Reviewerly/exHarmony huggingface dataset:
The role of Institutional quality, human capital development on economic growth in Sub-Saharan Africa

Purpose – The paper examined the role of institutional quality and human capital development on economic growth in Sub-Saharan African (47) countries. Methodology – The study employed secondary data from all the countries within the Sub-Saharan African countries region for the period of nineteen years, with the exclusion of South Sudan due to insufficient data; all the data was sourced from the World Bank Database and was analyzed via the use of fixed effects regression based on the nature of the study (i.e., Panel data). Findings – This study's findings revealed that IQ and HDI are statistically insignificant in the economic growth of the SSA region. Originality/Novelty – The study covered the existing gaps by adequately and sufficiently measuring the institutional quality and human capital index's role on the stated region's economic growth. Implications - – The study, therefore, recommends that various authorities should enhance both health and educational systems, allocating adequate resources to both sectors to foster economic growth, and there should be proper monitoring and evaluation measures to curtail the corruption and insecurity within this region.
CAENet: Contrast adaptively enhanced network for medical image segmentation based on a differentiable pooling function

Pixel differences between classes with low contrast in medical image semantic segmentation tasks often lead to confusion in category classification, posing a typical challenge for recognition of small targets. To address this challenge, we propose a Contrastive Adaptive Augmented Semantic Segmentation Network with a differentiable pooling function. Firstly, an Adaptive Contrast Augmentation module is constructed to automatically extract local high-frequency information, thereby enhancing image details and accentuating the differences between classes. Subsequently, the Frequency-Efficient Channel Attention mechanism is designed to select useful features in the encoding phase, where multifrequency information is employed to extract channel features. One-dimensional convolutional cross-channel interactions are adopted to reduce model complexity. Finally, a differentiable approximation of max pooling is introduced in order to replace standard max pooling, strengthening the connectivity between neurons and reducing information loss caused by downsampling. We evaluated the effectiveness of our proposed method through several ablation experiments and comparison experiments under homogeneous conditions. The experimental results demonstrate that our method competes favorably with other state-of-the-art networks on five medical image datasets, including four public medical image datasets and one clinical image dataset. It can be effectively applied to medical image segmentation.
The impact of cryptocurrencies capitalization on banking deposits variability in the UAE: evidence from the NARDL approach

Purpose This study aims to analyze the effect of cryptocurrency capitalization market development on bank deposits variability in the United Arab Emirates (UAE) spanning the period 2005M1–2020M4 using the novel nonlinear autoregressive distributive lag (NARDL). Design/methodology/approach The study employs the NARDL recently developed by Shin et al . (2014) to estimate the long and short-run relationships between the variables rather than the widely known ARDL (Pesaran et al., 2001), which suffers from a complex structure in the estimation equation that usually includes lags and differences in both short and long terms. The implementation of NARDL required several proceedings after plotting the descriptive data, commencing with unit root tests, selection of lag length, estimating the long-and-short variables coefficients, heteroscedasticity test and Wald test for symmetries. Findings The long-run estimations of the positive and negative asymmetric coefficients indicate that cryptocurrencies capitalization has a negative impact on bank deposits in the UAE. Further, the short-run estimations coefficients exhibit that both significant positive and negative partial sum squares of cryptocurrencies decrease bank deposits. Research limitations/implications The study has applied to the UAE spanning the period 2005M1–2020M4 using the NARDL. Practical implications The short-run estimations coefficients exhibit that both significant positive and negative partial sum squares of cryptocurrencies decreases bank deposits, which means that the increase in the magnitude of cryptocurrencies capitalization stimulates depositors and speculators to adjust their portfolios towards contracting their deposits in banks to invest partially in cryptocurrencies, on the other hand, the decline in cryptocurrencies capitalization process spur depositors and speculators to reduce their deposits for purchasing cryptocurrencies at lower prices. Social implications The study infers that individuals and businesses are cautious when investing in cryptocurrencies, and they need more certainty and trust to include these types of assets in their portfolios. The fluctuation in cryptocurrencies capitalization prompts speculators to change their deposits according to the cryptocurrencies' prices. Originality/value This study explores the short-and long-run asymmetric impacts of cryptocurrencies capitalization development on bank deposits volatility in the UAE, based on a NARDL, for providing a manifest depiction of whether the cryptocurrencies industry might be a threat to conventional banking system performance in the potential future.
MGDUN: An interpretable network for multi-contrast MRI image super-resolution reconstruction

