Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Mobile App Privacy Preferences with User Profiles via Federated Learning

Permission managers in mobile devices allow users to control permissions requests, by granting of denying application's access to data and sensors. However, existing managers are ineffective at both protecting and warning users of the privacy risks of their permissions' decisions. Recent research proposes privacy protection mechanisms through user profiles to automate privacy decisions, taking personal privacy preferences into consideration. While promising, these proposals usually resort to a centralized server towards training the automation model, thus requiring users to trust this central entity. In this paper we propose a methodology to build privacy profiles and train neural networks for prediction of privacy decisions, while guaranteeing user privacy, even against a centralized server. Specifically, we resort to privacy-preserving clustering techniques towards building the privacy profiles, that is, the server computes the centroids (profiles) without access to the underlying data. Then, using federated learning, the model to predict permission decisions is learnt in a distributed fashion while all data remains locally in the users' devices. Experiments following our methodology show the feasibility of building a personalized and automated permission manager guaranteeing user privacy, while also reaching a performance comparable to the centralized state of the art, with an F1-score of 0.9.
JK System, a New Structural System and Construction Technology

A new structural system and construction technology, called JK system, is introduced in the current study. JK system is a box-type bearing wall system in which both gravitational and lateral loads supposed to be supported by interior and exterior JK walls. JK wall is a concrete shear wall constructed by lightweight/low strength expanded polystyrene (EPS) concrete and reinforced with a 3D interconnected steel mesh, called JK panel. Using a pre-validated numerical model, seismic performance of a 3-story residential building, constructed with JK system, is examined followed by required design procedure. Obtained results indicate that JK system, similar to other box-type structures, has quite good seismic performance due to its high level of redundancy, lightness, and ductility of the used EPS concrete.
Genetic variation of E6, E7, and L1 genes of human papillomavirus 51 from central China

The incidence of cervical cancer is closely related to high‐risk human papillomavirus (HR‐HPV). Women in Jingzhou had relatively high susceptibility to HPV‐51, whose ratio was 9.61% (456/4743) among HR‐HPV‐positive samples and ranked fifth in all analyzed HR‐HPV types. In this study, variations and phylogenetic trees of HPV‐51 E6−E7 and L1 sequences were analyzed by MEGA‐X. The selective pressure was estimated using PAML. The B‐cell epitope of L1 amino acid sequences and T‐cell epitope of E6 and E7 amino acid sequences were further predicted by ABCpred server and IEDB website, respectively. In the E6−E7 sequences 14 single nucleotide variants occurred, among which 4 were nonsynonymous variants and 10 were synonymous variants. A total of 41 single nucleotide variants were identified in the L1 sequences, including 10 nonsynonymous variants and 31 synonymous variants. All the isolates of both E6−E7 and L1 were classified into the A variant lineage. In HPV‐51 E6−E7 and L1 sequences, no positively selected site was found. Two nonconservative substitutions, H119Y and N176S in L1, affected multiple hypothetical B‐cell epitopes. Three nonconservative substitutions, T86P, S100L in E6, and F29L in E7, affected multiple hypothetical T‐cell epitopes. Elucidation of the HR‐HPV prevalence characteristics and genetic variations of HPV‐51 in central China may contribute to future investigations of diagnostic probes, therapeutic or preventative vaccines with wider coverage.
Design and Magnetic Field Analysis for Novel Module Integrated Permanent Magnet Synchronous Motor

