Miracle-dz/UniFAR-FTUs huggingface dataset:
Duration of daily smartphone usage as an antecedent of nomophobia: exploring multiple mediation of loneliness and anxiety

ABSTRACT This study aims to investigate the multiple mediation of loneliness and anxiety in the relationship between adolescents’ duration of daily smartphone usage and their nomophobia levels, based on social cognitive theory. The data were collected from 274 adolescent smartphone users and analysed through ordinary least-squares regression analysis and bootstrap methods, as well as descriptive statistics and the Pearson correlation. The findings primarily indicated that there is a significant correlation between the duration of daily smartphone usage, loneliness, anxiety, and nomophobia. Additionally, both the single and multiple mediation effects of loneliness and anxiety in the relationship between duration of daily smartphone usage and nomophobia are significant. The findings further indicated that the single mediation effect of anxiety is more powerful compared with other models in which the pair of loneliness and anxiety, and loneliness as a single variable, significantly mediated the relationship. Based on our findings, it was concluded, as consistent with the relevant literature, that as adolescents’ daily smartphone usage increases, they feel more lonely and anxious, and consequently demonstrate more nomophobic behaviours. The current study contributed to the relevant literature by modelling the relationships among duration of daily smartphone usage, loneliness, anxiety, and nomophobia.
Design of A Convolutional Neural Network System to Increase Diagnostic Efficiency of Alzheimer’s Disease

The most common degenerative neural disease, Alzheimer’s disease (AD), is insidious and almost always requires imaging modalities to be diagnosed early. MRI is the most common one used, but requires timely interpretation. Here we develop a convolutional neural network (CNN)-based system that determines whether a brain MR image has AD or normal. First, feature extraction is performed to separate various parts of the brain. Then, the data is processed to differentiate normal brain from AD brain, solely using MR image. Finally, the neural network is supplemented using data from the patient’s history and physical examination. In this first phase, we were able to extract features from the brain MR image, initially by masking the image and separating the white matter, grey matter, and cerebrospinal fluid called the grey level co-occurrence method (GLCM). This method is able to using a convolutional neural network.
Bilateral Control with Task Learning and Adaptation to Environment

The Motion Copying System permits to save an operator task in terms of position and force references for the action reproduction whenever the operator isn't available or to train new users. This thesis analyzes the MCS design and limitations, the Bilateral Control System on which the MCS is based, and proposes a model to adapt the saved task to new environmental conditions.
Evaluating and Improving LXC Container Migration Between Cloudlets Using Multipath TCP

The advent of the Cloudlet concept—a “small data center” close to users at the edge is to improve the Quality of Experience (QoE) of end users by providing resources within a one-hop distance. Many researchers have proposed using virtual machines (VMs) as such service-provisioning servers. However, seeing the potentiality of containers, this thesis adopts Linux Containers (LXC) as Cloudlet platforms. To facilitate container migration between Cloudlets, Checkpoint and Restore in Userspace (CRIU) has been chosen as the migration tool. Since the migration process goes through the Wide Area Network (WAN), which may experience network failures, the Multipath TCP (MPTCP) protocol is adopted to address the challenge. The multiple subflows established within a MPTCP connection can improve the resilience of the migration process and reduce migration time. Experimental results show that LXC containers are suitable candidates for the problem and MPTCP protocol is effective in enhancing the migration process.
Vesti: Energy-Efficient In-Memory Computing Accelerator for Deep Neural Networks

