Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors Affecting RFID Subcutaneous Microchips Usage

Microchips are used for many years for different purposes. Nevertheless, recently applications of radio frequency identification technology have been progressed tremendously with the potential to permeate throughout society as valuable tool for enabling automatic identification and management. However, they are perceived as threats at several levels that impede its implementation. Most of the research discusses the adoption of Radio Frequency Identification (RFID) devices from the provider’s point of view. Thus, it would be of interest to examine the factors of RFID subcutaneous microchip (RFID-SM) usage, adoption and diffusion by individuals from various perspectives, particularly in developing countries. Knowledge upon the factors that drive RFID adoption is a crucial step in creating the policies required for its successful implementation. This quantitative, descriptive study investigated whether young people in Serbia would be willing to use subcutaneous microchip. Preliminary findings indicate that positive attitudes prevail for the perceived usefulness and ease of use, while suspicions of confidentiality and privacy are strong, and what is most surprising none of the respondents showed willingness to use microchip implants for everyday home activities. Since the process of individual users’ acceptance of technology is very complex, the full range of variables should be investigated from broader perspective.
Current-Fed Isolated LCC-T Resonant Converter With ZCS and Improved Transformer Utilization

Resonant converters with capacitive output filters are quite popular for their higher voltage gain and compact designs. But the majority of the converters underutilize the transformer as they are subjected to discontinuous currents. This discontinuous current possesses huge current ripples that produce huge core losses and aggravate the temperature rise of the transformer. This often leads to saturation of the transformer. This paper presents an LCC-T resonant dc–dc converter with a capacitive output filter operating all switches in the zero current switching (ZCS) switching mode and whose transformer current is a continuous sinusoidal with minimum stress on resonant tank components. This is achieved by bringing the transformer in series to a resonant inductor. The proposed converter is simulated in power simulation (PSIM) 11.0. 1. A proof-of-concept model rated 288 W is designed and developed, and hardware results are presented as verification to theory.
Performance Verification of a New Integrated Droop Controller with a Novel Virtual-Impedance Based PLL for Parallel Operation of Inverters

This paper presents a novel droop control method for decentralized paralleling of inverters in distributed generation (DG) system. The controller satisfies two basic objectives simultaneously: It provides active power ($P$) and reactive power ($Q$) based droops on voltage and frequency references using d-axis and q-axis currents, respectively and also incorporates virtual impedance loop. A novel PLL (Phase Locked Loop) structure and an integrated droop control (IDC) strategy are devised to achieve the same. This control method enables parallel inverters to share any type of load viz. linear and nonlinear proportional to the inverter power ratings under any transmission line condition namely resistive and resistive-inductive type. Performance of the proposed control method is verified through experimental results.
On the Modeling Ion Motion in Electromagnetic Field

The approach to the study of influence of low frequency and low intensity electromagnetic fields on living organism in medical practice is considered. It is based on approximate calculation of magnetic field in given points of the space and modeling the motion of ions under action of the field. We present the imitation model of this motion with visualization of results in Para View package. The proposed tool may be useful both for designers of magnetoterapy devices and physicians using such devices in practice.
Meta-Path Based Anchor Link Inference in Multiple Partially Aligned Social Networks

To enjoy fantastic services or fulfill their multifarious needs, people usually get involved in multiple online social networks at the same time. Due to the protection of privacy and the separation of different networks, the multiple accounts of the same user in different social networks are generally isolated. Finding the correspondence between users can benefit a variety of applications, such as cross-network recommendation and cold start. In this paper, we focus on inferring the correspondence between user pairs across partially aligned networks where some common users are shared, which is formally defined as multi-network anchor link inference problem. To solve the problem, an integrated anchor link inference framework, MALI (multi-network anchor link inference) is proposed, which consists of two parts: firstly, building structure-rich probabilistic networks with PU link prediction model to against noises. Secondly, taking advantage of cross-network social meta path as a powerful tool to extract useful features to train a classifier. Then by incorporating username with the ranking list which is based on the output probability of the classifier, an improved stable match model is designed to infer anchor links which are restricted to one-to-one relationships. Extensive experiments on two real-world partially aligned heterogeneous networks show that our proposed MALI can solve the multi-network anchor link inference problem very well.
Application of system fault detection and intelligent reconstruction method based on machine learning in micro inertial pedestrian navigation system

