Large-scale Training Datasets Research Articles

J-LOUCR: Joint learned optimized undersampling and constrained reconstruction for accelerated MRI by reference-driven deep image prior

Undersampled magnetic resonance (MR) image reconstruction performance is related to two fundamental problems: where to sample and how to reconstruct the image. Previously developed deep learning-based magnetic resonance imaging (MRI) methods, including undersampled mask optimization and reconstruction, require large-scale, high-quality, and patient-based datasets for training, which presents a challenge in practical clinical scenarios. To reduce the dependence on datasets and perform undersampling and reconstruction simultaneously, we propose a novel joint learned optimized undersampling and constrained reconstruction method for accelerated MRI by a reference-driven deep image prior (J-LOUCR). Inspired by the deep image prior (DIP), J-LOUCR involves no pretraining procedure, resulting in no dependence on large-scale training datasets. The implementation of J-LOUCR includes learned optimization of the undersampled mask and constrained reconstruction with the reference-driven DIP. In the first step, based on the traditional DIP, we introduce a reference image as the network input and propose a joint optimization problem of the undersampled mask and network parameters. Then, the joint optimization problem is deconstructed into subproblems and solved by using the alternative iteration strategy to obtain the optimal undersampled mask guided by the reference image. In the second step, for the optimal undersampled mask, we consider and recommend that the structural similarity between the reference and target MR images be used as prior information. Under the dual drivers of the reference image as the network input and prior constraint, J-LOUCR optimizes the network parameters and then completes reconstruction. Experiments on invivo MR scans demonstrate that J-LOUCR can achieve more accurate reconstruction with more details and features.

Membership inference attack on differentially private block coordinate descent.

The extraordinary success of deep learning is made possible due to the availability of crowd-sourced large-scale training datasets. Mostly, these datasets contain personal and confidential information, thus, have great potential of being misused, raising privacy concerns. Consequently, privacy-preserving deep learning has become a primary research interest nowadays. One of the prominent approaches adopted to prevent the leakage of sensitive information about the training data is by implementing differential privacy during training for their differentially private training, which aims to preserve the privacy of deep learning models. Though these models are claimed to be a safeguard against privacy attacks targeting sensitive information, however, least amount of work is found in the literature to practically evaluate their capability by performing a sophisticated attack model on them. Recently, DP-BCD is proposed as an alternative to state-of-the-art DP-SGD, to preserve the privacy of deep-learning models, having low privacy cost and fast convergence speed with highly accurate prediction results. To check its practical capability, in this article, we analytically evaluate the impact of a sophisticated privacy attack called the membership inference attack against it in both black box as well as white box settings. More precisely, we inspect how much information can be inferred from a differentially private deep model's training data. We evaluate our experiments on benchmark datasets using AUC, attacker advantage, precision, recall, and F1-score performance metrics. The experimental results exhibit that DP-BCD keeps its promise to preserve privacy against strong adversaries while providing acceptable model utility compared to state-of-the-art techniques.

Real Time Volumetric MRI for MR-Guided 3D Motion Tracking via Sparse Prior-Augmented Neural Representation Learning

