Reviewerly/exHarmony huggingface dataset:
A novel denoising method for CT images based on U-net and multi-attention

Reducing the radiation dose may lead to increased noise in medical computed tomography (CT), which can adversely affect the radiologists' judgment. Many efforts have been devoted to the denoising of low-dose CT (LDCT) images. However, it is often observed that denoised medical images usually lose some important clinical lesion edge information and may affect doctors’ clinical diagnosis. For a denoising neural network, it is expected that the neural network can well retain the detailed features and make the network more anthropomorphic, and to simulate the attention mechanism of observation, being a valuable feature of the thinking process of human brain. Based on U-network (U-Net) and multi-attention mechanism, a novel denoising method for medical CT images is proposed in this study. To obtain different feature information in CT images, three attention modules are proposed in our method. The local attention module is developed to localize the surrounding information of the feature map and calculate each pixel from the context extracted from the feature map. The multi-feature channel attention module can automatically learn and extract features, suppress some invalid information and add different weights to each channel in the feature map according to different tasks. The hierarchical attention module allows the deep neural network to extract a large amount of feature information. This study also introduces an enhanced learning module to learn and retain the detail information in the image by stacking multi-layer convolution layer, batch normalization (BN) layer and activation function layer to increase the network depth. Experimental studies are conducted, and comparisons with the state-of-the-art networks are made, and the results demonstrate that the developed method can effectively remove the noise in CT images and improve the image quality in the evaluation metrics of peak signal to noise ratio (PSNR) and structural similarity (SSIM). Our method achieved 34.7329 of PSNR and 0.9293 of SSIM for σ = 10 on the QIN_LUNG_CT dataset, and achieved 28.9163 of PSNR and 0.8602 of SSIM on the Mayo Clinic LDCT Grand Challenge dataset.
Locomotion Policy Guided Traversability Learning using Volumetric Representations of Complex Environments

Despite the progress in legged robotic locomotion, autonomous navigation in unknown environments remains an open problem. Ideally, the navigation system utilizes the full potential of the robots' locomotion capabilities while operating within safety limits under uncertainty. The robot must sense and analyze the travers ability of the surrounding terrain, which depends on the hardware, locomotion control, and terrain properties. It may contain information about the risk, energy, or time consumption needed to traverse the terrain. To avoid hand-crafted traversability cost functions we propose to collect traversability information about the robot and locomotion policy by simulating the traversal over randomly generated terrains using a physics simulator. Thousand of robots are simulated in parallel controlled by the same locomotion policy used in reality to acquire 57 years of real-world locomotion experience equivalent. For deployment on the real robot, a sparse convolutional network is trained to predict the simulated traversability cost, which is tailored to the deployed locomotion policy, from an entirely geometric representation of the envi-ronment in the form of a 3D voxel-occupancy map. This rep-resentation avoids the need for commonly used elevation maps, which are error-prone in the presence of overhanging obstacles and multi-floor or low-ceiling scenarios. The effectiveness of the proposed travers ability prediction network is demonstrated for path planning for the legged robot ANY mal in various indoor and natural environments.
Distinctive Measurement Scheme for Security and Privacy in Internet of Things Applications Using Machine Learning Algorithms

More significant data are available thanks to the present Internet of Things (IoT) application trend, which can be accessed in the future using some platforms for data storage. An external storage space is required for practical purposes whenever a data storage platform is created. However, in the IoT, certain cutting-edge storage methods have been developed that compromise the security and privacy of data transfer processes. As a result, the suggested solution creates a standard mode of security operations for storing the data with little noise. One of the most distinctive findings in the suggested methodology is the incorporation of machine learning algorithms in the formulation of analytical representations. The aforementioned integration method ensures high-level quantitative measurements of data security and privacy. Due to the transmission of large amounts of data, users are now able to assess the reliability of data transfer channels and the duration of queuing times, where each user can separate the specific data that has to be transferred. The created system is put to the test in real time using the proper metrics, and it is found that machine learning techniques improve security more effectively. Additionally, for 98 percent of the scenarios defined, the accuracy for data security and privacy is maximized, and the predicted model outperforms the current method in all of them.
Towards Making the Most of ChatGPT for Machine Translation

