Reviewerly/exHarmony huggingface dataset:
PSO based Hyperparameter tuning of CNN Multivariate Time- Series Analysis

Convolutional Neural Network (CNN) is an effective Deep Learning (DL) algorithm that solves various image identification problems. The use of CNN for time-series data analysis is emerging. CNN learns filters, representations of repeated patterns in the series, and uses them to forecast future values. The network performance may depend on hyperparameter settings. This study optimizes the CNN architecture based on hyperparameter tuning using Particle Swarm Optimization (PSO), PSO-CNN. The proposed method was evaluated using multivariate time-series data of electronic journal visitor datasets. The CNN equation in image and time-series problems is the input given to the model for processing numbers. The proposed method generated the lowest RMSE (1.386) with 178 neurons in the fully connected and 2 hidden layers. The experimental results show that the PSO-CNN generates an architecture with better performance than ordinary CNN.
Witnessing Nonequilibrium Entanglement Dynamics in a Strongly Correlated Fermionic Chain

Many-body entanglement in condensed matter systems can be diagnosed from equilibrium response functions through the use of entanglement witnesses and operator-specific quantum bounds. Here, we investigate the applicability of this approach for detecting entangled states in quantum systems driven out of equilibrium. We use a multipartite entanglement witness, the quantum Fisher information, to study the dynamics of a paradigmatic fermion chain undergoing a time-dependent change of the Coulomb interaction. Our results show that the quantum Fisher information is able to witness distinct signatures of multipartite entanglement both near and far from equilibrium that are robust against decoherence. We discuss implications of these findings for probing entanglement in light-driven quantum materials with time-resolved optical and x-ray scattering methods.
Designing an Image Encryption Algorithm Based on Hyperchaotic System and DCT

In the field of secure communications, the robustness of cipher images transmitted in various channels is becoming increasingly important. In this paper, a robust image encryption algorithm combining a new chaotic system and discrete cosine transform is proposed, which is interlinked with plain information and is resistant to high-intensity noise attacks. First, a 5D continuous hyperchaotic system is proposed, leading to an interrelated sequence of five chaotic sequences. Second, the plain image is subjected to discrete cosine transform. Then the transform domain image is quantized, and some high-frequency components are removed, and then the high-frequency components are filled with chaotic sequences. Next, the transform domain image is scrambled, and inverse discrete cosine transform is performed, and its gray value is mapped to obtain a spatial domain image. Finally, the spatial image is scrambled by the spiral transformation, and then the diffusion operation is performed to obtain the encrypted image. Through the simulation experiment, the histogram, correlation, differential attack, and robustness are analyzed. The experimental results show that the proposed encryption algorithm can resist high-intensity noise attacks and has good encryption performance.
Optical quantum states based on hot atomic ensembles and their applications

The four-wave mixing process in atomic ensembles has many important applications in quantum information. We review recent progress on the generation of optical quantum states from the four-wave mixing process in hot atomic ensembles, including the production of two-beam, multi-beam, and multiplexed quantum correlated or entangled states. We also review the applications of these optical quantum states in implementing quantum information protocols, constructing SU(1,1) quantum interferometers, and realizing quantum plasmonic sensing. These applications indicate that the four-wave mixing process in hot atomic ensembles is a promising platform for quantum information processing, especially for implementing all-optical quantum information protocols, constructing SU(1,1) interferometers, and realizing quantum sensing.
Backdoor Attacks Against Dataset Distillation

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models.It encapsulates the knowledge from a large dataset into a smaller synthetic dataset.A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset.However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility.The security risks stemming from them have not been explored.This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain.Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed.We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING.NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure.We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques.Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases.Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance.Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.
Object Detection Algorithm of UAV Aerial Photography Image Based on Anchor-Free Algorithms

