Reviewerly/exHarmony huggingface dataset:
Mathematical morphology based on stochastic permutation orderings

Abstract The extension of mathematical morphology to multivariate data has been an active research topic in recent years. In this paper we propose an approach that relies on the consensus combination of several stochastic permutation orderings. The latter are obtained by searching for a smooth shortest path on a graph representing an image. This path is obtained with a randomized version nearest of neighbors heuristics on a graph. The construction of the graph is of crucial importance and can be based on both spatial and spectral information to enable the obtaining of smoother shortest paths. The starting vertex of a path being taken at random, many different permutation orderings can be obtained and we propose to build a consensus ordering from several permutation orderings. We show the interest of the approach with both quantitative and qualitative results.
Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection

This paper considers the unsupervised domain adaptation problem for neural machine translation (NMT), where we assume the access to only monolingual text in either the source or target language in the new domain.We propose a cross-lingual data selection method to extract in-domain sentences in the missing language side from a large generic monolingual corpus.Our proposed method trains an adaptive layer on top of multilingual BERT by contrastive learning to align the representation between the source and target language.This then enables the transferability of the domain classifier between the languages in a zero-shot manner.Once the in-domain data is detected by the classifier, the NMT model is then adapted to the new domain by jointly learning translation and domain discrimination tasks.We evaluate our cross-lingual data selection method on NMT across five diverse domains in three language pairs, as well as a real-world scenario of translation for COVID-19.The results show that our proposed method outperforms other selection baselines up to +1.5 BLEU score.
Semi-Automatic Spectral Image Stitching for a Compact Hybrid Linescan Hyperspectral Camera towards Near Field Remote Monitoring of Potato Crop Leaves

The miniaturization of hyperspectral cameras has opened a new path to capture spectral information. One such camera, called the hybrid linescan camera, requires accurate control of its movement. Contrary to classical linescan cameras, where one line is available for every band in one shot, the latter asks for multiple shots to fill a line with multiple bands. Unfortunately, the reconstruction is corrupted by a parallax effect, which affects each band differently. In this article, we propose a two-step procedure, which first reconstructs an approximate datacube in two different ways, and second, performs a corrective warping on each band based on a multiple homography framework. The second step combines different stitching methods to perform this reconstruction. A complete synthetic and experimental comparison is performed by using geometric indicators of reference points. It appears throughout the course of our experimentation that misalignment is significantly reduced but remains non-negligible at the potato leaf scale.
Radar-based maritime path planning with static obstacles in a Frenet frame

<p indent=0mm>Navigation safety at sea is important for autonomous surface vehicles, and involves the problems of motion planning in dynamic environments and their robust tracking through feedback control. We present a practical path planning method that generates smooth trajectories for a high-speed marine vehicle traveling in an unknown environment, where obstacles are detected in real time by marine radar. Our approach uses the polynomial curve to describe lateral and longitudinal trajectories in the Frenet frame, known as motion primitives, whose combination ensures that the planning area is properly covered. In addition, we can select a feasible, optimal, and collision-free trajectory from such a set of motion primitives that is generated by considering the vehicle dynamics and comfort. The effectiveness of the proposed algorithm is demonstrated in an experiment with static and dynamic obstacles.
Distant Assist Cursor (DAC): Designing an Augmented Reality System to Facilitate Remote Collaboration for Novice Users

Many existing Augmented Reality (AR) systems facilitate remote collaboration by allowing users to share activity cues such as cursors. However, a lot of these systems require expensive hardware or non-negligible calibration phases which may deter novice users from using them. To overcome these barriers, we propose the Distant Assist Cursor (DAC): an AR remote collaboration system for sharing a virtual cursor. DAC relies on a camera and a projector sharing the same optical center. This design allows to use cheap hardware and a straightforward calibration phase. In this paper, we present the design of this system made to help novice users. We then evaluate DAC by assessing its benefits and usability for remote collaboration tasks.
Low-resource Taxonomy Enrichment with Pretrained Language Models

Taxonomies are symbolic representations of hierarchical relationships between terms or entities.While taxonomies are useful in broad applications, manually updating or maintaining them is labor-intensive and difficult to scale in practice.Conventional supervised methods for this enrichment task fail to find optimal parents of new terms in low-resource settings where only small taxonomies are available because of overfitting to hierarchical relationships in the taxonomies.To tackle the problem of low-resource taxonomy enrichment, we propose Musubu, an efficient framework for taxonomy enrichment in low-resource settings with pretrained language models (LMs) as knowledge bases to compensate for the shortage of information.Musubu leverages an LM-based classifier to determine whether or not inputted term pairs have hierarchical relationships.Musubu also utilizes Hearst patterns to generate queries to leverage implicit knowledge from the LM efficiently for more accurate prediction.We empirically demonstrate the effectiveness of our method in extensive experiments on taxonomies from both a SemEval task and real-world retailer datasets.
A Compound Poisson Perspective of Ewens–Pitman Sampling Model

