Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Enhancement in P300 ERP Single Trial by Machine Learning Adaptive Denoising Mechanism

The P300-based lie detection scheme is yet another and advantageous tactic for unadventurous polygraphy. In the proposed scheme, the raw electroencephalogram (EEG) signals are assimilated from 15 subjects during deception detection. After the assimilation, EEG signals are separated using an independent component analysis. The proposed adaptive denoising approach, extracts three kinds of features from denoised wave to reproduce P300 waveform and identify the P300 components at the <inline-formula> <tex-math notation="LaTeX">${P_{z}}$ </tex-math></inline-formula> electrode. Finally, in order to enhance the performance, four classifiers are used, i.e., support vector machine, linear discriminant analysis, <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-nearest neighbor, and back propagation neural network, achieving the accuracy of 74.5%, 79.4%, 97.9%, and 89%, respectively.
A robotic assistant to support the social and cognitive development of children from low-income families

According to latest estimates of the UNESCO, 38% of children leave primary school without learning how to read, write and do simple arithmetic operations. In the same way, approximately 124 million children and adolescents are denied opportunities to enter and complete school. In light of the above, in this paper, we present a robotic assistant aimed at supporting the development of social and cognitive skills in children of low-income families. This robotic assistant is part of the educational program "Sciences classroom for children" that is aimed at providing an educational and fun space where children can learn several concepts organized in 5 interactive ludic micro-worlds: electricity, magnetism, digital electronic, ecology, and robotic. To determine the real feasibility of the robotic assistant, we have conducted a pilot experiment with 68 children of low-income families, and the results achieved shows high levels of acceptation by children.
Student Centred Design of a Learning Analytics System

Current Learning Analytics (LA) systems are primarily designed with University staff members as the target audience; very few are aimed at students, with almost none being developed with direct student involvement and undertaking a comprehensive evaluation. This paper describes a HEFCE funded project that has employed a variety of methods to engage students in the design, development and evaluation of a student facing LA dashboard. LA was integrated into the delivery of 4 undergraduate modules with 169 student sign-ups. The design of the dashboard uses a novel approach of trying to understand the reasons why students want to study at university and maps their engagement and predicted outcomes to these motivations, with weekly personalised notifications and feedback. Students are also given the choice of how to visualise the data either via a chart-based view or to be represented as themselves. A mixed-methods evaluation has shown that students' feelings of dependability and trust of the underlying analytics and data is variable. However, students were mostly positive about the usability and interface design of the system and almost all students once signed-up did interact with their LA. The majority of students could see how the LA system could support their learning and said that it would influence their behaviour. In some cases, this has had a direct impact on their levels of engagement. The main contribution of this paper is the transparent documentation of a User Centred Design approach that has produced forms of LA representation, recommendation and interaction design that go beyond those used in current similar systems and have been shown to motivate students and impact their learning behaviour.
Layer-Wise Relevance Propagation for Explainable Deep Learning Based Speech Recognition

We develop a framework for incorporating explanations in a deep learning based speech recognition model. The most cited criticism against deep learning based methods across domains is the non-interpretability of the model. This means that the model in itself provides very less or no insight into which features of the input are most responsible for the model‘s predictions, Layer-wise relevance propagation is an emerging technique for explaining the predictions of deep neural networks. It has shown great success in computer vision applications, but to the best of our knowledge there has been no application of its use in a speech-recognition setup. In this paper, we develop a bi-directional GRU based speech recognition model in such a way that layer-wise relevance propagation can be suitably applied to explain the recognition task. We show through simulation results that the benefit of explainability does not compromise on the model accuracy of speech recognition.
Cross Domain Person Re-Identification With Large Scale Attribute Annotated Datasets

We propose a novel deep convolutional neural network framework called deep augmented attribute network (DAAN) to learn augmented attribute features for cross-domain person re-identification (person Re-ID) task. We observed that in some cases, different persons could have similar attributes (e.g., wearing similar clothes). It motivates us to distinguish such pedestrians by further learning complementary image features. We thus construct a deep neural network with three branches: 1) the attribute branch predicts the attributes of the input image; 2) the augmentation branch generates complementary features that are fused with the output of the attribute branch to form the augmented attribute features; and 3) the reconstruction branch to refine augmented attribute features on the target dataset. In order to learn precise and detailed attributes for pedestrian, we manually labeled two large datasets (CUHK03 and Market-1501) with 25 pre-defined mid-level semantic attributes. We evaluate the DAAN on a series of cross-domain person Re-ID tasks, where DAAN demonstrates superior performance (around 6%) to the prior state-of-the-art cross-domain algorithms.
Investigates Review of Leadership challenges in a Smart environment

