Reviewerly/exHarmony huggingface dataset:
Intermediate Task Fine-Tuning in Cancer Classification

Reducing the amount of annotated data required to train predictive models is one of the main challenges in applying artificial intelligence to histopathology. In this paper, we propose a method to enhance the performance of deep learning models trained with limited data in the field of digital pathology. The method relies on a two-stage transfer learning process, where an intermediate model serves as a bridge between a pre-trained model on ImageNet and the final cancer classification model. The intermediate model is fine-tuned with a dataset of over 4,000,000 images weakly labeled with clinical data extracted from TCGA program. The model obtained through the proposed method significantly outperforms a model trained with a traditional transfer learning process.
Research on the Demand and Training Path Mechanism of Talents for Rural Revitalization in Ankang

Talents play a pivotal role in rural revitalization. In the new era, the development of Ankang City prioritizes the advancement of rural human resources. It organizes and implements a city-wide plan to rejuvenate rural talents. The city firmly seizes the "gateway" to rural revitalization by grasping the four essential elements of "retaining, utilizing, attracting, and educating" in development. It vigorously fosters and trains local talents while rejuvenating the foundational talent pool. Additionally, it intensifies efforts to actively attract foreign talents and expand the growth of talent capital. The city establishes and enhances mechanisms for talent attraction and employment, all with the aim of retaining and nurturing talents. It adopts multiple measures to support the sustainable growth of agriculture and rural areas.
SLSL-QPSO: Quantum-behaved particle swarm optimization with short-lived swarm layers

SLSL-QPSO is a software that can find the optimal value of a function. It improves over the Quantum-behaved Particle Swarm Optimization (QPSO) algorithms by leveraging the concept of living and death as swarm layers like the parameter optimization in the Optimized PSO (OPSO) but without the super swarm, the Lévy mutation, the scoped contradiction-expansion coefficient, and the selection of effective layers. Experimental results demonstrate that SLSL-QPSO has superior performance in finding better optimal than QPSO and several other variants thus providing a competitive solution to optimization problems.
Overcoming Periodic Stripe Noise in Infrared Linear Array Images: The Fourier-Assisted Correlative Denoising Method

Infrared linear array detectors frequently experience vertical, low-frequency, and periodic stripe noise during imaging, stemming from electro-mechanical interference. Unlike conventional periodic disturbances, this interference showcases long periodicities and is uniquely columnar in orientation. Its presence, especially within the low-frequency domain, renders conventional filtering techniques ineffective and, at times, detrimental to image quality. Addressing this challenge, we introduce Fourier-Assisted Correlative Denoising (FACD), a correlation-centric denoising approach tailored for such unique interference patterns. This mechanism begins with the capture of a pure background image, inclusive of periodic noise, during the non-uniform correction phase of the infrared detector. Leveraging the noise's frequency domain attributes, we extract a one-dimensional single-cycle noise signal. The infrared image is subsequently segmented into parts, and using the detected noise periodicity, the one-dimensional signals for each segment are computed. By leveraging the correlation between these signals and the benchmark one-dimensional noise pattern, we ascertain the noise profile within each segment. This profile is then employed for spatial domain denoising across the entire image frame. Empirical assessments confirm that the FACD outperforms contemporary denoising techniques by augmenting the peak signal-to-noise ratio by approximately 2.5 dB, underscoring its superior robustness. Furthermore, in light of its specificity to this noise model, FACD rapidly denoises high-resolution real infrared linear array scans, thus meeting the stringent real-time and resolution imperatives of advanced infrared linear array scanning apparatuses.
Radical design praxis and the problematics of intent

This article explores counter-design as a radical meaning-making practice for community co-design. Radicalism, here, is an ongoing process of complicating and unsettling daily practices to reveal and thus acknowledge socio-political context. Semiotic analysis of ethnographic research with grassroots organisations is taken as the point of departure to understand how communities co-design meaning, and in turn how we might co-design new meanings through countering. Through counter-design in place, we observe small semiotic moments that problematise the rhetoric of horizontal democratic organising amongst the realities of coercion and power asymmetries. These semiotic moments are thus significant for how they reveal the reproduction of meaning and how this constructs, and can subsequently limit, the possible actions for these communities. In this sense, our approach constitutes a design praxis, through which we analyse what it means to employ countering against existing institutional power, both for our participants and for ourselves as designers. Here, we reveal the tenuousness of (designer/ly) intent in relation to and in combination with the artifice of readymade community designs, as these are shaped by work ‘on the ground’ and consider how this might impact the co-creation of democratic communities.
Diagnosis of Alzheimer’s Disease Based on Accelerated Mirror Descent Optimization and a Three-Dimensional Aggregated Residual Network

