Reviewerly/exHarmony huggingface dataset:
In Defence of Design Patterns for AI Planning Knowledge Models

Design patterns are widely used in various areas of computer science, the most notable example being software engineering. They have been introduced also for supporting the encoding of automated planning knowledge models, but up till now, with little success.In this paper, we investigate the merits of design patterns, as an example of the broader class of reusable abstractions, in the automated planning context; particularly, we aim at drawing attention to their potential usefulness for the explainability of domain-independent planning systems. Further, we argue that to foster the use of design patterns, there is a need for a centralised repository, and we describe the functionalities that such repository should provide to support knowledge engineers.
The quality of training future teachers in the context of digitalization of education

Modern information technologies as a product of scientific research essentially determine the future of education. They laid the foundation for the development of the world and the appropriate conditions for people to adapt to it, influencing all aspects of their lives. Hence, they play an important role in the workplace, business, education and entertainment. Various recent events in the world, including the COVID-19 epidemic, have shown the importance of effective use of modern information technologies in the educational process. Many governments have initiated global investments in information communications technology to improve teaching and learning in schools. The ability to effectively integrate ICT into the learning process is an important competence that a future teacher must master. This directly affects the quality of training students in pedagogical specialties. The article presents the results of a study to identify the opinions of participants in the educational process regarding the value of using ICT in their educational process. We considered the experience of the Batumi State University named after Shota Rustaveli in conducting training courses on information and communication technologies as part of the training of future teachers. Future teachers gain knowledge through actions and strategies, knowledge of which will contribute to improving the quality of education, motivating students and their involvement in the learning process.
OntoVidRec: A Staged Knowledge Aggregation Scheme for Annotations-Based Video Retrieval Using Ontology Matching

The amounts of multimedia data on the Web have increased in recent times and there are very few approaches for video recommendations. There is a need for a semantically driven video recommendation system as the current structure of the Web is progressing into a rational Semantic Web. The increase in the number of videos has also enhanced the annotations and the metadata associated with the video recommendation system. Due to the luxury of the availability of an increase in the amounts of metadata on the Web specifically for the video contents, an annotations-based video retrieval scheme by aggregating the meta information of the video is a mandated requirement as it can reduce the computational complexity in video recommendation than the traditional content-based video recommendation systems. In this paper, an OntoVidRec framework which aggregated video-related meta information from the query, user profiles, and the dataset has been proposed. The approach builds formal ontologies from the individual sources and encompasses a strategic model for Ontology Matching to yield the most appropriate Query relevant Ontological Entities that are semantically matched with the video annotations and are recommended based on the semantic similarity and the Kullback-Leibler divergence measure. An overall F-Measure of 95.37% has been achieved by the OntoVidRec which is the best in class performance for such systems.
PPCSA: Partial Participation-Based Compressed and Secure Aggregation in Federated Learning

Federated Learning (FL) enables users devices (UDs) to collaboratively train a Deep Learning (DL) model on an individual’s gathered data, without revealing their privacy sensitive information to the centralized cloud server. Those UDs usually have limited data plans with a slow network connection to a centralized cloud server, which causes limited communication bandwidth between the contributing mobile users. To mitigate this problem, we propose a novel Partial Participation-based Compressed and Secure Aggregation (PPCSA) algorithm. To implement the PPCSA, we use a Sparse Compression Operator (SCO) that reduces the communication bits between the cloud server and the users while maintaining the FL requirements. In particular, PPCSA utilizes a novel compression method and introduces a Local Differential Privacy (LDP) based framework to achieve the communication-efficiency at a new level. Our experiments on a commonly used FL dataset show that PPCSA distinctively outperforms the state-of-the-art schemes in terms of convergence accuracy and communication bits.
Multi Speaker Speech Synthesis System for Indonesian Language

Generally, text-to-speech models only produce voice from a single speaker. The most straightforward method to produce another speaker's voice, is to build a standalone synthesis model for each desired speaker's voice. But such approach needs large amount of training data and computational resource. To overcome the problem, several architectures has been successful in producing synthesized speech from various speakers efficiently in terms of data and computation. One of the architectures is Deep Voice 3. In this work, a multi speaker speech synthesis system is built for Indonesian language. The system is using Deep Voice 3 architecture, with several additional components for preprocessing dan post-processing. Some of the components are specifically implemented for Indonesian language. The system is built using a multi speaker dataset, consists of speech data from 145 Indonesian speaker. This system is evaluated subjectively to assess naturalness, similarity to original speaker, and intelligibility of the produced speech. The result shows that the system has MOS (mean opinion score) of 3.39 for speech naturalness dan 3.11 for speech similarity. In assessing speech intelligibility using SUS (semantically unpredictable sentence), the test gives 73.88% for sentence accuracy and 93.48% for word accuracy.
Formation of a mixture with the specified quality characteristics from the available small batches of multicomponent mixtures, provided that the total batch of the largest volume is created