Magnetic resonance imaging (MRI) Super-Resolution (SR) aims to obtain high resolution (HR) images with more detailed information for precise diagnosis and quantitative image analysis. Deep unfolding networks outperform general MRI SR reconstruction methods by providing better performance and improved interpretability, which enhance the trustworthiness required in clinical practice. Additionally, current SR reconstruction techniques often rely on a single contrast or a simple multi-contrast fusion mechanism, ignoring the complex relationships between different contrasts. To address these issues, in this paper, we propose a Model-Guided multi-contrast interpretable Deep Unfolding Network (MGDUN) for medical image SR reconstruction, which explicitly incorporates the well-studied multi-contrast MRI observation model into an unfolding iterative network. Specifically, we manually design an objective function for MGDUN that can be iteratively computed by the half-quadratic splitting algorithm. The iterative MGDUN algorithm is unfolded into a special model-guided deep unfolding network that explicitly takes into account both the multi-contrast relationship matrix and the MRI observation matrix during the end-to-end optimization process. Extensive experimental results on the multi-contrast IXI dataset and the BraTs 2019 dataset demonstrate the superiority of our proposed model, with PSNR reaching 37.3366 and 35.9690 respectively. Our proposed MGDUN provides a promising solution for multi-contrast MR image super-resolution reconstruction. Code is available at https://github.com/yggame/MGDUN.
Using XR (Extended Reality) for Behavioral, Clinical, and Learning Sciences Requires Updates in Infrastructure and Funding

Extended reality (XR, including augmented and virtual reality) creates a powerful intersection between information technology and cognitive, clinical, and education sciences. XR technology has long captured the public imagination, and its development is the focus of major technology companies. This article demonstrates the potential of XR to (1) deliver behavioral insights, (2) transform clinical treatments, and (3) improve learning and education. However, without appropriate policy, funding, and infrastructural investment, many research institutions will struggle to keep pace with the advances and opportunities of XR. To realize the full potential of XR for basic and translational research, funding should incentivize (1) appropriate training, (2) open software solutions, and (3) collaborations between complementary academic and industry partners. Bolstering the XR research infrastructure with the right investments and incentives is vital for delivering on the potential for transformative discoveries, innovations, and applications.
SDA-Net: Self-distillation driven deformable attentive aggregation network for thyroid nodule identification in ultrasound images

Early detection and accurate identification of thyroid nodules are the major challenges in controlling and treating thyroid cancer that can be difficult even for expert physicians. Currently, many computer-aided diagnosis (CAD) systems have been developed to assist this clinical process. However, most of these systems are unable to well capture geometrically diverse thyroid nodule representations from ultrasound images with subtle and various characteristic differences, resulting in suboptimal diagnosis and lack of clinical interpretability, which may affect their credibility in the clinic. In this context, a novel end-to-end network equipped with a deformable attention network and a distillation-driven interaction aggregation module (DIAM) is developed for thyroid nodule identification. The deformable attention network learns to identify discriminative features of nodules under the guidance of the deformable attention module (DAM) and an online class activation mapping (CAM) mechanism and suggests the location of diagnostic features to provide interpretable predictions. DIAM is designed to take advantage of the complementarities of adjacent layers, thus enhancing the representation capabilities of aggregated features; driven by an efficient self-distillation mechanism, the identification process is complemented with more multi-scale semantic information to calibrate the diagnosis results. Experimental results on a large dataset with varying nodule appearances show that the proposed network can achieve competitive performance in nodule diagnosis and provide interpretability suitable for clinical needs.
From beasts to bytes: Revolutionizing zoological research with artificial intelligence

Since the late 2010s, Artificial Intelligence (AI) including machine learning, boosted through deep learning, has boomed as a vital tool to leverage computer vision, natural language processing and speech recognition in revolutionizing zoological research. This review provides an overview of the primary tasks, core models, datasets, and applications of AI in zoological research, including animal classification, resource conservation, behavior, development, genetics and evolution, breeding and health, disease models, and paleontology. Additionally, we explore the challenges and future directions of integrating AI into this field. Based on numerous case studies, this review outlines various avenues for incorporating AI into zoological research and underscores its potential to enhance our understanding of the intricate relationships that exist within the animal kingdom. As we build a bridge between beast and byte realms, this review serves as a resource for envisioning novel AI applications in zoological research that have not yet been explored.
Wavelet Scattering Transform based Doppler signal classification

