Miracle-dz/UniFAR-FTUs huggingface dataset:
T-Center: A Novel Feature Extraction Approach Towards Large-Scale Iris Recognition

For large-scale iris recognition tasks, the determination of classification thresholds remains a challenging task, especially in practical applications where sample space is growing rapidly. Due to the complexity of iris samples, the classification threshold is difficult to determine with the increase of samples. The key issue to solving such threshold determination problems is to obtain iris feature vectors with more obvious discrimination. Therefore, we train deep convolutional neural networks based on a large number of iris samples to extract iris features. More importantly, an optimized center loss function referred to Tight Center (<inline-formula> <tex-math notation="LaTeX">$\mathcal {T}$ </tex-math></inline-formula>-Center) Loss is used to solve the problem of insufficient discrimination caused by the traditional Softmax loss function. In order to evaluate the effectiveness of our proposed method, cosine similarity is used to estimate the similarity between the features on the published iris recognition datasets ND-IRIS-0405, CASIA-Thousand and IITD. Our experiment results prove that the <inline-formula> <tex-math notation="LaTeX">$\mathcal {T}$ </tex-math></inline-formula>-Center loss can minimize intra-class variance and maximize inter-class variance, which achieve significant performance on the benchmark experiments.
Preliminary Design of DEMO PF Coils Based on EU DEMO 2018 Baseline

The dimensioning of Winding Packs and allocation of materials in DEMO PF coils is proposed based on the requirements defined by the EUROfusion 2018 DEMO baseline. The coils are pancake-wound and use two types of forced flow cable-in-conduit conductors. The conductor which uses NbTi has a high void fraction but has no separated low-impedance cooling channel. On the other hand, the conductor made using Nb3Sn has a separated low-impedance cooling channel and therefore low void fraction in the remaining cable space. Both the conductors have a rectangular cross-section. The design addresses the dimensioning of the Winding Pack (WP) based on the electromagnetic field calculations and stress analysis. The amount of superconductor, copper, and cable void fraction in the conductor are determined by the coil current, magnetic field and assumed temperature margin. The amount of structural material in the conductor jacket depends mainly on the fatigue stress corresponding to the pulsed operation of these coils. For PF1 and PF6 coils, the comparison of NbTi and Nb3Sn design options suggest a more efficient allocation of structural and superconducting material in the latter case. The WP dimensions do not significantly depend on the winding technique (pancake vs layer winding).
Comparison of Voltage Map Calculation Methods Using Non-Fractionated EGM Signals in a Persistent AF Patient

Catheter ablation targeting low voltage areas (LVA) is commonly being used to treat atrial fibrillation (AF) in patients with persistent AF. However, it is not always certain that the areas marked as low voltage (LV) are correct. This can be related to how the voltage is calculated. Therefore, this paper focuses on comparing different calculation methods, specifically, with regards to spatial distribution.Two voltage maps obtained in AF were used, removing points which did not meet the required specifications. The peaks for the remaining points, in regions of the left atrium, were then found and the voltage was calculated based on taking the peak to peak (p2p) for different beats.For around 30% of the points on the map, the voltage only changed by 0.1mV when taking one beat versus all beats. However, for some individual points, the difference was substantial, around 0.8mV, depending on the beat chosen. Additionally, the inter-method variability increased by around 0.1mV when considering all methods compared to only methods calculated using more than one point.It was found that taking the median over all p2p values in each point would be a more appropriate method for calculating the voltage. Thus, providing a technique, which could improve the accuracy of identifying LVA in an AF map.
RIBS: Risky Blind-Spots for Attack Classification Models

Nowadays, there has been an increment in the use of machine learning methods for cyber-security applications. These methods can be prone to generalization, especially in a binary attack classification setting, where the objective is to differentiate between benign vs. malicious behavior. This generalization creates risky security blind-spot weaknesses that make the system vulnerable. Current attackers are well aware of these blind-spots and as a counter-strategy, they exploit such vulnerabilities to bypass security measures and achieve their nefarious objectives. In this work, we propose a methodology to mitigate the problem, RIsky Blind-Spot (RIBS), by making the classification more robust. Our proposed approach creates a generator model that can learn the real characteristics of the data, and consequently, sample real examples targeting the blind-spots of a classifier. We validate our methodology in the context of power grids, where we show how this framework can improve the detection of unknown malicious behavior. Our approach provides an increment of 10% in terms of accuracy and detected attacks when compared to the baseline method.
Visual Odometry Offloading in Internet of Vehicles with Compression at the Edge of the Network

