Reviewerly/exHarmony huggingface dataset:
The Ontological Approach in Organic Chemistry Intelligent System Development

The amount of knowledge in organic chemistry grows exponentially inducing a need for robust intelligent systems that can promote the process of R&D. Although the methods of intelligent system design vary significantly juxtaposing expert systems, neural networks, genetic algorithms, and fuzzy logic, effective intelligent system development can start only after answering the following essential questions: “How is the application area structured? What is its ontology?”. Ever since the DENDRAL Project, the challenge of knowledge representation has been embraced by the scientific community. The notion of ontology has appeared in knowledge engineering delivering a possible solution. As the practice shows, taxonomies provide little expressiveness. Therefore, we suggest that the ontological approach advocates consider applied logic methodology. This framework proposes that complex-structured domains, such as organic chemistry, be represented as interconnected modules of applied logic theories. Employing the described technique, we introduce the model of organic chemistry intelligent system. Most special aspects of this methodology are depicted together with a historical overview of intelligent systems and the roots of knowledge representation models.
Identification of Territorial Resources Based on Domain Ontology (DOTK) for Sustainability

Ontology is one of the useful tool that can explicit and represent the territorial knowledge for sustainability within industry. This research proposed a descriptive ontology for territorial knowledge (DOTK) which can help the sustainability and enhance the sustainable knowledge of actors of industries. Moreover, this ontology is applied in the specific territory to identify the intangible and tangible resources of this real case. In this way, it is proved that DOTK can act as a guide to identify the territorial resources which can assist the sustainability within industries.
Classification conducting knowledge acquisition by an evolutionary robust GRBF-NN model

Diverse machine learning methods have been successfully used to discover classifying rule among classification data. Sometimes, executing a decision may however indirectly alter feature values of the classified objects, further influencing their classes under the discovered classifying rule. Actually, mining such classification conducting knowledge (CC-knowledge) hidden under the decision from related data can be very helpful to future decision-makings. Hence, this paper proposes an evolutionary robust GRBF-NN model to imitate the mathematical mapping between the feature values before and after executing the decision. A dual-loop nested robust training (DNRT) method is correspondingly developed to determine the weights and parameters using M-AdaBoost and NSGAII respectively in the inner and outer loop. Its remarkable merit is that it considers the classification information by integrating the given classifying rule into both training loops, ensuring the reasonability of prediction. In order to enhance the model’s generalization, the outer loop defines a regularized term and regards it as another optimizing objective of NSGAII besides the training error. Finally, several datasets are employed to verify the effectiveness of the proposed method for CC-knowledge acquisition.
Technology of forming students' foreign-language professional competence in the educational space of higher education institution

Educational space of a higher education institution constantly and continuously influences student’s personality as well as his / her developing as a competent specialist. The research was aimed at substantiating pedagogical technologies that optimize the process of forming students’ foreign-language professional competence within the educational space of a higher education institution. Theoretical (analysis, synthesis, comparison, systematization, pedagogical modelling), empirical (observation, pedagogical experiment, peer review method, reflexive evaluation method) and statistical methods were applied to achieve the set goal. Educational space of a higher education institution is defined as a purposefully created, methodically provided continuum of students’ foreign-language professional communication under conditions of which the development of their personality and effective formation of their foreign-language professional competence take place. Foreign-language professional competence as an integrative personality trait, determined by the synthesis of knowledge and skills, professional and life experience, values that ensure long-term readiness and ability to pursue professional activity in a foreign language, is developed effectively if the educational space of a higher education institution is multilingual and multicultural in its nature, broad, rich in foreign-language professional content, open, directed outwards. When designing educational space, it is necessary to rationally combine educational and non-educational components, provide in-class and out-of-class / extracurricular foreign-language educational and cognitive activities of students, use facilities of virtual educational environment, apply technics of contextual teaching, implement imitative and playing activities, interactive forms of training (games, project activity, case-study). In-class educational activity influences such components of foreign-language competence as “knowledge” and “skills”, whereas an extracurricular one aims at forming “values” and “experience”. Keywords: educational space of a higher education institution, foreign-language professional competence, virtual educational environment, educational and non-educational components, imitative and playing activities.
Light Field Reconstruction Using Dynamically Generated Filters

