Reviewerly/exHarmony huggingface dataset:
Discrete residual diffusion model for high-resolution prostate MRI synthesis

Objective.High-resolution magnetic resonance imaging (HR MRI) is an effective tool for diagnosing PCa, but it requires patients to remain immobile for extended periods, increasing chances of image distortion due to motion. One solution is to utilize super-resolution (SR) techniques to process low-resolution (LR) images and create a higher-resolution version. However, existing medical SR models suffer from issues such as excessive smoothness and mode collapse. In this paper, we propose a novel generative model avoiding the problems of existing models, called discrete residual diffusion model (DR-DM).Approach.First, the forward process of DR-DM gradually disrupts the input via a fixed Markov chain, producing a sequence of latent variables with increasing noise. The backward process learns the conditional transit distribution and gradually match the target data distribution. By optimizing a variant of the variational lower bound, training diffusion models effectively address the issue of mode collapse. Second, to focus DR-DM on recovering high-frequency details, we synthesize residual images instead of synthesizing HR MRI directly. The residual image represents the difference between the HR and LR up-sampled MR image, and we convert residual image into discrete image tokens with a shorter sequence length by a vector quantized variational autoencoder (VQ-VAE), which reduced the computational complexity. Third, transformer architecture is integrated to model the relationship between LR MRI and residual image, which can capture the long-range dependencies between LR MRI and the synthesized imaging and improve the fidelity of reconstructed images.Main results.Extensive experimental validations have been performed on two popular yet challenging magnetic resonance image super-resolution tasks and compared to five state-of-the-art methods.Significance.Our experiments on the Prostate-Diagnosis and PROSTATEx datasets demonstrate that the DR-DM model significantly improves the signal-to-noise ratio of MRI for prostate cancer, resulting in greater clarity and improved diagnostic accuracy for patients.
High-performance reconstruction method combining total variation with a video denoiser for compressed ultrafast imaging

Compressed ultrafast photography (CUP) is a novel two-dimensional (2D) imaging technique to capture ultrafast dynamic scenes. Effective image reconstruction is essential in CUP systems. However, existing reconstruction algorithms mostly rely on image priors and complex parameter spaces. Therefore, in general, they are time-consuming and result in poor imaging quality, which limits their practical applications. In this paper, we propose a novel reconstruction algorithm, to the best of our knowledge, named plug-in-plug-fast deep video denoising net-total variation (PnP-TV-FastDVDnet), which exploits an image's spatial features and correlation features in the temporal dimension. Therefore, it offers higher-quality images than those in previously reported methods. First, we built a forward mathematical model of the CUP, and the closed-form solution of the three suboptimization problems was derived according to plug-in and plug-out frames. Secondly, we used an advanced video denoising algorithm based on a neural network named FastDVDnet to solve the denoising problem. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) are improved on actual CUP data compared with traditional algorithms. On benchmark and real CUP datasets, the proposed method shows the comparable visual results while reducing the running time by 96% over state-of-the-art algorithms.
Super-resolution reconstruction of underwater polarized images with a fused attention mechanism

The polarization imaging technique leverages the disparity between target and background polarization information to mitigate the impact of backward scattered light, thereby enhancing image quality. However, the imaging model of this method exhibits limitations in extracting inter-image features, resulting in less-than-optimal outcomes in turbid underwater environments. In recent years, machine learning methodologies, particularly neural networks, have gained traction. These networks, renowned for their superior fitting capabilities, can effectively extract information from multiple images. The incorporation of an attention mechanism significantly augments the capacity of neural networks to extract inter-image correlation attributes, thereby mitigating the constraints of polarization imaging methods to a certain degree. To enhance the efficacy of polarization imaging in complex underwater environments, this paper introduces a super-resolution network with an integrated attention mechanism, termed as SRGAN-DP. This network is a fusion of an enhanced SRGAN network and the high-performance deep pyramidal split attention (DPSA) module, also proposed in this paper. SRGAN-DP is employed to perform high-resolution reconstruction of the underwater polarimetric image dataset, constructed specifically for this study. A comparative analysis with existing algorithms demonstrates that our proposed algorithm not only produces superior images but also exhibits robust performance in real-world environments.
Potential cognitive risks of generative transformer-based AI chatbots on higher order executive functions.

