Reviewerly/exHarmony huggingface dataset:
Automatic infarct segmentation in rat brain MR images after stroke using an adaptive deformable model

Experimental ischemic stroke models play an important role in realizing the mechanism of cerebral ischemia and evaluating the development of pathological extent. An efficient and reliable image segmentation tool to automatically identify the infarct region in the diffusion weighted imaging (DWI) and T2-weighted MRI (T2WI) images is critical for subsequent processing applications. This paper develops an automatic infarct segmentation algorithm in both rat brain DWI and T2WI images after stroke for further evaluation of neurological damages. The proposed framework consists of four major steps including image preprocessing, image registration, image enhancement, and infarct segmentation. To achieve complete automation, the input rat brain is first divided into two hemispheres, from which the initial infarct mask is acquired after a series of image registration, image subtraction, and image enhancement processes. Subsequently, an adaptive deformable model is exploited to perform infarct region segmentation. The proposed deformable model employs two-phase level set evolution, which is regularized by a local region integration. The integration of the difference between the local intensities and the global mean intensity is restricted in the inward and outward normal directions to minimize the influence of the intensity inhomogeneity. Moreover, the time step is dynamically modified towards annealing for performance refinement. Massive MR images were utilized to evaluate this new infarct segmentation algorithm. Adequate infarct segmentation results were obtained, which outperformed other competitive methods both qualitatively and quantitatively. Our infarct segmentation framework is of potential in providing a decent tool to facilitate preclinical stroke investigation and relevant neuroscience research using DWI and T2WI images.
Towards Computational Support with Language Models for TTRPG Game Masters

Tabletop role-playing games require game masters to mediate between the desires of a group of players and the affordances of a mechanically rich game world. In this paper, we explore the potential for large language models like GPT-3 to assist with the improvisational needs of a game master. We build upon Shoelace, a computational assistant for the GUMSHOE One-2-One role-playing framework; using GPT-3, we extend Shoelace to provide dialogue suggestions for non-player characters as well as to highlight relevant game module information. We emphasize the potential for language models to facilitate new role-playing tools and mechanics and suggest that the user experience problems presented by language models and role-playing are still underexplored and unresolved.
Sentiment Analysis on Consumers Behaviour Buying Computer Hardware

In this paper we present a deep literature review on existing system for sentimental analysis. Basically, sentimental analysis (SA) is the measurement of preference of people’s thoughts via natural language processing. This study leads to the behaviour pattern of consumer’s thinking process and various aspects factors of buying. The main aim of sentiment analysis is to know the orientation of the sentiment described in script. In recent decades the researcher focuses on the study various algorithms for relevant research results of the sentiment analysis. This research paper provides a comprehensive overview of this field's latest update. In this review, some recent proposed improvements of algorithms and various SA applications are explored and briefly described. The aim of this paper is to provide knowledge about the different method related to sentimental analysis also how they are classified, what the applications of this analysis. Keywords: - Behaviour- Sentimental Analysis, Algorithm comparison, word cloud, Naïve Bayes, Social Media, python-Programming, Machine Learning, data collection,PandaML
HOW EFFECTIVE IS CLOUD CRYPTOGRAPHY?

Nowadays cloud is been widely used all over the world. Cloud computing creates a virtual space on Internet, on which the user can store the data. Through cloud you can store huge amount of data virtually, rather than storing it on any physical system which will acquire space. One question comes here , “How secure is the data on cloud?”, “Will it be accessed by any third-party?”. Cloud computing has given solution to all these question and problems by providing “Cloud Cryptography” mechanism. Cloud cryptography adds a high layer of security to data and prevents it from any type of hacking or any third-party by encrypting the data on cloud. Currently Cloud cryptography uses different methods to secure the data. This research will discuss different methods which are used by cloud cryptography. How effective are these methods in securing the data stored on cloud. What is the future of cloud cryptography?, Will any other technology overlaps cloud cryptography? these all questions will be discussed in this paper. Keywords: ciphertext, cryptography, encryption, decryption
Data Security using Variant of Hill Cipher

