Reviewerly/exHarmony huggingface dataset:
Does the writing exposition text ability correlate to reading habit and discourse markers mastery?

This study aims to determine 1) the relationship between students’ reading habits with the ability to write exposition text, 2) the relationship between students’ discourse markers mastery with the ability to write exposition text, and (3) the relationship between students’ reading habits and discourse markers mastery with the ability to write exposition text. This research was conducted in the tenth grade students of SMA Negeri 4 Serang in the odd semester of the 2018/2019 school year. The research method used was a survey method with correlational techniques. The population was the students of grade X. There were 450 students consisting of 180 male and 270 female. The sample was taken using purposive sampling technique. The sample was 88 students. The test was used to obtain data on the students’ ability to write exposition texts and students’ mastery of discourse markers, while the questionnaire was used to to obtain data on students' reading habits. The results showed that: 1) there was a positive and significant relationship between the students’ reading habits and the students' ability to write exposition texts; 2) there was a positive and significant relationship between the students’ discourse markers mastery and the students' ability to write exposition texts; and 3) there was a positive and significant relationship between the students’ reading habits and the students’ discourse markers mastery with the students’ ability to write exposition texts.
COVID-19 and the Digital Transformation of Saudi Higher Education

This paper examines the effectiveness of the fourth industrial revolution technologies in mitigating the COVID 19 effects on higher education, through using digital learning and technological work systems as substitutes for traditional learning and work systems in Saudi Arabia. However, due to COVID 19 pandemic, all universities shut down and continued its role of providing educational services and keeping work going on using digital learning and technological work systems. Using a survey implemented in different universities for various colleges directed to faculty staff and students, the study tries to explain how the fourth industrial revolution is influencing higher education and how universities are prepared to face their challenges resulted from COVID 19 pandemic in Saudi Arabia. The study tries to explain how the fourth industrial revolution can play the role of catalyst for improving productivity, learning outcome, and wellbeing -work environment in these uncertain circumstances. Results display that digital learning is distinct compared to traditional learning, it improves the students learning outcome, enhance the faculty member’s capabilities and implementation of technical systems, develops staff productivity and work environment.
Evaluasi software aplikasi Senayan Library Management System (SLIMS) di lingkungan Universitas Lancang Kuning Pekanbaru menggunakan pendekatan ISO 9126 Usability

This research focuses on evaluating Senayan library management system (Slims) application software in the Lancang Kuning University environment using the ISO 9126.1 Usability approach. Research related to evaluation or application quality testing has been done a lot but for the evaluation of slims software in the Lancang Kuning University environment has never been done. In this research the method used is descriptive quantitative. With the method of collecting observation data, distributing questionnaires, and interviews. The sample in this study were 7 respondents from different libraries who used the slims application. Data analysis was performed using a Likert scale percentage formula. The results of this study indicate that based on the percentage of data using ISO 9126.1 usability on each attribute, namely Understandability with a percentage value of 116 predicate strongly agree, Learnability with a percentage value of 137 predicate agree, Opportunity with a value of 349 predicate strongly agree, Attactiveness with a value of 115 predicate strongly agree, and Usability Compliance with a rating of 115 is very much in agreement. From the results explained above, it can be concluded that Slims meet ISO 9126.1 usability standards.
Clasificación y descripción de sistemas categoriales sobre interacción sociocognitiva entre pares

A Socio-constructivist psychology proposes the interactive nature of development and knowledge. In this sense, the analysis of socio-cognitive interaction has been a central aspect within this tradition.The systematic and reliable analysis of the interaction involves a great challenge since its complexity, dynamicity and multiplicity. The literature shows a wide variety of methodological proposals for the analysis of socio-cognitive interaction, ranging from more holistic approaches to more systematic approaches based on the construction of categories. The latter constitute the focus of interest of this work. The aim is to propose a classification and description of the variety of categorical systems usedin recent years for the analysis of socio-cognitive peer interaction. From a systematic review in different international databases, 24 categorical systems were selected and analyzed. The classification was based on two relevant elements in the definitionof a system of categories: (a) the unit of coding or analysis (molecular and molar); (b) the type of empirical indicator considered (predominantly linguistic, predominantly behavioral and mixed). The results suggest a predominance of linguistic molecular systems (50%), compared to molar and / or predominantly behavioral or mixed systems. These results are discussed from the concept of inter-subjectivity and the evolutionary-educational moment of the participants of the studies analyzed (primary level). At the same time, it concludes about the need to incorporate new molar systems that more faithfully reflect the emergent and irreducible nature of socio-cognitive.
Back Propagation Neural Network for Controlling Coupled Water Tank