To reconstruct volumetric MRI from orthogonal cine acquisition aided by sparse priors of 2 static 3D MRI through implicit neural representation (NeRP) learning, with the goal of eliminating large-scale training datasets for data-driven sparse MRI reconstruction and supporting clinical workflow of real time 3D motion tracking during MR-guided radiotherapy. A multi-layer perceptron network was trained to learn the NeRP of a patient-specific MRI dataset, where the network takes 4D data coordinates of voxel locations and motion states as inputs and outputs corresponding voxel intensities. By first learning the NeRP of 2 static 3D MRI with different breathing motion states, prior knowledge of patient breathing motion was embedded into network weights through optimization. The prior knowledge was then augmented from 2 to 31 motion states by querying the optimized network at interpolated/extrapolated motion state coordinates. Starting from the prior-augmented network as an initialization point, the network was further trained using sparse samples of 2 orthogonal cine slices. The final volumetric reconstruction was obtained by querying the trained network at desired 3D spatial locations. We evaluated the proposed method using 5-minute volumetric MRI time series with 340 ms temporal resolution collected from 7 liver carcinoma patients. The time series was acquired using golden-angle radial MRI sequence and reconstructed through retrospective sorting. Two MRI with inhale and exhale states respectively were selected from the first 30 sec of the time series for prior embedding and augmentation. The remaining 4.5-min time series was used for volumetric reconstruction evaluation, where we retrospectively subsampled each MRI to 2 orthogonal slices and compared network-reconstructed images to ground truth images in terms of image quality and the capability of supporting 3D target motion tracking. Across the 7 patients evaluated, the peak signal to noise ratio between model reconstruction and ground truth was 54.66 ± 6.16 dB and the structural similarity index measure was 0.99 ± 0.01. Gross tumor volume (GTV) contours estimated by deforming a reference state MRI to model-reconstructed and ground truth MRI showed good consistency. The 95-percentile Hausdorff distance between GTV contours was 1.89 ± 1.13 mm, which is less than the voxel dimension. The mean GTV centroid position difference between ground truth and model estimation was less than 1 mm in all 3 orthogonal directions. Volumetric MRI from orthogonal cine acquisition with sparse priors is feasible by modeling prior knowledge through implicit neural representation learning. The model-reconstructed images showed sufficient accuracy in supporting 3D motion tracking of abdominal targets. By eliminating the need for large scale training datasets, the method promises to enable clinical implementation of 3D motion tracking for precision radiation therapy.

Summarization of Lengthy Legal Documents via Abstractive Dataset Building: An Extract-then-Assign Approach

Development of effective automatic summarization approaches for legal documents suffer from several challenges like extremely long document-summary pairs, lack of large scale training datasets with tractable document-summary token lengths, etc. In this work, we deal with the problem of legal document summarization by building a modified abstractive dataset from the original dataset. This ensures that the length of each document-summary pair is manageable and can be processed by the state of the art summarization approaches (such as BART). Secondly, we deal with the data scarcity problem by creating more number of training samples, from each of the original document-summary pair. This is done by creating multiple extractive summaries from each sample in the original dataset, following which ground-truth summary sentences are assigned to each of the extractive summary to generate new training samples. This results in a larger training dataset that can be utilized for fine-tuning summarization models. Our proposed approach has been evaluated on two different legal datasets- BillSum and Forum of Information Retrieval Evaluation (FIRE). With respect to the ROUGE metrics, the proposed approach is able to outperform pre-trained BART model fine-tuned on original dataset by (3−8)% for FIRE test sets, and by (1−3)% for the BillSum test sets. Considering the BERTScore metrics, the proposed approach obtains (1−2)% improvements on the FIRE test sets, while for the BillSum test sets (3−8)% improvements are observed. Such improvements suggest that the proposed dataset building approach can help achieve improved abstractive summarization of lengthy legal documents.

2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning

Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5,000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline.

Deep transfer learning for automatic speech recognition: Towards better generalization

Automatic speech recognition (ASR) has recently become an important challenge when using deep learning (DL). It requires large-scale training datasets and high computational and storage resources. Moreover, DL techniques and machine learning (ML) approaches in general, hypothesize that training and testing data come from the same domain, with the same input feature space and data distribution characteristics. This assumption, however, is not applicable in some real-world artificial intelligence (AI) applications. Moreover, there are situations where gathering real data is challenging, expensive, or rarely occurring, which cannot meet the data requirements of DL models. deep transfer learning (DTL) has been introduced to overcome these issues, which helps develop high-performing models using real datasets that are small or slightly different but related to the training data. This paper presents a comprehensive survey of DTL-based ASR frameworks to shed light on the latest developments and helps academics and professionals understand current challenges. Specifically, after presenting the DTL background, a well-designed taxonomy is adopted to inform the state-of-the-art. A critical analysis is then conducted to identify the limitations and advantages of each framework. Moving on, a comparative study is introduced to highlight the current challenges before deriving opportunities for future research.

