Reviewerly/exHarmony huggingface dataset:
Dbswin: Transformer Based Dual Branch Network for Single Image Deraining

Rain streaks severely affect the perception of the content and structure of an image sohigh-performance deraining algorithms are needed in order to eliminate the effects ofvarious rain streaks for high-level computer vision tasks. Although much progress hasbeen made with existing deraining methods, the task of single image deraining remainschallenging. In this paper, we first point out that existing Transformers lack sufficientability to capture channel attention which restricted the ability of models in deraining.To improve the performance of deraining model, we propose a dual branch derainingnetwork based on Transformer. One branch uses dense connections to connect Transformermodules which embed the attention of a composite channel. This branch captureschannel attention more finely to learn the representation of rain streaks features.The other branch first obtains features at different scales by gradually expanding thereceptive field, then uses these features to obtain attention for regional features, and finallyuses the attention to guide the model to focus on areas of high rain streaks densityand large scales. By fusing these two branches, the model is able to capture channel attentionmore finely and to focus on regions of high rain streaks density and large scales.The extensive experimental results on synthetic and real datasets demonstrate that theproposed method outperforms most advanced deraining methods.
Automatic Detection and Recognition of Object to Help Visually Impaired People while Visiting Liberation War Museum in Bangladesh

This paper represents an analysis of object detection by multi-image classification and machine learning techniques for blind people. Our system has been developed based on Speech Recognition that helps blind people to know the information about the different components in the liberation war museum in Bangladesh. From the initial survey it concerned that most of the blind people are excited to know what is inside in the liberation war museum, but there is no automated system in the museum to recognize the object as an explanation for them. For this thoughtfulness, we built a model for visual imperial people that will detect automatically and that provide an explanation about the object by speech recognition technique. For the whole process, we used Different Machine Learning(ML) tools like Scikit-learn, Pandas, Matploatlib, Numpy, TensorFlow. For preprocessing images TensorFlow, for machine learning we have used Scikit-learn. For measuring the accuracy of our work we used five different Machine Learning(ML) algorithms K-Nearest Neighbor(KNN), Support Vector Machine(SVM), Decision Tree Classifier, Random Forest, Naive Bayes and most popular image processing algorithm Convolutional Neural Network(CNN) to find out which algorithm gave the highest accuracy. Finally, we detect museum spectacles by this algorithm which produced the highest accuracy.
Contextual Sentence Analysis for the Sentiment Prediction on Financial Data

Newsletters and social networks can reflect opinions about individual stocks from the perspective of analysts and the general public on their products and/or services. Therefore, sentiment analysis of these texts can provide useful information to help investors make investment decisions. In this paper, a hierarchical stack of Transformers model is proposed to identify the sentiment associated with companies and stocks, by predicting a score (of data type real) in a range between -1 and +1. Specifically, we fine-tuned a RoBERTa model to process headlines and microblogs. Furthermore, we combined it with additional Transformer layers to process the sentence analysis with sentiment dictionaries to improve the sentiment analysis. We evaluated it on financial data released by SemEval-2017 task 5. Our proposition outperformed the best systems of SemEval-2017 task 5 and strong baselines. We verified that the combination of contextual sentence analysis with the financial and general sentiment dictionaries indeed provided useful information to our model and allowed it to generate more reliable sentiment scores.
Learning to Optimize Black-Box Functions with Extreme Limits on the Number of Function Evaluations

We consider black-box optimization in which only an extremely limited number of function evaluations, on the order of around 100, are affordable and the function evaluations must be performed in even fewer batches of a limited number of parallel trials. This is a typical scenario when optimizing variable settings that are very costly to evaluate, for example in the context of simulation-based optimization or machine learning hyperparameterization. We propose an original method that uses established approaches to propose a set of points for each batch and then down-selects from these candidate points to the number of trials that can be run in parallel. The key novelty of our approach lies in the introduction of a hyperparameterized method for down-selecting the number of candidates to the allowed batch-size, which is optimized offline using automated algorithm configuration. We tune this method for black box optimization and then evaluate on classical black box optimization benchmarks. Our results show that it is possible to learn how to combine evaluation points suggested by highly diverse black box optimization methods conditioned on the progress of the optimization. Compared with the state of the art in black box minimization and various other methods specifically geared towards few-shot minimization, we achieve an average reduction of 50% of normalized cost, which is a highly significant improvement in performance.
Public sentiment towards face-to-face activities during the COVID-19 pandemic in Indonesia

