Reviewerly/exHarmony huggingface dataset:
Towards an Organisation-Centred Semantics for Argumentation-Based Dialogues

Argumentation plays an important role in human social interaction, and it requires participants considering their social relationships and context in order to be effective during dialogues. Recently, argumentation techniques have been applied to multi-agent systems in order to enable rich dialogues in which agents are able to explain their positions. However, although multi-agent systems have evolved to (open) organisation-oriented paradigm, inspired by human societies, introducing social relationship and context for multi-agent systems, few works have considered the context of the participants in argumentation-based dialogues within such organisations. In other words, few works have considered the role of each participant in that society and the relationship between the different roles. In this work, we propose an organisation-centred semantics for argumentation-based dialogues in multi-agent systems, in which agents consider the participants' context in order to define preferences between arguments.
DQN for Multi-layer Game Based Mining Competition in VEC Network

Blockchain has been considered as a promising technology to improve the efficiency of data sharing in Vehicular Edge Computing (VEC) by data mining among RSUs and vehicles. In order to obtain more mining reward, RSUs and vehicles will compete to accomplish the data sharing mining task. However, it is easier for RSUs equipped with stronger computing capabilities to win the mining reward. In this condition, some vehicles provide their own computing resources to form a shared resource pool for the competition with RSUs. Meanwhile, the competition among the vehicles sharing the resource pool is non-negligible since some selfish vehicles divide the group reward but without providing resources. In this way, we provide a multilayer game model that involving a deformed N Iterated Prisoners Dilemma (NIPD) among vehicles and a bargain game between resource pool and RSUs. Further, we use multi-agent Deep Q Network (DQN) to achieve the equilibrium between vehicles and RSUs in mining. Finally, numerical results show the optimal strategy can attain a stable data sharing mining system in VEC network.
Quality Framework for Ontologies Evaluation Based on Structural Characteristics

In the domain of medical, biomedical ontologies play an important role for extracting the information and knowledge about the specific domain and present the problem in more understandable form. Now a day there are a lot of work in done in this field. Also, many evaluation techniques are made to check the correctness or quality of the ontologies. In this paper we proposed an evaluation technique that use the structure characteristics to examine the quality of ontologies. For this we select anatomy domain ontology and apply the proposed evaluation criteria. In our evaluation criteria, we check the quality of ontology on the basis of structured characteristics and compare the results of all ontologies and chose the best one.
Development of Interactive Learning Media based on Android Education Geography

Abstract This research is a type of product development that aims to determine the feasibility of developing interactive learning media products based on Android. The research method used is the design of the development model used in this study is the adaptation of the Luther model. The results of this study are: (1) Android-based interactive learning media applications on national cultural material and global interactions are arranged by getting input from validators (2) the results of the feasibility of the percentage of android-based interactive learning media by media experts are seen based on the aspects of animation, video aspects, video aspects, packaging aspects, packaging aspects, packaging aspects and aspects of use.
Model Of Management Of Resources Production In 4P-Environment Of Project-Oriented Enterprise

In the article authors present model of management of project and operational production what base on using of Monte-Carlo method. The main feature of the model is providing the ability to effective manage every stage of creating and using of resources in project activity of project-oriented enterprise.
Coverage Path Planning (CPP)

The coverage path planning (CPP) problem is determining a path that passes over all the points of an area or volume of interest while avoiding obstacles. Such a path planner falls under the exhaustive area coverage paradigm where visiting everywhere is of utmost importance. This is useful for applications like vacuum cleaning, window cleaning, demining, mowing, harvesting, etc., using mobile robots. This chapter presents several lines of work in this setting, spanning wireless sensing, combinatorics, and robotics.
Cultural Inheritance in Generalized Darwinism

Generalized Darwinism models cultural development as an evolutionary process, where traits evolve through variation, selection, and inheritance. Inheritance describes either a discrete unit’s transmission or a mixing of traits (i.e., blending inheritance). In this article, we compare classical models of cultural evolution and generalized population dynamics with respect to blending inheritance. We identify problems of these models and introduce our model, which combines relevant features of both. Blending is implemented as success-based social learning, which can be shown to be an optimal strategy.
A Journey in (Interpolated) Sound

Graphical interpolation systems provide a simple mechanism for the control of sound synthesis systems by providing a level of abstraction above the parameters of the synthesis engine, allowing users to explore different sounds without awareness of the synthesis details. While a number of graphical interpolator systems have been developed over many years, with a variety of user-interface designs, few have been subject to user-evaluations. We present the testing and evaluation of alternative visualizations for a graphical interpolator in order to establish if the visual feedback provided through the interface, aids the navigation and identification of sounds with the system. The testing took the form of comparing the users' mouse traces, showing the journey they made through the interpolated sound space when different visual interfaces were used. Sixteen participants took part and a summary of the results is presented, showing that the visuals provide users with additional cues that lead to better interaction with the interpolators.
OntoPPI: Towards Data Formalization on the Prediction of Protein Interactions