Densely-sampled light fields have already show unique advantages in applications such as depth estimation, refocusing, and 3D presentation. But it is difficult and expensive to access. Commodity portable light field cameras, such as Lytro and Raytrix, are easy to carry and easy to operate. However, due to the camera design, there is a trade-off between spatial and angular resolution, which can not be sampled intensively at the same time. In this paper, we present a novel learning-based light field reconstruction approach to increase the angular resolution of a sparsely-sample light field image. Our approach treats the reconstruction problem as the filtering operation on the sub-aperture images of input light field and uses a deep neural network to estimate the filtering kernels for each sub-aperture image. Our network adopts a U-Net structure to extract feature maps from input sub-aperture images and angular coordinate of novel view, then a filter-generating component is designed for kernel estimation. We compare our method with existing light field reconstruction methods with and without depth information. Experiments show that our method can get much better results both visually and quantitatively.
Artificial selection of maize as a bridging case to teach natural selection: a simulation proposal

In this paper, we present a simulation of artificial selection of maize that can be used as a bridging case for the subsequent introduction of natural selection in school. The proposed simulation takes up essential biological elements but also has a cultural meaning for the inhabitants of some regions of Latin America. After implementing a test of the simulation with secondary school teachers, we identified fundamental ideas that are introduced as a result of participating in the simulation: intentionality, recognition of variants, gradual process and population. These ideas are relevant for understanding evolution as natural selection. Therefore, the simulation could be used as a bridging case because it focuses the attention on relevant ideas for artificial selection and leaves aside others that are not productive.
To What Extent Can Text Classification Help with Making Inferences About Students’ Understanding

In this paper we apply supervised machine learning algorithms to automatically classify the text of students’ reflective learning journals from an introductory Java programming module with the aim of identifying students who need help with their understanding of the topic they are reflecting on. Such a system could alert teaching staff to students who may need an intervention to support their learning. Several different classifier algorithms have been validated on the training data set to find the best model in two situations; with equal cost for a positive or negative classification and with cost sensitive classification. Methods were used to identify those individual parameters which maximise the performance of each algorithm. Precision, recall and F1-score, as well as confusion matrices were used to understand the behaviour of each classifier and choose the one with the best performance. The classifiers that obtained the best results from the validation were then evaluated on a testing data set containing different data to that used for training. We believe that although the results could be improved with further work, our initial results show that machine learning could be applied to students’ reflective writing to assist staff in identifying those students who are struggling to understand the topic.
Teaching Software Process Models to Software Engineering Students: An Exploratory Study

A software process model (SPM) provides an abstract description of the order in which related activities of software development will be undertaken. Many process models available that can be adapted for software development. However, the selection of the best suitable process model with reference to the problem definition, constraints, and stakeholder requirements is a challenging task. Typically, in a Software Engineering (SE) course, students gain knowledge about SPM and realize their usage via classroom lectures and course projects. It is felt that if the basic knowledge imparted, through the fundamental SE course, is supplemented with some focused sessions about the SPM, then it will not only enable students to think in terms of the SPM but will also motivate them to harness the best practices of software development. This paper presents a preliminary study highlighting our experience on SPM-oriented teaching to impart the concept of requirement elicitation and process modeling, by performing a play (drama skit) annotating real-world scenarios. The feedbacks of students have been collected to evaluate whether this exercise helped them in understanding the processes they have to undergo during software development. Additionally, we have compared the student's feedback and performance in the project and reported the finding of the study.
Towards Self-confidence-based Adaptive Learning for Lunar Exploration

Future space science exploration missions will require collaboration between humans and autonomous robotic systems, resulting in increased science return. However, autonomous systems will occasionally require intervention from human assistants when faced with large uncertainties in their environments. Since human operator and astronaut time are valuable commodities, it is undesirable for autonomous systems to require frequent human assistance during missions. This work examines a simple strategy for adaptive learning which assesses and attempts to improve the capability of a simulated autonomous lunar exploration system as it completes navigation tasks when presented with environmental uncertainties. Specifically, the autonomous system’s perceived machine self-confidence (i.e. self-trust in resulting outcomes) is used to close the loop in a model-based reinforcement learning algorithm. This adaptive learning algorithm allows for effective and safe online learning, which allows the autonomous system to proceed with its tasks and thereby delay the need for human intervention. This paper defines a simulated lunar exploration grid-world problem to demonstrate a proof of concept for this ‘competency aware’ online learning algorithm, and its performance is evaluated in terms of effectiveness, safety, and speed of learning to reduce the uncertainty in its environment. The results for the simulated lunar grid world navigation task show that this adaptive learning strategy achieves good performance on a variety of terrain maps when compared to other simpler but more traditional learning strategies, as it enables automatic adjustment of exploration hyperparameters on the fly in order to better balance exploration and exploitation.
Performance Comparison of Type-1 and Type-2 Neuro-Fuzzy Controllers for a Flexible Joint Manipulator

