Reviewerly/exHarmony huggingface dataset:
Editorial paper for pattern recognition letters VSI on advances in graph-based recognition for pattern recognition

Deep learning based stereo image super-resolution algorithm can make use of the additional information in the stereo image pairs to improve the quality of the reconstructed images. However, extracting the similarity information of one image to another precisely is challenging when partially occluded regions exist in stereo images. In this paper, we propose an adaptive modulation alignment mechanism to modulate the aligned features calculated from the parallax attention mechanism and effectively deal with the inaccuracy caused by occlusion. Furthermore, because disparities of stereo image exist only along the epipolar line, we use rectangular convolution kernel in some convolution layers, to extend the receptive field horizontally. Finally, experimental results demonstrate that our method achieves state-of-the-art performance on the Middlebury, KITTI 2012 and KITTI 2015 stereo benchmarks.
Computing (optimal) embeddings of directed bigraphs

Bigraphs and bigraphical reactive systems are a well-known meta-model successfully used for formalizing a wide range of models and situations, such as process calculi, service oriented architectures, multi-agent systems, biological systems, etc.A key problem in the theory and the implementations of bigraphs is how to compute embeddings, i.e., structure-preserving mappings of a given bigraph (the pattern or guest) inside another (the target or host).In this paper, we present an algorithm for computing embeddings for directed bigraphs, an extension of Milner's bigraphs which take into account the request directions between controls and names.This algorithm solves the embedding problem by means of a reduction to a constraint satisfaction problem.We first prove soundness and completeness of this algorithm; then we present an implementation in jLibBig, a general Java library for manipulating bigraphical reactive systems.The effectiveness of this implementation is shown by several experimental results.Finally, we show that this algorithm can be readily adapted to find the optimal embeddings in a weighted variant of the embedding problem.
IMPROVING SIMPLE READING ABILITY IN SLBC STUDENTS CLASS D5 SLB KEMBAR KARYA DEVELOPMENT 3 USED CITIES

Learning objectives must be a predetermined behavior formulation to belong and appear to students as a result of making learning that has been carried out by the teacher's clarity in conveying, informing and explaining systematically. This study aims to determine how much the increase in learning achievement in reading simple sentences consisting of 3-5 words in mentally retarded children in grade D2 using the drill method. The research method used is Action Research. This study uses the model developed by Kurt Lewin based on the basic concept that action research consists of four main components, namely: Planning (Planning); Action (Acting); Observation (Observing); Reflection (Reflecting). The results of the study on the ability to read simple sentences consisting of 3-5 words, showed that before being given a drill approach class action with picture and picture aids, children became interested in reading word for word simple sentences that were read or spoken to him.
Sarcastic RoBERTa: A RoBERTa-Based Deep Neural Network Detecting Sarcasm on Twitter

Sarcastic RoBERTa is an approach to recognizing sarcastic tweets written in English. It is based on a pre-trained RoBERTa model supported by a 3-layer feed-forward fully-connected neural network. It establishes a new state-of-the-art result on the iSarcasm dataset, attaining the $$F_1$$ score of 0.526, and being not far from the human performance of 0.616.
Utilizing digital technology in the public health system during an epidemic

The people-centric public health measures and an integrated model of health service delivery can be obtained through the efficient utilization of digital technologies during the epidemics. The classical epidemic control measures like contact tracing, social distancing, and quarantine should be supported by the efficient analytical digital tools. This paper aims to explore the feasibility of digital interventions during a pandemic situation such as COVID-19 to enhance monitoring, surveillance, detection, and prevention of the spread of disease, along with to mitigate the impact on health services. The accessible health services can be ensured with the effective use of digital tools; web-based and cloud-based services, Internet of things, smartphones mHealth, data analytics, etc., to support an emergency public health response, real-time tracking, data monitoring, virtual clinics, and telemedicine/teleconsultation during a disease outbreak. Digital technologies to advance the management of public health services and to balance between data privacy and of public health measures should be considered for the effective management of an epidemic.
Text-Based Causal Inference on Irony and Sarcasm Detection

The state-of-the-art NLP models' success advanced significantly as their complexity increased in recent years. However, these models tend to consider the statistical correlation between features which may lead to bias. Therefore, to build robust systems, causality should be considered while estimating the given task's data generating process. In this study, we explore text-based causal inference on the irony and sarcasm detection problem. Additionally, we model the latent confounders by performing unsupervised data analysis, particularly clustering and topic modeling. The obtained results also provide insight for the causal explainability in irony detection.
Neural Network Information Leakage Through Hidden Learning

We investigate the problem of making an artificial neural network perform hidden computations whose result can be easily retrieved from the network’s output. In particular, we consider the following scenario. A user is provided a neural network for a classification task by a third party. The user’s input to the network contains sensitive information and the third party can only observe the output of the network. I this work, we provide a simple and efficient training procedure, which we call hidden learning, that produces two networks: (i) one that solves the original classification task with performance near to state of the art; (ii) a second one that takes as input the output of the first, retrieving sensitive information to solve a second classification task with good accuracy. Our result might expose important issues from an information security point of view, as for the use of artificial neural networks in sensible applications.
Comparing Deep Learning and Conventional Machine Learning Models for Predicting Mental Illness from History of Present Illness Notations.

