Miracle-dz/UniFAR-FTUs huggingface dataset:
A Classification Framework of Mobile Health CrowdSensing Research: A Scoping Review

Mobile Health CrowdSensing (MHCS) is the use of sensor-based smart mobile devices to generate, collect, share and analyse real-time patient data from a population for improving health. The purpose of the paper is to report on a scoping review of research conducted on MHCS using a proposed classification framework. The framework has five categories of attributes (purpose; adoption and barriers; system attributes; software; and benefits and outcomes). Repositories searched were Medline/PubMed, Embase/Science Direct, PsycINFO, EBSCOHOST, IEEE, ACM Digital and accessible University library repositories from January 1998 to December 2018. Certain exclusion criteria were applied. 'Mobile crowd sensing' and 'mobile crowdsensing' keywords were separately used as search terms. Thirteen studies met the inclusion criteria. The findings revealed that MHCS applications in healthcare are scant. Most MHCS studies were from conference proceedings rather than traditional journals because of the fast-pace of technology-driven research. Healthcare research is costly and time-consuming thus there is a need to adopt MHCS as it can contribute towards low cost research due to the affordable devices used in generating, collecting and providing access to real-time data.
Prediction of Tariff Package Model Using ROF-LGB Algorithm

With the slowing growth of the telecommunication market and the intense competition for existing customers, Customer Churn Management has become a crucial task for all mobile network operators. Recommendation models based on customer behaviors are widely used by operators to provide diverse telecom tariff packages for suitable people and thus improve customer satisfaction. To address the low precision rate and data granularity of prior studies, this study combined rotation forest (ROF) and LightGBM and construct a hybrid algorithm (ROF-LGB). Grid search method was used in parameter tuning, and ten-fold cross-validation method was used to prevent overfitting. Using mobile data generated by operators, ROF-LGB method was tested and compared with other five traditional machine learning methods. The results showed that ROF-LGB method achieved better performance with better precision rate and execution efficiency in telecom tariff package recommendation.
Constructivist Assistive Technology in a Mathematics Classroom for the Deaf: Going Digital at a Rural Namibian Primary School

Within the context of almost nine million children with hearing disabilities in Sub-Saharan Africa, their education is an important topic. The problem was the lack of conclusive research about the effects of digital assistive technologies for educating deaf learners in Sub-Saharan African countries, such as Namibia. The question was could a digital assistive technology improve the mathematics achievement of deaf children? The research objective was to gather scientific evidence by conducting a quantitative experiment with constructivist digital assistive technology and qualitative interviews with the teachers involved. The findings from the experiment suggest that the constructivist digital assistive technology may have had a positive effect on the mathematics achievement of the learners, which was supported by the findings from the interviews. This makes an original contribution to the domain and offers an intervention that was feasible, practical and potentially effective for improving the teaching and learning of mathematics for deaf learners.
Extending the National Lake Database of Malaysia (MyLake) as a Central Data Exchange using Big Data Integration

With the rise of heterogeneous large spatial and non-spatial data, systems are developed to manage these data sets. Big data emphasizes heterogeneity among systems leading to data integration issues due to the nature of big data which includes volume, variety and velocity. MyLake is a National Lake Database to manage information and knowledge sharing on lakes in Malaysia. At the moment, data are uploaded by each agency using MyLake as a platform. Nevertheless, this is carried out manually and require timely human effort. Each agency does one-to-one data integration (in-silo) where the integration is developed according to the agency-specific needs resulting from a possibility of integration issue across agencies. Therefore, this paper introduces a big data integration approach that extends the MyLake repository. The big data integration platform is a preliminary idea of how data can be shared, integrated, retrieved, and disseminated within a reliable and authenticated environment. The proposed centralized platform consisting of a set of standards, tools, repository and registry that enable multiple integrations between different agencies. The platform offers the potential to provide a reliable platform that acts as data retriever and disseminator.
Adoption Factors of E-Marketplace and Instagram for Micro, Small, and Medium Enterprises (MSMEs) in Indonesia

