Miracle-dz/UniFAR-FTUs huggingface dataset:
Anomaly Detection Techniques in Mobile App Usage Data among Older Adults

We are living in an era of demographic ageing, and new technologies that support independent living are constantly being created. In this context, more and more mobile applications are developed for this target group. In this paper, we are presenting a multidimensional application that targets older adults. We are monitoring the usage of all different aspects of the app, the amount of daily activity in the form of daily steps and the resting time throughout the day from a connected bracelet the user is wearing. Data amounting to 402 user-days of 6 different users are collected. A set of different datasets are manufactured, and various anomaly detection techniques are employed to identify the abnormalities in the datasets. The results demonstrate that clustering can be of use to detect anomalies in the older adults’ patterns that could be the trigger of appropriate actions, like informing family members or professional caregivers.
Study on Modeling and State Estimation of Ultrasonic Motor Based on Data Driving

Control of ultrasonic motor requires high accuracy of the mathematical model, and their states estimation requirements are also increasing. Only on this basis can the control be performed under high-performance indicators. In this paper, using the basic ideas of data-driven, a discrete-state model is established for a kind of standing-wave ultrasonic motor by using a nonlinear time series modeling method. And on this basis, a state estimation study is performed. Experiments show that the application of data-driven modeling method can establish a high-precision motor mathematical model. The motor state is estimated based on it. Meanwhile, it can provide a good condition for ultrasonic motor control accurately and efficiently.
Comparative Study of Control Structures for Maglev Systems

This paper presents the design of three control structures that control the ferromagnetic sphere position of a magnetic levitation (Maglev) system. The control structures are a Tensor Product-based model transformation control structure, an anti-windup Proportional-Integral-Derivative Gain-Scheduling control structure (aw-PID-GS-CS) and the combination of these two structures (CCS). Some real time experiments and a comparative study are given for two control structures: aw-PID-GS-CS and CCS.
Secrecy Performance of Multi-User MISO VLC Broadcast Channels With Confidential Messages

We study, in this paper, the secrecy performance of a multi-user (MU) multiple-input single-output visible light communication broadcast channel with confidential messages. The underlying system model comprises <inline-formula> <tex-math notation="LaTeX">$K +1$ </tex-math></inline-formula> nodes: a transmitter (Alice) equipped with <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> fixtures of LEDs and <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> spatially dispersed users, each equipped with a single photo-diode. The MU channel is modeled as deterministic and real-valued and assumed to be perfectly known to Alice, since all users are assumed to be active. We consider typical secrecy performance measures, namely, the max–min fairness, the harmonic mean, the proportional fairness, and the weighted fairness. For each performance measure, we derive an achievable secrecy rate for the system as a function of the precoding matrix. As such, we propose algorithms that yield the best precoding matrix for the derived secrecy rates, where we analyze their convergence and computational complexity. In contrast, what has been considered in the literature so far is zero-forcing (ZF) precoding, which is suboptimal. We present several numerical examples through which we demonstrate the substantial improvements in the secrecy performance achieved by the proposed techniques compared with those achieved by the conventional ZF. However, this comes at a slight increase in the complexity of the proposed techniques compared with ZF.
An educational environment based on digital image processing to support the learning process of biomaterials degradation in stem cells

The Poly(DL-lactide-co-glycolide) copolymers (PDLGA) have designed and performed as biomaterials, taking into account their biodegradability and biocompatibility properties. These materials have a wide range of application in medicine such as orthopedic implants, general surgical implants (suture materials), osteosynthesis, bone cement, among many others. For these reasons, in this paper, we present an intelligent educational environment that can be used for both, researchers and students interested in the analysis of the biomaterial behavior under certain conditions. Our platform includes a Learning Objects (LOs) for MOODLE, and in the same way, implements several digital image processing techniques as well as a decision support module based on a random forest algorithm and statistical modeling. With the aim of determining the real feasibility of this proposal, we have presented the system to 34 Ecuadorian engineering students. After testing the platform, the students answered a survey aimed at determining their perception of the system. The results provide several guidelines to continue with the developing of the platform.
Homomorphic Hash and Blockchain Based Authentication Key Exchange Protocol for Strangers

Modern communication technologies and cryptographic technologies have pushed social networks into the virtual world, but they have also ensured the real existence of social proximity. In the evaluation of social proximity, some decentralized scenarios require the participants bootstrapping tust, and the existing authentication key exchange scheme can hardly satisfy the above requirements. In this study, we have designed a homomorphic hash and Blockchain based authenticated key exchange protocol with privacy protection, and prove its security under the standard model based on hash one-way, discrete logarithm and Blockchain transaction-level security assumption, and discussed the attack that the proposed scheme can resist. Compared with the existing scheme, the proposed scheme does not need the default Unit of Trust, is safer and more flexible, suitable for the scenarios that require strangers to bootstrap trust.
Online Timely Status Updates with Erasures for Energy Harvesting Sensors

