Reviewerly/exHarmony huggingface dataset:
Big data measurement model in achieving maximum accuracy using the model Hierarchy of Grid Partition (HGP) method

The development of the amount of data stored through online-based storage systems in cloud computing systems is very large, so there is a possibility that there will be problems in processing such huge data. Therefore in the conference part of the results of this study of course presents an opinion about the new method model that researchers developed is to analyze the data using an integrated analysis model and in measuring the accuracy of large data of course on this occasion using the hierarchy of grid partition (HGP) and as a tool for design that uses the Unified Modeling Language (UML). The big data measurement model in the framework reaches the maximum of the needs of the measuring object entity or which will praise the accuracy of the data. Therefore, there needs to be a further process to support the results of this method model in order to get accurate test results. The working pattern of this big data measurement method model is, of course, agreed on the integration of data stored through the Cloud Server, or Database Management System Server.
Frequency-dependent body-Q and coda-Q in Karlıova Triple Junction and its vicinity, Eastern Turkey

A dataset obtained from six broadband stations of 111 small- to moderate-sized local events that occurred between 2010 and 2018 was analyzed to reveal the frequency-dependent seismic body (body-Q) and coda (coda-Q) wave attenuation characteristics of three tectonic branches of the Karlıova Triple Junction: the Varto Fault Zone (VFZ), North Anatolian Fault Zone (NAFZ), and East Anatolian Fault Zone. The magnitude range was between 2.8 and 6.1 with shallow (
Privacy-Preserving Locally Weighted Linear Regression Over Encrypted Millions of Data

The emerging development of cloud computing makes a trend that the cloud becomes a outsourced agglomeration for storing big data that generally contains numerous information. To mine the rich value involved in big data, the machine learning methodology is widespread employed due to its ability to adapt to data changes. However, the data mining process may involve the privacy issues of the users, hence they are reluctant to share their information. This is the reason why the outsourced data need to be dealt with securely, where data encryption is considered to be the most straightforward method to keep the privacy of data, but machine learning on the data in ciphertext domain is more complicated than the plaintext, since the relationship structure between data is no longer maintained, in such a way that we focus on the machine learning over encrypted big data. In this work, we study locally weighted linear regression (LWLR), a widely used classic machine learning algorithm in real-world, such as predict and find the best-fit curve through numerous data points. To tackle the privacy concerns in utilizing the LWLR algorithm, we present a system for privacy-preserving locally weighted linear regression, where the system not only protects the privacy of users but also encrypts the best-fit curve. Therefore, we use Paillier homomorphic encryption as the building modular to encrypt data and then apply the stochastic gradient descent in encrypted domain. After given a security analysis, we study how to let Paillier encryption deal with real numbers and implement the system in Python language with a couple of experiments on real-world data sets to evaluate the effectiveness, and show that it outperforms the state-of-the-art and occurs negligible errors compared with performing locally weighted linear regression in the clear.
Analysis of Strain Field Heterogeneity at the Microstructure Level and Inverse Identification of Composite Constituents by Means of Digital Image Correlation

The present paper is devoted to the theoretical study on the estimation of the full-field strain at the microstructural level of composite materials by means of Digital Image Correlation (DIC). The main aim of the paper is to investigate the influence of speckle size on the accuracy of the strain field measurement at the microscale. The DIC analysis was conducted based on artificial speckle patterns generated numerically and the deformation behavior of the composites was simulated by using the finite element method (FEM). This approach gives the opportunity to compare the results of the DIC in terms of speckle size with the reference FEM solution. Moreover, the paper focuses on the inverse identification of the material constants of the composite constituents by using information associated with the measured strain field. The inverse problem is solved by using a novel two-step optimization procedure, which reduces the problem complexity. The feasibility and accuracy of the proposed approach are presented by analysis of two exemplary microgeometries representing the microstructures of fiber reinforced composites.
Visualizing Financial Stock Data withinan Augmented Reality Trading Environment

In this paper, we present a novel augmented reality (AR) system for visualization of financial stock data.The system enhances the equity traders' working environment with virtual charts and live business TV streams, overlaid onto the real trading workspace.The presented research investigates how human cognitive capabilities can be extended by the use of artificial computer-generated 3D representation of financial data and interaction with such data representation using hand gestures.We combined the nVisor ST50 headset with InteriaCube4 and Leap Motion devices to enable tracking of head orientation and controlling the AR environment with hands.With the use of hand gestures, a user is able to create virtual charts displaying real-time stock data and financial TV streams within his/her surroundings.To evaluate the system, we conducted experimental tests with 22 users.Our objective was to evaluate tasks completion times and users' experience while creating and parametrizing virtual stock charts through the AR interface.The obtained results are promising and demonstrate that most users were able to finish tasks in acceptable time without significant difficulties.
A Model of a Weighted Agent System for Personalised E-Learning Curriculum