Mental illness, a serious problem across the globe, requires multi-pronged solutions including effective computational models to predict illness. Mental illness diagnosis is complicated by the pronounced sharing of symptoms and mutual pre-dispositions. Set in this context we offer a systematic comparison of seven deep learning and two conventional machine learning models for predicting mental illness from the history of present illness free-text descriptions in patient records. The models tested include a new architecture CB-MH which ranks best for F1 (0.62) while another attention model is best for F2 (0.71). We also explore model decisions using Integrated Gradients interpretability method which we use to identify key influential features. Overall, the majority of true positives have key features appearing in meaningful contexts. False negatives are most challenging with most key features appearing in unclear contexts. False positives are mostly true positives in actuality as supported by a small-scale clinician-based user judgement study.
Becoming: An Interactive Musical Journey in VR

Becoming is an operatic VR piece based on a Persian poem by Mowlana Rumi. The critical content of the piece is about the spiritual evolution of humans on Earth. The artistic expression of the piece takes advantage of an advanced ray-tracing audio spatialization system (Space3D), which is capable of creating realistic spatial impressions within changing acoustic environments in real-time. In this piece the user can interact with the environment and influence the progression of the music by touching various elements and by changing the spatialization paths and speeds of various layers of the music. Two audience members can be connected through the network and interact with each other via haptic effects.
Local Scale Adaptation for Augmenting Hand Shape Models

The accuracy of hand pose and shape recovery algorithms depends on how closely the geometric hand model resembles the user’s hand. Most existing methods rely on learned shape space, e.g. MANO; but this shape model fails to generalize to unseen hand shapes with large deviations from the training set. We introduce a new hand shape model, aMANO, that augments MANO by introducing local scale adaptation that enables modeling substantially different hand sizes. We use both MANO and aMANO for calibrating the shape to new users from a stream of depth images and observe the improvement of aMANO over MANO. We believe that our new hand shape model is a significant step in improving the robustness and accuracy of existing hand tracking solutions.
Synchronized Hand Difference Visualization for Piano Learning

When learning a dexterous skill such as playing the piano, people commonly watch videos of a teacher. However, this conventional way has some downsides such as limited information to be retrieved and less intuitive instructions. We propose a virtual training system by visualizing differences between hands to provide intuitive feedback for skill acquisition. After synchronizing the data, two visual cues are proposed including a hand-overlay manner and a two-keyboards visualization. A pilot study confirm the superiority of the proposed methods over conventional video-viewing.
Determining the Orientation of Low Resolution Images of a De-Bruijn Tracking Pattern with a CNN

Inside-out optical 2D tracking of tangible objects on a surface oftentimes uses a high-resolution pattern printed on the surface. While De-Bruijn-torus patterns offer maximum information density, their orientation must be known to decode them. Determining the orientation is challenging for patterns with very fine details; traditional algorithms, such as Hough Lines, do not work reliably. We show that a convolutional neural network can reliably determine the orientation of quasi-random bitmaps with 6 × 6 pixels per block within 36 × 36 pixel images taken by a mouse sensor. Mean error rate is below 2°. Furthermore, our model outperformed Hough Lines in a test with arbitrarily rotated low-resolution rectangles. This implies that CNN-based rotation-detection might also be applicable for more general use cases.
Real-time lens distortion algorithm on embedded GPU systems

The lens distortion is essential for displaying VR contents on a head-mounted display (HMD) with a distorted display surface. We propose a novel lens distortion algorithm on an embedded GPU system. To minimize the memory access overhead, we propose a compressed form of a lookup table. We also utilize the integrated memory architecture of the edge GPU system (e.g., NVIDIA’s Jetson devices) to reduce data communication overhead between host and device. As a result, our method shows up to 1.72-times higher performance than prior lookup table-based lens distortion approaches while it consumes up to 28.93% less power. Finally, our algorithm achieved real-time performance for high-resolution images on edge GPU systems (e.g., 94 FPS for 8K image on Jetson NX). These results demonstrate the benefits of our approach from the perspectives of both performance and energy.
Spectral image compression

