Reviewerly/exHarmony huggingface dataset:
Chapter 8. A Constraint-Based Approach to Learning and Reasoning

Neural-symbolic models bridge the gap between sub-symbolic and symbolic approaches, both of which have significant limitations. Sub-symbolic approaches, like neural networks, require a large amount of labeled data to be successful, whereas symbolic approaches, like logic reasoners, require a small amount of prior domain knowledge but do not easily scale to large collections of data. This chapter presents a general approach to integrate learning and reasoning that is based on the translation of the available prior knowledge into an undirected graphical model. Potentials on the graphical model are designed to accommodate dependencies among random variables by means of a set of trainable functions, like those computed by neural networks. The resulting neural-symbolic framework can effectively leverage the training data, when available, while exploiting high-level logic reasoning in a certain domain of discourse. Although exact inference is intractable within this model, different tractable models can be derived by making different assumptions. In particular, three models are presented in this chapter: Semantic-Based Regularization, Deep Logic Models and Relational Neural Machines. Semantic-Based Regularization is a scalable neural-symbolic model, that does not adapt the parameters of the reasoner, under the assumption that the provided prior knowledge is correct and must be exactly satisfied. Deep Logic Models preserve the scalability of Semantic-Based Regularization, while providing a flexible exploitation of logic knowledge by co-training the parameters of the reasoner during the learning procedure. Finally, Relational Neural Machines provide the fundamental advantages of perfectly replicating the effectiveness of training from supervised data of standard deep architectures, and of preserving the same generality and expressive power of Markov Logic Networks, when considering pure reasoning on symbolic data. The bonding between learning and reasoning is very general as any (deep) learner can be adopted, and any output structure expressed via First-Order Logic can be integrated. However, exact inference within a Relational Neural Machine is still intractable, and different factorizations are discussed to increase the scalability of the approach.
Retrofitting Video Surveillance Systems using Deep Learning Technologies

Steps toward the development of an embedded system that is able to enhance the capabilities of a video surveillance system are described within the proposed paper. The hardware support for our solution is based on the newly introduced Google Coral development board having, as a prominent feature, the on-board Edge Tensor Processing Unit, capable of execute state-of-the-art mobile vision models such as MobileNet v2 at 100+ FPS, in a power efficient manner. The Google Coral is proven to outperform competing Machine Learning hardware accelerators currently available on the market and so represents the best accuracy/power efficiency choice. The software part includes some deep learning-based modules for video analytics such as detection, classification and tracking objects, including cars and people, using MobileNet + SSD architecture. Also, the experimental setup is described: compiling OpenCV for Google Coral, connecting to the video stream of the surveillance cameras. The system supports video equipment by any manufacturer by interfacing to it using Real Time Streaming Protocol/Open Network Video Interface Forum connections.
Interests of pupils of the third and fourth grade of the high school for reading

The culture of reading, and interests of students in the school reading, changes from generation to generation. We have opted for gymnasium students of the third and fourth grade because we regard them as active readers with developed reading tastes and habits. Students who have home libraries and are surrounded by books, and whose parents have a higher level of education, read more on average than those who have no books at home and live in a less developed cultural and economic environment. Information about student literary interests is set as a methodological requirement and growing scientific attention is paid to it. The paper analyses the research findings on reading interests of students of the third and fourth grade of the general secondary education in schools in the City of East Sarajevo. All data should indicate: the level of reading culture that has been achieved; whether students regularly read the required reading and works outside the compulsory programme; which topics they are interested in; which works they prefer to read; and whether there are differences in interests depending on the gender of students, their academic achievements, and family and socioeconomic opportunities. The method of systematic empirical non-experimental research was applied.
Multivalent Entailment Graphs for Question Answering

Drawing inferences between open-domain natural language predicates is a necessity for true language understanding.There has been much progress in unsupervised learning of entailment graphs for this purpose.We make three contributions: (1) we reinterpret the Distributional Inclusion Hypothesis to model entailment between predicates of different valencies, like DEFEAT(Biden, Trump) WIN(Biden);(2) we actualize this theory by learning unsupervised Multivalent Entailment Graphs of open-domain predicates; and (3) we demonstrate the capabilities of these graphs on a novel question answering task.We show that directional entailment is more helpful for inference than non-directional similarity on questions of fine-grained semantics.We also show that drawing on evidence across valencies answers more questions than by using only the same valency evidence.
Artificial neural network