Progressive developments in the world of Information and Communications Technology open up many frontiers in the educational sector. One of such is adaptive e-learning systems, which is currently attracting a lot of research and development. Several conceptualisations and implementations rely on single parameters, or at most three or four parameters. This is not sufficient to account for the wide range of factors which can affect the learning process in an unconventional learning environment such as the web. Being able to choose relevant parameters for personalisation in different learning scenarios is vital to accommodate a wide range of these factors. In this paper, we'll do a review of the basic concepts and components of an adaptive e-learning system. Afterwards, we'll present a model of an adaptive e-learning system which generates a specialised curriculum for a learner based on a multi-parameter approach, thereby allowing for more choices in the process of creating a personalised and learner-oriented experience for such user. This will involve assembling (and/or suggesting) learning resources encompassed in a general curriculum and adapting it to specific personalities and preferences of users. The degree of adaptation (of the curriculum) is dependent on a weighted algorithm matching user features (relevant in each learning scenario) to the corresponding features of available learning resources.
A Supervised Method for Extractive Single Document Summarization Based on Sentence Embeddings and Neural Networks

Extractive summarization consists of generating a summary by ranking sentences from the original texts according to their importance and salience. Text representation is a fundamental process that affects the effectiveness of many text summarization methods. Distributed word vector representations have been shown to improve Natural Language Processing (NLP) tasks, especially Automatic Text Summarization (ATS). However, most of them do not consider the order and the context of the words in a sentence. This does not fully allow grasping the sentence semantics and the syntactic relationships between sentences constituents. In this paper, to overcome this problem, we propose a deep neural network model based-method for extractive single document summarization using the state-of-the-art sentence embedding models. Experiments are performed on the standard DUC2002 dataset using three sentence embedding models. The obtained results show the effectiveness of the used sentence embedding models for ATS. The overall comparison results show that our method outperforms eight well-known ATS baselines and achieves comparable results to the state-of-the-art deep learning based methods.
Perancangan Media Pembelajaran Interaktif Berbasis Android Pada Mata Pelajaran Ilmu Pengetahuan Sosial Untuk Anak Sekolah Dasar

Technology is increasingly developing in all aspects of life, even in the teaching and learning process. Children at Elementary School level are more interested in learning with interactive learning media that are more interesting than conventional learning from textbooks. Learning to use textbooks makes it difficult for elementary school age children to concentrate because of the large amount of material, books that are less interesting and lack of interaction so that when learning students must be guided especially for Social Sciences subjects. With the Android-based interactive learning media application, students can learn while playing. The media is designed to contain material in the form of images, audio and video so as to attract the attention and concentration of students in the learning process independently. This media design uses the RAD (Rapid Application Development) and Unified Modeling Language (UML) methods as a tool for modeling media, android studio for its design. Based on the results of trials conducted on elementary school students feel more interested and easier to remember material with interactive media than learning to use textbooks.
Intelligent Detection of Object's Anomalies for Road Surveilance Cameras

Nowadays intelligent data processing tools are widely applying into cities infrastructure in order to make citizent's life safer and convenient. One of the most important part of it is video surveillance, which plays important role in many traffic flow monitoring applications. Recent advances in hardware, computer vision and machine learning techniques makes it possible to solve different tasks for applications in real time without human, including recognition of different objects, anomalies and incidents detection. In this paper, we analyzed the impact of image pre-processing technique such as grayscaling on our AI based object detection and tracking model. We also used machine learning algorithms to detect object's frequent trajectories and anomalies on real time video from road surveillance cameras. Based on this, we suggested an approach to detect violations of legal trajectories by vehicles or pedes trians.
Grounding principles for (relevant) implication

Most of the logics of grounding that have so far been proposed contain grounding axioms, or grounding rules, for the connectives of conjunction, disjunction and negation, but little attention has been dedicated to the implication connective. The present paper aims at repairing this situation by proposing adequate grounding principles for relevant implication. Because of the interaction between negation and implication, new grounding principles concerning negation will also arise.
SUM'20: State-based User Modelling