Pedestrian navigation assisted by human body characteristics is a new research branch in indoor and outdoor positioning field in recent years. Aiming at the problem that the pedestrian navigation system of inertial measurement unit foot installation cannot effectively measure the information when the foot inertial measurement unit fails, and as a result the navigation function malfunctions, this paper presents a pedestrian navigation method with installation of inertial measurement units at the other parts of the human body beside foot. The output data of foot inertial measurement unit are simulated by machine learning methods such as neural networks. With fault detection and system intelligent reconstruction principle, the reliability and the performance index of pedestrian navigation system under complex gait conditions can be improved. The experiment results show that, while different BP neural networks are used under different gaits the complexity of neural network model can be reduced. Part of the positioning performance of the pedestrian navigation system based on this method, is equal to that of the foot inertial navigation system with same sensor precision under the condition of no sensor fault. While the foot inertial measurement unit fails, the pedestrian navigation system can also realize the navigation and positioning function of certain accuracy.
A New Transfer Learning Method and its Application on Rotating Machine Fault Diagnosis Under Variant Working Conditions

Effective data-driven rotating machine fault diagnosis has recently been a research topic in the diagnosis and health management of machinery systems owing to the benefits, including safety guarantee, labor saving, and reliability improvement. However, in vast real-world applications, the classifier trained on one dataset will be extended to datasets under variant working conditions. Meanwhile, the deviation between datasets can be triggered easily by rotating speed oscillation and load variation, and it will highly degenerate the performance of machine learning-based fault diagnosis methods. Hence, a novel dataset distribution discrepancy measuring algorithm called high-order Kullback–Leibler (HKL) divergence is proposed. Based on HKL divergence and transfer learning, a new fault diagnosis network which is robust to working condition variation is constructed in this paper. In feature extraction, sparse filtering with HKL divergence is proposed to learn sharing and discriminative features of the source and target domains. In feature classification, HKL divergence is introduced into softmax regression to link the domain adaptation with health conditions. Its effectiveness is verified by experiments on a rolling bearing dataset and a gearbox dataset, which include 18 transfer learning cases. Furthermore, the asymmetrical performance phenomenon found in experiments is also analyzed.
Robust Direct Power Control of PWM Rectifier with DC Voltage Ripple Elimination Under Unbalanced Network

Conventional direct power control (DPC) presents highly distorted grid currents and twice grid-frequency ripple in the dc bus voltage under unbalanced network. Various improved DPC methods have been proposed to tackle the problems above. However, they are usually complicated and not easy to use due to the positive/negative sequence extraction of grid and converter voltages/currents, additional controllers and filters, more tuning work, and so on. This paper proposes an improved DPC by simply adding a compensation term to the original power references. The compensation power is obtained by analyzing the power ripple in the input line inductance based on extended power theory. The reference voltage vector is subsequently calculated from the new power reference and synthesized by space vector modulation (SVM). To improve the robustness of the proposed DPC against inductance variation, an online inductance identification technique based on gradient correction method is proposed and combined with the proposed DPC. The presented simulation and experimental results prove that the proposed method can achieve sinusoidal grid currents and reduce the de voltage ripple significantly even with mismatched inductance.
A Gate Driver Based Approach to Improving the Current Density in a Power Module by Equalizing the Individual Die Temperatures

The current sharing in a high current power module can have a dramatic impact on the total current handling capability of that power module. Hence, in an effort to improve the utilization of the individual dies within a power module, this paper explores a gate driver based approach which aims to equalize the virtual junction temperatures of its composite dies. The implementation of the die-level junction temperature estimation, the generation of a target temperature reference and the selective die activation to control the individual die losses is presented as the key enabling elements for the proposed gate driver and power module. Experimental results confirm the reduction in the peak die temperatures, and consequently, the current output is shown to be able to increase by a factor of 20%.
MSMCT: Multi-State Multi-Camera Tracker

Visual tracking of multiple persons simultaneously is an important tool for group behaviour analysis. In this paper, we demonstrate that multi-target tracking in a network of non-overlapping cameras can be formulated in a framework, where the association among all given target hypotheses both within and between cameras is performed simultaneously. Our approach helps to overcome the fragility of multi-camera-based tracking, where the performance relies on the single-camera tracking results obtained at input level. In particular, we formulate an estimation of the target states as a multi-state graph optimization problem, in which the likelihood of each target hypothesis belonging to different identities is modeled. In addition, we learn the target-specific model to improve the similarity measure among targets based on the appearance cues. We also handle the occluded targets when there is no reliable evidence for the target’s presence and each target trajectory is expected to be fragmented into multiple tracks. An iterative procedure is proposed to solve the optimization problem, resulting in final trajectories that reveal the true states of the targets. The performance of the proposed approach has been extensively evaluated on challenging multi-camera non-overlapping tracking data sets, in which many difficulties, such as occlusion, viewpoint, and illumination variation, are present. The results of systematic experiments conducted on a large set of sequences show that the proposed approach outperforms several state-of-the-art trackers.
It’s not you, it’s me: corollary discharge in the precerebellar nuclei of sleeping infant rats

