Reviewerly/exHarmony huggingface dataset:
Improving Student’s Coding Skill with Gamification Website-based Program

People's dependence on digital technology has increased rapidly since the global Covid-19 pandemic. This causes the world's need for digital products and human resources to make digital products higher. Informatics Engineering Program is one of the providers of human resources who are proficient in the field of information technology. One of the basic skills that must be possessed to create digital products is programming or coding skills. The more skilled a person is at typing and understanding coding, the more complex the digital products he can create. This is why the coding ability of students in the Informatics Engineering Program must be improved. This website-based coding training program aims to improve the coding skills of Informatics Engineering students with interesting and fun gamification methods. Gamification methodology will greatly benefit student of Informatics Engineering Program in Riau University to improve his or her coding skill without consuming too much time and energy.
New, flexible and open-source fisheries self-reporting app: The Shiny4SelfReport

Self-reporting applications are considered a promising solution for fisheries data monitoring. However, they are still failing in providing accurate information and engaging users. We introduce the Shiny4SelfReport, an application for self-reporting data in fisheries that aims to address these shortcomings. Instead of using expensive proprietary software, we demonstrate how common and affordable technologies can be used to fill gaps in fisheries management of developing nations. The tool, developed in R, works by gathering fishers’ inputs and storing them in the cloud. It was designed to be simple and adaptable. Our application improves the data assembly on small-scale fisheries, provides fishers’ engagement and data accuracy, and may integrate fisheries’ knowledge worldwide while filling the gaps on data-poor fisheries. The app is available at http://triatlas.shinyapps.io/Shiny4SelfReport under a General Public License (GPLv3).
ESTIME: Estimation of Summary-to-Text Inconsistency by Mismatched Embeddings

We propose a new reference-free summary quality evaluation measure, with emphasis on the faithfulness.The measure is based on finding and counting all probable potential inconsistencies of the summary with respect to the source document.The proposed ESTIME, Estimator of Summary-to-Text Inconsistency by Mismatched Embeddings, correlates with expert scores in summary-level SummEval dataset stronger than other common evaluation measures not only in Consistency but also in Fluency.We also introduce a method of generating subtle factual errors in human summaries.We show that ESTIME is more sensitive to subtle errors than other common evaluation measures.
Thoughts on the Regulation of Content on Social Media in Latin America: Authors’ Rights, Limitations and Content Filtering

The array of issues involved in the regulation of social media, and their cardinal importance in Latin America -from privacy, freedom of expression, liabilities, disinformation, right to erasure, and copyright -is compelling, yet staggering in its expanse and substance.This review is not meant to be exhaustive, but it does provide thoughts and selected examples on the regulatory framework of social media content in Latin America.The first part of this paper aims to give an overview of some of the more noteworthy developments regarding internet and social media regulations in certain countries.Then, the analysis will focus on authors' rights in Latin America, analyzing how successfully -or not -traditional exceptions and limitations of international copyright law regulate copyrighted content on social media.
Application of pollution assessment models in soil contaminated by heavy metals in two steel rods markets, Port Harcourt, Rivers State, Nigeria