Abstract Statements, algorithms of the decision and the corresponding examples of tasks of forming of new mixes with the set characteristics or closest to the set characteristics from the available parties of multicomponent mixtures are given in work. The specified tasks have arisen in the course of the analysis of problem of drawing up by livestock complexes of the balanced nutritious mixes from the available small consignments of mixes. Solutions of the given tasks can be demanded also in other spheres of production. For example, in metallurgy at production of alloys, pharmacology at production of drugs.
Success Factors of Distributed Software Development Projects in Israel

Geographically Distributed Development (GDD) has now become a prominent software development paradigm. Software companies are increasingly adopting geographically distributed development approaches to produce high quality software [1]. geographically distributed development ’s popularity has attracted some research, but research work related to global distributed software development performed in Israel is limited. This contrasts with the relatively large-scale distributed software development conducted in Israel or by Israeli companies. Therefore, this work aims at better understanding the success factors of geographically distributed development in Israel. The research is done using triangulation and comparison of success factors identified by the literature and the opinions of software development practitioners, experts, and managers in Israel. Our findings show that in addition to worldwide common geographically distributed development success factors, some success factors are unique to Israel.
Synchronization of Spontaneous Eyeblink during Formula Car Driving

Formula car racing is a highly competitive sport. Previous studies have investigated the physiological characteristics and motor behaviors of drivers; however, little is known about how they modulate their cognitive states to improve their skills. Spontaneous eyeblink is a noteworthy factor because it reflects attentional states and is important for drivers to minimize the chance of losing critical visual information. In this study, we investigated whether the blink rate, blink synchronization among laps in each driver, and synchronization across drivers were related to their performance. Toward this end, we recorded the blinks and car behavior data of two professional drivers in quasi-racing environments. The results showed higher synchronization in higher-performance laps of each driver and across drivers but no significant change in blink rate. These results suggest that blink synchronization could reflect the changes in performance mode during formula car driving.
Image Registration with Optimal Regularization Parameter Selection by Learned Auto Encoder Features

In this paper, we propose a method that optimizes a regularization parameter for the regularized Free Form Deformation (FFD) non-rigid image registration. The developed process utilizes autoencoder generated image representations to assess image data generalization quality by the regularization parameter. Both pixel intensity and learned features are used to improve the overall accuracy and regularity of the resulting inverse problem solution. We implement the new selection criterion with its use in the non-rigid image FFD registration based on multi-level Bspline with L2-regularization, and validate the method with synthetic and real histopathology image datasets. Both qualitative and quantitative results suggest the efficacy of our developed method for fine-tuning histopathology microscope images.
Performance trade-offs of hybrid cryptosystem for medical images encryption - decryption

With the growth of Computer Network and Communication Technology, medical images need to be exchanged safely by public communication networks. High security and high speed of image transmission become much more important. In this paper, an improved AES-ECC hybrid cryptosystem is proposed to be implemented in the DE2-115 board combining benefits of the Advanced Encryption Standard (AES) with CTR mode to accelerate data encryption and the Elliptic curve cryptography (ECC) to secure the exchange for symmetric session key. To reduce the execution time in the encryption process, both shift rows and mix columns operation in the AES are removed and replaced with a random permutation. Only six rounds of encryption are performed in a loop. In the ECC, both point addition and point doubling are optimized to use only two reused multipliers. Our hardware implementation on ALTERA CYCLONE IV.E FPGA, uses only 14% of logic elements achieving a maximum frequency of 236 MHz and consumed 1.35 mw of thermal power dissipation. Security analysis is successfully performed and our experiments prove that the suggested technique provides the basis of cryptography with more simplicity and correctness.
Robust and Verifiable Information Embedding Attacks to Deep Neural Networks via Error-Correcting Codes