ChatGPT shows remarkable capabilities for machine translation (MT). Several prior studies have shown that it achieves comparable results to commercial systems for high-resource languages but lags behind in complex tasks, e.g, low-resource and distant-language-pairs translation. However, they usually adopt simple prompts which can not fully elicit the capability of ChatGPT. In this report, we aim to further mine ChatGPT's translation ability by revisiting several aspects: temperature, task information, and domain information, and correspondingly propose two (simple but effective) prompts: Task-Specific Prompts (TSP) and Domain-Specific Prompts (DSP). We show that: ❶ The performance of ChatGPT depends largely on temperature, and a lower temperature usually can achieve better performance; ❷ Emphasizing the task information further improves ChatGPT's performance, particularly in complex MT tasks; ❸ Introducing domain information can elicit ChatGPT's generalization ability and improve its performance in the specific domain; ❹ ChatGPT tends to generate hallucinations for non-English-centric MT tasks, which can be partially addressed by our proposed prompts but still need to be highlighted for the MT/NLP community. We also explore the effects of advanced in-context learning strategies and find a (negative but interesting) observation: the powerful chain-of-thought prompt leads to word-by-word translation behavior, thus bringing significant translation degradation.
A real-time semantic segmentation model using iteratively shared features in multiple sub-encoders

Recent studies show a significant growth in semantic segmentation. However, many semantic segmentation models still have a large number of parameters, making them unsuitable for resource-constrained embedded devices. To address this issue, we propose an efficient Shared Feature Reuse Segmentation (SFRSeg) model containing several novelties: a new yet effective shared-branch multiple sub-encoders design, a context mining module and a semantic aggregating module for better context granularity. In particular, our shared-branch approach improves the entire feature hierarchy by sharing the spatial and context knowledge in both shallow and deep branches. After every shared point in each sub-encoder, a proposed cascading context mining (CCM) module is deployed to filter out the noisy spatial details from the feature maps and provides a diverse size of receptive fields for capturing the latent context between multi-scale geometric shapes in the scene. To overcome the gradient vanishing issue at the early stage, we reduce the number of layers in the first sub-encoder and employ a unique multiple sub-encoders design which reprocesses the rich global feature maps through multiple sub-encoders for better feature refinement. Later, the rich semantic features generated by the efficient sub-encoders at different levels are fused by the proposed Hybrid Path Attention Semantic Aggregation (HPA-SA) module that effectively reduces the semantic gap between feature maps at different levels and alleviate the well-known boundary degeneration effect. To make it computationally efficient for resource-constrained embedded devices, a series of lightweight methods such as a lightweight encoder, a squeeze-and-excitation design, separable convolution filters, channel reduction (CR) are carefully exploited. With an exceptional performance on Cityscapes (70.6% test mIoU) and CamVid (74.7% test mIoU) data sets, the proposed model is shown to be superior over existing light real-time semantic segmentation models whilst having only 1.6 million parameters.
Learning Many-Body Hamiltonians with Heisenberg-Limited Scaling

Learning a many-body Hamiltonian from its dynamics is a fundamental problem in physics. In this Letter, we propose the first algorithm to achieve the Heisenberg limit for learning an interacting $N$-qubit local Hamiltonian. After a total evolution time of $\mathcal{O}({\ensuremath{\epsilon}}^{\ensuremath{-}1})$, the proposed algorithm can efficiently estimate any parameter in the $N$-qubit Hamiltonian to $\ensuremath{\epsilon}$ error with high probability. Our algorithm uses ideas from quantum simulation to decouple the unknown $N$-qubit Hamiltonian $H$ into noninteracting patches and learns $H$ using a quantum-enhanced divide-and-conquer approach. The proposed algorithm is robust against state preparation and measurement error, does not require eigenstates or thermal states, and only uses $\text{polylog}({\ensuremath{\epsilon}}^{\ensuremath{-}1})$ experiments. In contrast, the best existing algorithms require $\mathcal{O}({\ensuremath{\epsilon}}^{\ensuremath{-}2})$ experiments and total evolution time. We prove a matching lower bound to establish the asymptotic optimality of our algorithm.
Fully Passive Quantum Key Distribution

We propose a fully passive linear optical quantum key distribution (QKD) source that implements both random decoy-state and encoding choices with postselection only, thus eliminating all side channels in active modulators. Our source is general purpose and can be used in, e.g., BB84, the six-state protocol, and reference-frame-independent QKD. It can even potentially be combined with measurement-device-independent QKD to achieve robustness against side channels in both detectors and modulators. We also perform a proof-of-principle experimental source characterization to show its feasibility.
High-Resolution Iterative Feedback Network for Camouflaged Object Detection