Alzheimer's disease (AD), a neuropsychiatric disorder, continually arises in the elderly. To date, no targeted medications have been developed for AD. Early and fast diagnosis of AD plays a pivotal role in identifying potential AD patients, enabling timely medical interventions, and mitigating disease progression. Computer-aided diagnosis (CAD) becomes possible with the burgeoning of deep learning. However, the existing CAD models for processing 3D Alzheimer's disease images usually have the problems of slow convergence, disappearance of gradient, and falling into local optimum. This makes the training of 3D diagnosis models need a lot of time, and the accuracy is often poor. In this paper, a novel 3D aggregated residual network with accelerated mirror descent optimization is proposed for diagnosing AD. First, a novel unbiased subgradient accelerated mirror descent (SAMD) optimization algorithm is proposed to speed up diagnosis network training. By optimizing the nonlinear projection process, our proposed algorithm can avoid the occurrence of the local optimum in the non-Euclidean distance metric. The most notable aspect is that, to the best of our knowledge, this is the pioneering attempt to optimize the AD diagnosis training process by improving the optimization algorithm. Then, we provide a rigorous proof of the SAMD's convergence, and the convergence of SAMD is better than any existing gradient descent algorithms. Finally, we use our proposed SAMD algorithm to train our proposed 3D aggregated residual network architecture (ARCNN). We employed the ADNI dataset to train ARCNN diagnostic models separately for the AD vs. NC task and the sMCI vs. pMCI task, followed by testing to evaluate the disease diagnostic outcomes. The results reveal that the accuracy can be improved in diagnosing AD, and the training speed can be accelerated. Our proposed method achieves 95.4% accuracy in AD diagnosis and 79.9% accuracy in MCI diagnosis; the best results contrasted with several state-of-the-art diagnosis methods. In addition, our proposed SAMD algorithm can save about 19% of the convergence time on average in the AD diagnosis model compared with the gradient descent algorithms, which is very momentous in clinic.
Advancements in combining electronic animal identification and augmented reality technologies in digital livestock farming

Modern livestock farm technologies allow operators to have access to a multitude of data thanks to the high number of mobile and fixed sensors available on both the livestock farming machinery and the animals. These data can be consulted via PC, tablet, and smartphone, which must be handheld by the operators, leading to an increase in the time needed for on-field activities. In this scenario, the use of augmented reality smart glasses could allow the visualization of data directly in the field, providing for a hands-free environment for the operator to work. Nevertheless, to visualize specific animal information, a connection between the augmented reality smart glasses and electronic animal identification is needed. Therefore, the main objective of this study was to develop and test a wearable framework, called SmartGlove that is able to link RFID animal tags and augmented reality smart glasses via a Bluetooth connection, allowing the visualization of specific animal data directly in the field. Moreover, another objective of the study was to compare different levels of augmented reality technologies (assisted reality vs. mixed reality) to assess the most suitable solution for livestock management scenarios. For this reason, the developed framework and the related augmented reality smart glasses applications were tested in the laboratory and in the field. Furthermore, the stakeholders' point of view was analyzed using two standard questionnaires, the NASA-Task Load Index and the IBM-Post Study System Usability Questionnaire. The outcomes of the laboratory tests underlined promising results regarding the operating performances of the developed framework, showing no significant differences if compared to a commercial RFID reader. During the on-field trial, all the tested systems were capable of performing the task in a short time frame. Furthermore, the operators underlined the advantages of using the SmartGlove system coupled with the augmented reality smart glasses for the direct on-field visualization of animal data.
Predicting Potential Fires in Indonesia by Analyzing VIIRS Night Data during 2018 – 2022

