Reviewerly/exHarmony huggingface dataset:
Efficient framework with sequential classification for graphic vehicle identification number recognition

In vehicle monitoring, recognizing graphic vehicle identification number (VIN) on the car frame is a particularly important step. While text recognition methods have made great progress, automatic graphic vehicle VIN recognition is still challenging. In VIN images, the VIN text is engraved on the car frame, with complex background and arbitrary orientation, which make it extremely difficult for recognition. We propose an efficient framework for recognizing rotational VIN. First, combining lightweight convolutional neural network and per-pixel segmentation in the output layer, we achieve fast and excellent VIN detection. Second, we take the VIN recognition task as a sequential position-dependent classification problem. By attaching sequential classifiers, we predict VIN text without character segmentation. Finally, we introduce a VIN dataset, which contains 2000 raw rotational VIN images and 90,000 horizontal VIN images for validating our framework. Experiments results show that the framework we proposed achieves good performance in VIN detection and recognition. By automatically identifying the VIN, we can quickly confirm the vehicle's identity and help vehicle monitoring and tracking.
Refer360∘: A Referring Expression Recognition Dataset in 360: A Referring Expression Recognition Dataset in 360∘ Images Images

We propose a novel large-scale referring expression recognition dataset, Refer360°, consisting of 17,137 instruction sequences and ground-truth actions for completing these instructions in 360°scenes.Refer360°differs from existing related datasets in three ways.First, we propose a more realistic scenario where instructors and the followers have partial, yet dynamic, views of the scene -followers continuously modify their field-of-view (FoV) while interpreting instructions that specify a final target location.Second, instructions to find the target location consist of multiple steps for followers who will start at random FoVs.As a result, intermediate instructions are strongly grounded in object references and followers must identify intermediate FoVs to find the final target location correctly.Third, the target locations are neither restricted to predefined objects nor chosen by annotators; instead, they are distributed randomly across scenes.This "point anywhere" approach leads to more linguistically complex instructions, as shown in our analyses.Our examination of the dataset shows that Refer360°manifests linguistically rich phenomena in a language grounding task that poses novel challenges for computational modeling of language, vision, and navigation.
Analysis of Three Small-Scale Photovoltaic Systems Based on Simulation and Measurement Data

Sunlight is converted into electrical energy due to the photovoltaic effect in photovoltaic arrays. The energy yield of photovoltaic systems depends on the solar array location, orientation, tilt, tracking and local weather conditions. Currently, simulation software is most often used to analyze the operation of photovoltaic (PV) systems and to estimate the energy yield. In this article, the differences in energy yield calculations given by the simulation software and the measured data are determined. The analysis was carried out based on mathematical models and real measurement data, regarding the dependence of the average temperature of PV arrays on variable and difficult to predict ambient conditions. For the purpose of this analysis, thermal models for flat-plate photovoltaic arrays were used. The photovoltaic installations PV1, PV2a and PV2b, belonging to the hybrid power plant of the Bialystok University of Technology in Poland, were indicated as the data source. There is no universal mathematical model to determine the average temperature of the PV modules for every type of the installation with a small normalized root-mean-squared error. The Skoplaki model proved to be the best method in the case of a free-standing solar system. On the other hand, the data values obtained from building integrated installations were better modeled by a method which used parameters under NOCT (Normal Operating Cell Temperature) conditions.
Sustainable supply chain management framework in a higher education laboratory using intuitionistic fuzzy cognitive map

Purpose: This research aims to develop framework in the sustainable supply chain management (SCM) and to provide causal model of service industry specifically in higher education laboratory.Design/methodology/approach: The concepts of sustainable SCM in higher education laboratory were obtained by in-depth interviews and organized using Delphi method. While to identify the relationship between concepts, intuitionistic fuzzy cognitive map was utilized.Findings: As many as 15 concepts were identified to assess sustainability in the higher education laboratory SCM. These 15 concepts were classified into four categories according to its importance level, and there are two most important concepts: legal requirement and social responsibility. It is recommended for higher education laboratories to constantly obey national and regional government regulations and to satisfy current and prospective employers by providing work-ready graduates. This suggestion is expected to make higher education achieving its sustainability goals. Originality/value: This research is the first to develop a framework for sustainable SCM and to provide a causal model of the service industry especially in the education sector using intuitionistic fuzzy cognitive map.
Neural Multi-task Text Normalization and Sanitization with Pointer-Generator