Aiming at the problems of the difficult extraction of small target feature information, complex background, and variable target scale in unmanned aerial vehicle (UAV) aerial photography images. In this paper, an anchor-free target detection algorithm based on fully convolutional one-stage object detection (FCOS) for UAV aerial photography images is proposed. For the problem of complex backgrounds, the global context module is introduced in the ResNet50 network, which is combined with feature pyramid networks (FPN) as the backbone feature extraction network to enhance the feature representation of targets in complex backgrounds. To address the problem of the difficult detection of small targets, an adaptive feature balancing sub-network is designed to filter the invalid information generated at all levels of feature fusion, strengthen multi-layer features, and improve the recognition capability of the model for small targets. To address the problem of variable target scales, complete intersection over union (CIOU) Loss is used to optimize the regression loss and strengthen the model’s ability to locate multi-scale targets. The algorithm of this paper is compared quantitatively and qualitatively on the VisDrone dataset. The experiments show that the proposed algorithm improves 4.96% on average precision (AP) compared with the baseline algorithm FCOS, and the detection speed is 35 frames per second (FPS), confirming that the algorithm has satisfactory detection performance, real-time inference speed, and has effectively improved the problem of missed detection and false detection of targets in UAV aerial images.
Effect of Changing Targeted Layers of the Deep Dream Technique Using VGG-16 Model

The deep dream is one of the most recent techniques in deep learning. It is used in many applications, such as decorating and modifying images with motifs and simulating the patients' hallucinations. This study presents a deep dream model that generates deep dream images using a convolutional neural network (CNN). Firstly, we survey the layers of each block in the network, then choose the required layers, and extract their features to maximize it. This process repeats several iterations as needed, computes the total loss, and extracts the final deep dream images. We apply this operation on different layers two times; the former is on the low-level layers, and the latter is on the high-level layers. The results of applying this operation are different, where the resulting image from applying deep dream on the high-level layers are clearer than those resulting from low-level layers. Also, the loss of the images of low-level layers ranges between 31.1435 and 31.1435, while the loss of the images of upper layers ranges between 20.0704 and 32.1625.
Eliminating temporal correlation in quantum-dot entangled photon source by quantum interference

Semiconductor quantum dots, as promising solid-state platform, have exhibited deterministic photon pair generation with high polarization entanglement f\textcompwordmark idelity for quantum information applications. However, due to temporal correlation from inherently cascaded emission, photon indistinguishability is limited, which restricts their potential scalability to multi-photon experiments. Here, by utilizing quantum interferences to decouple polarization entanglement from temporal correlation, we improve multi-photon entanglement f\textcompwordmark idelity from $(58.7\pm 2.2)\%$ to $(75.5\pm 2.0)\%$. Our work paves the way to realize scalable and high-quality multi-photon states from quantum dots.
Restoring broken symmetries using quantum search “oracles”

We present a new method to perform variation after projection in many-body systems on quantum computers that does not require performing explicit projection. The technique employs the notion of ``oracle'', generally used in quantum search algorithms. We show how to construct the oracle and the projector associated with a symmetry operator. The procedure is illustrated for the parity, particle number, and total spin symmetries. The oracle is used to restore symmetry by indirect measurements using a single ancillary qubit. An illustration of the technique is made to obtain the approximate ground state energy for the pairing model Hamiltonian.
Knowing What and How: A Multi-modal Aspect-Based Framework for Complaint Detection

With technological advancements, the proliferation of e-commerce websites and social media platforms has created an avenue for customers to provide feedback to enterprises based on their overall experience. Customer feedback serves as an independent validation tool that could boost consumer trust in the brand. Whether it is a recommendation or review of a product, it provides insight allowing businesses to understand what they are doing right or wrong. By automatically analyzing customer complaints at the aspect-level enterprises can connect to their customers by customizing products and services according to their needs quickly and deftly. In this paper, we introduce the task of Aspect-Based Complaint Detection (ABCD). ABCD identifies the aspects in the given review about a product and also finds if the aspect mentioned in the review signifies a complaint or non-complaint. Specifically, a task solver must detect duplets (What, How) from the inputs that show WHAT the targeted features are and HOW they are complaints. To address this challenge, we propose a deep-learning-based multi-modal framework, where the first stage predicts what the targeted aspects are, and the second stage categorizes whether the targeted aspect is associated with a complaint or not. We annotate the aspect categories and associated complaint/non-complaint labels in the recently released multi-modal complaint dataset (CESAMARD), which spans five domains (books, electronics, edibles, fashion, and miscellaneous). Based on extensive evaluation our methodology established a benchmark performance in this novel aspect-based complaint detection task and also surpasses a few strong baselines developed from state-of-the-art related methods (Resources available at: https://github.com/appy1608/ECIR2023_Complaint-Detection ).
Corporate Culture and Its Effects on Organizational Performance: Multi-Group Analysis Evidence from Developing Countries

