Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Security, Privacy, Familiarity, and Trust on Users’ Attitudes Toward the Use of the IoT-Based Healthcare: The Mediation Role of Risk Perception

The Internet of Things (IoT) refers to the network of devices which contain electronics, sensors or software that enables them to connect at anytimeand anywhere through a cyber-physical system. Before the establishment of such a system, it should be considered to what extent the users are ready to adopt and use it in their daily routines. Therefore, this paper explores users’ attitudestowardsusing IoT technologies to receive healthcare services. This is in contrast to most previous research, which has studied the technical requirements or devices of the IoT that are required in healthcare services, or ways in which connectivity and performance can be improved using the IoT. Based on known models of technology acceptance, an integrated framework was developed to investigate the impact of security and privacy concerns, and familiarity with the technology, on users’ trust in the IoT, and then to measure the effect of that trust on Omani users’ attitudes regarding use ofIoT technologies to receive healthcare services. This framework enabled the measurement of risk perception as a mediator between user trust and their attitudes towards using the IoT. Data were collected from 387 respondents and were analysed using SPSS 25 and AMOS 25 statistics software. Exploratory and confirmatory analysis and structural equation modelling were applied. The findings showed that levels of security, privacy and familiarity affected trustin the IoT. Furthermore, these levels of trust in the IoT were found to affect both users’ perceptions of risk in, and their attitude towards, using the IoT. The users’ risk perception partially mediated the relations between users’ trustand their attitude regarding use of the IoT. The framework was supported and interpreted by 40 per cent of the variance in the attitude towards usingthe IoT in healthcare, while the mediator showed 47 per cent of the variance in the attitude towards using the IoT inhealthcare.
Optimal Tuning of Fractional Order PID Controller for DC Motor Speed Control via Chaotic Atom Search Optimization Algorithm

In this paper, atom search optimization (ASO) algorithm and a novel chaotic version of it [chaotic ASO (ChASO)] are proposed to determine the optimal parameters of the fractional-order proportional+integral+derivative (FOPID) controller for dc motor speed control. The ASO algorithm is simple and easy to implement, which mathematically models and mimics the atomic motion model in nature, and is developed to address a diverse set of optimization problems. The proposed ChASO algorithm, on the other hand, is based on logistic map chaotic sequences, which makes the original algorithm be able to escape from local minima stagnation and improve its convergence rate and resulting precision. First, the proposed ChASO algorithm is applied to six unimodal and multimodal benchmark optimization problems and the results are compared with other algorithms. Second, the proposed ChASO-FOPID, ASO-FOPID, and ASO-PID controllers are compared with GWO-FOPID, GWO-PID, IWO-PID, and SFS-PID controllers using the integral of time multiplied absolute error (ITAE) objective function for a fair comparison. Comparisons were also made for the integral of time multiplied squared error (ITSE) and Zwe-Lee Gaing’s (ZLG) objective function as the most commonly used objective functions in the literature. Transient response analysis, frequency response (Bode) analysis, and robustness analysis were all carried out. The simulation results are promising and validate the effectiveness of the proposed approaches. The numerical simulations of the proposed ChASO-FOPID and ASO-FOPID controllers for the dc motor speed control system demonstrated the superior performance of both the chaotic ASO and the original ASO, respectively.
Continuous Glucose Monitoring in the Real World Using Photosurveillance of #Dexcom on Instagram: Exploratory Mixed Methods Study

Background Individuals with diabetes are using social media as a method to share and gather information about their health via the diabetes online community. Infoveillance is one methodological approach to examine health care trends. However, infoveillance, while very effective in identifying many real-world health trends, may miss opportunities that use photographs as primary sources for data. We propose a new methodology, photosurveillance, in which photographs are analyzed to examine real-world trends. Objective The purpose of this research is to (1) assess the use of photosurveillance as a research method to examine real-world trends in diabetes and (2) report on real-world use of continuous glucose monitoring (CGM) on Instagram. Methods This exploratory mixed methods study examined all photographs posted on Instagram that were identified with the hashtag #dexcom over a 3-month period—December 2016 to February 2017. Photographs were coded by CGM location on the body. Original posts and corresponding comments were textually coded for length of CGM device wear and CGM failure and were analyzed for emerging themes. Results A total of 2923 photographs were manually screened; 12.08% (353/2923) depicted a photograph with a CGM site location. The majority (225/353, 63.7%) of the photographs showed a CGM site in an off-label location, while 26.2% (92/353) were in an FDA-approved location (ie, abdomen) and 10.2% (36/353) were in an unidentifiable location. There were no significant differences in the number of likes or comments based on US Food and Drug Administration (FDA) approval. Five themes emerged from the analysis of original posts (N=353) and corresponding comments (N=2364): (1) endorsement of CGM as providing a sense of well-being; (2) reciprocating information, encouragement, and support; (3) reciprocating CGM-related frustrations; (4) life hacks to optimize CGM use; and (5) sharing and learning about off-label CGM activity. Conclusions Our results indicate that individuals successfully used CGM in off-label locations, posting photos of these areas with greater frequency than of the abdomen, with no indication of sensor failure. While these photographs only capture a snapshot in time, these posts can be used to inform providers and industry leaders of real-world trends in CGM use. Additionally, there were instances in which sensors were worn beyond the FDA-approved 7-day period; however, they represented the minority in this study.
Data for Image Recognition Tasks: An Efficient Tool for Fine-Grained Annotations

