Reviewerly/exHarmony huggingface dataset:
Silicon Programmable Photonic Circuits Based on Periodic Bimodal Waveguides

Abstract Programmable photonic circuits are dense assemblies of waveguide meshes in which the flow of light can be reconfigured by software to implement a wide variety of functions, ranging from radiofrequency filtering to optical computing. However, most programmable architectures to date rely on rather bulky Mach‐Zehnder interferometers (MZIs), which are not suitable for large‐scale and high‐density integration. Here, an alternative approach to MZI‐based programmable photonic circuits by using slow‐light‐enhanced periodic bimodal waveguides (PBWs) as programmable units is presented. This study experimentally demonstrates low‐loss short tuning elements of 30× 1.7 µm 2 in area, achieving a two‐orders of magnitude integration density improvement compared to conventional MZIs. A rectangular arrangement of these tunable units is proposed for 3× 3 and 4× 4 matrix multiplication operations to design a feedforward circuit with a footprint of only 100× 250 µm 2 . Finally, the performance trade‐off and benchmark with alternative programmable unit cells are analyzed in order to address the ever‐growing demand for large computing requirements in next‐generation applications such as artificial intelligence and quantum information processing.
Security Techniques in Cyber Physical System for Pharmaceutical Care Services

The use of information technology by the pharmaceutical sector is currently being expanded in order to support the advancement and evolution of workable development strategies in response to the growing urbanization threat. Privacy, security, confidentiality, and authenticity are considered to be the most important features in pharmaceutical care data management alliances and are therefore crucial components of pharmaceutical care services. This paper does a thorough analysis of the Pharmaceutical Care Services (PCS) security requirements. Pharmaceutical manufacturing, supply chain, and other companies are under pressure from Cyber Physical System (CPS) to provide security.
AI-Assisted Learning with ChatGPT and Large Language Models: Implications for Higher Education

The recent progress in generative AI models, particularly large language models (LLMs), has brought about a transformation in the field of education. Conversational LLM services, such as Google's Bard and OpenAI's ChatGPT, offer students access to many abilities such as summarization and generation of text and code, and on-demand replies to questions on expert topics. In this paper, we observe ChatGPT to explore how LLM services impact learning and instruction in higher education. First, we mapped the capabilities of the system by reviewing the grey literature on ChatGPT and using the system ourselves for two months. Second, we selected a Bachelor level computer science curriculum from a Finnish university, and examined the impact of ChatGPT on the offered courses. As an outcome of this study, we highlight 13 implications for students' learning in higher education, and discuss the contemporary future of AI-assisted learning in universities and beyond.
The geology and geochemistry of the Rhyacian Josephine gold deposit, Northwest Ghana

ABSTRACTThis paper focuses on the geology and geochemistry of the Rhyacian Josephine gold deposit in northwestern Ghana, where gold is primarily hosted in quartzites, with other lithologies observed, including granitoids, mafic intrusions, and gneisses. Four deformational events, DJO0, DJO1, DJO2, and DJO3, are observed. DJO0 is synonymous with the primary preserved sedimentary crossbedding in the quarzitic rocks. Gold mineralization within the deposit is mainly associated with the DJO1 NNW-trending dextral and steeply dipping sinusoidal shear zone. Gold occurs primarily as free gold and invisible gold within the lattices of the disseminated arsenopyrite along the shear foliation planes of the host rock. The ore body geometry is 15–40 m wide and strikes 800 m in the NNW direction. Silicification, sericitization, chloritization, and sulphidation are the main alteration assemblages associated with the gold mineralization zones within the Josephine deposit. Pathfinder elements for gold are stibnite, tellurium, sulphur, copper, silver, and Lead.KEYWORDS: Deformationalterationmineralisationore bodyJosephine deposit AcknowledgementsThe authors would like to express their gratitude to Azumah Resources Limited, the owners of the Josephine deposit, for granting them access to the tenement and drill cores. Additionally, the authors would like to extend their sincere appreciation to the Agate project for providing financial support for this research through student scholarship awards received by the first author in 2021 and 2022.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingAdditionally, the authors would like to extend their sincere appreciation to the Agate project for providing financial support for this research through student scholarship awards received by the first author in 2021 and 2022.
Professional Digital Competence: Definition, Frameworks, Measurement, and Gender Differences: A Systematic Literature Review

