Reviewerly/exHarmony huggingface dataset:
Latin Square and Artificial Intelligence Cryptography for Blockchain and Centralized Systems

The recent advances in cryptanalysis techniques and the leakage of information about the cryptosystem used are major threats to information systems. An adversary may succeed in decrypting ciphertexts, while users of a particular cryptosystem unknowingly continue to use the compromised cryptosystem. In this paper, we present a novel cryptosystem based on Latin Square and AI/ML for Blockchain and Covert Communications. This cryptosystem is capable of operating in quadruple modes keyless, symmetric, asymmetric, and hybrid encryption to Cipher In Cipher, and hence we call it CipherInCipher. Unlike all contemporary techniques including obscurity, CipherInCipher is a public-based approach that does not depend on the secrecy of any of its related components. It attains a high level of security that protects private information not only by having strong ciphertext but also by preventing an adversary from obtaining the actual ciphertext. The presented cryptanalysis validation demonstrates the robust capabilities of achieving the cryptographic goal.
Fog Images Generation About Unmanned Surface Vessels with Improved Generative Adversarial Network

This paper proposes a fog weather data augmentation method for the unmanned surface vessels (USVs) via improved Generative Adversarial Network(GAN) model. First, a generator scheme for GAN is proposed with the guided generation of the atmospheric scattering model in this paper. A Laplacian Pyramid Based Depth Residuals model is added to the generator which reduces the difficulty of generating fog images caused by the degradation of water surface image and improves the quality of generated images. Finally, fog images are generated from sunny weather images collected with HUST-12C by LPBDR-GAN model and experiments show that generated images are very close to real fog images.
WDNet: A Water Depth Perception Network with Global Information

Deep perception of the unmanned surface vehicle's surroundings is an inaccessible part of its fully autonomous navigation mission. The existing methods, whether based on traditional stereo matching or deep learning, do not fully consider the characteristics of water environment, resulting in severe error depths in weak textures (sky, calm lake) and water reflections regions, that increases the risk of running aground or collision. What is worse that there is not a public dataset for depth estimation in the water environment. Therefore, this work proposes a self-supervised model for depth estimation named Water Depth Perception Network (WDNet) to address these problems. The decoder of this network has a wider receptive field and can effectively handle the depth error in the weak texture region. Besides, the WDNet is trained with a novel and effective loss function which assist the network to reduce errors in sky and water region, and some indexes are proposed to evaluate the model's performances in sky and water region. Finally, our proposed WDNet achieves a 0.1056 absolute relative error in ranging, the average number of error pixels in the sky area drops from 15803.87 to 580.91, which only accounted for 0.29% of the image, and the error in water region drops from 51.04 to 6.75, all of them are superior to the performance of baseline model.
USV Path Planning Based on ACO-SA Algorithm

This paper solves USV path planning problem constrained by multiple factors via ant-colony optimization algorithm. First, this paper uses the ways of multi-objective optimization to model the USV path planning problem. An improved ant colony algorithm named ACO-SA is put forward afterwards to effectively solve the problem. The algorithm is a combination of ACO algorithm(ant colony algorithm) and SA algorithm(simulated annealing algorithm), which has three improments: change the initial distribution of pheromone to guide the search when the algorithm has just started running; change the heuristic function and state transition probability taking three factors into consideration; change the pheromone update rule and make the ants compete for the right to update pheromone by simulated annealing algorithm, and update the best solution by the same algorithm. Finally, simulation experiment and field experiment are conducted to check the validity of ACO-SA algorithm.
Time and Networks: Reflections and Good Practices for the AI-Based Digital Era

This position paper aims to explore the interplay between networks, time, and digitalization. With the convergence of networks and digital technologies, the concept of time has undergone a significant transformation, influencing various aspects of our lives. More specifically, the paper highlights the transformative power of AI in shaping our understanding and experience of time, from the acceleration of information dissemination to the optimization of processes. By drawing on examples from different domains, this paper provides some insights into the implications of networked time in the context of AI-based digitalization, while suggesting a set of good practices that can prevent the negative implications of such transformation.
Broad Spectrum Image Deblurring via An Adaptive Super-Network

