Miracle-dz/UniFAR-FTUs huggingface dataset:
MUBIDUS-I: A multibiometric and multipurpose dataset

Individual biometric traits can seldom fulfill the requirements of security systems in the wild, so researchers were led to investigate multi-biometric/multi-modal systems. This has produced increasing demand for datasets suitable for validating multi-trait and multi-modal biometric systems. Recent devices available for image acquisition and processing can provide a wide range of data sources for biometric applications. The purpose of this work is to present a new multi-biometric dataset that includes a number of traits and acquisition devices wider than most existing datasets. It includes images and videos acquired from 80 subjects in an indoor and outdoor environment, in controlled and non-controlled conditions. Traits such as face, periocular regions, ear, iris, and others are acquired by cameras, mobile devices, and a drone. The data are structured to support experiments adhering to the most common protocols in the literature.
Deep Learning Multi-layer Fusion for an Accurate Iris Presentation Attack Detection

Iris presentation attack detection (PAD) algorithms are developed to address the vulnerability of iris recognition systems to presentation attacks. Taking into account that the deep features successfully improved computer vision performance in various fields including iris recognition, it is natural to use features extracted from deep neural networks for iris PAD. Each layer in a deep learning network carries features of different level of abstraction. The features extracted from the first layer to the higher layers become more complex and more abstract. This might point our complementary information in these features that can collaborate towards an accurate PAD decision. Therefore, we propose an iris PAD solution based on multi-layer fusion. The information extracted from the last several convolutional layers are fused on two levels, feature-level and score-level. We demonstrated experiments on both, off-the-shelf pre-trained network and network trained from scratch. An extensive experiment also explores the complementary between different layer combinations of deep features. Our experimental results show that feature-level based multi-layer fusion method performs better than the best single layer feature extractor in most cases. In addition, our fusion results achieve similar or better results than the state-of-the-art algorithms on the Notre Dame and IIITD-WVU databases of the Iris Liveness Detection Competition 2017 (LivDet-Iris 2017).
A Survey on Multimodal Data Stream Mining for e-Learner’s Emotion Recognition

Emotions play a crucial role in learning. To improve and optimize electronic learning (e-Learning) outcomes, many researchers have investigated the role of emotions. Also, researchers have come up with many approaches to utilize one or many data modalities to achieve this goal, and they have been successful. But the recent advancements in technology and the internet of things (IoT) devices have brought a new dimension in e-Learning, with many input devices (such as webcams, fit-bands etc.) for interacting with e-Learners. This new dimension brings not only massive amounts of data with volume, variety, and velocity called multimodal data streams but also more challenges of mining those data in real-time. In this work, we have focused on state-of-the-art emotion recognition in e-Learning utilizing the multimodal data streams of learners. Also, we have thoroughly investigated the past research and surveys on emotion recognition methods in e-Learning to find the affecting emotions and their relations with the emotion measurement channels; and we have compared several data-stream classifiers for emotion recognition by utilizing multimodal physiological data streams. Finally, the future research opportunities to be addressed are also discussed.
Two-stage Sequential Recommendation via Bidirectional Attentive Behavior Embedding and Long/Short-term Integration

In E-commerce applications, to predict what users will buy next is a crucial mission of sequential recommendation. Most frontier researches build end-to-end training models for sequential recommendation tasks via RNNs, CNNs or attentive models. However, a user’s historical behavior sequence carries more complex contextual information than words. In this paper, we propose a two-stage user modeling framework for sequential recommendation, which is consisted by a Bidirectional Self-attentive Behavior Embedding and a Long/Short-term Sequential Behavior Predictor. Firstly, in order to expand perceivable information, a novel self-attentive behavior embedding method is proposed to learn semantic representations not only for items, but also for other important contextual factors (e.g. actions, categories and time). Then, with the pre-trained behavior embeddings, we propose a personalized memory network for Top-N recommendation. We use recurrent network to encode the short-term intent and learn the personalized long-term memory by a self-attention block. To integrate the long/short-term preferences, we generate the predicted behavior representation by using the present intent as a query to match with user’s historical preferences via attentive memory reader. Finally, we conduct extensive experiments on two benchmark datasets provided by Tmall and Amazon. Compared with state-of-the-art techniques, experimental results demonstrate the effectiveness of our proposed framework.
A Hybrid Simulation Model for Electromagnetic Launchers Including the Transient Inductance and Electromotive Force