In the era of deep learning, a user often leverages a third-party machine learning tool to train a deep neural network (DNN) classifier and then deploys the classifier as an end-user software product (e.g., a mobile app) or a cloud service. In an information embedding attack, an attacker is the provider of a malicious third-party machine learning tool. The attacker embeds a message into the DNN classifier during training and recovers the message via querying the API of the black-box classifier after the user deploys it. Information embedding attacks have attracted growing attention because of various applications such as watermarking DNN classifiers and compromising user privacy. State-of-the-art information embedding attacks have two key limitations: 1) they cannot verify the correctness of the recovered message, and 2) they are not robust against post-processing (e.g., compression) of the classifier.
Ontology-Based Map Data Quality Assurance

A lane-level, high-definition (HD) digital map is needed for autonomous cars to provide safety and security to the passengers. However, it continues to prove very difficult to produce error-free maps. To avoid the deactivation of autonomous driving (AD) mode caused by map errors, ensuring map data quality is a crucial task. We propose an ontology-based workflow for HD map data quality assurance, including semantic enrichment, violation detection, and violation handling. Evaluations show that our approach can successfully check the quality of map data and suggests that violation handling is even feasible on-the-fly in the car (on-board), avoiding the autonomous driving mode's deactivation.
Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation

Automotive scene understanding under adverse weather conditions raises a realistic and challenging problem attributable to poor outdoor scene visibility (e.g. foggy weather). However, because most contemporary scene understanding approaches are applied under ideal-weather conditions, such approaches may not provide genuinely optimal performance when compared to established a priori insights on extreme-weather understanding. In this paper, we propose a complex but competitive multi-task learning approach capable of performing in real-time semantic scene understanding and monocular depth estimation under foggy weather conditions by leveraging both recent advances in adversarial training and domain adaptation. As an end-to-end pipeline, our model provides a novel solution to surpass degraded visibility in foggy weather conditions by transferring scenes from foggy to normal using a GAN-based model. For optimal performance in semantic segmentation, our model generates depth to be used as complementary source information with RGB in the segmentation network. We provide a robust method for foggy scene understanding by training two models (normal and foggy) simultaneously with shared weights (each model is trained on each weather condition). Our model incorporates RGB colour, depth, and luminance images via distinct encoders with dense connectivity and features fusing, and leverages skip connections to produce consistent depth and segmentation predictions. Using this architectural formulation with light computational complexity at inference time, we are able to achieve comparable performance to contemporary approaches at a fraction of the overall model complexity. Evaluation over several foggy weather condition datasets including synthetic and real-world examples illustrates our approach competitive performance compared to other contemporary state-of-the-art approaches.
Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition

The objective of Open set recognition (OSR) is to learn a classifier that can reject the unknown samples while classifying the known classes accurately. In this paper, we propose a self-supervision method, Detransformation Autoencoder (DTAE), for the OSR problem. This proposed method engages in learning representations that are invariant to the transformations of the input data. Experiments on several standard image datasets indicate that the pre-training process significantly improves the model performance in the OSR tasks. Moreover, our analysis indicates that DTAE can yield representations that contain some class information even without class labels.
PENGARUH PENERAPAN MEDIA PEMBELAJARAN POP-UP BOOK TERHADAP HASIL BELAJAR SISWA SEKOLAH DASAR

&lt;p&gt;&lt;em&gt;This research is motivated by the low student learning outcomes. The purpose of this study was to determine the differences in post-test results in a class using Pop-up book learning media with a class that did not use Pop-up book learning media and to find out the difference in the improvement of student learning outcomes in science class II SDN 1 Winduhaji. The method used in this research is a quasi-experimental design nonequivalent control group design. The research subjects consisted of two classes, class A consisting of 23 students as the experimental class and class B consisting of 23 students as the control class. The research instrument used a written test in the form of multiple choice questions. Furthermore, analysis of instruments and data analysis is carried out. Data analysis in the control class and experimental class using SPSS version 22 assistance. Data analysis in the experimental class and control class shows that the sig (2-tailed) value is 0.000 &amp;lt;0.05. So the results of the study show that there are posttest differences in student learning outcomes between students who use Pop-up book learning media and students who don't use Pop-up book learning media. Then there is a difference in the increase in student learning outcomes in classes using Pop-up book learning media with an increase in student learning outcomes in classes that do not use Pop-up book learning media. From these data it can be explained that the Pop-up book learning media has a significant effect on student learning outcomes and can be used as an alternative learning media for teachers in improving learning outcomes.&lt;/em&gt;&lt;/p&gt;
Harmonization Centered Ensemble For Small And Highly Imbalanced Medical Data Classification

