Miracle-dz/UniFAR-FTUs huggingface dataset:
Template-Based Insoles for the Center of Pressure Estimation in Different Foot Sizes

In this letter, we present a novel simplified approach for the center of pressure (CoP) estimation across different foot sizes using insole-based systems. A concept of insole templates is introduced to realize the adaptability of a single bunch of sensors across different template sizes. The proposed insole system is validated against the force platform during walking and weight shifting tasks. The experimental results show estimation accuracy with root mean square error of 1.98 ± 0.57 cm during walking and 1.28 ± 0.60 cm during weight shifting using UK7 insole size. The pearson correlation coefficient of 0.97 ± . 03 during walking and 0.97 ± 0.05 during weight shifting was observed. The nonsignificant difference (p>0.05) in root mean square error and Pearson's correlation coefficient between two significantly varying foot sizes (UK7 and UK10; p<0.05) indicates the suitability of the present approach for CoP estimation in different foot sizes at low cost during weight shifting.
Text Mining of Power Secondary Equipment Based on BiLSTM-Attention

A large number of idle defect and fault texts are accumulated in the process of long-term operation, maintenance and repair of power secondary equipment. In order to realize the effective utilization and deep mining of defect texts, a method of defect text classification of secondary equipment based on BiLSTM-Attention is proposed. Firstly, the text is cleaned according to the natural language characteristics of the historical defect texts of the secondary equipment. Secondly, the text content is vectorized through Word2Vec model, and the semantic information of the text is extracted in depth through BiLSTM-Attention to realize the accurate classification of the defect degree of the equipment. Finally, an example is built through the historical defect texts of a power company. The result shows that the proposed model can accurately classify the defect text of power secondary equipment and help operation and maintenance personnel to judge the defect degree quickly and accurately.
Dynamic Resource Optimization Allocation for 5G Network Slices Under Multiple Scenarios

The significant improvement of 5G technology will be a huge driving force for the Internet of Things (IoT). Furthermore, the concept of network slice provides the possibility of customization for IoT applications. How to optimize resource allocation in multiple scenarios has become a challenge we are facing. In this paper, we propose a bandwidth resource’s dynamic allocation scheme which is suitable for both Enhance Mobile Broadband (eMBB) scenario and Ultra Reliable and Low Latency Communication (uRLLC) scenario. Based on the classical Software Defined Network (SDN) architecture, admission control is introduced to maintain the resource utilization of the network, and the delay penalty factor we set can guarantee the low delay of the network. Besides, we make a tradeoff between the benefit and the cost of network when adjusting resources. Simulation results show that the algorithm can meet the performance requirements of different application scenarios while minimizing the cost of dynamic resource adjustment.
Demo Abstract: Elastic Deployment of Robust Distributed Control Planes with Performance Guarantees

Recent control plane solutions in a software-defined network (SDN) setting assume physically distributed but logically centralized control instances: a distributed control plane (DCP). As networks become more heterogeneous with increasing amount and diversity of network resources, DCP deployment strategies must be both fast and flexible to cope with varying network conditions whilst fulfilling constraints. However, many approaches are too slow for practical applications and often address only bandwidth or delay constraints, while control-plane reliability is overlooked and control-traffic routability is not guaranteed. We demonstrate the capabilities of our optimization framework [1]–[3] for fast deployment of DCPs, guaranteeing routability in line with control service reliability, bandwidth and latency requirements. We show that our approach produces robust deployment plans under changing network conditions. Compared to state of the art solvers, our approach is magnitudes faster, enabling deployment of DCPs within minutes and seconds, rather than days and hours.
DIDA: Distributed In-Network Defense Architecture Against Amplified Reflection DDoS Attacks