Text normalization and sanitization are intrinsic components of Natural Language Inferences.In Information Retrieval or Dialogue Generation, normalization of user queries or utterances enhances linguistic understanding by translating non-canonical text to its canonical form, on which many state-of-the-art language models are trained.On the other hand, text sanitization removes sensitive information to guarantee user privacy and anonymity.Existing approaches to normalization and sanitization mainly rely on hand-crafted heuristics and syntactic features of individual tokens while disregarding the linguistic context.Moreover, such context-unaware solutions cannot dynamically determine whether out-of-vocab tokens are misspelt or are entity names.In this work, we formulate text normalization and sanitization as a multi-task text generation approach and propose a neural pointer-generator network based on multihead attention.Its generator effectively captures linguistic context during normalization and sanitization while its pointer dynamically preserves the entities that are generally missing in the vocabulary.Experiments show that our generation approach outperforms both token-based text normalization and sanitization, while the pointer-generator improves the generator-only baseline in terms of BLEU4 score, and classical attentional pointer networks in terms of pointing accuracy.
Improving Slot Filling by Utilizing Contextual Information

Slot Filling (SF) is one of the sub-tasks of Spoken Language Understanding (SLU) which aims to extract semantic constituents from a given natural language utterance.It is formulated as a sequence labeling task.Recently, it has been shown that contextual information is vital for this task.However, existing models employ contextual information in a restricted manner, e.g., using self-attention.Such methods fail to distinguish the effects of the context on the word representation and the word label.To address this issue, in this paper, we propose a novel method to incorporate the contextual information in two different levels, i.e., representation level and task-specific (i.e., label) level.Our extensive experiments on three benchmark datasets on SF show the effectiveness of our model leading to new state-of-theart results on all three benchmark datasets for the task of SF.
Many-to-Many Data Trading Algorithm Based on Double Auction Theory

Nowadays, data-based analysis plays a crucial role in both academia and industry, and thus data has been recognized a valuable resource to be traded. Because data generated from various end devices is dispersive, heterogeneous and stored in a distributed way, an impartial and trustworthy platform is indeed essential to make a market for trading entities (data buyers and sellers). In a practical market, all the entities tend to maximize their profits rather than the overall social welfare, while the platform, instead, should focus on social welfare maximization by applying a sophisticated mechanism that is imperative to guide the entities to distribute data efficiently. In this paper, we propose the Many-to-Many Data Trading Algorithm (MMDTA), and then formulate a data trading model with multiple entities based on MMDTA. Finally, the simulations validate convergence behavior and economic properties of MMDTA which is effective in different market scales and with different market power.
BIT’s system for the AutoSimTrans 2020

This paper describes our machine translation systems for the streaming Chinese-to-English translation task of AutoSimTrans 2020.We present a sentence length based method and a sentence boundary detection model based method for the streaming input segmentation.Experimental results of the transcription and the ASR output translation on the development data sets show that the translation system with the detection model based method outperforms the one with the length based method in BLEU score by 1.19 and 0.99 respectively under similar or better latency.
Boundary Morphology for Hierarchical Simplification of Archaeological Fragments

Abstract When fitting archaeological artifacts, one would like to have a representation that simplifies fragments while preserving their complementarity. In this paper, we propose to employ the scale-spaces of mathematical morphology to hierarchically simplify potentially fitting fracture surfaces. We study the masking effect when morphological operations are applied to selected subsets of objects. Since fitting locally depends on the complementarity of fractures only, we introduce ‘Boundary Morphology’ on surfaces rather than volumes. Moreover, demonstrating the Lipschitz nature of the terracotta fractures informs our novel extrusion method to compute both closing and opening operations simultaneously. We also show that in this proposed representation the effects of abrasion and uncertainty are naturally bounded, justifying the morphological approach. This work is an extension of our contribution earlier published in the proceedings of ISMM2019 [10].
Improving Autoregressive NMT with Non-Autoregressive Model

Autoregressive neural machine translation (NMT) models are often used to teach nonautoregressive models via knowledge distillation.However, there are few studies on improving the quality of autoregressive translation (AT) using non-autoregressive translation (NAT).In this work, we propose a novel Encoder-NAD-AD framework for NMT, aiming at boosting AT with global information produced by NAT model.Specifically, under the semantic guidance of source-side context captured by the encoder, the nonautoregressive decoder (NAD) first learns to generate target-side hidden state sequence in parallel.Then the autoregressive decoder (AD) performs translation from left to right, conditioned on source-side and target-side hidden states.Since AD has global information generated by low-latency NAD, it is more likely to produce a better translation with less time delay.Experiments on WMT14 En⇒De, WMT16 En⇒Ro, and IWSLT14 De⇒En translation tasks demonstrate that our framework achieves significant improvements with only 8% speed degeneration over the autoregressive NMT.
Exploring aspects of similarity between spoken personal narratives by disentangling them into narrative clause types