To enable essential deep learning computation on energy-constrained hardware platforms, including mobile, wearable, and Internet of Things (IoT) devices, a number of digital ASIC designs have presented customized dataflow and enhanced parallelism. However, in conventional digital designs, the biggest bottleneck for energy-efficient deep neural networks (DNNs) has reportedly been the data access and movement. To eliminate the storage access bottleneck, new SRAM macros that support in-memory computing have been recently demonstrated. Several in-SRAM computing works have used the mix of analog and digital circuits to perform XNOR-and-ACcumulate (XAC) operation without row-by-row memory access and can map a subset of DNNs with binary weights and binary activations. In the single array level, large improvement in energy efficiency (e.g., two orders of magnitude improvement) has been reported in computing XAC over digital-only hardware performing the same operation. In this article, by integrating many instances of such in-memory computing SRAM macros with an ensemble of peripheral digital circuits, we architect a new DNN accelerator, titled Vesti. This new accelerator is designed to support configurable multibit activations and large-scale DNNs seamlessly while substantially improving the chip-level energy-efficiency with favorable accuracy tradeoff compared to conventional digital ASIC. Vesti also employs double-buffering with two groups of in-memory computing SRAMs, effectively hiding the row-by-row write latencies of in-memory computing SRAMs. The Vesti accelerator is fully designed and laid out in 65-nm CMOS, demonstrating ultralow energy consumption of <20 nJ for MNIST classification and $< 40~ \mu \text{J}$ for CIFAR-10 classification at 1.0-V supply.
Tensor factorization to extract patterns in multimodal EEG data

Noisy multi-way data sets are ubiquitous in many domains. In neuroscience, electroencephalogram (EEG) data are recorded during periodic stimulation from diﬀerent sensory modalities, leading to steady-state (SS) recordings with at least four ways: the channels, the time, the subjects and the modalities. Improving the signal-to-noise ratio (SNR) of the SS responses is crucial to enable their practical use. Supervised spatial ﬁltering methods can be considered for this purpose to relevantly guide the extraction of speciﬁc activity patterns. Nevertheless, such approaches are diﬃcult to validate with few subjects and can process at most two data ways simultaneously, the remaining ones being either averaged or considered independently despite their dependencies. This paper hence designs unsupervised tensor factorization models to enable identifying meaningful underlying structures characterized in all ways of multimodal SS data. We show on EEG recordings from 15 subjects that such factorizations faith-fully reveal consistent spatial topographies, time courses with enhanced SNR and subject variations of the periodic brain activity.
Understanding User Preferences of Digital Privacy Nudges - A Best-Worst Scaling Approach

Digital nudging in privacy has become more important to protect users of information systems while working with privacy-related data. Nudging is about altering a user’s behavior without forbidding any options. Several approaches exist to “nudge” users to change their behavior. Regarding the usage of digital privacy nudges, research still has to understand the meaning and relevance of individual nudges better. Therefore, this paper compares the preferences of users for different digital nudges. To achieve this goal, it presents the results of a so-called best-worst scaling. This study contributes to theory by providing a better understanding of user preferences regarding design variations of digital nudges. We support practitioners by giving implications on how to design digital nudges in terms of user preferences.
A remote sensing based approach for optimizing sampling strategies in tree monitoring and agroforestry systems mapping

Characterizing agroforestry systems (AFS) at landscape scale is of a great importance for development planning at regional scale in Africa. Therefore, the major constraint to effective AFS mapping with remote sensing is the high diversity within landscapes. To have a robust and representative sample of training data, this study proposes an optimized sampling strategy guided by the AFS functioning and allowing to take into account the landscape diversity. A simple and reproducible approach based on unsupervised classification of remote sensing data and an a priori knowledge on the environment functioning is developed. The study is conducted on AFS of the Senegalese Peanut Basin. Assuming that AFS landscapes with similar trees and crop cover composition will have similar phenological development, a multiresolution segmentation was performed on Sentinel-2 NDVI time series to obtain homogeneous landscape units. Then for each unit, landscape diversity proxies were derived from various geospatial data sources, namely vegetation productivity and its temporal dynamic, actual evapotranspiration, woody cover rates and soil type. Using a hierarchical clustering, four classes of typical unit of the landscape heterogeneity gradient were obtained. On this basis an optimized sampling plan was produced and used to carry out an inventory campaign of tree biodiversity (figure). The results showed a well-defined landscape diversity gradient, confirmed by the field inventory of tree species.
Using Evolutionary Process for Cross-Version Software Defect Prediction

