Reviewerly/exHarmony huggingface dataset:
Programmable photon pair source

Photon pairs produced by the pulse-pumped nonlinear parametric processes have been a workhorse of quantum information science. Engineering the spectral property of the photon pairs is crucial in practical applications. In this article, we demonstrate a programmable photon pair source by exploiting a two-stage nonlinear interferometer with a phase-control device. The phase-control device introduces phase shifts by a programmable phase function that can be arbitrarily defined. With a properly designed phase function, the output spectrum of the source can be freely customized and changed without replacing any hardware component in the system. In addition to demonstrating the generation of photon pairs with factorable, positively-correlated, and negatively-correlated spectra, respectively, we show that the output of the source can be tailored into multi-channel spectrally factorable photon pairs without sacrificing efficiency. Such a source, having the ability to modify the spectrum of the photon pairs at will according to the chosen application, is a powerful tool for quantum information science.
The Role of Communication Time in the Convergence of Federated Edge Learning

Federated Edge Learning (FEL) enables a massive number of edge devices (e.g. smart phones) to train machine learning models collaboratively. Due to the inherent unreliability of participating edge devices and the unpredictable network conditions, the final model accuracy is largely determined by the communication time between edge devices and the parameter server (PS). However, there exists very limited work to quantify the influence of the communication time in FEL and show how to minimize its negative impacts from a theoretic perspective. In this paper, we are among the first to develop a formal model of the communication time in FEL and its influence on the final model accuracy. In our work, the set of edge devices involved in each global iteration is defined as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ECP (Engaged Client Pool)</i> . We model the communication time cost as a function with respect to the response time distribution of individual devices and the ECP size. By incorporating communication time cost into the convergence rate analysis, we propose the ECPA and H-ECPA algorithms to automatically adjust the size of the ECP so as to maximize the model accuracy in both homogeneous and heterogeneous networks. We also analyze how the tail shape of response time affects the convergence rate, and prove that the heavy tail shape can significantly lower the model accuracy. Finally, we conduct extensive experiments with real datasets, and the results confirm the correctness of our analysis and demonstrate the superiority of our proposed algorithms.
Dream Net: a privacy preserving continual leaming model for face emotion recognition

Continual learning is a growing challenge of artificial intelligence. Among algorithms alleviating catastrophic forgetting that have been developed in the past years, only few studies were focused on face emotion recognition. In parallel, the field of emotion recognition raised the ethical issue of privacy preserving. This paper presents Dream Net, a privacy preserving continual learning model for face emotion recognition. Using a pseudo-rehearsal approach, this model alleviates catastrophic forgetting by capturing the mapping function of a trained network without storing examples of the learned knowledge. We evaluated Dream Net on the Fer-2013 database and obtained an average accuracy of 45% ± 2 at the end of incremental learning of all classes compare to 16% ± 0 without any continual learning model.
Deep Convolutional Neural Network with ResNet-50 Learning algorithm for Copy-Move Forgery Detection

One of the most well-known kind of image forgery is copy-move forgery (CMF). In CMF, one segment of an image is copied and pasted into another segment of an identical image in order to hide crucial data or include new data in the image. For that reason, falsification revealing methods are necessary to recognize altered territories. A new, quick, and capable copy-move forgery detection (CMFD) method is constructed and tested in this study. To classify CMF in a rapid and reliable manner, the proposed method utilizes a Deep Convolutional Neural Network (DCNN) that integrates Residual network framework with 50 layers (ResNet-50). The residual network uses the concept of skip connection which adds the original input to the output of the convolutional layer and will remove the problem of vanishing and exploding gradient that occurs in the traditional Convolutional Neural Network (CNN) model. The accuracy and logarithmic loss (LogLoss), which is a function of cross-entropy, have been employed to assess the performance and efficiency of the algorithm. The results are evaluated on the CoMoFoD dataset after dividing it into training and test set. According to the results, the proposed algorithm achieved better accuracy and reduced the LogLoss function at 100 epochs.
Jump-time unraveling of Markovian open quantum systems

We introduce jump-time unraveling as a distinct description of open quantum systems. As our starting point, we consider quantum jump trajectories, which emerge, physically, from continuous quantum measurements, or, formally, from the unraveling of Markovian quantum master equations. If the stochastically evolving quantum trajectories are ensemble-averaged at specific times, the resulting quantum states are solutions to the associated quantum master equation. We demonstrate that quantum trajectories can also be ensemble-averaged at specific jump counts. The resulting jump-time-averaged quantum states are then solutions to a discrete, deterministic evolution equation, with time replaced by the jump count. This jump-time evolution represents a trace-preserving quantum dynamical map if and only if the associated open system does not exhibit dark states. In the presence of dark states, on the other hand, jump-time-averaged states may decay into the dark states and the jump-time evolution may eventually terminate. Jump-time-averaged quantum states and the associated jump-time evolution are operationally accessible in continuous measurement schemes, when quantum jumps are detected and used to trigger the readout measurements. We illustrate the jump-time evolution with the examples of a two-level system undergoing relaxation or dephasing, a damped harmonic oscillator, and a free particle exposed to collisional decoherence.
Image Analysis of Cells Acute Lymphoblastic Leukemia Using Ensemble Learning of Deep Bagging