A year after the COVID-19 pandemic took place, activities that were carried out online gradually switched back to face-to-face. This has caused controversy given the high transmission. Therefore, this study aims to analyze public sentiment by utilizing Twitter data. Latent Dirichlet Allocation (LDA) was also conducted in this study to classify public opinion. It was found that face-to-face learning was the highlight of public conversation and was dominated by negative sentiment, followed by neutral and positive sentiment. Meanwhile, the LDA model produced topics about vaccination, public preference, school reopening, public sentiment, students' longing for face-to-face learning and face-to-face learning plan.
Impact of Turkish National Culture on Agile Software Development in Turkey

The effect of national culture in the software development especially in Agile Software Development industry has a considerable place since national culture affects and shapes organizations and individuals. Our study examines the impact of Turkish national culture on Agile software transformations and developments in Turkey, as the first instance in/for Turkey scope, to the best of our knowledge. We conducted semi-structured interviews with fourteen experts in prominent nine companies from three major industries including TechFin, Aviation, and Telecommunication. In the study, motivations of organizations for transforming Agile, challenges with transitioning to Agile, Agile culture specific to Turkey and preferences on Agile frameworks in Turkey were investigated. The results were discussed along with their implications for Agile in Turkey by considering Hofstede’s model which is designed to investigate country-level cultural traits. Our results are largely parallel with the existing knowledge of Hofstede Insights specific to Turkish culture, yet we additionally present the impacts of this national culture of Turkey on the country’s Agile Software Development. Consequently, it was observed that the national cultural background has a considerable effect on the Turkish Agile software development domain. We have witnessed some similar effects in the Eastern culture as well. By providing the country’s cultural patterns through a localized lens, the study may contribute to those who may have a practical interest to Turkish in terms of which potential challenges they need to be prepared for once they move into the adoption of agile working in/with this country and more generally in/with the countries with which has similar cultures as in the Eastern civilizations. Our study also comes with global insights to the other countries in terms of understanding the use of agile methods and practices in companies located outside of the early adopters of agile methods.
PRL: A game theoretic large margin method for interpretable feature learning

The crucial role played by interpretability in many practical scenarios has led a large part of the research on machine learning towards the development of interpretable approaches. In this work, we present PRL, a game-theory-based method capable of achieving state-of-the-art accuracy, yet keeping the focus on the interpretability of the predictions. The proposed approach is an instance of the more general preference learning framework. By design, the method identifies the most relevant features even when dealing with high-dimensional problems. This is possible thanks to an online features generation mechanism. Moreover, the algorithm is proven to be theoretically well-founded, thanks to a game-theoretical analysis of its convergence. To assess the quality of the proposed approach, we compared PRL against state-of-the-art methods in a plethora of different classification settings. The experimental evaluation focuses on interpretability, with an in-depth analysis of visualization, feature selection, and explainability.
Research on Intelligent Diagnosis Model of Electronic Medical Record Based on Graph Transformer

Intelligent diagnosis based on the Electronic Medical Record (EMR) is an important research content of biomedical informatics. Recent studies have showed that graph representation learning methods can improve the performance of diagnosis prediction tasks by utilizing the graph structural information of EMR. However, the original EMR data often do not contain such complete and explicit structural information. It is challenging to learn EMR structural information from EMR data. In this paper, we propose a Statistics and Knowledge based Graph Transformer (S_K_GT) model to jointly learn EMR graph and perform diagnosis prediction tasks. Specifically, we firstly use conditional probability to initially construct the adjacency matrix of EMR graph and send it to Transformer encoder to further learn the connection relationship between EMR nodes. Then, we construct a knowledge attention network based on medical knowledge graph as expert knowledge to optimize the EMR graph deep learning process. Finally, we learn precise EMR graph representation for intelligent diagnosis. Experiments on EMR data in both Chinese and English show that the proposed model outperforms the state-of-the-art methods in all diagnosis prediction tasks.
Event-related Potentials-based Evaluation of Attention Allocation while Watching Virtual Reality

