Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Identifies Brain Cognitive Load Via EEG Signals

The process of EEG signal analysis involves multi-frequency non-stationary brain waves from many channels. Appropriate segmentation of these signals, extracting discriminatory features to obtain the imperative properties, and classification are the key aspects of a pattern detection algorithm. The performance of the machine learning models is highly dependent on feature extraction and selection techniques. Identification of best possible features requires intricate domain knowledge and cumbersome exploration. To evade such dependencies, deep learning methods have evolved for EEG detection, which automatically extract the best features needed for the classification. However, the time to train such networks is substantial due to the complex structure of the model. To address these issues, the present paper proposes an automatic system for brain cognitive state detection using a simple 12-layer 1D network architecture. The developed algorithm yields ceiling level classification performance for both the experimental and the benchmark dataset.
Classification of Motor Imagery Waves using Hybrid-Convolutional Neural Network

Brain Computer Interfaces augments, alters, or replaces a lost biological function. Recently, classification methods using CNN were proposed to achieve higher accuracy levels. Nonetheless, they use a single convolution for classification while the best scale differs from subject to subject. This paper proposes a different architecture of Deep learning that takes 'n' different uncorrelated features of same signal parallelly into non-shareable convolution input layers in the same network to predict kinetic motion of patients. That is referred to as 1-D Hybrid-Convolutional Neural Network. The general motivation towards this being accurate feature extraction when minimal dataset is available. This approach is performed over three features namely Power, Frequency Spectrum components and Power Spectral Density of a same segment of a signal. A detailed analysis on more than 1500 EEG recordings from 109 healthy subjects and a comparative edge to this study was performed using previous algorithms and the relative strength highlighted.
Efficient Implementation of HashSieve Algorithm for Lattice-Based Cryptography

The security of lattice-based cryptosystems that are secure for the post-quantum period is based on the difficulty of the shortest vector problem (SVP) and the closest vector problem (CVP). In the literature, many sieving algorithms are proposed to solve these hard problems. In this paper, efficient implementation of HashSieve sieving algorithm is discussed. A modular software library to have an efficient implementation of HashSieve algorithm is developed. Modular software library is used as an infrastructure in order for the HashSieve efficient implementation to be better than the sample in the literature (Laarhoven’s standard HashSieve implementation). According to the experimental results, it is observed that HashSieve efficient implementation has a better running time than the example in the literature. It is concluded that both implementations are close to each other in terms of the memory space used.
How Successful is a Lockdown During a Pandemic?

Social distancing, leading to lockdown, has been followed as a measure to restrict the spread of COVID-19 infection in almost every country. In this paper, we study the impact of the United States of America's lockdown measures during this pandemic in restricting the spread of infection. We have split the lockdown into two types - hard lockdown and soft lockdown. A hard lockdown is defined as the period when only essential services are provided. On the other hand, in the soft lockdown period, non-essential services are allowed, but with prevailing social distancing measures. Our findings suggest that a properly implemented Hard Lockdown followed by a soft Lockdown is indeed effective in reducing the reproduction number (R0). Although R0 increases during the soft lockdown, the rate of increase is restricted if the hard lockdown was implemented favorably. As exceptions to this general trend, we have observed that in Washington state Hard Lockdown has failed to reduce R0, and Vermont’s soft lockdown implementation was successful in controlling the increase in R0 post-Hard Lockdown.
Few-mode Multi-core Fibres: Weakly-coupling and Randomly-coupling

Few-mode multi-core fibre (FM-MCF) design suitable for SDM transmission is described. For weakly-coupled FM-MCF, we explain how to determine fibre parameters and investigate the maximum spatial density in a limited cladding diameter. For randomly-coupled FM-MCF, we discuss how to enhance random mode-mixing between different mode-groups.
Topological Editing of Cross-Relaxation Networks

We have used the elements of graph theory to describe NMR cross-relaxation networks in macromolecules and to analyze different experiments used for topological editing. We propose a new experiment, block decoupled NOESY (B.D. NOESY), that splits the cross-relaxation network of a macromolecular system into two noninteracting subdomains. Splitting is achieved by arranging for the effective z-components of the magnetization in one subdomain, as viewed from the other subdomain, to be zero. The B.D. NOESY experiment, in favorable cases, can usefully simplify the analysis of cross-relaxation spectra by removing certain ambiguities. We demonstrate the method by separating the aromatic resonances from the rest of the cross-relaxation network of a small globular protein: turkey ovomucoid third domain, Mr 6000. The resulting spectrum provides a clearer picture of cross-relaxation pathways that involve only aromatic or only aliphatic spins. By comparing the B.D. NOESY spectrum with the normal NOESY spectrum, we were able to identify cross peaks that contain contributions from indirect magnetization transfer (spin diffusion) mediated by the aromatic side chains. In the terminology of graph theory, these experiments decompose topological networks of cross-relaxation into two subgraphs, the join of which generates the original graph.
A New Cost-sensitive SVM Algorithm for Imbalanced Dataset