Using large datasets is essential for machine learning. In practice, training a machine learning algorithm requires hundreds of samples. Multiple off-the-shelf datasets from the scientific domain exist to benchmark new approaches. However, when machine learning algorithms transit to industry, e.g., for a particular image classification problem, hundreds of specific purpose images are collected and annotated in laborious manual work. In this paper, we present a novel system to decrease the effort of annotating those large image sets. Therefore, we generate 2D bounding boxes from minimal 3D annotations using the known location and orientation of the camera. We annotate a particular object of interest in 3D once and project these annotations on to every frame of a video stream. The proposed approach is designed to work with off-the-shelf hardware. We demonstrate its applicability with an example from the real world. We generated a more extensive dataset than available in other works for a particular industrial use case: fine-grained recognition of items within grocery stores. Further, we make our dataset available to the interested vision community consisting of over 60,000 images. Some images were taken under ideal conditions for training while others were taken with the proposed approach in the wild.
A 28-nm 75-fsrms Analog Fractional- $N$ Sampling PLL With a Highly Linear DTC Incorporating Background DTC Gain Calibration and Reference Clock Duty Cycle Correction

An analog fractional-<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> sampling phase-locked loop (PLL) is presented. It achieves 75-fs rms jitter, integrated from 10 kHz to 10 MHz, and a −249.7-dB figure of merit (FoM) at the fractional-<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> mode with a 52-MHz reference clock. The measured fractional spur is less than −64 dBc across the 5.5–7.3-GHz output frequency band. The PLL employs digital-to-time converter (DTC)-based sampling PLL architecture, high linearity DTC design techniques, background DTC gain calibration, and reference clock duty cycle correction (DCC) to improve the integrated phase noise (IPN) and fractional spur. This design meets the performance requirement of the 5G cellular 64-quadratic-amplitude modulation (QAM) standard in the 28-/39-GHz band, supporting <inline-formula> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula> multi-in multi-out (MIMO). This paper, implemented in a 28-nm CMOS process, is integrated in a 5G millimeter-wave cellular transceiver. This PLL consumes 18.9 mW and occupies 0.45 mm<sup>2</sup>.
Robust Cylinder Estimation in Point Clouds from Pairwise Axes Similarities

The ubiquitous presence of cylindrical shapes in both natural and man-made environments makes their automated extraction a pivotal task for a broad range of applications such as robot manipulation, reverse engineering and automated industrial inspection. Albeit conceptually simple, the task of fitting cylinders from 3D data can quickly become challenging if performed "in-the-wild", when no prior is given to the number of primitives to find or when the point cloud is noisy and not oriented. In this paper we introduce a new robust approach to iteratively extract cylindrical primitives from a 3D point cloud by exploiting mutual consensus of different cylinder candidates. First, a set of possible axes is generated by slicing the point cloud with multiple random planes. Then, a game-theoretic inlier selection process is performed to extract a subset of axes maximizing the fitness against a payoff function based on the shortest geodesic path in SE(3) between pairs of corresponding 3D lines. Finally, the probability distribution resulting from the previous selection step is used to weight the input candidates and robustly obtain the final cylinder coefficients. Compared to other methods, our approach does not require point normals, offers superior resilience to noise and does not depend on delicate tuning of multiple parameters.
Sport governing bodies’ influence on non-transactional fan behaviours

