Reviewerly/exHarmony huggingface dataset:
An Innovative Design of Substitution Box Using Trigonometric Transformation

As the number of hacking events and cyber threats keeps going up, it is getting harder and harder to communicate securely and keep personal information safe on the Internet. Cryptography is a very important way to deal with these problems because it can secure data by changing it from one form to another. In this study, we show a new, lightweight algorithm that is based on trigonometric ideas and offers a lot of security by making it less likely that cryptanalysis will work. The performance of our suggested algorithm is better than that of older methods like the Hill cipher, Blowfish, and DES. Even though traditional methods offer good security, they may have more work to do, which slows them down. The suggested algorithm tries to close this gap by offering a solution based on trigonometric ideas that are both fast and safe. The main goal of this study is to come up with a small but strong encryption algorithm that cannot be broken by cryptanalysis and keeps Internet communication safe. We want to speed up the coding process without making it less secure by using trigonometric principles. The suggested algorithm uses trigonometric functions and operations to create non-linearity and confusion, making it resistant to both differential and linear cryptanalysis. We show that the suggested algorithm is more secure and faster than traditional methods like the Hill cipher, Blowfish, and DES by doing a lot of research and testing. Combining trigonometric ideas with a simple design makes it workable for real world uses and offers a promising way to protect data on the Internet.
Seeing Beyond Cancer: Multi-Institutional Validation of Object Localization and 3D Semantic Segmentation using Deep Learning for Breast MRI

The clinical management of breast cancer depends on an accurate understanding of the tumor and its anatomical context to adjacent tissues and landmark structures. This context may be provided by semantic segmentation methods; however, previous works have been largely limited to a singular focus on the tumor alone and rarely other tissue types. In contrast, we present a method that exploits tissue-tissue interactions to accurately segment every major tissue type in the breast including: chest wall, skin, adipose tissue, fibroglandular tissue, vasculature and tumor via standard-of-care Dynamic Contrast Enhanced MRI. Comparing our method to prior state-of-the-art, we achieved a superior Dice score on tumor segmentation while maintaining competitive performance on other studied tissues across multiple institutions. Briefly, our method proceeds by localizing the tumor using 2D object detectors, then segmenting the tumor and surrounding tissues independently using two 3D U-nets, and finally integrating these results while mitigating false positives by checking for anatomically plausible tissue-tissue contacts. The object detection models were pre-trained on ImageNet and COCO, and operated on MIP (maximum intensity projection) images in the axial and sagittal planes, establishing a 3D tumor bounding box. By integrating multiple relevant peri-tumoral tissues, our work enables clinical applications in breast cancer staging, prognosis and surgical planning.
Streaming Lossless Volumetric Compression of Medical Images Using Gated Recurrent Convolutional Neural Network

Deep learning-based lossless compression methods offer substantial advantages in compressing medical volumetric images. Nevertheless, many learning-based algorithms encounter a trade-off between practicality and compression performance. This paper introduces a hardware-friendly streaming lossless volumetric compression framework, utilizing merely one-thousandth of the model weights compared to other learning-based compression frameworks. We propose a gated recurrent convolutional neural network that combines diverse convolutional structures and fusion gate mechanisms to capture the inter-slice dependencies in volumetric images. Based on such contextual information, we can predict the pixel-by-pixel distribution for entropy coding. Guided by hardware/software co-design principles, we implement the proposed framework on Field Programmable Gate Array to achieve enhanced real-time performance. Extensive experimental results indicate that our method outperforms traditional lossless volumetric compressors and state-of-the-art learning-based lossless compression methods across various medical image benchmarks. Additionally, our method exhibits robust generalization ability and competitive compression speed
Effect of Quantum Information Scrambling on Bound Entangled States

Spreading of information in physical systems is a common phenomenon, but when the information is quantum, then tracking, describing, and quantifying the information is a challenging task. Quantum Information (QI) scrambling defines the quantum information propagating chaotically over the physical system. This article describes the effect of Quantum Information (QI) scrambling on bound entangled states. A bound entangled state is a particular type of entangled state that carries noisy entanglement. The distillation of this type of entangled state is very difficult. In recent times, the usefulness of these states has been depicted in different applications. The outcome of this study exhibits that Quantum Information (QI) scrambling develops entanglement in the separable portion of the bound entangled states. Although Quantum Information (QI) scrambling reduces free entanglement, it is also found from the study that Quantum Information (QI) scrambling plays a significant role in activating the bound entangled states by introducing a certain amount of approximately stable free entanglement.
Pre-trained Language Models Do Not Help Auto-regressive Text-to-Image Generation