Forest fires have been detected to occur in Indonesia since 1998. The forest fires mostly resulted from human activities in order to expand their land, especially oil palm lands. Once a land clearing is carried out, it covers areas of tens to hundreds of thousands of hectares. The peak of the forest fires occurred in 2015 when almost half of the world was affected especially by smoke from the fire. In 2011, VIIRS (Visible Infrared Imaging Radiometer Suite) was launched by NOAA (National Oceanic and Atmospheric Administration). In their collected data, there is data on Indonesia issued daily for areas in the islands of Java, Sumatra, Kalimantan, Sulawesi, and the general areas of Indonesia. The VIIRS night data shows the possibility of some burning at night. We need to identify burnings that potentially become fires, which are potentially producing smoke. It is important to observe areas where large-scale forest fires frequently occur, so the authority could do some prevention beforehand. Using existing data released per day per area, we utilize ANN (Artificial Neural Network) to identify a potential fire. The processes start with data cleaning and processing, Neural Network creation, and finally ANN training and testing. By using the ANN prediction model with the MinMax Scaler, we choose variables Temperature, Radiant Heat Intensity, and Source Footprint. Simulations are made to show the ranges of these variables that predict the possibility of ’flame’ status occurring. It concludes that the ANN method gives more accurate results than the existing classification Keywords: VIIRS Night, Forest Fires, ANN, MinMax Scaller, Fire Radiative Power
Far-right populism in alt-tech: A challenge for democracy?

This article examines how far-right communities in alt-tech platforms depict and engage with the values of democracy. The study contributes to the ongoing discussion on the impact of far-right populism on democracy and the role of alt-tech social media as an enabling platform for such ideologies. Through applying the concept of polyarchal democracy as an analytical framework, the article conducts a case study of Australian alt-tech communities to explore discourses of democracy. The dataset comprises of text-centric social media posts from the Telegram and Gab platforms during the 2022 federal election period. The findings reveal that, overall, far-right populist discourses in alt-tech communities predominantly support the processes of democracy and uphold most (though not all) of its values. Understanding these discourses is crucial for safeguarding liberal democracy in a context of rising illiberalism.
Understanding the role of participatory-moral abilities, motivation, and behavior in European adolescents’ responses to online hate

Researchers have repeatedly discussed how to strengthen supportive and pro-social responses to online hate, such as reporting and commenting. Researchers and practitioners commonly call for the promotion of media literacy measures that are believed to be positively associated with countermeasures against online hate. In this study (conducted in 2021), we examined relationships between media literacy proficiencies of (1) moral-participatory motivation and abilities and, consequently, (2) the establishment of moral-participatory behaviors and the correspondence with prosocial responses to online hate. A sample of 1489 adolescents and young adults (16–22 years old) from eight European countries is examined. Results confirmed that higher participatory-moral motivation and behavior were significantly associated with stronger intentions to report online hate. Commenting on hateful online content, on the other hand, was significantly related to participatory-moral abilities and past experiences with online harassment. Implications for the role of social media literacy in the context of online hate are discussed.
Digital and Audiovisual Literacy Culture Among University Students

In the contemporary era, digital literacy has become an unavoidable imperative, with university students as part of the generation that has grown up immersed in the digital world. The objective was to analyze the culture of digital literacy in students. A descriptive, observational and transversal study was followed, the sample was 320 students, resulting in the attitude in relation to w technologies being good due to the familiarity of their use. The use of ICT represents invaluable support for students by facilitating their research in the scientific and medical field.
A Controlled Experiment on the Impact of Intrusion Detection False Alarm Rate on Analyst Performance

Organizations use intrusion detection systems (IDSes) to identify harmful activity among millions of computer network events. Cybersecurity analysts review IDS alarms to verify whether malicious activity occurred and to take remedial action. However, IDS systems exhibit high false alarm rates. This study examines the impact of IDS false alarm rate on human analyst sensitivity (probability of detection), precision (positive predictive value), and time on task when evaluating IDS alarms. A controlled experiment was conducted with participants divided into two treatment groups, 50% IDS false alarm rate and 86% false alarm rate, who classified whether simulated IDS alarms were true or false alarms. Results show statistically significant differences in precision and time on task. The median values for the 86% false alarm rate group were 47% lower precision and 40% slower time on task than the 50% false alarm rate group. No significant difference in analyst sensitivity was observed.
SpearSim-V<sub>2</sub>: Synthetic Task Environment for Evaluating Attacker Behaviors

Despite extensive research on phishing, a severe lack of work centered on attackers has resulted in a limited understanding of the adversarial behaviors conducive to attack success and failures. This work describes a novel method for conducting controlled laboratory studies of cognitive vulnerabilities that attackers experience during the design and execution phases of spear-phishing attacks. Based on the SpearSim platform, the new simulation environment integrates cognitive agents that model and predict end-user responses to spear-phishing attacks. This advancement to SpearSim allows the generation of real-time, automated, “human-like” responses to simulated spear-phishing attacks. This enables the execution of experiments focused on attackers and attacker behaviors. We describe the proposed simulation framework, provide details about the implemented simulation environment, and present results to evaluate the performance of the simulation environment. Compared to the earlier version of SpearSim involving human end-users, the new approach generates responses at a much faster rate (3 times faster than human end-users) and importantly with less variance in the time to respond. The cognitive agents used in the simulation predicted human responses to phishing and spear-phishing attackers with moderate accuracy (about 60%). Our proposed method intends to provide an effective and robust way to conduct laboratory experiments on spear-phishing attacks and further understand attackers' decision-making processes that could be exploited to thwart future attacks.
DRS: A deep reinforcement learning enhanced Kubernetes scheduler for microservice‐based system

