Reviewerly/exHarmony huggingface dataset:
Survey of Denoising, Segmentation and Classification of Pancreatic Cancer Imaging

Background: Pancreatic cancer is one of the most serious problems that has taken many lives worldwide. The diagnostic procedure using the traditional approaches was manual by visually analyzing the large volumes of the dataset, making it time-consuming and prone to subjective errors. Hence the need for the computer-aided diagnosis system (CADs) emerged that comprises the machine and deep learning approaches for denoising, segmentation and classification of pancreatic cancer. Introduction: There are different modalities used for the diagnosis of pancreatic cancer, such as Positron Emission Tomography/Computed Tomography (PET/CT), Magnetic Resonance Imaging (MRI), Multiparametric-MRI (Mp-MRI), Radiomics and Radio-genomics. Although these modalities gave remarkable results in diagnosis on the basis of different criteria. CT is the most commonly used modality that produces detailed and fine contrast images of internal organs of the body. However, it may also contain a certain amount of gaussian and rician noise that is necessary to be preprocessed before segmentation of the required region of interest (ROI) from the images and classification of cancer. Methods: This paper analyzes different methodologies used for the complete diagnosis of pancreatic cancer, including the denoising, segmentation and classification, along with the challenges and future scope for the diagnosis of pancreatic cancer. Results: Various filters are used for denoising and image smoothening and filters as gaussian scale mixture process, non-local means, median filter, adaptive filter and average filter have been used more for better results. Conclusion: In terms of segmentation, atlas based region-growing method proved to give better results as compared to the state of the art whereas, for the classification, deep learning approaches outperformed other methodologies to classify the images as cancerous and non- cancerous. These methodologies have proved that CAD systems have become a better solution to the ongoing research proposals for the detection of pancreatic cancer worldwide.
&lt;p&gt;Revisiting the Determinants of Investment- The Case of Tunisia&lt;/p&gt;

This study aims to analyse the determinants of investment for developing countries, through the case of Tunisia, which is strongly affected by such a dynamic. Using the ARDL model on annual data for the period 1987 to 2020, we found evidence of a short- and long-term relationship between various social and economic variables and the investment. These findings have important policy implications for economic agents, politicians, and policymakers. This study is useful to identify the socioeconomic determinants of total investment in Tunisia.
The role of the curator in the educational and training process of the university

Curatorship is one of the important areas of university’s educational and training work, it contributes to the formation of competent, highly moral young professionals who clearly represent their place in society. The work of the curator of the student group should be purposeful and systematic. The system of interaction between the teacher and the student helps to solve educational issues, transfer to students the accumulated experience, knowledge, traditions, resolve disputes and conflict situations, influence the formation of personality and professionalism. Educational work requires the curator to make a big calculation in terms of spending time on the group. Its main content represents the most difficult process — it is to live the life of a supervised group, the problems and joys of student life. Only then the educational work of the curator will have a significant impact on the quality of training of students.
Metaverse and Its Impact on Climate Change

A novel idea called the metaverse combines various technologies to offer a lifelike experience in a virtual setting. To realize its full potential, the idea must still be refined. People's opinions of the phrase are conflicted. Whether the metaverse is a beneficial thing or whether it will harm our future has been heavily disputed. Many critics think it might cause more harm than good, while many regard it as the next logical step for humanity after the internet. The impact of the metaverse on the climate is just one of many worries. The enormous computer power needed to implement artificial intelligence (AI) techniques could increase carbon emissions. A metaverse may also decrease the need for physical travel, saving a significant amount of energy and resources and lowering fuel use and the emission of toxic gases. This article is a study of various technologies used to provide an immersive experience in the virtual world and their overall contribution to environmental climate change.
A STUDY ON FACE SUPER-RESOLUTION RECONSTRUCTION ALGORITHM BASED ON IMPROVED SRGAN

In this paper, we propose a novel approach to address the challenges of Super-Resolution Generative Adversarial Network (SRGAN) in face image super-resolution reconstruction. We introduce a new improved SRGAN algorithm, named Wasserstein SRGAN (W-SRGAN), which addresses the limitations of the original model by enhancing the loss function, generator, and discriminator. Our approach utilizes the embedded residual structure combined with feature fusion as the new generator, while removing the Sigmoid of the last layer of the discriminator of SRGAN by borrowing the idea of Wasserstein GAN (W-GAN). Additionally, we replace the Kullback–Leibler (KL) divergence of SRGAN with Wasserstein distance. The contributions of our research are twofold. Firstly, we propose a new face super-resolution reconstruction algorithm that outperforms existing methods in terms of visual quality. Secondly, we introduce a new loss function and generator–discriminator architecture that can be applied to other image super-resolution tasks, extending the applicability of GANs in this domain. Experimental results demonstrate that our proposed W-SRGAN outperforms Bicubic, Super-Resolution Convolutional Neural Network (SRCNN), and SRGAN in terms of visual quality on all Celeb A datasets. These results confirm the effectiveness of our proposed algorithm and provide a new solution for face super-resolution reconstruction.
Comparative Analysis of AI-Based Facial Identification and Expression Recognition Using Upper and Lower Facial Regions