In blurry images, the degree of image blurs may vary drastically due to different factors, such as varying speeds of shaking cameras and moving objects, as well as defects of the camera lens. However, current end-to-end models failed to explicitly take into account such diversity of blurs. This unawareness compromises the specialization at each blur level, yielding sub-optimal deblurred images as well as redundant post-processing. Therefore, how to specialize one model simultaneously at different blur levels, while still ensuring coverage and generalization, becomes an emerging challenge. In this work, we propose Ada-Deblur, a super-network that can be applied to a “broad spectrum” of blur levels with no re-training on novel blurs. To balance between individual blur level specialization and wide-range blur levels coverage, the key idea is to dynamically adapt the network architectures from a single well-trained super-network structure, targeting flexible image processing with different deblurring capacities at test time. Extensive experiments demonstrate that our work outperforms strong baselines by demonstrating better reconstruction accuracy while incurring minimal computational overhead. Besides, we show that our method is effective for both synthetic and realistic blurs compared to these baselines. The performance gap between our model and the state-of-the-art becomes more prominent when testing with unseen and strong blur levels. Specifically, our model demonstrates surprising deblurring performance on these images with PSNR improvements of around 1 dB. Our code is publicly available at https://github.com/wuqiuche/Ada-Deblur.
Phantom touch illusion, an unexpected phenomenological effect of tactile gating in the absence of tactile stimulation

Abstract We report the presence of a tingling sensation perceived during self-touch without physical stimulation. We used immersive virtual reality scenarios in which subjects touched their body using a virtual object. This touch resulted in a tingling sensation corresponding to the location touched on the virtual body. We called it “phantom touch illusion” (PTI). Interestingly, the illusion was also reported when subjects touched invisible (inferred) parts of their limb. We reason that this PTI results from tactile gating process during self-touch if there is no tactile input to supress. The reported PTI when touching invisible body parts indicates that tactile gating is not exclusively based on vision, but rather on multi-sensory, top-down input involving body schema. This supplementary finding shows that representations of one's own body are defined top-down, beyond the available sensory information.
Student Youth Scientific and Project Activities as a Factor in their Socialization (Case Study of North Caucasian Federal University)

The relevance of studying the topic of scientific and project activity as a factor of socialization of student youth is due to the need of modern society for competitive specialists. The novelty of the study consists in the applica-tion of the subjective-activity and socio-cultural approaches to the analysis of students’ socialization in the cho-sen type of activity. The specificity of extracurricular scientific design is emphasized. University role as a collec-tive agent of socialization, which provides the resource base for the process of project activity, is substantiated. The paper describes the activities of structural units of North Caucasus Federal University that manage stu-dents’ research design. The significance of individual agents of socialization, i.e. teachers who have the main place in the process of group and individual management of students' projecting is emphasized. As an infor-mation base of the author’s conclusions the documents and information from the websites of North Caucasian Federal University are given. The results obtained can serve as a theoretical and methodological basis for fur-ther development and conducting applied research of students’ scientific project activity as a factor of their so-cialization.
Data from Accelerating Cancer Histopathology Workflows with Chemical Imaging and Machine Learning

&lt;div&gt;&lt;p&gt;Histopathology has remained a cornerstone for biomedical tissue assessment for over a century, with a resource-intensive workflow involving biopsy or excision, gross examination, sampling, tissue processing to snap frozen or formalin-fixed paraffin-embedded blocks, sectioning, staining, optical imaging, and microscopic assessment. Emerging chemical imaging approaches, including stimulated Raman scattering (SRS) microscopy, can directly measure inherent molecular composition in tissue (thereby dispensing with the need for tissue processing, sectioning, and using dyes) and can use artificial intelligence (AI) algorithms to provide high-quality images. Here we show the integration of SRS microscopy in a pathology workflow to rapidly record chemical information from minimally processed fresh-frozen prostate tissue. Instead of using thin sections, we record data from intact thick tissues and use optical sectioning to generate images from multiple planes. We use a deep learning–based processing pipeline to generate virtual hematoxylin and eosin images. Next, we extend the computational method to generate archival-quality images in minutes, which are equivalent to those obtained from hours/days-long formalin-fixed, paraffin-embedded processing. We assessed the quality of images from the perspective of enabling pathologists to make decisions, demonstrating that the virtual stained image quality was diagnostically useful and the interpathologist agreement on prostate cancer grade was not impacted. Finally, because this method does not wash away lipids and small molecules, we assessed the utility of lipid chemical composition in determining grade. Together, the combination of chemical imaging and AI provides novel capabilities for rapid assessments in pathology by reducing the complexity and burden of current workflows.&lt;/p&gt;Significance:&lt;p&gt;Archival-quality (formalin-fixed paraffin-embedded), thin-section diagnostic images are obtained from thick-cut, fresh-frozen prostate tissues without dyes or stains to expedite cancer histopathology by combining SRS microscopy and machine learning.&lt;/p&gt;&lt;/div&gt;
Supplementary Figure 2 from Accelerating Cancer Histopathology Workflows with Chemical Imaging and Machine Learning

&lt;p&gt;Method of choosing discrete frequency bands from hyperspectral SRS measurements.&lt;/p&gt;
Supplementary Figure 3 from Accelerating Cancer Histopathology Workflows with Chemical Imaging and Machine Learning