Although electromagnetic launchers (EMLs) are superior to classical gun-powder-based launchers, they have to withstand extreme electrical and mechanical conditions. Therefore, the optimal design and precise simulations of these devices are crucial. In this article, a new simulation strategy for EMLs is proposed in order to achieve high accuracy and reduced complexity. The inductance and electromotive force (EMF) variations in the transient, which have a considerable influence on the launch process, are modeled using the finite element method (FEM) coupled with electrical circuit simulation. The proposed method has a good agreement with the experimental results of two EMLs (EMFY-1 and EMFY-2), which have 25- and 50-mm square bores and 3-m-length launchers. The study showed that the hybrid model with transient inductance and EMF calculations showed a good agreement with experiments that have 625 kJ–3.241-MJ input energies.
DrunaliaCap: Image Captioning for Drug-Related Paraphernalia With Deep Learning

Image captioning is a process of generating textual descriptions of images. In recent years, research on publicly available large-scale datasets and deep learning-based algorithms has promoted the development of this field. However, little research has been conducted on captioning images of drug-related paraphernalia that, despite being an important topic for both drug prevention and police enforcement, is not covered by existing image captioning studies. In this paper, we propose DrunaliaCap—a deep learning-based system for autogenerating both “factual” (what is in the image) and “functional” (the usage of each paraphernalia during drug-taking) descriptions of images of drug-related paraphernalia. We constructed a new dataset containing 20 categories of drug-related items and trained deep learning-based models for the proposed system. We further proposed a method to evaluate and optimize the generation of captions to prevent them from missing important knowledge. Experiments were conducted to validate the performance of the newly proposed dataset and method. We analyzed the experimental results and discussed the significance, limitations, and potential applications of our work.
Proportional-Derivative State-Feedback Control for Singular Systems With Input Quantization

This paper deals with an admissibilization problem of singular systems with uniform input quantization. The aim is to design a controller to guarantee the admissibility of the closed-loop system. To achieve this, this paper proposes a proportional-derivative state-feedback controller which includes non-linear control part to reject the effect of uniform input quantization. Based on the proposed controller, sufficient conditions are obtained in terms of linear matrix inequalities. Two examples show the feasibility of the proposed controller.
Direct DC Voltages Comparison between two Programmable Josephson Voltage Standards at SCL

Standards and Calibration Laboratory (SCL) setup in 2018 a liquid helium based programmable Josephson voltage standard (PJVS) developed by the National Institute of Standards and Technology (NIST). The system was validated by direct comparison with a NIST transportable PJVS system at 1.018 V, 4 V, 6 V, 8 V and 10 V. The difference between the systems was within 0.5 nV, with an expanded uncertainty of less than 2.2 nV ($k=2$). In this paper, the setup and the results of the direct comparison method are presented.
Additively Printed Multi-Material Temperature Sensor Realized using Screen Printing

Additively printed sensors allow for measurement of environmental parameters close to the point of interest in addition to better integration with the underlying asset. In addition, the use of additive technologies for printed sensors allows for asset monitoring in health, defense and automotive applications even if the underlying system was designed without a compliant interface. Printed sensors can be used for creating a digital fingerprint that can be used to authenticate devices. The performance of new materials and processing strategies can be assessed using data on the prior storage conditions from the printed sensors. In this article, screen-printing technique is used to realize a Resistance Temperature Detector/Sensor (RTD) on a flexible substrate using commercially available inks. Initially, the sensor is printed using single material in the size and shape of standard SMD resistors to understand the temperature coefficient behavior. In a second configuration, the temperature sensor is printed on a Wheatstone bridge configuration with both Positive and Negative Temperature Coefficient (PTC and NTC) materials. Electrical characteristics such as resistance have been studied in detail, along with Temperature Coefficient of Resistance (TCR) of the materials. For the Wheatstone bridge configuration, a bridge voltage is supplied and output voltage will be measured and related to temperature. The repeatability of measurement for the same sensor, sensor-to-sensor variation, accuracy of the measurement, hysteresis of the measured temperature and the extended time stability has been quantified.
Machine Learning Analysis of Heart Rate Variability for the Detection of Seizures in Comatose Cardiac Arrest Survivors