Recent advances in image tokenizers, such as VQ-VAE, have enabled text-to-image generation using auto-regressive methods, similar to language modeling. However, these methods have yet to leverage pre-trained language models, despite their adaptability to various downstream tasks. In this work, we explore this gap by adapting a pre-trained language model for auto-regressive text-to-image generation, and find that pre-trained language models offer limited help. We provide a two-fold explanation by analyzing tokens from each modality. First, we demonstrate that image tokens possess significantly different semantics compared to text tokens, rendering pre-trained language models no more effective in modeling them than randomly initialized ones. Second, the text tokens in the image-text datasets are too simple compared to normal language model pre-training data, which causes the catastrophic degradation of language models' capability.
Mapping quantum circuits to shallow-depth measurement patterns based on graph states

The paradigm of measurement-based quantum computing (MBQC) starts from a highly entangled resource state on which unitary operations are executed through adaptive measurements and corrections ensuring determinism. This is set in contrast to the more common quantum circuit model, in which unitary operations are directly implemented through quantum gates prior to final measurements. In this work, we incorporate concepts from MBQC into the circuit model to create a hybrid simulation technique, permitting us to split any quantum circuit into a classically efficiently simulatable Clifford-part and a second part consisting of a stabilizer state and local (adaptive) measurement instructions, a so-called standard form, which is executed on a quantum computer. We further process the stabilizer state with the graph state formalism, thus enabling a significant decrease in circuit depth for certain applications. We show that groups of fully commuting operators can be implemented using fully-parallel, i.e., non-adaptive, measurements within our protocol. In addition, we discuss how such circuits can be implemented in constant quantum depths by employing quantum teleportation. Finally, we demonstrate the utility of our technique on two examples of high practical relevance: the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE).
Darknet Traffic Analysis A Systematic Literature Review

The primary objective of an anonymity tool is to protect the anonymity of its users through the implementation of strong encryption and obfuscation techniques. As a result, it becomes very difficult to monitor and identify users activities on these networks. Moreover, such systems have strong defensive mechanisms to protect users against potential risks, including the extraction of traffic characteristics and website fingerprinting. However, the strong anonymity feature also functions as a refuge for those involved in illicit activities who aim to avoid being traced on the network. As a result, a substantial body of research has been undertaken to examine and classify encrypted traffic using machine learning techniques. This paper presents a comprehensive examination of the existing approaches utilized for the categorization of anonymous traffic as well as encrypted network traffic inside the darknet. Also, this paper presents a comprehensive analysis of methods of darknet traffic using machine learning techniques to monitor and identify the traffic attacks inside the darknet.
A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learning

Quadratic Unconstrained Binary Optimization (QUBO) is a generic technique to model various NP-hard Combinatorial Optimization problems (CO) in the form of binary variables. Ising Hamiltonian is used to model the energy function of a system. QUBO to Ising Hamiltonian is regarded as a technique to solve various canonical optimization problems through quantum optimization algorithms. Recently, PI-GNN, a generic framework, has been proposed to address CO problems over graphs based on Graph Neural Network (GNN) architecture. They introduced a generic QUBO-formulated Hamiltonian-inspired loss function that was directly optimized using GNN. PI-GNN is highly scalable but there lies a noticeable decrease in the number of satisfied constraints when compared to problem-specific algorithms and becomes more pronounced with increased graph densities. Here, We identify a behavioral pattern related to it and devise strategies to improve its performance. Another group of literature uses Reinforcement learning (RL) to solve the aforementioned NP-hard problems using problem-specific reward functions. In this work, we also focus on creating a bridge between the RL-based solutions and the QUBO-formulated Hamiltonian. We formulate and empirically evaluate the compatibility of the QUBO-formulated Hamiltonian as the generic reward function in the RL-based paradigm in the form of rewards. Furthermore, we also introduce a novel Monty Carlo Tree Search-based strategy with GNN where we apply a guided search through manual perturbation of node labels during training. We empirically evaluated our methods and observed up to 44% improvement in the number of constraint violations compared to the PI-GNN.
Simultaneous Energy Harvesting and Hand Gesture Recognition in Large Area Monolithic Dye-Sensitized Solar Cells

Internet of Things (IoT) devices have become prevalent, embedding intelligence into our environment. It is projected that over 75 billion IoT devices will be connected by 2025 worldwide, with the majority being operated indoors. Dye-sensitized solar cells (DSSC) have recently been optimized for ambient light, having the capabilities of providing sufficient energy for self-powered IoT devices. Interaction with digital technologies, termed Human Computer Interaction (HCI), is often achieved via physical mechanisms (e.g. remote controls, cell phones) which can hinder the natural interface between users and IoT devices, a key consideration for HCI. What if the solar cell that is powering the IoT device can also recognize hand gestures which would allow the user to naturally interact with the system? Previous attempts to achieve this have necessarily employed an array of solar cell/photodiodes to detect directionality. In this work, we demonstrate that by monitoring the photocurrent output of an asymmetrically patterned monolithic (i.e., single cell) DSSC, and using machine learning, we can recognize simple hand gestures, achieving an accuracy prediction of 97.71%. This work shows that, DSSCs are the perfect choice for self-powered interactive technologies, both in terms of powering IoT devices in ambient light conditions and having aesthetic qualities that are prioritized by users. As well as powering interactive technologies, they can also provide a means of interactive control.
Characterizing Video Question Answering with Sparsified Inputs