A recent trend in the IoT is to shift from traditional cloud-centric applications towards more distributed approaches embracing the fog and edge computing paradigms. In autonomous robots and vehicles, much research has been put into the potential of offloading computationally intensive tasks to cloud computing. Visual odometry is a common example, as realtime analysis of one or multiple video feeds requires significant on-board computation. If this operations are offloaded, then the on-board hardware can be simplified, and the battery life extended. In the case of self-driving cars, efficient offloading can significantly decrease the price of the hardware. Nonetheless, offloading to cloud computing compromises the system's latency and poses serious reliability issues. Visual odometry offloading requires streaming of video-feeds in real-time. In a multivehicle scenario, enabling efficient data compression without compromising performance can help save bandwidth and increase reliability.
Efficient similarity-based alignment of temporally-situated graph nodes with Apache Spark

Topic evolution networks are widely used to represent the evolution of research topics in scientific document archives. These networks might contain thousands of topics and alignment edges which are computed by comparing millions of topic pairs with some similarity function. In this work, we are addressing the problem of computing a very large number cosine-based topic alignments on top of Apache Spark. We present the native map-reduce implementation proposed by Spark and a more efficient implementation which is tuned for alignment computation. Both implementations are evaluated on three real-world datasets.
A Full Soft Switched Bridgeless Power Factor Corrected AC-DC Converter

In this study, a soft swithed and bridgeless power factor corrected (BPFC-FSS) boost converter with an active snubber cell is presented. The converter is operated with pulse with modulation (PWM) and the average current mode control is used to generate PWM signals. The soft switching operation of all semiconductors is achieved by a snubber circuit in introduced converter. The snubber circuit allows zero voltage transition (ZVT) turn on and zero voltage switching (ZVS) turn off for the boost switch. In addition, zero current switching (ZCS) turn on and ZVS turn off of the snubber switch are provided. The boost diode and the other snubber diodes work with soft switching. Moreover, the current stress of the snubber switch is descended by the soft switching energy delivery to the output. Thes soft switching operation of all semiconductors is accomplished for different load case. Thus, the conduction and switching losses are reduced and the efficiency is increased. The theoretical analysis of the BPFC-FSS is presented and validated with a simulation work operating at 100 kHz, with 1 kW output power and 400 V output voltage.
A Pulse Repetition Rate Compressive Sampler for Radar Target Detection

Compressive sampling (CS) is an attractive method to implement analog-to-information conversion (AIC) for a sub-Nyquist radar, where random demodulation (RD) is the most successful AIC. However, RD only considers the sparse characteristic of one single radar pulse, and its mixing circuit still works at the Nyquist sampling rate. To exploit the strong correlation of radar echo pulses, this letter presents a pulse repetition rate compressive sampler for radar target detection. It can acquire these highly correlated pulses by setting a different sampling start time for each pulse and compresses each pulse into one single sample, which means that the low compressive sampling rate is equal to the pulse repetition rate. Compared with RD, this new system reduces the complexity of the hardware circuit and improves the signal compression ratio. Experimental results verify that the proposed radar system outperforms RD by achieving target detection from compressive samples without signal recovery.
Comparison of time-domain and time-scale data in bearing fault detection

Recently various machine learning techniques have been applied as a solution to the problem of timely and accurate detection of motor bearing faults. Conventional decision support systems consist of feature extraction and classification phases which require manual design and optimization of each block separately with increased computational cost. In our work, one dimensional Convolutional Neural Networks (1D CNN) with an inherent adaptive design are applied to efficiently combine the feature extraction and classification into a single learning body. Wavelet packet decomposition (WPD) is applied to represent the bearing vibration signals in time-scale domain efficiently. We then compare performance of raw time-domain vibration input and time-scale transformed input signals for classification of bearing faults using real experimental data.
Recommender System-Based Diffusion Inferring for Open Social Networks

Open social network (OSN) plays a more significant role in information propagation through the rapid developing of information technology. Since information diffusion is an essential process happens in OSN, it has been studied in many studies. Several models have been proposed to infer the diffusion process and reproduce diffusion network. However, these methods have two critical problems: 1) ignoring the effects of user social characteristics and 2) inaccuracy resulted from calculating the influence of different features independently. To address these limitations, a diffusion inferring method based on a recommender system (DIM-SPTF) was proposed. The DIM-SPTF method considers the propagation process between the users as the recommendation process of information and employs a recommender system to infer the propagation relationship. Through determining the propagation relations among all users in the observed topic data set, an information diffusion network can be finally obtained. Experimental results show that DIM-SPTF leads to improvements in performance compared with the state-of-the-art methods.
FinDX: A Versatile, Low-Resource Approach to Financial Website Classification