Sharing personal narratives is a fundamental aspect of human social behavior as it helps share our life experiences.We can tell stories and rely on our background to understand their context, similarities, and differences.A substantial effort has been made towards developing storytelling machines or inferring characters' features.However, we don't usually find models that compare narratives.This task is remarkably challenging for machines since they, as sometimes we do, lack an understanding of what similarity means.To address this challenge, we first introduce a corpus of real-world spoken personal narratives comprising 10,296 narrative clauses from 594 video transcripts.Second, we ask non-narrative experts to annotate those clauses under Labov's sociolinguistic model of personal narratives (i.e., action, orientation, and evaluation clause types) and train a classifier that reaches 84.7% F-score for the highest-agreed clauses.Finally, we match stories and explore whether people implicitly rely on Labov's framework to compare narratives.We show that actions followed by the narrator's evaluation of these are the aspects non-experts consider the most.Our approach is intended to help inform machine learning methods aimed at studying or representing personal narratives.
Analisis Keamanan Sistem Informasi Berbasis Website Dengan Metode Open Web Application Security Project (OWASP) Versi 4: Systematic Review

Abstract -- OWASP (Open Web Application Security Project) version 4 issued by a non-profit organization called owasp.org which is dedicated to the security of web-based applications. This systematic review is intended to review whether the Open Web Application Security Project (OWASP) method is widely used to detect security in a website-based Information System. In this systematic review, we review 3 literature from several publisher sources and make a comparison regarding OWASP version 4 results and the security level of a web server from the publisher's source.Keywords— OWASP, Website Vulnerability, Website Security Detection
Automatic extraction of personal events from dialogue

In this paper we introduce the problem of extracting events from dialogue.Previous work on event extraction focused on newswire, however we are interested in extracting events from spoken dialogue.To ground this study, we annotated dialogue transcripts from fourteen episodes of the podcast This American Life.This corpus contains 1,038 utterances, made up of 16,962 tokens, of which 3,664 represent events.The agreement for this corpus has a Cohen's κ of 0.83.We have open sourced this corpus for the NLP community.With this corpus in hand, we trained support vector machines (SVM) to correctly classify these phenomena with 0.68 F1, when using episodefold cross-validation.This is nearly 100% higher F1 than the baseline classifier.The SVM models achieved performance of over 0.75 F1 on some testing folds.We report the results for SVM classifiers trained with four different types of features (verb classes, part of speech tags, named entities, and semantic role labels), and different machine learning protocols (under-sampling and trigram context).This work is grounded in narratology and computational models of narrative.It is useful for extracting events, plot, and story content from spoken dialogue.
Improving the Identification of the Discourse Function of News Article Paragraphs

Identifying the discourse structure of documents is an important task in understanding written text.Building on prior work, we demonstrate an improved approach to automatically identifying the discourse function of paragraphs in news articles.We start with the hierarchical theory of news discourse developed by van Dijk (1988) which proposes how paragraphs function within news articles.This discourse information is a level intermediate between phrase-or sentence-sized discourse segments and document genre, characterizing how individual paragraphs convey information about the events in the storyline of the article.Specifically, the theory categorizes the relationships between narrated events and (1) the overall storyline (such as MAIN EVENTS, BACKGROUND, or CONSEQUENCES) as well as ( 2) commentary (such as VERBAL REAC-TIONS and EVALUATIONS).We trained and tested a linear chain conditional random field (CRF) with new features to model van Dijk's labels and compared it against several machine learning models presented in previous work.Our model significantly outperformed all baselines and prior approaches, achieving an average of 0.71 F 1 score which represents a 31.5% improvement over the previously bestperforming support vector machine model.
Mass Media Internships in Vocational Training of Students Majoring in Journalism

The purpose of this research was to answer the questions of how and to what extend the internship programme, seen as a part of their vocational training, could contribute to students' learning process and professional growth and how the students perceived the internship programme.This was a quasi-experimental study utilising quantitative and qualitative methods.Such statistical tools as a cumulative grade point average, the career motivation test, and Mettl's aptitude online test for journalists, the semi-structured interview for the focus-group, and references from internship host company were used for calculations of the variables.The dependence of variables on the internship was analysed using a two-way ANOVA.The study found that mass media internships significantly improve vocational training system of students majoring in Journalism.Due to involvement in this programme, students' academic performance and career motivation increased, journalism skills developed and the quality of this education rose.This internship programme fosters students' job-related skills like working in a team and fast-paced environment, time-management, editing and publishing, communication, and social networking, using software to create digital and printed content.This study implies that universitybased journalism education is likely to soon become a secondary process.This study implies and experimentally proves that universitybased journalism education should soon become a secondary thing as de-institutionalised education is emerging laying the basics for "entrepreneurial journalism".
On the number of shortest paths by neighborhood sequences on the square grid

