Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable Video Streaming for Real-Time Multimedia Applications over DDS Middleware for Future Internet Architecture

The significant advancements achieved in wireless communications over the past few years has facilitated successful deployment of LTE-A, and heralded great efforts in 5G development. However, with the increase in the number and variety of connected devices, wireless video transmission in real-time is challenging for the aforementioned network paradigm. Many studies have shown that the centric focus of communications should be the content type, rather than the communication itself, which means real-time multimedia communications are considered crucial for future Internet architectures. This requires high capacity channels and techniques to mitigate inherent wireless channel errors. Thus, we propose an application-layer and middleware-based solutions that increase network reliability and flexibility and provide Quality of Service (QoS) control based on Scalable Video Coding (SVC). Due to the real-time and QoS support of the Data Distribution Service (DDS) middleware, it can be used to implement the three types of SVC scalability: Viz. Temporal, Spatial, Quality (SNR). The open source Scalable Video-streaming Evaluation Framework (SVEF) tool has been used to assess the video transmission performance with performance metrics, Viz. Peak Signal to Noise Ratio (PSNR), Mean Opinion Score (MOS), and frame delay. The results showed a graceful degradation of video quality when using the DDS-based SVC, particularly when the number of receivers is increased. The acquired results show excellent improvements which can be applied to different Future Internet architectures.
Towards Smart Educational Recommendations with Reinforcement Learning in Classroom

In this paper, we propose to construct a cyber-physical-social system that uses multiple sensors such as cameras and a quiz creator to track the learning process of the students and applies reinforcement learning techniques to provide learning guidance based on the multi-modal sensing data in smart classroom. More specifically, the smart learning recommendation system measures the heartbeats, quiz scores, blinks and facial expressions of each student to formulate the learning states and applies reinforcement learning to recommend the effective learning activities for students based on their current learning states. The interactive learning recommendation process in a smart classroom with multiple sensors can be modeled as a Markov decision process. Our simulation results have preliminarily demonstrated the effectiveness of this smart learning recommendation system. This work may provide insights into constructing a future intelligent learning environment for enriched personalized experiences.
Self- Triggered Control under Actuator Delays

In this paper we address the problem of self-triggered control of nonlinear systems under actuator delays. In particular, for globally asymptotically stabilizable systems we exploit the Lipschitz properties of the system's dynamics, and present a self-triggered strategy that guarantees the stability of the sampled closed-loop system with bounded actuator delays.
Variance Amplification of Accelerated First-Order Algorithms for Strongly Convex Quadratic Optimization Problems

We study performance of accelerated first-order optimization algorithms in the presence of additive white stochastic disturbances. For strongly convex quadratic problems, we explicitly evaluate the steady-state variance of the optimization variable in terms of the eigenvalues of the Hessian of the objective function. We demonstrate that, as the condition number increases, variance amplification of both Nesterov's accelerated method and the heavy-ball method by Polyak is significantly larger than that of the standard gradient descent. In the context of distributed computation over networks, we examine the role of network topology and spatial dimension on the performance of these first-order algorithms. For $d$-dimensional tori, we establish explicit asymptotic dependence for the variance amplification on the network size and the corresponding condition number. Our results demonstrate detrimental influence of acceleration on amplification of stochastic disturbances and suggest that metrics other than convergence rate have to be considered when evaluating performance of optimization algorithms.
Damage to museum objects by visible radiation (light) and ultraviolet radiation (uv)

Light destroys many colours at a rate which can be predicted by the ISO light fastness scale and by other relevant light fastness datings. The data on which the ratings are based are examined for visible ultraviolet light. The forms of damage, the wavelenth dependence and the rates of damage are presented for both these sources of light. The patterns of damage exhibited by paint, paper textiles, feathers, hair, insects glass and minerals are summarised. The author concludes that every light source in museums should be filtered.
Transfer Learning of Spatio-Temporal Information Using 3D-CNN for Person Re-identification

Video based person re-identification has received significant attention in the recent past due to advancement in deep learning techniques. However, it is still a challenging problem because of inherent dynamic nature of videos. Additionally, lack of sufficient annotated dataset may lead to overfitting issues while training deep networks. In this paper, we propose a spatio-temporal transfer learning approach using 3D-CNN for video based person re-identification. To address the issue of insufficient labelled data and transfer the knowledge, we use a pre-trained 3D-CNN model of Sports-1M dataset and perform fine-tuning on multiple domain datasets such as PRID-2011, iLIDS-VID, MARS and an aerial video dataset simultaneously. Learning features from multiple domain data is of significant value because of large variation which otherwise is not possible to obtain from small individual datasets. In our experiments, we show that the fine-tuned transferred features encode robust representations and enhance the re-identification accuracy. Further, to boost the performance, we apply XQDA metric learning. Experiments conducted on all the four datasets show that the proposed framework outperforms the popular methods by an average improvement of 4% or more. In addition, we analyse the network's robustness against adversarial examples and show that the proposed 3D-CNN network has better resilience compared to 2D-CNN used in most of the existing algorithms.
A Small Java Application for Learning Blockchain

