Reviewerly/exHarmony huggingface dataset:
Statistical Destriping of Pushbroom-Type Images Based on an Affine Detector Response

Remote sensing pushbroom-type imaging systems acquire entire columns of an image with a single detector.As a consequence, the miss-calibration of the detectors produces stripes on the image.In this context, this paper introduces a new self-calibration destriping method based on an affine response model for the detectors, called Statistical Affine Destriping (SAD).In contrast, some previous contributions were limited to a purely linear model, while many others only considered an additive structured noise model.It is based on the maximum a posteriori estimation of the gain and offset parameters attached to each detector given the observed image.Simple statistical prior assumptions are adopted: respectively, a Gaussian white noise model for the gains and offsets, and a first-order, homogeneous Markov model for the observed scene.Based on a simplification of the posterior likelihood, we propose a very efficient optimization scheme based on a constrained Majorize-Minimize principle, allowing us to process large dimension images.Moreover, simple empirical rules are given to tune the hyperparameters of the destriping method for high-resolution PLEIADES-type images.Compared to the performance of a destriping method limited to gain correction, we observe that the new version provides reliable results in a wider range of situations.We also extend the method in two directions.On the one hand, we consider that some detectors may be atypical, with very high or very low gains or offsets.On the other hand, we extend the method to multispectral image destriping.
Predicting Hate Intensity of Twitter Conversation Threads

Tweets are the most concise form of communication in online social media, wherein a single tweet has the potential to make or break the discourse of the conversation. Online hate speech is more accessible than ever, and stifling its propagation is of utmost importance for social media companies and users for congenial communication. Most of the research barring a recent few has focused on classifying an individual tweet regardless of the tweet thread/context leading up to that point. One of the classical approaches to curb hate speech is to adopt a reactive strategy after the hate speech postage. The ex-post facto strategy results in neglecting subtle posts that do not show the potential to instigate hate speech on their own but may portend in the subsequent discussion ensuing in the post's replies. In this paper, we propose DRAGNET++, which aims to predict the intensity of hatred that a tweet can bring in through its reply chain in the future. It uses the semantic and propagating structure of the tweet threads to maximize the contextual information leading up to and the fall of hate intensity at each subsequent tweet. We explore three publicly available Twitter datasets -- Anti-Racism contains the reply tweets of a collection of social media discourse on racist remarks during US political and Covid-19 background; Anti-Social presents a dataset of 40 million tweets amidst the COVID-19 pandemic on anti-social behaviours; and Anti-Asian presents Twitter datasets collated based on anti-Asian behaviours during COVID-19 pandemic. All the curated datasets consist of structural graph information of the Tweet threads. We show that DRAGNET++ outperforms all the state-of-the-art baselines significantly. It beats the best baseline by an 11% margin on the Person correlation coefficient and a decrease of 25% on RMSE for the Anti-Racism dataset with a similar performance on the other two datasets.
Resonate: Website on Text to Speech

Abstract: Synthesizing speech is quite complex as it is heavily reliant on language. Meaning, the language processing section in a TTS system inherently has the largest chunk of linguistic knowledge for a particular language. The technical as well as theoretical challenges faced while building such a high-quality system can be quite daunting and hard to navigate. To ensure that the system has relevant and updated linguistic information, one must make sure it has access to the most natural and unrestricted text to ensure quality and authenticity. We will also need extensive studies to achieve the same. At the heart of this software engine lies an OCR Engine (Optical Character Recognizer) which inherits crucial morphological operations required for image conditioning &amp; transformation, accompanied with python libraries used for character classification. Further the processed textual data is transformed into speech signals using various Text-to-Speech synthesis techniques
OPTIMIZING GRADIENTS WEIGHT OF ENHANCED PAIRWISE-POTENTIAL ACTIVATION LAYER IN CNN FOR FABRIC DEFECT DETECTION

Imperfection classification is the most involved task in the cotton sector for finding Fabric Defects (FDs) and improving fiber productivity.Several approaches have been suggested in ancient times to automatically classify FDs.Presently, an Enhanced Pairwise-Potential Activation Layer in Convolutional Neural Network (EPPAL-CNN) approach depends on improved external memory and Dynamic Conditional Random Fields (DCRFs) to solve the complex pattern correlation of FDs and detect the defective fabrics from the given images.On the contrary, the gradient-based optimization schemes for learning the weights of CNN tend to unusual convergence nature, resulting in inefficient classification.Hence in this paper, an EPPAL-Optimized CNN (EPPAL-OCNN) approach is proposed which introduces an individual weight optimization scheme depending on NWM-Adam for solving the unwanted convergence of CNN.In this approach, a novel first-order gradient descent optimization method is introduced, which applies an adaptive exponential decay percentage for second-moment approximation rather than a preconfigured and constant one.Also, it can simply modify the grade to which how much the previous gradients weigh in the approximation.This novel exponential moving mean deviation is designed based on the fact that assigning additional memory to the previous gradients compared to the current gradients.Thus, it guarantees effective convergence and increases detection accuracy.At last, the testing results reveal that the EPPAL-OCNN achieves 94.64% of accuracy to different state-of-the-art approaches on the TILDA database.
Rectify ViT Shortcut Learning by Visual Saliency