Flexible joint manipulators are extensively used in several industries and precise control of their nonlinear dynamics has proven to be a challenging task. In this work, we want to compare two intelligent controllers by proposing two Takagi-Sugeno-Kang Neuro-Fuzzy Approaches (Type-1 and Type-2) to control a flexible joint. For both controllers, The inverse models are found using identification techniques, then they are put in series as inverse controllers to control the flexible joint in an online structure. Interval weights are trained by gradient descent approaches using backpropagation algorithms. Results reveal that, without any knowledge about the dynamic of the robot, the methods can control the flexible joint which is highly unstable. As illustrated in result section, One level more fuzziness of Type-2 in compare to type-1 fuzzy controllers helps this controller to more effectively deals with information from a knowledge base. The proposed models can effectively handle uncertainties arising from friction and other structural nonlinearities.
Bengali Poem Generation Using Deep Learning Approach

This research proposes a deep learning based effective Bangla poem generation model that can generate any famous Bengali poet style poem. Unigram, bigram, and trigram word level approach has been followed to make the poem generation model, which is a very early study for this language. From various online website and social media about 35K Bangla poem containing more than 90 million words has been scrapped using python programming language. After preprocessing and tokenizing the data, text data is converted to the n-dimensional numerical vector space using word embedding tool. To build the language model various extension of Recurrent Neural Network (RNN) like LSTM, BiLSTM has experimented. Also, another merge mode for BiLSTM such as sum, avg, mul, concat has been tested. The generated poems are evaluated via various modern evaluation methods like BLEU, GLEU, and a Turing test. Among diverse neural network, BiLSTM performs better for unigram, bigram, trigram where for bigram it gives the best outcomes against BLEU and GLEU more than 87% and Turing test 89% satisfactory level. This study is an excellent contribution to the Bengali text generation research.
An Image Denoising Algorithm Based on Singular Value Decomposition and Non-local Self-similarity

Image denoising is a basic but important step in image pre-processing, computer vision, and related areas. Based on singular value decomposition (SVD) and non-local self-similarity, This paper proposed an image denoising algorithm which is simple in computation. The proposed algorithm is divided into three steps: firstly, the block matching technique is used to find similar patches to construct one matrix, which is of low rank; secondly, SVD is performed on this matrix, and the singular value matrix is processed by principal component analysis (PCA); finally, all similar patches are aggregated to retrieve the denoised image. Since the noise in the image will affect the computation of similar patches, this procedure is iterated many times to enhance the performance. Simulated experiments on different images show that the proposed algorithm performs well in denoising images. Compared with most denoising algorithms, the proposed algorithm is of high efficiency.
Study on the Possibility of Detecting Objects in Real Time on a Mobile Robot

Today, a task of current interest in the field of artificial intelligence in digital image processing is the detection of objects using a convolutional neural network. The purpose of this work is to study the processing of video stream in real-time with the help of a modified tracking module on the client-server system used in robotic complexes. The modified tracking module proposed in this paper, which is a combination of the KCF and SORT algorithms, eliminates object detection duplicates and levels the low speed of the convolutional neural network. By measuring the operating time of each module in the system, was obtained the frame rate of each module. The obtained time characteristics of the client-server system modules confirm the effectiveness of the proposed modified tracking module. The practical significance of the work consists of the hypothesis confirmation is about reducing the impact of the object detection rate on the overall performance of the system when using the tracking module.
Extension Support Vector Machine for Extension Classification