Corporate culture is one of the most critical intangible resources that substantially affect the overall organizational performance in developing countries. Hence, this study focused on developing countries' energy sectors to evaluate corporate culture's influence on performance. A quantitative research approach was used in this study to achieve the set goals. The study population comprised 408 employees of General Electric Company of Libya (GECOL) who were sampled during the data collection stage of this study. Structural Equation Modelling (SEM) was used to evaluate the impact of organizational culture on performance. The study’s outcome evidenced a significant positive impact of culture on corporate performance; this effect was more significant for administrative employees than technical employees. This study offers some recommendations for the direction of future research, as well as criticism of its limitations.
View-aware attribute-guided network for vehicle re-identification

Vehicle re-identification is one of the essential application of urban surveillance. Due to enormous variation in inter-class and intra-class resemblance creates a challenge for methods to distinguish between the same vehicles. Additionally, varying illumination and complex environments create significant hurdles for the existing methods to re-identify vehicles. We present a multi-guided learning method in this paper that uses multi-attribute and view point information, while also enhancing the robustness of feature extraction. The multi-attribute sub-network learns discriminative features like, i.e. color and type of vehicle. Moreover, the view predictor network adds extra information to the feature embedding and To validate the effectiveness of our framework, experiments on two benchmark datasets VeRi-776 and VehicleID are conducted. Experimental results illustrate our framework achieved comparative performance.
Rapid Seismic Waveform Modeling and Inversion With Neural Operators

Seismic waveform modeling is a powerful tool for determining earth structure models and unraveling earthquake rupture processes, but it is usually computationally expensive. We introduce a scheme to vastly accelerate these calculations with a recently developed machine learning paradigm called the neural operator. Once trained, these models can simulate a full wavefield at negligible cost. We use a U-shaped neural operator to learn a general solution operator to the 2D elastic wave equation from an ensemble of numerical simulations performed with random velocity models and source locations. We show that full waveform modeling with neural operators is nearly two orders of magnitude faster than conventional numerical methods, and more importantly, the trained model enables accurate simulation for velocity models, source locations, and mesh discretization distinctly different from the training dataset. The method also enables convenient full-waveform inversion with automatic differentiation.
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection

Abstract Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to the extraction of dataset-specific, non-robust features. Therefore, such methods do not generalize well across multiple datasets. We present a universal domain adaptation method, DeepAstroUDA , as an approach to overcome this challenge. This algorithm performs semi-supervised domain adaptation (DA) and can be applied to datasets with different data distributions and class overlaps. Non-overlapping classes can be present in any of the two datasets (the labeled source domain, or the unlabeled target domain), and the method can even be used in the presence of unknown classes. We apply our method to three examples of galaxy morphology classification tasks of different complexities (three-class and ten-class problems), with anomaly detection: (1) datasets created after different numbers of observing years from a single survey (Legacy Survey of Space and Time mock data of one and ten years of observations); (2) data from different surveys (Sloan Digital Sky Survey (SDSS) and DECaLS); and (3) data from observing fields with different depths within one survey (wide field and Stripe 82 deep field of SDSS). For the first time, we demonstrate the successful use of DA between very discrepant observational datasets. DeepAstroUDA is capable of bridging the gap between two astronomical surveys, increasing classification accuracy in both domains (up to <?CDATA $40\%$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>40</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> on the unlabeled data), and making model performance consistent across datasets. Furthermore, our method also performs well as an anomaly detection algorithm and successfully clusters unknown class samples even in the unlabeled target dataset.
Quantum-enhanced interferometer using Kerr squeezing

Abstract One of the prime applications of squeezed light is enhancing the sensitivity of an interferometer below the quantum shot-noise limit, but so far, no such experimental demonstration was reported when using the optical Kerr effect. In prior setups involving Kerr-squeezed light, the role of the interferometer was merely to characterize the noise pattern. The lack of such a demonstration was largely due to the cumbersome tilting of the squeezed ellipse in phase space. Here, we present the first experimental observation of phase-sensitivity enhancement in an interferometer using Kerr squeezing.
Deep Gaussian Scale Mixture Prior for Image Reconstruction