Summary Recently, Kubernetes is widely used to manage and schedule the resources of microservices in cloud‐native distributed applications, as the most famous container orchestration framework. However, Kubernetes preferentially schedules microservices to nodes with rich and balanced CPU and memory resources on a single node. The native scheduler of Kubernetes, called Kube‐scheduler, may cause resource fragmentation and decrease resource utilization. In this paper, we propose a deep reinforcement learning enhanced Kubernetes scheduler named DRS. We initially frame the Kubernetes scheduling problem as a Markov decision process with intricately designed state , action , and reward structures in an effort to increase resource usage and decrease load imbalance. Then, we design and implement DRS mointor to perceive six parameters concerning resource utilization and create a thorough picture of all available resources globally. Finally, DRS can automatically learn the scheduling policy through interaction with the Kubernetes cluster, without relying on expert knowledge about workload and cluster status. We implement a prototype of DRS in a Kubernetes cluster with five nodes and evaluate its performance. Experimental results highlight that DRS overcomes the shortcomings of Kube‐scheduler and achieves the expected scheduling target with three workloads. With only 3.27% CPU overhead and 0.648% communication delay, DRS outperforms Kube‐scheduler by 27.29% in terms of resource utilization and reduces load imbalance by 2.90 times on average.
Spatial organization of riverside territories in Irkutsk area: mid XX – early XXI centuries

The paper presents a design of a scientifically-based, theoretical model for organization of riverside territories of the Irkutsk area under contemporary conditions. The work set out to analyze the factors influencing the formation of the riverside territories, problems and possible directions for necessary transformations; to systematize and adapt the current theoretical and methodological base; to develop a theoretical design model for spatial organization of the territories. The research methodology involves system and landscape-geographical approaches. Urban ecological analysis of riverside territories included analysis of spatial and strategic planning documents, field investigation of the territories, graphical analysis of world experience in terms of patterns and trends in organizing riverside territories, as well as conceptual modeling with consideration to the urbanization level of the territories. The study consists in developing the following definitions: scientific ground for spatial organization of riverside territories in urban area; global trends in organizing riverside territories in urban area; theoretical design model and perspectives for transformations in the territories. The study results include the development of a theoretical design model for the riverside territories in the Irkutsk area, enabling the growth potential and organization principles of the riverside territories to be effectively identified with consideration to their urbanization level. In addition, the authors offer a definition for the concept of “riverside territories in the urban area.”
Dilations and information flow axioms in categorical probability

Abstract We study the positivity and causality axioms for Markov categories as properties of dilations and information flow and also develop variations thereof for arbitrary semicartesian monoidal categories. These help us show that being a positive Markov category is merely an additional property of a symmetric monoidal category (rather than extra structure). We also characterize the positivity of representable Markov categories and prove that causality implies positivity , but not conversely. Finally, we note that positivity fails for quasi-Borel spaces and interpret this failure as a privacy property of probabilistic name generation.
The Influence of Children's Literature on Bilingual Education in China

Children's literature has emerged as a significant resource in bilingual education, particularly in China because it helps students become more proficient in multiple languages, have a deeper understanding of different cultures, and have increased opportunities for communication and future academic success. This paper provides an overview of the current state of employing children's literature in bilingual education and explores its influence on language skills, cultural understanding, and literacy development. The paper highlights the need for effective teaching materials and strategies in bilingual education and analyzes the challenges faced in using children's literature in this context. Further, several key strategies are proposed for the use of children's literature in bilingualism. It discusses the influence of children's literature on cultural comprehension and bilingual vocabulary learning and suggests practical activities like book club discussions and creative writing for better application in bilingual education. It also promotes a comprehensive approach involving cultural integration, teacher training, material development, creative activities, and research-based strategies to enhance learning outcomes for bilingual students.
Giving mixed signals

Giving mixed signals&#x0D; On gendered readings of Late Iron Age figurines