Support vector machine (SVM) method has been successfully applied to classification, in which a classifying rule between features and class is pursued. Sometimes, a specific transformation can influence the feature values of an example, according to the classifying rule, further leading to a possible change of its corresponding class. How to apply such examples to obtain knowledge about the transformation is a new issue, called extension classification (EC). In this paper, an extension support vector machine (ESVM) is developed, in which a mathematic model involving a series of sub-models between feature values of the examples before and after transformation is considered. Dependent function is established to replace the classifying rule. Based on dependent function, nonlinear constraints are requested to ensure the reasonability of the model, in which sub-models are coupled with each other. By introducing the concept of dependent radius, each original constraint is decoupled into several linear sub-constraints so that each sub-model can be established independently. For each sub-model, a non-convex example-dependence loss function is proposed and concave-convex programming (CCCP) procedure is suggested to process the non-convexity. Experimental results on two cases confirm the effectiveness of the ESVM.
Recognition Matrix for Comparing Pages of Text by a Robot

A method is proposed for express estimation of the degree of similarity of two pages of text with the help of recognition matrices in the conditions of a shortage of computing resources of an autonomous robot. The method is based on the geometrical approach of finding the hashes of words, taking into account their location on the page. An algorithm for generating recognition matrices of hashes of words and their coordinates on the page is proposed. The method and algorithm for comparing matrices using a single pass based on the method of a sweeping curve are considered. The assessment of the contribution of quantitative and qualitative factors in the formation of the magnitude of the degree of similarity.
FDE as a Base for Constructive Logic

In this paper I explore whether First Degree Entailment (FDE) might serve as a suitable base for a constructive logic and a constructive account of language roughly along the lines of Michael Dummett’s project. I present three interpretation of FDE’s truth values that hold some promise in this setting. The paper also includes an extensive critical discussion of work by Shramko, Dunn and Takenaka on constructive readings of FDE-like values and the possible need to further extend the space of truth values.
Can A User Guess What Her Followers Want?

Whenever a social media user decides to share a story, she is typically pleased to receive likes, comments, shares, or, more generally, feedback from her followers. As a result, she may feel compelled to use the feedback she receives to (re-)estimate her followers' preferences and decide which stories to share next to receive more (positive) feedback. Under which conditions can she succeed? In this work, we first investigate this problem from a theoretical perspective and then provide a set of practical algorithms to identify and characterize such behavior in social media.
Classification and Risk-Mapping of River Water Quality in Surabaya with Semantic Visualitzation

River water pollution is one of the environmental problems that occur in Surabaya. The amount of industrial waste and household waste makes Surabaya River water easily polluted every day, besides that there are also many people who are not aware about the quality of river water in Surabaya. In this paper, we present a new system to classify water quality of river in surabaya. The system involve a semantic visualization of risk-mapping for the river, so that the people of Surabaya are easier to get information about the quality of Surabaya River water. In this paper, we measured the water quality of Surabaya River using Horiba sensor measuring instruments using 5 parameters, namely temperature, PH, DO, Turbidity, TDS. These five parameters are input variables for calculating water quality with the methods applied in this research. We use the Storet Method to determine the quality of Surabaya River water. The results of the Storet Method explained that there were 0.03% of the data on lightly polluted water quality and there were 37.41% of the data being moderately polluted and there were 59.29% of the data heavily polluted. The results of the calculation using the Storet method concluded that the condition of Surabaya River water quality was not good. We also apply the rule of the Storet Method to the Neural Network by using Surabaya River water quality data as learning data and gave performance 70.02% accuracy.
Smart Recognition System for Business Predictions (You Only Look Once – V3) Unified, Real-Time Object Detection

Object recognition is viewed as a standout amongst the most difficult issues in this field of PC vision, as it includes the mix of item arrangement and object confinement inside a scene. Lately, Deep Neural Network systems (DNNs) have been shown to accomplish better item discovery execution through different methodologies, with YOLOv2 (an enhanced You Only Look Once model) being one of the cutting edges in DNN-based object location strategies in both speed and precision. Even though YOLOv2 can accomplish constant execution on a ground-breaking GPU, it stays exceptionally trying to utilize this methodology for continuous item identification in video on installed figuring gadgets with constrained computational power and restricted memory. In this paper, we propose another system called Fast YOLOv3, a quick You Only Look Once structure that quickens YOLOv2 to have the capacity to perform object location in video on inserted gadgets in a continuous way. To begin with, we use the developmental profound knowledge system to advance the YOLOv3 engineering and deliver an improved design that has 2.8× fewer parameters with only an ~2% IOU drop. To additionally lessen control utilization on installed gadgets while looking after execution, a movement versatile surmising technique is brought into the proposed Fast YOLO structure to decrease the recurrence of profound derivation with O-YOLOv3 dependent on transient movement qualities.