In Video Question Answering, videos are often processed as a full-length sequence of frames to ensure minimal loss of information. Recent works have demonstrated evidence that sparse video inputs are sufficient to maintain high performance. However, they usually discuss the case of single frame selection. In our work, we extend the setting to multiple number of inputs and other modalities. We characterize the task with different input sparsity and provide a tool for doing that. Specifically, we use a Gumbel-based learnable selection module to adaptively select the best inputs for the final task. In this way, we experiment over public VideoQA benchmarks and provide analysis on how sparsified inputs affect the performance. From our experiments, we have observed only 5.2%-5.8% loss of performance with only 10% of video lengths, which corresponds to 2-4 frames selected from each video. Meanwhile, we also observed the complimentary behaviour between visual and textual inputs, even under highly sparsified settings, suggesting the potential of improving data efficiency for video-and-language tasks.
Derivation of the generalized Zurek's bound from a detailed fluctuation theorem

The minimal heat that must be released to the reservoir when a single input $x$ is mapped to a single output $y$ is considered. It was previously argued by Zurek in an informal style that the minimal heat should be $K(x|y)$. Recently, Kolchinsky has rigorously proved a generalized Zurek's bound in the Hamiltonian "two-point measurement" scheme, which implies a trade-off between heat, noise and protocol complexity in a computation from $x$ to $y$. Here we propose another proof of the generalized Zurek's bound from a detailed fluctuation theorem, showing that the generalized Zurek's bound holds in any system that satisfies a DFT. Several applications of the bound are also discussed.
Quantum-classical simulation of quantum field theory by quantum circuit learning

We employ quantum circuit learning to simulate quantum field theories (QFTs). Typically, when simulating QFTs with quantum computers, we encounter significant challenges due to the technical limitations of quantum devices when implementing the Hamiltonian using Pauli spin matrices. To address this challenge, we leverage quantum circuit learning, employing a compact configuration of qubits and low-depth quantum circuits to predict real-time dynamics in quantum field theories. The key advantage of this approach is that a single-qubit measurement can accurately forecast various physical parameters, including fully-connected operators. To demonstrate the effectiveness of our method, we use it to predict quench dynamics, chiral dynamics and jet production in a 1+1-dimensional model of quantum electrodynamics. We find that our predictions closely align with the results of rigorous classical calculations, exhibiting a high degree of accuracy. This hybrid quantum-classical approach illustrates the feasibility of efficiently simulating large-scale QFTs on cutting-edge quantum devices.
Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations

In this work, we address the challenge of multi-task image generation with limited data for denoising diffusion probabilistic models (DDPM), a class of generative models that produce high-quality images by reversing a noisy diffusion process. We propose a novel method, SR-DDPM, that leverages representation-based techniques from few-shot learning to effectively learn from fewer samples across different tasks. Our method consists of a core meta architecture with shared parameters, i.e., task-specific layers with exclusive parameters. By exploiting the similarity between diverse data distributions, our method can scale to multiple tasks without compromising the image quality. We evaluate our method on standard image datasets and show that it outperforms both unconditional and conditional DDPM in terms of FID and SSIM metrics.
Reducing Gender Bias in Machine Translation through Counterfactual Data Generation

Recent advances in neural methods have led to substantial improvement in the quality of Neural Machine Translation (NMT) systems. However, these systems frequently produce translations with inaccurate gender (Stanovsky et al., 2019), which can be traced to bias in training data. Saunders and Byrne (2020) tackle this problem with a handcrafted dataset containing balanced gendered profession words. By using this data to fine-tune an existing NMT model, they show that gender bias can be significantly mitigated, albeit at the expense of translation quality due to catastrophic forgetting. They recover some of the lost quality with modified training objectives or additional models at inference. We find, however, that simply supplementing the handcrafted dataset with a random sample from the base model training corpus is enough to significantly reduce the catastrophic forgetting. We also propose a novel domain-adaptation technique that leverages in-domain data created with the counterfactual data generation techniques proposed by Zmigrod et al. (2019) to further improve accuracy on the WinoMT challenge test set without significant loss in translation quality. We show its effectiveness in NMT systems from English into three morphologically rich languages French, Spanish, and Italian. The relevant dataset and code will be available at Github.
A Categorization of Bugs in Quantum Programs