Deficient and highly imbalanced medical data often makes it challenging for accurate classification without bias. In this paper, we propose an ensemble learning framework to harmonize the discriminating information between the majority class and minority class, which is called Harmonization Centered Ensemble (HCE). The proposed framework leverages the classification hardness and neighborhood information concept during the under-sampling procedure to emphasize the importance of the minority class. It also applies the boosting technique, which refines the ratio of easily distinguishable and difficult to distinguish the majority class to calibrate the deficient attention on majority class. Extensive experimental results indicate that our framework outperforms state-of-the-art under-sampling ensemble frameworks on two highly imbalanced medical datasets with small data size, and offers more balanced performance for different target classes.
Engineering Slidable Graphical User Interfaces with Slime

Intra-platform plasticity regularly assumes that the display of a computing platform remains fixed and rigid during interactions with the platform in contrast to reconfigurable displays, which can change form depending on the context of use. In this paper, we present a model-based approach for designing and deploying graphical user interfaces that support intra-platform plasticity for reconfigurable displays. We instantiate the model for E3Screen, a new device that expands a conventional laptop with two slidable, rotatable, and foldable lateral displays, enabling slidable user interfaces. Based on a UML class diagram as a domain model and a SCRUD list as a task model, we define an abstract user interface as interaction units with a corresponding master-detail design pattern. We then map the abstract user interface to a concrete user interface by applying rules for the reconfiguration, concrete interaction, unit allocation, and widget selection and implement it in JavaScript. In a first experiment, we determine display configurations most preferred by users, which we organize in the form of a state-transition diagram. In a second experiment, we address reconfiguration rules and widget selection rules. A third experiment provides insights into the impact of the lateral displays on a visual search task.
A Novel Method of Light Source Calibration under Near-field Lighting Condition

Abstract The paper presents a novel method of photometric stereo calibration that works under near-field condition, and eight LED lights are installed on the system. In order to estimate the pose of the light source, light source position estimation based on iPad imaging is proposed. However, it is difficult to estimate principal optical axis direction of the light source which would influence the reconstruction result. Considering the problems of above, the new convolution kernel is used to find the brightest point on the chessboard paper, and the principal optical axis direction of light source can be precisely calculated according to geometric transformation. The experiments show the high-quality of 3-D reconstruction can be obtained by using the novel calibration method.
Prediction of the Quantity of Boxed Meals Preparation by High-Speed Train Based on ELM

Abstract Meal service is an important part of the railway transportation service, and when preparing the number of boxed meals before departure, the train must meet the needs of the train passengers for self-catering and food service. The demand of meals avoids causing too much waste. Extreme learning machine has the characteristics of fast learning speed and generalization ability, and has a wide range of applications in neural network prediction, this paper from the train perspective, by applying and improving the ELM model and using some of the high-speed train data from the Beijing-Qingdao section for model training. A prediction error analysis was performed to verify the reasonableness and feasibility of the model for the prediction of boxed meals.
Telling and the reasons of testimony*

Philosophy and Phenomenological ResearchVolume 102, Issue 3 p. 708-714 SYMPOSIUM Telling and the reasons of testimony* Sanford C. Goldberg, Corresponding Author s-goldberg@northwestern.edu Northwestern University Correspondence Sanford C. Goldberg, Northwestern University. Email: s-goldberg@northwestern.eduSearch for more papers by this author Sanford C. Goldberg, Corresponding Author s-goldberg@northwestern.edu Northwestern University Correspondence Sanford C. Goldberg, Northwestern University. Email: s-goldberg@northwestern.eduSearch for more papers by this author First published: 25 May 2021 https://doi.org/10.1111/phpr.12775 * I thank Matt Chrisman, Peter Graham, and Jennifer Lackey for helpful discussions of Moran’s work. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Volume102, Issue3May 2021Pages 708-714 RelatedInformation
Classification and Spam Image Detection in Smartphones

Use of social media applications have increased exponentially and sharing of images has increased tremendously. Applications like WhatsApp shares 4.5 billion photos on average a day as of date. These images are a mixture of important and unimportant images. Thus, it becomes infeasible for a user to organize and review these bulks of images and may lead to storage inefficiencies. So a solution is presented on this issue of inefficient storage. This issue is addressed by providing a user-friendly interface to users involving categorization of images into number of categories. In the proposed solution the quality of images is improved so as to help in the accurate classification of text images. Finally users are provided with classification of images to be deleted for review.