In this paper we are addressing a counting problem of discrete mathematics, more precisely of digital geometry.In the Euclidean plane the shortest path between any two points is given by the straight line segment connecting the points.In discrete mathematics, the shortest path is usually not unique, e.g., in graphs there could be several shortest paths between two vertices.In this paper, a special infinite graph, the square grid, (i.e., the usual digital plane) is used.In digital geometry there are various digital, i.e., path based distance functions.A neighborhood sequence B gives the condition for each step of a B-path separately what type of neighborhood is used in that step.Therefore, the length and also the number of the shortest paths between two points depend not only on the respective positions (coordinate differences) of the points but also on the neighborhood sequence B. We give an algorithm and also closed formulae to compute the number of shortest B-paths.
New approach for Arabic named entity recognition on social media based on feature selection using genetic algorithm

Many features can be extracted from the massive volume of data in different types that are available nowadays on social media. The growing demand for multimedia applications was an essential factor in this regard, particularly in the case of text data. Often, using the full feature set for each of these activities can be time-consuming and can also negatively impact performance. It is challenging to find a subset of features that are useful for a given task due to a large number of features. In this paper, we employed a feature selection approach using the genetic algorithm to identify the optimized feature set. Afterward, the best combination of the optimal feature set is used to identify and classify the Arabic named entities (NEs) based on support vector. Experimental results show that our system reaches a state-of-the-art performance of the Arab NER on social media and significantly outperforms the previous systems.
Examining the Formation of Integrative Readiness of Pre-Service Teachers to work in Inclusive Classrooms in Ukraine

The article addresses potential issues surrounding the development of the integrative readiness of pre-service teachers to work in inclusive classrooms in Ukraine. Integrative readiness is an open, dynamic, synergistic system functioning of cognitive, motivational, affective, and selective processes which is developed through the purposeful training of future teachers for work in inclusive, which is included in the educational social environment as a tool for acquiring productive knowledge, forming integrative skills. The structure of integrative readiness includes personality orientation; ability to perform their duties, professional competences; productive knowledge that the student receives from environmental information based on personal prior knowledge, skills, personal and professional qualities, and integrative skills that demonstrate the ability of an individual to make independent decisions and appropriate actions in unpredictable new situations. The levels of integrative readiness were investigated, covering the following interrelated stages: theoretical analysis, synthesis, comparison; modelling and technological design; generalization; observational; pedagogical experiment. A study was conducted. The research involved 285 pre-service teachers from the Ternopil Volodymyr Hnatiuk National Pedagogical University, enrolled in the Speciality 016: Special Education unit. A structural model of the formation of integrative readiness of future teachers for professional activity was developedwhich consisting of the following blocks: target, content and procedural, and resultant. The research finding reported that the effectiveness of corrective, developmental, and educational work in the educational inclusive space largely depended upon the quality of training of the pre-service teacher, and the use of active and interactive training.
Ship Target Detection Based on LightGBM Algorithm

Along with the increasingly prominent role of ship automatic detection in the search and rescue of distressed ships, combating illegal fishing, marine traffic control and marine warfare, this paper proposes a ship detection method based on shape and texture features. Firstly, this article extracts the ship candidate area; combines the feature extraction algorithm to extract 5 shape characteristics such as unevenness, eccentricity, and symmetric regularity index, and then uses the LBP(Local Binary Pattern) rotation invariant uniform pattern algorithm to extract the LBP value of the ship candidate area and combine the LBP values For statistical histograms, the statistical histograms are finally connected into a multi-dimensional vector; shape features and LBP multi-dimensional vectors are combined into feature vectors, and the feature vectors are used to complete the training of the LightGBM classifier, and the trained LightGBM classifier is used to complete the final ship Detection. The detection algorithm in this paper is based on the 768 pixel * 768 pixel ship image provided by the Airbus Ship Detection Challenge, and finally forms a 46-dimensional feature vector with a recall rate of 93.3% and an accuracy rate of 92.9%. The results show that this method can not only effectively exclude the influence of clouds, small islands, and light, but also has a high recognition rate, and effectively realize accurate and rapid detection of ships at sea.