Reviewerly/exHarmony huggingface dataset:
An Approach for Interesting Subgraph Mining from Web Log Data Using W-Gaston Algorithm

Graph-Based Data Mining (GBDM) is an emerging research topic nowadays, for the retrieval of the essential information from the graph database. There exist many algorithms that find frequent patterns in a given graph database. One such algorithm, GASTON uses support based on frequency to discover frequent patterns. The discovery phase in the Gaston algorithm is time-consuming, and the pages captured the interest of the users are ignored by the existing GASTON algorithm. This paper proposes an algorithm, Weighted-Gaston (W-Gaston) algorithm, by modifying the existing Gaston algorithm. Here, four interesting measures are developed based on the frequency, entropy, and the page duration, for the retrieval of the interesting sub-graphs. The proposed interesting measures include four types of support: (1) Support based on the page duration (W-Support), (2) Support based on the entropy (E-Support), (3) Support based on the page duration and the entropy (WE-Support), and (4) Support based on the frequency, page duration, and the entropy (FWE-Support). The simulation of the proposed work is done using the MSNBC and the weblog databases. The experimental results show that the proposed algorithm performed well as compared with the existing algorithms.
The Correlation of Solar Radiation and Atmospheric Elements including Air Pollution

Purpose: In order to forecast the energy production of photovoltaic systems, precise solar radiation prediction is required with the expansion of solar power generation systems and the application of distributed power. In this study, the current status of air pollution in urban areas is analyzed, and the correlation between air pollution and solar radiation is analyzed. Method: The air pollution level is analyzed by year and season, and the correlation between atmospheric elements such as atmospheric pollution and meteorological factors is analyzed. The correlation is analyzed by using SPSS 25 statistical program by season. Principal component analysis is used to analyze the main factors affecting extraterrestrial ratio. Result: The air quality of urban areas is generally improving; however, the concentrations of some air pollutants such as O₃, NO₂, and PM2.5 are increasing or remain steady. Because seasonal factors affecting solar radiation are different, a seasonal solar radiation prediction model is required. As a result of factorial analysis of winter variables, all variables were classified into three factors, namely gaseous pollutants, particulate pollutants, and atmospheric water vapor.
A Video Representation Method Based on Multi-View Structure Preserving Embedding for Action Retrieval

The content-based video retrieval is a popular topic in computer vision filed, especially, action retrieval. This paper proposes a novel and effective video representation module for content-based action retrieval framework, in which feature learning can be conducted with complementary information and intrinsic structure, where the relationship between appearance feature and geometry can be preserved. Based on multi-view analysis and graph embedding, the target features are generated to minimize the inter-class discrepancy and maximize intra-class discrimination. Applied to the content-based retrieval task, the proposed method can be combined with Euclidean distance for the comparison of low-dimensional features. As demonstrated in the extensive experiments on the benchmark datasets, the performance of the proposed framework is superior to the state-of-the-art methods.
Estimation of surface solar irradiation using sky view factor, sunshine factor and solar irradiation models according to geometry and buildings

This study aims to estimate surface solar irradiation using sky view factor (SVF), sunshine factor (SF) and solar irradiation models by the fish-eye image. The SVF and the SF contribute the solar irradiation for shielding from the sky and obtaining from the sunshine. It is possible to generate the SVF and the SF values for calculating from the simple geometric measurements, fisheye images, and simple model. The case study is a new building in Ulsan and looking for the best orientation and slope using the drone with a fisheye lens. The study demonstrates that the SVF and the SF analysis using fish-eye image is a useful and effective tool for design, optimization and performance evaluation of solar technologies and building energy for any geographical locations and buildings.
Robust Online Signature Verification Using Long-term Recurrent Convolutional Network

The explosively increasing use of personal computing devices that contain a touchscreen as input interface and the inconvenience of manually pressing password on the devices lead to studies on alternative biometric authentication methods. Among these, handwritten signature has an advantage that using signature as a means of authentication is already familiar to people, but it also has disadvantage that forger can easily attempt to imitate the signature of others. In this context, the purpose of this paper is to boost the forgery verification performance using the machine learning approach. This paper proposes an online signature verification using long-term recurrent convolutional network (LRCN) that ensures extracting distinguishable features between genuine and forged signature. In the proposed method, CNN and time interval embedding are used for feature extraction of signature strokes and LSTM is used for modeling long-term temporal characteristics of stroke sequences. Besides, a forgery-sensitive loss is proposed for robust signature verification against forged signatures. With the loss function, the proposed network provides a feature vector of signature to support vector machine-based classifier for distinguishing a genuine signature from forgeries. The signature verification experiments on the SUSIG dataset show that our proposed work outperforms other state-of-the-art methods on forgery verification performance.
Cost Benefits of Multi-cloud Deployment of Dynamic Computational Intelligence Applications