High power electric machine requires high operational reliability and regular maintenance. To meet such requirements, a novel module integrated structure of permanent magnet synchronous motor is designed and the dynamic mathematical model is established, and the coupled magnetic field of stator winding among multiple modules is analyzed. An integral circular stator can be split into multiple modules, and then these modules are integrated according to design rules to form different module groups. Meanwhile, each module group is able to operate independently. In order to classify and distinguish the integrated modules, the concepts of master-module and sub-module are defined. By integrating multiple sub-modules into one master-module, the coupled magnetic field among multiple master-modules is decoupled. Because the windings in adjacent master-modules are disconnected, the parameters of three-phase windings of different master-modules are unequal. Thus, by phase exchange of the sub-modules, the total length of each phase winding in this master-module is equal and the inductance value of each phase winding is the same. Finally, the coupled magnetic field analysis is carried out by using the finite element method. The experimental results verify the feasibility of the proposed structural for permanent magnet motor.
A Modified Decomposition Based Multi-objective Optimization Algorithm for High Dimensional Feature Selection

Feature selection (FS) is an important research topic in the field of data preprocessing. For this reason, a modified decomposition based multi-objective optimization algorithm, namely M-MOEA/D, is proposed for high dimensional FS, in which an efficient elimination and repair strategy and a modified binary differential evolution (DE) operator are implemented in the decomposition-based framework. Specifically, the elimination and repair strategy is designed based on the symmetric uncertainty. In order to increase the global search capability of the algorithm, a modified binary DE operator is further proposed to cooperate with the elimination and repair strategy. Finally, six different real-world high dimensional data sets are adopted in experiment. The experimental results have validated that M-MOEA/D greatly reduced the size of features set to be selected, and our accuracy was also very competitive when compared to other FS algorithms.
Doubly Pessimistic Algorithms for Strictly Safe Off-Policy Optimization

We study offline reinforcement learning (RL) in the presence of safety requirements: from a dataset collected a priori and without direct access to the true environment, learn an optimal policy that is guaranteed to respect the safety constraints. We address this problem by modeling the safety requirement as an unknown cost function of states and actions, whose expected value with respect to the policy must fall below a certain threshold. We then present an algorithm in the context of finite-horizon Markov decision processes (MDPs), termed Safe-DPVI that performs in a doubly pessimistic manner when 1) it constructs a conservative set of safe policies; and 2) when it selects a good policy from that conservative set. Without assuming the sufficient coverage of the dataset or any structure for the underlying MDPs, we establish a data-dependent upper bound on the suboptimality gap of the safe policy Safe-DPVI returns. We then specialize our results to linear MDPs with appropriate assumptions on dataset being well-explored. Both data-dependent and specialized bounds nearly match that of state-of-the-art unsafe offline RL algorithms, with an additional multiplicative factor $\frac{\Sigma_{h=1}^{H}\alpha_{h}}{H}$, where αh characterizes the safety constraint at time-step $h$. We further present numerical simulations that corroborate our theoretical findings. A full version referred to as technical report of this paper is accessible at: https://offline-rl-neurips.github. io/2021/pdf/21.pdf
The Moderating Effects of Perceived Risk: A Study of Users’ Attitude Toward Uber App

The vast use of smartphones and the coverage of the Internet service in the information age provide a friendly environment for the sharing economy products, such as Uber, to obtain a competitive advantage to win the market share. However, the constant controversies have ever temporarily stopped Uber’s operation in some countries, such as Taiwan. Since Uber has been gaining popularity worldwide and claimed for continuous improvement of the App features to construct social safety network, it is worthwhile to search into the current usage status of Uber from consumers’ perspectives. This research constructs a conceptual model by applying the Technology Acceptance Model (TAM) to learn how consumers’ perceived usefulness and easy-of-use of the Uber App may affect their attitude toward the behavioral intention of using Uber service. Meanwhile, whether consumers’ perceived risk may have moderating effects on the above listed linkages is also investigated. The results may provide the practical references for the future establishments of related policies and legitimacy.
Control of a subsea PMSM with a very long feeding cable using a top-side converter – design and performance evaluation

