Miracle-dz/UniFAR-FTUs huggingface dataset:
A High-Intensity Exercise Intervention Improves Older Women Lumbar Spine and Distal Tibia Bone Microstructure and Function: A 20-Week Randomized Controlled Trial

Introduction: The effects of ageing on bone can be mitigated with different types of physical training, such as power training. However, stimuli that combine increasing external and internal loads concomitantly may improve bone quality. The goal of this study was to assess the efficacy of a combined power and plyometric training on lumbar spine and distal tibia microstructure and function. Methods: 38 sedentary elderly women between 60 and 70 years were randomly allocated in experimental (N = 21) and control group (N = 17). The effects of the 20-week protocol on lumbar spine microstructure and tibia microstructure and function were assessed by trabecular bone score (TBS), high resolution peripheral quantitative computed tomography (HR-pQCT) and microfinite element analysis. Results: when compared to the effects found in the control group, the experimental group showed significant improvements in lumbar spine TBS (Hedges’ g = 0.77); and in distal tibia trabecular thickness (g = 0.82) and trabecular bone mineral density (g=0.63). Conclusion: our findings underscore the effectiveness of the proposed intervention, suggesting it as a new strategy to slow down and even reverse the structural and functional losses in the skeletal system due to ageing.
Delay-dependent stabilization of positive continuous-time Takagi-Sugeno fuzzy systems: An LP approach

This paper studies the stability and stabilization problem of Takagi-Sugeno (T-S) fuzzy systems with time-varying delay by using linear programming (LP) technique while imposing positivity in closed-loop. New delay-dependent stabilization conditions are derived using a new Lyapunov-Krasovskii Functional (LKF). To show the advantages of the proposed design procedure, a numerical example is studied.
A New Approach for Face Anti-Spoofing Using Handcrafted and Deep Network Features

In biometrics, face recognition methods are achieving momentum with recent progress in the computer vision(CV). Face recognition is widely used in the identification of an individual's identity. Unfortunately, in recent research work has revealed this face biometrics system is unprotected to spoofing attacks using by very low price instrument such as printed 2D photos attack, 3D masking attack and taking videos using smart devices (reply attack). Therefore, a Liveness Attack Detection (LAD) approach is needed to improve the high-quality security of face recognition system. Most of the earlier worked LAD methods for face anti-spoofing methods have highlight on using the handcrafted features, which are developed by expert knowledge of researcher. As example Gabor filter, Histogram of Oriented Gradients, local ternary pattern, and the Local Binary Pattern. Because of that, the extracted features consider limited factors of the problem, yielding a capture accuracy that is very low and changes with the point of presentation in attack face images. The deep learning method has developed in the computer vision research community, which is proven to be suitable for automatically training. In this article, we approach to mix or combine the handcrafted features and deep neural network features to design the discriminant face spoofing detection. The handcrafted features were based on LBP analysis. We examine the features information from the brightness and the chrominance channels using LBP descriptor. In deep features, we present an approach based on pre-trained convolutional neural network VGG-16 model using static features to recognize video and printed(2D) photo attacks. By attaching this two types of image features on our dataset and public databases, we get good results to identify real and attack images feature, called hybrid features, which has better discrimination ability to understand spoofing image feature.
A Novel MI-EEG Imaging With the Location Information of Electrodes

Combination of the Motor Imagery EEG (MI-EEG) imaging and Deep Convolutional Neural Network is a prospective recognition method in brain computer interface. Nowadays, the frequency or time-frequency analysis has been applied to each channel of MI-EEG signal to obtain a spatio-frequency or time-frequency image, and even the images from several channels are infused to generate a combined image. However, the real position information of channels or electrodes is lost in these MI-EEG images, and this is contradictory to the activation area of MI-tasks. In this paper, the MI period and the frequency band covered by $\mu $ and $\beta $ rhythms are divided into ten time windows and three sub-bands, respectively. Then, for each electrode, Fast Fourier Transform (FFT) is employed to transform each time window to spectrum, and its inverse FFT is calculated for each sub-band. The time-domain powers of ten time windows are averaged for the same sub-band. So, three average powers are generated as the time-frequency features of each electrode of MI-EEG. They are further arranged to the electrode coordinate figure by using Clough-Tocher interpolation algorithm, and a complicated image, in which the time-frequency features are correctly located at the real position of each electrode, is obtained to embody the MI-EEG in detail. Furthermore, a VGG network is modified to perform effective recognition for MI-EEG image, and it is called mVGG. Extensive experiments are conducted on three publicly available datasets, and the 10-folds cross validation accuracies of 88.62%, 92.28% and 96.86% are achieved respectively, and they are higher than that of the state-of-the-art imaging methods. Kappa values and ROC curves demonstrate our method has lower class skew and error costs. The experimental results show that the effectiveness of proposed MI-EEG imaging method, and it is well-matched with mVGG.
Rumor Stance Classification via Machine Learning with Text, User and Propagation Features