Leukemia (ALL) is a form of blood cancer that claimed the lives of 111,000 people worldwide in 2015. Recent advances in deep learning (DL) techniques have made it possible to diagnose Everything using microscopic image analysis. However, as with most medical issues, there are deficiency training samples and visual flaws that distinguish leukaemia from normal. The body is made up of cells, making image analysis a difficult task. To address the aforementioned issues, an augmented image enhanced bagging ensemble learning with elaborately developed training subsets was proposed. The preliminary and final Fl-scores are 0.85 and 0.89, respectively, in the calculated tests. In the Classification of Normal and Malignant WBCs contest, we used our ensemble model predictions and placed in the top ten percent. Our findings show that using Deep learning-based techniques to analyse leukaemia (ALL) cells images can be efficient.
Kernel Quantization for Efficient Network Compression

This paper presents a novel network compression framework, Kernel Quantization (KQ), targeting to efficiently convert any pre-trained full-precision convolutional neural network (CNN) model into a low-precision version without significant performance loss.Unlike existing methods struggling with weight bit-length, KQ has the potential in improving the compression ratio by considering the convolution kernel as the quantization unit.Inspired by the evolution from weight pruning to filter pruning, we propose to quantize in both kernel and weight level.Instead of representing each weight parameter with a low-bit index, we learn a kernel codebook and replace all kernels in the convolution layer with corresponding low-bit indexes.Thus, KQ can represent the weight tensor in the convolution layer with low-bit indexes and a kernel codebook with limited size, which enables KQ to achieve significant compression ratio.Then, we conduct a 6-bit parameter quantization on the kernel codebook to further reduce redundancy.Extensive experiments on the ImageNet classification task prove that KQ needs 1.05 and 1.62 bits on average in VGG and ResNet18, respectively, to represent each parameter in the convolution layer and achieves the state-of-the-art compression ratio with little accuracy loss.
CUP: Cluster Pruning for Compressing Deep Neural Networks

We propose CUP, a new method for compressing and accelerating deep neural networks. At its core, CUP achieves compression by clustering and pruning similar filters in each layer. For clustering, CUP uses hierarchical clustering which allows for an elegant parameterization of model capacity through a single hyper-parameter t. We observe that by increasing t, CUP can dynamically reduce model capacity through non-uniform layer-wise pruning leading to two advantages. First, CUP can effectively compress a model to within the desired compute budget through a simple line-search on t. Second, through a simple extension, CUP can obtain the pruned model in a single training pass leading to large savings in training time. On Imagenet, CUP leads to a 2.47× FLOPS reduction on Resnet-50 with less than 1% drop in top-5 accuracy. Notably, in the retrain-free setting, CUP-RF saves over 10 hours of training time on 3 GPUs, in comparison to state-of-the-art methods. The code for CUP is open sourced <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
HRM-CenterNet: A High-Resolution Real-time Fittings Detection Method

Most successful fittings detectors are anchor-based, which is challenging to meet the lightweight and real-time requirements of the edge computing system. We propose a high-resolution real-time network HRM-CenterNet. Firstly, the lightweight MobileNetV3 is used to extract multi-level features from images. Then, to improve the resolution of the feature maps and reduce the spatial semantic information loss during the image downsampling process, a high-resolution feature fusion network based on iterative aggregation is introduced. Finally, we conduct experiments on the PASCAL VOC dataset and fittings dataset. The results show that HRM-CenterNet improves accuracy as well as robustness, and meets the performance requirements of real-time edge detection.
Image Steganography Using Steg with AES and LSB

Nowadays mobile phone becoming one of the most popular communication system. Information shared through this medium is a very sensitive to the users. Hence it is highly needed to secure the message from the intruders. This paper proposed an android based secured system named Steg! developed by combining the cryptography and steganography. Here the algorithm used for cryptography is Advanced Encryption Standard (AES) and Least Significant Bit is used for the steganography. This hybrid approach increases the level of secretion of information from unauthorized the access by encrypting the message and hiding into the image. The application helps the user to hide/unhide the text to/from the image. The proposed system above is proven to be powerful and robust than those system which implements cryptography and steganography alone.
Mining High Utility Subgraphs