Spotting camouflaged objects that are visually assimilated into the background is tricky for both object detection algorithms and humans who are usually confused or cheated by the perfectly intrinsic similarities between the foreground objects and the background surroundings. To tackle this challenge, we aim to extract the high-resolution texture details to avoid the detail degradation that causes blurred vision in edges and boundaries. We introduce a novel HitNet to refine the low-resolution representations by high-resolution features in an iterative feedback manner, essentially a global loop-based connection among the multi-scale resolutions. To design better feedback feature ﬂow and avoid the feature corruption caused by recurrent path, an iterative feedback strategy is proposed to impose more constraints on each feedback connection. Extensive experiments on four challenging datasets demonstrate that our HitNet breaks the performance bottleneck and achieves significant improvements compared with 29 state-of-the-art methods. In addition, to address the data scarcity in camouflaged scenarios, we provide an application example to convert the salient objects to camouflaged objects, thereby generating more camouflaged training samples from the diverse salient object datasets. Code will be made publicly available.
Use of ChatGPT at University as a Tool for Complex Thinking: Students' Perceived Usefulness

Artificial intelligence (AI) and AI-based chatbots, such as ChatGPT, are transforming the approach to education. In particular, ChatGPT's potential to process large amounts of data and learn from user interactions makes it a beneficial resource for students, albeit with some reluctance from some teachers. This study aimed to explore the acceptance of ChatGPT by university students. The researchers administered an online survey to 400 Spanish university students aged 18-64 (M = 21.80; SD = 6.40). The results of the methodological approach based on the UTAUT2 model for technology adoption showed that: 1) gender was not a determining variable in any construct while the experience of use was a factor conditioning a higher score on all constructs; 2) experience, performance expectancy, hedonic motivation, price value, and habit were influential in behavioral intention to use ChatGPT; 3) facilitating conditions, habit, and behavioral intention were conditioning factors in user behavior. Finally, this report discusses the findings and practical implications of the work and recommends some good uses for ChatGPT.
A Bi-objective Genetic Algorithm Optimization of Chaos-DNA Based Hybrid Approach

Abstract The paper implements and optimizes the performance of a currently proposed chaos-deoxyribonucleic acid (DNA)-based hybrid approach to encrypt images using a bi-objective genetic algorithm (GA) optimization. Image encryption is a multi-objective problem. Optimizing the same using one fitness function may not be a good choice, as it can result in different outcomes concerning other fitness functions. The proposed work initially encrypts the given image using chaotic function and DNA masks. Further, GA uses two fitness functions – entropy with correlation coefficient (CC), entropy with unified average changing intensity (UACI), and entropy with number of pixel change rate (NPCR) – simultaneously to optimize the encrypted data in the second stage. The bi-objective optimization using entropy with CC shows significant performance gain over the single-objective GA optimization for image encryption.
Comments on “Fractional Extreme Value Adaptive Training Method: Fractional Steepest Descent Approach”

In this comment, we raise serious concerns over the derivation of the rate of convergence of fractional steepest descent algorithm in fractional adaptive learning approach presented in “Fractional Extreme Value Adaptive Training Method: Fractional Steepest Descent Approach.” We substantiate that the estimate of the rate of convergence is grandiloquent. We also draw attention toward a critical flaw in the design of the algorithm stymieing its applicability for broad adaptive learning problems. Our claims are based on analytical reasoning supported by experimental results.
Cell Segmentation Based on FOPSO Combined With Shape Information Improved Intuitionistic FCM

Fuzzy c-means (FCM) clustering algorithms have been proved to be effective image segmentation techniques. However, FCM clustering algorithms are sensitive to noises and initialization. They cannot effectively segment cell images with inhomogeneous gray value distributions and complex touching cells. Aiming to overcome these disadvantages, this paper proposes a cell image segmentation algorithm using fractional-order velocity based particle swarm optimization (FOPSO) combined with shape information improved intuitionistic FCM (SI-IFCM) clustering. Iterations are carried out between FOPSO and SI-IFCM to achieve final cell segmentation. Experimental results demonstrate that the proposed algorithm has advantages on cell image segmentation, with the highest recall (90.25%) and lowest false discovery rate (0.28%) compared with the state-of-the-art algorithms.
Status Quo in Requirements Engineering: A Theory and a Global Family of Surveys