An energy harvesting sensor that is sending status updates to a destination through an erasure channel is considered, in which transmissions are prone to being erased with some probability $q$, independently from other transmissions. The sensor, however, is unaware of erasure events due to lack of feedback from the destination. Energy expenditure is normalized in the sense that one transmission consumes one unit of energy. The sensor is equipped with a unit-sized battery to save its incoming energy, which arrives according to a Poisson process of unit rate. The setting is online, in which energy arrival times are only revealed causally after being harvested, and the goal is to design transmission times such that the long term average age of information $(AoI)$, defined as the time elapsed since the latest update has reached the destination successfully, is minimized. The optimal status update policy is first shown to have a renewal structure, in which the time instants at which the destination receives an update successfully constitute a renewal process. Then, for $ q\leq \frac {1}{2}$, the optimal renewal policy is shown to have a threshold structure, in which a new status update is transmitted only if the AoI grows above a certain threshold, that is shown to be a decreasing function of $q$. While for $q \gt \frac {1}{2}$, the optimal renewal policy is shown to be greedy, in which a new status update is transmitted whenever energy is available.
SDCAE: Stack Denoising Convolutional Autoencoder Model for Accident Risk Prediction Via Traffic Big Data

Traffic accident is considered as one of main causes for traffic congestion in cities. There are many causal factors that may give rise to traffic accidents, e.g. driver characteristics, road conditions, traffic flows and weather conditions, etc. Due to uncertain factors as well as the contingency of accident occurrences, it is very difficult to predict traffic accidents. Many existing works have utilized classical prediction models to predict the risk of accidents on highways or road segments. However, predicting the risk of citywide accidents remains an open issue. To address this problem, we propose SDCAE, a novel Stack Denoise Convolutional Auto-Encoder algorithm to predict the risk of traffic accident in the city-level. First, we divided the city into regions by counting the number of accidents and traffic flows in each region. Second, we employed a deep model of stack denoise convolutional autoencoder which considers spatial dependencies to learn the hidden factors in accidents. Third, we conducted extensive experiments on two real-world cross-domain traffic big datasets from a major city of China for accident risk prediction. Experimental results demonstrate that SDCAE could outperforms five baseline methods.
Community Detection Method Based on User Influence Probability and Similarity

With the development of the social network, the rapid spread of micro-blog information and its convenience have enabled a large number of users to share their daily activities. On the basis of interest, users on Twitter formed a community by exchanging views and establishing friendships with others. Most existing community division methods use classification or clustering method to separate communities by single user interest weighting, and the effect is not ideal. In this paper, we propose a community detection method based on user impact probability and interest model called IPS(base on Influence Probability and Similarity). This paper predicts the probability of influence between two users. Finally, we assign users to communities with higher interest and impact probability and higher public connection topology. In addition, we test the ability of our algorithm by experiments on the actual micro-blog dataset. The experimental results show that the algorithm is effective for micro-blog.
A Novel Frequency-Dependent Coupling With Flexibly Controllable Slope and Its Applications on Substrate-Integrated Waveguide Filters

This letter reports a novel realization of frequency-dependent coupling (FDC) structure, which can realize both the positive and negative slopes. The FDC is constructed by two coupled stepped-impedance grounded coplanar waveguides with opened ends. The characteristics of the FDC are studied, which indicates that the proposed FDC has advantages of the flexibly controllable slope, zero of position, and weak/strong strength. For the verification of the characteristics of the proposed FDC, an inline-line fourth-order substrate integrated waveguide bandpass filter (BPF) with one large positive slope and a third-order triplet BPF with one small negative slope for additional finite transmission zero with controllable position were designed, fabricated, and measured. Measured results agree well with simulated ones.
Design and Usability Testing of the Stroke Caregiver Support System: A Mobile-Friendly Website to Reduce Stroke Caregiver Burden