Synthetic data for face recognition: Current state and future prospects

Over the past years, deep learning capabilities and the availability of large-scale training datasets advanced rapidly, leading to breakthroughs in face recognition accuracy. However, these technologies are foreseen to face a major challenge in the next years due to the legal and ethical concerns about using authentic biometric data in AI model training and evaluation along with increasingly utilizing data-hungry state-of-the-art deep learning models. With the recent advances in deep generative models and their success in generating realistic and high-resolution synthetic image data, privacy-friendly synthetic data has been recently proposed as an alternative to privacy-sensitive authentic data to overcome the challenges of using authentic data in face recognition development. This work aims at providing a clear and structured picture of the use-cases taxonomy of synthetic face data in face recognition along with the recent emerging advances of face recognition models developed on the bases of synthetic data. We also discuss the challenges facing the use of synthetic data in face recognition development and several future prospects of synthetic data in the domain of face recognition.

Fast CO2 saturation simulations on large-scale geomodels with artificial intelligence-based Wavelet Neural Operators

Carbon capture and storage (CCS) projects rely on numerical reservoir simulations for determining where and how much CO2 can safely be stored underground. Conventional simulations are computationally expensive and time consuming, thereby increasing the turn-around time of decision-making processes and projects. Recent research on artificial intelligence (AI) based approaches for solving the underlying two-phase flow equations has shown enormous promise, as the computational burden can be shifted to the training time, while simulations can be performed in fractions of a second at test time. Applications of AI methods for simulating CO2 flow and other differential equations have so far been limited to 2D or small 3D problems with relatively simple geometries. We introduce a cloud-native framework for parallel simulations of large-scale training data sets using the Open Porous Media (OPM) simulator and we propose a new AI architecture based on Fourier Neural Operators (FNOs) with 3D wavelet transforms. This architecture performs better than FNOs on data with shock fronts, such as simulating CO2 saturation. We also provide a new training dataset for CO2 flow based on the Sleipner CO2 storage geomodel from the Norwegian continental shelf and show that both FNOs and Wavelet Neural Operators (WNOs) can be trained on models with over 200,000 cells and lead to significant speed-ups of up to 50,000× (FNOs) and 100× (Wavelet NOs) over numerical simulations of CO2 saturation with OPM.

Pseudo-Label Guided Image Synthesis for Semi-Supervised COVID-19 Pneumonia Infection Segmentation.

Coronavirus disease 2019 (COVID-19) has become a severe global pandemic. Accurate pneumonia infection segmentation is important for assisting doctors in diagnosing COVID-19. Deep learning-based methods can be developed for automatic segmentation, but the lack of large-scale well-annotated COVID-19 training datasets may hinder their performance. Semi-supervised segmentation is a promising solution which explores large amounts of unlabelled data, while most existing methods focus on pseudo-label refinement. In this paper, we propose a new perspective on semi-supervised learning for COVID-19 pneumonia infection segmentation, namely pseudo-label guided image synthesis. The main idea is to keep the pseudo-labels and synthesize new images to match them. The synthetic image has the same COVID-19 infected regions as indicated in the pseudo-label, and the reference style extracted from the style code pool is added to make it more realistic. We introduce two representative methods by incorporating the synthetic images into model training, including single-stage Synthesis-Assisted Cross Pseudo Supervision (SA-CPS) and multi-stage Synthesis-Assisted Self-Training (SA-ST), which can work individually as well as cooperatively. Synthesis-assisted methods expand the training data with high-quality synthetic data, thus improving the segmentation performance. Extensive experiments on two COVID-19 CT datasets for segmenting the infections demonstrate our method is superior to existing schemes for semi-supervised segmentation, and achieves the state-of-the-art performance on both datasets. Code is available at: https://github.com/FeiLyu/SASSL.

