvement of 16% for sinusoidal wind and an average improvement of 8% are observed.
Affordances and IT Design: A Typology for Social Media and Platform Affordances

Affordance is a key term for understanding digital products' interface and design, implying what a digital platform allows its users to do. Due to lack of previous studies, the present study aims to provide a typology for affordances. Three theories of Gibson's relational affordances; Norman's Perceived affordances; and Gaver's Technology affordances are used as the framework. As a conceptual research, this study adopted typology approach. Typology researches contribute through distinguishing and categorizing of extant knowledge about a phenomenon, construct, or theory. First, a bibliometric analysis of literature in the subject of affordances was conducted and then, the identified affordances were classified in categories. Cognitive, physical, sensory, functional, explicit, graphic, language, hidden, pattern, metaphorical, false, negative and animated affordances are the major types that are identified in this research.
A Convex-based Computation Offloading Mechanism in Vehicular Edge Computing

Mobile edge computing (MEC) has been an effective paradigm for supporting computation-intensive vehicular applications by offloading resources at network edge. However, due to the unique characteristics of vehicular networks, including high mobility of vehicles, dynamic distribution of vehicle densities and heterogeneous network resources, it is still challenging to implement efficient computation offloading mechanism in MEC-assisted vehicular networks. In this paper, we investigate a novel scenario of computation offloading in MEC-assisted architecture, where task upload coordination between multiple vehicles, task migration between MEC/cloud servers and heterogeneous computation capabilities of MEC/cloud severs, are comprehensively investigated. On this basis, we formulate cooperative computation offloading (CCO) problem by modeling the procedure of task upload, migration and computation based on queuing theory, which aims at minimizing the delay of task completion. To tackle the CCO problem, we propose a probabilistic computation offloading (PCO) algorithm, which achieves the optimal allocation probability in an iterative way based on a convex framework called Alternating Direction Method of Multipliers (ADMM). Last but not the least, we implement the simulation model. The comprehensive simulation results show the superiority of the proposed algorithm under a wide range of scenarios.
Adaptive DRX Method for MTC Device Energy Saving by Using a Machine Learning Algorithm in an MEC Framework

Machine-type communication (MTC) is recognized as an enabling building block for constructing the Internet of Things (IoT) through cellular networks. Since MTC devices (MTCDs) are usually powered by batteries, reducing the energy consumption of an MTCD is an important requirement in the design of an IoT system. The third-generation partnership project (3GPP) specified discontinuous reception (DRX) to save energy in user equipment (UE) by making a UE device sleep whenever it is not involved in data transmission or reception. However, UE is used for human-type communications (HTCs), and MTCs are different from HTCs in many ways, namely, in terms of traffic arrival patterns, mobility, and information priority. Thus, using the DRX developed for UE to save the energy of an MTCD leads to inefficiency in terms of the amount of power saved and the data transmission delay. In the literature, many efforts have been made to dynamically vary the DRX cycle based on traffic activities to reduce the energy consumption of an MTCD. These assume a specific distribution for the traffic arrival process and predict the next packet arrival time to control the DRX cycle. However, since the characteristics of the MTC traffic arrival pattern vary widely according to the types of applications and the network status, previous approaches are limited in the achievable energy efficiency and the range of MTC applications that they can support. To address these issues, we devise adaptive DRX (ADRX) by adopting a machine learning algorithm called bank-of-experts. Without making any assumption regarding the underlying traffic process, ADRX predicts the next packet arrival time using only the observed packet arrival times. Using the predicted next packet arrival time, ADRX dynamically adjusts the sleep interval. In terms of implementation, ADRX is executed in an eNB by following the Multi-access Edge Computing (MEC) framework. Thus, an MTCD avoids consuming the energy required to execute a sophisticated energy-saving algorithm while obtaining the benefits provided by ADRX. Since different IoT services have different requirements for the amount of saved energy and data transmission delay, we design a loss function by combining these two performance metrics so that ADRX can support a wide range of application services. We first analyze the bound of the cumulative regrets of ADRX, and then through simulations, we compare the performance of ADRX with the standard DRX with optimal sleep duration (OSDRX) and a generalized and autonomous DRX (GDRX) scheme. The results show that ADRX outperforms GDRX in terms of the loss function and has almost the same performance as OSDRX.
Backstepping tracking control for nonholonomic mobile robot