Support Vector Machine(SVM) is a popular machine learning algorithm for its excellent generalization ability. However, similar to most of traditional algorithms, the proposal of SVM is based on an assumption that the dataset is nearly balanced, and when SVM is applied in imbalanced dataset, the result may be bias towards majority class which leads to poor performance. To solve this problem, a new cost-sensitive SVM algorithm based on samples density are proposed in this paper. In the proposed algorithm, samples’ weights are depended on sample density estimated from Kernel Density Estimation(KDE) method, and furthermore, the samples’ weights are modified to enlarge the weights of border samples and reduce the weights of noise samples based on Support Vector Data Description(SVDD) algorithm. The experiments result shows that the proposed algorithm could achieve satisfactory performance.
Effect of Platinum-Catalysed Silicone Elastomer Encapsulation on the Performance of Embedded Stretchable Capacitive Multimodal Sensor

This paper presents the study of the effect of Platinum-catalysed silicone elastomer encapsulation material on the performance of a stretchable multimodal sensor embedded inside it. The novel stretchable multimodal sensor is designed based on a parallel plate capacitor structure to measure strain, shear stress, and pressure. A prototype of the sensor is fabricated from thin copper-polyimide laminate and encapsulated inside Ecoflex 00–30 platinum-catalyzed silicone rubber and Polydimethylsiloxane (PDMS). Empirical tests are conducted on the prototype under different environment temperatures, which is known to affect the modulus of elasticity of silicone rubber encapsulation. For the temperature settings of 24, 30, and 37 °C, sensitivity values for strain sensor are recorded to be −18.2, −16.0, and −13.6 fF. %−1 respectively, whereas the sensitivities of the shear sensor are found to be 1.27, 1.24, and 1.16 fF.kPa−1 under the same temperature settings. The pressure sensor tested under the temperatures of 24, 30, and 37 °C shows an opposite trend with recorded sensitivity values of 0.087, 0.088, and 0.089 fF.kPa−1 respectively. In terms of cross-sensitivity performance, the pressure and shear sensors exhibit a small negative sensitivity to strain stimulus. Strain and pressure sensors also give slight negative sensitivities to the shear stimulus. Pressure stimulus, on the other hand, induces a significant positive response and small negative response by the shear and strain sensor respectively. The experimental results are validated using a series of finite element analysis (FEA). Simulation results confirm that the change in the embedded sensor sensitivity with temperature is due to the change in the stiffness of the encapsulation material. The cross-sensitivity performance on the other hand is due to the movement and deformation of the encapsulation material under mechanical stimuli.
Apparent DC conductivity of silicone rubber compounds

HVDC cable accessories play a significant role for the reliable operation of polymeric HVDC cable systems. The majority of materials used for HVDC cable accessories are based on EPDM or EPR compounds. However, the development and qualification tests conducted on HVDC cable systems show positive insights on the general application of silicone rubber compounds for HVDC components. For this purpose, a deeper investigation of the conduction mechanisms of silicone rubber compounds is required. Within this paper, the apparent conductivity of silicone rubber compounds under variation of electric field and temperature is studied and novel modelling aspects are presented. Besides the general conductivity dependence on electric field and temperature, the time dependent long-term behaviour under application of a dynamic temperature profile is considered. The measurement results show a positive agreement to empirical model descriptions based on thermal activated conduction extended by parameters for the modelling of time-dependent aspects. A minor and temperature-dependent electric field dependency is found for both material configurations.
Autonomic Runtime Adaptation Framework for Power Management in Large-Scale High-Performance Computing Systems

Power consumption is one of the most critical issues in the viability and sustainability of exascale computing systems. It is challenging to achieve a quintillion (1018) computations per second within the sustainable power budget of 20 MW. We need to improve energy efficiency at all HPC ecosystem levels to achieve exascale performance, such as subsystem level, node level, system level, scheduling level, software level, HPC site level, and power supply level. The power consumption and performance are the conflicting optimization concerns and that we need to manage intelligently. Hence, we need an efficient and comprehensive power management framework. In this paper, we have presented an agent-based autonomic runtime adaptation framework (ARAF). We investigated the Q-learning algorithm for devising optimal operating point (V, F) based control policy. The experimental results show that the described control policy provides 5-12% of energy-saving with minimal performance degradation. The proposed architecture offers a comprehensive and efficient power management framework for next-generation HPC systems.
Research on test case description language