The World Wide Web provides an excellent platform for investors to discover new partnership opportunities with a variety of companies. Analysts can categorize websites according to their business domains to retain relevant investment opportunities. Classifying websites manually is too expensive and time-consuming; thus, automatic classification tools are necessary. In this paper, we present FinDX (Financial Data EXploration), a tool for automatic website content classification for the financial technology (fintech) domain. At the core of our system is a keyword-based web crawler that extracts text from the landing page and relevant subpages, such as the About or Product pages of company websites. After cleaning the text and filtering it using part-of-speech tagging, we use a Linear Support Vector Machine (SVM) or Multilayer Perceptron (MLP) to classify a company website as fintech or non-fintech. FinDX achieves high binary classification accuracy on two different datasets of business websites, attaining a maximal F-score of 96%. In addition, our flexible tool is easily adaptable to any business domain and is not resource-expensive. This makes FinDX ideal for use in startup environments.
Detecting errors in short-term electricity demand forecast using people dynamics

The landscape of power grids is gradually changing. The growing number of electrical appliances as well as the outbreak of Electric Vehicles (EVs) is increasing the need for electricity. As a consequence, high-accuracy consumption predictions are necessary in order to both schedule and plan production and operations accordingly. The emergence of connected “tracking” devices opens up new data-sets into both Internet-of-Things and Big Data worlds. It provides information on human dynamics (people mobility behavior) and with it several opportunities. Electricity consumption is impacted by people movements as while moving they are not “connected” to the power grid. Therefore, predicting such movement patterns and volume could help electricity providers improve their own consumption predictions. This paper presents a system and methods used to predict people movement behaviors as well as detect any anomaly. A scoring system is used to both evaluate the dynamics predictions and raise alerts when the computed score surpasses established thresholds. This proposal is tested over a scenario using available datasets and demonstrates that modifications in people movement behavior is affecting the consumption profile.
Real-Time Neural Network-Based Gait Phase Estimation Using a Robotic Hip Exoskeleton

Lower limb exoskeletons provide assistance during the gait cycle using a state variable, one in particular is gait phase. This is crucial for the exoskeleton controller to provide the user accurate assistance. Conventional methods often utilize an event marker to estimate gait phase by computing the average stride time. However, this strategy has limitations in adapting to dynamic speeds. We developed a sensor fusion-based neural network model to estimate the gait phase in real-time that can adapt to dynamic speeds ranging from 0.6 to 1.1 m/s. Ten able-bodied subjects walked with an exoskeleton using our estimator and were provided with corresponding torque assistance. Our best performing model had RMSE below 29 ms and 4% for real-time estimation and torque generation, respectively, reducing the estimation error by 36.0% ( ${p} < 0.01$ ) and torque error by 40.9% ( ${p} < 0.001$ ) compared to conventional methods. Our results indicate that creating a general user-independent model and additionally training on user-specific data outperforms the user-specific model and user-independent model. Our study validates the feasibility of using a sensor fusion-based machine learning model to accurately estimate the user’s gait phase and improve the controllability of a lower limb exoskeleton.
State Recognition of Food Images Using Deep Features

State recognition of food images is a recent topic that is gaining a huge interest in the Computer Vision community. Recently, researchers presented a dataset of food images at different states where unfortunately no information regarding the food category was included. In practical food monitoring applications it is important to be able to recognize a peeled tomato instead of a generic peeled item. To this end, in this paper, we introduce a new dataset containing 20 different food categories taken from fruits and vegetables at 11 different states ranging from solid, sliced to creamy paste. We experiment with most common Convolutional Neural Network (CNN) architectures on three different recognition tasks: food categories, food states, and both food categories and states. Since lack of labeled data is a common situation in practical applications, here we exploits deep features extracted from CNNs combined with Support Vector Machines (SVMs) as an alternative to the End-to-End classification. We also compare deep features with several hand-crafted features. These experiments confirm that deep features outperform hand-crafted features on all the three classification tasks and whatever is the food category or food state considered. Finally, we test the generalization capability of the most performing deep features by using another, publicly available, dataset of food states. This last experiment shows that the features extracted from a CNN trained on our proposed dataset achieve performance quite close to the one achieved by the state of the art method. This confirms that our deep features are robust with respect to data never seen by the CNN.
MSSTN: Multi-Scale Spatial Temporal Network for Air Pollution Prediction

Air pollution has become an important factor constraining city development and threatening public health in recent years. Air pollution prediction has been considered as the key part for the early warning of pollution event. Considering the multi-scale nature of geo-sensory data such as air pollution signal, in this paper we adopt a multi-level graph data structure for better utilization of multi-scale spatio-temporal information. We further present a novel deep convolutional neural network model, named Multi-Scale Spatial Temporal Network (MSSTN), for the learning task on this data structure. The MSSTN is specially designed to better discover multi-scale spatial temporal patterns and their high-level interactions, by explicitly using multi-scale neural network structure in both spatial and temporal component. We conduct extensive experiments and ablation studies on Urban Air Pollution Datasets in North China, where the MSSTN can make hourly PM2.5 concentration predictions jointly for a number of cities. And our results shows an outstanding prediction accuracy as well as high computational efficiency compared to existing works.
Deep Tensor Factorization for Multi-Criteria Recommender Systems