The Ewens–Pitman sampling model (EP-SM) is a distribution for random partitions of the set {1,…,n}, with n∈N, which is indexed by real parameters α and θ such that either α∈[0,1) and θ&gt;−α, or α&lt;0 and θ=−mα for some m∈N. For α=0, the EP-SM is reduced to the Ewens sampling model (E-SM), which admits a well-known compound Poisson perspective in terms of the log-series compound Poisson sampling model (LS-CPSM). In this paper, we consider a generalisation of the LS-CPSM, referred to as the negative Binomial compound Poisson sampling model (NB-CPSM), and we show that it leads to an extension of the compound Poisson perspective of the E-SM to the more general EP-SM for either α∈(0,1), or α&lt;0. The interplay between the NB-CPSM and the EP-SM is then applied to the study of the large n asymptotic behaviour of the number of blocks in the corresponding random partitions—leading to a new proof of Pitman’s α diversity. We discuss the proposed results and conjecture that analogous compound Poisson representations may hold for the class of α-stable Poisson–Kingman sampling models—of which the EP-SM is a noteworthy special case.
Perceptual Fusion of Electronic Chart and Marine Radar Image

Electronic charts and marine radars are indispensable equipment in ship navigation systems, and the fusion display of these two parts ensures that the vessel can display dangerous moving targets and various obstacles on the sea. To reduce the noise interference caused by external factors and hardware, a novel radar image denoising algorithm using the concept of Generative Adversarial Network (GAN) using Wasserstein distance is proposed. GAN focuses on transferring the image noise distribution between strong and weak noise, while the perceptual loss approach is to suppress the noise by comparing the perceptual characteristics of the output after denoising. Afterwards, an image registration method based on image transformation is proposed to eliminate the imaging difference between the radar image and chart image, in which the visual attribute transfer approach is used to transform images. Finally, the sparse theory is used to process the high frequency and low frequency subband coefficients of the detection image obtained by the fast Fourier transform in parallel to realizing the image fusion. The results show that the fused contour has a high consistency, fast training speed and short registration time.
Behaviour detection in crowded classroom scenes via enhancing features robust to scale and perspective variations

Detecting human behaviours in images of crowded classroom scenes is a challenging task, due to the large variations of humans in scale and pose perspective. In this paper, two modules are proposed to tackle these two variations. First, an attention-based RoI (region-of-interest) extractor is designed to handle scale variation. Feature fusion and attention mechanism are used to improve the RoI feature with more local and global information. Second, a transformation-based detection head is introduced to handle perspective variation. The spatial transformation is adopted to extract consistent representation under various perspectives. Moreover, since there is a lack of proper datasets for human behaviour detection in classroom scenes, a new dataset is created, namely CLBD. The experiments on the proposed dataset demonstrate that the modules obtain significant improvements of performance over the state-of-the-art detectors.
Beyond Text: Incorporating Metadata and Label Structure for Multi-Label Document Classification using Heterogeneous Graphs

Multi-label document classification, associating one document instance with a set of relevant labels, is attracting more and more research attention.Existing methods explore the incorporation of information beyond text, such as document metadata or label structure.These approaches however either simply utilize the semantic information of metadata or employ the predefined parent-child label hierarchy, ignoring the heterogeneous graphical structures of metadata and labels, which we believe are crucial for accurate multi-label document classification.Therefore, in this paper, we propose a novel neural network based approach for multi-label document classification, in which two heterogeneous graphs are constructed and learned using heterogeneous graph transformers.One is metadata heterogeneous graph, which models various types of metadata and their topological relations.The other is label heterogeneous graph, which is constructed based on both the labels' hierarchy and their statistical dependencies.Experimental results on two benchmark datasets show the proposed approach outperforms several stateof-the-art baselines.
An Intelligent Approach for Performing Energy-Driven Classification of Buildings Utilizing Joint Electricity–Gas Patterns