A well-prepared abstract enables the reader to identify the basic content of the level and flow control in tanks is the heart of all chemical engineering systems. The control of liquid level in tanks and flow between tanks is a basic problem in the process industries. Many times the liquids will be processed by chemical or mixing treatment in the tanks, but always the level of fluid in the tanks must be controlled and the flow between tanks must be regulated in presence of non-linearity. Therefore, in this paper will use neural network based on backpropagation (BP) to control of level 2 in the tank 2 with the setpoint of 10 centimeters and can follow the setpoint changes to 8 centimeters given in 225 seconds. The results show that a neural network based on backpropagation can follow setpoint given with steady-state error is 0 cm, overshoot is 0%, the rising time is 49 seconds, settling time is 52 seconds and can follow setpoint changes in 51 seconds.
Reducing field depth: annular Hadamard masks

For optically narrowing field depth, we explore the use of annular masks that are coded with the elements of Hadamard matrices. We show that with no further reduction of light throughput, these annular masks can enhance the influence of focus error on the modulation transfer functions (MTF). We report numerical evaluations of the irradiance point spread functions (PSFs), their associated MTFs, and of some digitally generated images. For complementing our assessments, we evaluate the Fisher information of these Walsh–Hadamard, annular masks. We suggest an optical technique for breaking 3-D scenes into several 2-D sliced frames by translating out-of-focus images into background noise.
Machine Translation Using Improved Attention-based Transformer with Hybrid Input

Machine Translation (MT) refers to the automated software-based translation of natural language text. The embedded complexities and incompatibilities of natural languages have made MT a daunting task facing numerous challenges, especially when it is to be compared to a manual translation. With the emergence of deep-learning AI approaches, the Neural Machine Translation (NMT) has pushed MT results closer to human expectations. One of the newest deep learning approaches is the sequence-to-sequence approach based on Recurrent Neural Networks (RNN), complex convolutions, and transformers, and employing encoders/decoder pairs. In this study, an attention-based deep learning architecture is proposed for MT, with all layers focused exclusively on multi-head attention and based on a transformer that includes multi-layer encoders/decoders. The main contributions of the proposed model lie in the weighted combination of layers' primary input and output of the previous layers, feeding into the next layer. This mechanism results in a more accurate transformation compared to non-hybrid inputs. The model is evaluated using two datasets for German/English translation, the WMT'14 dataset for training, and the newstest'2012 dataset for testing. The experiments are run on GPD-equipped Google Colab instances and the results show an accuracy of 36.7 BLEU, a 5% improvement over the previous work without the hybrid-input technique.
Context-Aware Healthcare Adaptation Model for COPD Diseases

Nowadays, ubiquitous computing and mobile applications are controlling all our life’s aspects, from social media and entertainment to the very basic needs like commerce, learning, government, and health. These systems have the ability to self-adapt to meet changes in their execution environment and the user’s context. In the healthcare domain, information systems have proven their efficiency, not only by organizing and managing patients’ data and information but also by helping doctors and medical experts in diagnosing disease and taking precluding procedure to avoid serious conditions. In chronic diseases, telemonitoring systems provide a way to monitor the patient’s state and biomarkers within their usual life’s routine. In this article, we are combining the healthcare telemonitoring systems with the context awareness and self-adaptation paradigm to provide a self-adaptive framework architecture for COPD patients.
Sketching a Generative Model of Intention Management for Characters in Stories: Adding Intention Management to a Belief-Driven Story Planning Algorithm

Previous work on story planning has shown success in the generation of plans that are both intention-coherent and demonstrate aspects of inter-character conflict. However, the initial models of intention and conflict have been limited, in that they lack methods to generate story plots wherecharacters drop sub-plans to achieve their goals in believably consistent and expressive ways and adopt new sub-plans in the face of plan failure. In current work, we have developed models of failed actions in stories that go hand in hand with erroneous belief models for character. Motivated by characterizations of rational agents' intentions as choice combined with commitment, we provide a framing of the plan generation process that is intended to show how characters form their own plans to achieve their own goals, act upon those plans until they feel that conditions no longer support their plans, and then re-plan in the face of adversity to achieve their goals. We show an example story plan that contains several types of character-based intention dynamics targeted by our approach.
Lexicalization of Probabilistic Linear Context-free Rewriting Systems

In the field of constituent parsing, probabilistic grammar formalisms have been studied to model the syntactic structure of natural language.More recently, approaches utilizing neural models gained lots of traction in this field, as they achieved accurate results at high speed.We aim for a symbiosis between probabilistic linear context-free rewriting systems (PLCFRS) as a probabilistic grammar formalism and neural models to get the best of both worlds: the interpretability of grammars, and the speed and accuracy of neural models.A combination of these two could be achieved by applying supertagging to PLCFRS.This approach requires lexical grammar formalisms.Here, we present a procedure which turns any PLCFRS G into an equivalent lexical PLCFRS G .Moreover, we show how the derivations in G can be transformed to obtain their corresponding original derivations in G. Our construction for G preserves the probability assignment and does not increase parsing complexity compared to G.
Detecting Suspicious Timber Trades