Software testing is crucial in the development of software interfaces or web pages. In this paper, a test case description language (TCDL) is proposed. TCDL can conveniently describe the process of web UI testing with a grammar that is close to natural language and conforms to manual operation logic. In this paper, the manual UI testing process is abstracted by TCDL, and the syntax specification of TCDL is designed, and the parsing of TCDL is realized with the help of ANTLR tool. Using TCDL, testers can quickly write test scripts with manual test logic. TCDL reduces the learning cost of users and improves the testing efficiency.
Research and Performance Analysis of Single Intersection Online Q-Iearning Model Based on Queue Length Equilibrium

In order to solve the problem of intersection phase queue equilibrium, this paper uses reinforcement learning theory to establish online Q-Iearning model with the difference of intersection queue length as the optimization goal. At the same time, in order to accelerate the convergence speed and avoid the dimension disaster, the state space and reward function are reconstructed, and the prior knowledge is introduced to judge to improve the robustness of the model. The second development of VISSIM is carried out by using MATLAB to establish online simulation platform and carry out experimental analysis. The experimental results show that taking the difference of queue length as the optimization objective can improve the balance of queue length in all directions; and the selection of parameters and applicability of the model are analyzed. The results show that the online Q-Iearning model established in this paper has the learning ability and faster calculation speed, and the convergence of the model is affected by the periodic value and the number of optional behaviors.
Petrochemical Facility Lightning Protection Primer: Part 1: History and Basic Science

The development of lightning protection standards for petrochemical processing and storage facilities has progressed significantly over the past 20 years. Standard requirements have become more stringent and prescriptive. Understanding of the development and propagation of lightning has grown with the advent of 3D-detection systems. This is part 1 of a three-part primer on lightning protection systems (LPSs) for petrochemical production, storage, and processing facilities. Part 1 covers the basic science behind lightning strikes and the history of lightning research, protection systems, and models of attachment. The purpose of this article and this primer series is to update the design and operating engineer? s knowledge of lightning protection at petrochemical facilities and to increase worker and equipment safety in these facilities.
Windowed Fractional Fourier Transform on Graphs: Fractional Translation Operator and Hausdorff-Young Inequality

Designing transform method to identify and exploit structure in signals on weighted graphs is one of the key challenges in the area of signal processing on graphs. So we need to account for the intrinsic geometric structure of the underlying graph data domain. In this paper we generalize the windowed fractional Fourier transform to the graph setting. First we review the windowed fractional Fourier transform and introduce spectral graph theory. Then we define a fractional translation operator with interesting property for signals on graphs. Moreover, we use the operator to define a windowed graph fractional Fourier transform, and explore the reconstruction formula. Finally, the Hausdorff-Young inequality established on this new transform is obtained.
Localization Cues Preservation in Hearing Aids by Combining Noise Reduction and Dynamic Range Compression

Dynamic range compression (DRC) and noise reduction algorithms are commonly used in hearing aids. They are known to have opposite objectives concerning the Signal-to-Noise Ratio (SNR) and to affect negatively the localization performance. Yet, the study of their interaction received few attention. In this work, we improve an existing combined approach of DRC and noise reduction to bridge the gap between the algorithms proposed independently in their respective communities. The proposed solution is then compared to state-of-the-art algorithms thanks to objective criteria assessing the spatial fidelity preservation, the SNR improvement and the output dynamic range reduction. Experimental results show that the standard serial concatenation of noise reduction and DRC stages is unable to improve the SNR and preserve the noise component acoustic characteristics. They suggest that the proposed design restores the noise localization cues and manages to improve the output SNR.
The Evaluation of Non-contact Electrodes Applied for Bathing Electrocardiogram Measurement

Bathing is a daily routine for Japanese, however, there is an increased risk of death for seniors during bathing. Electrocardiogram (ECG) collection and analysis during bathing may be an effective measure for health monitoring. But the quality of ECG should be guaranteed when collecting. In this work, we aim to evaluate the applicability of 4 different sizes of non-contact electrodes (electrodes 1-4) for bathing ECG measurement. The study is conducted with 7 male participants, each with 15-min bathing, and the signals are collected with limbs leads I, II, II. Several signal quality indices (SQIs): kSQI (kurtosis), pSQI (the relative power in the QRS complex), pcaSQI (principal component analysis), and rSQI (undetected error of R peaks) are calculated based on the bathing ECG. The results obtained indicate that the electrode 3 is more suitable than others for bathing ECG collection.
Image Segmentation of Tiny Parts Based on Template Prior