Abstract Purpose The purpose of this study was to design a mobile-friendly, Internet-based website, modeled on previously described websites for Alzheimer caregivers, to equip stroke caregivers and potentially reduce caregiver burden. Design A mixed-methods study was performed to design and test the usability of the Stroke Caregiver Support System (SCSS). Methods An iterative, user-centered design approach was employed in three phases: (I) Focus Groups, (II) Structured Interviews, and (III) Usability Testing. Phase I and Phase II provided information for the development of the SCSS website, whereas Phase III helped in gathering data regarding the usability and efficacy of the newly implemented SCSS website. Findings Qualitative data on caregiving and the content and design of the SCSS were obtained from focus groups and interviews. In the usability test, the nine caregivers who completed Phase III (78% women, mean age = 46, SD = 17) exhibited a high level of burden and depressive symptoms (median [Q1, Q3] Zarit burden score = 18 [16, 23], Center of Epidemiologic Studies-Depression Scale = 15 [8, 17]). Caregivers conveyed the usability of the SCSS but also expressed several needed improvements. Conclusions Participants reported the value of the SCSS, but further refinements are needed to maximize its usability and potential efficacy. Clinical Relevance The SCSS has potential to reduce caregiver burden in stroke.
How to Assess the Dependability of Applications on Top of the Blockchain: Novel Research Challenges

It is nowadays extremely popular to devise applications of top of the blockchain technology, due to the flourishing of several open-source platforms and the progressive success of this technology outside its traditional cryptocurrency domain. Testing these applications is particularly challenging and traditionally limited to the performance and functionality verification. How-ever, the progressive adoption of such a technology in critical domains is calling for dependability guarantees. This paper highlights the challenges related to the dependability assessment of these applications, and sketches a possible research direction.
Impact of simulation training on performance and outcomes of endobronchial ultrasound-guided transbronchial needle aspiration performed by trainees in a tertiary academic hospital.

Background Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) has a high diagnostic yield and low complication rate. Whilst it has been included in international guidelines for the diagnosis and staging of lung cancer, current results are mostly based on EBUS experts performing EBUS-TBNA in centres of excellence. The impact of simulation training on diagnostic yield, complications, scope damage and repair cost in a real-world teaching hospital is unclear. Methods A review of our hospital EBUS-TBNA registry from August 2008 to December 2016 was performed. A positive diagnosis was defined as a confirmed histological or microbiological diagnosis based on EBUS sampling. Complications were classified as major or minor according to the British Thoracic Society guidelines. In addition, we assessed the cost of repairs for scope damage before and after simulation training was implemented. Using CUSUM analysis, the learning curves of individual trainees and the institution were plotted. Results There were 608 EBUS-TBNA procedures included in the study. The number of procedures performed by trainees who underwent conventional training was 331 and those who underwent simulation training performed 277 procedures. Diagnostic yield for trainees without simulation training was 88.2% vs. 84.5% for trainees with simulation training (P=0.179). There was no statistical difference in the diagnostic yield between the groups of trainees (OR: 0.781, 95% CI: 0.418-1.460, P=0.438) after adjusting for risk factors. There was an increase in overall complications from 13.6% to 16.6% (OR: 2.247, 95% CI: 1.297-3.891, P=0.004) after introduction of the simulation training, but a trend to decrease in major complications 3.6% to 0.7% (P=0.112). The cost for scope repairs for the trainees without simulation training was SGD 413.88 per procedure vs. SGD 182.79 per procedure for the trainees with simulation training, with the mean difference being SGD 231.09 per procedure (95% CI: 178.40-640.60, P=0.268). CUSUM analysis showed an increasing learning curve for the trainees with simulation training after an initial competency period. Conclusions There was no statistical difference in diagnostic yield from EBUS-TBNA and cost of scope damage after simulation training was introduced into our training program. Interestingly, there was an increase in minor complications. CUSUM analysis can provide additional information on institutional learning curves. The value of simulation training in EBUS-TBNA remains uncertain.
Towards Simulation of the Processes Related to Transport Movement within Industrial Objects

The paper presents an approach to modeling the processes associated with movement of the vehicles at production facilities. The authors define main tasks that have to be solved when modeling each particular process. A model of spatio-temporal and other essential characteristics of movement is described. The model proposed considers that the movement of the vehicle is monitored by different monitoring devices such as access control checkpoints, video cameras and weight measuring devices. The software implementation of the generator of movement logs has been developed. The tasks for the future research are defined.
Determination of Optimal Thread Pool for Cloud Based Concurrent Enhanced No-Escape Search