Soil samples were collected at a depth of 0-30cm within two steel markets and a control site in Port Harcourt, Rivers State Nigeria to assess the level of heavy metals (Fe, Pb, Cu, Cd, Cr, Ni and As) in the environment. Atomic Adsorption Spectrophotometer was used to analyze the samples for heavy metals. The concentrations of all the heavy metals in the steel rods markets exceeded that of the control. The results indicated that heavy metals concentrations in the sites were in the order; Mile III &gt; Kala &gt; RSU. The average levels of contamination of heavy metals recorded followed the order Fe &gt; Cr &gt; Cu &gt; Pb &gt; Ni &gt; As &gt; Cd in Mile III, Fe &gt; Pb &gt; Cu &gt; Cr &gt; Ni &gt; As &gt; Cd in Kala and Fe &gt; Cu &gt; Pb &gt; Cr &gt; Ni &gt; As &gt; Cd in RSU (control). Mean concentrations obtained for heavy metals within the months of investigation were; 1420.931±9.155, 7.753±0.184, 8.730±0.050, 2.843±0.124, 9.428±0.122, 7.433±0.047 and 3.732±0.047 mg/Kg for Fe, Pb, Cu, Cd, Cr, Ni and As respectively at the mile III station, while the mean concentrations of heavy metals observed at the Kala station were; 1161.173±1.823, 9.425±0.054, 7.596±0.027, 1.425±0.020, 6.507±0.006, 5.455±0.033 and 1.901±0.010 mg/Kg for Fe, Pb, Cu, Cd, Cr, Ni and As respectively. The mean values of heavy metals concentrations observed at the RSU station within the period were; 892.064±1.025,5.603±0.007, 5.841±0.051, 0.173±0.005, 3.389±0.009, 2.309±0.010 and 0.706±0.006 mg/Kg for Fe, Pb, Cu, Cd, Cr, Ni and As respectively. Pollution assessment models used for assessing the anthropogenic input on the quality of the soil in the area using the control site as the basis of judgment were: contamination factor (CF), pollution load index (PLI), contamination degree (CD), modified contamination degree (mCD), potential ecological risk coefficient (Eir), potential ecological risk index (RI), Geo-accumulation index (Igeo) and anthropogenicity. These indices revealed that the steel markets were contaminated and polluted and poses ecological risks by heavy metals, even though the values obtained were still below the WHO acceptable limits. The steel rods markets need to be adequately monitored and regulated to avoid further soil contamination by heavy metals to a degree that will be dangerous to human health.
Energy-Constrained LOCC-Assisted Quantum Capacity of the Bosonic Dephasing Channel

We study the LOCC-assisted quantum capacity of a bosonic dephasing channel with energy constraint on the input states. We start our analysis by focusing on the energy-constrained squashed entanglement of the channel, which is an upper bound for the energy-constrained LOCC-assisted quantum capacity. As computing energy-constrained squashed entanglement of the channel is challenging due to a double optimization (over the set of density matrices and the isometric extensions of a squashing channel), we first derive an upper bound for it, and then, we discuss how tight that bound is for the energy-constrained LOCC-assisted quantum capacity of the bosonic dephasing channel. In doling so, we prove that the optimal input state is diagonal in the Fock basis. Then, we analyze two explicit examples of squashing channels through which we derive explicit upper and lower bounds for the energy-constrained LOCC-assisted quantum capacity of the bosonic dephasing channel in terms of its quantum capacity with different noise parameters. As the difference between upper and lower bounds becomes smaller by increasing the dephasing parameter, the bounds become tighter.
A Fine-Grained Domain Adaption Model for Joint Word Segmentation and POS Tagging

Domain adaption for word segmentation and POS tagging is a challenging problem for Chinese lexical processing.Self-training is one promising solution for it, which struggles to construct a set of high-quality pseudo training instances for the target domain.Previous work usually assumes a universal sourceto-target adaption to collect such pseudo corpus, ignoring the different gaps from the target sentences to the source domain.In this work, we start from joint word segmentation and POS tagging, presenting a fine-grained domain adaption method to model the gaps accurately.We measure the gaps by one simple and intuitive metric, and adopt it to develop a pseudo target domain corpus based on finegrained subdomains incrementally.A novel domain-mixed representation learning model is proposed accordingly to encode the multiple subdomains effectively.The whole process is performed progressively for both corpus construction and model training.Experimental results on a benchmark dataset show that our method can gain significant improvements over a vary of baselines.Extensive analyses are performed to show the advantages of our final domain adaption model as well.
Synchronous Dual Network with Cross-Type Attention for Joint Entity and Relation Extraction