Requirements Engineering (RE) has established itself as a software engineering discipline over the past decades. While researchers have been investigating the RE discipline with a plethora of empirical studies, attempts to systematically derive an empirical theory in context of the RE discipline have just recently been started. However, such a theory is needed if we are to define and motivate guidance in performing high quality RE research and practice. We aim at providing an empirical and externally valid foundation for a theory of RE practice, which helps software engineers establish effective and efficient RE processes in a problem-driven manner. We designed a survey instrument and an engineer-focused theory that was first piloted in Germany and, after making substantial modifications, has now been replicated in 10 countries worldwide. We have a theory in the form of a set of propositions inferred from our experiences and available studies, as well as the results from our pilot study in Germany. We evaluate the propositions with bootstrapped confidence intervals and derive potential explanations for the propositions. In this article, we report on the design of the family of surveys, its underlying theory, and the full results obtained from the replication studies conducted in 10 countries with participants from 228 organisations. Our results represent a substantial step forward towards developing an empirical theory of RE practice. The results reveal, for example, that there are no strong differences between organisations in different countries and regions, that interviews, facilitated meetings and prototyping are the most used elicitation techniques, that requirements are often documented textually, that traces between requirements and code or design documents are common, that requirements specifications themselves are rarely changed and that requirements engineering (process) improvement endeavours are mostly internally driven. Our study establishes a theory that can be used as starting point for many further studies for more detailed investigations. Practitioners can use the results as theory-supported guidance on selecting suitable RE methods and techniques.
Knockoffs for the Mass: New Feature Importance Statistics with False Discovery Guarantees

An important problem in machine learning and statistics is to identify features that causally affect the outcome. This is often impossible to do from purely observational data, and a natural relaxation is to identify features that are correlated with the outcome even conditioned on all other observed features. For example, we want to identify that smoking really is correlated with cancer conditioned on demographics. The knockoff procedure is a recent breakthrough in statistics that, in theory, can identify truly correlated features while guaranteeing that false discovery rate is controlled. The idea is to create synthetic data—knockoffs—that capture correlations among the features. However, there are substantial computational and practical challenges to generating and using knockoffs. This paper makes several key advances that enable knockoff application to be more efficient and powerful. We develop an efficient algorithm to generate valid knockoffs from Bayesian Networks. Then we systematically evaluate knockoff test statistics and develop new statistics with improved power. The paper combines new mathematical guarantees with systematic experiments on real and synthetic data.
Optical image authentication scheme using dual polarization decoding configuration

We report on an optical image authentication scheme using dual polarization decoding configuration. We examine a sparse encoding method based on image division to process the original image. Next, the sparse original image is separated into two noise-like structure images with random polarization parameters which are digitally encoded. During the decoding stage, the encoded images are sent into a dual polarization decoding configuration where a pixelated polarizer with sparse distribution of transmission angle is employed for image recovering. The proposed scheme avoids complicated recording of complex information and resolves the alignment problem of previous polarization encoding methods. Then we examine numerical simulations to show that the proposed scheme is well suited to recover the original image when decryption keys are correctly used. We find evidence that the verification system exhibits high robustness and flexibility against attacks and interference. We expect that these results might be useful for polarization-encoding based optical verification.
Exploiting Generalization in the Subspaces for Faster Model-Based Reinforcement Learning

Due to the lack of enough generalization in the state space, common methods of reinforcement learning suffer from slow learning speed, especially in the early learning trials. This paper introduces a model-based method in discrete state spaces for increasing the learning speed in terms of required experiences (but not required computation time) by exploiting generalization in the experiences of the subspaces. A subspace is formed by choosing a subset of features in the original state representation. Generalization and faster learning in a subspace are due to many-to-one mapping of experiences from the state space to each state in the subspace. Nevertheless, due to inherent perceptual aliasing (PA) in the subspaces, the policy suggested by each subspace does not generally converge to the optimal policy. Our approach, called model-based learning with subspaces (MoBLeSs), calculates the confidence intervals of the estimated Q-values in the state space and in the subspaces. These confidence intervals are used in the decision-making, such that the agent benefits the most from the possible generalization while avoiding from the detriment of the PA in the subspaces. The convergence of MoBLeS to the optimal policy is theoretically investigated. In addition, we show through several experiments that MoBLeS improves the learning speed in the early trials.
Weighted Abstract Dialectical Frameworks through the Lens of Approximation Fixpoint Theory

Weighted abstract dialectical frameworks (wADFs) were recently introduced, extending abstract dialectical frameworks to incorporate degrees of acceptance. In this paper, we propose a different view on wADFs: we develop semantics for wADFs based on approximation fixpoint theory, an abstract algebraic theory designed to capture semantics of various non-monotonic reasoning formalisms. Our formalism deviates from the original definition on some basic assumptions, the most fundamental is that we assume an ordering on acceptance degrees. We discuss the impact of the differences, the relationship between the two versions of the formalism, and the advantages each of the approaches offers. We furthermore study complexity of various semantics.