The Linking Open Data (LOD) cloud is a global data space for publishing and linking structured data on the Web. The idea is to facilitate the integration, exchange, and processing of data. The LOD cloud already includes a lot of datasets that are related to the biological area. Nevertheless, most of the datasets about protein interactions do not use metadata standards. This means that they do not follow the LOD requirements and, consequently, hamper data integration. This problem has impacts on the information retrieval, specially with respect to datasets provenance and reuse in further prediction experiments. This paper proposes an ontology to describe and unite the four main kinds of data in a single prediction experiment environment: (i) information about the experiment itself; (ii) description and reference to the datasets used in an experiment; (iii) information about each protein involved in the candidate pairs. They correspond to the biological information that describes them and normally involves integration with other datasets; and, finally, (iv) information about the prediction scores organized by evidence and the final prediction. Additionally, we also present some case studies that illustrate the relevance of our proposal, by showing how queries can retrieve useful information.
Colorblind-Shareable Videos

The two distinctive visual experiences of binocular display, with and without stereoscopic glasses, have been recently utilized for visual sharing between the colorblind and the normal-vision audiences. However, all existing methods only work for still images, and lack of temporal consistency for video application. In this paper, we propose the first synthesis method for colorblind-sharable videos that possess the temporal consistency for both visual experiences of colorblind and normal-vision, and retains all other crucial characteristics for visual sharing with colorblind. We formulate this challenging multi-constraint problem as a global optimization and minimize an objective function consisting of temporal term, color preservation term, color distinguishability term, and binocular fusibility term. Qualitative and quantitative experiments are conducted to evaluate the effectiveness of the proposed method comparing to existing methods.
Extension of a Task-Based Model to Functional Programming

Recently, efforts have been made to bring together the areas of high-performance computing (HPC) and massive data processing (Big Data). Traditional HPC frameworks, like COMPSs, are mostly task-based, while popular big-data environments, like Spark, are based on functional programming principles. The earlier are know for their good performance for regular, matrix-based computations; on the other hand, for fine-grained, data-parallel workloads, the later has often been considered more successful. In this paper we present our experience with the integration of some dataflow techniques into COMPSs, a task-based framework, in an effort to bring together the best aspects of both worlds. We present our API, called DDF, which provides a new data abstraction that addresses the challenges of integrating Big Data application scenarios into COMPSs. DDF has a functional-based interface, similar to many Data Science tools, that allows us to use dynamic evaluation to adapt the task execution in runtime. Besides the performance optimization it provides, the API facilitates the development of applications by experts in the application domain. In this paper we evaluate DDF's effectiveness by comparing the resulting programs to their original versions in COMPSs and Spark. The results show that DDF can improve COMPSs execution time and even outperform Spark in many use cases.
The Needs for Development of Flash-based Learning Media on Civics Education Study in the Junior High Schools

Abstract Civic Education is designed as a way to develop citizen skills of 21st century. For this reason, Civic Education teachers are required to be creative and innovative in managing and developing their learning. One effort in the development of learning is done by developing flash-based learning media. This study aims to determine the needs of the Civic Education teachers at Junior High Schools in Semarang related to the development of Flash-based learning media. This study uses descriptive quantitative and qualitative methods that collect and analyze data using web-based applications. The results showed that Civic Education teachers desperately needed flash-based interactive learning media. The teacher’s needs are related to these indicators: first, the teacher still has difficulty in determining Flash media that is easy to use, according to the development of students and supports the achievement of competencies, and encourage students to be involved in active learning; second, flash media designed contextually and in accordance with the needs of students, cheap, and easy to make; third, the development of flash-based learning media that can build collaboration between students, be easily accessed individually and in groups, and can be used for learning in schools and independent assignments at home.
Hyperparameter optimization for autoencoders that perform content-based image retrieval

Content-based image retrieval problem is important in a wide range of computer science-based applications. One of the best solutions for this problem is the application of deep autoencoders. The accuracy of a neural network may be significantly dependent on the chosen of hyperparameters. In this work, we applied three methods of hyperparameter optimization (Tree of Parzen Estimator[TPE], annealing, and random search methods) to the recently proposed deep autoencoder architecture that solves the problem of image classification and content-based image retrieval. The parameters of AdamOptimizer were chosen as hyperparameters. Rather than a simple linear relationship between parameters and neural network accuracy, we observe significant difficulties in obtaining convergence with optimal hyperparameter values in all analyzed methods.
Focus on Area Tracking Based on Deep Learning for Multiple Users