Cost-Efficient Image Semantic Segmentation for Indoor Scene Understanding Using Weakly Supervised Learning and BIM

Image segmentation is an essential step in vision sensing and image processing. It enables the understanding of the object’s classes, spatial locations, and extents in the scene, which can be used to support a wide range of construction applications such as progress monitoring, safety management, and productivity analysis. The recent ground-breaking achievements of deep learning-based approaches for semantic segmentation are at the cost of expensive large-scale training datasets annotated at the pixel level. Although building information modeling (BIM) has been leveraged to alleviate labeling costs using automatically generated, color-coded images as semantic labels, the differences between the BIM models and the real-world scenes make it difficult to apply networks trained on BIM-generated labels to real images. Furthermore, it takes nontrivial efforts to reduce such differences. To address these problems, this paper proposes a weakly supervised segmentation approach that uses inexpensive image-level labels. The missing boundary information in image-level labels is compensated by BIM-extracted object information. The proposed method consists of three modules: (1) detect initial object locations from image-level labels; (2) extract object information from BIM as prior knowledge; and (3) incorporate the prior knowledge into the network to enhance the detected object locations. Three extensive experiments are designed to evaluate the effectiveness of the proposed method. Results show that the proposed method substantially improves the detected object areas by using prior knowledge of target objects from BIM and outperforms the state-of-the-art weakly supervised methods.

Tropical Cyclone Wind Speed Estimation: A Large Scale Training Data Set and Community Benchmarking

AbstractTropical cyclones (TCs) cause significant disruptions to infrastructure and livelihood. The scale of loss due to TCs may be mitigated by prompt and accurate advisories about TC wind speed. Current advisories are consensus based and have a time delay of about 6 hr between each new update. As part of efforts to increase the frequency of wind speed estimations without compromising on the accuracy, we curate a high‐quality hurricane image and wind speed data set and develop a machine learning (ML) model for frequent wind speed estimation. The image and wind speed pairings are consolidated from different sources. The wind speeds are interpolated hourly to curate the TC wind speed estimation data set. The data set is supplemented with metadata that eases adoption of the data set by the ML community. We have also designed a competition to garner interest and encourage community involvement to build state‐of‐the‐art ML models to estimate wind speeds from satellite images of TCs. The competition winners were able to improve the wind speed estimation by almost 50% over the benchmark model.

Twin Broad Learning System for Fault Diagnosis of Rotating Machinery

As rotating machines are more and more widely used in modem equipment, their fault diagnosis is important to guarantee the instrument’s reliability and safety. Although intelligent fault diagnosis based on deep learning (DL) has achieved great performance in many fault diagnosis tasks, these models are highly dependent on the large-scale training dataset and can be time-consuming during training and testing. These limitations largely affect the efficiency of diagnosis in real-world applications. An effective alternative way is to develop the single-layer feedforward networks (SLFNs) based Broad Learning System (BLS) for fault diagnosis tasks, which enjoy fast training speed as well as strong generalization ability. However, we found that the least square classifier used in classical BLS can have a problem distinguishing the possibly overlapping fault patterns. In this paper, we propose a novel twin broad learning system (TBLS) for fault diagnosis of rotating machinery. Rather than using the classical least square method, the proposed TBLS learns to find two nonparallel hyper-planes to deal with the classification problem, which shows stronger generalization ability in fault diagnosis problems. Experimental results on two fault diagnosis benchmark datasets for typical rotating machinery further illustrate the effectiveness of the TBLS methods, which offer a high-efficiency solution for rotating machinery fault diagnosis.

An Intelligent Vision-Based Nutritional Assessment Method for Handheld Food Items

Dietary assessment has proven to be an important tool to evaluate the dietary intake of patients with diabetes and obesity. The traditional approach of accessing the dietary intake is to conduct a 24-hour dietary recall (24HR), a structured interview designed to obtain information on food categories and volume consumed by the participants. Due to the unconscious biases in this kind of self-reporting approach, many research studies have explored the use of vision-based approaches to provide accurate and objective assessments. Despite the promising results of food recognition by deep neural networks, there still exist several hurdles in deep learning-based food volume estimation ranging from domain shift between synthetic and raw 3D models, shape completion ambiguity and, most importantly, lack of large-scale paired training dataset. Therefore, this paper proposed an intelligent nutritional assessment approach via weakly-supervised point cloud completion. It aims to close the reality gap in 3D point cloud completion tasks and address the targeted challenges by proposing the Point Cloud Auxiliary Classifier GAN (PC-ACGAN), a novel and robust point cloud completion architecture that can be trained in a weakly-supervised manner. Then the volume can be easily estimated from the completed representation of the food. In addition to advantages with regard to efficiency, another major merit of our system is that it can be used to estimate the volume of handheld food items without requiring the constraints including placing the food items on a table or next to fiducial markers, which facilitate the implementation on both wearable and handheld cameras. Comprehensive experiments have been carried out on major benchmark datasets and self-constructed volume-annotated dataset respectively, in which the proposed PC-ACGAN demonstrates comparable results with several strong fully-supervised baseline methods and shows superior completion ability in handling food volume estimation.