Capturing and effectively utilising user states and goals is becoming a timely challenge for successfully leveraging intelligent and usercentric systems in differentweb search and data mining applications. Examples of such systems are conversational agents, intelligent assistants, educational and contextual information retrieval systems, recommender/match-making systems and advertising systems, all of which rely on identifying the user state in order to provide the most relevant information and assist users in achieving their goals. There has been, however, limited work towards building such state-aware intelligent learning mechanisms. Hence, devising information systems that can keep track of the user's state has been listed as one of the grand challenges to be tackled in the next few years [1]. It is thus timely to organize a workshop that re-visits the problem of designing and evaluating state-aware and user-centric systems, ensuring that the community (spanning academic and industrial backgrounds) works together to tackle these challenges.
Study of Imaginative Play in Children using Neutrosophic Cognitive Maps Model

This paper studies the imaginative play in young children using a model based on neutrosophic logic, viz, Neutrosophic Cognitive Maps (NCMs). NCMs are constructed with the help of expert opinion to establish relationships between the several concepts related with the imaginative play in children in the age group 1-10 years belonging to socially, economically and educationally backward groups. The NCMs are important in overcoming the hindrance posed by complicated and often imprecise nature of psychological or social data. Data was collected by video recording of children playing and the interpretations given by experts. Fifteen attributes / concepts related with children playing with the same toy were observed and according to experts several concepts were related and for some the relations between concepts were indeterminate, so it was appropriate to use NCMs. These NCMs were built using five expert’s opinion and the hidden patterns of them happened to be a fixed point.
A Configurable Pruning Gaussian Image Filter for Energy-Efficient Edge Detection

This paper presents a new configurable pruning Gaussian image filter CMOS architecture to address energy efficiency requirements regarding edge detection applications. Low-energy consumption is key for Internet of Things (IoT) devices. Many emerging IoT applications rely on cameras to extract video or image features by running power-hungry computer vision algorithms. The Gaussian image filter is one of the most compute intensive tasks for pre-processing edge detection techniques which are widely adopted in the computer vision domain. Therefore, our proposed 2D Gaussian filter architecture enables: i) a low power and low area overhead run-time configuration scheme based on clock gating technique to prune the Gaussian filter (GF) window size, and ii) run-time capability to balance the tradeoff between edge detection quality and energy efficiency. Our proposed configurable architecture is synthesized and mapped onto 45 nm technology for an ASIC implementation. Results show that for 6 different run-time profiles our proposed configurable architecture provides power dissipation reduction of up to 64% with multiple levels of edge detection quality, which is assessed by considering the performance conformance metric.
Akıllı Bir Çoklu Model Arttırılmış Gerçeklik Uygulaması Ameliyat Öncesi Prosedür Becerilerinin Öğretilmesi

Despite the current essential rules to set up the surgical instruments on the mayo table, there is no standard implementation for this stage of the preoperative preparation. The nursing students do not have enough opportunity to gain experience because of the high-risk conditions and they faced a lack of feedback during their learning. This study introduces a smart multimodal augmented reality application which developed to solve these issues. 10-correct and 10-incorrect instrument arrangement of the expert nurses were used to train this system. During using this application, the learners become able to perform pre-operative operating room preparation procedures; then they can receive individual feedback about the suitability of their arrangements. Meta2 AR goggles used to provide gesture interaction. This study represents the interface, features and smart system structure of a multimodal AR.
Sampling-based motion planning of 3D solid objects guided by multiple approximate solutions

Sampling-based motion planners are often used to solve motion planning problems for robots with many degrees of freedom. These planners explore the related configuration space by random sampling. The well-known issue of the sampling-based planners is the narrow passage problem. Narrow passages are small collision-free regions in the configuration space that are, due to their volume, difficult to cover by the random samples. The volume of the narrow passages can be artificially increased by reducing the size of the robot, e.g., by scaling-down its geometry, which increases the probability of placing the random samples into the narrow passages. This allows us to find an approximate solution (trajectory) and use it as a guide to find the solution for a larger robot. Guiding along an approximate solution may, however, fail if this solution leads through such parts of the configuration space that are not reachable or traversable by a larger robot. To improve this guiding process, we propose to compute several approximate solutions leading through different parts of the configuration space, and use all of them to guide the search for a larger robot. We introduce the concept of disabled regions that are prohibited from the exploration using the sampling process. The disabled regions are defined using trajectories already found in the space being searched. The proposed method can solve planning problems with narrow passages with higher success rate than other state-of-the-art planners.
Revisiting Deep Learning Parallelism: Fine-Grained Inference Engine Utilizing Online Arithmetic