Cost savings is one of the main motivations for deploying commercial applications in the Cloud. These savings are more pronounced for applications with varying computational needs, like Computational Intelligence (CI) applications. However, continuously deploying, adapting, and decommissioning the provided Cloud resources manually is challenging, and autonomous deployment support is necessary. This paper discusses the specific challenges of CI applications and provide calculations to show that dynamic use of Cloud resources will result in significant cost benefits for CI applications.
360° Panorama Generation using Drone Mounted Fisheye Cameras

We propose a system that produces a high quality 360° panorama from images captured by fisheye cameras mounted on a drone. The hardware of the system is designed in such a way that no user input is required to stitch the individual images. The auxiliary equipment such as drone wings and legs are not a part of the automatically stitched panorama. Hence, no additional post processing is required to remove unwanted artifacts. Automatic stitching is performed using Nvidia VRWorks 360 SDK. The stitched panorama has no visible seams in it. Furthermore, the panorama is automatically straightened with respect to the horizon.
Machine Tango

In Argentine tango, dancers typically respond to fixed musical recordings with improvised movements, each movement emerging in a wordless dialog between leader and follower. In the interactive work Machine Tango, this relation between dancers and music is inverted, enabling tango dancers to drive musical outcomes. Motion sensors are attached to dancer limbs, and their data is sent wirelessly to a computer, where algorithms turn the movement into sound. In doing so, the computer inserts itself in this on-going nonverbal conversation. Instead of traditional tango instruments such as the bandoneón, dancers generate and transform the sounds of typewriters and found sounds. System musical response to movement shifts during the dance, becoming more complex. The two dancers must navigate the resulting unstable musical structures as one body, responding with stylized tango movements. The difficulty of this task and the juxtaposition of the traditional with the experimental are integral to the performance aesthetic.
Accuracy improvement in the estimation of epicentral distance inferred from initial P-waves by considering local heterogeneity

Epicentral distance can be estimated from growth of the initial P-waves in real time, and this estimation is used in the earthquake early-warning system in Japan, especially for railways. This estimation is based on the relationship between the growing initial P-waves and epicentral distance. The initial P-waves grow gradually with increasing distance from the epicentre because geometrical spreading, scattering and intrinsic attenuation blunt their shape. Therefore, the growing form of the initial P-waves can be used to estimate the epicentral distance. However, in practice, the relationship is not uniform and the growth curves (i.e. gradient) are not a perfect function of epicentral distance. Therefore, the epicentral distance estimated from the initial P-waves involves significant uncertainty, which decreases the accuracy of the early-warning system for strong motions. In this study, we examined the causes of error in this relationship using theoretical calculations based on the Born approximation, which indicate that the growth curves of the P-waves fluctuate due to regionally different heterogeneous conditions in the subsurface medium. Therefore, we propose a robust relationship between the initial P-waves and epicentral distance considering regional heterogeneous conditions by analysing real earthquakes (magnitude 4–5) in Japan.
Improving Robustness of Random Forest Under Label Noise

Random forest is a well-known and widely-used machine learning model. In many applications where the training data arise from real-world sources, there may be labeling errors in the data. In spite of its superior performance, the basic model of random forest dose not consider potential label noise in learning, and thus its performance can suffer significantly in the presence of label noise. In order to solve this problem, we present a new variation of random forest - a novel learning approach that leads to an improved noise robust random forest (NRRF) model. We incorporate the noise information by introducing a global multi-class noise tolerant loss function into the training of the classic random forest model. This new loss function was found to significantly boost the performance of random forest. We evaluated the proposed NRRF by extensive experiments of classification tasks on standard machine learning/computer vision datasets like MNIST, letter and Cifar10. The proposed NRRF produced very promising results under a wide range of noise settings.
VeGAN: Using GANs for Augmentation in Latent Space to Improve the Semantic Segmentation of Vehicles in Images From an Aerial Perspective

