Miracle-dz/UniFAR-FTUs huggingface dataset:
Behind Our Words: Psychological Paths Underlying the Un/Supportive Stance Toward Immigrants in Social Media

The present work is aimed at exploring the psychological paths underlying supportive and unsupportive social media stances toward immigrants. This investigation is based on a particular case of supportive communication on immigration promoted on line by a public figure (Morandi, a famous Italian singer). The used methodology integrated methods from data science and psychology, and it was based on a machine learning approach combined with a psycho-lexical analysis of online discussions. Results showed that from the words used by social media commenters can be extracted peculiar psychological processes behind supportive and unsupportive stance toward immigrant hosting. In particular, words were qualitatively grouped by defining the underlying value dimension, cognitive, emotional and also attributional processes for both supportive and unsupportive commenters. Thus this study allowed to find fine grained socio-psychological shades by overcoming the simple polarized online stances detection.
Comparision of Four Machine Learning Techniques for the Prediction of Prostate Cancer Survivability

Prostate cancer is regarded as the most prevalent cancer in the word and the main cause of deaths worldwide. Many traditional machine learning classification techniques has been applied to prostate patient survivability prediction, such as k Nearest Neighbors (KNN), Decision Tree (DT), Naïve Bayes (NB)and Support Vector Machine (SVM). In recent years, deep learning has been proved as a strong technique and became a research hotspot. As a kind of deep learning method, in this study, artificial neural network and several traditional machine learning techniques are applied to SEER (the Surveillance, Epidemiology, and End Result program)database to classify mortality rate in two categories including less than 60 months and more than 60 months. The result shows that neural network has the best accuracy (85.64%)in predicting survivability of prostate cancer patients.
MPED: A Multi-Modal Physiological Emotion Database for Discrete Emotion Recognition

To explore human emotions, in this paper, we design and build a multi-modal physiological emotion database, which collects four modal physiological signals, i.e., electroencephalogram (EEG), galvanic skin response, respiration, and electrocardiogram (ECG). To alleviate the influence of culture dependent elicitation materials and evoke desired human emotions, we specifically collect an emotion elicitation material database selected from more than 1500 video clips. By the considerable amount of strict man-made labeling, we elaborately choose 28 videos as standardized elicitation samples, which are assessed by psychological methods. The physiological signals of participants were synchronously recorded when they watched these standardized video clips that described six discrete emotions and neutral emotion. With three types of classification protocols, different feature extraction methods and classifiers (support vector machine and k-NearestNeighbor) were used to recognize the physiological responses of different emotions, which presented the baseline results. Simultaneously, we present a novel attention-long short-term memory (A-LSTM), which strengthens the effectiveness of useful sequences to extract more discriminative features. In addition, correlations between the EEG signals and the participants’ ratings are investigated. The database has been made publicly available to encourage other researchers to use it to evaluate their own emotion estimation methods.
An Intra-Frame Classification Network for Video Anomaly Detection and Localization

Anomaly detection and localization in the surveillance scenes is still a challenge in the filed of computer vision. Previous methods take this task as a one-class deviation problem, where the deviations between test samples and normal patterns are computed. In this paper, an Intra-frame Classification Network (ICN) is proposed to take the advantages of deep learning to transform this task to a multi-class classification problem. For video sequences, the feature maps are extracted by a set of Spatial Temporal Convolutional Layers (STCLs). The feature maps are then split into sub-regions by an Adaptive Region Pooling Layer (ARPL). The feature vectors of each sub-region are labelled as one class. The classification result for a sub-region is used to evaluate the abnormality of it. The proposed method is examined on UCSD Ped l dataset and UCSD Ped2 dataset. The results are further compared with previous state-of-the-art approaches to confirm the effectiveness and the efficiency of our method.
The Recognition of Driving Action Based on EEG Signals Using Wavelet-CSP Algorithm

The ability of recognizing driving actions could help building a more advanced driving assitance system, and could even be applied in automated driving to improve the driving safety. In this paper, we investigate the offline recognition of three classes of driving actions (turning left, turning right and braking), based on electroencephalography (EEG) signals. A simulated experiment was conducted to collect EEG data of participants. The proposed algorithm includes Wavelet Analysis and Common Spatial Patterns (CSP), to extract the discriminative features. The classification results were obtained using the Linear Discriminant Analysis (LDA). The results yielded an average single trial classification accuracy of 70.25% for all subjects, showing the discrimination of different actions and the correlation between driving actions and EEG signals.
Predicting Appliance Usage Status In Home Like Environments