In oil and gas extraction applications, Permanent Magnet Synchronous Motors (PMSMs) are becoming a more preferred choice to drive Electric Submersible Pumps in order to achieve a higher efficiency than Induction Motors (IMs). In the drive system, a subsea PMSM is fed from a top-side Variable Speed Drive (VSD) through a transmission system including a step-up transformer and a long feeding cable. In this work, both the series impedance and the shunt admittance of the transmission system are taken into consideration. Sensorless control of such a subsea PMSM is implemented and evaluated in this thesis work. The sensorless control is realized by the combination of the Static Compensated Voltage Model (SCVM) method and the High Frequency Injection (HFI) method. An equivalent IM model current controller is developed to deal with the transformer shunt branch and a static estimator is implemented to estimate the PMSM voltage and current. The developed controller can successfully start and accelerate the PMSM up to 6000 rev/min within 5 s and the efficiency during steady state operation achieves 91.65% excluding the iron-core loss of the PMSM. The steady state angle estimation error with perfect parameters is -0.8506° with peak dynamic angle estimation errors at +0.7063° and -4.7335° respectively. A ±10% parameter error is acceptable for a successful PMSM start-up except for a -10% PMSM magnet flux linkage error which generates a too large angle estimation error during the start-up and leads to a start-up failure. In conclusion, the field-oriented sensorless control of a PMSM through a transmission system is possible. A five-level inverter needs to be applied to reduce the harmonic contents to realize a successful start-up of the PMSM. It is also suggested that the accuracy of the PMSM magnet flux linkage and the stator inductance are of essential importance.
Acne and Rosacea Detection from Images using Deep CNN's EfficientNet

Detecting skin diseases is dire and perilous as per the perspective of contamination of everything, like diseases, much of the time spread in human skin. Regardless, it is testing, routinely common, and drawn-out to see skin infections with manual strategies. In this manner, motorized acknowledgment of skin diseases is outstandingly needed in the field of dermatological mechanization. The capacity to recognize ailments utilizing acne or rosacea pictures of skin has been shown because of ongoing advances in computer vision. In light of everything, the massive piece of the proposed models can break down illnesses quickly. Thus, in this segment, we investigate an ideal acne and rosacea unmistakable confirmation model combining skin assurance. Regardless of multi-class grouping, the model utilizes a multilabel format system to recognize both acne and rosacea infection. For the examination and evaluation, we have blended two data sets from various internet-based sources that included skin pictures of skin inflammation and rosacea. In our study, we complete different well-known convolutional neural organization (CNN) plans. The exploratory results endorse that the EfficientNet perform better in multilabel skin diseases classification undertakings. Furthermore, we have found that a CNN design, skip affiliations, spatial convolutions, and more restricted mystery layer network sway better results in skin inflammation and rosacea recognizable proof portrayal.
Automatic Pavement Type Recognition based on Mobile Deep Learning

Pavement deformation detection is an essential task in order to maintain driving safety. In recent years, convolutional neural networks (CNN), one of the machine learning methods, have been widely studied for pavement condition detection. In this paper, we propose a method that can achieve optimal pavement deformation detection in each region, assuming a mobile terminal.
To what extent do thermo physical properties of a metallurgical reactor affect the performance of a virtual sensor used for predicting the ledge profile

A non-invasive virtual sensor is employed for the inverse prediction of the time-varying ledge profile that forms inside high-temperature metallurgical reactors filled with a load of phase change material (PCM). The virtual sensor is tested for thermo physical properties of the vessel wall and of the PCM that fall outside the range for which it was originally designed. The results are analyzed and presented in terms of the shift of key thermo physical properties from the reference case. Results indicate that the virtual sensor is more sensitive to the variation of the properties of the phase change material than that of the vessel walls. The virtual sensor response remains accurate for reactor loads of high thermal inertia. The virtual sensor may still be used for reactor loads of low thermal inertia provided that thermo physical properties of the PCM are well-known.
Data augmentation for Improving Rice Leaf Disease Classification on Residual Network Architecture