Most eye-tracking experiments are limited to single subjects because gaze points are difficult to track when multiple users are involved and environmental factors might cause interference. To overcome this problem, this paper proposes a method for gaze tracking that can be applied for multiple users simultaneously. Four models, including FASEM, FAEM and FAFRCM in the signal-user environment, as well as FAEM and FAMAM in the multiple-user environment, are proposed, and we collected raw data of gazing behaviors to train the models. Through a modified VGG19 architecture and adjusting the Number of Convolutional Layers (NoCL), we obtained and compared the accuracy of various models to determine the most suitable architecture. Since data for multiple-users is not easy to obtain, in this paper, we trained the model first with single users, then extended it to multiple users with transfer learning. Finally, we propose an adaptive method to integrate the benefits of FAEM and FAMAM.
Two-Way Image Based CAPTCHA

CAPTCHA is a technology used to protect web applications from one of the malicious threats called BOT. CAPTCHA’s primary goal is to avail web services only to legitimate users and thereby foreseeing effective usage of web services. In this paper, a variant of image-based CAPTCHA is designed for visually challenged people. The main challenge is to identify human users from software robots. A novel two-way approach for image selection is designed to enhance the existing image-based CAPTCHAs. Randomly generated homography transformation function is used to find a match between the two images. Image-based CAPTCHAs have overwhelmed the text-based CAPTCHAs, and the proposed approach promises a better test for legitimate users to accomplish higher web security while retaining ease of use.
Design and Implementation of Hybrid Plate Tectonics Neighborhood-Based ADAM’s Optimization and Its Application on Crop Recommendation

Agriculture is the primary employment of people in India. Even after years of experience in farming, farmers are not able to take the right decision when it comes to the crop selection. This paper proposes to use plate tectonics neighborhood-based classifier along with Adam’s algorithm for more optimized results. The final aim of this paper is to create an algorithm that will correctly determine the most suitable crop to be grown in a particular region given various input factors. As part of the paper, plate tectonics-based optimization is used in combination with Adam’s algorithm, and a hybrid technique is developed from it. The hybrid is tested on the benchmarks to compare its convergence to global minima in comparison with its predecessor PBO. The classifier that was developed is employed for finding the best class of crops that can be grown in a place given a set of features associated with the place. We also test our proposed algorithm on soybean dataset to predict disease class based on the various symptoms leading to the disease. The Chapter also emphasizes accuracies found on few datasets that are collected online or collected independently. Of the data collected online, it was found to be 90% accurate and had a Cohen Kappa Score of 0.84 from 1 while for the data collected independently, it was 98% accurate and had a Cohen Kappa Score of 0.984 from 1.
Kalman Filter Changepoint Detection and Trend Characterization

In this paper we describe a data-driven change detection algorithm based on the Kalman filter. The algorithm models trends in the underlying data through the use of a Kalman filter. A learning rate is applied to the Kalman filter gain to allow for trends to be locked in. This forces the estimates to rely more on the prediction and less on the observation as time goes on. Statistical thresholds are set to detect a change, i.e. changepoint, and a new Kalman filter is started to track the new trend in the data. We show results using neural electrocorticography time-series data.
Research on Evaluation Index of Energy Efficiency Design of Multi-level Sports Complex Based on Synergistic Effect

Abstract Transforming the single-storey large space of traditional sports buildings into vertical extension space of multi-level venues, the multi-level sports complex achieves efficient use of resources by introducing a combination of recreation and social functions. Analyzing the key design points of the cascade-type gymnasium based on relevant criteria and standards of evaluating sports buildings, this paper establishes a multi-layer evaluation system to achieve synergistic energy efficiency design. It also provides a reference for expanding the efficient design of sports complexes.
Knowledge Acquisition with Deep Fuzzy Inference Model and Its Application to a Medical Diagnosis

In this paper, we reduce the number of fuzzy rules in the fuzzy inference model and acquire knowledge as fuzzy rules. The number of input items used for the inference model is reduced by randomly selecting the number of input items in each layer. Therefore, it turns out that the number of rules in the whole of this model can be reduced more than that of rules in an inference model that uses all the original input items at one time. However, in the previous model by Zhang, although the consequent part of the fuzzy rule was learned, the antecedent part was not learned. Since we need to deal with the situation where there is no prior knowledge in the problem to apply and it will be necessary to acquire knowledge from data, it is required to learn the antecedent part. In this paper, we propose a learning method for the antecedent fuzzy sets in fuzzy rules in order to obtain relationship between input and output of the learning data from the actual data. Then, as an example, the proposed method is applied to medical diagnosis of diabetes, the accuracy of the previous method is compared with that of the proposed method.
Perturbation-induced Data Augmentation Considering Shape of Distribution — An Application of Generative Adversarial Networks —