E-commerce has proliferated globally, as well as nationally in Indonesia. Unfortunately, Indonesian commodities, including Micro, Small, and Medium Enterprises (MSMEs) products, have been negatively affected by the increase of imported goods traded on the country's e-marketplace and social commerce. It is a huge challenge as the MSMEs contributed to more than half of the national Gross Domestic Product (GDP). On the other hand, previous studies indicated that most Indonesian MSMEs have not fully harnessed the potential of e-commerce and social commerce. To address this problem, a research was conducted to understand factors influencing MSMEs in Indonesia adopting e-marketplace and Instagram. E-marketplace was a traditional media for e-commerce, whilst Instagram was identified as a promising social media for business. The research model was constructed based on the Technology, Organization, Environment (TOE) framework. For this context, a questionnaire was designed and distributed to MSMEs in Indonesia. There were 110 valid responses, which were analyzed with Partial Least Square-Structural Equation Modeling (PLS-SEM). The statistical findings were followed up by interviews with selected MSMEs as inputs to develop recommendation. The results show that Competitive Pressure, Financial Resources, Perceived Risk, and Top Management Support affect Indonesian MSMEs to adopt e-marketplace. Besides, factors influencing their Instagram adoption are Competitive Pressure, Perceived Benefit, Top Management Support, and Vendor Support.
The Influence of the Secondary Tasks on Driving Performance and Mental Workload at Different Speeds

To research the influence of the secondary task on driving performance at different speed, the driving simulation experiment of four specified speed conditions and four secondary task conditions was designed. The mental workload of 16 driving conditions was evaluated by DAIL subjective scale. The changes of time required to complete secondary tasks, speed maintenance ability, lateral lane keeping ability and mental workload under different driving conditions were analyzed, and the effects of driving mental workload on speed maintenance and lateral lane keeping were analyzed. The results showed that speed and secondary task type have significant differences in secondary tasks completion time and driving mental workload, but their interaction has no effect on secondary tasks completion time or driving mental workload. For different secondary tasks, the mental workload has a quadratic function relationship with the speed, and the mental workload increases rapidly with the increase of the speed. The increase of the mental workload reduces the speed maintenance ability and makes the throttle input value smaller and stable. There is no significant relationship between mental workload and lane keeping ability, but the increase of the mental workload makes the steering wheel input value larger but more unstable.
Predicting Learners' Demographics Characteristics: Deep Learning Ensemble Architecture for Learners' Characteristics Prediction in MOOCs

Author Profiling (AP), which aims to predict an author's demographics characteristics automatically by using texts written by the author, is an important mechanism for many applications, as well as highly challenging. In this research, we analyse various previous machine learning models for AP, with respect to their potential for our research problem. Based on this, we propose a Deep Learning Architecture to predict the demographics characteristics of the learners in MOOCs, incorporating multi-feature representations and ensemble learning methods. Specifically, we employ a novel pipeline, combining the most successful deep learning classifiers, Convolution Neural Networks, Recurrent Neural Networks and Recursive Neural Networks, to learn from a text. Moreover, beside the state-of-the-art training involving character and word-level input, we additionally propose phrase-level input. With this approach, we aim at deepening our understanding of the writing style of learners, and thus, predict the author profile with high accuracy. In this paper, we propose the model and architecture, and report on initial tests of our model on a large dataset from the FutureLearn platform, to predict the demographics characteristics of the learners.
A Survey on Gait Recognition via Wearable Sensors

Gait is a biometric trait that can allow user authentication, though it is classified as a “soft” one due to a certain lack in permanence and to sensibility to specific conditions. The earliest research relies on computer vision, especially applied in video surveillance. More recently, the spread of wearable sensors, especially those embedded in mobile devices, has spurred a different research line. In fact, they are able to capture the dynamics of the walking pattern through simpler one-dimensional signals. This capture modality can avoid some problems related to computer vision-based techniques but suffers from specific limitations. Related research is still in a less advanced phase with respect to other biometric traits. However, many factors - the promising results achieved so far, the increasing accuracy of sensors, the ubiquitous presence of mobile devices, and the low cost of related techniques - contribute to making this biometrics attractive and suggest continuing investigating. This survey provides interested readers with a reasoned and systematic overview of problems, approaches, and available benchmarks.
A Characterization of Digital Native Approaches To Mobile Privacy and Security

Despite their familiarity with the digital, so-called 'digital natives' are not tech-savvy, particularly with respect to privacy and security. In this study we characterize this problem by looking at a cohort of South African students. We employ a web-based survey of 100 students, supplemented by in-depth interviews with 10 additional students. In both cases we inquired about, and observed knowledge of permissions, encryption and application installation practices. Our findings show that most students (80%) do not look for or understand permissions or encryption, and use location-based services unsafely. Based on these results we argue that digital natives lack the technical skills and understanding to properly engage with mobile privacy and security. We further argue that this generation has been so over-exposed to mobile requests that violate their privacy and security that they have become desensitized and their definition of privacy and security has changed. Lastly, we discuss the implications of our findings for higher education institutions, policy, and mobile application design.
Wireless Powered Cognitive Communications with Asynchronous Channel Access