In week-old rats, somatosensory input arises predominantly from stimuli in the external environment or from sensory feedback associated with myoclonic twitches during active (REM) sleep. A previous study of neural activity in cerebellar cortex raised the possibility that the brainstem motor structures that produce twitches also send copies of motor commands (or corollary discharge, CD) to the cerebellum. Here, by recording from two precerebellar nuclei—the inferior olive and lateral reticular nucleus—we demonstrate that CD does indeed accompany the production of twitches. Within both structures, the CD signal comprises a surprisingly sharp activity peak within 10 ms of twitch onset. In the inferior olive, this sharp peak is attributable to the opening of slow potassium channels. We conclude that a diversity of neural activity is conveyed to the developing cerebellum preferentially during sleep-related twitching, enabling cerebellar processing of convergent input from CD and reafferent signals.
Compressed Sensing Method for IGBT High-Speed Switching Time On-Line Monitoring

Condition monitoring (CM) has been considered as a promising technique to improve the reliability of insulated gate bipolar transistors (IGBTs). Among various condition parameters, switching time is a good health status indicator to detect IGBT failures. However, on-line monitoring of the IGBT high-speed switching time is still difficult in practice due to the requirement of the extremely high sampling rate for signal acquisition. To overcome the technical difficulty, this paper provides an innovative compressed sensing (CS) method to achieve equivalent sampling performance for high-speed IGBT switching time monitoring with a lower sampling rate. By utilizing the sparse characteristics of an IGBT switching signal, the sampling rate in the CS method could be far less than the traditional Nyquist sampling rate. To clarify the method, the CM mechanism using IGBT switching time is first analyzed. Then, three key points in the CS method are studied, i.e., the selection of sparsifying basis, the design of a measurement matrix, and the implementation of a reconstruction algorithm. Finally, experiments are carried out to investigate the performance of the CS method for on-line CM. Experimental results show that the IGBT turn-off time at different temperatures can be accurately monitored with a highly compressed sampling rate, which validates the feasibility and effectiveness of the proposed method.
Securing Services in Networked Cloud Infrastructures

In this paper, we propose techniques and architecture for securing services that are hosted in a multi-tenant networked cloud infrastructures. Our architecture is based on trusted virtual domains and takes into account both security policies of the tenant domains as well as specific security policies of the virtual machines in the tenant domains. We describe techniques for detecting a range of attacks such as attacks between the virtual machines within a trusted virtual domain, attacks between the virtual machines in different domains, malicious insider attacks and attacks against specific services such as DNS, database and web servers within a domain. We address security policies for trusted virtual domain management such as secure addition and deletion of a virtual machine and the revocation of privileges associated with a virtual machine in a domain. We also discuss forensic analysis of attacks and fine granular detection of malicious entities and mechanisms for restoration of services. Furthermore the proposed architecture provides mechanisms for enhancing the assurance of communications between the virtual machines in different domains. Finally, we present the implementation of our security architecture using Xen and illustrate how our architecture is able to secure services in networked cloud infrastructures.
Exploiting Implicit Trust and Geo-Social Network for Recommendation

Recommender system (RS) seeks to predict the rating or preference a user would give to an item, this system often relies on collaborative filtering (CF). CF suffers from the problems of data sparsity, cold start and location insensitive. Existing RSs do not consider the spatial extent of users, we analyze the users' location data from four four commercial websites, and conclude that people with close social relationships prefer to purchase in places that are also physically close. State-of-the-art recommendation algorithm TrustSVD extends RS with social trust information, we propose Trust-location SVD (TLSVD) by incorporating the location information and implicit trust into TrustSVD. The improved TLSVD helps to quantitatively analyze the spatial closeness and preference similarity between users. Experimental results indicate that the accuracy of our method is better than other multiple counterparts', especially when active users have location information or few ratings.
Draft Genome Sequences of the Nitrate-Dependent Iron-Oxidizing Proteobacteria Acidovorax sp. Strain BoFeN1 and Paracoccus pantotrophus Strain KS1

The draft genomes of the nitrate-dependent iron-oxidizing bacteria Acidovorax sp. strain BoFeN1 and Paracoccus pantotrophus strain KS1 are presented. ABSTRACT The draft genomes of the nitrate-dependent iron-oxidizing bacteria Acidovorax sp. strain BoFeN1 and Paracoccus pantotrophus strain KS1 are presented. These genomes supply supporting data to investigations of the mechanisms underlying this anaerobic form of microbial biogeochemical iron cycling.
Design of a Miniaturized Microstrip Conformal Antenna