Generative Adversarial Networks (GANs) are a new network architecture capable of delivering state-of-the-art performance in generating synthetic images in various domains. We train a network called VeGAN (Vehicle Generative Adversarial Network) to generate realistic images of vehicles that look like images taken from a top-down view of an unmanned aerial vehicle (UAV). The generated images are used as additional training data for a semantic segmentation network, which precisely detects vehicles in recordings of traffic on highways. While images are commonly generated randomly for a content-based augmentation, we leverage ideas from the domain of active learning. Using a network which is based on the InfoGAN architecture allows mapping existing vehicle images to a latent space representation. After mapping the complete training dataset, we perform the augmentation in the latent space. The applied techniques include creating variations of given hard negative samples and generating samples in sparsely occupied areas of the latent space. We improve the IoU of the semantic segmentation network from 93.4% to 94.9% and reduce the mean positional error of the detected vehicles' centers from 0.51 to 0.37 pixels in longitudinal and from 0.21 to 0.17 pixels in lateral direction.
The Effectiveness of Interactive E-Book for Self-Study and Increasing Students’ Critical Thinking Skills in Electromagnetic Radiation Topic

This research aims to develop interactive e-book on electromagnetic radiation topic, which can be used for self-study, and to increase students critical thinking skill. The methodology in this research is based on ADDIE model research and development (R&D) design which consist of analysis, design, development, implementation, and evaluation. The data resource was the students and teachers of XII grade of senior high schools in Lampung Province. The instrument used were questionnaire and test instrument. Data analysis technique was conducted descriptively with interval using n-gain, independent t-test, and paired t-test. The research showed that interactive e-book on electromagnetic radiation was effective for self-study and to increase critical thinking skill of the students significantly at a confidence level of 95%, with N-gaining experimental group <g> = 0.76 which was higher than in control group <g> = 0.55. Therefore, it can be concluded that the product which was developed namely interactive e-book on electromagnetic radiation is effective for self-study and to increase students' critical thinking skill.
Studying Task Processes for Improving Programmer Productivity

Productivity of a software development organization can be enhanced by improving the software process, using better tools/technology, and enhancing the productivity of programmers. This work focuses on improving programmer productivity by studying the process used by a programmer for executing an assigned task, which we call the task process. We propose a general framework for studying the impact of task processes on programmer productivity and also the impact of transferring task processes of high-productivity programmers to average-productivity peers. We applied the framework to a few live projects in Robert Bosch Engineering and Business Solutions Limited, a CMMI Level 5 company. In each project, we identified two groups of programmers: high-productivity and average-productivity programmers. We requested each programmer to video capture their computer screen while executing his/her assigned tasks. We then analyzed these task videos to extract the task processes and then used them to identify the differences between the task processes used by the two groups. Some key differences were found between the task processes, which could account for the difference in productivities of the two groups. Similarities between the task processes were also analyzed quantitatively by modeling each task process as a Markov chain. We found that programmers from the same group used similar task processes, but the task processes of the two groups differed considerably. The task processes of high-productivity programmers were transferred to the average-productivity programmers by training them on the key steps missing in their process but commonly present in the work of their high-productivity peers. A substantial productivity gain was found in the average-productivity programmers as a result of this transfer. The study shows that task processes of programmers impact their productivity, and it is possible to improve the productivity of average-productivity programmers by transferring task processes from high-productivity programmers to them.
The Applications of Digital Reality in Creative and Oceanic Cultural Industries

Digital reality technologies have become a key component of promoting creative and cultural industries in Taiwan. In 2016, Taiwan's Ministry of Culture funded 45 projects to promote creative and cultural industries in this island country. A total of USD$25.6 million dollars have been granted to this project. Among these projects, the applications of virtual reality (VR) and augmented reality (AR) technologies have been found to be the latest trend in Taiwan's creative and cultural industries. This chapter employs a case study approach to survey the current state of digital reality technology applications particularly in the area of creative and oceanic cultural industries in Taiwan. Using a detailed description of these best practices among creative and cultural industries to promote Taiwanese oceanic culture, this chapter aims to provide a detailed examination of digital reality applications in the creative and cultural industry sectors in a non-Western context.
Rate adaptation with Bayesian attractor model for MPEG-DASH

Recently, over-the-top video service providers focus on the quality of experience (QoE) as an important factor when they provide video content. Today, most video streaming service providers, such as Youtube and Netflix, provide video content to users with adaptive bitrate (ABR) control techniques for increasing the user QoE. To maximize the QoE under a fluctuating network condition, in this paper, we propose an ABR algorithm using the Bayesian attractor model, which models cognition and decision making of the human brain, as the name suggests, according to the Bayesian inference. Simulation results show that our proposed method achieves a higher video bitrate with less video quality switching to improve the user QoE compared to the methods even in the situation where network available bandwidth greatly fluctuates.
Attribute-Sentiment Pair Correlation Model Based on Online User Reviews