ABSTRACT The purpose of the paper is to present an advanced knowledge of a sport governing body’s – instead of club’s or team’s – influence on non-transactional fan behaviours. A higher understanding is achieved via the development of a global Fan-Relationship-Management-Model and local subgroup-specific models. A partial least squares path modelling approach is used to test the global model among a sample of five hundred and one individuals. Further, a response-based procedure for detecting unit segments is implemented to uncover local models. A series of chi-square tests are conducted to determine whether the local models differ on, for example, demographic measures and the fan’s devotedness to the team. We found three local models. Each local model exhibits different relationship strengths between its variables. These different relationship strengths are not confounded by demographic characteristics, but by a fan’s devotedness. This paper is useful to appreciate the sport governing body’s impact on non-transactional fan behaviours. The insights shared are essential for practitioners, who want to incorporate their sport governing body into a holistic and segment-specific marketing strategy. For the first time, a sport governing body’s influence on non-transactional fan behaviours is examined. The presentation of three local models, based on a response-based procedure for detecting unit segments, is new to the sports management theory.
Conceptual Analysis of Different Clustering Techniques for Static Security Investigation

Power system contingency studies play a pivotal role in maintaining the security and integrity of modern power system operation. However, the number of possible contingencies is enormous and mostly vague. Therefore, in this paper, two well-known clustering techniques namely K-Means (KM) and Fuzzy C-Means (FCM) are used for contingency screening and ranking. The performance of both algorithms is comparatively investigated using IEEE 118-bus test system. Considering various loading conditions and multiple outages, the IEEE 118-bus contingencies have been generated using fast-decoupled power flow (FDPF). Silhouette analysis and fuzzy partition coefficient techniques have been profitably exploited to offer an insight view of the number of centroids. Moreover, the principal component analysis (PCA) has been used to extract the dominant features and ensure the consistency of passed data with artificial intelligence algorithms’ requirements. Although analysis of comparison results showed excellent compatibility between the two clustering algorithms, the FCM model was found more suitable for power system static security investigation.
Real-Time Drilling Operation Activity Analysis Data Modelling with Multidimensional Approach and Column-Oriented Storage

Real-time data stream in the format of WITSML which can have frequency as low as 1 Hz is one of the best candidate to produce KPIs for the drilling operation activity. The KPIs generated from this calculation will have a relationship with other information from other data sources, known as metadata. The question is how can this KPI information be utilized for further analysis, wider/more complex analysis process which needs to be combined with metadata? An OLTP model is not the recommended model for data analytics but OLAP is. Another question is how will this data be stored in terms of the physical storage? We argue to use column-oriented for the physical storage which can perform analytical queries 10x to 30x faster than the row-oriented storage. The implementation of an OLAP model for storing KPIs data is proven to improve the performance of the analytical query significantly and combined with the implementation of column-oriented in the OLAP model improves more performance. This concludes that the implementation of OLAP with column-oriented data model can be used as the solid foundation for storing KPI data.
Future Interaction between Man and Robots from Islamic Perspective

Technology improves exponentially every second and becomes more complex beyond our grasp. Without we realize, everything about ourselves can be found on the internet from mundane things such as our food preferences to private matters such as saving accounts. To manage these information, an artificial intelligence system is required to safeguard, validate and even facilitate our daily tasks. Hypothetically, this issues will lead to the creation of Artificial General Intelligence (AGI), a (hypothetical) machine that could successfully perform any intellectual task that a human being can. Without a doubt, AGI will be a central component to our future society. As AGI further improved itself, roaming fully robotic android may be a common sight in future cities. This is a common plot setting in science fiction stories and is commonly discussed from Western perspective. However, this issue is rarely discussed from the Islamic perspective. Will AGI differentiate between fitna and facts (tabayyun)? Is a humanized android considered human (nas)? Does matter of fiqh relevant to a humanized android? Although seems fictional, it is imperative for the Muslim world to initiate discussion on these issues.
Code-switching in Italo-Romance: a variationist study of convergence in bilingual speech

A BSTRACT : This paper deals with grammatical aspects of code-switching between Italian and Italo-Romance dialects, and focuses on the case of negative constructions featuring a MICA -type particle (i.e. a particle deriving from the Latin MICA ( M ) “crumb”) in a corpus of Italian and bresciano speech data. The case at hand, which will be examined following the so-called comparative variationist method (cf. Poplack & Tagliamonte 2001 inter alia ), will offer valuable insight into the conventionalization of mixing patterns and the interplay between code-switching and convergence in Italo-Romance.
Ground-Truth Adversarial Examples

