Reviewerly/exHarmony huggingface dataset:
Generative Adversarial Attacks on Fingerprint Recognition Systems

As a recent distribution of mobile and fintech systems, user authentication via biometric recognition has been widely used. Specifically, deep learning-based fingerprint recognition methods became popular due to their high accuracy in fingerprint liveness detection. However, existing deep learning-based approaches contain inherent vulnerabilities such as adversarial attack. In this paper, we propose a new method for generative adversarial attack on fingerprint recognition systems. Our method generates fingerprint images which effectively break existing deep learning-based fingerprint recognition systems while maintaining real-looking appearance of target fingerprints.
Deep discriminative image feature learning for cross-modal semantics understanding

Deep hashing image feature learning methods has attracted attentions of cross-modal semantics understanding researchers due to its low storage costs and efficient query speed. Usually heterogeneous cross-modal data are embedded in semantic space, then they may be converted to their own corresponding binary hash code through the learning hash function. But when mapping heterogeneous data to common Hamming space, some works ignore joint cross correlation that contributes to interactively explore the latent semantic information between different modalities, resulting in sub-optimal feature. To address the above issues, we present a novel deep discriminative feature learning methods for cross-modal semantics understanding named Deep Discriminant Semantic Joint Hashing(DDSJH). In order to maximize joint cross correlation, we use mutual information which contribute to semantics understanding. Features in semantic space are exchanged with the pairwise features to calculate loss between semantic space and Hamming space. Thus, the corresponding information in cross-modal data are collaboratively utilized to realize exploration of underlying mutual joint semantic correlation. Hash codes of similar categories are as close as possible, but it should be considered that hash codes of different categories data should be as discriminative as possible. So we harness linear classifier to learn discriminative hash code. Extensive experiments on two image–text cross-modal datasets show that our proposed approach achieves better accuracy than several state-of-the-art methods.
A Static Hand Gesture Based Sign Language Recognition System using Convolutional Neural Networks

Sign language (SL) is the basic means by which deaf and dumb person communicate. However, these SLs are not known to the majority of healthy people. In our paper, we have developed a SL interpreter that takes the input sign gesture and gives the output in a display device. We have used Convolutional Neural Networks (CNNs) to train the system with a given database. For our work, we have used Indian SL database, consisting of 26 alphabets along with 10 digits. We have applied Histogram BackProjection technique for segmentation of images. These datasets are then formed into classes which are fed to a CNN for training and testing. After training we find out the testing accuracy of 99.89% and validation accuracy of 99.85% at 5 epoch. After this, we test the system with real time input and find out the result in the display device.
An Optimal Footprint Based Coverage Planning for Hydro Blasting Robots

The hydro blasting of metallic surfaces is an essential maintenance task in various industrial sites. Its requirement of a considerable labour force and time, calls for automating the hydro blasting jobs through mobile robots. A hydro blasting robot should be able to cover the required area for a successful implementation. If a conventional robot footprint is chosen, the blasting may become inefficient, even though the concerned area is completely covered. In this work, the blasting arm’s sweeping angle is chosen as the robot’s footprint for hydro blasting task, and a multi-objective optimization-based framework is proposed to compute the optimal sweeping arc. The genetic algorithm (GA) methodology is exploited to compute the optimal footprint, which minimizes the blasting time and energy simultaneously. Multiple numerical simulations are performed to show the effectiveness of the proposed approach. Moreover, the strategy is successfully implemented on our hydro blasting robot named Hornbill, and the efficacy of the proposed approach is validated through experimental trials.
Quantum-Resistant Lightweight Authentication and Key Agreement Protocol for Fog-Based Microgrids

A microgrid is a flexible and localized grid that can disconnect from the main grid and operate independently on the traditional and/or renewable resource.It also can work with the main grid, and even provide power to the main grid when it has excess power to help relieve the pressure of energy.The flow of power between main grid and microgrid is monitored by smart meter, which kept the end user's privacy information and his/her energy usage in real time.Hence, ensuring the privacy and security of these information is a considerable challenge.In this paper, we investigate the relationship between microgrids and the main grid, and design a quantum-resistant lightweight authentication and key agreement (AKA) protocol based on fog computing.The proposed protocol provides the mutual authentication and generates a session key between a smart meter and a fog node.More importantly, it can resist against quantum computing attacks, as the security of our scheme relies on the hardness of the learning parity with noise (LPN) problem, an NP-complete problem.Finally, we evaluate the performance of our proposed protocol and prove that our protocol is more lightweight and efficient.
Comparative Convolutional Dynamic Multi-Attention Recommendation Model