The present paper aims to develop a control law approach for controlling a wheeled mobile robot. The present work investigates both the kinematic and dynamic control for guiding the wheeled mobile robot to follow the desired trajectory. Firstly, the kinematic model of the mobile robot is introduced. Secondly, the Backstepping controller (BSC) is required for controlling the wheeled mobile robot in order to follow the desired trajectory. The stability of the system is achieved using the Lyapunov stability theory. Then, the dynamic model is investigated in order to control the mobile robot in the real time. It is formulated by using Euler-Lagrange approach. To ameliorate the performance of the backstepping controller, a genetic algorithm was adopted to design an optimal control law. At the end, to prove the efficiency of the developed approach, simulation results are presented.
Coordinated Multi-Point Transmission in 5G and Beyond Heterogeneous Networks

Over the years, there has been a significant increase in the human population as well as the autonomous devices requiring ubiquitous wireless connectivity. The current wireless technology is no longer meeting the prodigious demands of wireless capacity and data rates. This problem bounds the wireless network operators to have second thought regarding the available networks and their resources that have become narrow and limited to accommodate the entire traffic. In this paper, we discuss LTE Heterogeneous Networks (HetNets) as the most competitive solution to meet the ever-increasing demand for wireless data rates. The concept of HetNets has risen out of the need of cellular network operators to be able to operate networks consisting of a variety of radio access technologies, formats of cells and many other aspects, and combining them to operate in a seamless fashion. This article focuses on the Coordinated Multipoint (CoMP) technique and its effectiveness to overcome the interference and security challenges in HetNets. CoMP has proven to be a tremendous interference mitigation technique along with physical layer security (PLS) improvement as well. It provides high secrecy coverage probability and interference performance through careful coordination among the Base Stations (BSs). We will also provide simulation results to show how CoMP based resource allocation techniques allow a substantial increase in terms of capacity and hence reduced interference.
Narrowband IoT Device To Device Pairing Scheme To Save Power

Narrowband Internet of Things (Nb-IoT) has emerged as one of the most promising wireless technology for the Internet of things. IoT devices have resource constraints that require novel techniques to optimize the devices' connectivity with the base station. Device to device connectivity is one of the promising solutions to form the IoT devices network with robust connectivity. The paper presents a technique that finds the potential relay device for an IoT device based on the least distance, the least bit error rate (BER), and the least power loss. Based on this combination the transmitting power can be reduced and the battery life of the IoT device can be improved. Simulation results demonstrates the supremacy of our proposed technique.
The Effects of Two Game Interaction Modes on Cortical Activation in Subjects of Different Ages: A Functional Near-Infrared Spectroscopy Study

Increasing age and various pathological factors lead to cognitive function decline among the elderly. The most serious cognitive dysfunctions among the elderly include mild cognitive impairment (MCI), Alzheimer’s disease (AD), and vascular dementia (VAD). Cognitive training is an effective approach to mitigate the decline in cognitive function. Recent studies have confirmed that emerging training methods using new technologies, such as virtual reality (VR) and mobile phones, can be used effectively for cognitive training. This study used functional near-infrared spectroscopy (fNIRS) to compare the brain activation of young and elderly people during VR and mobile phone training when performing a cognitive training game. fNIRS has been shown to be an effective tool for monitoring cognitive decline. In the current study, the MMSE scale was used to measure cognitive performance and fNIRS was used to measure brain activation among 20 youth (mean age 25.33± 1.59 years) and 17 elderly people (mean age 63± 4.35 years). The results showed that the mobile phone game produced significant activation of the prefrontal lobe (PFC) and the VR game produced significant activation of the parietal lobe (MC). The average MMSE scale score of the elderly group was lower than that of the young group and was strongly correlated with PFC activation. This study confirms that elderly people have reduced cognitive function compared to young people. The results indicate that mobile phone games have a positive training effect on reducing cognitive decline, and that VR is a suitable means for cognitive function training among the elderly.
Exploration of Solutions for Smart Cities: Challenges in Privacy and Security