Abstract: Data is first collected, and the Pixel Repetition Technique is used to perform pre-processing, Payload capacity, attack protection, and visual quality. To rise the security of information concealing, The LSB information hiding algorithm of data using secret key has been presented to strengthen the security of information concealing. It increases human visual abilities, integrates information concealment and cryptography, and uses identity identification based on digital signature and encryption technology. It is based on a scheme where the receiver can retrieve secret data by applying the A* algorithm backwards. Last but not least, we tested safety and peak signal-to-noise ratio (PSNR). With stronger security and higher PSNR, the enhanced LSB data steganography technique using encryption technology is superior to the standard LSB data steganography method
Motivation for Learning in Virtual Environments

The use of state-of-art immersive technologies enhanced undergraduate students’ motivation to learn. The main variables for the comparative analysis are the opinions of surveyed students for three academic terms and the perception of motivation as an engine for learning. The data used for this article comes from undergraduate students at a Mexican university, Tecnológico de Monterrey, during January 2019 up to June 2020. The virtual activity designed for fostering motivation for is called: Welcome to Ur, Mesopotamia. The activity consists of a recreation of the ancient city of Ur using 3D animation and virtual reality goggles. Each student was able to travel back on time and explore by foot the streets, temples, and palaces of the Mesopotamian city. The courses where the educational experiences took place, "Sustainable Cities and Communities" and "Citizenship and Democracy" reported higher satisfaction and motivation to learn that those courses that did not use the virtual tool. The use of technological tools helped students to have better understanding of the academic content. The students reported feeling motivated to learn more about political history due to the immersive learning activity.
QR based Hospital Health Card

The proposed web application is designed to provide a platform for patients and doctors to interact efficiently and effectively.The patient panel includes several features that enable patients to manage their medical data, book hospital appointments related to their health issues, read blogs on their diseases, purchase online medicines through the pharmacy, and use QR codes to access their medical information.The doctor panel, on the other hand, enables doctors to manage their appointments with patients, retrieve patient data through QR code scanning, and check the contents of medicines prescribed to patients.The application is built on modern web development frameworks such as ReactJS, NodeJS, and MongoDB, which provide a secure and scalable environment for data management.The proposed methodology includes a development process that incorporates rigorous testing and quality control to ensure that the application meets the highest standards of usability and security.The technology stack includes modern web development frameworks that offer the flexibility and scalability necessary to accommodate future expansion and growth.The potential results and outputs of the proposed web application are significant.By enabling patients to manage their medical data and access healthcare services online, the application can enhance patient engagement and satisfaction.Doctors can also benefit from the application by managing their appointments and accessing patient data quickly and efficiently.The web application has the potential to reduce healthcare costs by enabling patients to access healthcare services remotely, reducing the need for physical consultations.The application can also improve patient outcomes by providing patients with easy access to healthcare services, enabling them to manage their health more effectively.
THE INSTITUTION OF MENTORING AS A SOCIAL PHENOMENON

The subject of research in the article is mentoring as a social institution and a universal social technology of assistance in the formation of meta-competencies, meanings and values of young people in the process of social partnership. For the successful implementation of the functions of mentoring in the current conditions of the crisis of society, it is necessary to revise the main theoretical and methodological guidelines of the mentoring institute, the fundamental principles and anthropopraktics of self-development in the activities of the mentor. The role of the mentor in the student period of personality development is shown.
Validation of the Attitude Scale on Prospective Teachers’ Perceptions of the Consequences on Their Psychological State: Well-Being and Cognition

The aim of this study was to analyse the validated psychometric characteristics of the “Scale of Attitudes towards New Post-Pandemic Scenarios” (SANPS) tool using a short version on Perceptions of Future Teachers towards the New Post-Pandemic Educational Scenarios; describe the attitudes of future teachers towards motivation, collaboration, and emerging active pedagogies; and determine the internal consistency and reliability of the tool. The design structure of the instrument consists of the following three latent factors, which were obtained through an exploratory factor analysis (EFA): empowerment/motivation, autonomy/situated learning and emerging digital pedagogies. The questionnaire was administered to a sample of 966 participants. In the confirmatory factor analysis (CFA), a previous hypothesis was established regarding the relationship of the factors and their number and nature, specifying the number of factors and the way in which the variables are related to each other. The 66.53% of total variance was explained. The reliability, calculated with Cronbach’s alpha, reached a global value of over 0.90 (α = 0.94). This valid and reliable questionnaire, which incorporates a dimension that measures the transfer of learning in hybrid and multimodal models of digital ecosystems in Higher Education, can be applied in the evaluation of online education processes.
Multimedia Interaktif Pengenalan Museum Bali