Cross-version defect prediction can effectively reduce the construction cost of a model, which is of great significance for understanding the causes of defects in subsequent software versions and improving the quality of software products. The objective of this work is to validate the feasibility of the cross-version defect predictor using the evolutionary process in different scenarios and to investigate practical guidelines for the choice of evolutionary process and historical data of a given project. Firstly, we verified the effectiveness of the evolutionary process attributes in the cross-version prediction model. Then, we analyzed the cross-version model prediction performance based on multi-version historical data. Finally, multiple benchmark models were selected for experimental comparison to further verify the effectiveness of the proposed method. The experimental conclusions can not only effectively reduce the construction cost of the defect prediction model in the version evolution scenarios, but also help developers understand the defect generation mechanism in the subsequent versions and improve the software development process in a targeted way
Design and evaluation of mobile sensing technologies for identifying medicines by people with visual disabilities

PurposePeople with visual impairments may have difficulty in identifying information, either on a bottle or package of medicine. As a result, many of them need the help of a third party to discern which medication they should take. In this perspective, this study aims to assist people with visual impairments to identify their medicine by using mobile sensing technologies.Design/methodology/approachThe authors have developed three versions of a mobile application. They use three technologies, namely, near field communication, QR code and magnetic field, for drug identification. The entire development process was based on user-centered design. And, the authors also performed an initial usability assessment of these applications with ten users who were blind.FindingsPreliminary results show user preference for the near field communication approach and reinforce some known drawbacks concerning the use of QR code by people with visual impairments.Research limitations/implicationsAlthough the authors have evaluated these technologies with few mobile devices, they perceived that the acquired values of the magnetic field suffered distortions culminating, in some cases, in a non-identification of the medicine, or even in incorrect identification.Practical implicationsCurrent mobile device magnet sensors are not yet robust to be used for medicine identification.Social implicationsMobile apps combined with medicine identification can contribute to the autonomy of people who are blind.Originality/valueThe main contribution of this paper is to integrate and analyze distinct mobile-based sensing technologies for identifying medicines by people with visual impairments.
Cooperative Tracking by Multi-Agent Systems Using Signals of Opportunity

The omnipresent signals of opportunity (SOOP) enable an effective way for passive target tracking using multiple agents. However, cooperative target tracking via non-cooperative SOOP is challenging since the positions of agents are not precisely known. In this paper, we determine the performance bounds of cooperative tracking using SOOP by multiple asynchronous agents equipped with antenna arrays. The Fisher information matrix of joint target and agent positions can be decomposed as the sum of the information from SOOP and self-localization networks, where the correlation of signals introduces an additional Fisher information component and the multipath effect is characterized by path-overlap coefficients. We demonstrate how the location information coupling between the target and agents affects localization accuracy and how the cooperation among agents improves tracking performance. Moreover, the angular information is shown to mitigate multipath and asynchronous effects by spatiotemporal separation and time-independent measurements, respectively. Then we propose a distributed hybrid belief propagation based algorithm for cooperative tracking and network synchronization via likelihood consensus. Finally, numerical results validate our theoretical analysis and the performance of the proposed algorithm.
Optimizing Wireless Sensor Network Installations by Visibility Analysis on 3D Point Clouds