This paper introduces a small Java application named ChainTutor for learning basic Blockchain concepts. Although the term Blockchain is widely known and Blockchain technologies are finding applications in various areas such as banking, health care and Internet of Things, some concepts of Blockchain are not easy for beginners to understand. Fully text-based tutorials are often difficult to follow. General picture of Blockchain operations gets lost in lengthy textual descriptions. With the Java application introduced in this paper, users can experiment with key Blockchain concepts through a graphical user interface. They can generate keys, hashes, transactions, blocks and wallets. They can see the low level details of a blockchain such as encryption keys and hashes. They can see how mining works and how blocks are added to a blockchain. Parameters of a blockchain can also be varied in order to observe their impact on performance or even to make a blockchain invalid. The Java application is intended to be used in classroom environment by instructors when they teach introductory Blockchain courses.
Energy-Efficient Clustering Algorithm for Magnetic Induction-Based Underwater Wireless Sensor Networks

Magnetic induction (MI) communication is a promising technology for next-generation low-power underwater wireless sensor networks (UWSNs). Clustering algorithm design becomes an important and challenging issue in today’s MI-based UWSNs. In contrast to the conventional approaches which suffer from continuous movement of ocean current and traffic loads in different areas of the network, we consider a clustering algorithm based on the Voronoi diagram and node density distribution to improve the energy efficiency and to prolong the network lifetime. In particular, we propose a jellyfish breathing process for cluster head selection and an automatic adjustment algorithm for sensor nodes. The simulation results show that the proposed clustering algorithm achieves a high network capacity rate and a good equalization for the remaining energy.
Contraction Patterns of Neck Muscles during Phonating by High-Density Surface Electromyography

Phonation is generated by sequentially closing and opening of vocal folds, which is controlled by the laryngeal musculature. Pitch that is the highest contributor to generate natural voice depends on the contraction of muscles over the neck. Proper evaluation of muscular activities from neck muscles would be helpful to understand the phonation functions. While surface electromyography (sEMG) technique has been chosen to investigate physiological characteristics of neck muscles for pitch estimation in previous studies, it remains unclear what are the contracting patterns on both left and right sides of the neck muscles. In this study, 80 channels of high-density (HD) sEMG electrodes were recorded from three healthy volunteers when they phonated vowel (/a/) during varying pitch from normal to high. The root mean square (RMS) of the HD sEMG signals was computed within a series of analysis windows. An averaged RMS was used to construct HD sEMG energy maps for offering average energy distribution of the neck muscles. Normalized RMS (NRMS) was calculated to quantitatively display the correlation of the neck muscles between the left and right sides. The results of sEMG waveforms and NRMS values showed obvious symmetry between the left and right sides of the front neck muscles. Additionally, the NRMS across all five rows revealed a left-right significant correlation (r=0.9432 ± 0.018, p<0.01). This pilot study suggests that HD sEMG would be a potential tool to assess contraction patterns of the neck muscles during phonation. Also, it might pave way for evaluating the muscle synergy during phonating.
Deep Domain Adaptation to Predict Freezing of Gait in Patients with Parkinson's Disease

Freezing of gait (FoG) is a common gait impairment in patients with advanced Parkinson's disease (PD), which manifests as sudden difficulties in starting or continuing locomotion. FoG often results in falls and negatively affect a patient's quality of life. Real-time detection algorithms have been developed, which detect FoG events using signals derived from wearable sensors. However, predicting FoG before it actually occurs opens the possibility of preemptive cueing, which can potentially avoid (or reduce the intensity and duration of) the episodes. Moreover, human gait involves significant subject-based variability and a machine learning model trained on a particular patient's data may not generalize well to other patients. In this paper, we study the performance of advanced deep learning algorithms to predict FoG events in short time durations before their occurrence. We further study the performance of domain adaptation (or transfer learning) algorithms to address the domain disparity between data from different subjects, in order to develop a better prediction model for a particular subject. To the best of our knowledge, this is the first research effort to study domain adaptation algorithms to predict FoG episodes in patients with PD. Our extensive empirical studies on a publicly available dataset (collected from 10 PD patients) demonstrate the potential of our algorithms to accurately identify FoG events before their onset. We believe this research will serve as a stepping stone toward the development of more advanced FoG prediction algorithms for patients with PD.
Application of Logistic Regression in WEB Vulnerability Scanning