Virtual reality (VR) has been applied to several fields such as entertainment, education, and medicine in recent years. VR is characterized by a high sense of immersion, which can be represented by the attention allocation from the real world to the virtual space. Although a high degree of attention allocation is significant in VR technology, most existing evaluation methods of VR applications are based on subjective questionnaires. Thus, quantitative and objective VR application evaluation methods are needed to realize advanced VR applications. In this study, we adopted a probe stimulus method to evaluate the attention allocation quantitatively and objectively in VR technology. Ten young adult participants underwent an auditory oddball task while they experienced VR content. The amount of attention directed to the VR content could be quantified based on the decrease in the event-related P300 wave response in the case of the oddball task. The participants watched two-dimensional and three-dimensional VR contents on a liquid crystal display and a head-mounted display, respectively, while brain activity was recorded in the form of electroencephalographic signals. A total of 230 probe stimuli at 1800 Hz (standard stimulus), 2000 Hz (target stimulus), and 500 Hz (deviant stimulus) were presented randomly via an earphone for 70 ms at 1000-ms intervals at the fractions of 70, 15, and 15%, respectively. Additionally, the reaction time and false reaction rate during the oddball task were measured as behavioral measures, and a questionnaire was used for subjective evaluation after the task. Based on a comparison of the subjective measure, behavioral measure, and amplitudes of P300 measured with the target stimulus from Pz and deviant stimulus from Cz, we found that attention allocation to the VR content can be quantitatively estimated using the amplitude of P300 for the deviant stimulus. These results suggest that the proposed method involving event-related potentials can be used as an indicator for attention allocation while watching VR content.
Elements of Task Theory

Tasks are of primary importance for artificial intelligence (AI), yet no theory about their characteristics exists. The kind of task theory we envision is one that allows an objective comparison of tasks, based on measurable physical properties, and that can serve as a foundation for studying, evaluating, and comparing learning controllers of various kinds on a variety of tasks by providing principled ways for constructing, comparing, and changing tasks with particular properties and levels of difficulty. In prior papers we have outlined an approach towards this goal; in this paper we present further principles for its development, including causal relations. We use these principles to expand our prior ideas, with the aim of laying the groundwork for covering levels of detail, prior knowledge of the learner/performer and task difficulty, to name some of the complex issues that must be solved for a useful task theory.
Automatic ICD-10 Coding Based on Multi-Head Attention Mechanism and Gated Residual Network

Classifying diseases in electronic medical records into corresponding ICD codes requires not only a large amount of medical knowledge but also a large number of coders, which is time-consuming and labor-consuming. Therefore, automatic coding is of great significance. This paper aims to build a deep learning model for automatic ICD-10 coding from a batch of Chinese electronic medical records. The data enhancement, convolutional neural network, attention mechanism, and the gating residual network proposed by the author were used to code ICD code corresponding to the distribution of medical record information by supervised learning. The benchmark model and ablation model were tested on a data set of Chinese electronic medical records. The effectiveness of the proposed modules, such as feature aggregation, multi-head attention mechanism, dilated convolution, and gating residuals, was verified. In the automatic ICD coding task for 104 diseases, the accuracy of the proposed method is 91.71%, and the F1-Score is 92.11%. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Scalable and Robust Decentralized Cargo Transport via Genetic Fuzzy Systems

Genetic algorithms provide the adaptability necessary to construct complex interactions between inputs and outputs in fuzzy logic controllers. Fuzzy Logic provides visually interpretable correlations which for low-dimensionality feature spaces can be directly observed for flaws. By carefully staging trials in an auto-test structure, individual behaviors have been hand-picked to train thrusts of point-mass drones sharing a single payload via damped spring cables with slack modes considered. The final system is capable (in real time) of (1) actively bringing the shared payload to target altitudes, (2) maintaining the respective drone cable pitch (and thereby drone contribution), (3) maintaining yaw spacing from neighboring drones, and (4) performing basic linear navigation in the horizontal plane. As an intentional byproduct of this approach, the system attains a passive resilience against drone losses and reasonable sensor noise. All of this is achieved through what is assumed to be visually attainable information (that is, neighbor position and velocity), at a user-defined sample rate, with a long-term goal of making the controller easily deployable to any cargo drone team using nothing more than sufficient depth perception and GPS. Ongoing areas of expanded research and their natural/practical inspirations are presented.
A holistic analysis of web-based public key infrastructure failures: comparing experts' perceptions and real-world incidents