Future machine type networks, such as the Internet of Things (IoT), will be characterized by sporadic transmissions of short packets. In this paper, we study an asynchronous channel access model performed by a primary ad hoc network underlaid with a cognitive secondary wireless-powered ad hoc network. Specifically, we consider that the primary transmitters are connected to the power grid, whereas the cognitive secondary transmitters have radio frequency energy harvesting capabilities and their asynchronous channel access is established based on certain energy and interference based criteria. The sporadic channel traffic is modeled by time-space Poisson point processes and we provide an analytical framework, based on stochastic geometry, for the performance of this asynchronous system.
Industrial-Scale Stateless Network Functions

While the industry is still struggling to embrace the network function virtualization paradigm, recently a novel approach has appeared with the promise of improving the state-of-the-art: stateless virtualized network functions. Rooted in cloud-native computing, this design outsources the state embedded in virtual network functions to a dedicated "state storage" layer, facilitating elastic scaling and resiliency. While related work mostly focuses on performance, we in this paper pinpoint all other factors that weigh in when it comes to deploying the stateless design in a carrier-grade operator network. Among those we argue that reliability and flexibility are key, and we propose a system design that can be adapted to any telco use case without the need for complex coordination among the network control, the stateless network functions, and the state storage backend. Then, in extensive evaluations on synthetic use cases we show that the additional flexibility provided by our design does not come at a performance penalty; in fact, in certain cases our design outperforms the state-of-the-art significantly. Finally, we present what to our knowledge is the first product-phase realization of the stateless paradigm, an operational virtualized IP Multimedia Subsystem that can restore the live call records of thousands of mobile subscribers under a couple of seconds with half the resources required by a traditional "stateful" design.
A Convolutional Neural Network based on Batch Normalization and Residual Block for P300 Signal Detection of P300-speller System

How to detect P300 signal efficiently and accurately is great importance to improve the performance of P300-spe11er system, one of a type brain-computer interface (BCI) system. In present study, we proposed a novel convolutional neural network (CNN) for P300 signal detection of P300-spe11er system based on traditional CNN, batch normalization, and residual block, which can extract the feature of P300 signal from spatial and temporal domain with little preprocessing. We compared the results of P300 signal detection and character recognition between traditional CNN and novel CNN, and the results showed that the proposed CNN got greater accuracy of P300 detection and character recognition than traditional CNN.
Towards Scalable k-out-of-n Models for Assessing the Reliability of Large-Scale Function-as-a-Service Systems with Bayesian Networks

Typically, Function-as-a-Service (FaaS) involves state-less replication with very large numbers of instances. The reliability of such services can be evaluated using Bayesian Networks and k-out-of-n models. However, existing k-out-of-n models do not scale to the larger number of hosts of FaaS services. Therefore, we propose a scalable k-out-of-n model in this paper with the same semantics as the standard k-out-of-n voting gates in fault trees, enabling the reliability analysis of FaaS services.
Reacting to Multi-Obstacle Emergency Scenarios Using Linear Time Varying Model Predictive Control

Emergency scenarios may require trajectory planning at actuator and friction limits within tight obstacle constraints. This paper presents an approach to handle such scenarios using Linear Time Varying Model Predictive Control, in the presence of both static and dynamic obstacles. With the proposed approach, the controller transitions between a kinematic vehicle model, which is stable at low velocities, and a dynamic vehicle model, which is more accurate at high velocities, based on a threshold velocity. This ensures the functionality of the controller in emergency braking scenarios and enables manoeuvre initialization with a full braking trajectory. An adjustable safety distance concept is introduced that considers the criticality of the scenario and tightens or loosens the obstacle avoidance and friction constraints accordingly. Simulation results show that the controller is capable of handling various critical situations, including those when the vehicle comes to a stop at the end of a high speed evasive manoeuvre. Furthermore, it is more effective at keeping a safety distance from obstacles in such manoeuvres than by using fixed safety distance concept.
Improved Shrinkage Estimators of Covariance Matrices With Toeplitz-Structured Targets in Small Sample Scenarios

Shrinkage regularization is an effective strategy to estimate the covariance matrix of multi-variate random vector in small sample scenarios. The purpose of this paper is to propose improved linear shrinkage estimators of covariance matrix as two types of Toeplitz-structured target matrices are respectively employed in the shrinkage procedure. Under Gaussian and non-Gaussian distributions, the corresponding shrinkage estimators are respectively obtained in closed form by unbiasedly estimating the unknown scalar quantities which involve the true covariance matrix. Compared with the existing estimators of same type, the proposed covariance estimators show a significant improvement on the mean squared error in numerical simulations. Moreover, example applications including portfolio risk estimation and classification of real data are provided for verifying the performance of proposed covariance estimators in small sample scenarios.
Probabilistic Modeling of Vehicle Acceleration and State Propagation With Long Short-Term Memory Neural Networks