With each new DDoS attack potentially becoming a higher intensity attack than the previous ones, current ISP measures of over-provisioning or employing a scrubbing service are becoming ineffective and inefficient. We argue that we need an in-network solution (i.e., entirely in the data plane), to detect DDoS attacks, identify the corresponding traffic and mitigate promptly. In this paper, we propose the first distributed in-network defense architecture, DIDA, to cope with the sophisticated amplified reflection DDoS (AR-DDoS) attacks. We leverage programmable stateful data planes and efficient data structures and show that it is possible to keep track of per-user connections in an automated and distributed manner without overwhelming the network controller. Building on top of this data, DIDA can easily detect if unsolicited attack packets are sent towards a victim within an ISP network. Once an attack is detected, the routers at the network edge automatically block the malicious sources. We prototype DIDA in P4. Our preliminary experiments show that DIDA can detect and mitigate 99.8% of amplification attacks containing 7, 000 different sources while requiring less than 1% of the memory of current programmable switches.
Defect prediction of low pressure die casting in crankcase production based on data mining methods

Low Pressure Die Casting (LPDC) is widely used in crankcase production. Owing to the unstable production situation and the complex shape of crankcase, porosity defect usually occurs in LPDC process which leads to loss in quality and productivity. In this paper, we apply data mining methods to predict the porosity defect in advance, so that we can take actions to prevent it from reoccurring in the next production part, thereby increasing the quality and productivity. To do it, firstly, we collect production data from a real-world casting line. Secondly, we prepare the data for prediction by feature extraction and feature selection. Thirdly, we apply an ensemble algorithm named Forest of Local Trees (FLT) for defect prediction. Finally, we present a thorough experimental study of the proposed method. The results show that our method outperforms other five algorithms on five real-world datasets in terms of three indicators, recall, precision and F-measure.
The Influence of Managerial Entrenchment on Corporate Mergers and Acquisitions——The Mediating Effect of Equity Concentration

Mergers and Acquisitions is one of the most important decisions in enterprise investment. An important M&A error often puts a company in trouble or even in the danger of bankruptcy. So, do managers' managerial entrenchment motives affect corporate M&A? In order to explore the relationship, this article uses the Shanghai and Shenzhen A-share listed companies as a sample to empirically test the influence of managerial entrenchment on corporate M&A. The research shows that: (1)Managerial entrenchment has a significant inverted U-type relationship with corporate M&A, which indicates that low managerial entrenchment promotes M&A and high managerial entrenchment inhibits M&A, and this relationship is more obvious in manufacturing and non-state-owned enterprises; (2)Equity concentration plays a mediating effect in the process of the influence of managerial entrenchment on corporate M&A.
Heart rate measurement based on video acceleration magnification

The aim of this study is to improve the effect of Eulerian video magnification in non-contact heart rate measurement. Extracting the heart rate information from subtle head movements caused by blood circulation, we overcome the adverse condition that the person in the measurement should be completely still. Firstly, the temporal acceleration filter is used to preprocess the video and filter out the large low-frequency motion in the video. Secondly, face recognition is performed on each frame of the pre-processing video and face regions are cut out. The tracked face regions are reorganized to further reduce the impact of large movements. Heart rate information could be extracted from this reconstituted video. Experiment results indicate that the proposed method is more accurate in dynamic heart rate monitoring.
Reconciliation Of Group Sparsity And Low-Rank Models For Image Restoration

Image nonlocal self-similanty (NSS) property has been widely exploited via various sparsity models such as joint sparsity (JS) and group sparse coding (GSC). However, the existing NSS-based sparsity models are either too restrictive, i.e., JS enforces the sparse codes to share the same support, or too general, i.e., GSC imposes only plain sparsity on the group coefficients, which limit their effectiveness for modeling real images. In this paper, we propose a novel NSS-based sparsity model, namely low-rank regularized group sparse coding (LR-GSC), to bridge the gap between the popular GSC and JS. The proposed LR-GSC model simultaneously exploits the sparsity and low-rankness of the dictionary-domain coefficients for each group of similar patches. To make the proposed scheme tractable and robust, an alternating minimization with an adaptive adjusted parameter strategy is develope- d to solve the proposed optimization problem. Experimental results on both image deblocking and denoising demonstrate that the proposed LR-GSC image restoration algorithms outperform many popular or state-of-the-art methods, in terms of both the objective and perceptual quality.
A Numerical Simulation and Experimental Comparison of Atmospheric Thermal Plasma Spray Coatings Between Internal and External Powder Injection Processes