Stance classification of user responses plays a key role in rumor detection on social media platforms. Stances are commonly divided into four categories: support, deny, query and comment, where the first three are particularly important for determining the confidence of rumors. Since people seldom express definite stance under rumors with unclear authenticity, it is difficult to judge such stances. Machine learning approaches have been proposed to address this problem with either manually designed features or automatically extracted features. However, the problem is related to many aspects of attributes and it is somehow difficult to summarize a comprehensive feature template or to learn an effective feature representation. In this work, we conduct an in-depth study on the feature engineering for this task. We screen out 18 salient features in three aspects including text content, user portrait and propagation state. The experimental results on the RumorEval dataset show that coupled with these 18 features, a traditional logistic regression classifier even achieves the state-of-the-art performance and outperforms some complex neural networks such as long-short term memory networks that uses the same feature template or automatic feature extraction.
What Factors Influence Online Product Sales? Online Reviews, Review System Curation, Online Promotional Marketing and Seller Guarantees Analysis

This paper proposes an SFNN (a sales factor model using a neural network), which uses a back-propagation multilayer perceptron neural network and weight matrix operation, to study the mechanism of the influencing factors of online product sales in the e-commerce platform. To achieve this objective, this study analyzes the factors and relative strength of online product sales based on four aspects: online reviews, review system curation, online promotional marketing, and seller guarantees. The empirical analysis of the SFNN model based on the data of Taobao.com shows whether the 14 factors, in relation to the four aspects, have any impact on product sales. In addition, the findings indicate that the number of sentiment words greatly affects product sales. Other factors affecting online product sales significantly include the review volume, the number of uploaded pictures, the negative review rate, the discount rate, 7+day returns and money-back guarantees, and the freight insurance. This study examines the interactions among the various factors affecting product sales on the e-commerce platform and provides management inspiration for e-commerce enterprises to manipulate online reviews, undertake effective promotion and fulfill after-sales promises.
Flexible conductivity, temperature, and depth sensor for marine environment monitoring*

This paper presents lightweight and flexible sensors for conductivity, temperature, and depth (CTD). CTD sensors are heavily used in oceanography to determine the salinity, which is a critical factor in the determination of mixingdensity, currents, weather and more. The graphene sensors are fabricated by laser printing on flexible polyimide. They combine corrosion resistance with a good conductivity and piezoresistivity in a robust platform, enabling the detection of conductance, temperature, and pressure. The performance of the graphene sensor was demonstrated in harsh environments with pressures up to 12 MPa and prolonged exposure to highly saline seawater. The printed sensors are durable and lightweight, making ideal for use as stationary monitory sensors or attached to a wide range of marine animals.
25 Moral Reasoning: A Network Neuroscience Perspective

Humans are moral creatures – uniquely capable of drawing on beliefs about morality in order to distinguish between actions that are right or wrong. Neuroscience research has investigated the neural substrates that underlie this reasoning process, identifying multiple regions of the brain that are recruited to facilitate performance on moral reasoning tasks. Current evidence indicates that reasoning about morality recruits a distributed collection of brain areas functioning variously in conflict or agreement (Pascual, Rodrigues, and Gallardo-Pujol, 2013). Recently, dual-process frameworks have been employed to operationalize moral reasoning in terms of two distinct cognitive systems (Crockett, 2013; Cushman, 2013; Greene, 2017; Greene et al., 2001). This dual-process framework posits that, when faced with a moral dilemma, individuals will predominantly rely on one of two reasoning systems (heuristic, or model-based) to produce deontological or consequentialist moral judgments. Fundamental questions remain, however, as to whether these two reasoning processes depend on distinct neurobiological substrates, and to what extent moral cognitions may result from dynamic interactions between these two cognitive systems. To expand our understanding of how judgments about morality might depend on the brain’s systems and organization, this chapter considers moral reasoning in terms of the topology and dynamics of brain networks. The emerging field of network neuroscience uses tools and methods from the network sciences to characterize the topology and dynamics of brain networks (see Bassett and Sporns, 2017; Bressler and Menon, 2010). Regions of neural tissue are defined as topological elements (nodes) in a network, and physiological signatures are measured and used to infer their relationships (edges). Evidence from this literature suggests that the brain is a complex, self-organized structure constructed to be at once highly efficient, flexible, and specialized. Network neuroscience’s set of conceptual and empirical tools affords the opportunity to investigate neural circuits for moral reasoning at a new level of resolution – that of communication and interactions occurring within and between large-scale functional brain networks (see Chiong et al., 2013; Young and Saxe, 2008). Current neuroscience accounts recognize that moral reasoning depends on relationships between a number of regions and circuits in the brain, but have yet to address the
LT Codes with Feedback: Accelerate the Distributed Matrix-Vector Multiplication with Stragglers