Chat generative retrained transformer (ChatGPT) represents a groundbreaking advancement in Artificial Intelligence (AI-chatbot) technology, utilizing transformer algorithms to enhance natural language processing and facilitating their use for addressing specific tasks. These AI chatbots can respond to questions by generating verbal instructions similar to those a person would provide during the problem-solving process.ChatGPT has become the fastest growing software in terms of user adoption in history, leading to an anticipated widespread use of this technology in the general population. Current literature is predominantly focused on the functional aspects of these technologies, but the field has not yet explored hypotheses on how these AI chatbots could impact the evolutionary aspects of human cognitive development. Thesis: The "neuronal recycling hypothesis" posits that the brain undergoes structural transformation by incorporating new cultural tools into "neural niches," consequently altering individual cognition. In the case of technological tools, it has been established that they reduce the cognitive demand needed to solve tasks through a process called "cognitive offloading." In this theoretical article, three hypotheses were proposed via forward inference about how algorithms such as ChatGPT and similar models may influence the cognitive processes and structures of upcoming generations.By forecasting the neurocognitive effects of these technologies, educational and political communities can anticipate future scenarios and formulate strategic plans to either mitigate or enhance the cognitive influence that these factors may have on the general population. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Molecular Epidemiology of Plasmodium falciparum Infections Using PCR-based Assays in Jos, Nigeria-cross-sectional Study

Background: Malaria remains a significant health threat globally, with Plasmodium falciparum being the predominant and lethal parasite in Africa. Nigeria is still faced with ongoing cases of asymptomatic malaria, hindering effective control measures.&#x0D; Aim: The aim was to generate epidemiological data that will provide good background and guide strategies for driving malaria control efforts, research, and resource allocation in the region.&#x0D; Place and Duration of Study: This study was conducted in Jos, Plateau State, Nigeria, where the samples were originally collected within about 16 months between October 2019 and January 2021.&#x0D; Methodology: A cross-sectional molecular epidemiological study was conducted using 136 microscopically screened 2 plus (++) and above positive malarial whole blood samples obtained in EDTA bottles from two hospitals in Jos, Plateau State, Nigeria. The DNA extraction was performed according to the manufacturer's instructions using Zymo Research extraction kits. Plasmodium genus and Plasmodium falciparum were detected in the samples using the PCR method and gel electrophoresis.&#x0D; Results: In the results, using PCR techniques, 47.8% (65/136) of the total malaria-positive samples collected were confirmed for the presence of the Plasmodium genus. Out of these 65 positive samples, 63 were found to be Plasmodium falciparum.&#x0D; Conclusion: This study demonstrates that Plasmodium falciparum remains the predominant malaria species in Jos, Plateau State, comprising approximately 96.9% (63/65) of the malarial cases. This indicates that only about 3% of malaria cases affecting the residents of Jos, Plateau State might be caused by the other four species of malaria parasites (Plasmodium vivax, Plasmodium malariae, Plasmodium ovale, and Plasmodium knowlesi).
Application solutions of highway freight information systems based on quantum communication

Abstract To improve the security of data transmission in the highway freight information system, this study is an application plan for the highway freight information system based on quantum communication. This solution is based on quantum communication technology to encrypt and transmit key sensitive data [1] ; it realizes unified management of quantum keys through the quantum key cloud terminal and provides key services for the highway freight information system; it realizes access to the system through the quantum key cloud service platform. The secure use of mobile terminal quantum keys improves the overall security of the road freight information system. This scheme uses the quantum encryption key generated only once, effectively protecting the entire system's security. The quantum key management server and quantum key cloud platform defined in this plan manage terminals and quantum keys respectively, and jointly produce and distribute quantum keys with the help of other hardware facilities and software to provide secure transmission of key information.
CROWD MANAGEMENT DURING MASS EVENTS

Mass events organized in Poland in recent years, including sports, entertainment, as well ascultural and social (religious) gatherings, have been consistently gaining popularity, as reflectedin the large number of participants. This is undoubtedly due to the attractiveness of the events onone hand and the positive assessment of the safety level during these events on the other. Yet itis crucial to recognize that beyond their positive aspects, mass events frequently present specificchallenges and threats. Unpredictable and irrational behaviour among participants can result froma variety of factors, posing risks to their health and lives as well as that of bystanders and property.Effective managing of these situations is a genuine challenge for governmental authorities,services and responsible entities. This challenge stems from comprehensive preparatory andorganizational measures designed to prevent crisis situations and, if they occur, promptly regaincontrol. This study aims to present a model of organizational-procedural solutions and explorethe application of modern information technologies to enhance participant safety during massevents. The research methodology involved a comprehensive analysis of scientific, industry, andlegal materials. Additionally, valuable insights were drawn from the author’s first-hand experiencegained through direct observations while fulfilling official duties in ensuring the safety of massevents during real incidents in Poland.
Data-based approach for time-correlated closures of turbulence models