Recently, an attention mechanism has been used to help recommender systems grasp user interests more accurately. It focuses on their pivotal interests from a psychology perspective. However, most current studies based on it only focus on part of user interests; they have not mined user preferences thoroughly. To address the above problem, we propose a novel recommendation model: comparative convolutional dynamic multi-attention (CCDMA). This model provides a more accurate approach to represent user and item features and uses multi-attention-based convolutional neural networks to extract user and item latent feature vectors dynamically. The multi-attention mechanism considers both self-attention and cross-attention. Self-attention refers to the internal attention within users and items; cross-attention is the mutual attention between users and items. Moreover, we propose an optimized comparative learning framework that can mine the ternary relationships between one user and a pair of items, focusing on their relative relationship and the internal link between a pair of items. Extensive experiments on several real-world data sets show that the CCDMA model significantly outperforms state-of-the-art baselines in terms of different evaluation metrics.
FPGA Based Blockchain System for Industrial IoT

Industrial IoT (IIoT) is critical for industrial infrastructure modernization and digitalization. Therefore, it is of utmost importance to provide adequate protection of the IIoT system. A modern IIoT system usually consists of a large number of devices that are deployed in multiple locations and owned/managed by different entities who do not fully trust each other. These features make it harder to manage the system in a coherent manner and utilize existing security mechanisms to offer adequate protection. The emerging blockchain technology provides a powerful tool for IIoT system management and protection because the IIoT nature of distributed deployment and involvement of multiple stakeholders fits the design philosophy of blockchain well. Most existing blockchain construction mechanisms are not scalable enough and too heavy for an IIoT system. One promising way to overcome these limitations is utilizing hardware based trusted execution environment (TEE) in blockchain construction. However, most of the existing works on this direction do not consider the characteristics of IIoT devices (e.g., fixed functionality and limited supply) and face several limitations when they are applied for IIoT system management and protection, such as high energy consumption, single root-of-trust, and low decentralization level. To mitigate these challenges, we propose a novel field programmable gate array (FPGA) based blockchain system. It leverages the FPGA to build a simple but efficient TEE for IIoT devices, and removes the single root-of-trust by allowing all stakeholders to participate in the management of the devices. The FPGA based blockchain system shifts the computation/storage intensive part of blockchain management to more powerful computers but still involves the IIoT devices in the block construction to achieve a high level of decentralization. We implement the major FPGA components of the design and evaluate the performance of the whole system with a simulation tool to demonstrate its feasibility for IIoT applications.
Performance Analysis of Kalman Filter in Computed Tomography Thorax for Image Denoising

: Medical image processing is a very important field of study due to its large number of applications in human life. For diagnosis of any disease, several methods of medical image acquisition are possible such as Ultrasound (US), Magnetic Resonance Imaging (MRI) or Computed Tomography (CT). Depending upon the type of image acquisition, different types of noise can occur. Background: The most common types of noises in medical images are Gaussian noise, Speckle noise, Poisson noise, Rician noise and Salt &amp; Pepper noise. The related noise models and distributions are described in this paper. We compare several filtering methods for denoising the mentioned types of noise. Objective: The main purpose of this paper is to compare well-known filtering methods such as arithmetic mean, median and enhanced lee filter with only rarely used filtering methods like Kalman filter as well as with relative new methods like Non-Local Means (NLM) filter. Methods: To compare these different filtering methods, we use comparative parameters like Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Mean Structural Similarity (MSSIM), Edge Preservation Index (EPI) and the Universal Image Quality Index (UIQI). Results: The processed images are shown for a specific noise density and noise variance. We show that the Kalman filter performs better than Mean, Median and Enhanced Lee filter for removing Gaussian, Speckle, Poisson and Rician noise. Conclusion: Experimental results show that the Kalman filter provides better results as compared to other methods. It could be also a good alternative to NLM filter due to almost equal results and lower computation time.
A Multi-modal Machine Learning Approach and Toolkit to Automate Recognition of Early Stages of Dementia Among British Sign Language Users

The ageing population trend is correlated with an increased prevalence of acquired cognitive impairments such as dementia. Although there is no cure for dementia, a timely diagnosis helps in obtaining necessary support and appropriate medication. Researchers are working urgently to develop effective technological tools that can help doctors undertake early identification of cognitive disorder. In particular, screening for dementia in ageing Deaf signers of British Sign Language (BSL) poses additional challenges as the diagnostic process is bound up with conditions such as quality and availability of interpreters, as well as appropriate questionnaires and cognitive tests. On the other hand, deep learning based approaches for image and video analysis and understanding are promising, particularly the adoption of Convolutional Neural Network (CNN), which require large amounts of training data. In this paper, however, we demonstrate novelty in the following way: a) a multi-modal machine learning based automatic recognition toolkit for early stages of dementia among BSL users in that features from several parts of the body contributing to the sign envelope, e.g., hand-arm movements and facial expressions, are combined, b) universality in that it is possible to apply our technique to users of any sign language, since it is language independent, c) given the trade-off between complexity and accuracy of machine learning (ML) prediction models as well as the limited amount of training and testing data being available, we show that our approach is not over-fitted and has the potential to scale up.
The Round Complexity of Perfect MPC with Active Security and Optimal Resiliency