High utility pattern mining discovers more realistic and important knowledge than the traditional frequent pattern mining by considering non-binary occurrences of items inside transactions/sequences and different weight values for different items. Research has been done for high utility itemset and sequence mining. However, graphs have ubiquitous applications, including social networks, road networks, webpage access networks, citation networks, and biological protein-protein interaction networks. Moreover, it is more challenging to devise a graph mining approach than the traditional itemset/sequence mining due to the structural complexity of graphs. In this work, we propose a generic framework for utility-based graph pattern mining. A complete algorithm is presented for high utility subgraph mining and to the best of our knowledge, we are the first to develop a complete framework for high utility subgraph mining. The number of candidates generated in the search space grows exponentially for weighted or utility-based pattern mining. To address this problem, we introduce a pruning technique for effective pruning of the search space. We conducted experiments on various datasets to analyze the performance of the algorithm. Our experimental results show the effectiveness of our proposed algorithm to extract high utility subgraph patterns.
Seismic Fault Analysis Using Graph Signal Regularization

Graph signal processing (GSP) is a process that deals with the processing of data that graphs can support. The concepts of digital signal processing (DSP) are mapped to define the processing techniques for signals on a graph. Furthermore, a seismic fault is nothing but a discontinuity in a volume of seismic data across which there has been significant seismic displacement. In this paper, we propose an approach to interpret the presence of a fault in seismic data using GSP. First, we extract 2D patches of data from a seismic volume and present the seismic data in the graph domain by considering a seismic patch as a vector space node. K-nearest neighbour graph is used to present data on graphs. Next, we do the fault classification by implementing a graph signal regularization technique that uses the graph shift matrix.
Blockchain Security Detection Condition Light Module

The commercial web application can benefit from Blockchain technology as it provides a high level of security. Blockchain technology has demonstrated its effectiveness in a multitude of fields, including online banking, supply chain management, machine learning, international payments, the Internet of Things, customer relationship management, and many others.The blockchain has the potential to be employed in the disruption detection area, as it can provide data storage integrity and process transparency. In this paper we can handle the condition of cybersecurity threats according to the propose blockchain detection condition model process.The aim of the model is to identify cyber threats and potential accidents using interruption detection systems, which are commonly used in computer networks. Collaborative interruption detection networks, which enable interruption detection system nodes to share data with each other, have been designed to improve the detection capabilities of a single interruption detection system.Current detection architecture is still plagued by data and trust management issues, which could jeopardize the efficacy of such systems. The blockchain technology has gained widespread adoption in the financial industry, mainly through the use of cryptocurrencies. Keywords: Interruption Detection Systems, Blockchain, Cryptocurrencies, Cybersecurity
Locally Weighted Ensemble-Detection-Based Adaptive Random Forest Classifier for Sensor-Based Online Activity Recognition for Multiple Residents

In recent years, various approaches for multiresident human activity recognition (HAR) in a smart indoor environment have been developed and improved along with the rapid development of sensors and AI technologies. Research in data stream-based online learning (OL) for multiresident HAR is relatively new and a majority of the existing works have been developed based on training batches of data that cannot recognize real-time activities. To address the challenges of OL for multiresident HAR, we propose a novel OL architecture based on a locally weighted ensemble detection-based adaptive random forest (LED-ARF) classifier. We conduct a comprehensive performance comparison of eight famous OL classification techniques and our LED-ARF method. The comparison is evaluated based on the two benchmarking CASAS and ARAS data sets. Our experimental results show that LED-ARF achieves the best performance with the highest robustness for online multiresident HAR.
Adaptive Coding and Ordered-Index Extended Scrambling Based RDH in Encrypted Images

Reversible data hiding in encrypted images (RDHEI) technique can be used to realize privacy protection and management in the image outsourcing scenario. Most existing RDHEI schemes focus on increasing the maximum embedding rate (Max-ER), but not paying much attention to the security improvement under various attacks. In this paper, a RDHEI method based on the adaptive bit-plane (ABP) coding is proposed to improve the Max-ER. The order-index extended scrambling (OIES) encryption scheme is also developed to strengthen the RDHEI's ability of thwarting various attacks. The effectiveness of ABP coding is achieved by proper selections of the threshold. The OIES enables the design of a novel scramble-key (SK) generation method to greatly reduce the probability of generating the same SK by the same user-key. This significantly improves the ability of resisting various attacks in that the attack on the scrambling encryption is mainly via the SK rather than the user-key estimation. Analysis shows that the probability of OIES obtaining the same SK is reduced from 1.0 to 0.01 for different images and to 1/2 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup>α</sup></i> for the same image. Simulation results demonstrate that the proposed ABP coding and OIES schemes outperform the state-of-the-art RDHEI algorithms in terms of the Max-ER and ability against various attacks.
Differential Privacy in Social Networks Using Multi-Armed Bandit