With the popularization of Internet applications and the rapid development of e-commerce, online shopping has become a widespread and important pattern of consumption. Online user comments are an important data asset on e-commerce sites and have a great potential value for online users and merchants. However, accurate and effective extraction of the characteristics of products and users’ sentiment evaluation from a tremendous amount of comments is a significant challenge. Based on the concept of the LinLog energy model, this paper proposes an online review attribute-sentiment pair correlation model that evaluates user comments. After preprocessing the comment data of mobile phones and constructing an attribute dictionary, the proposed model conducts a clustering analysis of attributes and sentiment pairs to gain accurate assessment of attributes in order to explore potential information from user comments. Experiments conducted on one real-world dataset with comprehensive measurements verify the efficacy of the proposed model.
Shared representations of actions for alternative suggestion with incomplete information

Common coding theory posits there are cycles between perceptions and actions at the fundamental logic of the nervous system. An action performed by an agent modifies its external environment and/or internal states. The agent is able to complete a given goal by performing a sequence of actions. In this work, shared representations for both perception and action and their processing algorithm are presented that suggest goal-oriented alternative actions even with incomplete information. The alternative actions provide more opportunities for a service robot to achieve goals. Knowledge plays significant roles to successfully complete service tasks. Most knowledge inference mechanisms assume complete and correct knowledge about the environment. Real world environments are often uncertain and only partially observable. Thus, intelligent service robots may have an incomplete knowledge base which includes false negatives and false positives as well as true positives. False negatives and false positives can prevent service robots from completing their service tasks. A case study reveals that the proposed method has proved valuable to suggest alternatives for false information as well as to build reactive plans with customary efficiency.
Vision-based fall detection for elderly people using body parts movement and shape analysis

Falls are the major cause of serious injuries and even death for elderly people. Fall detectors are usually based on wearable devices such as gyroscope, accelerometers, etc. Unfortunately, elderly people often forget to wear them especially those with dementia. In this paper, we present a new vision-based method for automatic fall detection in smart home environment. First, we extract efficiency the person silhouette based on background subtraction method and active contour. Then, motion and shape features are extracted from person body parts and analyzed in order to classify fall from other daily activities using rule-based classification. Evaluation results demonstrate the effectiveness of the proposed method in smart home environment.
A TUI to Explore Cultural Heritage Repositories on the Web

This article presents a paper-based Tangible User Interface (TUI) that facilitates the production of complex queries on a Cultural Heritage (CH) repository. The system helps to easily make use of the data elements and Boolean logic that describe the collections. This research presents a design methodology divided into two main phases: A User Experience (UX) and User Centred Design (UCD) where potential users' behaviours are analysed, followed by the development and evaluation of the TUI prototype. The TUI uses off the shelf electronics and a paper-based set of tokens to engage the user with the system, thus facilitating the exploration with CH collections through querying.
Passive Tracking of Underwater Targets Using Dual Observation Stations

Passive tracking method has a unique role in underwater surveillance. When using passive sonar to track underwater targets, the only available measurement data are those called bearing angles As we know, traditionally used single bearings-only observing system is difficult to achieve a complete observability of the target tracking. Bearings-only dual observing stations system is fully observable to track target and easy to apply. In order to track the single maneuvering underwater target, we use Interacting Multiple Model (IMM) combined Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) to track maneuvering target. Simulation results show that when measurement error increases, the tracking performance of IMM-UKF is more stable and accurate than IMM-EKF. In addition, we propose to track multiple targets with Bearings-only dual observing stations system in clutter environment, here PDA and NN algorithm are compared with this system. Simulation results show that algorithm based on PDA is more accurate than NN.
The use of android-based teaching materials in physics learning process at vocational high school

This study is purposed to describe the effectiveness of Android based teaching materials for physics learning at vocational school (SMK). Digital teaching materials that are made consist of learning materials equipped with video and animation that can be played through an Android-based smartphone. This research implemented pre experimental design based on one-shoot study case. The effectiveness of Android-based digital teaching materials is examined based on concepts understanding and respons of students. The subject of the research is 40 students of SMK in Surabaya. The concepts understanding is measured by exam test and the students' response is measured based on Likert-scales. The data analysis used is a descriptive statistic. Android-based digital teaching materials are effective if the average score of students' conceptual understanding ≥ 60 and the percentage of students' responses ≥ 61% (good or very good). The results obtained are: the average score of students' conceptual understanding x = (85 ± 6) and students' response is categorized very good (percentage of effectiveness are 85%). The results showed that Android-based digital teaching materials are effective to use in Physics learning process at vocational school.