Chapter Contents: 4.1 Introduction 4.2 Artificial neural networks 4.2.1 Artificial neuron 4.3 Activation functions 4.3.1 Sigmoid function 4.3.2 Hyperbolic tangent function (tanh) 4.3.3 Rectified linear unit function 4.3.4 The bias 4.4 Forward and backward propagation 4.4.1 The gradient descent 4.4.2 Description 4.4.3 Stochastic gradient descent 4.4.4 Full batch gradient descent 4.4.5 Mini-batch gradient descent 4.4.5.1 Tips 4.4.6 Batch normalisation 4.4.7 The vanishing gradient 4.4.7.1 Solutions 4.4.8 The gradient explosion 4.4.8.1 Solution 4.4.9 The back propagation 4.4.10 A numerical example 4.5 Overfitting and underfitting 4.5.1 Bias and variance 4.5.2 Early stopping of training 4.5.3 Dropout 4.6 Regularisation techniques 4.6.1 Parameter norm penalties 4.6.1.1 Regularisation L2 4.6.1.2 Regularisation L1 4.7 Conclusions References
Neutrosophic Bi-topological Group

Neutrosophic topological groups are neutrosophic groups in an algebraic sense together with neutrosophic continuous group operations. In this article, we have presented neutrosophic bi-topological groups with illustrative examples. We have also defined eight new models of neutrosophic bi-topological groups. Neutrosophic bi-topological group that depends on two neutrosophic topologies group is more general than the neutrosophic topological group. Finally, Some basic properties of neutrosophic bi-topological groups were studied.
Implementaion for Used Trading Management System Based BlockChain (Case: Used Car)

Internet on e-commerce management system is gradually increasing and various items in the transaction made it. In particular, the used car market scale continuously growing there. But online on sale transactions and the car as high the transaction the seller and purchaser between reliability is very important. Several of the areas used transactions, but the amount is big owned the car for transaction management system by developing reliable improvement was increased. This system uses smart contracts to ensure reliability based ethereum without third party intervention. In addition, this system mitigated information asymmetry between buyers and sellers, and reduced and prevented brokerage fees in the distribution process without third parties.
Data Journalist : One Data Indonesia (SDI) Weak of Data

ABSTRACT&#x0D; The government built Satu Data Indonesia (SDI) as part of implementing the principle of open government. Through SDI, all data from the government from in Indonesia including data from government agencies can be easily accessed by visiting the data.go.id website.&#x0D; This situation should be a good ecosystem to develop data journalism in Indonesia, because the government provides abundant data. This study aims to see the implementation of SDI from the perspective of data journalists. Data journalist is a profession that is directly related to SDI, they are tasked with managing data to be presented to the public in a simple and easy-to-understand manner for the general public. By interviewing data journalists from Katadata and Lokadata (two online media who declare themselves as data journalism), this qualitative research is expected to provide constructive input on the implementation of One Data Indonesia. In general, the existence of SDI is appreciated by data journalists. But data journalists from Katadata and Lokadata do not use SDI as part of their news-seeking activities. They see SDI has basic problems, the lack of data availability, data relevance problems, problems with easy data access, no data updates (updates) to data reliability. In addition, problems with data updating and data reliability, on the other hand, not all regions in Indonesia are connected to SDI. As a result, data journalists do not use Satu Data Indonesia as an instrument to make news. They prefer to dig up data through data mining on the internet and collect data gradually in their data bank for processing at a later date.&#x0D; &#x0D; Keywords: One Data Indonesia, Open Government, Online Media, Data Journalism
Efficient Pre-Processing Techniques for Improving Classifiers Performance

Data pre-processing plays a vital role in the life cycle of data mining for accomplishing quality outcomes. In this paper, it is experimentally shown the importance of data pre-processing to achieve highly accurate classifier outcomes by imputing missing values using a novel imputation method, CLUSTPRO, by selecting highly correlated features using Correlation-based Variable Selection (CVS) and by handling imbalanced data using Synthetic Minority Over-sampling Technique (SMOTE). The proposed CLUSTPRO method makes use of Random Forest (RF) and Expectation Maximization (EM) algorithms to impute missing. The imputed results are evaluated using standard evaluation metrics. The CLUSTPRO imputation method outperforms existing, state-of-the-art imputation methods. The combined approach of imputation, feature selection, and imbalanced data handling techniques has significantly contributed to attaining an improved classification accuracy (AUC curve) of 40%–50% in comparison with results obtained without any pre-processing.
Sign Language Recognition System

We witness many people who face disabilities like being deaf, dumb, blind etc. They face a lot of challenges and difficulties trying to interact and communicate with others. This paper presents a new technique by providing a virtual solution without making use of any sensors. Histogram Oriented Gradient (HOG) along with Artificial Neural Network (ANN) have been implemented. The user makes use of web camera, which takes input from the user and processes the image of different gestures. The algorithm recognizes the image and identifies the pending voice input. This paper explains two way means of communication between impaired and normal people which implies that the proposed ideology can convert sign language to text and voice.
An AR/VR-Hybrid Interaction System for Historical Town Tour Scenes Incorporating Mobile Internet