In the anomaly detection case of the web site, the traditional vulnerability scanning technology is com-monly used for active defense at present, but there are some problems such as incomplete data crawling and time consuming iteration of rule set, Therefore, this paper puts forward a logistic regression model based on machine learning to detect anomaly of web traffic, using TF-IDF algorithm to complete the feature extraction of URL, then training the logistic regression model, it is found that using LBFGS algorithm can be used as a loss function to obtain better model accuracy. At the same time, the experimental results show that the model can distinguish abnormal traffic well for newly generated web traffic.
Tri-band $2\times 2$ 5G MIMO Antenna Array

A tri-band $\pmb{2}\times \pmb{2}$ MIMO antenna array for 5G applications is presented. It consists of two orthogonally-placed compact tri-band 28/38/60 GHz antennas with the realized gain varying between 3.5 and 8.5 dBi over the operating frequencies. Each individual antenna maintains a monopole-like radiation pattern across these bands. In addition, the MIMO characterization of the proposed array is discussed. The simulation results show that the envelope correlation coefficient of the array is excellent with a maximum value of less than 0.0015 whereas the diversity gain almost attains 10 dB over the entire operating frequency. The simple planar structure of the MIMO array also offers an ease of fabrication and practical integration with other electronic components
A Thin-Layer Volume Integral Equation Formulation for Linear and Non-Linear Magnetostatic Problems

A thin-layer approximation of the magnetostatic volume integral equation (VIE) method is presented. This method, referred to as the thin-VIE (TVIE), utilizes a thin approximation of Green’s function. The TVIE approximates both the transverse and the normal magnetization in the material represented by a surface mesh, dramatically improving the accuracy of the method. The TVIE is solved via a locally corrected Nyström formulation, supporting high-order meshing and solution accuracy. A high-order singularity extraction method is used to compute the hypersingular terms required by near-field local corrections, leading to a very efficient algorithm. The method is applied to linear media, as well as non-linear, anisotropic, and hysteretic medium. The method is also hybridized with the VIE method, allowing for mixed meshes for solving general problems.
Blended Shared Control with Subgoal Adjustment

Combining the benefits of robust situational awareness of human operators with the efficiency and precision of automatic control has been an important topic of human-machine shared control. The emphasis is on keeping human operators in the loop while automatic control providing assistance to improve task performance. Given a task with specific subgoals, execution of a task using blended shared control involves predicting the operator’s intent of subgoal transitions and deciding the blending weights for inputs from the human operator and automatic control. In this paper we address the problem of subgoal adjustment in blended shared control which is typically initiated by the operator’s intent and necessary to sustain the shared control performance for changing subgoal conditions. First, we provide a method to predict operator’s intent of visiting a subgoal. Based on intent prediction, we propose a method for subgoal adjustment where the adjustment is encoded by a hyperrectangle. The volume of the hyper-rectangle is obtained by using a hyperbolic slope transition function which is based on the distance between subgoals. The adjustment actions within the hyper-rectangle are facilitated by a skill-weighted action integral that takes into consideration the skill level of the operator. The approach is tested on a scaled hydraulic excavator platform with multiple novice operators and a skilled operator. Experimental results are presented and discussed.
An Analysis Workflow-Aware Storage System for Multi-Core Active Flash Arrays

The need for novel data analysis is urgent in the face of a data deluge from modern applications. Traditional approaches to data analysis incur significant data movement costs, moving data back and forth between the storage system and the processor. Emerging Active Flash devices enable processing on the flash, where the data already resides. An array of such Active Flash devices allows us to revisit how analysis workflows interact with storage systems. By seamlessly blending together the flash storage and data analysis, we create an analysis workflow-aware storage system, AnalyzeThis. Our guiding principle is that analysis-awareness be deeply ingrained in each and every layer of the storage, elevating data analyses as first-class citizens, and transforming AnalyzeThis into a potent analytics-aware appliance. To evaluate the AnalyzeThis system, we have adopted both emulation and simulation approaches. In particular, we have evaluated AnalyzeThis by implementing the AnalyzeThis storage system on top of the Active Flash Array's emulation platform. We have also implemented an event-driven AnalyzeThis simulator, called AnalyzeThisSim, which allows us to address the limitations of the emulation platform, e.g., performance impact of using multi-core SSDs. The results from our emulation and simulation platforms indicate that AnalyzeThis is a viable approach for expediting workflow execution and minimizing data movement.
Error Backpropagation with Attention Control to Learn Imbalanced Data for Regression

The imbalanced data problem refers to a skewed distribution of data over each class. Learning with imbalanced data constitutes a significant challenge for both research and industry applications. This often impairs performance of even powerful machine learning algorithms, such as Deep Artificial Neural Networks (DNN). To alleviate this problem, this paper proposes a neuroscience-inspired approach to train DNN, in which each neuron learns to filter the input while minimizing the training error though error back propagation. The proposed framework possesses two important structural and functional characteristics of biological vision systems: (1) each layer of the visual hierarchy has a non-uniform receptive field distribution and (2) the visual cortex learns to control its attention during development. The learned filter makes each neuron react only to a specific input range, encouraging some neuron committed to learn imbalanced samples. We demonstrate that the proposed model achieves improved performance in various imbalanced data-learning scenarios.
SoC Logic Compatible Multi-Bit FeMFET Weight Cell for Neuromorphic Applications