Image reconstruction from partial observations has attracted increasing attention. Conventional image reconstruction methods with hand-crafted priors often fail to recover fine image details due to the poor representation capability of the hand-crafted priors. Deep learning methods attack this problem by directly learning mapping functions between the observations and the targeted images can achieve much better results. However, most powerful deep networks lack transparency and are nontrivial to design heuristically. This paper proposes a novel image reconstruction method based on the Maximum a Posterior (MAP) estimation framework using learned Gaussian Scale Mixture (GSM) prior. Unlike existing unfolding methods that only estimate the image means (i.e., the denoising prior) but neglected the variances, we propose characterizing images by the GSM models with learned means and variances through a deep network. Furthermore, to learn the long-range dependencies of images, we develop an enhanced variant based on the Swin Transformer for learning GSM models. All parameters of the MAP estimator and the deep network are jointly optimized through end-to-end training. Extensive simulation and real data experimental results on spectral compressive imaging and image super-resolution demonstrate that the proposed method outperforms existing state-of-the-art methods.
Investigating the Privacy and Security of the SimpliSafe Security System on Android and iOS

The emergence of the Internet of Things technologies and the increase and convenience of smart home devices have contributed to the growth of self-installed home security systems. While home security devices have become more accessible and can help users monitor and secure their homes, they can also become targets of cyberattacks and/or witnesses of criminal activities, hence sources of forensic evidence. To date, there is little existing literature on forensic analysis and the security and privacy of home security systems. In this paper, we seek to better understand and assess the forensic artifacts that can be extracted, the security and privacy concerns around the use of home security devices, and the challenges forensic investigators might encounter, by performing a comprehensive investigation of the SimpliSafe security system. We investigated the interaction of the security system with the SimpliSafe companion app on both Android and iOS devices. We analyzed the network traffic as the user interacts with the system to identify any security or privacy concerns. Our method can help investigators working on other home security systems, and our findings can further help developers to improve the confidentiality and privacy of user data in home security devices and their applications.
Multiple-photon bundle emission in the n-photon Jaynes-Cummings model

We study the multiple-photon bundle emission in the n -photon Jaynes-Cummings model composed of a two-level system coupled to a single-mode optical field via the n -photon exciting process. Here, the two-level system is strongly driven by a near-resonant monochromatic field, and hence the system can work in the Mollow regime, in which a super-Rabi oscillation between the zero-photon state and the n -photon state can take place under proper resonant conditions. We calculate the photon number populations and the standard equal-time high-order correlation functions, and find that the multiple-photon bundle emission can occur in this system. The multiple-photon bundle emission is also confirmed by investigating the quantum trajectories of the state populations and both the standard and generalized time-delay second-order correlation functions for multiple-photon bundle. Our work paves the way towards the study of multiple-photon quantum coherent devices, with potential application in quantum information sciences and technologies.
Benchmarking 2D Multi-Object Detection and Tracking Algorithms in Autonomous Vehicle Driving Scenarios

Self-driving vehicles must be controlled by navigation algorithms that ensure safe driving for passengers, pedestrians and other vehicle drivers. One of the key factors to achieve this goal is the availability of effective multi-object detection and tracking algorithms, which allow to estimate position, orientation and speed of pedestrians and other vehicles on the road. The experimental analyses conducted so far have not thoroughly evaluated the effectiveness of these methods in road driving scenarios. To this aim, we propose in this paper a benchmark of modern multi-object detection and tracking methods applied to image sequences acquired by a camera installed on board the vehicle, namely, on the videos available in the BDD100K dataset. The proposed experimental framework allows to evaluate 22 different combinations of multi-object detection and tracking methods using metrics that highlight the positive contribution and limitations of each module of the considered algorithms. The analysis of the experimental results points out that the best method currently available is the combination of ConvNext and QDTrack, but also that the multi-object tracking methods applied on road images must be substantially improved. Thanks to our analysis, we conclude that the evaluation metrics should be extended by considering specific aspects of the autonomous driving scenarios, such as multi-class problem formulation and distance from the targets, and that the effectiveness of the methods must be evaluated by simulating the impact of the errors on driving safety.