Developed turbulent motion of fluid still lacks an analytical description despite more than a century of active research. Nowadays, phenomenological ideas are widely used in practical applications, such as small-scale closures for numerical simulations of turbulent flows. In the present paper, we use a shell model of turbulence to construct a closure intended to have a solid theoretical background and to capture intrinsic probabilistic features of turbulence. Shell models of turbulence are dynamical deterministic systems used to model energy cascade and other key aspects of the Navier-Stokes such as intermittency. We rescale the variables of the Sabra model in a way which leads to hidden symmetries and universal distributions. We then use such fine distributions to write closures, i.e., missing expressions for some of the Sabra variables. Our closures rely on approximating probability density functions using a Gaussian mixture model, which makes them probabilistic by nature and allows us to write time-correlated closures. We also provide a framework where other machine learning tools can be employed with reduced black-box aspects.
Security issues of the gold industry chain based on smart blockchain in the context of the Internet of Things

Abstract The purpose is to solve the safety production and management problems of the gold Industrial Chain and give early warning of the safety situation of the gold Industrial Chain. According to the theory of industrial chain security governance and the basic situation of the gold Industrial Chain, this work establishes a gold Industrial Chain model based on smart blockchain and system dynamics (SD), and discusses the application of the gold Industrial Chain in the Internet of Things (IoT) environment. The overall goal of the application of IoT technology to the safety management of intelligent gold mines is to take the gold mine production demand as the driving force. The digitalization of production, electromechanical, safety, dispatching, and other information realizes intelligent digital perception, assists decision-making, guides the safety management of gold mining operations, continuously improves the operation efficiency of the gold mining industry, and drives the development of the industry. Finally, it takes the resource reserve of China’s gold industry from 2011 to 2021 as the research data introduces the weighting method to assess the security situation of China’s gold Industrial Chain from 2011 to 2021. The safety performance of China's environmental industry chain is evaluated through the detailed introduction of the basic information of the gold Industrial Chain. The result shows that the security situation of China’s gold Industrial Chain from 2011 to 2021 shows an overall growth trend, 88.42% higher than in 2014. The security situation of China’s gold Industrial Chain from 2011 to 2021 positively impacted the opening of the domestic gold market and entering the international gold market, improving the security level of China’s gold Industrial Chain. In this work, a gold Industrial Chain security model based on smart blockchain and SD is established to solve the safety problem of the gold Industrial Chain, which can improve the safety level of the gold industry and promote its sustainable development.
Covering Hierarchical Dirichlet Mixture Models on binary data to enhance genomic stratifications in onco-hematology

Onco-hematological studies are increasingly adopting statistical mixture models to support the advancement of the genomically-driven classification systems for blood cancer. Targeting enhanced patients stratification based on the sole role of molecular biology attracted much interest and contributes to bring personalized medicine closer to reality. In onco-hematology, Hierarchical Dirichlet Mixture Models (HDMM) have become one of the preferred method to cluster the genomics data, that include the presence or absence of gene mutations and cytogenetics anomalies, into components. This work unfolds the standard workflow used in onco-hematology to improve patient stratification and proposes alternative approaches to characterize the components and to assign patient to them, as they are crucial tasks usually supported by a priori clinical knowledge. We propose (a) to compute the parameters of the multinomial components of the HDMM or (b) to estimate the parameters of the HDMM components as if they were Multivariate Fisher’s Non-Central Hypergeometric (MFNCH) distributions. Then, our approach to perform patients assignments to the HDMM components is designed to essentially determine for each patient its most likely component. We show on simulated data that the patients assignment using the MFNCH-based approach can be superior, if not comparable, to using the multinomial-based approach. Lastly, we illustrate on real Acute Myeloid Leukemia data how the utilization of MFNCH-based approach emerges as a good trade-off between the rigorous multinomial-based characterization of the HDMM components and the common refinement of them based on a priori clinical knowledge.
Untouchable them: the effect of chatbot gender on angry customers