In this paper, we revisit the parallelism of neural inference engines. In a departure from the conventional coarse-grained neuron-level parallelism, we propose a synapse-level parallelism by performing highly parallel fine-grained neural computations. Our method employs online Most Significant Digit First (MSDF) digit-serial arithmetic to enable early termination of the computation. Using online MSDF bit-serial arithmetic for DNN inference (1) enables early termination of ineffectual computations, (2) enables mixed-precision operations (3) allows higher frequencies without compromising latency, and (4) alleviates the infamous weights memory bottleneck. The proposed technique is efficiently implemented on FPGAs due to their concurrent fine-grained nature, and the availability of on-chip distributed SRAM blocks. Compared to other bit-serial methods, our Fine-Grained Inference Engine (FGIE) improves energy efficiency by ×1.8 while having similar performance gains.
POTENSI PENGEMBANGAN ENERGI BARU DAN ENERGI TERBARUKAN DI KOTA SEMARANG

Semarang is the largest metropolitan city in Central Java, Indonesia with a high electricity consumption to support community activities, Main electricity of Semarang City is supplied by the State Electricity Company (PLN), and sourced from fossil fuels which are in fact unsustainable and high risk of environmental damage. The national government attempt to reduce dependence on fossil fuels by establishing a National Energy Policy (KEN) to increase the utilization of renewable energy (EBT) by around 23% in 2025 and 31% in 2050. To support this target, local governments need play a role to realize the utilization of EBT in their respective regions. The purpose of this study is to identify the source of EBT in Semarang which has the greatest potential to be developed as an energy substitute for electricity supply. This study uses quantitative descriptive methods and Geographic Information Systems (GIS) analysis to identify the potential of EBT in each region. The results of this study indicate that solar panels are the largest potential source of EBT with an estimated substitution in 2025 is about 19% of the total electricity demand in Semarang and 12% in 2050.
Ground Truth OCR Sample Data of Finnish Historical Newspapers and Journals in Data Improvement Validation of a re-OCRing Process

The National Library of Finland (NLF) has digitized historical newspapers, journals and ephemera published in Finland since the late 1990s. The present collection consists of about 16.51 million pages mainly in Finnish and Swedish. Out of these about 7.64 million pages are freely available on the web site&nbsp;https://digi.kansalliskirjasto.fi/etusivu. The copyright restricted part of the collection can be used at six legal deposit libraries in different parts of Finland. The time period of the open collection is from 1771 to 1929. The last nine years, 1921–1929, were opened in January 2018. This paper presents briefly the ground truth Optical Character Recognition data of about 500 000 words that has been compiled at the NLF for development of an improved OCR process for the Finnish collection. We discuss compilation of the data generally and show results of the new OCR process in comparison to current OCR, using the ground truth data as an evaluation benchmark. We also show with real newspaper data of 30 years and 109 million words that the re-OCRing process is improving the quality of the OCRed data.
A RRT*FN Based Path Replanning Algorithm

A path replanning algorithm based on RRT*FN(Rapidly-exploring Random Tree Fixed Nodes) is proposed for online local path planning of robot. First, we propose a procedure to reuse the tree from the last planning. Second we design a strategy to balance the exploitation of the old tree and the exploration of the current environment. Finally, the RRT*FN’s strategy is adopted to control the size of the tree. Empirical studies have shown that when the positions of the starting point, the goal and the dynamic obstacles change within a certain range, the proposed algorithm can significantly improve the quality of the solution within a limited time compared to totally starting a new planning using RRT*FN. We also compared the proposed algorithm with the two related replanning algorithms, ORRT* (Online Rapidly-exploring Random Tree*) and RT-RRT* (Real Time Rapidly-exploring Random Tree). The proposed algorithm is better with respect to the time used to find the first feasible path and the cost of the first feasible path.
Using Bayesian Network to Estimate the Value of Decisions within the Context of Value-Based Software Engineering: A Multiple Case Study

Context: Companies must make a paradigm shift in which both short- and long-term value aspects are employed to guide their decision-making. Such need is pressing in innovative industries, such as ICT, and is the core of Value-based Software Engineering (VBSE). Objective: This paper details three case studies where value estimation models using Bayesian Network (BN) were built and validated. These estimation models were based upon value-based decisions made by key stakeholders in the contexts of feature selection, test cases execution prioritization, and user interfaces design selection. Methods: All three case studies were carried out according to a Framework called VALUE — improVing decision-mAking reLating to software-intensive prodUcts and sErvices development. This framework includes a mixed-methods approach, comprising several steps to build and validate company-specific value estimation models. Such a building process uses as input data key stakeholders’ decisions (gathered using the Value tool), plus additional input from key stakeholders. Results: Three value estimation BN models were built and validated, and the feedback received from the participating stakeholders was very positive. Conclusions: We detail the building and validation of three value estimation BN models, using a combination of data from past decision-making meetings and also input from key stakeholders.