In the current context of increasing digitization, professionals need to be digitally competent. In addition, women’s low participation in the technology field indicates the persistence of a digital gender gap in the economic and social spheres. A key aspect to help reducing digital inequality is the role that the digital competence (DC) plays in the professional development of women, allowing them to enter to a job market still coped by men. The current systematic literature review, following the PRISMA protocol, analyzes the existing definitions of professional DC, the frameworks used to develop it at the workplace, and the gender differences observed. Four main ideas emerge from the review of the 41 selected articles: (1) the need of an enabling professional DC definition to help understand how it operates specifically in professional environments; (2) the expanding role of the DigComp framework to carry out initiatives for assessing, training, developing, advising, or certifying digital competence in professional environments; (3) the identification of seven key dimensions of professional DC; and (4) the need of future studies that go further in the measurement of women’s professional DC, as a response of the lack of data about gender differences in this field. Although the limitations of a systematic literature review, such as publications and database bias, these results are aimed at fostering a shared definition and framework of professional DC that standardizes the measurement and development of this competence, allowing workers, and women in particular, to adapt to the digital transformation, assuring equal access to qualified jobs.
Offline Decentralized Multi-Agent Reinforcement Learning

In many real-world multi-agent cooperative tasks, due to high cost and risk, agents cannot continuously interact with the environment and collect experiences during learning, but have to learn from offline datasets. However, the transition dynamics in the dataset of each agent can be much different from the ones induced by the learned policies of other agents in execution, creating large errors in value estimates. Consequently, agents learn uncoordinated low-performing policies. In this paper, we propose a framework for offline decentralized multi-agent reinforcement learning, which exploits value deviation and transition normalization to deliberately modify the transition probabilities. Value deviation optimistically increases the transition probabilities of high-value next states, and transition normalization normalizes the transition probabilities of next states. They together enable agents to learn high-performing and coordinated policies. Theoretically, we prove the convergence of Q-learning under the altered non-stationary transition dynamics. Empirically, we show that the framework can be easily built on many existing offline reinforcement learning algorithms and achieve substantial improvement in a variety of multi-agent tasks.
Filtering of Audio Signals Using Discrete Wavelet Transforms

Nonlinear diffusion has been proved to be an indispensable approach for the removal of noise in image processing. In this paper, we employ nonlinear diffusion for the purpose of denoising audio signals in order to have this approach also recognized as a powerful tool for audio signal processing. We apply nonlinear diffusion to wavelet coefficients obtained from different filters associated with orthogonal and biorthogonal wavelets. We use wavelet decomposition to keep signal components well-localized in time. We compare denoising results using nonlinear diffusion with wavelet shrinkage for different wavelet filters. Our experiments and results show that the denoising is much improved by using the nonlinear diffusion process.
General Sigmoid Relied Banach Space Valued Neural Network Approximation

Here we study the univariate quantitative approximation of Banach space valued continuous functions on a compact interval or all the real line by quasi-interpolation Banach space valued neural network operators. We perform also the related Banach space valued fractional approximation. These approximations are derived by establishing Jackson type inequalities involving the modulus of continuity of the engaged function or its Banach space valued high order derivative or fractional derivaties. Our operators are defined by using a density function induced by a general sigmoid function. The approximations are pointwise and with respect to the uniform norm. The related Banach space valued feed-forward neural networks are with one hidden layer. We finish with a convergence analysis.
Banach Space Valued Multivariate Multi Layer Neural Network Approximation Based on q-Deformed and Parametrized Half Hyperbolic Tangent

Here we research the multivariate quantitative approximation of Banach space valued continuous multivariate functions on a box or $$\mathbb {R}^{N}$$ , $$N\in \mathbb {N},$$ by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We investigate also the case of approximation by iterated multilayer neural network operators of the last four types. These approximations are achieved by establishing multidimensional Jackson type inequalities involving the multivariate modulus of continuity of the engaged function or its partial derivatives. Our multivariate operators are defined by using a multidimensional density function induced by a q-deformed and parametrized half hyperbolic tangent sigmoid function. The approximations are pointwise and uniform. The related feed-forward neural network are with one or multi hidden layers.
Adaptive Fast Image Encryption Algorithm Based on Three-Dimensional Chaotic System

This paper introduces a novel three-dimensional chaotic system that exhibits diverse dynamic behaviors as parameters vary, including phase trajectory offset behaviors and expansion-contraction phenomena. This model encompasses a broad chaotic range and proves suitable for integration within image encryption. Building upon this chaotic system, the study devised a fast image encryption algorithm with an adaptive mechanism, capable of autonomously determining optimal encryption strategies to enhance algorithm security. In pursuit of heightened encryption speed, an FPGA-based chaotic sequence generator was developed for the image encryption algorithm, leveraging the proposed chaotic system. Furthermore, a more efficient scrambling algorithm was devised. Experimental results underscore the superior performance of this algorithm in terms of both encryption duration and security.
3D-Memory efficient listless set partitioning in hierarchical trees for hyperspectral image sensors