Currently Oman government is running the strategy of building a strong Special Economic Zone Area in Al Duqum area, south of Muscat City. Area is one with the following characteristics: A smart city and With business leaders having technical knowledge of ICT and advanced managerial and leadership skills. This paper is about analyzing the effectiveness of the current leadership challenge in Smart city. Hence, the e-business will keep run remotely and customers are setting virtually in virtual online market to purchase. However, the leaders today are in face of technology which run the business called e-business. Therefore, this research will investigate though other literatures about the leadership challenges with dealing in new demand e-business. The recommended solutions over this paper will enhance the transformation of leadership to new skills based in smart business requirements.
An Inductive Voltage-/Current-Mode Integrated Power Management With Seamless Mode Transition and Energy Recycling

An integrated power management (IPM) with the unique capabilities of seamless-voltage-/current-mode (SVCM) operation and energy recycling is presented for robust inductive power delivery. Utilizing parasitic bulk diodes with lower voltage drop, this IPM seamlessly transitions between voltage mode (VM) and current mode (CM) in a safe and robust fashion, extending the input-voltage range (removing dead zone) and significantly improving power-conversion efficiency (PCE) in CM operation. The IPM first provides the required load power (<inline-formula> <tex-math notation="LaTeX">$\text{P}_{L}$ </tex-math></inline-formula>) by one-step rectification/regulation, and then stores the surplus energy into a storage capacitor to extend the receiver (Rx) operation time via energy recycling when the input power is insufficient. The theory behind the energy recycling is presented. A proof-of-concept chip was fabricated in a 0. 35-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process. In measurements, the chip safely achieved a regulated voltage of 3 V for a wide input-voltage range (without dead zone) by switching the Rx LC-tank at 142 kHz. With zero input power, the chip extended the Rx operation time by 250% thanks to the energy recycling.
A 3-D Sparse FIR Frustum Filter for Enhancing Broadband Plane Waves

A 3-D finite-extent impulse response (FIR) frustum filter having sparse coefficients is proposed for enhancing broadband plane waves (PWs) received by uniform aperture arrays. Motivated by the design technique of 1-D interpolated FIR filters, the proposed filter is realized by cascading a spatially-upsampled 3-D FIR prototype filter having a frustum-shaped passband and a 3-D FIR masking filter having a parallelepiped passband. Sparsity of the filter coefficients is achieved through the spatial upsampling of the coefficients of the prototype filter and by hard-thresholding the coefficients of both prototype and masking filters. The proposed 3-D FIR frustum filter saves 39.5% multiplications and 66.6% additions compared to the state-of-the-art 3-D FIR frustum filters without degrading the performance in enhancing broadband PWs.
An Optical Communication's Perspective on Machine Learning and Its Applications

Machine learning (ML) has disrupted a wide range of science and engineering disciplines in recent years. ML applications in optical communications and networking are also gaining more attention, particularly in the areas of nonlinear transmission systems, optical performance monitoring, and cross-layer network optimizations for software-defined networks. However, the extent to which ML techniques can benefit optical communications and networking is not clear and this is partly due to an insufficient understanding of the nature of ML concepts. This paper aims to describe the mathematical foundations of basic ML techniques from communication theory and signal processing perspectives, which in turn will shed light on the types of problems in optical communications and networking that naturally warrant ML use. This will be followed by an overview of ongoing ML research in optical communications and networking with a focus on physical layer issues.
Using Transcranial Alternating Current Stimulation (tACS) to Improve Romantic Relationships Can Be a Promising Approach

The romantic relationship refers to the specific relationship in which partners are dependent upon each other to obtain satisfactory outcomes and facilitate the pursuit of their most important needs and goals. Satisfying romantic relationships is a strong predictor of better psychological well-being, better physical health, and longer life expectancy. However, romantic relationships are not all smooth-sailing and lovers are often confronted with a variety of unavoidable issues that constantly challenge the stability of their romantic relationships. Dissatisfying romantic relationships are harmful and even destructive. Dyads of lovers engage in a variety of efforts to protect and maintain their romantic relationships based on qualitative research methods including theories- and psychological consultation-based approaches. Unfortunately, those existing approaches do not seem to effectively improve romantic relationships. Thus, it is necessary to seek an efficient approach regulating dyads of lovers in romantic relationships simultaneously. Transcranial alternating current stimulation (tACS) with advantages over existing approaches satisfies this purpose. We discuss the practicability of tACS in detail, as well as why and how tACS can be utilized to improve romantic relationships. In summary, this review firstly introduced the concept of romantic relationship and the necessity of enhancing it. Then, it discussed methods to improve romantic relationships including some existing approaches. This review next discussed the practicability of using tACS to improve romantic relationships. Finally, it shone a spotlight on potential future directions for researches aiming to improve romantic relationships.
Confidential Communication in C-RAN Systems with Infrastructure Sharing