Developing algorithms that identify potentially illegal trade shipments is a non-trivial task, exacerbated by the size of shipment data as well as the unavailability of positive training data. In collaboration with conservation organizations, we develop a framework that incorporates machine learning and domain knowledge to tackle this challenge. Modeling the task as anomaly detection, we propose a simple and effective embedding-based anomaly detection approach for categorical data that provides better performance and scalability than the current state-of-art, along with a negative sampling approach that can efficiently train the proposed model. Additionally, we show how our model aids the interpretability of results which is crucial for the task. Domain knowledge, though sparse and scattered across multiple open data sources, is ingested with input of domain experts to create rules that highlight actionable results. The application framework demonstrates the applicability of our proposed approach on real world trade data. An interface combined with the framework presents a complete system that can ingest, detect and aid in the analysis of suspicious timber trades.
Unsupervised Reinforcement Learning

Conventionally, reinforcement learning algorithms are goal-directed: they aim to acquire policies that most effectively maximize a given reward signal. However, if we consider agents that must master very large repertoires of behaviors -- such as general-purpose robots that must perform a diverse array of tasks in the real world -- then it makes sense to instead frame the reinforcement learning process as an unsupervised learning procedure, which has the aim of extracting a large and diverse array of skills that can later be utilized for the many tasks that the agents may be asked to perform. Such a formulation not only makes it feasible to acquire diverse behaviors before any reward signal is actually observed, but can actually make learning much more tractable for tasks with delayed or sparse reward signals. In this talk, I will discuss recent advances in unsupervised reinforcement learning, many of which draw on an information-theoretic formulation for the unsupervised skill acquisition problem. I will discuss how this formulation can provide us with a principled view of unsupervised skill acquisition, and furthermore provides some tantalizing clues about how to quantify the usefulness of learned behaviors. I will also present experimental results showing that unsupervised reinforcement learning not only provides good results in a variety of simpler simulated environments, but in fact can be utilized with real-world robotic systems to learn sophisticated behaviors with minimal human input.
Exploring the Use of Lexicons to aid Deep Learning towards the Detection of Abusive Language.

Detecting abusive language is a significant research topic, which has received a lot of attention recently. Our work focused on detecting personal attacks in online conversations. State-of-the-art research on this task has largely used deep learning with word embeddings. We explored the use of sentiment lexicons as well as semantic lexicons towards improving the accuracy of the baseline Convolutional Neural Network (CNN) using regular word embeddings. This is a work in progress, limited by time constraints and appropriate infrastructure. Our preliminary results showed promise for utilizing lexicons, especially semantic lexicons, for the task of detecting abusive language.
Deep Learning for Speech Accent Detection in Videogames

In video games, a wide range of characters make up the world players inhabit. These characters, NPCs, have traits, such as their appearance and speech accent, that determine certain things about them, including moral inclination, levels of trustworthiness, social class, levels of education, and ethnic background. But what does an accent say about a character in a video game? We use deep learning to train a neural network to detect speech accents and establish the degree to which machines can be used to recognize these accents. This research aims to help sociolinguists and discourse analysts establish critical study and content analytical findings for instance about stereotypical uses of speech accents, to better analyze who has what accent in video games, and what kind of language ideologies and social value judgments the use of accents in games construct and perpetuate. This paper presents the results of the first deep learning experiments, which were conducted on Standard North American, British Received Pronunciation, and Spanish English. We discuss our methodological considerations and some early deep learning results, which show relatively low levels of accuracy (61%). We discuss possibilities of improving our method, and of enriching our training datasets.
A Semantic Approach to Constraint-Based Reasoning in Geographical Domains

Various models have been developed to manage geographic data but most of them integrate heterogeneous techniques to support knowledge representation and reasoning. This is far from optimal because it requires mapping data between different representation formats; moreover, as it fragments knowledge, it limits the possibility to use complete information about the problem to be solved for the execution of inferences. In order to address this issue, we adopt a unified approach, in which we use Semantic Web techniques to manage both knowledge representation and reasoning rules with particular attention to constraint verification that is central to several geographic reasoning tasks. Our model exploits an ontological description of spatial constraints which supports the specification of their properties, facilitating the automated selection of the relevant ones to be applied to a given problem. The model supports different types of inferences, such as checking the compliance of a given geographical area to a set of constraints, or suggesting a suitable aggregation of land patches that satisfy them. We test our model by applying it to the management of Ecological Networks, which describe the structure of existing real ecosystems and help planning their expansion, conservation and improvement by introducing constraints on land use.
Kujang And Batik Bogor Educational Games To Grow Millennial Generation Enthusiasm For Local Wisdom Through Digital Media