Household activities nowadays heavily rely on electrical and electronic devices, the operation of which are largely reflected in the household energy usage. With the advance of sensor technology, smart meters are increasingly adopted in people’s homes, which makes it easier to access finer-grained energy consumption data and more importantly enables the study of household activities via the patterns in energy consumption. In this paper, we investigate the application of $k -$nearest Neighbours algorithm $(k -$NN) and Convolutional Neutral Network (CNN) to predict whether specific appliances are being used (on/off status) at different times based on the total energy consumption of a whole house. The experiment results on three types of appliances in one household show that CNN in general achieves better performance than $k -$NN and both methods perform better on the appliances with relatively large energy consumption.
Automatic Activity Classification Based on Human Body Kinematics and Dynamic Time Wrapping

Human movement analysis often relies on obtaining and processing digital signals from the lab-based biomechanical equipment such as motion capture system and force plate. This paper introduced a machine-learning based method, known as the Dynamics Time Wrapping (DTW) for human movement analysis. The DTW is used to classify four basketball playing movements including shoot, layup, dribble and pass. The kinematic raw data were obtained during an experiment session. The sample kinematic data were selected and normalized to create the templates. The DTW compared the kinematic data from each movement with the template. A 3-fold cross validation was used to validate the method. The results show that this method can achieve a high activity classification accuracy.
A Dual-Polarized Linear Antenna Array With Improved Isolation Using a Slotline-Based 180° Hybrid for Full-Duplex Applications

This letter presents a simple and compact dual-polarized linear antenna array characterizing high interport isolation for in-band full-duplex communications. By using the symmetrically shared-interface configuration integrated with a 180° hybrid, the self-interference resulting from the impedance mismatching between the antenna and feeding network and the coupling among the antenna elements can be well suppressed theoretically. Furthermore, to achieve the high isolation in practice among a wide frequency band, a slotline-based 180° hybrid is proposed and studied, which features a high isolation between the sum and differential ports. For further verification, a demonstrator of a 1 × 4 array centered at 5.0 GHz is developed and fabricated. The measurements denote that the shared −10 dB impedance bandwidth of the two ports is from 4.75 to 5.18 GHz, and the interport 50 dB isolation bandwidth is from 4.55 to 5.66 GHz (over 21.7%).
A Low-Profile, Directional, Ultrawideband Antenna

A compact, low-profile, ultrawideband (UWB) antenna providing directional radiation pattern is proposed. The antenna is based on a modified Vivaldi antenna and is characterized by a three-dimensionally bended structure. Each of the two radiation fins is capacitively loaded with an invert trapezoidal metallic plate at the end. In order to conveniently mount the antenna on the metal surface, a corrugated plate is added as a ground. Owing to the bended structure and capacitively loaded scheme, the antenna has a low profile of 0.216<inline-formula><tex-math notation="LaTeX">$\lambda _m$</tex-math></inline-formula>, where <inline-formula><tex-math notation="LaTeX">$\lambda _m$</tex-math></inline-formula> is the wavelength at the minimum operating frequency. The prototype of the antenna is fabricated and measured. The measured and simulated results show good agreement. The measured results show that the antenna has VSWR <inline-formula><tex-math notation="LaTeX">$< $</tex-math></inline-formula> 2.5 from 0.9 to 10 GHz and measured gain from 3.6 to 8.2 dBi.
A Blind 3D Point Cloud Watermarking Algorithm Based on Azimuth Angle Modulation

A new blind watermarking algorithm of 3D point cloud model by using azimuth angle modulation is proposed. The concave vertices with mean curvature lesser than 0 are used as feature vertices, and the rest vertices are considered to establish an invariant space for the greater robustness. The watermarking information is embedded repeatedly by modulating azimuth angle of feature vertices lying in different regions. Sub-regional embedding and redundant embedding enhance the performance of algorithm against common attacks. Experimental results demonstrate the proposed algorithm offers the better imperceptibility and robustness.
Automatic Semantic Labelling of Images by Their Content Using Non-Parametric Bayesian Machine Learning and Image Search Using Synthetically Generated Image Collages

In this paper, we describe a novel algorithm for fully unsupervised discovery of meaningful object categories in images, and their semantic labelling. Our approach works bottom-up, starting with superpixel segmentation and the representation of images as bags of quantized superpixel based visual words. Inference over these is achieved automatically using hierarchical non-parametric Bayesian learning. This visual learning process is built upon by the association of class labels using two complementary methods. The first of these is primarily suited for conventional object categories, whereas the second one, which employs a reverse image search using synthetically generated superpixel collages, is used for amorphous image content (such as 'grass', 'sky', etc.). The proposed algorithm does not rely on any dataset specific parameter tuning and (by exploiting auxiliary Google retrievable "big data"' in the wild) can be applied on any kind of corpora (no labelling, weak labelling, or fully annotated images).
Quantitative Comparison of EEG Compressed Sensing using Gabor and K-SVD Dictionaries