This study presents two 3-D atmospheric plasma spray models, namely, internal and external powder injections, by using the computational fluid dynamics technique to study the status of the melting of a particle and its in-flight velocity during a porous ceramic layer fabrication process. A bulk fluid model, which solves the time-dependent standard <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> model, is used in combination with a Lagrangian to track the trajectories of alumina particles. The results of the fluid-analytical models are compared with experimental measurements, and it is seen that the spray angle increases as the flow rate of the carrier gas increases, which is more significant for an internal than for an external powder injector. Internal powder injection gives a particle a greater in-flight velocity and particle surface temperature because of the location of the powder injector. It is seen that the particle injection velocity and the particle penetration depth vary with the particle size and the flow rate of the carrier gas and that the dispersion for the internal type is more concentrated than that for the external type because of the momentum. The surrounding setup also plays an important role in the process of plasma spraying.
ECF-MRS: An Efficient and Collaborative Framework With Markov-Based Reputation Scheme for IDSs in Vehicular Networks

Vehicle Ad Hoc Networks (VANETs) are considered to be a next big thing that will remarkably change our lives, since this kind of technology is able to make our lives and roads safer. Intrusion Detection Systems (IDS) is an important technology that can mitigate both inner and outer threats for the vulnerable networks like VANETs. However, it is difficult to adopt the same IDSs that have been appropriately used in wired networks, due to the fast moving and highly dynamic nature of VANETs. Thus, in this article, an Efficient and Collaborative Framework with a Markov-based Reputation Scheme is proposed, namely ECF-MRS. In the proposed framework, the collaborative mechanism is achieved by using Non-dominant Sorting Genetic Algorithm-III (NSGA-III)-Collaboration to merge the advantages of IDSs in VANETs to generate a more superior IDS, while Non-Linear Programming (NLP)-Optimization is designed as the efficient mechanism to reduce the execution time of IDSs in VANETs. Moreover, considering the security risks of collaboration, a Reputation Scheme based on the Hidden Generalized Mixture Transition Distribution (HgMTD) model, namely RS-HgMTD, is proposed for each vehicle in VANETs to evaluate the creditworthiness of their neighbors. Experiments show that the IDS with ECF-MRS has better performance than other existing IDSs in terms of detection rate, detection time and overhead.
A UAV-Based Machine Vision Algorithm for Industrial Gauge Detecting and Display Reading

Unmanned Aerial Vehicle (UAV) has overwhelming superiority on the completion of difficult missions in the industrial production or implementation scenarios. Its brilliant navigation and on-board perception abilities endow the aerial platform a considerable potential in the industrial applications. Since manipulation stability and computational capacity of UAVs are crucial for industrial missions, it is challenging to develop a reliable and safe UAV platform for indoor industrial operation. Focusing on the measurement of industrial-standard gauges, we propose a vision algorithm which is capable of fast detecting the industrial-standard gauges and the readings and integrate the algorithm into a quadrotor drone platform with indoor and outdoor navigation. In our work, we demonstrate how to improve the simplicity and efficiency of the UAV-based visual recognition by implementing and adjusting a YOLO v3 framework with Darknet [1]. Moreover, our vision algorithm is combined with the image geometric correction module and the gauge detecting and reading module to overcome the detection problems caused by the harsh industrial conditions, such as an obscure image and the under-exposure condition. And the results show that accuracy of detection in the experimentation is sufficient for industrial missions.
Computational Multifocal Near-Eye Display with Hybrid Refractive-Diffractive Optics