Joint entity and relation extraction is challenging due to the complex interaction of interaction between named entity recognition and relation extraction.Although most existing works tend to jointly train these two tasks through a shared network, they fail to fully utilize the interdependence between entity types and relation types.In this paper, we design a novel synchronous dual network (SDN) with cross-type attention via separately and interactively considering the entity types and relation types.On the one hand, SDN adopts two isomorphic bi-directional type-attention LSTM to encode the entity type enhanced representations and the relation type enhanced representations, respectively.On the other hand, SDN explicitly models the interdependence between entity types and relation types via crosstype attention mechanism.In addition, we also propose a new multi-task learning strategy via modeling the interaction of two types of information.Experiments on NYT and WebNLG datasets verify the effectiveness of the proposed model, achieving state-of-the-art performance.
Nonexistence of some ternary linear codes with minimum weight -2 modulo 9

<p style='text-indent:20px;'>One of the fundamental problems in coding theory is to find <inline-formula><tex-math id="M3">\begin{document}$ n_q(k,d) $\end{document}</tex-math></inline-formula>, the minimum length <inline-formula><tex-math id="M4">\begin{document}$ n $\end{document}</tex-math></inline-formula> for which a linear code of length <inline-formula><tex-math id="M5">\begin{document}$ n $\end{document}</tex-math></inline-formula>, dimension <inline-formula><tex-math id="M6">\begin{document}$ k $\end{document}</tex-math></inline-formula>, and the minimum weight <inline-formula><tex-math id="M7">\begin{document}$ d $\end{document}</tex-math></inline-formula> over the field of order <inline-formula><tex-math id="M8">\begin{document}$ q $\end{document}</tex-math></inline-formula> exists. The problem of determining the values of <inline-formula><tex-math id="M9">\begin{document}$ n_q(k,d) $\end{document}</tex-math></inline-formula> is known as the optimal linear codes problem. Using the geometric methods through projective geometry and a new extension theorem given by Kanda (2020), we determine <inline-formula><tex-math id="M10">\begin{document}$ n_3(6,d) $\end{document}</tex-math></inline-formula> for some values of <inline-formula><tex-math id="M11">\begin{document}$ d $\end{document}</tex-math></inline-formula> by proving the nonexistence of linear codes with certain parameters.
Developing chatbots in the field of healthcare: A systematic review

Considering the context of Computer Science, chatbots are computer programs that use Artificial Intelligence techniques to simulate human behavior in dialogues. The use of chatbots applied to the health area has been growing, especially in scenarios for dealing with pandemics, such as COVID-19, as they help to avoid the burden of face-to-face care. Thus, this article proposes a systematic review of the work carried out in this line of research. After the review, it was found which technologies, strategies and frameworks are most used in recent times, as well as which specific areas of health are having more focus on the use of chatbots.
Formation of the complex characteristic of the image of text fonts for the purpose of optimization of font design process of book editions

of drawing of text fonts which are subject to the analysis in the course of a choice of font design of book editions and are criteria of formation of library of text fonts is defined.The analysis of ways of completing the headsets with graphic modifications and their systematization is performed.Variants of the choice of pairs of graphic variations of the font pattern for the font design of the main continuous and selected text are offered.
The Realization of Automobile Fog Lamp Intelligent Control System Based on Jetson Nano

In order to improve the driving safety of vehicles in foggy environment, a set of intelligent control system for vehicle fog lamp that can automatically control the fog lamp on and off by identifying the weather conditions is designed. Firstly, Jetson Nano, IMX219 camera, Raspberry PI display and LED indicator simulating fog lamp were selected as the system hardware components. Secondly, a weather recognition algorithm based on capsule network was designed. In the process of testing images which contain foggy, rainy, cloudy and sunny days, the recognition accuracy of the algorithm is 97.5%. Finally, the algorithm model was transplanted to Jetson Nano for debugging and testing. The test results show that the intelligent control system of vehicle fog lamp based on Jetson Nano basically meets the design goals and functional requirements of the system, that is, the fog lamp can be automatically turned on/off in foggy/nonfoggy environment.
Synthetic Textual Features for the Large-Scale Detection of Basic-level Categories in English and Mandarin