Smart cities are improving the urban quality of life. The cities are providing more and more facilities to enhance the living standards of city residents. Implementation of smart cities has improved the services provided by various urban systems. Out of the various challenges being faced by smart cities, most important are the privacy / security of its residents and sanctity of data systems. Traditional cyber security protection policies cannot be applied directly to smart city applications. Furthermore, it is important to identify these cyber threats so as to design and implement effective countermeasures. In this paper we will focus on current measures being taken to counter privacy and security threats to smart cities, identify limitations of these measures and also present future trends in this field. This research allows users of the domain to select an appropriate technology for relevant security threat and to design smart cities application according to their needs.
Towards Distributed Edge-based Systems

In the past few years, researchers from academia and industry stakeholders suggest adding more computational resources (i.e., storage, networking, and processing) closer to the end-users and IoT domain, respectively, at the edge of the network. Such computation entities perceived as edge devices aim to overcome high-latency issues between the cloud and the IoT domain. Thus, processing IoT data streams closer to the end-users and IoT domain can solve several operational challenges. Since then, a plethora of application-specific IoT systems are introduced, mainly hard-coded, inflexible, and limited extensibility for future changes. Additionally, most IoT systems maintain a centralized design to operate without considering the dynamic nature of edge networks. In this paper, we discuss some of the research issues, challenges, and potential solutions to enable: i) deploying edge functions on edge resources in a distributed manner and ii) deploying and scaling edge applications on-premises of Edge-Cloud infrastructure. Additionally, we discuss in detail the three-tier Edge-Cloud architecture. Finally, we introduce a conceptual framework that aims to enable easy configuration and deployment of edge-based systems on top of heterogeneous edge infrastructure and present our vision within a smart city scenario.
Indian Road Conditions and Accident Risk Predictions using Deep Learning Approach – A Review

Indian road conditions are very concerning as they are traffic prone in many locations. Further, the road surface conditions are not uniform in several places which may lead to a sudden impact on the vehicle dwellers on the highways. Furthermore, the driver may be the cause of accidents due to ignorance and improper focus on the road during driving. Statistical sources show that there are many reasons regarding the risk of road accidents to consider while calculating the reasons and the risk of road accidents. Various factors lead to road accidents and hence a strong methodology and the support of computer-aided support is required to solve such critical problems. The current work deals with the application of the deep learning approach to predict critical road accidents.
Physical-layer Cyberattack Resilient OFDM Automotive Radars

In this paper, we present a physical-layer cyberattack resilient OFDM radar design for automotive applications. A typical FMCW radar system sends multiple chirp signals in a coherent processing interval, whereas an OFDM radar sends multiple OFDM frames [1]. OFDM radar approach has a potential advantage for automotive applications, which is the use same RF hardware and bandwidth for both AV radar and V2V/V2X communication [2]. Cyberattack resilience of FMCW radars has been studied in [3], [4], [5], [6], in this paper we address the same problem for OFDM radars, propose a new radar algorithm called OFDM-i, and a cyberattack detector D1. The proposed idea is based on selecting a fixed percentage of subcarriers as null, and changing these null bands randomly for each frame. To minimize the impact of this on range resolution, we also require no more than two neighboring null bands in each frame. We demonstrate the performance of OFDM-i both by using simulations, and then using real data obtained from a National Instruments 39 GHz mmwave system. We also provide an upper bound for the false cyberattack alarm rate, study the resilience of the proposed system using a combinatorial analysis, and then using simulated attacks. Finally, we summarize possible future research directions.