Shortcut learning is common but harmful to deep learning models, leading to degenerated feature representations and consequently jeopardizing the model's generalizability and interpretability. However, shortcut learning in the widely used Vision Transformer framework is largely unknown. Meanwhile, introducing domain-specific knowledge is a major approach to rectifying the shortcuts, which are predominated by background related factors. For example, in the medical imaging field, eye-gaze data from radiologists is an effective human visual prior knowledge that has the great potential to guide the deep learning models to focus on meaningful foreground regions of interest. However, obtaining eye-gaze data is time-consuming, labor-intensive and sometimes even not practical. In this work, we propose a novel and effective saliency-guided vision transformer (SGT) model to rectify shortcut learning in ViT with the absence of eye-gaze data. Specifically, a computational visual saliency model is adopted to predict saliency maps for input image samples. Then, the saliency maps are used to distil the most informative image patches. In the proposed SGT, the self-attention among image patches focus only on the distilled informative ones. Considering this distill operation may lead to global information lost, we further introduce, in the last encoder layer, a residual connection that captures the self-attention across all the image patches. The experiment results on four independent public datasets show that our SGT framework can effectively learn and leverage human prior knowledge without eye gaze data and achieves much better performance than baselines. Meanwhile, it successfully rectifies the harmful shortcut learning and significantly improves the interpretability of the ViT model, demonstrating the promise of transferring human prior knowledge derived visual saliency in rectifying shortcut learning
LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning

Abstract We describe LineageD—a hybrid web‐based system to predict, visualize, and interactively adjust plant embryo cell lineages. Currently, plant biologists explore the development of an embryo and its hierarchical cell lineage manually, based on a 3D dataset that represents the embryo status at one point in time. This human decision‐making process, however, is time‐consuming, tedious, and error‐prone due to the lack of integrated graphical support for specifying the cell lineage. To fill this gap, we developed a new system to support the biologists in their tasks using an interactive combination of 3D visualization, abstract data visualization, and correctable machine learning to modify the proposed cell lineage. We use existing manually established cell lineages to obtain a neural network model. We then allow biologists to use this model to repeatedly predict assignments of a single cell division stage. After each hierarchy level prediction, we allow them to interactively adjust the machine learning based assignment, which we then integrate into the pool of verified assignments for further predictions. In addition to building the hierarchy this way in a bottom‐up fashion, we also offer users to divide the whole embryo and create the hierarchy tree in a top‐down fashion for a few steps, improving the ML‐based assignments by reducing the potential for wrong predictions. We visualize the continuously updated embryo and its hierarchical development using both 3D spatial and abstract tree representations, together with information about the model's confidence and spatial properties. We conducted case study validations with five expert biologists to explore the utility of our approach and to assess the potential for LineageD to be used in their daily workflow. We found that the visualizations of both 3D representations and abstract representations help with decision making and the hierarchy tree top‐down building approach can reduce assignments errors in real practice.
Sheaf Neural Networks with Connection Laplacians

A Sheaf Neural Network (SNN) is a type of Graph Neural Network (GNN) that operates on a sheaf, an object that equips a graph with vector spaces over its nodes and edges and linear maps between these spaces. SNNs have been shown to have useful theoretical properties that help tackle issues arising from heterophily and over-smoothing. One complication intrinsic to these models is finding a good sheaf for the task to be solved. Previous works proposed two diametrically opposed approaches: manually constructing the sheaf based on domain knowledge and learning the sheaf end-to-end using gradient-based methods. However, domain knowledge is often insufficient, while learning a sheaf could lead to overfitting and significant computational overhead. In this work, we propose a novel way of computing sheaves drawing inspiration from Riemannian geometry: we leverage the manifold assumption to compute manifold-and-graph-aware orthogonal maps, which optimally align the tangent spaces of neighbouring data points. We show that this approach achieves promising results with less computational overhead when compared to previous SNN models. Overall, this work provides an interesting connection between algebraic topology and differential geometry, and we hope that it will spark future research in this direction.
А. Blok’s comprehension of the principles of Romanticism during his work at the Bolshoi Drama Theater (1919–1921)