Building type identification is an important task that may be used in confirming and verifying its legitimate operation. One of the main sources of information over the operation of a building is its energy consumption, with the analysis of electricity patterns being at the spotlight of a non-intrusive identification approach. However, electricity patterns are the only source of information, and therefore, their analysis imposes several restrictions. In this work, we introduce a new approach in energy-driven identification by adding one more source of information beyond the electricity pattern that may be utilized, namely the gas consumption pattern. In particular, we propose a new intelligent approach that jointly analyzes the electricity–gas patterns to provide the type of building at hand. Our approach exploits the synergism of the matrix profile data analysis technique with a feed-forward artificial neural network. This approach has applicability in the energy waste elimination through the implementation of different energy efficiency solutions, as well as the optimization of the demand-side process management, safer and reliable operation through fault detection, and the identification and validation of the real operation of the building. The obtained results demonstrate the improvement in identifying the type of the building by employing the proposed approach for joint electricity–gas patterns as compared to only using the electricity patterns.
Analisis Kemampuan Menulis Artikel Ilmiah Mahasiswa Pendidikan Guru Sekolah Dasar Angkatan 2019

As a student who will prepare for a final project, it is necessary to strengthen and write scientific papers, as a novice writer, students are given a test to make individual scientific articles. This writer aims to analyze 1) the ability of students to write systematic scientific articles, 2) the ability of students to write the contents of scientific articles according to the systematics, and 3) the ability to use Indonesian spelling in writing scientific articles. The research approach is descriptive qualitative. The data source is PGSD students at State University of Malang batch 2019. The data is the result of student work, namely writing scientific articles. The data collection technique in this research is a test. The data analysis technique used is the Miles and Huberman model. The steps are, data reduction, data presentation, and drawing conclusions. The results of the first research, the ability to write systematic scientific articles. 30 students or 86% categorized as being able to write systematic scientific articles. The rest, 5 students or 14% are categorized as needing guidance. Second, the ability of students to write the contents of scientific articles. Students are categorized as good because more than 75% can write. 25% of students are categorized as needing guidance. Third, the ability to use Indonesian spelling. There are errors, because students do not read scientific papers, so that knowledge of the use of Indonesian spelling is still relatively lacking.
An Integrated BM3D Method for Removing Mixed Noise in Remote Sensing Image

BM3D (block matching and 3D filtering) method is an effective Gaussian noise removing method, but it shows limitation when removing Gaussian and impulse mixed noise in remote sensing images. The integrated method is a more effective way to remove mixed noise. In order to make full use of the advantages of BM3D method, an improved integration method (integrated BM3D method) has been developed. And a modified method M-Liu method is raised and used for estimating the priori noise level as an input parameter for the integrated BM3D method. According to algorithm validation, this paper draws the conclusion that the integrated BM3D method, taking the removal of image noise and the retention of detail signals into account, has good denoising performance. The method can be used to provide a new method for image denoising, and it is useful for the later application of remote sensing image.
Students' Scientific Literacy Skills by Using Big book-based Science Textbooks (Changes in Form of Objects)

Big books are learning media that can be used as learning resources by presenting of contextual and interactive images in increasing of students' literacy skills. Good literacy skills will make easy for students to understand the meaning of the information provided and they can improve their interpersonal skills well. This research is a quantitative research with a survey approach. Participants in this study are 237 students. Data collection was obtained from interviews and questionnaires, then analyzed using the percentage technique. The results of this study illustrate that using big book-based science textbooks, students' scientific literacy skills can increase. The results shows that literacy in context, knowledge, competence, and attitude are categorized good.
Transforming Multi-Conditioned Generation from Meaning Representation

Our study focuses on language generation by considering various information representing the meaning of utterances as multiple conditions of generation.Generating an utterance from a Meaning representation (MR) usually passes two steps: sentence planning and surface realization.However, we propose a simple one-stage framework to generate utterances directly from MR.Our model is based on GPT2 and generates utterances with flat conditions on slot and value pairs, which does not need to determine the structure of the sentence.We evaluate several systems in the E2E dataset with 6 automatic metrics.Our system is a simple method, but it demonstrates comparable performance to previous systems in automated metrics.In addition, using only 10% of the dataset without any other techniques, our model achieves comparable performance, and shows the possibility of performing zero-shot generation and expanding to other datasets.
Markov Matrix and Entropy based Tamper Detection Technique for Text Images

In this paper, a novel watermarking technique for the tamper detection of text images is pro- posed. Entropy of every sentence is computed and Markov matrix using the occurrences of the characters is used to generate a character pattern. Entropy and character patterns are converted to Unicode Zero Width Characters (ZWCs) by using a lookup table. The ZWCs of entropy of each sentence is embedded at the end of every sentence after terminator. ZWCs of the character patterns are embedded in the end of the text of the image. On receiver side, ZWCs are extracted and converted to numerical form using the same lookup table. Entropy of every sentence and character patterns are recalculated and compared with extracted values for tamper detection. Comparison of technique with existing state-of-art techniques shows the effectiveness of the proposed technique.
Knowledge Dynamics and Behavioural Equivalences in Multi-Agent Systems