In this modern era, various advanced deep learning architectures are evolving to categorize plant diseases. Deep learning is being considered to be an important aspect of feature extraction and image classification through various computer vision tasks. In this paper, Resnet-32 model is well-thought-out for the early recognition of rice plant diseases and data augmentation is proposed to improve classification accuracy. As well, a comparative study on various deep learning architectures is made. Deep learning model with small number of datasets is the major challenge that's result in over fitting. And for this data augmentation is contemplated to be the solution. Data augmentation plays a crucial role in improvising classification accuracy with its ability to grow the training samples in size. This happens by applying some kind of transformations to an input image. Resnet architecture is used for feature extrication. Thus, in our work, classification of augmented rice-leaf image data set is performed with Resnet model. A Resnet based model is used to train around 2215 rice leaves images obtained from plant village datasets. With seven data augmentation techniques being applied the number of images increased to 12684, which is used to identify different plant species and different disease categories. The trained model could achieve an accuracy of 99.55% on test dataset and a validation loss of 0.0029, demonstrating the efficacy of this approach. The outcome of this findings shows that proposed approach achieved higher performance on the identification and early prevention of rice leaf diseases.
Cyclist Speed Estimation Using Accelerometer and Gyroscope in Smartphones

Improving the safety of cyclists is a critical traffic issue. Cyclists’ riding speed and acceleration often indicate their intentions. Currently, most autonomous vehicle manufacturers are working toward developing vehicle automation technologies that fully rely on the vehicle’s onboard sensing and intelligence. However, onboard vehicle intelligence may still encounter difficulties when detection capability or range is physically obstructed or weakened. This paper proposes to develop a speed estimation function intended to be applied in cyclists’ smartphones. The proposed system provides the accurate speed of cyclists in an effort of establishing vehicle-to-everything communication. By making use of the accelerometer and gyroscope sensors in a smartphone attached to a bicycle, cyclists’ speed can be estimated using one of the most prominent deep neural networks, namely Long Short-Term Memory. The experiment results demonstrate the proposed method can outperform the conventional speed estimation method.
Spatial models of pattern formation during phagocytosis

Phagocytosis, the biological process in which cells ingest large particles such as bacteria, is a key component of the innate immune response. Fcγ receptor (FcγR)-mediated phagocytosis is initiated when these receptors are activated after binding immunoglobulin G (IgG). Receptor activation initiates a signaling cascade that leads to the formation of the phagocytic cup and culminates with ingestion of the foreign particle. In the experimental system termed “frustrated phagocytosis”, cells attempt to internalize micropatterned disks of IgG. Cells that engage in frustrated phagocytosis form “rosettes” of actin-enriched structures called podosomes around the IgG disk. The mechanism that generates the rosette pattern is unknown. We present data that supports the involvement of Cdc42, a member of the Rho family of GTPases, in pattern formation. Cdc42 acts downstream of receptor activation, upstream of actin polymerization, and is known to play a role in polarity establishment. Reaction-diffusion models for GTPase spatiotemporal dynamics exist. We demonstrate how the addition of negative feedback and minor changes to these models can generate the experimentally observed rosette pattern of podosomes. We show that this pattern formation can occur through two general mechanisms. In the first mechanism, an intermediate species forms a ring of high activity around the IgG disk, which then promotes rosette organization. The second mechanism does not require initial ring formation but relies on spatial gradients of intermediate chemical species that are selectively activated over the IgG patch. Finally, we analyze the models to suggest experiments to test their validity. Author Summary Phagocytosis, the process by which cells ingest foreign bodies, plays an important role in innate immunity. Phagocytosis is initiated when antibodies coating the surface of a foreign body are recognized by immune cells, such as macrophages. To study early events in phagocytosis, we used “frustrated phagocytosis”, an experimental system in which antibodies are micropatterned in disks on a cover slip. The cytoskeleton of cells attempting to phagocytose these disks organizes into “rosette” patterns around the disks. To investigate mechanisms that underlie rosette formation we turned to mathematical modeling based on reaction-diffusion equations. Building on existing models for polarity establishment, our analysis revealed two mechanisms for rosette formation. In the first scenario an initial ring of an intermediate signaling molecule forms around the disk, while in the second scenario rosette formation is driven by gradients of positive and negative pathway regulators that are activated over the disk. Finally, we analyze our models to suggest experiments for testing these mechanisms.
Intradiscal Inflammatory Stimulation Induces Spinal Pain Behavior and Intervertebral Disc Degeneration In Vivo