The article raises the issue of Alexander Blok’s participation in the creation of the repertoire for Petrograd's Bolshoi Drama Theatre in the period from 1919 to 1921, the theoretical basis of which includes the idea of reviving the classics and returning to romanticism. In the process of work, the poet came to expand the category of romanticism, asserting the idea of its timelessness. As a result, the entire repertoire of the Bolshoi Drama Theatre acquired a single system, the purpose of which was to provide the viewer with the opportunity to look at the classics with new eyes in the light of the 1917 Revolution; then values were being breaking down and new principles of art, laid. Alexander Blok wanted to contribute to the updated system of priorities, highlighting his romantic hero – a revolutionary, a human with a pure, kind soul eager to create a just society built on the principles of goodness, which would be based on eternal values in the form of classics.
Understanding the Drivers’ Factors for Choosing an Accounting Student’s Career as A Professional Accountant

A career is a person's main goal in obtaining education in the field of interest. Students who choose an accounting major certainly want to have a career related to accounting. However, a student's choice of major may not be following the career he is involved in. This research examines the effect of intrinsic value, career prospects, labor market considerations, work environment, financial rewards, and individuals on the career selection of accounting students as a public accounting profession. The object of this research is undergraduate accounting students at Binus Online Learning. The sampling technique used is the Slovin technique, with a total sample of 171 respondents. Data collection techniques were taken by distributing questionnaires using Google Form Link and distributed through electronic media. The analytical method used is Multiple Linear Regression analysis with the SPSS analysis tool. The result showed that the variables of intrinsic value, career prospects, labor market considerations, work environment, financial rewards, and individuals have a significant effect on the career choices of accounting students to become public accounting profession. With the low attractiveness of the public accounting profession among accounting students themselves, public accounting professional organizations and academics can recommend encouraging students to take the Certified Public Accountant (CPA) exam, namely supporting, and helping students who really intend to have a career as public accountants and encourage them not to change their career choices. The results of this study are input to the university that it is necessary to introduce the accounting profession, students who hear and follow the development of accounting science in the form of seminars or share knowledge from lecturers will provide an overview and motivation for students to choose a career as an accountant
Arabic-English Code-Switching among KKU Students on Social Media “Twitter”

Code-switching is one of the apparent linguistic phenomena in bilingual or multilingual communication. This study investigated the occurrence of CS between Arabic and English languages among KKU students on social media, particularly Twitter. It sought to find out the frequent types and reasons for CS that appeared on KKU students’ Twitter accounts. The study was conducted using quantitative and qualitative methods. The primary data of this study was collected to explore the occurrence and the types of CS by taking screenshots of 300 tweets from Twitter accounts of 20 KKU students. The secondary data of this study was responses to an online questionnaire in order to examine the reasons for CS. The findings of this study proved that Arabic-English CS occurred among KKU students on Twitter. Also, it showed that the most frequent type of CS was intra-sentential CS. In addition, it was found that the most frequent reason that motivated KKU students to CS was the reason for talking about a particular topic. The current study provided significant information about using CS among KKU students on Twitter. It also encouraged to conduct further researches in this field, especially among Arab universities.
ESIM (ELECTRONIC STRATEGIC INTERVENTION MATERIAL) IN SCIENCE 9: AN AID TO IMPROVE ONLINE LEARNING PERFORMANCE

This research utilizes ESIM (Electronic Strategic Intervention Material) to improve online learning performance in Science, assisting the goal of providing high-quality teaching and learning in the face of a pandemic.The effectiveness and acceptability of ESIM were studied in this study (Electronic Strategic Intervention Material).Its goal is to determine whether ESIM (Electronic Strategic Intervention Material) is acceptable in terms of subtasking, congruence, functionality, and replicability.It also determines the pre-and post-test performance levels of students.It also determines how much Learners value the E-SIM in terms of cognitive, emotional, and psychomotor benefits.It also examines if the learners' pre-and post-test results are significantly different.The developed ESIM targeted the least mastered skills.To describe the level of acceptability of ESIM, this study employed a descriptive research design and a quasi-experimental research design with pre-and post-tests.The purposive sample technique was utilized to gather 70 respondents, including science instructors and ICT experts, and 40 Grade-9 students.The mean and standard deviation were utilized to assess ESIM's acceptance, as well as the learners' pre-and post-test results and perceived benefits.A paired t-test was used to evaluate the significant difference between the pre-test and post-test scores.In terms of subtasking, congruence, usefulness, and replicability, data analysis demonstrated that ESIM has a very high level of acceptability.The level of learners' science proficiency improved dramatically after the material was implemented.As a result, learners' perceived cognitive, emotional, and psychomotor advantages were extremely high.There is a significant difference in learners' Science performance, as evidenced by the test difference between the pre-test and post-test.According to the study, ESIM had a significant impact on learners' performance in the subject.Based on the findings and conclusions, the developed Electronic Strategic Intervention Material (ESIM) is highly acceptable and effective in improving learners' performance.
Belief-Desire-Intention (BDI) Multi-agent System for Cloud Marketplace Negotiation