Advances in NISQ devices have allowed quantum researchers and enthusiasts to design and develop quantum programs for different applications. As the hardware progresses from noisy to fault-tolerant, the size of the programs implemented will increase. As the circuits get larger, testing and debugging these circuits will become more complex. To overcome the counterintuitive nature of quantum circuits and help developers better understand their programs and locate bugs during the development process's testing and debugging stages, we must understand the different types of bugs that occur in quantum circuits and how they affect the state of the qubits and propagate through the circuit. In this work, we categorize some bugs that could occur when implementing quantum circuits. This poster is part of a bigger work that aims to assist developers in debugging their circuits. This is done by bridging some testing and debugging techniques from the classical world, such as hypothesis and binary-like testing, and debugging-assistance functions, such as tracking gates and detecting qubits' light cones. In addition, we collected and studied bugs from Stack Exchange, Stack Overflow, and GitHub and categorized these bugs based on their source to provide developers with tools and information for an efficient and systematic debugging process for their programs.
Robust Quantum Control for Set-Membership Hamiltonian Uncertainty

We provide a brief review of a recently proposed approach to robust quantum control which rests on three theoretical foundations: the classic method of averaging, set-membership uncertainty modeling, and the known topological properties of the quantum control landscape. Application of the method of averaging directly results in a multi-criterion optimization problem consisting of the uncertainty-free fidelity competing with a generic robustness measure, the latter being a norm of a time-domain product of a function of the uncertainty-free system and the character of the uncertainty set. If this term is sufficiently small, then at most, only fourth order error effects can accrue. Lastly, the topological features promote a two-stage algorithm: in stage-1 the uncertainty-free fidelity is set to a high threshold, then in stage-2 the robustness measure is minimized while maintaing the threshold. Each stage-2 control update is found by solving a convex optimization problem.
Software Architecture Challenges in Integrating Hybrid Classical-Quantum Systems

The emergence of quantum computing proposes a revolutionary paradigm that can radically transform numerous scientific and industrial application domains. The ability of quantum computers to scale computations exponentially imply better performance and efficiency for certain algorithmic tasks than current computers provide. However, to gain benefit from such improvement, quantum computers must be integrated with existing software systems, a process that is not straightforward. In this paper, we investigate challenges that emerge from building larger hybrid classical-quantum computers, and discuss some approaches that could be employed to overcome these challenges.
DR-SASV: A deep and reliable spoof aware speech verification system

A spoof-aware speaker verification system is an integrated system that is capable of jointly identifying impostor speakers as well as spoofing attacks from target speakers. This type of system largely helps in protecting sensitive data, mitigating fraud, and reducing theft. Research has recently enhanced the effectiveness of countermeasure systems and automatic speaker verification systems separately to produce low Equal Error Rates (EER) for each system. However, work exploring a combination of both is still scarce. This paper proposes an end-to-end solution to address spoof-aware automatic speaker verification (ASV) by introducing a Deep Reliable Spoof-Aware-Speaker-Verification (DR-SASV) system. The proposed system allows the target audio to pass through a “spoof aware” speaker verification model sequentially after applying a convolutional neural network (CNN)-based spoof detection model. The suggested system produces encouraging results after being trained on the ASVSpoof 2019 LA dataset. The spoof detection model gives a validation accuracy of 96%, while the transformer-based speech verification model authenticates users with an error rate of 13.74%. The system surpasses other state-of-the-art models and produces an EER score of 10.32%.
Possibility of decryption speed-up by parallel processing in CCA secure hashed ElGamal

In order to prove the ElGamal CCA(Chosen Ciphertext Attack) security in the random oracle model, it is necessary to use the group where ICDH(Interactive Computational Diffie Hellman) assumption holds. Until now, only bilinear group with complex algebraic structure has been known as the ICDH group. In this paper, we introduce the ICDH group with simple algebraic structure. In other words, we prove that ICDH assumption holds in the integer group with composite modulus. On the basis of this, we propose the CCA secure hashed ElGamal and its fast variant to speed up decryption by parallel processing. Our parallel scheme has the fastest decryption among all CCA secure PKE(Public Key Encryption) schemes implemented in integer group and gives the possibility that ElGamal protocol could be practical when the big modulus numbers are used to resist the quantum attack.
Gaussian Boson Sampling for Binary Optimization

In this study, we consider a Gaussian Boson Sampler for solving a Flight Gate Assignment problem. We employ a Variational Quantum Eigensolver approach using the Conditional Value-at-risk cost function. We provide proof of principle by carrying out numerical simulations on randomly generated instances.