Degeneration of the intervertebral disc (IVD) is known to occur naturally over time, with the severity of pain varying widely. Other components of the degenerative environment, including structural disruption and inflammatory cytokine levels, and their correlation with pain severity have been studied. However, the role of the inflammatory environment in activating degenerative changes that manifest as a pain phenotype has not been elucidated. Previous studies have aimed to recreate the sustained inflammatory environment exhibited during human disc degeneration in a rat model. Most commonly, a puncture injury has been used causing structural damage and only initiating an acute inflammatory response. This study utilized injection of lipopolysaccharide (LPS), a pro-inflammatory stimulus, into the rat disc in vivo to create the desired sustained inflammatory environment independent of physical disruption. LPS injections resulted in upregulation of pro-inflammatory cytokines and an immunogenic response. The structural integrity of the IVD was also altered demonstrated by changes in histological score, disc height, and mechanical properties. Ultimately, a sustained inflammatory environment led to both local and radiating mechanical sensitivity, demonstrating that the pain phenotype experienced during disc degeneration can be initiated solely by a sustained inflammatory profile. Markers indicative of nerve ingrowth into the IVD were also expressed suggesting a potential mechanism for the pain exhibited by animals. This rat injury model will allow for future study of the direct relationship between inflammation and pain in the degenerative environment.
Outside-in: intracellular vesicles in giant sulfur bacteria contain peptidoglycan

Until recently, the cellular envelopes of bacteria were regarded as static and rigid relative to those of eukaryotes. While investigating peptidoglycan synthesis in populations of giant sulfur bacteria, Candidatus Thiomargarita spp. , we observed internal vesicle-like features (VLFs). VLFs, as imaged following the active incorporation of D-amino acids, appear to begin as invaginations and delaminations of the cellular envelope. Staining with wheat germ agglutinin confirmed the presence of peptidoglycan in VLFs, while polymyxin B revealed that the outer membrane is present in some VLFs. Transmission electron microscopy revealed a complex network of interconnected VLFs. Genomes of Ca. Thiomargarita nelsonii lack a canonical divisome, while possessing homologs to genes such as actin, membrane scaffolding proteins, and dynamins that are associated with phagocytosis in eukaryotes. The physiological role of VLFs remains unclear, but the presence of sulfur globules in some suggests compartmentalization of metabolism and energy production. This is the first report of peptidoglycan and outer membrane bound intracellular vesicles within prokaryotic cells. These findings transform the canonical view of the inflexible bacterial cell envelope and further narrow the divide between prokaryotes and eukaryotes.
Stair Recognition for Robotic Exoskeleton Control using Computer Vision and Deep Learning

Computer vision can be used in robotic exoskeleton control to improve transitions between different locomotion modes through the prediction of future environmental states. Here we present the development of a large-scale automated stair recognition system powered by convolutional neural networks to recognize indoor and outdoor real-world stair environments. Building on the ExoNet database - the largest and most diverse open-source dataset of wearable camera images of walking environments – we designed a new computer vision dataset, called StairNet, specifically for stair recognition with over 515,000 images. We then developed and optimized an efficient deep learning model for automatic feature engineering and image classification. Our system was able to accurately predict complex stair environments with 98.4% classification accuracy. These promising results present an opportunity to increase the autonomy and safety of human-exoskeleton locomotion for real-world community mobility. Future work will explore the mobile deployment of our automated stair recognition system for onboard real-time inference.