With the evolution of cloud computing, there has been a rise of large enterprises extending their infrastructure and workloads into the public cloud. This paper proposes a full-fledged framework for a Belief-Desire-Intention (BDI) multi-agent-based cloud marketplace system for cloud resources. Each party in the cloud marketplace system supports a BDI agent for autonomous decision making and negotiation to facilitate automated buying and selling of resources. Additionally, multiple BDI agents from an enterprise competing for the same cloud resource can consult with each other via Master Negotiation Clearing House to minimize the overall cost function for the enterprise while negotiating for a cloud resource. The cloud marketplace system is further augmented with assignments of behavior norm and reputation index to the agents to facilitate trust among them.
Encrypted Short Message Service Design Using Combination of Modified Advanced Encryption Standard (AES) and Vigenere Cipher Algorithm

&#x0D; Despite the popularity of social media and Internet-based messaging services, Short Message Service (SMS) is still widely used by a lot of people worldwide. However, in this modern era, Short Message Service (SMS) is not a secure way of sending and receive text messages. Anyone with the right privilleges can easily read all the messages sent using SMS service. Therefore, appropriate cryptographic algorithms should be applied to ensure confidentiality of the message content. In this paper, a modified AES with Vigenere Cipher Algorithm will be used to encrypt the text message, hence greatly increasing the security of the sent messages. Avalanche Effect is chosen as a metric for measuring performance of the proposed algorithm and its implementation. The proposed algorithm shows high Avalanche Effect compared to standard AES algorithm.&#x0D;
Characteristics of Primary Glaucoma in Glaucoma Clinic of Dr. Mohammad Hoesin General Hospital, Palembang

Introduction: Glaucoma is the second leading cause of blindness for over 70 million people worldwide. Bilateral blindness occurs with an estimated 10%. In Indonesia, the prevalence of glaucoma is 0,46%. That means that 4 to 5 out of 1.000 people suffer from glaucoma. Considering glaucoma as the second cause of blindness after cataracts in the world and its irreversible nature, this study was conducted to determine the characteristics of patients with primary glaucoma in the Glaucoma Clinic, Dr. Mohammad Hoesin General Hospital in 2021 based on age, gender, diagnosis, and intraocular pressure.&#x0D; Methods: This research is a descriptive retrospective study using secondary data through the medical record of patients in the Glaucoma Clinic, Dr. Mohammad Hoesin General Hospital Palembang from January to December 2021. The sample of this study was 20 people, and there were 40 eyes diagnosed with primary glaucoma.&#x0D; Results: The number of Primary Glaucoma patients was more in female patients, namely 11 patients (55,0%) with age varies at ≤ 50 years of age (45,0%), namely 51-60 patients (35,0%) and aged ≥ 61 years as many as 4 patients (25%), with an age range from 40 years to 70 years. Most patients were POAG (85%), with the highest eye pressure was 38,8mmHg, and 43,4 mmHg in PACG.&#x0D; Conclusion: There were 20 cases of primary glaucoma at the Glaucoma Clinic of Dr. Mohammad Hoesin General Hospital Palembang in 2021 with 40 affected eyes. Most patients were female, with most age group &lt;50 years, and the most common type of glaucoma was open-angle.
Customizing Summon for a Specific User Population: A Health Sciences Library’s Experience

The Harrell Health Sciences Library: Research and Learning Commons (HHSL) tailored their discovery solution, Ex Libris Summon, to better meet the needs of its health sciences users. The customizations encourage more sophisticated searching behaviors and improves usability of the library website for browsing and finding known items. HHSL worked with Penn State University Libraries and their Ex Libris Customer Success Manager to create customized search panels as well as turn on features available in Summon. While these customizations are specific to a health sciences library, any library can use these practices to customize their instance of Summon for their user group(s).
On the exhaustive generation of discrete figures with connectivity constraints

Abstract This paper deals with a generalization of polyominoes called ( a, b ) -connected discrete figures , where a and b respectively denotes the connectivity of the foreground (i.e. black pixels ) and background (i.e. white pixels ). Formally, a finite set of pixels P is ( a, b )-connected if P is a -connected and P̄ is b -connected. By adapting a combinatorial structure enumeration algorithm due to Martin, we successfully generate ( a, b )-connected discrete figures up to size n = 18.