This paper introduces a digital interaction system enabled by hybrid augmented and visual reality (AR/VR) technology for historical town tour scenes. The proposed system combines AR, VR and mobile Internet technologies together to enable online digital interaction, so that to improve the user experience. By using the unmanned airborne vehicle (UAV) photography method, detailed panoramic views of various scenic spots in a historical town are captured. Moreover, the three-dimensional (3D) models of several cultural landmarks are also collected to enhance the elementary information for online interaction. Subsequently, the panoramic views of various scenic spots are utilized as the visual reality scene to set up a complete and continuous route for tour guidance. Then, the 3D models are presented in the forms of augmented reality and online viewing, which can provide users with novel and rich experience.
Trustworthy collaborative trajectory scheme for continuous LBS

With the high demand for using location-based services (LBSs) in our daily lives, the privacy protection of users' trajectories has become a major concern.When users utilise LBSs, their location and trajectory information may expose their identities in continuous LBSs.Using the spatial and temporal correspondences on users' trajectories, adversaries can easily gather their private information.Using collaboration between users instead of location service providers (LSPs) reduces the chance of revealing private information to adversaries.However, there is an assumption of a trusting relationship between peers.In this paper, we propose a trustworthy collaborative trajectory privacy (TCTP) scheme, which anonymises users' trajectories and resolves the untrustworthy relationship between users based on peer-to-region LBSs.Moreover, the TCTP scheme provides query content preservation based on a fake query concept in which we conceal the user's actual query among a set of queries.The results of several experiments with different conditions confirm that our proposed scheme can protect users' trajectory privacy successfully in a trustworthy and efficient manner.
Using Deep Learning for the Image Recognition of Motifs on the Center of Sukhothai Ceramics

The motifs on the center of Sukhothai ceramics are essential elements for determining the age of the ceramics. Sukhothai ceramics in each kiln were made with different pattern production techniques, and thus one specific pattern appears only in a particular kiln. Thus, archaeologists can determine which ceramic was produced from which particular kiln site by investigating its motif. Motif identification requires a well-experienced expert to identify the tracery of the pattern on the center of a ceramic. Thus, identifying such archaeological evidence is complex even for general archaeologists. The aim of this research was to study the use of deep convolutional neural networks for classifying seven motif patterns on the center of Sukhothai ceramics (i.e. Chrysanthemum bouquet, Classic scroll, Conch shell, Fish pattern, Flower head pattern, Printed Chrysanthemum head, and Tibetan Buddhist vajra ). We collected a new dataset, including 557 images of ceramics, from two kiln sites. Each ceramic’s motif was labeled by Thai ceramic experts. The collection of the motifs on the center of the Sukhothai ceramic dataset was called CMC Sukhothai Ceramic Dataset. The efficiency of the motif identification on the center of Sukhothai ceramics was evaluated by comparing five pretrained convolutional neural network models: DenseNet121, InceptionV3, VGG16, GoogLeNet, and AlexNet. Then, the models that were efficient for our dataset were selected and trained by fine tuning. Results showed that the motif recognition of VGG16 + our classification layers generated the best efficiency at 500 epochs of training and 86.54% of accuracy in the test dataset.
Pixel Transformations

In the previous chapter, we introduced a model for on-screen images that allowed us to load them from files and to change them by moving their pixels around. In this chapter, we will modify images by changing the colors of individual pixels.
THE EFFECTIVENESS OF MIND MAPPING TECHNIQUE IN ENHANCING STUDENTS’ READING ACHIEVEMENT

Reading is considered challenging for high students in which students are not massively exposed to English input. Despite the challenge, the students are required to have good reading ability. One of the most proposing techniques is mind mapping. This research aims to reveal to what extent the mind mapping technique enhances students’ reading achievement. Moreover, the research was quasi-experimental research which involved one experimental groups and one control group. The samples were tenth grade students at SMAN 2 Kuala Kapuas, Central Kalimantan. Moreover, mind mapping technique served as the independent variable; while, students’ achievement in reading served as dependent variable. The findings revealed that the mind mapping technique enhances students’ reading achievement. In other words, it signifies the effectual mind mapping technique in enhancing students’ reading achievement
Analog Circuits Sizing Using Multi-Objective Evolutionary Algorithm Based on Decomposition