Learning Dual-Level Deep Representation for Thermal Infrared Tracking

The feature models used by existing Thermal InfraRed (TIR) tracking methods are usually learned from RGB images due to the lack of a large-scale TIR image training dataset. However, these feature models are less effective in representing TIR objects and they are difficult to effectively distinguish distractors because they do not contain fine-grained discriminative information. To this end, we propose a dual-level feature model containing the TIR-specific discriminative feature and fine-grained correlation feature for robust TIR object tracking. Specifically, to distinguish inter-class TIR objects, we first design an auxiliary multi-classification network to learn the TIR-specific discriminative feature. Then, to recognize intra-class TIR objects, we propose a fine-grained aware module to learn the fine-grained correlation feature. These two kinds of features complement each other and represent TIR objects in the levels of inter-class and intra-class respectively. These two feature models are constructed using a multi-task matching framework and are jointly optimized on the TIR object tracking task. In addition, we develop a large-scale TIR image dataset to train the network for learning TIR-specific feature patterns. To the best of our knowledge, this is the largest TIR tracking training dataset with the richest object class and scenario. To verify the effectiveness of the proposed dual-level feature model, we propose an offline TIR tracker (MMNet) and an online TIR tracker (ECO-MM) based on the feature model and evaluate them on three TIR tracking benchmarks. Extensive experimental results on these benchmarks demonstrate that the proposed algorithms perform favorably against the state-of-the-art methods.

SiamON: Siamese Occlusion-Aware Network for Visual Tracking

Occlusion has been proven to be one of the most challenging factors faced by most visual trackers. There are mainly two difficulties, the first one is that the number of occlusion samples are very limited even though collecting a large-scale training data set, and another one is how to correctly learn the features of the target when comes to occlusion situations. In this paper, we tried to solve these two problems together in our proposed model. To this end, we propose a novel Siamese Occlusion-aware Network (SiamON) for high-performance visual tracking. In particular, we predefine some soft-masks to solve the problem of fewer occlusion samples, which perceive patterns of occlusion contents at different locations and take these masks as the conditions to guide occlusion-aware feature learning. Meanwhile, we propose a target-aware attention mechanism allows the model to pay more attention to the target and further weaken the impact of occlusion. Extensive experiments on several popular benchmarks show that our tracking method exceeds many state-of-the-art trackers especially in the presence of occlusion and meets the requirements of real-time.

Physics Guided Remote Sensing Image Synthesis Network for Ship Detection

Automatic detection and localization of objects in remote sensing images are of great significance for remote sensing systems. Existing frameworks usually train an object detection network using collected remote-sensing images. However, these models usually perform poorly due to the lack of large-scale training datasets, which is often the case for special remote sensing scenarios, e.g., the detection of ships in the open sea. Although image synthesis is a common strategy to alleviate the issue of data insufficiency, the trained model still performs poorly when being tested on real-world scenes. Aimed at this, a novel sensor-related image synthesis framework, dubbed as RS-ISP, is developed to address the lack of on-orbit remote sensing images. Specifically, our RS-ISP introduces two novel designs to ensure the distribution consistency between the generated images and the real images. 1) The first is a novel pipeline for modeling the physical process of noise production during image capture using specific sensors. 2) The second is the design of a detection-oriented image harmonization model. Similar to the existing design, our model first produces coarse synthetic images by copy-paste operation, on which the proposed harmonization process is used to reduce the variation of the pasted foreground and background. By incorporating these two designs into a unified framework, our RS-ISP is designed and used to produce large-scale synthetic images used to train the object detection model for detecting ships in remote sensing images. Comparative experiments demonstrated that RS-ISP increased the AP@.50 from 0.148 to 0.498 for the ship detection task. Code will be publicly available.