We present a multifocal computational near-eye display that employs a static diffractive optical element (DOE) in tandem with a refractive lens. The DOE is co-optimized with the con-volutional neural network-based preprocessing to achieve desired multifocal display point spread function in an optimal manner. In the simulations, we demonstrate a multifocal display that can deliver sharp images for three distinct depths sampling the dioptric depth range uniformly from 3 diopters to infinity.
Method for the Optimal Sensor Deployment of WSNs in 3D Terrain Based on the DPSOVF Algorithm

Maximizing coverage and maintaining connectivity are two major objectives in designing and deploying wireless sensor networks (WSNs). In this article, a novel approach is proposed to obtain better sensor deployment in three-dimensional (3D) terrain in terms of coverage and connectivity. The proposed approach is based on a combination of the distributed particle swarm optimization (DPSO) algorithm and a proposed 3D virtual force (VF) algorithm. The communication limit (CL) of the sensor nodes (SNs) is taken into consideration. A heuristic algorithm that is suitable for a limited communication environment is proposed to cluster the SNs. To effectively guide the SN deployment optimization and to speed up convergence, the addition of a VF term in the velocity update equation of each particle is adopted. To improve the efficiency of increase of coverage and connectivity of the WSN, the selected redundant sensor nodes (RSNs) move toward certain selected areas. In addition, measures are taken to guarantee population diversity in the early stage and the convergence speed of the proposed algorithm in a later stage. Finally, to verify the effectiveness of the proposed algorithm, some comparative experiments are performed. The experimental results show that the proposed algorithm performs well against other algorithms in deploying sensors in 3D terrain when considering CL.
Molecular and Morphological Redescriptions of Enteric Myxozoans, Enteromyxum Ieei (formerly Myxidium sp. TP) and Enteromyxum fugu comb. n. (syn. Myxidium fugu) from Cultured Tiger Puffer

The phylogenies of two enteric myxozoans, Myxidium fugu and Myxidium sp. TP, from cultured tiger puffer Takifugu rubripes were re-evaluated based on their small subunit ribosomal RNA (SSU rRNA) gene sequence and spore morphology. Phylogenetic analysis shows that M. fugu and Myxidium sp. TP are grouped with Enteromyxum species as a monophyletic clade, not with other Myxidium spp. It was also illustrated that spores of M. fugu satisfy the criteria for the genus Enteromyxum for spore morphology. SSU rRNA gene sequence of Myxidium sp. TP shared 99.6% identity to that of Enteromyxum leei. Experimental transmission of Myxidium sp. TP to red sea bream Pagrus major induced the formation of mature spores, which were indistinguishable from those of E. leei in morphology and morphometrics. Myxidium sp. TP is identified as E. leei, and M. fugu is reassigned as Enteromyxum fugu comb. n.
Torque analysis of axial-flux permanent magnet couplers with skewed slots using electromagnetic-thermal semi-analytical model

An improved structure for axial-flux permanent magnet couplers (AFPMCs) with skewed slots is proposed to inhibit the cogging torque. Then, a novel electromagnetic-thermal semi-analytical model is presented to analyze the cogging torque, electromagnetic torque and temperature distribution. Different from conventional electromagnetic models, the proposed model takes into account the strong interaction between the electromagnetic and thermal analysis. Firstly, an exact subdomain model with multi-slice method is established in cylindrical coordinate to study the electromagnetic field. Secondly, 3-D finite element thermal model is carried out, and electromagnetic-thermal analysis is conducted. Then, cogging torque and electromagnetic torque can be obtained, and the influence of various loads on electromagnetic torque is also analyzed. In addition, the temperature distribution of the copper plate is studied. The AFPMC with skewed slots has lower temperature rise, and the temperature change along radius direction is more gently, which indicates that it is an optimal structure design. The semi-analytical predicted results are compared with the results of finite element method (FEM). The comparison results confirm the validity of the semi-analytical model.
Robust Switching Control for Force-reflecting Telerobotic with Time-varying Communication Delays