In this paper, we propose a coding scheme for distributed matrix-vector multiplication that builds upon the Luby transform (LT) codes with feedback. The ideal soliton distribution is utilized in our LT coding scheme to encode the sub-matrices. Besides, the belief propagation (BP) decoding algorithm is modified to cooperate with the feedback information. Compared with other coded distributed computations with straggling servers, our approach achieves lower computation latency when the overall delay incurred by the encoding, mapping and decoding process is considered. Furthermore, we compare the storage loads of different schemes and show that the LT coding with feedback has a strong comparative advantage in these straggler-tolerant computation scenarios.
Study on the Reading of Energy-Harvested Implanted NFC Tags Using Mobile Phones

In this paper we study the read range of implanted sensors based on batteryless, Near-Field Communication (NFC) integrated circuits (IC) using an NFC-equipped smartphone as a reader. The most important challenges are the low coupling between loops of different sizes, the limited quality factor imposed by the bandwidth communication, the effects of the body on propagation, and the detuning of the antennas. Two systems are analyzed: a conventional system based on resonant coupling between two coils; and a system based on resonant coupling between three coils. With the latter, a relay antenna is attached to a patch, which is adhered to the skin. Simulations and measurements show that the quality factor of both antennas can be improved by including a spacer made of low-permittivity material. A circuit model is proposed for the implanted and relay antenna, which simplifies its usage in circuit simulators. Some implanted and relay antenna prototypes are analyzed and a system model that includes a nonlinear model of the tag is used to analyze the maximum depth at which the implant can be read. Our experimental results show that the system based on three coils performs much better performance at longer distances and is more robust to misalignments between coils. A $15\times 15$ mm-implanted tag with commercial NFC IC and energy harvesting can be read using commercial smartphones. It can feed sensors at a distance of up to 16 mm inside the body and at a distance of 3 cm from the skin. Our results also show that data previously stored in the IC memory can be transferred to the reader located at distances of up to 2 cm and 3.8 cm for the 2-coil and 3-coil systems, respectively. This study demonstrates the potential of batteryless NFC sensors for biomedical and wearable applications using mobile phones as readers.
Classification of 3D Terracotta Warrior Fragments Based on Deep Learning and Template Guidance

The Terracotta Warriors are terracotta sculptures created for China’s first emperor more than 2,000 years ago. They are among the most precious unearthed cultural relics of China. However, these relics have been predominantly found in fragments. Fragment classification is currently performed manually on enormous quantities of fragments, which is a time-consuming, inaccurate, and subjective task for archaeologists and conservators. In this study, an automatic method based on a deep learning network combined with template guidance is proposed to classify 3D fragments of the Terracotta Warriors. The fragments are initially classified using PointNet. Then, misclassified fragments are secondly categorized based on their best match to a complete Terracotta Warrior model. Extensive experiments were performed to verify the effectiveness of the proposed method. The promising results demonstrate that the method is the most accurate technique for classifying 3D Terracotta Warrior fragments to date. Moreover, the proposed method can significantly increase the efficiency of future fragment reassembly for the Terracotta Warriors.
Linear Machines for Long Stroke Applications—A Review

This document reviews the current state of the art in the linear machine technology. First, the recent advancements in linear induction, switched reluctance and permanent magnet machines are presented. The ladder slit secondary configuration is identified as an interesting configuration for linear induction machines. In the case of switched reluctance machines, the mutually-coupled configuration has been found to equate the thrust capability of conventional permanent magnet machines. The capabilities of the so called linear primary permanent magnet, viz. switched-flux, flux-reversal, doubly-salient and vernier machines are presented afterwards. A guide of different options to enhance several characteristics of linear machines is also listed. A qualitative comparison of the capabilities of linear primary permanent magnet machines is given later, where linear vernier and switched-flux machines are identified as the most interesting configurations for long stroke applications. In order to demonstrate the validity of the presented comparison, three machines are selected from the literature, and their capabilities are compared under the same conditions to a conventional linear permanent magnet machine. It is found that the flux-reversal machines suffer from a very poor power factor, whereas the thrust capability of both vernier and switched-flux machines is confirmed. However, the overload capability of these machines is found to be substantially lower than the one from the conventional machine. Finally, some different research topics are identified and suggested for each type of machine.
Performance Analysis for Underwater Cooperative Optical Wireless Communications in the Presence of Solar Radiation Noise