Abstract Public key infrastructure (PKI) is the foundation of secure and trusted transactions across the Internet. This paper presents an evaluation of web-based PKI incidents in two parts. We began with a qualitative study where we captured security and policy experts' perceptions of PKI in a set of interviews. We interviewed 18 experts in two conferences who include security academics and practitioners. We describe their perceptions of PKI failures. To evaluate whether perceived failures match real documented failures, we conducted a quantitative analysis of real-world PKI incidents on the web since 2001. Our data comprise reports from Bugzilla, root program operators, academic literature, security blogs, and the popular press. We determined the underlying causes of each and reported the results. We identified a gap between experts' perceptions and real-world PKI incidents. We conclude that there are significant sources of failures of PKI that neither the usability nor traditional computer security community is engaging, nor can arguably engage separately. Specifically, we found incidents illustrate systematic weaknesses of organizational practices that create risks for all who rely upon PKI. More positively, our results also point to organizational and configuration choices that could avoid or mitigate some of these risks. Thus, we also identify immediate mitigation strategies (where feasible).
Towards building a Bangla text recognition solution with a Multi-Headed CNN architecture

Bangla is among the ten most popular languages in the world by the number of speakers. The task of Bangla recognition is quite challenging than other languages because of the existence of graphemes of multiple single characters, and diacritics of vowels and consonants. The purpose of this study is to develop an innovative large-scale Bangla OCR solution based on character-level recognition. Two types of documents were used to test our method: handwritten and printed. In addition, our method was applied to the handwritten documents as well as three subdomains of the printed domain: computer-composed, letterpress, and typewritten documents using our proposed attentionbased multi-headed CNN architecture. Extensive testing shows that our method provides state-of-the-art performance on both handwritten and printed texts.
Cooking habits and food choices of individuals with Type 1 Diabetes Mellitus during the pandemic of COVID-19

ABSTRACT Objective To verify the association between cooking habits, socioeconomic data, and food choices of individuals with Type 1 Diabetes Mellitus during the pandemic of COVID-19. Methods Transversal study with individuals with Type 1 Diabetes Mellitus carried out in July 2020. Socioeconomic data and information about social distancing and food practices were collected with an online form. The research was approved by the university’s Ethics and Research Committee (Process number 4.147.663). Results Out of the 472 participants, 50.9% reported that they have been cooking more during the pandemic. An association between cooking more and having a university degree (p&lt;0.000) was observed. Not being able to comply with social distancing rules because of work necessities was associated with not cooking (p=0.006). Cooking more during the quarantine was associated with eating less than five meals per day (p=0.04), having an appropriate consumption of fruits (p=0.02) and vegetables (p=0.04), and increased water intake (p=0.01). Conclusion In Brazil, the habit of cooking during the pandemic may represent an increase in domestic work, reinforced by social inequalities. Therefore, comprehending the cooking habits and food choices of people with diabetes may widen the perspectives of health professionals involved in the treatment of the disease and contribute to the elaboration of public policies that take the country's inequalities into account. We emphasize the importance of investing in policies that encourage the development of culinary skills, as well as the habit of cooking as part of the actions of Food and Nutrition Education.
Locally Aligned Image Stitching Based on Multi-Feature and Super-Pixel Segmentation With Plane Protection

The number and accuracy of image feature matching directly affect the accuracy of image warping, which is gaining widespread attention in image stitching. From a single plane to a regular multi-plane warping model, the alignment accuracy is gradually improved. To further improve the accuracy of image alignment, this paper proposes a multi-plane alignment method based on superpixel segmentation, which preserves the integrity of local planes as much as possible to reduce ghosting. Recent warps prove that line features provide strong correspondences, especially in low-textured cases. Moreover, image segmentation methods such as superpixel segmentation have the function of protecting the integrity of the object. On the one hand, in the feature matching stage, our approach introduces superpixel segmentation to refine the matching. The homography combines line features to enrich as many matching points as possible. On the other hand, to solve the problem of misalignment caused by local warping, the proposed method makes full use of the characteristics of superpixels to perform irregular plane segmentation to avoid the traditional rectangular segmentation method from dividing different planes into the same grid. Experimental results demonstrate that the proposed method outperforms some state-of-the-art warps from both qualitative and quantitative aspects, including the as-projective-as-possible warp (APAP), the as-natural-as-possible warp (ANAP), the global similarity prior (GSP), and so on.
Automatic Generation of Intelligent Chatbots from DMN Decision Models

Decision models are the consolidated knowledge representation of the requirements and the logic of operational decisions in business organizations. Decision models defined in the Decision Model and Notation (DMN) standard can contribute significantly to the automation of business decision management. However, the current scope of decision support is quite limited in presenting the decision-making process to an end-user in a reliable, user-friendly way. This paper provides a framework for automatically generating chatbots from DMN models that can handle numerous user scenarios for effective and explainable decision-making during customer support inquiries. The method can improve the digitalization of customer services and give customers more transparency and trust in the decision-making through user-friendly chatbots.