Abstract&#x0D; Museum nowadays is not just a place for preserving historical objects, but also become a place of recreation and education. Bali Museum, located in Denpasar, Bali, is one of many museums that has collection more than 2500 pieces, most of them consist of ethnographic, Numismatic, and Archeological collection, The implementation of interactive multimedia for introducing Bali Museum as an innovation is made to help increase tourist attraction and also make it easier for tourists to obtain important information related to Balinese history packaged in an interesting and informative way. This interactive multimedia is made using Visual Basic and later will be displayed on a screen like queue machine which expected to make it easier for visitor. The expected result is this interactive can attract more tourists and make it easier for tourists to get know about the museum before entering the rooms in the museum.
“O QUE NÃO TE DESAFIA, NÃO TE TRANSFORMA”: A LIGA INTERDISCIPLINAR EM SAÚDE MENTAL E SUAS AÇÕES EXTENSIONISTAS NOS MEIOS DIGITAIS

Our objective was to report experiences of the Interdisciplinary League in Mental Health (LISAM) regarding the use of digital tools for extension activities development. By establishing a link between university and society, academic leagues play an important role in health promotion. Nevertheless, due to the health crisis of the New Coronavirus face-to-face activities were suspended, so these extension projects had to reinvent themselves by creating new methodologies for virtual environments. In this regard, LISAM has committed itself to using digital platforms to publish posts, and making live broadcasts, online events, and podcasts as a method of providing support to the population in the midst of the pandemic, in addition to transmitting scientifically-based information. Therefore, social networks were an important field for continuing the university extension activities, becoming a complementary means for education and health care improvement.
Are Hate Crime Enhancements Warranted in American Law?

Hate crimes are crimes that have an unusual bias upon a certain group due to their specific identity. In the American legal system, it has been a long-time controversy surrounding whether a hate crime enhancement is warranted for those classified as such. This paper first seeks to examine the rationality of hate crime enhancements, using established hate crime legislature to analyze its purpose. Then it will apply such a framework to evaluate whether the legislature abides with legal philosophy and constitutional rights through the lens of moral culpability, past landmark cases, and implementation observances. The paper will explain that legal philosophy deems it impossible to assess motive, a key component of hate crimes. It will also challenge the current supreme court and landmark rulings upon hate crime legislature and points out an unconstitutional logical loophole. Flaws of implementation on demographics and due process are also pointed out as limitations. Through the three-prong analysis, although a justification for current hate crime enhancements is present due to the potential physical and mental damages, it is concluded that they are unconstitutional, unwarranted, and must be limited to ensure the justice of the American legal system.
Semi-Supervised Mixture Learning for Graph Neural Networks With Neighbor Dependence

A graph neural network (GNN) is a powerful architecture for semi-supervised learning (SSL). However, the data-driven mode of GNNs raises some challenging problems. In particular, these models suffer from the limitations of incomplete attribute learning, insufficient structure capture, and the inability to distinguish between node attribute and graph structure, especially on label-scarce or attribute-missing data. In this article, we propose a novel framework, called graph coneighbor neural network (GCoNN), for node classification. It is composed of two modules: GCoNN <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{\Gamma}$</tex-math> </inline-formula> and GCoNN <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{\mathop{\Gamma}\limits^{\circ}}$</tex-math> </inline-formula> . GCoNN <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{\Gamma}$</tex-math> </inline-formula> is trained to establish the fundamental prototype for attribute learning on labeled data, while GCoNN <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{\mathring{\Gamma}}$</tex-math> </inline-formula> learns neighbor dependence on transductive data through pseudolabels generated by GCoNN <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{\Gamma}$</tex-math> </inline-formula> . Next, GCoNN <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{\Gamma}$</tex-math> </inline-formula> is retrained to improve integration of node attribute and neighbor structure through feedback from GCoNN <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{\mathring{\Gamma}}$</tex-math> </inline-formula> . GCoNN tends to convergence iteratively using such an approach. From a theoretical perspective, we analyze this iteration process from a generalized expectation–maximization (GEM) framework perspective which optimizes an evidence lower bound (ELBO) by amortized variational inference. Empirical evidence demonstrates that the state-of-the-art performance of the proposed approach outperforms other methods. We also apply GCoNN to brain functional networks, the results of which reveal response features across the brain which are physiologically plausible with respect to known language and visual functions.
Allez Go: Computer Vision and Audio Analysis for AI Fencing Referees