In this paper we present an effective and simple method of image compression based on spectral analysis rather than redundancy reduction. A significant portion of traffic transmitted over communication channels is static and dynamic images, and the volume of this traffic is growing at a faster rate than the capacity of communication channels is increasing. One of the ways to solve this problem is to compress the transmitted images. Data compression can be done in two ways – with and without loss of information. A distinctive feature of images as a form of information presentation is the presence of large internal redundancy, which allows the use of lossy compression methods. To exclude the loss of meaningful information, it is necessary to take into account the specifics of specific signals and divide them into groups according to the predominant concentration of information in the frequency or in spatial domain. To do this, one can use the analysis of the spatial spectrum of images and remove some part of the spectrum with an acceptable loss of information.
Iteration algorithm for multidimensional television signals superposition of optical triangulation sensor in speckle reducing issue

We describe iteration algorithm for multidimensional television signals superposition of optical triangulation sensor for speckle reducing, which distorts the measurements of object counter. The object of research is the drill-pipe thread. The measurements of such objects are very sensitive to the speckle. The estimation error can be about 0.20 mm and higher. But value of tolerance is about 0.05 мм. The developed method for speckle reducing includes the preliminary stage of superposition of two multidimensional signals. The location of speckle on signals is different. The different location is defined by object offset relatively the laser of optical triangulation sensor. The multidimensional television signals superposition allows estimating the location of the speckle noise on images. Removing of the image region with the speckle noise reduces the error of object contour estimation. In issue of television signals processing the one of important problem has been distortion of multidimensional signals. It connects with distortion of triangulation sensor optical elements. The method of superposition on background of optical distortions and speckle noise is described in this article.
Iteration algorithm for offsets, scale and rotate estimation for multidimensional signals superposition of optical triangulation sensor

We describe the iteration algorithm for multidimensional signals superposition of optical triangulation sensor. The multidimensional signals link by transformation. This transformation includes offsets, scale, and rotation. The processing of multidimensional signals has characterized some features, which make superposition difficult. This is an irregular sampling step and no matching between points of processing signals. We developed the algorithm on-base iteration procedure for this issue. The procedure includes the offsets estimation in the Cartesian system, and it includes the scale and rotates in the log-polar coordinate system. The numerical simulation allows the estimate of superposition parameters error. The simulation shows that superposition parameters error of developed algorithm likes error by brute force algorithm, but the developed algorithm is faster. The developed algorithm can use in a system that works in real-time.
Experience in providing a laboratory practice in the conditions of distance learning during a pandemic

Pandemic COVID-19 in 2020-2021 led to the largest disruption in education systems in history. Universities, institutes, and other institutions of higher education changed their students to study by using distance learning technologies. The greatest difficulties for educational organizations were caused by the implementation of a laboratory workshop in a distance form. In this work, we share the experience of leading laboratory classes in a remote form, using virtual laboratory installations.
Invited Talk: On Proactive Sentence Specific Popularity Forecasting

In this draft, we introduce the reader to the problem of popularity prediction. We highlight the existing directions of work in the area, and lay out the foundation for the task of proactively forecasting relative information popularity of individual sentences within online news documents. We discuss the key challenges and potential business applications for this novel task and note down the main contributions of our work presented at HT ’22 [7]. Lastly, we discuss some interesting avenues of future work.
Methods for Automatic Argumentation Structure Prediction

Argumentation mining is a natural language understanding task consisting of several subtasks: relevance detection, stance classification, argument quality assessment and fact checking. In this work we propose several architectures for the analysis of argumentative texts based on BERT. We also show, that models, which jointly learn argumentation mining subtasks outperform pipelines of models trained on a single tasks. Additionally we explore transfer learning approach based on pretraining for the natural language inference task, which achieves highest score on tasks of argumentation mining among the models trained on english corpora.
Video Anomaly Detection Using Optimization Based Deep Learning

Background: Excellence in the growing technologies enables innovative techniques to ensure the privacy and security of individuals. Manual detection of anomalies through monitoring is time-consuming and inefficient most of the time; hence automatic identification of anomalous events is necessary to cope with modern technology. Purpose: To enhance the security in public places as well as in the dwelling areas, surveillance cameras are employed to detect anomalous events. Methods: As a contribution, this research focuses on developing an anomaly detection model based on the deep neural network classifier which effectively classifies the abnormal events in the surveillance videos and is effectively optimized using the grey wolf optimization algorithm. The extraction of the features utilizing the Histogram of Optical flow Orientation and Magnitude (HOFM) based feature descriptor furthermore improves the performance of the classifier. Results: The experimental results are obtained based on the frame level and pixel levels with an accuracy rate of 92.76 and 92.13%, Area under Curve (AUC) rate of 91.76 and 92%, and the equal error rate (EER) is 7.24 and 9.37% which is more efficient compared with existing state-of-art methods. Conclusion: The proposed method achieved enhanced accuracy and minimal error rate compared to the state of art techniques and hence it can be utilized for the detection of anomalies in the video.
Study of Regional Language Translator Using Natural Language Processing