The COVID-19 pandemic has significantly impacted society, having led to a lack of social skills in children who became used to interacting with others while wearing masks. To analyze this issue, we investigated the effects of masks on face identification and facial expression recognition, using deep learning models for these operations. The results showed that when using the upper or lower facial regions for face identification, the upper facial region allowed for an accuracy of 81.36%, and the lower facial region allowed for an accuracy of 55.52%. Regarding facial expression recognition, the upper facial region allowed for an accuracy of 39% compared to 49% for the lower facial region. Furthermore, our analysis was conducted for a number of facial expressions, and specific emotions such as happiness and contempt were difficult to distinguish using only the upper facial region. Because this study used a model trained on data generated from human labeling, it is assumed that the effects on humans would be similar. Therefore, this study is significant because it provides engineering evidence of a decline in facial expression recognition; however, wearing masks does not cause difficulties in identification.
Secure Blockchain Transactions for Electronic Health Records based on an Improved Attribute-Based Signature Scheme (IASS)

Electronic Health Records (EHRs) are entirely controlled by hospitals, not patients, making it difficult to obtain medical advice from individual hospitals. Patients need to keep tabs on their health details and take back control of their medical data. The rapid development of blockchain technology has facilitated large-scale healthcare, including medical records and patient-related data. The technology provides comprehensive and immutable patient records and free access to electronic medical records for providers and treatment portals. To ensure the validity of the blockchain-connected EHR, the Improved Attribute-Based Signature Scheme (IASS) has considerable powers, allowing patients to approve messages based on attributes but not validated. In addition, it avoids the problem of having multiple authorities without a single or central source of trust for generating and distributing patient public/private keys and fits into the blockchain model for distributed data storage. By sharing a secret, pseudo-random activity seed between authorities, the protocol resists collusive attacks by corrupt officials. The technology provides patients with a comprehensive, immutable record and free access to their EHR from providers and treatment portals. To ensure the validity of blockchain-connected EHRs, propose an attribute-based multi-authority signature scheme that authorizes messages based on their attributes without revealing any information.
Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading

Eye movements during reading offer insights into both the reader's cognitive processes and the characteristics of the text that is being read. Hence, the analysis of scanpaths in reading have attracted increasing attention across fields, ranging from cognitive science over linguistics to computer science. In particular, eye-tracking-while-reading data has been argued to bear the potential to make machine-learning-based language models exhibit a more human-like linguistic behavior. However, one of the main challenges in modeling human scanpaths in reading is their dual-sequence nature: the words are ordered following the grammatical rules of the language, whereas the fixations are chronologically ordered. As humans do not strictly read from left-to-right, but rather skip or refixate words and regress to previous words, the alignment of the linguistic and the temporal sequence is non-trivial. In this paper, we develop Eyettention, the first dual-sequence model that simultaneously processes the sequence of words and the chronological sequence of fixations. The alignment of the two sequences is achieved by a cross-sequence attention mechanism. We show that Eyettention outperforms state-of-the-art models in predicting scanpaths. We provide an extensive within- and across-data set evaluation on different languages. An ablation study and qualitative analysis support an in-depth understanding of the model's behavior.
B-spline quarklets and biorthogonal multiwavelets

In this paper, we show that B-spline quarks and the associated quarklets fit into the theory of biorthogonal multiwavelets. Quark vectors are used to define sequences of subspaces [Formula: see text] of [Formula: see text] which fulfill almost all conditions of a multiresolution analysis. Under some special conditions on the parameters, they even satisfy all those properties. Moreover, we prove that quarks and quarklets possess modulation matrices which fulfill the perfect reconstruction condition. Furthermore, we show the existence of generalized dual quarks and quarklets which are known to be at least compactly supported tempered distributions from [Formula: see text]. Finally, we also verify that quarks and quarklets can be used to define sequences of subspaces [Formula: see text] of [Formula: see text] that yield non-orthogonal decompositions of [Formula: see text].
An Investigation of Open Access Availability of Library and Information Science Research