In this era of high demand for cloud-computing, concurrent and high-performance processing is the viable option to enhance performance and use available resources efficiently. Thread pool architecture is widely implemented in order to improve resource utilization and enhance performance. Studies in this field suggest that Thread pool size is mostly determined by heuristics, trials, or practical experience. This process lacks precision and theoretical justification. In this paper, a novel Hyperbola-based Thread-Pool Analysis (HTA) technique for determining optimal Thread Pool size by considering available bandwidth (upload/download speed) and workload (file-size) has been proposed for any cloud-based concurrent process. Here, HTA has been developed in the context of No-Escape Search (NES), a cloud-based content indexing and search system, proposed by authors in an earlier work. Results of HTA indicate an accuracy of 97.63% in estimation of optimal thread pool size. Incorporating HTA with No-Escape Search resulted a significant decrease in insertion time by 2236 folds and retrieval time by 1747 folds. Additionally, this paper also presents design of enhanced NES with features like pictorial representation of files for the visual summary, more efficient deletion algorithm using lazy delete and duplicate files detection in order to ensure efficient indexing.
High Permittivity, Low Loss, and Printable Thermoplastic Composite Material for RF and Microwave Applications

Interest in utilizing additive manufacturing for RF applications has increased remarkably in recent years thanks to the potential in producing complex and lightweight parts, as well as reducing costs in manufacturing processes. However, the materials available for 3D-printing are not developed for microwave applications and thus do not have the required properties for RF applications. In this paper, we present a new composite material based on COC (Cyclic Olefin Copolymer) polymer and loaded by titanium oxide (TiO2) at 36 vol%, which is perfectly compatible with the FDM (Fused Modeling Deposition) process. This new thermoplastic composite material offers high permittivity up to 9.2, low loss of 1.10-3 at 9.3GHz, and good behavior against temperature (glass transition temperature of 130°C). The CST simulation results on sub-wavelength prisms show many advantages when using this new material, mainly in reducing the component’s thickness and reducing the side lobes levels.
[Research Paper] Periodic Developer Metrics in Software Defect Prediction

Defect prediction studies have proposed several data-driven approaches, and recently, this field has put more emphasis on whether the people factor is associated software defects. Developer metrics can capture experience, code ownership, coding skills and techniques, and commit activities. These metrics have so far been measured at a specified snapshot of the codebase although developer's knowledge on a source module could change over time. In this paper, we propose to measure periodic developer experience with regard to contextual knowledge on files and directories. We extract periodic experience metrics capturing the previous activities of developers on source files and investigate the explanatory effect of these metrics on defects. We also use activity-based (churn) metrics to observe the performance of both metric types on defect prediction. We used two large-scale open source projects, Lucene and Jackrabbit, for model evaluation. We calculate periodic developer experience metrics and churn metrics at two granularity levels: file level and commit level. We build the models using five popular machine learning algorithms in defect prediction literature. The models with the two best performing algorithms are assessed in terms of Precision, Recall, False Positive Rate, and F-measure. The set of metrics that explains software defects the best is also identified using correlation-based feature selection method. Results show that periodic developer experience metrics extracted at file level are good merits for defect prediction, accompanied with churn. When there is not enough data to extract the contextual knowledge of developers on source files, churn metrics play an important role on defect prediction.
Design of a Location Based Adaptive MPP Tracker for PV System

As solar irradiance varies hourly from day to day throughout the year for a location the output characteristics of solar photovoltaic (SPV) also vary in accordance. The behaviour of PV systems is non linear in nature. So, in a large PV system where more numbers of PV modules are connected in strings and arrays, the effect of irradiance is not evenly distributed. Thus it's quite tough to track the maximum power point (MPP) in such a condition for a large system. The existing MPPT algorithms are also inefficient to track global maximum power point (GMPP) for such systems due to presence of lots of local maximas and hotspots. In this work, a 5 MW Spy based power plant is designed with SunPower SPR-EI9-315 modules (nominal efficiency 19.31% under STC condition) by connecting total 15,864 modules with the configuration of 12 nos. of modules per string and 1322 nos. of strings in parallel. The forecasted global horizontal irradiance (GHI) data of Agartala, Tripura, India were logged hourly basis from January to December using System Advisor Model (SAM) simulator. The system was tested under these GHI data and different $\mathbf{v}_{\mathbf{mpp}}$ and MPPs were calculated precisely. Further, a MPP tracker was designed in MATLAB and a database was developed with the data of different maximum power point voltage $(\mathbf{V}_{\mathbf{mpp}})$ under different GHI imported from SAM. This adaptive database based tracker successfully tracked the GMPPs for the $\pmb{5\text{MW}}$ plant quickly and precisely for this location time to time basis. This independent system is capable to track the GMMPs hourly for everyday basis throughout the year for this location even without tracking the GMPPs by scanning each time unlike perturb & observe method and incremental conductance method. So, this is simple, reliable and faster in performance in comparison to the existing ones.