With the fast development of wearable healthcare systems, compressed sensing (CS) has been proposed to be applied in electroencephalogram (EEG) acquisition. For CS, it is desired to build the best-fit dictionary in order to achieve good reconstruction accuracy. While most of existing works focused on static dictionaries such as Gabor, Fourier and wavelets. The dynamic nature of EEG signals motivates us to study learned dictionaries, which are supposed to provide better reconstruction accuracy and lower computation cost. In this paper, we provide the quantitative performance comparison of EEG CS using two different types of dictionaries, i.e., the well-known Gabor dictionaries versus K-SVD learned dictionaries. The performance comparison utilizes the well-established database of scalp EEG from Physiobank, which allows researchers in this field to compare their work with ours. In addition, it also attempts to inspire the systematic study of dictionary learning in EEG CS.
Recovery of Cycling Endurance Failure in Ferroelectric FETs by Self-Heating

This letter investigates the impact of self-heating on the post-cycling functionality of a scaled hafnium oxide-based ferroelectric field-effect transistor (FeFET). The full recovery of FeFET switching properties and data retention after the cycling endurance failure is reported. This is achieved by damage annealing through localized heating, which is intentionally induced by a large current flow through the drain (source)−body p−n junctions. The results highlight that the local thermal treatments could be exploited to extend the cycling endurance of FeFETs.
DMD-Unmanaged Long-Haul SDM Transmission Over 2500-km 12-Core × 3-Mode MC-FMF and 6300-km 3-Mode FMF Employing Intermodal Interference Canceling Technique

We demonstrate differential mode delay (DMD) unmanaged 2500-km 12-core × 3-mode multicore few-mode fiber (MC-FMF) and 6300-km 3-mode FMF transmission. Mode-division multiplexed signals exhibit different transmission behavior depending on excited spatial modes in the presence of mode-relevant physical phenomena including DMD and mode-dependent loss (MDL). In weakly coupled FMF transmission over long distance where overall DMD and MDL grow almost linearly with fiber length, these phenomena restrict achievable information capacity and/or transmission reach. This paper presents a newly developed transmission scheme and a multiple-input multiple-output (MIMO) signal processing technique that are designed for achieving long-haul FMF transmission. Permutating spatial channels cyclically at each span induced quasi-strongly coupled transmission regime and hence significantly suppressed DMD-induced pulse broadening. Layered MIMO signal processing comprising multiple stages performing successive signal detection and intermodal interference canceling mitigated MDL impact with more than 2-dB Q-factor improvement. The combined use of these techniques enabled us to achieve the first-ever transoceanic-class long-haul FMF transmission.
INCREMENTAL CALIBRATION OF SEAT SELECTION PREFERENCES IN AGENT-BASED SIMULATIONS OF PUBLIC TRANSPORT SCENARIOS

The calibration of agent-based pedestrian simulation models requires empirical data. To avoid cost-intensive real-world experiments, human-in-the-loop simulations can be applied in which simulated pedestrians interact with human-controlled agents. However, the experiment results may be unrealistic if the human participants are presented with agents acting according to an uncalibrated model. We propose an incremental calibration approach that aims to address the circular dependency between the behaviour of human and simulated pedestrians. By incrementally adapting the parameters of the simulated agents to match the behaviour of the human participants, we aim to gradually approach a realistic interaction. We evaluate our approach using the simulation of the boarding procedure of a public transport vehicle in 2D and virtual reality experiments. The calibration results are compared with those gathered from a traditional non-incremental calibration. Our results indicate the feasibility of our approach and highlight the necessity for future research on efficient simulation model calibration.
3-D Temperature Field Reconstruction for a Lithium-Ion Battery Pack: A Distributed Kalman Filtering Approach

Despite the ever-increasing use across different sectors, the lithium-ion batteries (LiBs) have continually seen serious concerns over their thermal vulnerability. The LiB operation involves heat generation and buildup effect, which manifests itself strongly, in the form of highly uneven thermal distribution, for a LiB pack consisting of multiple cells. If not well monitored and managed, the heating may accelerate aging and cause unwanted side reactions. In extreme cases, it will even cause fires and explosions. Toward addressing this threat, this brief, for the first time, seeks to reconstruct the 3-D temperature field of a LiB pack in real time. The major challenge lies in how to acquire a high-fidelity reconstruction with constrained computation time. In this brief, a 3-D thermal model is established first for a LiB pack configured in series, which captures the spatial thermal behavior with a combination of high integrity and low complexity. Given the model, the standard Kalman filter is then distributed to attain temperature field estimation with substantially reduced computational complexity. The arithmetic operation analysis and the numerical simulation illustrate that the proposed distributed estimation achieves a comparable accuracy as the centralized approach but with much less computation.
Design and Development of a Novel High Voltage Gain, High-Efficiency Bidirectional DC–DC Converter for Storage Interface