I Spy You

This paper presents iSpyU, a system that shows the feasibility of recognition of natural speech content played on a phone during conference calls (Skype, Zoom, etc) using a fusion of motion sensors such as accelerometer and gyroscope. While microphones require permissions from the user to be accessible by an app developer, the motion sensors are zero-permission sensors, thus accessible by a developer without alerting the user. This allows a malicious app to potentially eavesdrop on sensitive speech content played by the user's phone. In designing the attack, iSpyU tackles a number of technical challenges including: (i) Low sampling rate of motion sensors (500 Hz in comparison to 44 kHz for a microphone). (ii) Lack of availability of large-scale training datasets to train models for Automatic Speech Recognition (ASR) with motion sensors. iSpyU systematically addresses these challenges by a combination of techniques in synthetic training data generation, ASR modeling, and domain adaptation. Extensive measurement studies on modern smartphones show a word level accuracy of 53.3 - 59.9% over a dictionary of 2000-10000 words, and a character level accuracy of 70.0 - 74.8%. We believe such levels of accuracy poses a significant threat when viewed from a privacy perspective.

Generation of training data for named entity recognition of artworks

As machine learning techniques are being increasingly employed for text processing tasks, the need for training data has become a major bottleneck for their application. Manual generation of large scale training datasets tailored to each task is a time consuming and expensive process, which necessitates their automated generation. In this work, we turn our attention towards creation of training datasets for named entity recognition (NER) in the context of the cultural heritage domain. NER plays an important role in many natural language processing systems. Most NER systems are typically limited to a few common named entity types, such as person, location, and organization. However, for cultural heritage resources, such as digitized art archives, the recognition of fine-grained entity types such as titles of artworks is of high importance. Current state of the art tools are unable to adequately identify artwork titles due to unavailability of relevant training datasets. We analyse the particular difficulties presented by this domain and motivate the need for quality annotations to train machine learning models for identification of artwork titles. We present a framework with heuristic based approach to create high-quality training data by leveraging existing cultural heritage resources from knowledge bases such as Wikidata. Experimental evaluation shows significant improvement over the baseline for NER performance for artwork titles when models are trained on the dataset generated using our framework.

Automatic vision-based calculation of excavator earthmoving productivity using zero-shot learning activity recognition

Recently, vision-based methods have been widely used to analyze the construction productivity based on onsite videos owing to their low cost, simple deployment, and easy maintenance. However, existing vision-based methods rely on supervised learning for activity recognition, which is computationally intensive owing to the necessity of labeling large-scale training datasets. To address this problem, this paper describes a vision-based method for automatically analyzing excavators' productivities in earthmoving tasks by adopting zero-shot learning for activity recognition. The proposed method can identify activities of general construction machines (e.g., excavators and loaders) without pre-training or fine-tuning. To verify the feasibility, the proposed method has been tested on videos recorded from real construction sites. The accuracy values for activity recognition and productivity evaluation are 86% and 87.8%, respectively.

Machine Learning on Cloud With Blockchain: A Secure, Verifiable and Fair Approach to Outsource the Linear Regression

Linear Regression (LR) is a classical machine learning algorithm which has many applications in the cyber physical social systems (CPSS) to shape and simplify the way we live, work, and communicate. This paper focuses on the data analysis for CPSS when the Linear Regression is applied. The training process of LR is time-consuming since it involves complex matrix operations, especially when it gets a large scale training dataset In the CPSS. Thus, how to enable devices to efficiently perform the training process of the Linear Regression is of significant importance. To address this issue, in this paper, we present a secure, verifiable and fair approach to outsource LR to an untrustworthy cloud-server. In the proposed scheme, computation inputs/outputs are obscured so that the privacy of sensitive information is protected against cloud-server. Meanwhile, computation result from cloud-server is verifiable. Also, fairness is guaranteed by the blockchain, which ensures that the cloud gets paid only if he correctly performed the outsourced workload. Based on the presented approach, we exploited the fair, secure outsourcing system on the Ethereum blockchain. We analysed our presented scheme on theoretical and experimental, all of which indicate that the presented scheme is valid, secure and efficient.