A novel miniaturized microstrip conformal antenna with wide band performance is accomplished by using a microstrip patch structure with U-slot. The designed antenna realizes 10% relative bandwidth with a high dielectric constant $(\varepsilon_{r}=9.6)$ and $0.07\lambda_{0}$ thickness dielectric plate. The size of the patch is only $\pmb{0.13\lambda_{0}\times 0.13\lambda_{0}}$, while the ground size is $\pmb{0.33\lambda_{0}\times 0.33\lambda_{0}}$. At the same time, FR-4 substrate is adopted to satisfy the conformal requirement of the system. The test results show that the simulation is in good agreement with the measurement.
Large-scale, cross-flow based isolation of highly pure and endocytosis-competent extracellular vesicles

ABSTRACT Isolation of extracellular vesicles (EVs) from cell culture supernatant or plasma can be accomplished in a variety of ways. Common measures to quantify relative success are: concentration of the EVs, purity from non-EVs associated protein, size homogeneity and functionality of the final product. Here, we present an industrial-scale workflow for isolating highly pure and functional EVs using cross-flow based filtration coupled with high-molecular weight Capto Core size exclusion. Through this combination, EVs loss is kept to a minimum. It outperforms other isolation procedures based on a number of biochemical and biophysical assays. Moreover, EVs isolated through this method can be further concentrated down or directly immunopurified to obtain discreet populations of EVs. From our results, we propose that cross-flow/Capto Core isolation is a robust method of purifying highly concentrated, homogenous, and functionally active EVs from industrial-scale input volumes with few contaminants relative to other methods.
A Miniaturized Circularly Polarized Antenna for Wireless Applications

In this paper a circularly polarized slot antenna is proposed with a If-shaped microstrip feedline that provides a wide impedance bandwidth and wide axial ratio bandwidth (ARBW). The extensive simulation results demonstrate that the impedance bandwidth for $\mathbf{(S) < -10 \ dB}$ and ARBW for axial ratio (AR) $< 3$ are 74.50% and 33.33%, respectively. The antenna has a stable radiation pattern over the entire circular polarization frequency band with a gain above 3 dB for entire AR bandwidth.
What People Like in Mobile Finance Apps: An Analysis of User Reviews

Even though app store reviews provide highly valuable information on how people use mobile apps and what they expect from them, the systematic, timely analysis of an ever-growing volume of such unstructured reviews across many apps remains a challenge. We analyzed more than 300'000 review sentences belonging to 1'610 finance apps using a machine learning-based approach to investigate the impact that different aspects of finance apps have on their ratings. Additionally, we manually categorized all apps into sub-categories such as payment or trading apps to discuss our findings on an extra level of detail. This work illustrates how different aspects of mobiles apps affect their ratings, how this varies across sub-categories, and discusses the role of privacy, user interfaces, signup experiences, notifications, when the use of location services may be appropriate, and other aspects of mobile finance apps, to provide detailed insights into users' expectations and perception of finance apps.
Good things come in threes

Ectopic expression of PU.1, IRF8, and BATF3 reprograms mouse and human fibroblasts into dendritic cells. See related Research Article by Rosa et al. Ectopic expression of PU.1, IRF8, and BATF3 reprograms mouse and human fibroblasts into dendritic cells. See related Research Article by Rosa et al.
Judged by the Cover: Investigating the Effect of Adaptive Game Interface on the Learning Experience

Adaptive educational games have shown potential in enhancing the learning experience in educational setups. Most of the conducted research used the content and level of difficulty as the adaptive aspect. However, in this work we aim to evaluate the effect of changing the game interface including; background music and color as well as dynamics represented by the timer and scoring technique. We built adaptive educational games using subjective measures, where the adaptive features are manipulated based on reported emotions by the participants. We conducted a between subject study with 30 participants using adaptive and non-adaptive version of the game to assess the effectiveness of the proposed adaptive aspects on the learning experience, namely, learning gain and the engagement level. Our findings showed a significant increase in the learning gain and the engagement level of the participants who used the adaptive version of the system over the other. Finally, we discuss the implication of our findings and potential added value of our findings.
Emotion Enhancement through Ubiquitous Media Technology in a Smart Kitchen Environment

Today, Human Computer Interaction is increasingly shifting from 'information worlds' towards 'experience worlds'. Still, much research of smart environment technologies focuses merely on functional aspects. This paper recognizes the relevance of smart environment technologies on emotional experiences. We developed a ubiquitous media technology in a smart kitchen environment to enhance the user's emotions: A touch and voice controlled Visual Atmosphere App (VAA) that can change kitchen ambience through different atmospheric themes by using visual projection on the kitchen surfaces and sound effects. In a quantitative and qualitative laboratory user study (N=30) we measured the impact of the VAA on the user's emotional state. Our findings show that this ubiquitous media technology in a smart environment can have a significant (p<0.05) effect on emotion enhancement. This result is even stronger if the smart environment fits the user's personal preferences. This study is a step towards emotional experiences caused by smart environment technologies to foster desired psychological states.