Underwater optical wireless communications suffer from several challenges, such as large path loss and limited transmission distance. Cooperative communications are implemented by utilizing relays as virtual antennas to increase diversity gain and expand communication range. In this paper, we investigate the performance of the cooperative transmission for underwater optical wireless communications in the presence of solar radiation noise, which has a detrimental impact on the underwater communications during daytime and in the neritic zone. Firstly, the performance of amplify-and-forward relaying and decode-and-forward relaying based on line-of-sight (LOS) links and non-line-of-sight (NLOS) links is theoretically analyzed. Then, the effects of the horizontal positions and the depths of relays, as well as the allocations of transmitted power on cooperative communications are simulated and evaluated. The main factors affecting the system behavior and the optimal deployment of the relays for LOS and NLOS links are derived through balancing path loss, refraction loss, and solar radiation noise.
Fresh Tea Leaves Classification Using Inception-V3

Tea is a kind of favorite commodity all over the world. In the process of tea production, the classification of different kinds of fresh tea leaves is very important. In this paper, based on the Inception-v3 model Keras platform, we used transfer learning technology to conduct experiments on the fresh tea leaves dataset created by ourselves. In combination with the Inception-v3's inherent performance, saving parameters, accelerating computation, and reducing over-fitting, which can bring convenience in practical production applications. Through the experiment, the discrimination accuracy rate of fresh tea leaves can reach more than 95%. After that, through the data augmentation technology, we can make the discrimination accuracy rate of fresh tea leaves reach 98%, which brings good news to industrial production.
Vehicle-Terrain Parameter Estimation for Small-Scale Unmanned Tracked Vehicles

Unmanned vehicles are used extensively in a wide range of industries: from agriculture, mining, search and rescue, to hazardous waste management and disposal [1]. One common thread to these applications is that in these instances, the vehicle must traverse widely uncertain, highly unstructured terrain. Thus, having prior information or the ability to estimate vehicle-terrain interaction parameters as the vehicle moves along uncertain terrain provides the opportunity to optimize traction and/or power consumption in order to meet the increasingly complex, highly-critical mission objectives of today's robotic applications. This is particularly significant for tracked vehicles, which rely on skid-steering, and thus the deformability of the terrain, to achieve locomotion. Terrain parameters, such as internal friction angle and soil cohesion play a critical role in estimating track-terrain interaction from readily available odometry from wheel encoders and navigation sensors, such as gyroscopes and accelerometers. The work presented here establishes a need for a reliable means for estimating terrain parameters, particularly for deformable terrain. Methods, such as Extended Kalman Filters and Newton Raphson techniques for estimating vehicle-terrain parameters have been implemented and evaluated using experimental studies. The estimated results obtained are compared with experimental data and tested on uniform terrain using a small-scale unmanned tracked robot.
Accurate String Constraints Solution Counting with Weighted Automata

As an important extension of symbolic execution (SE), probabilistic symbolic execution (PSE) computes execution probabilities of program paths. Using this information, PSE can prioritize path exploration strategies. To calculate the probability of a path PSE relies on solution counting approaches for the path constraint. The correctness of a solution counting approach depends on the methodology used to count solutions and whether a path constraint maintains a one-to-one relation with program input values. This work focuses on the latter aspect of the solution counting correctness for string constraints. In general, maintaining a one-to-one relation is not always possible, especially in the presence of non-linear constraints. To deal with this issue, researchers that work on PSE for numerical domains either analyze programs with linear constraints, or develop novel techniques to handle solution counting of non-linear constraints. For the string domain, however, previous work on PSE mainly focuses on efficient and accurate solution counting for automata-based string models and has not investigated whether a one-to-one relationship between the strings encoded by automata and input string values is preserved. In this work we demonstrate that traditional automata-based string models fail to maintain one-to-one relations and propose to use the weighted automata model, which preserves the one-to-one relation between the path constraint it encodes and the input string values. We use this model to implement a string constraint solver and show its correctness on a set of non-trivial synthetic benchmarks. We also present an empirical evaluation of traditional and proposed automata solvers on real-world string constraints. The evaluations show that while being less efficient than traditional automata models, the weighted automata model maintains correct solution counts.
Autonomy of an EPAC in Mountainous Cities under Different Assistance Modes Using Energetic Macroscopic Representation

This paper deals with the application of the energetic macroscopic representation (EMR) for autonomy studies of an electric pedal-assisted cycle (EPAC) in mountainous terrains. Multiple slope and speed profiles from conventional cycles users are used for EPAC under different modes of electrical assistance. The results of the study show that EPAC autonomy is strongly affected by the road gradient forces in mountainous terrains: even 50% less in comparison with scenarios where road gradient forces are not considered. The use of the regenerative braking can give even an extra autonomy of 58%, depending on terrain and route conditions.