&#x0D; The study aims to investigate the Open Access (OA) availability of Library and Information Science (LIS) research and determine the extent to which research published in high-impact LIS journals is openly available to researchers via different OA routes. Using Google Scholar Metrics, ten core high-impact LIS journals were selected to collect the required data for the study. Bibliographic and citation details of each article published in these journals during 2016-2020 were extracted by searching the ISSN of each journal in the document search option of the Scopus database. The OA status of each article (Gold, Hybrid Gold, Green, and Bronze) was determined by querying the DOI API using the Simple Query Tool of Unpaywall. The findings show that LIS journals reviewed in this study demand $2500 to $3500 as an Article Processing Charge (APC). Almost all the journals have explicit self-archiving policies and lenient restrictions for making articles OA. However, accepted articles can be made available OA at designated Web locations after an embargo of 12months. The study’s findings indicate that 36.5 % of the total 5029 articles published during 2016-2020 in selected LIS journals were OA. Of the total OA articles, around 29 % were available through the publishers’ Website (Gold or Bronze OA), and 71 % were available through the repositories (Green OA). The average citation rate for OA articles is 13.37, which is higher than that of non-OA articles, showing a clear citation advantage for OA articles over non-OA articles.
Challenges and opportunities for improved contact tracing in Ghana: experiences from Coronavirus disease-2019-related contact tracing in the Bono region

In Ghana, contact tracing received heightened attention in the fight against the COVID-19 pandemic during its peak period. Despite the successes achieved, numerous challenges continue to limit the efforts of contact tracing in completely curtailing the effect of the pandemic. Despite these challenges, there are still opportunities that could be harnessed from the COVID-19 contact tracing experience for future eventualities. This study thus identified the challenges and opportunities associated with COVID-19 contact tracing in the Bono Region of Ghana.Using a focus group discussion (FGD) approach, an exploratory qualitative design was conducted in six selected districts of the Bono region of Ghana in this study. The purposeful sampling technique was employed to recruit 39 contact tracers who were grouped into six focus groups. A thematic content analysis approach via ATLAS ti version 9.0 software was used to analyse the data and presented under two broad themes.The discussants reported twelve (12) challenges that hindered effective contact tracing in the Bono region. These include inadequate personal protective equipment, harassment by contacts, politicisation of the discourse around the disease, stigmatization, delays in processing test results, poor remuneration and lack of insurance package, inadequate staffing, difficulty in locating contacts, poor quarantine practices, poor education on COVID-19, language barrier and transportation challenges. Opportunities for improving contact tracing include cooperation, awareness creation, leveraging on knowledge gained in contact tracing, and effective emergency plans for future pandemics.There is a need for health authorities, particularly in the region, and the state as a whole to address contact tracing-related challenges while simultaneously harnessing the recommended opportunities for improved contact tracing in the future for effective pandemic control.
MAS-UNet: a U-shaped network for prostate segmentation

Prostate cancer is a common disease that seriously endangers the health of middle-aged and elderly men. MRI images are the gold standard for assessing the health status of the prostate region. Segmentation of the prostate region is of great significance for the diagnosis of prostate cancer. In the past, some methods have been used to segment the prostate region, but segmentation accuracy still has room for improvement. This study has proposed a new image segmentation model based on Attention UNet. The model improves Attention UNet by using GN instead of BN, adding dropout to prevent overfitting, introducing the ASPP module, adding channel attention to the attention gate module, and using different channels to output segmentation results of different prostate regions. Finally, we conducted comparative experiments using five existing UNet-based models, and used the dice coefficient as the metric to evaluate the segmentation result. The proposed model achieves dice scores of 0.807 and 0.907 in the transition region and the peripheral region, respectively. The experimental results show that the proposed model is better than other UNet-based models.
The Concept of Teaching Russian History at University: from Project to Implementation

From September 1, 2023, in accordance with the order of the Ministry of Education and Science and Higher Education of the Russian Federation dated July 19, 2022 No. 662, the number of hours for studying the history of Russia in higher education institutions for students of non-historical specialties will increase. Students of all Russian universities will start studying the updated course of Russian history, expanded in its geographical and chronological framework and covering the events taking place in the country and the world, concerning every citizen of Russia. For teachers, the changes made to the federal state educational standards are detailed in the new concept of teaching the course “History of Russia” approved on February 2, 2023. The concept, representing a social order addressed to education, contains an important ideological message, the development of citizenship and patriotism among students, but does not disclose specific tools to achieve the goals. The article analyzes the psychological, pedagogical and methodological aspects of the most difficult complex problem that will affect the history teacher at the university as the executor of the decisions made. An approach to methodological solutions is determined through clarifying the essence of the phenomena of patriotism, identity, historical consciousness, self-awareness and the psychological mechanisms of their formation in education. The tasks of the teacher as the organizer of the learning situation in learning based on the knowledge of the individual characteristics of students and ways to optimize their personal development on the basis of history are revealed. The article sets the methodological guidelines for moral education in the course of teaching history, due to the requirements of the reform of higher education and modern social challenges. This is all the more important because universities have the right to determine them independently.
Data Analytic for Healthcare Cyber Physical System