The gradual increase in online fencing videos over the past decade has allowed for novel technical projects in fencing that rely heavily on data, such as artificial intelligence. This study resulted in a state-of-the-art lightweight Temporal Convolutional Network to referee fencing bouts and classify actions as either a touch for the fencer on the left, or the fencer on the right. To address this problem, we developed a pose estimation and audio analysis approach to autonomously referee fencing bouts. Using a custom dataset of international level fencing from the last 7 years, including ~4000 unique clips, our model achieved an accuracy of 89.1%, a 20% increase over previous state-of-the-art models. This model leverages advancements in human pose estimation to extract the position of both fencers and avoids high computational loads typically associated with CNNs. Additionally, it uses a novel technique to solve the issue of blade contact, a key component of refereeing fencing that was generally unaddressed in previous works. Our novel approach uses audio to ‘listen’ for the sound of blade contact rather than attempting to identify it visually.
EvoText: Enhancing Natural Language Generation Models via Self-Escalation Learning for Up-to-Date Knowledge and Improved Performance

In recent years, pretrained models have been widely used in various fields, including natural language understanding, computer vision, and natural language generation. However, the performance of these language generation models is highly dependent on the model size and the dataset size. While larger models excel in some aspects, they cannot learn up-to-date knowledge and are relatively difficult to relearn. In this paper, we introduce EvoText, a novel training method that enhances the performance of any natural language generation model without requiring additional datasets during the entire training process (although a prior dataset is necessary for pretraining). EvoText employs two models: $G$, a text generation model, and $D$, a model that can determine whether the data generated by $G$ is legitimate. Initially, the fine-tuned $D$ model serves as the knowledge base. The text generated by $G$ is then input to $D$ to determine whether it is legitimate. Finally, $G$ is fine-tuned based on $D$'s output. EvoText enables the model to learn up-to-date knowledge through a self-escalation process that builds on a priori knowledge. When EvoText needs to learn something new, it simply fine-tunes the $D$ model. Our approach applies to autoregressive language modeling for all Transformer classes. With EvoText, eight models achieved stable improvements in seven natural language processing tasks without any changes to the model structure.
CNN Based Missing Object Detection

Abstract: Missing object detection is an important problem in computer vision with applications in various fields such as autonomous driving, surveillance, and robotics. Deep neural networks, particularly convolutional neural networks (CNNs), have shown promising results in addressing this problem. In this literature survey, we review recent research papers that focus on using CNNs for missing object detection. We analyze the different approaches and techniques employed by these papers, including context-aware detection, generative adversarial networks, multi-task learning, and transfer learning. We also discuss the challenges and limitations of these approaches and suggest possible directions for future research. Overall, the literature survey highlights the potential of CNNs in addressing the missing object detection problem and provides a comprehensive understanding of the recent advancements in this field.
Labor Market Opportunities of Private and Public Education in Colombia

&#x0D; Colombia has presented high unemployment for the past decade which was worsened by the Covid-19 pandemic. Job inequality among the highly vs. poorly educated has sparked a debate whether employers treat candidates differently based on their high school institution. This experiment explores the present research question: Does graduating from a public or private high school affect a candidate's opportunities of getting employed in Colombia under the circumstances of Covid-19? To circumvent the quandary, a quantitative field experiment on a correspondence testing method was used to investigate minority outcomes in the labor market. To create a realistic representation of the actual employment situation of Colombia, CEOs and business managers of the top companies were part of the experiment. Results reflect differential treatment in labor opportunities favouring private school graduates. It was evident throughout the experiment that the effects of graduating institutions were not as relevant as the level of education. Applicants from public schools can overcome this hurdle by investing in observable skills to the employer, suggesting that educational training programs can contribute to the reduction of the inequality gap Colombia currently faces.&#x0D;