In STOC 1988, Ben-Or, Goldwasser, and Wigderson (BGW) established an important milestone in the fields of cryptography and distributed computing by showing that every functionality can be computed with perfect (information-theoretic and error-free) security at the presence of an active (aka Byzantine) rushing adversary that controls up to n/3 of the parties. We study the round complexity of general secure multiparty computation in the BGW model. Our main result shows that every functionality can be realized in only four rounds of interaction, and that some functionalities cannot be computed in three rounds. This completely settles the round-complexity of perfect actively-secure optimally-resilient MPC, resolving a long line of research. Our lower-bound is based on a novel round-reduction technique that allows us to lift existing three-round lower-bounds for verifiable secret sharing to four-round lower-bounds for general MPC. To prove the upper-bound, we develop new round-efficient protocols for computing degree-2 functionalities over large fields, and establish the completeness of such functionalities. The latter result extends the recent completeness theorem of Applebaum, Brakerski and Tsabary (TCC 2018, Eurocrypt 2019) that was limited to the binary field.
DenseNet model with RAdam optimization algorithm for cancer image classification

Application of deep learning algorithms to medical images recognition can improve diagnostic accuracy and efficiency. In recent years, computer-aided diagnosis has attracted the attention of a large number of researchers. The introduction of image processing in medicine is a important method to reduce unnecessary manual diagnosis costs and promote disease classification and detection. In this paper, we propose a novel method for metastatic cancer image classification which uses Densely Connected Convolutional Networks, Rectified Adam optimization algorithm, and focal loss. DenseNet can effectively capture the important features hidden in images. And RAdam optimization algorithm Radam is robust for model training. Our dataset is provided by the Kaggle competition, which is the modified version of the PatchCamelyon (PCam) benchmark dataset. The dataset packs the clinically relevant problem of metastasis detection into a straight-forward binary image classification problem. The experiments shows our approach can effectively identify metastatic cancer in small image patches which are taken from larger digital pathology scans on the dataset. And experimental results indicate that our proposed model is significantly better than Resnet34, Resnet50, Vgg19. The effectiveness of the DenseNet Block, Rectified Adam, focal loss is also verified.
Automatic Extraction of Segments from Resumes using Machine Learning

Online recruitment systems or automatic resume processing systems are becoming more popular because it saves time for both employers and job seekers. Manually processing these resumes and fitting to several job specifications is a difficult task. Due to the increased amount of data, it is a big challenge to effectively analyze each resume based on various parameters like experience, skill set, etc. Processing, extracting information and reviewing these applications automatically would save time and money. Automatic data extraction, focused primarily on skillset, experience and education from a resume. So, it extremely helpful to map the appropriate resume for the right job description. In this research study, we propose a system that uses multi-level classification techniques to automatically extract detailed segment information like skillset, experience and education from resume based on specific parameters. We have achieved state-of-the-art accuracy in the segment of the resumes to identify skill sets.
Transfer learning and subword sampling for asymmetric-resource one-to-many neural translation

There are several approaches for improving neural machine translation for low-resource languages: monolingual data can be exploited via pretraining or data augmentation; parallel corpora on related language pairs can be used via parameter sharing or transfer learning in multilingual models; subword segmentation and regularization techniques can be applied to ensure high coverage of the vocabulary. We review these approaches in the context of an asymmetric-resource one-to-many translation task, in which the pair of target languages are related, with one being a very low-resource and the other a higher-resource language. We test various methods on three artificially restricted translation tasks—English to Estonian (low-resource) and Finnish (high-resource), English to Slovak and Czech, English to Danish and Swedish—and one real-world task, Norwegian to North Sámi and Finnish. The experiments show positive effects especially for scheduled multi-task learning, denoising autoencoder, and subword sampling.
Calibration of geometrical aberration in transmitted wavefront testing of refractive optics with deflectometry