In this paper, a MATLAB tool for the automatic detection of the best locations to install a wireless sensor network (WSN) is presented. The implemented code works directly on high-resolution 3D point clouds and aims to help in positioning sensors that are part of a network requiring inter-visibility, namely, a clear line of sight (LOS). Indeed, with the development of LiDAR and Structure from Motion technologies, there is an opportunity to directly use 3D point cloud data to perform visibility analyses. By doing so, many disadvantages of traditional modelling and analysis methods can be bypassed. The algorithm points out the optimal deployment of devices following mainly two criteria: inter-visibility (using a modified version of the Hidden Point Removal operator) and inter-distance. Furthermore, an option to prioritize significant areas is provided. The proposed method was first validated on an artificial 3D model, and then on a landslide 3D point cloud acquired from terrestrial laser scanning for the real positioning of an ultrawide-band WSN already installed in 2016. The comparison between collected data and data acquired by the WSN installed following traditional patterns has demonstrated its ability for the optimal deployment of a WSN requiring inter-visibility.
Natural Image Classification based on Multi-Parameter Optimization in Deep Convolutional Neural Network

Traditional machine learning algorithms cannot adequately train the parameters of networks using massive data. A deep convolutional neural network based on multi-parameter optimization by the TensorFlow deep learning framework is designed in this paper. In order to improve the training speed and prevent over-fitting, we improve and optimize the multi-parameters, including the batch value, dropout, and momentum in the network structure. The experiment involves training and testing on the standard natural image data sets in cifar-10 and cifar-100. The experimental results show that the method achieve better classification accuracy in less time compared with other algorithms, such as Conv-KN, ImageNet-2010, SVM, LR, and Boosting.
Prototipe Akses Pintu Masuk Stadion Terintegrasi dengan Kartu RFID sebagai Tiket Berbasis Arduino Uno

ABSTRAKSepak bola merupakan olah raga yang digemari masyarakat sebagai hiburan. Di stadion sering dijumpai penonton tanpa tiket atau dengan tiket palsu, sehingga menyebabkan kerugian penyelenggara. Pada makalah ini dibuat prototipe akses masuk stadion dengan mengimplementasikan Radio Frequency Identification (RFID) untuk tiket dan kunci akses pintu masuk stadion, dengan tujuan mencegah penonton masuk ke stadion dengan tiket palsu atau tanpa tiket, dan tiket dapat dimanfaatkan kembali. Sistem ini menggunakan RFID tag berbentuk kartu sebagai tiket dan dibaca dengan RFID Reader RDM 6300 untuk dibandingkan dengan data yang disimpan pada SD Card, jika sesuai maka solenoid aktif dan penonton dapat masuk stadion, sedangkan jika tidak sesuai, maka pintu stadion tidak dapat dibuka. Dari hasil pengujian, sistem mekanik dapat berfungsi sesuai dengan yang diharapkan dan seluruh RFID tag dapat dikenali dalam jarak optimal 3 cm. Keseluruhan pengguna yang telah terdaftar dapat membuka pintu.Kata kunci: Tiket, Stadion, Arduino, RFID, RDM6300 ABSTRACTFootball is a sport that is loved by society as an entertainment. In stadiums, there are often spectators without tickets or with fake tickets, causing losses to the organizers. In this paper a prototype of stadium entrance access was made by implementing Radio Frequency Identification (RFID) for tickets and access keys to stadium entrances, with the aim of preventing viewers from entering the stadium with fake tickets or no tickets, and tickets can be reused. This system uses cardshaped RFID tags as tickets and is read with an RDM 6300 RFID Reader to be compared with data stored on the SD Card, if it matches then the solenoid is active and the viewer can enter the stadium, whereas if it is not appropriate, the stadium door cannot be opened. From the results of testing, the mechanical system can function as expected and the entire RFID tag can be identified at an optimal distance of 3 cm. All registered users can open the door.Keywords: Ticket, Stadium, Arduino, RFID, RDM6300
Multivariate small area estimation under nonignorable nonresponse*