There are diverse languages present all over the world and in countries too. India is also the diverse country with people following various culture and languages. Though there are many languages, the amendments, bills and the drafts made by the constitution, are scripted only by using Hindi and English. Most of the people in India know only their mother-tongue that is their regional languages. In this case, when a draft is passed in the Constitution, majority of the people will not understand the gist of the draft, its uses and its impacts clearly. They need a translator manually to convey the pros and cons of the drafts. Thus, in order to make the people easily understand the essence of the drafts or bills passed by government, an automatic regional language translator is required. This paper reviewed various kind of methods in order to translate one language to another language. In this proposed method in order to process the string and translate into another language we use Natural Language Processing (NLP) toolkit. Thus, this project enables people in various regions to understand the basic ideas of the amendments or drafts or bills passed by the Government summarize.
Blockchain Based Email Communication with SHA-256 Algorithm

The cryptocurrency transaction by utilizing bitcoins is the present work scenario in industries with an appealing feature of blockchain technology. Business is increasingly embracing blockchain systems because they make transactions expedite, cost effective, and efficient. Thus, the proposed system aims is to develop a decentralized application to make a secured email transmission using Secured Hash Algorithm(SHA)-256 hashing algorithm. The existing system for Email transmission is the Gmail application. The Gmail is centralized by google company in which all the authorities are at high level and personal data are been centralized and it leads to various security issues. A Decentralized Email Solution on a mission to protect email users’ digital rights in a decentralized manner.
Intelligent System for Diagnosis of Pulmonary Tuberculosis Using XGBoosting Method

Tuberculosis (TB) is a disease of human infection that affects the respiratory and other body systems. The WHO reported an average incidence of TB per annum of 2.2 million new cases contracted by individuals. A major public concern for the developing country is to reduce the reproductive rate of transmission and outbreaks of tuberculosis disease. It needs to improve the diagnosis process and encourage patient faithfulness to medical treatment. This research study includes several recent ensemble classification algorithms to choose our base model, achieving high-performance accuracy. The extreme gradient boosting/ XGBoosting model performs the highest testing score of AUC 95.86% using the full optimizing trained model.
Crisis Management of Covid-19 Cluster through Social Media Contents Analysis at @Inside Sampoerna

The Covid-19 phenomenon has implications for the crisis in almost all sectors in Indonesia, even globally.PT H.M. Sampoerna experienced a crisis when employees were infected with Covid19, there were casualties and became a Covid-19 cluster as in May 2020.To maintain trust & reputation, PT.H.M. Sampoerna managed this crisis through several methods, including producing relevant creative content on social media.This study aims to reveal how PT.H.M. conducted crisis management using qualitative content analysis methods at @insidesampoerna for the period 30 April 2020 -14 January 2021.Researchers found that the crisis communications conducted by Sampoerna were categorized into 4 main issues with 30 posts as follows: (a) 12 posts of health protocol, ( b) 6 posts of corporate news, (c) 10 posts of Corporate Social Responsibility (CSR), and (d) 2 posts of corporate activity.The health protocol category getting the most reactions from followers with 20,033 likes and 351 comments.Crisis communication applies primary and secondary crisis response strategies.There are several types of strategies applied in crisis communication through @insidesampoerna, namely: (a) informing and adjusting crisis information, (b) crisis response reduction strategies, (c) a strategy to respond to the crisis by rebuilding the reputation and public trust, and implementing strategies (d) maintaining consistency in crisis response strategies.
A Survey on Wild Creatures Alert System to Protect Agriculture Lands Domestic Creatures and People

Wild animal ready framework in ML is utilized to secure the property, fringe, and runway from wild creatures with enormous information study of a hazardous wild monster. Checking the exhibition of wild animals is utilized to secure the farmlands. In old ways of spotting creatures in paddy fields and residences embody crafted by regular eyes to notice the creature movement. It is not feasible for men to watch creature developments ceaselessly for the duration of the day. From where a desire of specific discovery of animals which penetrate appreciably in the rice fields and the agricultural lands of the individuals. The ways utilized for the prevalence of the creatures encapsulate division and item discovery strategies. This result requires difficulties in the development of returns and causes the ranch failure. This tosses back to ranchers destroying the land on account of incessant monstrosities by wild animals.