Deflectometry, with its noticeable advantages such as simple structure, large dynamic range, and high accuracy comparable to interferometry, has been one of the powerful metrological techniques for optical surfaces in recent years. In the “null” deflectometric transmitted wavefront testing of refractive optics, ray tracing of the test system model is required, in which both the miscalibration of system geometrical parameters and optical tolerances on tested optics could introduce significant geometrical aberrations in the testing results. In this paper, the geometrical aberration introduced by a system modeling error in the transmitted wavefront testing is discussed. Besides, a calibration method based on polynomial optimization of geometrical aberration is presented for the geometrical aberration calibration. Both simulation and experiment have been performed to validate the feasibility of the proposed calibration method. The proposed method can calibrate the optical tolerances on tested optics effectively, and it is feasible even with a large geometric error, providing a viable way to address the uncertainty in system modeling in transmitted wavefront testing of freeform refractive optics with large dynamic range.
Radix-2<i> <sup>w</sup> </i> Arithmetic for Scalar Multiplication in Elliptic Curve Cryptography

Elliptic curve scalar multiplication k. P, where k is a nonnegative constant and P is a point on the elliptic curve, requires two distinct operations: addition (ADD) and doubling (DBL). To reduce the number of ADDs without increasing the number of DBLs, a recoding of k with fewer nonzero digits is necessary. Based on Radix- 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sup> arithmetic, we introduce a principled w-bit windowing method where the properties of speed, memory, and security are described by exact analytic formulas as proof of superiority. Contrary to existing windowing algorithms, to minimize the number of ADDs the window size ( w) is guided by an optimum depending on the bit-length ( l) of the scalar k. The number of required precomputations is minimal regarding the value of w. The proposed method recodes the binary string k and evaluates the multiplication on-the-fly from right-to-left and left-to-right, likewise. Radix- 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sup> method is very easy to be used and highly reconfigurable, allowing speed-memory and speed-security trade-offs to satisfy different crypto-system constraints. Furthermore, the method shows a high resilience to side-channel attacks based on power, timing, and statistical analysis. All Radix- 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sup> properties are confronted to standard windowing methods' through an in-depth analysis of the complexities. An overall comparison is made via NIST-recommended GF( 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</sup> ) finite fields.
Diversified Mutual Learning for Deep Metric Learning

Mutual learning is an ensemble training strategy to improve generalization by transferring individual knowledge to each other while simultaneously training multiple models. In this work, we propose an effective mutual learning method for deep metric learning, called Diversified Mutual Metric Learning, which enhances embedding models with diversified mutual learning. We transfer relational knowledge for deep metric learning by leveraging three kinds of diversities in mutual learning: (1) model diversity from different initializations of models (2) temporal diversity from different frequencies of parameter update, and (3) view diversity from different augmentations of inputs. Our method is particularly adequate for inductive transfer learning at the lack of large-scale data, where the embedding model is initialized with a pretrained model and then fine-tuned on a target dataset. Extensive experiments show that our method significantly improves individual models as well as their ensemble. Finally, the proposed method with a conventional triplet loss achieves the state-of-the-art performance of Recall@1 on standard datasets: 69.9 on CUB-200–2011 and 89.1 on CARS-196.
Understanding experience complexity in a smart learning journey

This paper contributes to a pedagogical model for smart learning by establishing a framework of some considerations based on learner experience of smart learning journeys. Phenomenography is used to investigate variation of learner experience in smart learning journeys. Learners participate in 'Literary London', situated in London, UK, and 'Malta Democracy', situated in Valletta, Malta. An inclusive relational hierarchy of experience complexity is demonstrated with vertical, horizontal and diagonal relationships between four categories of experience variation for a smart learning journey. A pedagogical relevance structure for smart learning is discussed, supporting connectivist-inspired participatory pedagogies for smart learning environments. Sample participants consist of Education degree university students, with one other discipline represented (English Literature and Creative Writing). Various levels of study, cultural and international backgrounds are represented. Understanding learner experience of these kinds of activities may help to support today's growing culture of learning cities, to "promote lifelong learning opportunities for all" (Unesco SDG4), within a context of the European Commission 2018 Digital Competence Framework for Citizens.
Vehicle Detection at Night Time

Recent growth in deep learning has opened up many opportunities for the problem of vehicle detection. Detecting objects in poor visibility is catching scientists attention. In this study, we choose night as the challenge. We conducted training and evaluation of the YOLOv4 method in combination with image preprocessing methods: gamma, CycleGAN's night-day conversion model was retrained on DETRAC data. Night dataset (26,168 images) extracted from DETRAC were used. The results showed that the training on the primitive data is highly effective (64.51%mAP) compared to the image changed from night to day, particularly on the car class (92%AP), bus (91%AP). This is the premise for the next studies and the basis to develop intelligent traffic monitoring systems.