We demonstrate an SoC logic compatible ferroelectric-metal field effect transistor (FeMFET) digital 2-bit weight cell by monolithic BEOL integration of a ferroelectric (FE) capacitor with the gate of a conventional Si HK/MG MOSFET. Through optimization of the area ratio between the FE capacitor and the MOSFET, we show: 1) program/erase write voltages can be scaled down to logic compatible level, ±1.8 V, simplifying write circuitry; 2) write speed of 100ns; 3) write endurance $> 10^{10}$ cycles without degradation due to elimination of charge trapping in FE; 4) 2 bits/cell achieving software levels of accuracy for inference on MNIST training database; 5) state retention approaching 104 s for a depolarization field of 0.3 MV/cm; 6) Multi-port (independent read and write) operations.
Security, Performance and Energy Trade-Offs of Hardware-Assisted Memory Protection Mechanisms

The deployment of large-scale distributed systems, e.g., publish-subscribe platforms, that operate over sensitive data using the infrastructure of public cloud providers, is nowadays heavily hindered by the surging lack of trust toward the cloud operators. Although purely software-based solutions exist to protect the confidentiality of data and the processing itself, such as homomorphic encryption schemes, their performance is far from being practical under real-world workloads. The performance trade-offs of two novel hardware-assisted memory protection mechanisms, namely AMD SEV and Intel SGX - currently available on the market to tackle this problem, are ADD described in this practical experience. Specifically, we implement and evaluate a publish/subscribe use-case and evaluate the impact of the memory protection mechanisms and the resulting performance. This paper reports on the experience gained while building this system, in particular when having to cope with the technical limitations imposed by SEV and SGX. Several tradeoffs that provide valuable insights in terms of latency, throughput, processing time and energy requirements are exhibited by means of micro-and macro-benchmarks.
Generative Adversarial Framework for Learning Multiple Clinical Tasks

Inspired by the recent success of generative adversarial networks (GANs), we propose a novel adversarial network for learning multiple clinical tasks. We design a conditional generative adversarial network (cGAN) with a new selective weighted loss to mitigate imbalance data problem in medical image analysis. Our proposed method comprises two components: a generator and a discriminator. While the generator is trained on sequential magnetic resonance images (MRI) to learn semantic segmentation and disease classification, the discriminator classifies whether a generated output is real or fake. The generative model and the discriminator model are trained via adversarial loss with two player mini-max game, and with an additional proposed selective weighted loss. The proposed architecture has shown promising performance on the ACDC-2017 benchmark for prediction of cardiac disease beside of semantic segmentation of dual cavities and myocardium vessel. Moreover, we achieved competitive results for brain tumor semantic segmentation and brain disease classification on the BraTS-2017 challenge.
The Electricity Sales Forecasting Based on Leading Analysis and Factor Compensation

Electricity sales forecasting is a fundamental work for State Grid Corporation of China. Currently, after data preprocessing, X13 seasonal adjustment is applied to decompose electricity sales series into trend, season and random items. The decomposed items are predicted respectively to produce accurate forecasting results. On the basis of it, a new electricity sales forecasting method based on leading analysis and factor compensation is proposed in this paper. First of all, the leading factors are introduced to improve the ability in identifying sudden change of trend item. Then, the impact of the Spring Festival in random item is eliminated and the prediction of random item is categorized into normal month and abnormal month. Under normal month, the random item is forecasted by combining Mixture of Experts model with factor data and processed by Kalman Filter; under abnormal month, the prediction results are obtained by the method of factor elimination and compensation. Finally, combined with the impact of the Spring Festival, the adjustment of electricity sales forecasting result on January to March is achieved. The actual validation shows that the prediction accuracy and robustness of the proposed model is greatly improved.
Numerical Calculation of 2-D Inhomogeneous Media Green’s Function and Some Applications in Electromagnetic Scattering Problems

A method of moments-based numerical technique for the calculation of the electric-type Green’s function of inhomogeneous media is presented for a 2-D configuration. The problem is reduced to an integral equation by the use of a special representation through the decomposition of Green’s function into two parts, which allows the singularity is being expressed conveniently. However, this representation yields multiple singular terms to appear on the right-hand side of the integral equation system, which are treated by an approximation of the related integral through an equivalent cell approach. The method is validated by comparisons with classical solutions and some numerical examples are also given in order to show the applicability and the effectiveness.