This work considers a Cloud Radio Access Network (C-RAN) architecture in which the Radio Units (RUs) are shared among multiple operators or are managed by a separate infrastructure provider, while mobile users and cloud processor belong to the given service provider. In order to account for the resulting privacy concerns, the strict constraint is imposed that the RUs should only be able to infer a fraction of the information about the mobiles’ messages that vanishes with the transmission blocklength. The largest achievable rate under this constraint, or secrecy capacity, is characterized for a two-cell Gaussian model and for a multi-cell circular Wyner model.
Classification of Breast Cancer Histology Images Using Multi-Size and Discriminative Patches Based on Deep Learning

The diagnosis of breast cancer histology images with hematoxylin and eosin stained is non-trivial, labor-intensive and often leads to a disagreement between pathologists. Computer-assisted diagnosis systems contribute to help pathologists improve diagnostic consistency and efficiency. With the recent advances in deep learning, convolutional neural networks (CNNs) have been successfully used for histology images analysis. The classification of breast cancer histology images into normal, benign, and malignant sub-classes is related to cells’ density, variability, and organization along with overall tissue structure and morphology. Based on this, we extract both smaller and larger size patches from histology images, including cell-level and tissue-level features, respectively. However, there are some sampled cell-level patches that do not contain enough information that matches the image tag. Therefore, we propose a patches’ screening method based on the clustering algorithm and CNN to select more discriminative patches. The approach proposed in this paper is applied to the 4-class classification of breast cancer histology images and achieves 95% accuracy on the initial test set and 88.89% accuracy on the overall test set. The results are competitive compared to the results of other state-of-the-art methods.
Impact of Modulator Designs and Materials on Efficiency and Losses in Radial Passive Magnetic Gear

This paper presents an analysis of efficiency for passive magnetic gear resulting from the selection of materials and structures in the magnetic circuit. A particular attention is paid to the structure of modulator, which forms the crucial element of the system, and which can be designed in a variety of ways. In this paper, the considered alternatives include a modulator design manufactured from soft magnetic composite material and from circumferentially and axially stacked lamination. Based on the experimentally determined efficiency versus load curves, the study demonstrates that the soft magnetic modulator has similar performance as for the case of a circumferentially and axially stacked lamination. The physical reasons of this result are explained by means of the finite element analysis of magnetic flux waveforms in the crucial parts of the systems.
Probabilistic Appearance-Based Place Recognition Through Bag of Tracked Words

A key feature in robotics applications is to recognize whether the current environment observation corresponds to a previously visited location. Should the place be recognized by the robot, a Loop Closure Detection (LCD) has occurred. The letter in hand deploys a novel low complexity LCD method based on the representation of the route by unique visual features (VFs). Each of these VFs, referred to as “Tracked Word” (TW), is generated on-line through a tracking technique coupled with a guided-feature-detection mechanism and belongs to a group of successive images. During the robot's navigation, new TWs are added to the database forming a bag of tracked words. When querying the database seeking for loop closures, the new local-feature-descriptors are associated with the nearest neighboring TWs in the map casting votes to the corresponding instances. The system relies on a probabilistic method to select the most suitable loop closing pair, based on the number of votes each location polls. The proposed system depends solely on the appearance information of the scenes on the trajectory, without requiring any pre-training phase. The evaluation of the method is administered via a variety of tests with several community datasets, thus proving its capability of achieving high recall rates for perfect precision.
Screen-Printed Piezoresistive Sensors for Monitoring Pressure Distribution in Wheelchair

Prolonged sitting inadequacies cause pressure ulcer to many individuals, especially to disadvantaged with reduced mobility. The measurement of distributed pressure and detection of irregular sitting postures is essential for preventing the risk of developing pressure ulcer. In this paper, a pressure sensing system capable of recognizing sitting postures by means of measuring interface pressure through printed pressure sensors is presented. A thin and flexible large area sensor is screen-printed using silver flake and carbon particle inks and comprises 16 sensing elements. For the evaluation of practical usability, the sensor characterization is carried out by conducting stability, repeatability, drift, and bending tests. The performance of the sensor is checked under varying environmental conditions. Sitting posture detection accuracy above 80 % is achieved using a classification algorithm for four different sitting postures. Pressure distribution is monitored at a scanning rate of 10 Hz. A low-power and small form factor of readout electronics enables a compact packaging inside the seat cushion. The presented sensor design targets smart wheelchairs, but it is extendable to much larger areas, for example, to be used in beds. The proposed sensing system would be of a great assistance for caregivers and health professionals.
Screening Hidden N- $k$  Line Contingencies in Smart Grids Using a Multi-Stage Model