Machine Learning (ML) techniques need a tremendous volume of training data. However, in operation and maintenance of industrial facilities, it is difficult to get such a volume of data due to the lack of sensors or infrequency of target phenomena. A promising approach to solve this problem is so-called data augmentation, which generates training data by using a prior knowledge or adding noise (perturbation) to original data. For this, Gaussian noise is generally used because of its simplicity. However, when the distribution of original data is not isotropic, the Gaussian-based augmentation breaks its shape, which causes so-called over regularization. In this paper, we propose a novel perturbation-induced data augmentation method, which does not require any prior knowledge and makes it easy to control the magnitude of perturbation. The novelty of the proposed method is to keep certain characteristics of shape of original distribution. The perturbation for this is generated by combined use of generative adversarial networks and newly proposed objective functions. We experimentally show that the proposed method enables to keep the gap between peaks of a mixed normal distribution. The effectiveness of the proposed method is also demonstrated in the case of an image classification task.
Secure Cloud-Based E-Healthcare System Using Ciphertext-Policy Identity-Based Encryption (CP-IBE)

Healthcare organizations are adopting Electronic Health Record (EHR) for better and fast services. Due to flexibility, security, and efficiency, cloud data storage has become a frequent alternative for deploying EHR systems. Several cryptography techniques provide security to cloud user’s data by encrypting and decrypting data for intended users. The present paper proposes Ciphertext-Policy Identity-based Encryption (CP-IBE) as a cryptographic technique for cloud data. CP-IBE is combination of data encryption and identity-based approach. It is enhanced by integrating CP-IBE algorithm with Elliptic Curve Cryptography (ECC) that gives a novel scheme for public-key cryptography known as Ciphertext-Policy Identity-based Elliptic Curve Cryptography (CP-IBE-ECC). Torsion point concept is used in the Elliptic Curve Cryptography (ECC) system. An elliptic curve is the formation of curve under an algebraic set of coordinates which does not intersect with other points during curve formation. This merit makes us define a new security model in healthcare applications. None of the studies has explored the merits of applying torsion points on key distribution system. It provides dynamic data support, efficiency, security as well as privacy for E-Healthcare System. This system focus on storing and maintaining the Health Records of patients electronically so refer these records as EHR. The EHRs are migrated to the cloud data centers in order to prevent medical errors and to have efficient storage and access to those records. IBE encryption technique is also applied in order to make EHRs more secure. The Shared Nearest Neighbor (SNN) clustering is applied to resolve the issues with attribute clustering. SNN works on estimating the nearest neighbors for the most shared attributes and then clusters the records.
Effective Matrix Factorization for Recommendation with Local Differential Privacy

With the continuous upgrading of smart devices, people are using smartphones more and more frequently. People not only browse the information they need on the Internet, but also more and more people get daily necessities through online shopping. Faced with a variety of recommendation systems, it becomes more and more difficult for people to keep their privacy from being collected while using them. Therefore, ensuring the privacy security of users when they use the recommendation system is increasingly becoming the focus of people. This paper summarizes the related technologies. A recommendation algorithm based on collaborative filtering, matrix factorization as well as the randomized response is proposed, which satisfies local differential privacy (LDP). Besides, this paper also discusses the key technologies used in privacy protection in the recommendation system. Besides, This paper includes the algorithm flow of the recommendation system. Finally, the experiment proves that our algorithm has higher accuracy while guaranteeing user privacy.
A Classification of Grammar-Infused Templates for Ontology and Model Verbalisation

Involving domain-experts in the development, maintenance, and use of knowledge organisation systems can be made easier through the introduction of easy-to-use interfaces that are based on natural language. Well resourced languages make use of natural language generation techniques to provide such interfaces. In particular, they often make use of templates combined with computational grammar rules to generate grammatically complex text. However, there is no model of pairing templates and computational grammar rules to ensure suitability for less-resourced languages. These languages require a modular design that ensures grammar detachability so as to allow grammar re-use across domains and applications. In this paper, we present a model and classification scheme for grammar-infused templates suited for less-resourced languages and classify existing systems that make use of them. We have found that of the 15 systems that pair templates and grammar rules, and their 11 distinct template types, 13 have support for detachable grammars.