Educational game is a game that raises local wisdom in which there are values of local wisdom that can be remembered and implemented by users in everyday life. in this educational game made in accordance with the rules needed in the world of education. starting from the elements of game design and techniques used in making games. This application was built using MDLC (Multimedia Development Live Cycle) combined with FSM (Finite State Machine) so that this educational game is very easy to understand and understand by users, especially millennial generation. after the socialization to the millennial generation there was an increase in understanding of the local wisdom of Bogor, especially the Kujang and the batik of Bogor.
Image Caption Combined with GAN Training Method

In today’s world where the number of images is huge and people cannot quickly retrieve the information they need, we urgently need a simpler and more human-friendly way of understanding images, and image captions have emerged. Image caption, as its name suggests, is to analyze and understand image information to generate natural language descriptions of specific images. In recent years, it has been widely used in image-text crossover studies, early infant education, and assisted by disadvantaged groups. And the favor of industry, has produced many excellent research results. At present, the evaluation of image caption is basically based on objective evaluation indicators such as BLUE and CIDEr. It is easy to prevent the generated caption from approaching human language expression. The introduction of GAN idea allows us to use a new method of adversarial training. To evaluate the generated caption, the evaluation module is more natural and comprehensive. Considering the requirements for image fidelity, this topic proposes a GAN-based image description. The Attention mechanism is introduced to improve image fidelity, which makes the generated caption more accurate and more close to human language expression.
[Single-modal neuroimaging computer aided diagnosis for schizophrenia based on ensemble learning using privileged information].

Neuroimaging technologies have been applied to the diagnosis of schizophrenia. In order to improve the performance of the single-modal neuroimaging-based computer-aided diagnosis (CAD) for schizophrenia, an ensemble learning algorithm based on learning using privileged information (LUPI) was proposed in this work. Specifically, the extreme learning machine based auto-encoder (ELM-AE) was first adopted to learn new feature representation for the single-modal neuroimaging data. Random project algorithm was then performed on the learned high-dimensional features to generate several new feature subspaces. After that, multiple feature pairs were built among these subspaces to work as source domain and target domain, respectively, which were used to train multiple support vector machine plus (SVM+) classifier. Finally, a strong classifier is learned by combining these SVM+ classifiers for classification. The proposed algorithm was evaluated on a public schizophrenia neuroimaging dataset, including the data of structural magnetic resonance imaging (sMRI) and functional MRI (fMRI). The results showed that the proposed algorithm achieved the best diagnosis performance. In particular, the classification accuracy, sensitivity and specificity of the proposed algorithm were 72.12% ± 8.20%, 73.50% ± 15.44% and 70.93% ± 12.93%, respectively, on the sMRI data, and it also achieved the classification accuracy of 72.33% ± 8.95%, sensitivity of 68.50% ± 16.58% and specificity of 75.73% ± 16.10% on the fMRI data. The proposed algorithm overcomes the problem that the traditional LUPI methods need the additional privileged information modality as source domain. It can be directly applied to the single-modal data for classification, and also can improve the classification performance. Therefore, it suggests that the proposed algorithm will have wider applications.神经影像技术目前已经应用于精神分裂症的诊断。为了提升基于单模态神经影像的精神分裂症计算机辅助诊断（CAD）的性能，本文提出一种基于特权信息学习（LUPI）分类器的集成学习算法。该算法首先对单模态数据采用极限学习机-自编码器（ELM-AE）进行特征二次学习，然后通过随机映射算法将高维特征随机分成多个子空间，并进行两两组合形成源领域和目标领域数据对，用于训练多个支持向量机+（SVM+）弱分类器，最终通过集成学习获得一个强分类器，实现有效的模式分类。本算法在公开的精神分裂症神经影像数据库中进行了实验，包括结构磁共振成像和功能磁共振成像数据。结果表明该算法取得了最优的诊断结果，其在基于结构磁共振成像诊断的分类精度、敏感性和特异性分别可以达到 72.12% ± 8.20%、73.50% ± 15.44% 和 70.93% ± 12.93%，而基于功能磁共振成像诊断的分类精度、敏感性和特异性分别为 72.33% ± 8.95%、68.50% ± 16.58%、75.73% ± 16.10%。本文算法的主要创新点在于克服了传统的 LUPI 分类器需要额外的特权信息模态的不足，可以直接应用于单模态数据分类问题，而且还提升了分类性能，因此具有较为广泛的应用前景。.