&lt;p&gt;Representative examples of virtual staining&lt;/p&gt;
Supplementary Figure 4 from Accelerating Cancer Histopathology Workflows with Chemical Imaging and Machine Learning

&lt;p&gt;Representative examples of failure cases&lt;/p&gt;
Supplementary Figure 5 from Accelerating Cancer Histopathology Workflows with Chemical Imaging and Machine Learning

&lt;p&gt;Examples demonstrating the virtual sectioning capabilities of the implemented workflow.&lt;/p&gt;
TABLE 1 from Accelerating Cancer Histopathology Workflows with Chemical Imaging and Machine Learning

&lt;p&gt;Pathologists review from the survey for assigning section and stain quality score, where we report mean ± SD of the scores that have been assessed by pathologists to each domain&lt;/p&gt;
An adaptive n-gram transformer for multi-scale scene text recognition

While vision transformers have been highly successful in improving the performance in image-based tasks, not much work has been reported on applying transformers to scene text recognition due to the complexities in the visual appearance of multi-scale texts. To fill the gap, this paper proposes an adaptive n-gram transformer for multi-scale scene text recognition (ANT-STR). In ANT-STR, an adaptive n-gram embedding that is able to automatically determine the optimal size of each image patch is designed to fully explore the potential semantic correlations between neighboring visual patches, which is essential for feature extraction from multi-scale scene texts. On top of the adaptive n-gram embedding, a patch-based n-gram attention mechanism is introduced into ANT-STR to further process the feature maps for multi-scale texts. In addition, the loss function is rectified to take into account both multi-scale character-based identification and contextual coherence scoring. Comparative studies are conducted on five widely used benchmark datasets and a new multi-scale scene text dataset collected from tourism scenes in Indonesia. Our experimental results demonstrate that ANT-STR performs considerably better compared to the state-of-the-art, especially in handling complex multi-scale scene texts.
Suitability of photovoltaic development and emission reduction benefits based on geographic information sensing and multi-criteria decision making method—an example from Inner Mongolia Autonomous Region

With the rapid development of the photovoltaic (PV) industry, the problem of the blind construction of PV power plants is becoming increasingly prominent. Thus, the scientific evaluation of the suitability of regional PV products, power generation potential, and emission reduction benefits is of great significance to the sustainable development of the PV industry. In this study, geographic information sensing and multi-criteria decision making methods are to be combined to calculate a PV development suitability index based on meteorology, topography, location, and vegetation to characterize the spatial patterns of the PV development in the Inner Mongolia Autonomous Region. Subsequently, the power generation potential and emission reduction benefits under different development scenarios were quantitatively assessed. The particularly suitable, and more suitable areas for the PV development in the Inner Mongolia Autonomous Region are 23.66% of the total area. Among these, the more suitable areas are the most extensive. The suitability of PV development in the western part of the Inner Mongolia Autonomous Region is more than that in the central and eastern parts. The suitable areas are mainly distributed in the Alashan League and Bayannur City in the west. Thus, the Inner Mongolia Autonomous Region has enormous potential for electricity generation. The development of 12.57% of the particularly suitable areas can meet the electricity consumption of the Inner Mongolia Autonomous Region in 2021. Moreover, sensitivity analysis is conducted to investigate the impacts of system efficiency variation on energy output. The distribution of the power generation potential and electricity demand in the Inner Mongolia Autonomous Region has robust spatial heterogeneity. PV power generation can achieve remarkable energy saving and emission reduction. If all particularly suitable areas are developed, the annual carbon emission reduction of the Inner Mongolia Autonomous Region will be approximately 2,446,621,000 t, which is 4.46 times the total carbon emission of the Autonomous Region and 21.2% of the total carbon emission of China in 2021. Therefore, this study provides a scientific reference and guide for selecting suitable sites for PV power plants and their sustainable development in the Inner Mongolia Autonomous Region.
Pengaruh Kecerdasan Menghadapi Hambatan dan Kecerdasan Emosional Terhadap Prestasi Belajar Ekonomi Siswa

This study aims to analyze the influence of adversity quotient and emotional intelligence in influencing economic achievement of high school students in Madiun City. This research is a quantitative descriptive research. The research subjects studied were high school students in Madiun City who received economics subjects totaling 302 respondents. Data collection technique using purposive random sampling technique. Research data was taken using a questionnaire which was carried out offline by visiting schools. Data analysis technique using multiple linear regression analysis. The research results obtained are as follows. First, adversity quotient has a positive and significant effect on students' economic achievement. Second, emotional intelligence has a positive and significant effect on students' economic achievement. Third, adversity quotient and emotional intelligence has a positive and significant effect on students' economics learning achievement by 50.2%..
TEACHING DESCRIPTIVE TEXT USING YOUTUBE CHANNEL: AN ANALYSIS OF ENGLISH STUDENTS’ PERCEPTION