Purpose This study explores how chatbot gender and symbolic service recovery may improve the satisfaction of angry customers in the context of service failures. It provides a strategy for companies to deploy chatbots effectively in customer anger. Design/methodology/approach This research relies upon a systematic literature review to propose three hypotheses, and we recruit 826 participants to examine the effect of chatbot gender on angry customers through one lab study and one field study. Findings This research shows that female chatbots are more likely to increase the satisfaction of angry customers than male chatbots in service failure scenarios. In addition, symbolic recovery (apology vs. appreciation) moderates the effect of chatbot gender on angry customers. Specifically, male (vs. female) chatbots are more effective in increasing the satisfaction of angry customers when using the apology method, whereas female (vs. male) chatbots are more effective when using the appreciation method. Originality/value The rapid advancements in artificial intelligence technology have significantly enhanced the effectiveness of chatbots as virtual agents in the field of interactive marketing. Previous research has concluded that chatbots can reduce negative customer feedback following a service failure. However, these studies have primarily focused on the level of chatbot anthropomorphism and the design of conversational texts, rather than the gender of chatbots. Therefore, this study aims to bridge that gap by examining the effect of chatbot gender on customer feedback, specifically focusing on angry customers following service failures.
IHIBE: A Hierarchical and Delegated Access Control Mechanism for IoT Environments

Ensuring authorized access control in the IoT is vital for privacy and safety protection. Our study presents the novel IHIBE framework, which combines IOTA (a distributed ledger technology) with hierarchical identity-based encryption (HIBE), thereby enhancing both IoT security and scalability. This approach secures access tokens and policies while reducing the computational demand on data owners. Our empirical findings reveal a significant performance gap, with access rights delegation on the Raspberry Pi 4 exceeding those on AWS by over 250%. Moreover, our analysis uncovers optimal identity policy depths: up to 640 identities on AWS and 640 on the Raspberry Pi 4 for systems with higher tolerable delays, and 320 identities on AWS versus 160 on the Raspberry Pi 4 for systems with lower tolerable delays. The system shows practical viability, exhibiting insignificant operational time differences compared to Zhang et al.’s schemes, particularly in access rights verification processes, with a minimal difference of 33.35%. Our extensive security assessment, encompassing scenarios like encrypted token theft and compromise of authority, affirms the efficacy of our challenge-response and last-word challenge (LWC) mechanisms. This study underscores the importance of platform choice in IoT system architectures and provides insights for deploying efficient, secure, and scalable IoT environments.
The effect of metaverse on L2 vocabulary learning, retention, student engagement, presence, and community feeling

Abstract This study investigates the multifaceted impacts of Metaverse Based Language Teaching on high school L2 vocabulary learning and retention, engagement, community feeling, and diverse dimensions of presence, namely social, cognitive, teaching, and overall presence. Adopting a quasi-experimental design, the study provides an in-depth controlled trial. The participants in this study consist of 86 (43 male, 43 female) high school English language learners in Türkiye, who are assigned to two groups. The control group uses traditional instructional technologies while the experimental group engages with metaverse technology. The lessons are based on social constructivist theory for both groups. Pre- and post-tests are employed to quantitatively assess vocabulary learning and retention and reveal substantial improvements in both areas. A series of carefully selected psychometric scales capture core aspects of engagement, community feeling, and presence. The findings reveal increased levels of engagement and a sense of presence, and connectedness within the language learning community, which confirms the efficacy of the MBLT. However, the improvement in social presence does not reach statistical significance. Cumulatively, the findings of this research transcend mere quantifiable improvements in vocabulary learning and retention. They reveal a holistic transformation of the language learning experience by emphasizing the significance of social interactions and digital embodiment in high school second language education through metaverse. The research highlights the metaverse’s potential in shaping the future of language education, reimagining traditional paradigms, and proposing a new era of immersive, interactive, and transformative pedagogy.
Content Based Recommendation System on Netflix Data

After pandemic, OTT platforms are the most common platform to provide entertainment to users. Among all platforms, Netflix has become most the popular one. Data visualization of Netflix data can provide valuable insights and benefits in many ways like understanding viewer preferences, content optimization, personalized recommendation, quality and content performance evaluation, fraud detection to name a few. This research provides exploratory data visualization and provide a content based recommendation system on Netflix data as in real world applications, company uses these recommendation system algorithms to determine which system are better to improve users’ engagement of the platform.