Hyperspectral (HS) images contain rich spatial and spectral information. Due to its large size, it is difficult to store, process, analyze, or transmit the critical information contained in it. The compression of hyperspectral images is inevitable. Many transform based Hyper Spectral Image Compression Algorithms (HSICAs) have been proposed in the past that work for both lossy and lossless compression processes. The transform based HSICA uses linked lists or dedicated markers or array structure to keep track of significant and insignificant sets or coefficients of a transformed HS image. However, these algorithms either suffered from low coding efficiency, high memory requirements, or high coding complexity. This work proposes a transform based HSICA using a curvelet transform to improve the directional elements and the ability to represent edges and other singularities along curves. The proposed HSICA aims to provide superior quality compressed HS images by representing HS images at different scales and directions and to achieve a high compression ratio. Experimental results show that the proposed algorithm has a low coding memory requirement with a 2% to 5% increase in coding gain compared to the other state of art compression algorithms.
GAN-based watermarking for encrypted images in healthcare scenarios

Nowadays, it is very common for healthcare professionals or staff to transmit digital data in the form of images over public channels or store it on hard drives or third-party clouds. However, unauthorised users and cloud-service providers may view or abuse these sensitive images. This research proposes a generative adversarial network (GAN)-based watermarking for encrypted images to prevent data leakage in healthcare scenarios. The technique uses a combination of a chaotic map and randomised singular value decomposition (RSVD) to encrypt the image first. Subsequently, a GAN model is developed for watermark generation by hiding multiple marks within an image. Later, the encrypted image is marked by embedding the generated watermark for copyright protection and authentication. This fundamentally solves the problem of copyright violation and privacy leakage of medical data. Experimental results have demonstrated that the proposed method is imperceptible and successfully resists various attacks. The obtained results confirmed the superiority of this method over other techniques, which makes it more suitable for healthcare applications.
Underwater Target Detection Technology Based on YOLO v7

The field of underwater target detection has very important research value in many research fields, such as marine environment, resource exploration, marine biological research and so on. Underwater target detection still has the following challenges: (1) The underwater target has high similarity with the background and low identification; (2) Generally, the volume of the underwater target to be detected is very small and difficult to find; (3) The underwater acquisition image is fuzzy and has heterogeneous noise, which requires targeted image processing and data enhancement technology; (4) Camera jitter will cause changes in the background area and affect the accuracy of target detection methods based on background modeling. In addition, there are many occlusions of underwater targets, which have a certain impact on feature extraction. Therefore, this paper proposes an underwater target detection framework based on YOLO v7, which is composed of supervised feature learning technology. Based on the new model scaling technology, a new Cross Stage Merge is designed to reduce the impact of scaling on the calculation amount and accuracy. By extending the efficient aggregation network, a new framework of E-ELAN is proposed. Finally, the accuracy rate of YOLO v7 at 30 frames per second known in GPU 2080TI exceeded YOLO v5 and YOLO v4, reaching 59.3% AP. The final experimental results are based on the training method from scratch, without the use of pre-training weights.
Automatic Nuclei Segmentation Method using Median Filter for Denoising

Nuclei deliver vast information about the microenvironment. The workload of pathologists can be reduced and precise observations of the microenvironment for clinical research can be made with an automatic nucleus segmentation approach. Although the present deep-learning (DL) based nuclei segmentation techniques generally outperform the conventional ones, this task is still challenging particularly when the nuclei present are cohesive and overlap with one another. In the present work, we have proposed a new nuclei segmentation technique in which we are using a median filter for the pre-processing of the source image along with the U-Net convolution neural network (CNN) model. The median filter can remove the noise present in the source image keeping the edge information preserved. The pre-processing step used enhances the efficacy of the proposed segmentation technique. The performance of the proposed method is compared with the existing techniques for nuclei segmentation on a similar dataset. The proposed technique attained IOU(JI), Accuracy, Precision, and F1 score values as 0.8024, 96.23%, 0.8895, and 0.8704 respectively. The empirical findings indicate that the performance of our proposed technique is better as compared to the other existing methods.
Rethinking Lightweight Salient Object Detection via Network Depth-Width Tradeoff

Existing salient object detection methods often adopt deeper and wider networks for better performance, resulting in heavy computational burden and slow inference speed. This inspires us to rethink saliency detection to achieve a favorable balance between efficiency and accuracy. To this end, we design a lightweight framework while maintaining satisfying competitive accuracy. Specifically, we propose a novel trilateral decoder framework by decoupling the U-shape structure into three complementary branches, which are devised to confront the dilution of semantic context, loss of spatial structure and absence of boundary detail, respectively. Along with the fusion of three branches, the coarse segmentation results are gradually refined in structure details and boundary quality. Without adding additional learnable parameters, we further propose Scale-Adaptive Pooling Module to obtain multi-scale receptive field. In particular, on the premise of inheriting this framework, we rethink the relationship among accuracy, parameters and speed via network depth-width tradeoff. With these insightful considerations, we comprehensively design shallower and narrower models to explore the maximum potential of lightweight SOD. Our models are proposed for different application environments: 1) a tiny version CTD-S (1.7M, 125FPS) for resource constrained devices, 2) a fast version CTD-M (12.6M, 158FPS) for speed-demanding scenarios, 3) a standard version CTD-L (26.5M, 84FPS) for high-performance platforms. Extensive experiments validate the superiority of our method, which achieves better efficiency-accuracy balance across five benchmarks.
Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations

Clinical routine and retrospective cohorts commonly include multi-parametric Magnetic Resonance Imaging; however, they are mostly acquired in different anisotropic 2D views due to signal-to-noise-ratio and scan-time constraints. Thus acquired views suffer from poor out-of-plane resolution and affect downstream volumetric image analysis that typically requires isotropic 3D scans. Combining different views of multi-contrast scans into high-resolution isotropic 3D scans is challenging due to the lack of a large training cohort, which calls for a subject-specific framework. This work proposes a novel solution to this problem leveraging Implicit Neural Representations (INR). Our proposed INR jointly learns two different contrasts of complementary views in a continuous spatial function and benefits from exchanging anatomical information between them. Trained within minutes on a single commodity GPU, our model provides realistic super-resolution across different pairs of contrasts in our experiments with three datasets. Using Mutual Information (MI) as a metric, we find that our model converges to an optimum MI amongst sequences, achieving anatomically faithful reconstruction. (Code is available at: https://github.com/jqmcginnis/multi_contrast_inr/ ).
PMC-CLIP: Contrastive Language-Image Pre-training Using Biomedical Documents

Foundation models trained on large-scale dataset gain a recent surge in CV and NLP. In contrast, development in biomedical domain lags far behind due to data scarcity. To address this issue, we build and release PMC-OA, a biomedical dataset with 1.6M image-caption pairs collected from PubMedCentral’s OpenAccess subset, which is 8 times larger than before, PMC-OA covers diverse modalities or diseases, with majority of the image-caption samples aligned at finer-grained level, i.e., subfigure and subcaption. While pretraining a CLIP-style model on PMC-OA, our model named PMC-CLIP outperform previous state-of-the-art models on various downstream tasks, including image-text retrieval on ROCO, MedMNIST image classification, Medical VQA, for example, +8.1% R@10 on image-text retrieval, +3.9% accuracy on image classification.
RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection

With an excellent balance between speed and accuracy, cutting-edge YOLO frameworks have become one of the most efficient algorithms for object detection. However, the performance of using YOLO networks is scarcely investigated in brain tumor detection. We propose a novel YOLO architecture with Reparameterized Convolution based on channel Shuffle (RCS-YOLO). We present RCS and a One-Shot Aggregation of RCS (RCS-OSA), which link feature cascade and computation efficiency to extract richer information and reduce time consumption. Experimental results on the brain tumor dataset Br35H show that the proposed model surpasses YOLOv6, YOLOv7, and YOLOv8 in speed and accuracy. Notably, compared with YOLOv7, the precision of RCS-YOLO improves by 1%, and the inference speed by 60% at 114.8 images detected per second (FPS). Our proposed RCS-YOLO achieves state-of-the-art performance on the brain tumor detection task. The code is available at https://github.com/mkang315/RCS-YOLO .
Examining the critical factors of internal audit effectiveness from internal auditors’ perspective: Moderating role of extrinsic rewards

An effective internal auditor can support an organization in achieving its goals and protect its assets and funds. However, to be effective, the internal auditors need to be empowered with relevant resources. This study aims at probing the influence of the empowerments (management support, external auditors' collaboration, independence, size of internal audit units, and extrinsic rewards) on the internal auditors' effectiveness, and to examin whether extrinsic rewards moderate the association between respective empowerments and the effectiveness of Jordanian public sector internal auditors'. The current work relied on 117 surveys collected from Jordan's internal audit and financial managers in the public sectors. According to the Resource-Based View (RBV), the findings show that management support, external auditors cooperation, independence, and extrinsic rewards all have a significant influence on the effectiveness of internal auditor. The of the Internal Audit Department (IAD) size was discovered to be insignificant. Also, the results have revealed partial support for the influence of the extrinsic reward as a moderator. Because of the prominence of the public sector in the Jordanian market, this current research expands on the idea of enabling internal auditor (IA) to accomplish their assigned objectives. The findings might help authorities develop new norms and legislation for the internal audit profession. These empowering characteristics may also improve internal auditors' capacity to execute their duty in saving public monies and limiting corrupt practices inside public sector organizations.