The success of Intelligent Driver Assistance (IDA) depends on the system's ability to accurately model the state of traffic surrounding the ego vehicle and predict driving behavior of the surrounding vehicles in order to help the ego driver make the best informed decisions in real-time. The ability to predict acceleration behavior is crucial as a first step towards modeling traffic patterns. In this paper, we show that Long Short-Term Memory (LSTM) neural networks are capable of producing acceleration distributions from which accurate future acceleration values can be sampled. Furthermore, state values calculated from these acceleration predictions are used as input for future predictions, showing that these networks are capable of generating realistic simulated vehicle trajectories over short prediction horizons.
Enhancing Security for Voltage Control of Distribution Systems Under Data Falsification Attacks

We consider enhancing cyber security in voltage regulation of distribution systems in the presence of malicious attacks. Due to the increase in distributed generation, the regulation of voltage has become more complicated, requiring more sensor data to be used for control. Recently, we have developed an attack detection algorithm to find data falsification attacks on voltage measurements transmitted by sectionizing switches in the feeders to a centralized controller. In this paper, the security level of the system is further improved by introducing a controller that is capable of operating the regulation in the presence of attacks by utilizing the detection results. In particular, it identifies abnormal behavior in the sensor data and ignores measurements taking extreme values among those received. Through detailed simulation studies on a small scale distribution system, we show the effectiveness of the proposed control and analyze the relation between the number of attacks and the detectability of the attacks.
Route specific Driver Characterization for Data-Based Range Prediction of Battery Electric Vehicles

The development of accurate range prediction algorithms for battery electric vehicles (BEV) is an important requirement for gaining the long-lasting confidence of future users in this technology. The driver, who strongly contributes to the energy consumption of a vehicle, has to be considered for non-automated driving scenarios.This paper introduces a framework for the acquisition and processing of situation-related driving behavior in data-based models, such as support vector machines. The correlation of the driver’s behavior to route, environment as well as vehicle characteristics is identified through lazy learning of segmental characteristic values. Suitable parameters are selected by correlation analysis and neighborhood component feature selection. In this context, a distinction is made between several road topologies like traffic lights and crossroads. During the prediction, the expected driver’s behavior is determined by searching for the k-nearest-neighbors to the current segment parameters, representing the route, environment and vehicle. The identified driver-specific parameters are used afterwards together with other significant parameters for the training and the application of a data-based model, which allows an accurate prediction of the future demand for energy. The concept provides an online implementable solution, which uses existing information systems and thus could be used for real driving scenarios.
Mitigating Noise-Induced Tracking Loss in PI Controlled Systems with Saturating Actuators

This technical note addresses a problem of mitigating noise induced tracking loss in a closed-loop system with linear plant, PI controller, saturating actuator, and anti-windup. For such systems, it was recently discovered that with the presence of zero mean measurement noise the mean of the output process at steady-state exhibits a level of tracking error. This phenomenon was termed as “Noise-Induced Tracking Error (NITE). ” Low pass filtering the measurement noise may be a solution to mitigate NITE, however, adding a filter affects the stability and also the transient tracking performance. This paper proposes to use an observer as a filter to decouple filter dynamics and the system stability, avoid performance loss in transient tracking, and most importantly, eliminate the noise induced tracking error. To accomplish these, a standard is necessary for observer gain selection, which is provided based on Quasilinear Control Theory. Finally, proof of the separation principle for PI-controlled feedback system is derived and the region of stability that considers the actuator saturation nonlinearity is presented.
Reduction of Open-Circuit DC Winding Induced Voltage in Hybrid-Excited Switched -Flux Permanent Magnet Machine

The open-circuit DC winding induced voltage in a hybrid-excited switched-flux permanent magnet (HESFPM) machine is investigated in this paper. The phenomenon of the DC winding induced voltage under open-circuit condition is presented and the mechanism of this phenomenon is explained. Rotor step skewing and unequal rotor teeth are comparatively investigated by the finite element (FE) method to reduce the open-circuit DC winding induced voltage. FE results show that the peak to peak value of the open-circuit DC winding induced voltage can be effectively reduced by 78.87% with 5-step rotor skewing, whilst the average electromagnetic torque can be maintained at 96.74%. By using unequal rotor teeth, the peak to peak value of the open-circuit DC winding induced voltage can be reduced by 95.43% and 86.54% for the optimal machines 2 and 3, respectively, whilst the average electromagnetic torque can be maintained at 89.93% and 94.62%, respectively.