ABSTRACT We consider multivariate small area estimation under nonignorable, not missing at random (NMAR) nonresponse. We assume a response model that accounts for the different patterns of the observed outcomes, (which values are observed and which ones are missing), and estimate the response probabilities by application of the Missing Information Principle (MIP). By this principle, we first derive the likelihood score equations for the case where the missing outcomes are actually observed, and then integrate out the unobserved outcomes from the score equations with respect to the distribution holding for the missing data. The latter distribution is defined by the distribution fitted to the observed data for the respondents and the response model. The integrated score equations are then solved with respect to the unknown parameters indexing the response model. Once the response probabilities have been estimated, we impute the missing outcomes from their appropriate distribution, yielding a complete data set with no missing values, which is used for predicting the target area means. A parametric bootstrap procedure is developed for assessing the mean squared errors (MSE) of the resulting predictors. We illustrate the approach by a small simulation study.
Organisational project evaluation via machine learning techniques: an exploration

ABSTRACT This study explores ways an organisation can save time; review all proposed innovative, internal ideas; and, identify relevant start-up companies able to bring these ideas to fruition within a knowledge management framework. It uses text-mining techniques, including Python for data extraction and manipulation and topic modelling with Latent Dirichlet Allocation and Jaccard similarity indexes as a basis for evaluation of potentially valuable project ideas. Results show that internal organisational project ideas can be automatically matched with external data regarding potential implementation partners using big data knowledge management approaches. This ensures internal ideas are not overlooked or lost, but rather considered further so potentially profitable and viable opportunities are not missed. Increased use of big data to predict innovation and add value opens new channels to utilise text analysis in organisations and ensure internal innovation through a sustainable knowledge management approach.
Efficient and flexible hardware structures of the 128 bit CLEFIA block cipher

In this study, high-throughput and flexible hardware implementations of the CLEFIA lightweight block cipher are presented. A unified processing element is designed and shared for implementing of generalised Feistel network that computes round keys and encryption process in the two separate times. The most complex blocks in the CLEFIA algorithm are substitution boxes (S 0 and S 1 ). The S 0 S-box is implemented based on area-optimised combinational logic circuits. In the proposed S-box structure, the number of logic gates and critical path delay are reduced by using the simplification of computation terms. The S-box S 1 consists of three steps: a field inversion over F 2 8 and two affine transformations over F 2 . The inversion operation is implemented over the composite field F(2 4 ) 2 instead of inversion over F 2 8 which is an important factor for the reduction of area consumption. In addition, we proposed a flexible structure that can perform various configurations of CLEFIA to support variable key sizes: 128, 192 and 256 bit. Implementation results of the proposed architectures in 180 nm complementary metal-oxide-semiconductor technology for different key sizes are achieved. The results show improvements in terms of execution time, throughput and throughput/area compared with other related works.
Improvement of blood pressure estimation from face video using RGB camera

In this paper, we investigated the correlation between blood pressure (BP) and image-based pulse transit time (iPTT) acquired from only face using RGB camera. In general, the value of iPTT can be calculated from a transition time at peaks of hemoglobin amount which is extracted from RGB values of the camera. The transition time of peaks is obtained by time of the peak at the face and the palm. In the previous research, it is known that there is a correlation between BP and iPTT. Therefore, blood pressure can be estimated by acquiring iPTT from video taken using RGB camera. However, it is necessary to take video of face and palm simultaneously in this conventional iPTT measurement method. It is difficult to take the video of face and palm simultaneously. In order to solve this problem, we investigated whether iPTT can be acquired from a single part of body. At first, we took a video of face and palm using a high-speed camera and investigated whether the pulse wave flow can be observed. As a result, we were able to observe the propagation of the pulse wave in the regions of face and palm. Hence, iPTT acquisition from the single part of body is expected to be possible in these two parts. Next, we set a region of interest (ROI) on the chin and the forehead for the face, and the center of the palm and thenar for the palm. Then, pulse waves were extracted from each region, and iPTT was calculated from each part. We found that iPTT acquired from face is long and stable, but iPTT acquired from palm is short and unstable. Moreover, we examined the correlation between blood pressure and iPTT acquired from face. Consequently, a correlation was found between blood pressure and iPTT acquired from only face.