A novel bidirectional dc–dc converter having high voltage gain capability with high efficiency is proposed in this paper for interfacing storage in a range of applications. The proposed converter steps up (boost) the voltage in one direction while stepping it down (buck) in the other direction of operation. An attractive feature of the proposed converter is the symmetry of its operating modes. It also has inherent soft-switching capability during turn on of the switches, enabling high switching frequency operation. This, coupled with the fact that the proposed converter uses only one coupled inductor for both boost and buck modes, leads to a compact system. A clamped capacitor network is used to recover the leakage energy. Circuit description, design, and loss calculations for both buck and boost modes are explained. The bidirectional converter is designed for a voltage gain of 10 and (1/10) in the boost and buck modes, respectively. A 500 W laboratory prototype of the proposed converter has been developed and tested under rated conditions. Even for a large voltage gain of 10, a peak efficiency in excess of 94% is achieved, which is not possible with conventional converters. All the results of this work are included.
Effective Schrodinger equation for one-dimensional systems with rapidly oscillating boundary conditions

The method of the effective Schrodinger equation applied to the one-dimensional problem of a moving particle in a potential box with a potential function and high-frequency time-dependent boundary conditions is investigated. The method of converting the initial equation written for a system with time-dependent boundary conditions to an equation describing a system in a constant region is briefly presented. The resulting system can be considered as a particle in a potential box, located in the field of some effective potential, different from the initial one. Next we consider a particular case of wall motion, with the subsequent construction of the effective Schrodinger equation. Rapid oscillations of the potential box as a whole (i.e., without changing its width) lead to an additional term in the effective equation proportional to the second derivative of the potential. This, in particular, can lead to the formation of potential wells at potential peaks in which bound states of a particle are possible. In the considered case, it was assumed that the oscillation amplitudes are small in comparison with the characteristic dimensions of the system.
Nonlinear Circle-Criterion Observer Design for an Electrochemical Battery Model

Smart battery management systems require reliable information on the internal state of the battery, which are unavailable through direct measurements. Electrochemical models are relevant in this context as these describe the internal phenomena governing the battery dynamics. These models can thus be used to design observers, which provide estimates of the state variables online. In this paper, we consider a single particle electrochemical model of a lithium-ion battery given by a set of ordinary differential equations built from the spatial discretization of partial differential equations (PDEs) that locally describe the mass and charge transport of lithium. We then design a nonlinear observer, whose global exponential stability and robustness are guaranteed provided that a linear matrix inequality holds. The latter is shown to be verified for standard model parameters values. Simulation results based on plug-in hybrid electric vehicle tests on the original PDE model are presented, which illustrate the good performances of the observer in terms of speed of convergence and robustness.
Minimization of frequency error as a result of load increase on power system network

Frequency of supply is an important aspect of electrical power system. Active power balance and frequency control are important tasks in the daily management of a power system. Due to variation in power demand as a result of load changes based on consumer usage, resultant change in frequency impacts on power system operation. The phenomena i.e. load fluctuation creates a burden on the generator prime mover. When the load demand is greater than the generation, generator speed drops, thus initiating a drop-in frequency. An unstable power system condition could lead to generator falling out of synchronism or overheating machines or malfunction of equipment. Should the frequency drop below certain limits automatic load shedding will occur. Minimization of frequency error will enable the matching of frequency system generation to load frequency to nullify sudden blackouts and system imbalance. Increasing economic pressures for power system efficiency and reliability have led to a requirement for maintaining system frequency and power flows closer to specified values as much as possible. Therefore, in a modern power system, load frequency control plays a fundamental role, as a help service, in supporting and providing better conditions for the electricity trading. This paper presents a case study of PID controllers that can withstand load variations and produce improved functionality outputs (results) in a power system. The PID controller is incorporated with a trignometric function of a fourier series to mimimise the change in frequency. To ascertain the proficiency of the proposed technique, different versions of the PID controller in operation was undertaken. The proposed adaptive controller design provided the best results in terms of both frequency deviation and speed drop.
Classification of Style in Fine-Art Paintings Using Transfer Learning and Weighted Image Patches

With the ongoing expansion of digitized artworks, the automated analysis and categorization of fine art paintings have become a rapidly growing research field. However, due to the implicit subjectivity and nuances separating different artistic movements, numerical art analysis implies significant challenges. This paper describes a new efficient method that improves the classification accuracy of fine-art paintings compared to the existing baseline methods. The proposed approach is based on transfer learning and classification of sub-regions or patches of the painting. A weighted sum of the individual-patch classification outcomes gives the final stylistic label of the analyzed painting. The patch weights are optimized to maximize the overall style recognition accuracy. Experimental validation based on two standard art classification datasets and six different pre-trained convolutional neural network (CNN) models (AlexNet, VGG-16, VGG-19, GoogLeNet, ResNet-50 and Inceptionv3) shows that the proposed approach outperforms the baseline techniques and offers low computational and data costs.