Matrix factorization is one of the most successful methods for single-criterion recommender systems but not for multi-criteria recommender systems that contain multiple criterion-specific ratings. Tensor factorization methods have been developed to learn predictive models in multi-criteria recommender systems by dealing with the three-dimensional (3D) user-item-criterion ratings. However, they suffer from the data sparsity issues in real applications. In order to alleviate this problem, we propose deep tensor factorization (DTF) in this paper by integrating deep representation learning and tensor factorization, where the side information is embedded to provide an effective compensation for tensor sparsity. We exhibit two DTF instantiations by combining stacked denoising autoencoder (SDAE) and CANDECOMP/PARAFAC (CP) tensor factorization. Experimental results demonstrate that our DTF schemes outperform state-of-the-art methods in terms of multi-criteria recommendations.
Semi-Blind Receivers for Multi-User Massive MIMO Relay Systems Based on Block Tucker2-PARAFAC Tensor Model

Massive multiple-input multiple-output (MIMO) relay can significantly improve the capacity and throughput of wireless networks, thus has been a sought-after technique for future communication systems. However, the development of massive MIMO relay systems faces several major challenges. For example, the knowledge of instantaneous channel state information (CSI) is needed to estimate signals and optimize systems. Traditional estimation schemes need to transmit pilot sequences, which occupy the spectrum resources. In this paper, we propose a tensor-based method for joint signal and channel estimation for multi-user massive MIMO relay systems without using pilot sequences, and develop two tensor-based semi-blind receivers. Through multidimensional signaling scheme, the signals received by each user are formulated as the block Tucker2-PARAFAC (TP) tensor model. Then, two semi-blind receivers are proposed to jointly estimate the information signals and channel matrices. One is based on the tensor-based closed-form receiver, the other is based on the tensor-based iterative receiver. The proposed closed-form approach can also be used to initialize the iterative receiver for improving the convergence speed. In particular, the proposed schemes are practicable for both time division duplexing (TDD) and frequency division duplexing (FDD) modes. Uniqueness, identifiability and complexity are analyzed for our receivers. Compared with existing receivers, our receivers offer superior bit error rate (BER) and normalized mean square error (NMSE) performance. Numerical examples are shown to demonstrate the effectiveness of the proposed tensor-based receivers.
Hierarchical Demand Forecasting for Factory Production of Perishable Goods

Demand forecasting factory production is of particular importance for retailers of perishable goods, as they are produced daily with a fixed production lead time. Over- or underestimating demand can result in loss of profits due to stock-outs or overstock. However, demand forecasting and production planning for perishable goods represent a significant challenge due to factors such as high volatility, significant variation, the dynamics of store-level product demand, and the need for L day ahead forecasting that allows enough time for production planning. By collaborating with a leading perishable product retailer, we have analyzed (1) detailed internal supply chain data, including sales transaction records and day-end inventories, along with (2) environmental factors, including temperature, weather conditions and wind speed. With the aim of minimizing loss of profit caused by inaccurate forecasting, we propose the following three-stage hierarchical demand forecasting model that leverages the combined data for perishable goods production planning: 1. Identification of store-level demand patterns, 2. store clustering for aggregated production, and 3. a recurrent dynamic network based on a nonlinear autoregressive network with exogenous inputs (NARX) for L-day ahead demand forecasting. Finally, we validate the proposed approach by comparing the loss of profits using this model with other baselines along with the industry standard model used in the perishable goods industry. Our proposed model successfully reduces lost profits to 3.30% of total sales, representing a reduction of 1.71% when compared with the industry standard production system.
A New Charge-Recycling Approach for Reactivation-Noise Reduction

MTCMOS proves to be an effective technique in minimizing leakage power. But the basic problem with this technique is the reactivation or transition noise. Various techniques to minimize ground-bouncing noise have been analyzed in this paper. The techniques have been broadly classified in two categories: Sleep signal slew rate modulation techniques and Charge recycling technique. A new charge recycling technique with block dividing approach has also been proposed. All the techniques have been verified with a 32-bit TSPC adder implemented in 45nm technology. The proposed technique reduces the peak amplitude of reactivation noise by 95.2%, 58.5%, 51%, 37% and 73% compared to the compared to the Standard MTCMOS, simple rise-time delay, two-step rise time delay, three-step rise time delay and conventional charge recycling approaches respectively. The proposed technique also suppresses the leakage power and reactivation delay significantly.