There has been an exponential growth over the years in the number of users connected to social networks. This has spurred research interest in social networks to ensure the privacy of users. From a theoretical standpoint, a social network is modeled as a directed graph network and interactions among agents in the directed graph network can be analyzed with non-Bayesian learning and online learning strategies. The goal of the agents is to learn the underlying time-varying true state of the network through repeated cooperative interaction among themselves. To overcome privacy challenges in social networks, recent research works include differential privacy in the social network analysis to guarantee the privacy of shared information among the agents. However, the common online learning strategy adopted in most existing work is the stochastic multi-armed bandit approach which assumes that the loss distribution is independent and identically distributed. This does not account for the arbitrariness of the time-varying true state in the social network. Therefore, this paper proposes a tougher but realistic setting that removes the restriction on the loss distribution. Two non-stochastic multi-armed bandit algorithms are proposed. The first algorithm uses the Laplace mechanism to guarantee differential privacy against a third-party intruder. The second algorithm uses the Laplace mechanism to guarantee differential privacy against both a third-party intruder and any spying agent in the network. The simulation results show that the agents’ beliefs converge to the most dominant true state among the sequence of arbitrarily time-varying true states over the time horizon. The speed of convergence comes as a trade-off with privacy. Regret bounds are obtained for the proposed algorithms and compared to the non-private algorithm in the literature.
Stereoscopic Video Quality Assessment with Multi-level Binocular Fusion Network Considering Disparity and Multi-scale Information

Stereoscopic video quality assessment (SVQA) is of great importance to promote the development of the stereoscopic video industry. In this paper, we propose a three-branch multi-level binocular fusion convolutional neural network (MBFNet) which is highly consistent with human visual perception. Our network mainly includes three innovative structures. Firstly, we construct a multi-scale cross-dimension attention module (MSCAM) on the left and right branches to capture more critical semantic information. Then, we design a multi-level binocular fusion unit (MBFU) to fuse the features from left and right branches adaptively. Besides, a disparity compensation branch (DCB) containing an enhancement unit (EU) is added to provide disparity feature. The experimental results show that the proposed method is superior to other existing SVQA methods with state-of-the-art performance.
HCI curricula for sustainable innovation

research-article Open Access Share on HCI curricula for sustainable innovation: the humanitarian focus at Amrita Vishwa Vidyapeetham Authors: Maneesha Vinodini Ramesh Amrita Vishwa Vidyapeetham Amrita Vishwa VidyapeethamView Profile , Alexander Muir Amrita Vishwa Vidyapeetham Amrita Vishwa VidyapeethamView Profile , Krishna Nandanan Amrita Vishwa Vidyapeetham Amrita Vishwa VidyapeethamView Profile , Rao R. Bhavani Amrita Vishwa Vidyapeetham Amrita Vishwa VidyapeethamView Profile , Renjith Mohan Amrita Vishwa Vidyapeetham Amrita Vishwa VidyapeethamView Profile Authors Info & Claims InteractionsVolume 29Issue 1January - February 2022 pp 54–57https://doi.org/10.1145/3506564Online:06 January 2022Publication History 0citation398DownloadsMetricsTotal Citations0Total Downloads398Last 12 Months398Last 6 weeks52 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Limited Discriminator GAN using explainable AI model for overfitting problem

Data-driven learning is the most representative deep learning method. Generative adversarial networks (GANs) are designed to generate sufficient data to support such learning. The learning process of GAN models typically trains a generator and discriminator in turn. However, overfitting problems occur when the discriminator depends excessively on the training data. When this problem persists, the image created by the generator shows a similar appearance to the learning image. Images similar to learning images eventually lose the meaning of data augmentation. In this paper, we propose a limited discriminator GAN (LDGAN) model that explains the results of GAN, which is a model that cannot be analyzed externally, such as a black box. The part explained in LDGAN becomes the discriminator model of GAN, and it is possible to check which area of the image is used as the basis for determining fake/real by the discriminator. In the end, a method for limiting the learning of discriminator is proposed based on the described results. Through this, it is possible to avoid the overfitting problem of the discriminator and to generate various images different from the learning image. The LDGAN method allows users to perform meaningful data augmentation with only specific objects except for complex images or backgrounds that require analysis. Compare the LDGAN method with the existing DCGAN and present the extensive simulation results. The extensive simulation result shows that the image generated by the proposed LDGAN including the estimation area is about 10% more.