Nowadays, a large number of AI-powered healthcare cyber-physical systems (CPSs) have been used in healthcare services. In order to provide better care, AI-powered healthcare CPSs analyze the data they collect using a variety of techniques. Data analysis for artificial intelligence (AI)-driven healthcare CPS is one of these approaches. However, none of the techniques in data analysis can provide a good representation of contiguous and negative information. Therefore, we are the first to introduce the problem of contiguous negative sequential pattern mining. A novel algorithm called Contiguous Negative Sequential Pattern Miner (CNSPM) is proposed to discover and analyze contiguous negative sequential patterns (CNSPs) from the data collected by healthcare CPSs. Finally, we select some real medical and non-medical datasets to conduct numerous experiments. We further analyze the discovered patterns and show how healthcare services can use meaningful patterns for medical decision-making. The performance results on these datasets demonstrate that the proposed algorithm can discover more valuable patterns efficiently and effectively from the collected and transformed medical data.
Quantum Similarity Testing with Convolutional Neural Networks

The task of testing whether two uncharacterized quantum devices behave in the same way is crucial for benchmarking near-term quantum computers and quantum simulators, but has so far remained open for continuous-variable quantum systems. In this Letter, we develop a machine learning algorithm for comparing unknown continuous variable states using limited and noisy data. The algorithm works on non-Gaussian quantum states for which similarity testing could not be achieved with previous techniques. Our approach is based on a convolutional neural network that assesses the similarity of quantum states based on a lower-dimensional state representation built from measurement data. The network can be trained offline with classically simulated data from a fiducial set of states sharing structural similarities with the states to be tested, or with experimental data generated by measurements on the fiducial states, or with a combination of simulated and experimental data. We test the performance of the model on noisy cat states and states generated by arbitrary selective number-dependent phase gates. Our network can also be applied to the problem of comparing continuous variable states across different experimental platforms, with different sets of achievable measurements, and to the problem of experimentally testing whether two states are equivalent up to Gaussian unitary transformations.
Metaphorical User Simulators for Evaluating Task-oriented Dialogue Systems

Task-oriented dialogue systems (TDSs) are assessed mainly in an offline setting or through human evaluation. The evaluation is often limited to single-turn or is very time-intensive. As an alternative, user simulators that mimic user behavior allow us to consider a broad set of user goals to generate human-like conversations for simulated evaluation. Employing existing user simulators to evaluate TDSs is challenging as user simulators are primarily designed to optimize dialogue policies for TDSs and have limited evaluation capabilities. Moreover, the evaluation of user simulators is an open challenge. In this work, we propose a metaphorical user simulator for end-to-end TDS evaluation, where we define a simulator to be metaphorical if it simulates a user’s analogical thinking in interactions with systems. We also propose a tester-based evaluation framework to generate variants, i.e., dialogue systems with different capabilities. Our user simulator constructs a metaphorical user model that assists the simulator in reasoning by referring to prior knowledge when encountering new items. We estimate the quality of simulators by checking the simulated interactions between simulators and variants. Our experiments are conducted using three TDS datasets. The proposed user simulator demonstrates better consistency with manual evaluation than an agenda-based simulator and a seq2seq model on three datasets; our tester framework demonstrates efficiency and has been tested on multiple tasks, such as conversational recommendation and e-commerce dialogues.
Teleport to the Augmented Real-World with Live Interactive Effects (IFX)

Augmented telepresence provides rich communication for people at a distance with interactive blended information between the virtual and real world [Rhee et al. 2017, 2020; Young et al. 2022]. We push the boundaries of augmented telepresence with a novel live media technology, including live capturing, modeling, blending, and interactive effects (IFX) to augment telepresence. Using our technology, people at a distance can connect and communicate with creative storytelling, augmented with novel IFX.
Detecting Phishing Website Using Machine Learning

Trying to gather personal information through deceptive ways is becoming more common nowadays.In order to assist the user to be aware of the access to such websites, the implemented system notifies the user through email and also pop-up, when trying to access a phishing site.This paper proposes an approach of phishing detection system to detect blacklisted URL also known as phishing websites, so that individual can be alerted while browsing or accessing a particular website.Therefore, it can be utilized for identification and authentication and become a legitimate tool to prevent an individual from getting tricked.