Normal blood supply to the human brain may be marred by the presence of a clot inside the blood vessels. This clot structure called emboli inhibits normal blood flow to the brain. It is considered as one of the main sources of stroke. Presence of emboli in human's can be determined by the analysis of transcranial Doppler signal. Different signal processing and machine learning algorithms have been used for classifying the detected signal as an emboli, Doppler speckle, and an artifact. In this paper, we sought to make use of the wavelet transform based algorithm called Wavelet Scattering Transform, which is translation invariant and stable to deformations for classifying different Doppler signals. With its architectural resemblance to Convolutional Neural Network, Wavelet Scattering Transform works well on small datasets and subsequently was trained on a dataset consisting of 300 Doppler signals. To check the effectiveness of extracted Scattering transform based features for Doppler signal classification, learning algorithms that included multi-class Support vector machine, k-nearest neighbor and Naive Bayes algorithms were trained. Comparative analysis was done with respect to the handcrafted Continuous wavelet transform features extracted from samples and Wavelet scattering with Support vector machine achieved an accuracy of 98.89%. Also, with set of extracted scattering coefficients, Gaussian process regression was performed and a regression model was trained on three different sets of scattering coefficients with zero order scattering coefficients providing least prediction loss of 34.95%.
Central Vision based Super-resolution for 360-Degree Videos

With the rapid development of VR technology, 360-degree video is gradually coming into public view. Compared with normal video, it can bring a better immersive experience to the user with the trade-off of a huge bandwidth and transmission latency. Many studies choose to transmit low-resolution video frames to solve those problems and use super-resolution on the client side to reconstruct the resolution of the whole frame. However, in those methods, computational latency caused by super-resolution process of either a whole video frame or viewport portion of a frame, is critical problem. In this paper, the idea of central vision based super resolution is considered in 360-degree video streaming. Particularly, we adopt FOCAS - a super resolution method using foveated rendering for normal 2D video, to 360-degree video. We conduct an experiment in order to verify the effectiveness of human central vision. The experimental results show that it is able to reduce the computational latency by 82% while maintaining a competitive visual quality compared to existing super-resolution methods.
Quantum Mechanics in the Context of Heidegger’s Ontology

Aristotle was the first to notice that entity exists not only as real, but also as pos­sible. The Aristotelian concept of possibility has an insufficiently clear ontologi­cal sense. Aristotle does not ask about own Being of possibilities. For a classical thought it had no essential importance. In quantum physics the situation is differ­ent. Quantum possibilities have own action, so, it is necessary to recognize their own Being. However, it cannot be made within usual thinking, since quantum possibilities do not allow representation in terms of an entity present-at-hand. It sends us to Heidegger’s ideas. Classical philosophical thought does not notice an ontological difference between the “Being” and “Entity”; as a result “Being” is presented as “something”. Heidegger showed simplified nature of such ap­proach, however the natural sciences usually accept the specified identification. A surprising exception is the quantum mechanics. Having recognized own Being of quantum possibilities, we will see the attribute of Heidegger’s ontology. This “Being” is not “Something” and is designated by me as “Ψ-Being”. It possesses the internal temporality similar to existential time of Dasein. Also the difference between “Ψ-Being” and “Being” of Heidegger’s ontology is detected. The fact is that “Ψ-Being possesses action which is capable to change definiteness of quan­tum entity. It offers a heuristic prospect for philosophical ontology. According to Heidegger, Dasein is the existential Being of possibilities. However, these possibilities are not connected with personal definiteness. But the quantum me­chanics shows us that definiteness of “entity” (“something” or “who”) can change under the influence of “Being” that cannot be imagined in terms of “something”.
A Key Escrow-Free KP-ABE Scheme and Its Application in Stand-Alone Authentication in IoT

When users access the Internet of Things (IoT) devices, authenticating their identities and permissions is an important measure to ensure system security and achieve access control. Stand-alone authentication (SAA) is an efficient authentication method because it allows IoT devices to independently finish user authentication, avoiding the reliance on a single authentication center in traditional authentication methods. Attribute-based encryption (ABE) supports fine-grained access control and can be used to realize SAA. In certain SAA scenarios, the underlying ABE scheme should also meet some special requirements of security and function. For example, when a central authority seeks help from multiple proxy authorities to share the burden of managing users’ access rights, dishonest proxy authorities may grant access rights to illegal users to gain benefits. This situation where the authority abuses its capability of access rights management is referred to as the key escrow problem in ABE (because for ABE, users’ decryption keys represent their access rights). Therefore, we propose a key-policy ABE (KP-ABE) scheme without key escrow to prevent dishonest authorities from abusing their power. Compared with related schemes, the new scheme can relatively fully solve the key escrow problem, has high access policy expressiveness, and is efficient. These advantages ensure that the proposed scheme can be used to achieve secure and efficient SAA in IoT.
MO-AVC: Deep Reinforcement Learning Based Trajectory Control and Task Offloading in Multi-UAV enabled MEC Systems