The ability to deploy neural networks in real-world, safety-critical systems is severely limited by the presence of adversarial examples: slightly perturbed inputs that are misclassified by the network. In recent years, several techniques have been proposed for training networks that are robust to such examples; and each time stronger attacks have been devised, demonstrating the shortcomings of existing defenses. This highlights a key difficulty in designing an effective defense: the inability to assess a network's robustness against future attacks. We propose to address this difficulty through formal verification techniques. We construct ground truths: adversarial examples with a provably-minimal distance from a given input point. We demonstrate how ground truths can serve to assess the effectiveness of attack techniques, by comparing the adversarial examples produced by those attacks to the ground truths; and also of defense techniques, by computing the distance to the ground truths before and after the defense is applied, and measuring the improvement. We use this technique to assess recently suggested attack and defense techniques.
Reinforcement Learning with Deep Quantum Neural Networks

The advantage of quantum computers over classical computers fuels the recent trend of developing machine learning algorithms on quantum computers, which can potentially lead to breakthroughs and new learning models in this area. The aim of our study is to explore deep quantum reinforcement learning (RL) on photonic quantum computers, which can process information stored in the quantum states of light. These quantum computers can naturally represent continuous variables, making them an ideal platform to create quantum versions of neural networks. Using quantum photonic circuits, we implement Q learning and actor-critic algorithms with multilayer quantum neural networks and test them in the grid world environment. Our experiments show that 1) these quantum algorithms can solve the RL problem and 2) compared to one layer, using three layer quantum networks improves the learning of both algorithms in terms of rewards collected. In summary, our findings suggest that having more layers in deep quantum RL can enhance the learning outcome.
MedClick: Last Minute Medical Appointments No-Show Management

A no-show is one of the phenomena that leads to an efficiency decrease in various sectors, including in the health care sector. When a scheduled patient misses an appointment without cancelling, it will not only waste the clinic’s resources, but it will also deny medical service to another patient who could have benefited from the respective time slot. This paper describes the research that is being developed in the context of MedClick, an online platform that aims to help medical service providers increase the efficiency of their practices. The solution supports the reduction of no-shows by predicting their occurrence and finding replacements to fulfill “last-minute” vacancy slots. A supervised learning algorithm (logistic regression) is being implemented and it will be used to predict the probability of no-show for each patient. The system will run this algorithm 48 hours before each appointment so that there is still enough time to find a replacement, if necessary. The prediction is based on features related to the respective clinic and patient, which requires access to the database.
Comparative Analysis of Galaxy and Xiaomi on Switching Intention Process of Smartphone Using Structural Equation Modeling

As a result, this study may be considered significant in conducting an in-depth research on the purchasing behavior pattern of the smartphone users in Shanghai, China and in investigating the variables in the effect on the behavior after purchasing Galaxy and Xiaomi among the Chinese smartphone customers. In addition, this study implies that not only the price competitiveness but also technical capabilities should be secured to survive in the smartphone market[1]. The results of this study show that the effect on the channel depends on the brand types of Galaxy and Xiaomi. The results of the analysis are found to show no difference in all channels. As shown in the analysis results of the causal relationship of each channel by dividing Galaxy and Xiaomi into two groups based on all the models in order to prove the detailed difference in two brands towards their customers, the way of dealing with two models respectively shall be different due to the different customer behavior processes of Galaxy and Xiaomi. Accordingly, the marketing strategy to secure the competitive advantage among the competitors in the area of smartphone in China shall be applied differently, which will be helpful to the rise in market share. In addition, the results of this study may be used as the basic data required in establishing the market strategy differentiated from the competitors providing the smartphone in the Chinese smartphone market, which may be the contribution of this study. This study is aim to investigate the switching intention process which is the post purchase behavior of the smartphone users in China targeting the customers in Shanghai, China and to clarify the difference in high-priced Galaxy and middle-low priced Xiaomi targeting the Chinese customers in explaining the behavior after purchasing Galaxy and Xiaomi in terms of perceived value[1].
Compact Color Texture Representation by Feature Selection in Multiple Color Spaces

This paper presents a compact color texture representation based on the selection of features extracted from different configurations of descriptors computed in multiple color spaces. The proposed representation aims to take simultaneously into account several spatial and color properties of different textures. For this purpose, texture images are coded in five different color spaces. Then, texture descriptors with different neighborhood and quantization parameter settings, are calculated from this images in order to extract a high dimensionality feature vector describing the textures. Compact representation is finally obtained by means of a feature selection scheme. Our approach is applied with two well-known color texture descriptors for the classification of three benchmark image databases.