Objective: Heart rate variability (HRV) reflects autonomous nervous system disturbance and is used for seizure prediction. The aim of this study was to develop a real-time, continuous physiological medical signal data acquisition system in seizure detection for intensive care unit (ICU). Methods: This prospective study was conducted in National Taiwan University Hospital from August 2018 to October 2019. This study included 20 patients who (a) had a sustained return of spontaneous circulation following out-of-hospital cardiac arrest, (b) were over 18 years old, (c) and were admitted to the emergency ICU for post-cardiac-arrest care. One-lead electrocardiography and bilateral two-channel electroencephalography recorders were synchronically used to conduct measurements for a maximum of 72 hours. The recorded data were wirelessly real-time transmitted by a proxy transmitting module through an access point and a local gateway. A system with a novel algorithm processed the signals and conducted feature extraction and supervised learning for seizure detection. Results: A total of 89 nonseizure and 83 seizure events were detected by the system. Seizure occurred in two-thirds of the patients assessed by intensivists and neurologists. Four HRV parameters, namely standard deviation of normal-to-normal R-wave intervals, high frequency, low frequency–high frequency ratio, and sample entropy, were determined as potential features for identifying seizures. The sensitivity and specificity of the developed system were 0.74 and 0.81, respectively, and the positive predictive value was 0.82. Conclusion: The developed system can be used to identify seizure events through HRV features. Significance: The current study achieved real-time seizure detection and overcame previous limitations on continuity and accessibility.
Strain-Engineered MoTe2 Photodetector in Silicon Photonics at 1550 nm

Here we show how strain-engineering (∼4% tensile strain) lowers the bandgap (−0.2eV) of MoTe2 nanocrystals heterogeneously integrated around a silicon photonic waveguide, thus enabling photoabsorption at 1550nm, a responsivity of 0.5A/W and NEP of 90pW/Hz2.
Fault Tolerant multi-sensor Data Fusion for vehicle localisation using Maximum Correntropy Unscented Information Filter and α-Rényi Divergence

the paper presents a fault-tolerant multi-sensor fusion approach with Fault Detection and Exclusion (FDE) based on information theory. The Maximum Correntropy Criterion (MCC) in Unscented Information Filter (UIF) form, called (MCCUIF), is used as estimator. The Unscented Transformation (UT) provides an efficient tool to restrict the non-linear state estimation problem. However, the UIF works well with Gaussian noises, where its performance may decrease when dealing with non-Gaussian noises. The MCC is used to deal with non-Gaussian noises (for instance shot noises or Gaussian mixture noises). For detection and exclusion of erroneous measurements, a residual is designed using a- Rényi Divergence (a-RD) between a priori and a posteriori probabilities distributions. Then α-Rényi criterion (a-Rc) is used in the decision part of the proposed approach in order to calculate an adaptive threshold for FDE. In order to target both high integrity and accuracy of the navigation function of an autonomous vehicle in stringent environments (urban canyon, forests …), this paper presents a tightly coupled architecture by merging raw data of a Global Navigation Satellite System (GNSS) with odometer (odo) measurements through the proposed approach. The main contributions of this paper are: - the proposition of a multisensor fusion approach using MCCUIF, - the development of an FDE method using a residual based on a-RD with an adequate choice of a value and adaptive thresholding, - the validation of the proposed approach with real experimental data.
Automatic Cell Phone Detection in Large Volume of Baggage Processing

The research focuses on the detection of mobile phones that appear in the passenger baggage at airport arrivals. The aim of the research is to develop a method that detects mobile phones efficiently in passenger baggage at the custom scanner for the purpose to make sure that no mobile phone is passed undetected without payment of duties and taxes. It presents a machine learning based solution towards the airport security system by detecting mobile phones in a scanned image of passenger’s baggage at airport arrival. Classification is based on colour, density, size and pattern. It is challenging to ascertain if an electronic item is a cell phone or not from an x-ray image particularly when two objects are overlapping each other. The system’s performance is marred by the unavailability of high-quality x-ray images. The performance of the system increases manifolds when a high-quality image is provided as a test case. The system is able to classify the images correctly 80 percent of the time on average. The research project is of significant importance to the customs authorities as it helps them in profiling the passenger baggage at the arrival for imported mobile phones.
Data Driven Relational Constraint Programming

We propose a data-driven constraint programming environment that merges the power of two separate domains: databases and SAT-solvers. While a database system offers flexible data models and query languages, SAT solvers offer the ability to satisfy logical constraints and optimization objectives. In this paper, we describe a goal-oriented declarative algebra that seamlessly integrates both worlds. Bring from proven practices in functional programming, we express constants, variables and constraints in a unified relational query language. The language is implemented on top of industrial strength database engines and SAT solvers.In order to support iterative constraint programming with debugging, we propose several debugging operators to assist with interactive constraint solving.
Sentiment Analysis Based Churn Prediction in Mobile Games using Word Embedding Models and Deep Learning Algorithms