This study aimed to analyze English students' perceptions regarding the use of YouTube channels as a teaching tool for descriptive text in a seventh-grade class at SMP N 37 Semarang. Data was collected through the distribution of a questionnaire to the students. The study employed a quantitative approach, analyzing the responses obtained from the questionnaire. The findings indicate that YouTube offers numerous benefits in terms of enhancing students' writing skills and expanding their vocabulary. Furthermore, the use of YouTube as an instructional tool serves as a motivational factor and boosts students' engagement in the subject matter. The availability of visually appealing videos with subtitles provides additional support for students to comprehend the content effectively. Moreover, the accessibility of YouTube enables students to access learning materials both inside and outside the classroom, facilitating flexible and convenient learning experiences. Overall, the research underscores the positive impact of incorporating YouTube as a medium for descriptive text learning, showcasing its potential in improving students' learning outcomes and fostering their active participation in the subject.
Research on Formative Evaluation of College English Teaching Based on Online Learning Platform

In order to integrate information technology and teaching reasonably and improve teaching quality, this paper puts forward a formative evaluation study of college English teaching based on online learning platform. Take U campus as an example. The U campus is called the U campus intelligent teaching
A Study Related to the Evaluation of University Education by Companies Implementing a new Personnel Recruitment Management System

The development of human resource management practices, management scholars and practitioners is increasingly concerned with the strategic value of human resources. Since the acquisition, development and retention of human resources are embedded in the unique history and culture of a company, they a
Design and Application of Software Engineering Course Case Resource Library Based on Values Education through Curriculum

In view of the immature construction of software engineering course case resource database in universities. This project presents a software engineering case library based on curriculum ideology and politics. An effective method to establish and introduce ideological and political cases in curriculu
Motivation Learning English During Pandemic

This study aims to determine the perception of EFL students towards learning English during the COVID-19 pandemic at SMKN 3 Medan. The data collection is done through interviews. There were 6 students of grade XI APL 4 participating in this study. Since this research has explored students’ perception and motivation derived from students’ experiences during distance studying carried out due to the covid-19 pandemic. Descriptive research using qualitative technique is considered the right approach. The results of this study are presented descriptively to reveal students’ perception and motivation comprehensively. The result found that E-Learning got a bad perception because it was flexible and ineffective.
Development of a Virtual Reality Escape Room Game for Emotion Elicitation

In recent years, the role of emotions in digital games has gained prominence. Studies confirm emotions’ substantial impact on gaming, influencing interactions, effectiveness, efficiency, and satisfaction. Combining gaming dynamics, Virtual Reality (VR) and the immersive Escape Room genre offers a potent avenue through which to evoke emotions and create a captivating player experience. The primary objective of this study is to explore VR game design specifically for the elicitation of emotions, in combination with the Escape Room genre. We also seek to understand how players perceive and respond to emotional stimuli within the game. Our study involved two distinct groups of participants: Nursing and Games. We employed a questionnaire to collect data on emotions experienced by participants, the game elements triggering these emotions, and their overall user experience. This study demonstrates the potential of VR technology and the Escape Room genre as a powerful means of eliciting emotions in players. “Escape VR: The Guilt” serves as a successful example of how immersive VR gaming can evoke emotions and captivate players.
Experience of Using the Virtual Clinic "Dimedus" in the Work of the Simulation Center of the I. Kant Baltic Federal University

Since December 2021 the simulation center of the I. Kant Baltic Federal Universityactively uses the virtual clinic "Dimedus".The wide possibilities of using virtual technologies in simulation training have led to various options for using the Dimedus virtual clinic in this process.The experience of the simulation center of the I. Kant Baltic Federal Universityshows that virtual technologies can be used both as an independent teaching method and in combination with others.In general a virtual learning environment is a fairly effective method, tool and technology of learning.
TADAKU: Application of Research Questionnaire Collection as an Effort to Increase the Quantity and Quality of Research in Indonesia

Data is crucial in today's modern society. One of the most effective ways to obtain data is through online surveys using online questionnaires. However, there is a lack of online questionnaire creation applications that can motivate respondents to participate in surveys. As a result, survey creators often face difficulties in obtaining responses from respondents. The Tadaku application is designed to address this issue by providing a platform for survey creators to create questionnaires and helping them to reach targeted respondents quickly and accurately by implementing a reward system for participants who are willing to complete the questionnaires. Thus, the Tadaku application provides motivation and benefits for respondents. The questionnaire app system is developed using the waterfall method, which consists of four stages: needs analysis, system design, coding, system testing, and maintenance. The features available in the Tadaku questionnaire application include registration, creating new questionnaires, questionnaire completion, and point redemption for cash rewards.