Basic-level categories (BLC) are an important psycholinguistic concept introduced by Rosch et al. (1976); they are defined as the most inclusive categories for which a concrete mental image of the category as a whole can be formed, and also as those categories which are acquired early in life.Rosch's original algorithm for detecting BLC (called cue-validity) is based on the availability of semantic features such as 'has tail' for 'cat', and has remained untested at large.An at-scale algorithm for the automatic determination of BLC exists, but it operates without Rosch-style semantic features, and is thus unable to verify Rosch's hypothesis.We present the first method for the detection of BLC at scale that makes use of Rosch-style semantic features.For both English and Mandarin, we test three methods of generating such features for any synset within Wordnet (WN): extraction of textual features from Wikipedia pages, Distributional Memory (DM) and BART.The best of our methods outperforms the current SoA in BLC detection, with an accuracy of English BLC detection of 75.0%, and of Mandarin BLC detection 80.7% on a test set.When applied to all of WordNet, our model predicts that 1,118 synsets in English Wordnet (1.4%) are BLC, far fewer than existing methods, and with a precision improvement of over 200% over these.As well as confirming the usefulness of Rosch's cue validity algorithm, we also developed and evaluated our own new indicator for BLC, which models the fact that BLC features tend to be BLC themselves.
Modularity and Combination of Associative Commutative Congruence Closure Algorithms enriched with Semantic Properties

Algorithms for computing congruence closure of ground equations over uninterpreted symbols and interpreted symbols satisfying associativity and commutativity (AC) properties are proposed. The algorithms are based on a framework for computing a congruence closure by abstracting nonflat terms by constants as proposed first in Kapur's congruence closure algorithm (RTA97). The framework is general, flexible, and has been extended also to develop congruence closure algorithms for the cases when associative-commutative function symbols can have additional properties including idempotency, nilpotency, identities, cancellativity and group properties as well as their various combinations. Algorithms are modular; their correctness and termination proofs are simple, exploiting modularity. Unlike earlier algorithms, the proposed algorithms neither rely on complex AC compatible well-founded orderings on nonvariable terms nor need to use the associative-commutative unification and extension rules in completion for generating canonical rewrite systems for congruence closures. They are particularly suited for integrating into the Satisfiability modulo Theories (SMT) solvers. A new way to view Groebner basis algorithm for polynomial ideals with integer coefficients as a combination of the congruence closures over the AC symbol * with the identity 1 and the congruence closure over an Abelian group with + is outlined.
Character-based PCFG Induction for Modeling the Syntactic Acquisition of Morphologically Rich Languages

Unsupervised PCFG induction models, which build syntactic structures from raw text, can be used to evaluate the extent to which syntactic knowledge can be acquired from distributional information alone.However, many state-of-the-art PCFG induction models are word-based, meaning that they cannot directly inspect functional affixes, which may provide crucial information for syntactic acquisition in child learners.This work first introduces a neural PCFG induction model that allows a clean ablation of the influence of subword information in grammar induction.Experiments on child-directed speech demonstrate first that the incorporation of subword information results in more accurate grammars with categories that word-based induction models have difficulty finding, and second that this effect is amplified in morphologically richer languages that rely on functional affixes to express grammatical relations.A subsequent evaluation on multilingual treebanks shows that the model with subword information achieves state-ofthe-art results on many languages, further supporting a distributional model of syntactic acquisition.
On Camera Calibration and Distortion Correction Based on Bundle Adjustment

The accurate calibration and distortion correction of cameras are important subjects in visual measurement. The parameters of the camera need to be calculated both conveniently and accurately and the distorted images are corrected by using the obtained parameters. This article first analyses the imaging model and calibration theory of the camera. Then it is proposed to introduce the bundle adjustment method into the camera calibration. The internal and external parameters of the camera and the positions of the target corners are jointly optimized in the process of parameter optimization. Furthermore, the distortions of the original images are corrected with the final optimized parameters. Traditional calibration methods focus on physical methods and rely on high-precision calibration objects to improve parameter accuracy. The novelty of this paper is that the bundle adjustment method is used to eliminate the impact of standard point errors from the perspective of the algorithm. This method removes the reliance on high-precision calibration objects, reduces the cost and improves the calibration accuracy.
Novel Hybrid Interval Type-2 Fuzzy Adaptive Backstepping Control for a Class of Uncertain Discrete-Time Nonlinear Systems