Several analog circuit design has been suggested where a layout generator is used after a circuit sizing. But, many iterations between circuit sizing and layout generator stages are needed to obtain desired specifications. This paper proposes a new equation and simulation-based method for circuits sizing of CMOS operational amplifiers (op-amps) by considering layout effects. In the proposed method, layout effects are considered during the sizing step. Layout effects are devices parasitics and geometry information that are extracted from a new automated layout generator. Optimization is performed using multi-objective evolutionary algorithm based on decomposition (MOEA/D). In order to evaluate the performance of the proposed sizing method, the design of folded-cascode and three-stage op-amps are provided in a 0.18µm process CMOS technology with 1.8 V supply voltage. The simulation results exhibit the good performance of the proposed sizing method.
The problem of providing psychological comfort in the digital educational environment of rural schools

Currently, digitalization has penetrated into all spheres of society, including education. At the same time, the use of digital technologies has not only positive, but also negative sides, which affects the quality of interaction of all subjects of the educational process. Some aspects of the digitalization of education reduce the psychological comfort of students, parents and teachers. The purpose of this study is to identify and describe the existing difficulties, risks, barriers, major changes and prospects of rural schools in the context of digital transformation, which affect the psychological comfort of the educational environment. Taking into account various risks and negative factors for students, parents and teachers will create psychologically comfortable quality of the educational environment. It is necessary to take into account the content of the learning process and the organizational and communicative conditions of the educational environment for optimal interaction of students with the intra-school and external environment. Psychological comfort is a factor of identification of students with teachers in the educational environment of rural school, which contributes to the development of subjectivity of teachers and students. The condition for ensuring psychological comfort is the use of a variety of pedagogical tools by the teacher. The leading methods of studying the problem were the analysis of pedagogical and methodological literature, regulatory documents, and questionnaires. The scientific novelty of the article material is determined by the fact that an attempt is made to analyze the problems of ensuring psychological comfort in the conditions of the digital educational environment of rural school. Teachers, taking into account these risks and barriers, can find positive aspects and opportunities of «numbers» in the education of rural schoolchildren and create conditions for fully realised learning, student development, pedagogical support and interaction with parents. The materials of this article can be used by teachers to improve the quality of the digital educational environment of rural school.
Instrumentation of an Inspection Test Rig for Geometry Measurement of Fiber Bundles in Automated Composite Manufacturing

The major advantage of products made from composite materials is given by their superior weight-specific mechanical properties. These can be weakened by defects induced in the manufacturing process. Therefore, online detection and analysis of the processed fiber bundle geometry is a key factor for the quality assurance of the final part. In this article, the instrumentation and data evaluation for determining the surface geometry of fiber bundles by means of light sectioning was examined. Bundles of glass and carbon fibers were measured continuously on an inspection test rig. Different background materials have been used in order to validate the applicability of the approach. By utilization of a polynomial fitting algorithm, data segmentation of object and baseline was robustly achieved. By means of cross correlation, the data alignment could be evaluated faster and more reliable compared to a method previously presented by us. The information could then be used for the determination of the fiber bundle width, centerline, spatial changes, and oscillations. In addition, unwanted defects as well as lateral movement of the fiber bundles were reliably detected. The information revealed by the proposed algorithm provides the basis for robust online monitoring of fiber bundle geometry in highly automated composite manufacturing processes.
Tool-based persona for designing user interfaces in healthcare

Health sites impact healthcare delivery by using technology to improve healthcare outcomes. System complexity, and the difficulty for doctors to deal with those systems, made it necessary to consider how to understand the mental language of the target users. The purpose of this paper is to create a new Health Site Design tool for designing health sites based on doctors' behaviour, personae, and real-life scenarios. The tool was tested by participants from designing perspectives randomly divided into two groups: control group, who were asked to follow the instructions in watching, attending the tutorial session and then perform the tasks; and study group, who were asked to perform the tasks directly. The study results show no significant difference between participants in both groups for effectiveness and efficiency. The user satisfaction of the participants yielded a score of 84.6 on the system usability scale, mapping it to the A grade.
Empirical Analysis on the Internal Control of Iraqi Electronic Payments System (EPS): Evidence from Ministry of Iraqi Finance

The research aims to study the effect of the electronic payment system on the supervisory controls used in Iraqi banks registered in the Iraq money market using the COBIT framework. Also it aims to identify the discrepancy in the adoption of COBIT by academics and practitioners on the research sample (central bank of Iraq, the Gulf Commercial Bank, the United Investment Bank, and the Cooperative Agricultural Bank). The Control Objectives for Information and related Technology (COBIT) is a framework of generally applicable IS security and control practices for information technology control systems in order to achieve objective. The data been distributed on 89 items that represented by department managers (payments, accounts, information technology, control and inspection). These date were collected and analyzed for each bank under consideration of COBIT accordingly. The results showed that The percentage of the weakness varies between the banks, it is the strongest in the Central Bank of Iraq because it is the owner of the system. In order to improve the performance of Iraqi banks that are listed on Iraq Stock Exchange, international guidelines should be followed.