We define a process calculus to describe multi-agent systems with timeouts for communication and mobility able to handle knowledge. The knowledge of an agent is represented as sets of trees whose nodes carry information; it is used to decide the interactions with other agents. The evolution of the system with exchanges of knowledge between agents is presented by the operational semantics, capturing the concurrent executions by a multiset of actions in a labelled transition system. Several results concerning the relationship between the agents and their knowledge are presented. We introduce and study some specific behavioural equivalences in multi-agent systems, including a knowledge equivalence able to distinguish two systems based on the interaction of the agents with their local knowledge.
Investigation of Pre-Trained Bidirectional Encoder Representations from Transformers Checkpoints for Indonesian Abstractive Text Summarization

Text summarization aims to reduce text by removing less useful information to obtain information quickly and precisely. In Indonesian abstractive text summarization, the research mostly focuses on multi-document summarization which methods will not work optimally in single-document summarization. As the public summarization datasets and works in English are focusing on single-document summarization, this study emphasized on Indonesian single-document summarization. Abstractive text summarization studies in English frequently use Bidirectional Encoder Representations from Transformers (BERT), and since Indonesian BERT checkpoint is available, it was employed in this study. This study investigated the use of Indonesian BERT in abstractive text summarization on the IndoSum dataset using the BERTSum model. The investigation proceeded by using various combinations of model encoders, model embedding sizes, and model decoders. Evaluation results showed that models with more embedding size and used Generative Pre-Training (GPT)-like decoder could improve the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score and BERTScore of the model results.
On Machine Translation of User Reviews

This work investigates neural machine translation (NMT) systems for translating English user reviews into Croatian and Serbian, two similar morphologically complex languages.Two types of reviews are used for testing the systems: IMDb movie reviews and Amazon product reviews.Two types of training data are explored: large out-of-domain bilingual parallel corpora, as well as small synthetic in-domain parallel corpus obtained by machine translation of monolingual English Amazon reviews into the target languages.Both automatic scores and human evaluation show that using the synthetic in-domain corpus together with a selected subset of out-of-domain data is the best option.Separated results on IMDb and Amazon reviews indicate that MT systems perform differently on different review types so that user reviews generally should not be considered as a homogeneous genre.Nevertheless, more detailed research on larger amount of different reviews covering different domains/topics is needed to fully understand these differences.
CogView: Mastering Text-to-Image Generation via Transformers

Text-to-Image generation in the general domain has long been an open problem, which requires both a powerful generative model and cross-modal understanding. We propose CogView, a 4-billion-parameter Transformer with VQ-VAE tokenizer to advance this problem. We also demonstrate the finetuning strategies for various downstream tasks, e.g. style learning, super-resolution, text-image ranking and fashion design, and methods to stabilize pretraining, e.g. eliminating NaN losses. CogView (zero-shot) achieves a new state-of-the-art FID on blurred MS COCO, outperforms previous GAN-based models and a recent similar work DALL-E.
Making Your Tweets More Fancy: Emoji Insertion to Texts

In the social media, users frequently use small images called emojis in their posts.Although using emojis in texts plays a key role in recent communication systems, less attention has been paid on their positions in the given texts, despite that users carefully choose and put an emoji that matches their post.Exploring positions of emojis in texts will enhance understanding of the relationship between emojis and texts.We extend an emoji label prediction task taking into account the information of emoji positions, by jointly learning the emoji position in a tweet to predict the emoji label.The results demonstrate that the position of emojis in texts is a good clue to boost the performance of emoji label prediction.Human evaluation validates that there exists a suitable emoji position in a tweet, and our proposed task is able to make tweets more fancy and natural.In addition, considering emoji position can further improve the performance for the irony detection task compared to the emoji label prediction.We also report the experimental results for the modified dataset, due to the problem of the original dataset for the first shared task to predict an emoji label in Se-mEval2018.
APPLICATION OF THE RAPID APPLICATION DEVELOPMENT METHOD TO THE BAZNAS ZAKAT RECEIPT INFORMATION SYSTEM IN KARAWANG

Zakat is one of the pillars of Islam, it is obligatory for every Muslim to do it. In fact, there are still many Muslims who do not understand the types of zakat, zakat calculation, payment and processing of zakat, causing them forget to pay zakat. Moreover, in this modern era, people want do anything fast and practical. Even though if every Muslim and Muslimah is willing to pay zakat diligently, of course it can help improve the economy for the middle to lower class society. And of course zakat must be issued and processed according to the established rules. Information system development uses the Rapid Application Development System method, starting from the requitment planning, system design, and implementation phases, as result an objective information system. This website-based information system was built with HTML, CSS, Boostrap and PHP programming languages ​​and for the database uses MySQL and for its web service uses Apache