In current industrial production, there is a problem of low precision and slow speed in manual measurement of tiny parts. In order to solve the problem of part size detection, we propose an accurate segmentation algorithm of tiny parts images based on template priors. In this paper, the detection object is the watch stator. The accuracy of watch stator is required to be micron level, and its size is within 25 mm. This method first realizes coordinate transformation on the CAD part template to achieve overall template matching, then uses local transformation to adjust the registration of local part edges. Finally, deep neural networks are used to implement image segmentation of parts by integrating edge shape prior. Combining the depth object segmentation with the geometric information of the part edge provided by the prior template, the edge structure of the part can be better restored for the blurred or damaged part image. The experimental results show that the image segmentation method based on the template prior can achieve high-efficiency and high-precision segmentation in tiny parts, and meet the requirements of automatic production line.
Bacterial Image Classification Using Convolutional Neural Networks

Bacteria classification is an essential task in medical field, for the diagnosis and treatment of various diseases. Typically, classification has been done by clinical specialists using conventional techniques, which do not rely on prediction approaches. Manual classification of bacteria is a time consuming and challenging task which requires huge human efforts. As technology has advanced, classification of micro-organisms have been possible with the aid of novel machine learning algorithms implemented on computers. Deep Neural Network (DNN) is one such promising technology which has been widely used for image classification. One of the variant of DNN is Convolutional Neural Network (CNN), which is an efficient technique for classification problems, has been used in this paper for bacteria classification. We have used ResNet-50 CNN model for classifying bacterial images into twenty categories, which are medically relevant. Using our approach, we could get an accuracy of 99.9% for classification. Experimental results show that our technique gives better results compared to the state-of-art approaches for bacteria classification.
Bitmessage Plus: A Blockchain-Based Communication Protocol With High Practicality

We are in the Internet era, when protecting the security of personal information is both vital and challenging. As the most commonly used methods of communication, centralized systems cannot meet the increasing need for information security. Blockchain, with its characteristics of openness, decentralization, and tamper resistance, is an innovative technology underlying Bitcoin. There is potential to use blockchain in developing decentralized and transparent communication systems. Bitmessage is a well-known decentralized messaging system that enables users to exchange messages and prevents accidental eavesdropping. Bitmessage achieves anonymity and privacy by relying on the blockchain flooding propagation mechanism and asymmetric encryption algorithm. Unfortunately, Bitmessage uses proof-of-work as the solution to prevent spam, which wastes computational power and makes it inefficient to be used in practice. To address this problem, we improve Bitmessage with a novel antispam mechanism based on proof-of-space, which requires the user to dedicate a certain amount of disk space to send a message. This improvement reduces the time and computing resource costs by eliminating computationally heavy hash operations. Moreover, we achieve a high level of anonymity by using the stealth address as the destination of the delivered message, which can only be identified by the intended receiver. Finally, we improve the protocol’s reliability by taking the blockchain as an immutable database to store the delivered messages.
Comparison of Image Features Descriptions for Diagnosis of Leaf Diseases

The agricultural industries have always demanded technologies for the automatic discovery and diagnosis of plant diseases with high speed, accuracy, and low cost. Numerous studies have been conducted in response to this demand; however, significant issues remain in most cases where a large-scale dataset of field images is taken with different atmospheric conditions, lighting, scale, and in different directions. The large dataset often causes high computational and storage costs. To overcome this problem, we focus on methods based on efficient invariant image features. These methods are robust against such external factors added during image acquisitions with low computational cost and higher accuracy. We then use a well-known data clustering algorithm k-means to create visual features for lesions. We then create a group of robust visual features (BoVF) using the Term Frequency-Inverse Document Frequency (TF-IDF) weighting scheme that considers the most important visual features in the image for classification. Experimental results classify the BoVF using K-means clustering that categorizes a particular disease in the leaf image into their appropriate group.
A Novel Beamspace Channel Estimation Technique for Millimeter Wave Massive MIMO Systems

Millimeter-Wave (mmWave) systems fit out an incomparable chance for fulfilling the increasing data rate demands on Fifth Generation (5G) wireless networks. Beamspace with metal plate lens antenna array is a rising technique at mmWave massive MIMO systems in 5G for reducing the RF chains without the performance loss. Yet to manage the capacity of every beam should have perfect Channel State Information (CSI) in low RF chains condition. To overcome this limitation in this paper we propose a Regular Scanning Support Detection (RSSD) based channel estimation scheme. Especially we propose the use of a voltage doubler and a metal plate lens antenna where globose wavefront is reformed into plane wavefront but output wavefront is speeded up by material of lens which increases the spectral efficiency of the system. In the estimation of channel component we first estimate the support with the fading effect and then removed it from total channel estimation problem. Simulation results displays the satisfying accuracy of the proposed RSSD based channel estimation.