We investigate the joint trajectory control and task offloading (JTCTO) problem in multi-unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC). However, existing JTCTO solutions primarily focus on fixed UAV-enabled MEC scenario variations and necessitate extensive interaction to adapt to new scenarios. Moreover, we consider minimizing task latency and UAV’s energy consumption, and maximizing the quantity of tasks collected by the UAV as optimization goals. However, this optimization problem is characterized by multiple conflicting goals that should be adjusted according to their relative significance. In this paper, we present a Multi-Objective Actor-Variations Critic-based JTCTO solution (MO-AVC). First, a group of reinforcement learning strategies is utilized to collect experience on training scenarios, which are employed to learn embeddings of both strategies and scenarios. Further, these two embeddings are used as inputs to train the AVC, which explicitly estimates the total return in a space of JTCTO strategies and UAV-enabled MEC scenarios. When adapting to a new scenario, just a few steps of scenario interaction are enough to predict the scenario embedding, thus selecting strategies by maximizing the trained AVC. Second, we propose an actor-conditioned critic framework where the outputs are conditioned on the varying significance of goals, and present a weight dynamic memory-based experience replay to address the intrinsic instability of the dynamic weight context. Finally, simulation results show that MO-AVC can quickly adapt to new scenarios. Moreover, MO-AVC reduces the latency by 7.56%~10.57%, the energy consumption by 11.11%~17.27%, and increases the tasks number by 10.33%~15.54% compared to existing solutions.
Deciding on cloud-managed services: The cloud trilemma

The phenomenal growth in cloud managed services (i.e. provisioning) is evident. But, despite the burgeoning cloud provisioning marketplace, cloud provisioning comes in various forms. Therefore, companies need to carefully and deliberately consider their cloud-managed service choices. This teaching case, using the context of Telecom companies’ (Telcos) billing services provisioning, not only highlights various provisioning engagement models but also surfaces an interesting trilemma, competing facets, that companies need to consider as they embark upon and expand upon their existing services portfolio.
Clinical Application of Digital and Computational Pathology in Renal Cell Carcinoma: A Systematic Review

Context Computational pathology is a new interdisciplinary field that combines traditional pathology with modern technologies such as digital imaging and machine learning to better understand the diagnosis, prognosis, and natural history of many diseases. Objective To provide an overview of digital and computational pathology and its current and potential applications in renal cell carcinoma (RCC). Evidence acquisition A systematic review of the English-language literature was conducted using the PubMed, Web of Science, and Scopus databases in December 2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PROSPERO ID: CRD42023389282). Risk of bias was assessed according to the Prediction Model Study Risk of Bias Assessment Tool. Evidence synthesis In total, 20 articles were included in the review. All the studies used a retrospective design, and all digital pathology techniques were implemented retrospectively. The studies were classified according to their primary objective: detection, tumor characterization, and patient outcome. Regarding the transition to clinical practice, several studies showed promising potential. However, none presented a comprehensive assessment of clinical utility and implementation. Notably, there was substantial heterogeneity for both the strategies used for model building and the performance metrics reported. Conclusions This review highlights the vast potential of digital and computational pathology for the detection, classification, and assessment of oncological outcomes in RCC. Preliminary work in this field has yielded promising results. However, these models have not yet reached a stage where they can be integrated into routine clinical practice. Patient summary Computational pathology combines traditional pathology and technologies such as digital imaging and artificial intelligence to improve diagnosis of disease and identify prognostic factors and new biomarkers. The number of studies exploring its potential in kidney cancer is rapidly increasing. However, despite the surge in research activity, computational pathology is not yet ready for widespread routine use.
Does banning cryptocurrencies affect stock markets?

Purpose This paper aims to investigate the impact of banning cryptocurrencies on stock markets. Design/methodology/approach The paper uses an event study approach and data from stock market indices in nine countries that imposed a ban. It uses the constant mean model and the market model, with two different benchmarks for global returns, to analyze if any of the stock indices show abnormal returns on or around the announcement of a cryptocurrency ban. Findings The analysis shows that banning cryptocurrencies did not affect the returns of stock markets in any of the countries studied, indicating that the cryptocurrency market and stock markets are decoupled from each other, or the ban was not effectively implemented. Originality/value To the best of the author’s knowledge, this paper is the first to explore the potential spillover effect of a cryptocurrency ban on stock markets. It also bridges two strands of literature: the relationship between cryptocurrencies and traditional assets, and the impact of cryptocurrency regulation on their returns.