This paper addresses an issue in the contingency screening that the probabilities of some high-order N-k contingencies involving multiple element failures may be significantly underestimated in the traditional screening processes. Considering the severe consequences of these hidden contingencies, this practice would impose a potential risk to the system operations. In this paper, we propose a multi-stage approach to screen out these N-k contingencies and reveal their hidden probabilities. The approach is formulated as a bi-level mixed integer linear programming problem and validated by simulations on the IEEE 14-bus and 118-bus systems.
Planar 0.05–1.1 THz Laminate-Based Transition Designs for Integrating High-Frequency Photodiodes With Rectangular Waveguides

Compact planar laminate-based transitions for integrating high-frequency photodiodes (PDs) with rectangular waveguides (WRs) are presented for the WR15 to WR1 standard waveguide bands, i.e., from 0.05 THz up to 1.1 THz. The transitions couple the optically generated (e.g., via heterodyning) millimeter-wave or terahertz signals from the grounded coplanar waveguide (GCPW) output of the PD chip to the WR. To our knowledge, this is the first scalable integration concept that enables hermetic photodiode packaging up to the terahertz frequency range. For the WR15-WR6 bands, all transitions are designed on ultrathin microfiber reinforced PTFE composites (127-μm Rogers RT/duroid 5880 laminate). For the WR5-WR2.2 and the WR1.5-WR1 bands, liquid crystalline polymer ULTRALAM 3850 laminates are used with a thickness of 50 and 25 μm, respectively. The proposed GCPW-WR transition design is based on a double-slot antenna structure and enables the development of fully hermetic photonic packages, which is required to improve the durability of the PD chip. The transition designs are optimized by systematic EM-wave propagation modeling for achieving a wide operational bandwidth of up to 30% of the respective center frequency for each WR band. The optimized transitions exhibit an average insertion loss of about 1.5 dB and a return loss of about 10 dB for all waveguide bands (WR15-WR1). Based upon the systematic numerical modeling, generic guidelines are developed that allow designing a specific transition for any given waveguide band to 1.1 THz. In addition to the numerical analysis, GCPW-WR12 transitions for E-band (60–90 GHz) operation are fabricated and integrated with InP-based balanced-PD chips and GaAs HEMT MMIC medium power amplifiers in a fully hermetic package. Furthermore, hermetic WR12 coherent photonic mixer (CPX) modules are developed. The packaged WR12-type CPX allows direct optical-to-wireless conversion of optical baseband or IF-band signals, e.g., for radio-over-fiber fronthauling in mobile communications. The WR12-CPX module provides RF output power of up to +15 dB·m at 77.5 GHz.
Single-Carrier and Multi-Carrier 400 Gbps Transmission With Multi-Rate Multi-Format Real-Time Transceiver Prototypes

New customer usages such as on-demand high-definition videos, business analytics, and cloud services stress more and more optical transport networks. While 100 Gbps WDM systems have been widely adopted by operators to cope with the capacity growth of the past 6–7 years, a new generation of the optical transmission system is required to respond to the exponential traffic demands coming from mobile, fixed, and data center networks. Equipment manufacturers recently planned to enhance to 400 Gbps the data rate carried by WDM channels to meet this ever-increasing traffic requirement. Various trends are in competition to achieve this objective. Single-carrier and multi-carrier techniques operating at different symbol rates (i.e., 32, 45, > 60 Gbaud) and with diverse modulation formats (DP-QPSK, DP-8QAM, DP-16QAM) are proposed depending on whether transmission distance, spectral efficiency, or cost is the predominant driver. In this paper, we investigate the potential of these various 400 Gbps solutions by using real-time transceiver prototypes generating 32/64 Gbaud DP-QPSK, 45 Gbaud DP-8QAM, 32/61 Gbaud DP-16QAM formats over various fiber types (i.e., G.652, G.654, G.655 fibers). In particular, we demonstrate 400 Gbps WDM transmission with 61 Gbaud DP-16QAM channels using bandwidth-limited DAC/ADC (i.e., < 15 GHz) and real-time signal processing based on maximum likelihood sequence estimation equalization that mitigates bandwidth limitations. We conclude by analyzing the fiber non-linearity robustness of the 400 Gbps real-time transceivers under test thanks to the Gaussian noise model that permits to extract the ANL parameter and quantify non-linear interference affecting optical channels after fiber propagation.
It Would Make Me Happy if You Used My Guess: Comparing Robot Persuasive Strategies in Social Human–Robot Interaction

This letter presents an exploratory social human–robot interaction (HRI) study that investigates and compares the persuasive effectiveness of robots attempting to influence a user with different behavior strategies. Ten multimodal persuasive strategies were uniquely designed based on compliance gaining behaviors. These persuasive strategies were then compared using two competing social robots attempting to influence participant's estimate during a jelly bean guessing game. The results of our exploratory study with 200 participants showed that affective and logical strategies had a higher potential for persuasive influence and warrant further research.