Customer churn is one of the most important problems for many industries, including banking, telecommunications, and gaming. In the gaming market, it is observed that the demand on game applications rises with the usage of mobile devices such as smartphones. Because of this, it is important to predict when players tend to leave a game. Studies so far focus on churn prediction in mobile or online games by analyzing demographic, economic, and behavioral data about their customers. In this work, we introduce a sentiment analysis-based churn prediction model in mobile games using word embedding models and deep learning algorithms. To the best of our knowledge, this is the first study to evaluate the churn tendency of customers by analyzing sentiments of players from their comments on games using deep learning and word embedding models. For this purpose, we use deep learning algorithms for classification and word embedding models for text representation. The applied deep learning algorithms include convolutional neural networks, recurrent neural networks, long short-term memory networks. Word2Vec, GloVe, and FastText word embedding models are employed for text representation. To demonstrate the impact of proposed model, comprehensive experiments are carried out on Turkish four different game datasets. The experiment results show that sentiment analysis of players in mobile games can be powerful indicator in terms of predicting customer churn.
Broadband Second-Harmonic Generation from Electrically Tunable Nonlinear Polaritonic Metasurfaces

We report broadband second-harmonic generation based on Stark-tunable intersubband nonlinearities in multiple-quantum-well structures combined with plasmonic nanoresonators. Experimentally, 0.75 μm of the second-harmonic generation spectral peak tuning around pump wavelength of 9.8 μm was achieved.
Event Extraction for Criminal Legal Text

This paper concerns with the actual problems in the legal work. We apply event extraction technology to the case description part in the Chinese legal text. We define the event type, event argument and event argument role of the larceny case, and construct a larceny case event extraction dataset through data annotation. We divide event extraction into two steps: event trigger word and argument joint extraction and event argument role assignment. We use BERT to obtain Chinese character vectors, use the BiLSTM-CRF model for extraction at the first step, and combine additional features with the extraction results of the first step, then input them to the CRF model of the second step to obtain an improvement in extraction result. We display the extracted event information in time series to realize the litigation visualization. We format Chinese time expressions, sorts the event information in tine series, and develops a Web application to display the timeline of event information.
Fusing Multilevel Deep Features for Fabric Defect Detection Based NTV-RPCA

Fabric defect detection plays an important role in automated inspection and quality control in textile manufacturing. As the fabric images have complex and diverse textures and defects, traditional detection methods show a poor adaptability and low detection accuracy. Robust principal component analysis (RPCA) model that can be used to separate the image into object and background have proven applicable in fabric defect detection. However, how to represent texture feature of the fabric image more effectively is still problematic in this kind of method. In addition, the use of the traditional RPCA may result in low accuracy and more noises in sparse part. In this article, a novel fabric defect detection method based on multilevel deep features fusion and non-convex total variation regularized RPCA (NTV-RPCA) is proposed. Firstly, the image representation ability is well enhanced through multilevel deep features extracted by a convolutional neural network. Then, the non-convex total variation regularized RPCA is proposed in which total variation constraint significantly reduces the noises in sparse part and non-convex solution is more approximate to the authentic one. Next, multilevel saliency maps generated by the sparse matrixes are fused via RPCA to produce a more reliable detection result. Finally, the defect region is located by segmenting the fused saliency map via a threshold segmentation algorithm. Qualitative and quantitative experiments conducted on two public fabric image databases demonstrate that the proposed method improves the adaptability and detection accuracy comparing to the state-of-the-arts.
Predicting Particle Fineness in a Cement Mill

Cement production is a multi-billion dollar industry, of which one of the main sub-processes, cement milling, is complex and non-linear. There is a need to model the fineness of particles exiting the milling circuit to better control the cement plant. This paper explores the relationship between the particle size of cement produced and the operation of the cement mill circuit. This paper aims to provide a model for predicting the fineness of particles exiting the milling circuit using data on the current and past states of the plant. A comprehensive literature review of the problem, as well as a discussion of potential modelling solutions, is provided. Blaine (particle fineness)is modelled using many different linear and non-linear models on 5 months of data from a Chinese cement plant. On a holdout test set a multi-layered perceptron achieved an MAE of 8.799 and a linear regression achieved a $R^{2}$ of 0.481. discussion of the significance of various features for predicting Blaine is also presented. The results show some limited success from non-linear data-driven models and highlight some of the unique difficulties in modelling the cement mill and present recommendations for future research.
Research of edge caching strategy based on collaboration mechanism

This paper introduces a collaboration mechanism in a multi-layered and centered network topology model to achieve the sharing and effective use of cache content through edge cache clusters. Under this network architecture, a collaborative cache deployment algorithm and a cache elimination strategy based on a multi-level popularity index are proposed, and a cache strategy evaluation mechanism with cache hit rate and content acquisition delay as evaluation indicators is established. Finally, the two are combined into a caching strategy based on a collaboration mechanism. Simulation experiments and comparative analysis are performed using the caching strategy evaluation mechanism. The experimental results show that the proposed caching strategy can effectively improve cache performance and service quality.