The transparency problem of telerobotic system with time-varying delays is investigated in this paper. Based on a contact degree of freedom of the manipulator end-effector and Lyapunov function, a novel switching tracking control strategy is designed to guarantee the stability of the closed-loop system and the robustness of the tracking system. Simulation results demonstrate the effectiveness and feasibility of the proposed approach.
Modified-PBIL Based User Selection for Multi-user Massive MIMO Systems with Massive Connectivity

Inspired by the big data processing capability of the machine learning, we propose a user selection algorithm based on modified population-based incremental learning (MPBIL) for multi-user massive MIMO systems with massive connectivity. With the objective of enhancing the algorithm efficiency, the proposed algorithm evolves the population exploiting both the superior individuals and the best individual. In further, we design the orthogonal permutation to increase individual diversity and avoid the overfit of the classical population-based incremental learning. Simulation results demonstrate that the performance of the proposed algorithm is far better than the classical greedy based user selection method while maintaining low complexity, especially for the large number of MU-MIMO users and candidates.
Energy Harvesting for Self-Powered Systems

Harvesting energy from waste energy sources in ambient environmental is the accessible technology in the field of self-power electronic devices for high-efficiency energy due to the power consumption of modern electronic devices that are rapidly developed to microwatt, low-cost, lightweight, and small size. Since numerous research are investigated the technique for converting the ambient energy into useable electrical energy for practical application. Including light, thermal gradients, vibration or motion, and radio-frequency electromagnetic radiation, various forms of waste energy sources are proposed in this review. Their existing power density in the ambient environment is demonstrated. Moreover, the fundamental theory and the application of the energy harvesting devices are discussed as a foundation of the energy conversion technology for self-power electronic devices such as handheld, wearable, and portable devices.
Learning Intrinsic Decomposition of Complex-Textured Fashion Images

In this paper, we present a deep learning approach for shading decomposition from single garment images with complex textured albedos. We propose a multi-stream generator to infer albedo, shading, normal and lighting in a unified network and train it in a novel local-global adversarial learning framework. To support the training, we carefully build a large dataset with synthetic garment photos under various lighting conditions and textured albedos. Both quantitative and qualitative evaluations, on synthetic and real images, demonstrate the superiority of our method against the state-of-the-art.
Prediction of Stock Based on Convolution Neural Network

With the increasingly close connection between the market and computer technology. Stock forecasting is one of the task studies on the market economy. However, the information about the market economy contains a lot of noise and uncertainty, which makes the economic forecast more and more challenging. Ensemble learning and deep learning are the main methods to solve the stock forecast problem. In this paper, we forward a model combination of two methods, the advantages of two methods to forecast the change of stock price. The proposed method combines CNN and GBoost. The results of two market indexes show that this method has better performance for current popular methods.
Introduction to Dynamic Stochastic Computing

Stochastic computing (SC) is an old but reviving computing paradigm for its simple data path that can perform various arithmetic operations. It allows for low power implementation, which would otherwise be complex using the conventional positional binary coding. In SC, a number is encoded by a random bit stream of '0's and '1's with an equal weight for every bit. However, a long bit stream is usually required to achieve a high accuracy. This requirement inevitably incurs a long latency and high energy consumption in an SC system. In this article, we present a new type of stochastic computing that uses dynamically variable bit streams, which is, therefore, referred to as dynamic stochastic computing (DSC). In DSC, a random bit is used to encode a single value from a digital signal. A sequence of such random bits is referred to as a dynamic stochastic sequence. Using a stochastic integrator, DSC is well suited for implementing accumulation-based iterative algorithms such as numerical integration and gradient descent. The underlying mathematical models are formulated for functional analysis and error estimation. A DSC system features a higher energy efficiency than conventional computing using a fixed-point representation with a power consumption as low as conventional SC. It is potentially useful in a broad spectrum of applications including signal processing, numerical integration and machine learning.