A Novel hybrid backstepping interval type-2fuzzy adaptive control (HBT2AC) for uncertain discrete-time nonlinear systems is presented in this paper. The systems are assumed to be defined with the aid of discrete equations with nonlinear uncertainties which are considered as modeling errors and external unknown disturbances, and that the observed states are considered disturbed. The adaptive fuzzy type-2 controller is designed, where the fuzzy inference approach based on extended single-input rule modules (SIRMs) approximate the modeling errors, non-measurable states and adjustable parameters are estimated using derived weighted simplified least squares estimators (WSLS). We can prove that the states are bounded and the estimation errors stand in the neighborhood of zero. The efficiency of the approach is proved by simulation for which the root mean squares criteria are used which improves control performance.
An Introspective Performance Analysis of Threshold-based Segmentation Techniques on Digital Mammograms

Image segmentation, as a pre-processing step, plays a vital role in medical image analysis. The variants of threshold-based image segmentation methods are proved to offer feasible and optimal solutions to extract the region of interest (RoI), from medical images. Digital mammograms are used as a reliable source of breast cancer prognosis and diagnosis. Thresholding is a simple and effective strategy that finds applications in image processing and analysis. This research aimed to analyze the performance behaviour of a few threshold-based segmentation methods with respect to the intensity distribution of the input mammograms. For this analytical research, six automated thresholding segmentation techniques were chosen: Kapur, Otsu’s, Isoentropic, Ridler &amp; Calvard’s, Kittler &amp; Illingworth's, and Yen. The performance and behaviour of those methods were validated on the digital mammogram images of mini-MIAS database featured with Fatty (F), Fatty-Glandular (G), and Dense-Glandular (D) breast tissues. Those methods were analyzed on two metrics viz., Region Non-Uniformity (RNU) and computation time. The results of this research confirm that Ridler &amp; Calvard’s method gives the best segmentation results for Dense-Glandular, Isoentropic method gives better segmentation results for Fatty and Yen method works well on the Fatty-Glandular normal mammogram images.
Retraction Note: Non-point source pollution in river basin based on Bayesian network and intelligent translation system of English books

With the rapid development of social science and industrial technology, more and more attention has been paid to the evaluation of water environmental pollution. Bayesian network has strong probability theory inference ability, clear semantics, easy to understand, and other technical characteristics. This provides a natural and effective way to express dependency and causality, which can find potential relationships and patterns in datasets. Therefore, it has its own advantages in data mining. Bayesian network technology is used to study non-point source pollution in river basin and intelligent translation system of English books. Water pollution is one of the main problems that human beings are facing. In China, water pollution is more serious. First of all, the acceleration of urbanization and the change of land use will lead to the change of surface vegetation, resulting in the loss of soil and water. Secondly, many fertilizers, pesticides, and other chemical reagents are used in agricultural activities, some of which are absorbed by crops, and most of the rest are released from the soil in the process of rainfall. “Machine translation + post-translation editing” mode has been widely used in various translation projects, and the English Chinese subtitle translation project of “a little English” English learning software has adopted this mode. In practice, it is found that “machine translation + mode can improve the efficiency of subtitle E-C translation, but there are also limitations. In this paper, by analyzing the cases in the subtitle English-Chinese translation project of the English learning software, the advantages and typical errors of machine translation in subtitle English-Chinese translation are summarized, and the corresponding post-translation editing strategies are proposed for different error types. The main errors at the lexical level are the wrong choice of word meaning, no translation of abbreviations, and no translation of coined words. The proposed post-editing strategies are to locate polysemous words.