Reviewerly/exHarmony huggingface dataset:
Lifelog Generation Based on Informationally Structured Space

As the problem of the increased number of elderly people and the decreased number of children in Japan has arisen recently, the development of robot partner and intelligent room for monitoring and measurement system has become a main topic. On the other hand, the stability of both social rhythm and biological rhythm is very important for extension of healthy life expectancy. It is difficult for elderly people to understand the current stability of social rhythm and biological rhythm in daily life. First of all, we have to generate detail lifelog. We define lifelog composed of daily human behavior. In this paper, first, we show different types of classification methods for human activity. We explain our computation model for elderly care. Next, we introduce several human behavior measurement methods for lifelog. And we show lifelog generation in indoor, outdoor and by using robot partner. Finally, we discuss the effectiveness of the proposed methods and future works.
Research on New Sprinkler Heat Insulation System Based on 3D Printing Technology

Abstract Firstly, the mechanical structure of the sprinkler is designed by Solidworks in this paper. The wire of the sprinkler is made of waste PET. Then the theoretical feasibility is proved by further simulation analysis with ANSYS software, and the thermodynamic theory of heating and heat dissipation is analyzed and utilized. Finally, the practical operation feasibility of the new 3D printer print nozzle insulation system is verified by physical printing test.
Ensemble of Deep Belief Network and Bayesian Adaptive Aggregation for Regression

Ensemble modeling of Neural Networks is a strategy where multiple alternative models (ensemble members) are constructed and then their forecasts are ensembled using various combination approaches. Ensemble of Neural Networks has proved the concept behind this strategy. Deep neural network offers potential opportunities to overcome traditional ensemble of neural networks. This paper proposes an deep belief networks ensemble (DBN). The ensemble members of DBN are constructed with different number of epochs Which results in superior generalization ability. The outputs of these DBNs are aggregated by a Bayesian model averaging method. The proposed Bayesian adopted ensemble of DBNs is evaluated on two benchmark data sets. Comparison of the proposed model is evaluated with simple averaging and single DBN over a number of forecasting measuring that shows better performance of the proposed model.
Cocks’ Identity-Based Encryption in the Standard Model, via Obfuscation Techniques (Short Paper)

Identity-based encryption (IBE) is an attractive primitive in modern cryptography. Cocks first gave an elegant construction of IBE under Quadratic Residuosity ( $$\mathsf {QR}$$ ) assumption. Unfortunately, its security works only in the Random Oracle (RO) model. In this work, we aim at providing Cock’s scheme with provable security in the standard model. Specifically, we modify Cocks’ scheme by explicitly instantiating the hash function using indistinguishability obfuscation in two different ways which yield two variants of Cocks’ scheme. Their security are promised under well-defined selective-ID and adaptive-ID model respectively. As an additional contribution, we adapt the same method into the Boneh, LaVigne, Sabin (BLS) $$e^{th}$$ residuosity based IBE cryptosystem and obtain an adaptive chosen-ID secure scheme under Modified $$e^{th}$$ Residuosity ( $$\mathsf {MER}$$ ) assumption.
Non-linear Autoregressive Neural Networks to Forecast Short-Term Solar Radiation for Photovoltaic Energy Predictions

Nowadays, green energy is considered as a viable solution to hinder $$ CO_{2} $$ emissions and greenhouse effects. Indeed, it is expected that Renewable Energy Sources (RES) will cover $$ 40\% $$ of the total energy request by 2040. This will move forward decentralized and cooperative power distribution systems also called smart grids. Among RES, solar energy will play a crucial role. However, reliable models and tools are needed to forecast and estimate with a good accuracy the renewable energy production in short-term time periods. These tools will unlock new services for smart grid management. In this paper, we propose an innovative methodology for implementing two different non-linear autoregressive neural networks to forecast Global Horizontal Solar Irradiance (GHI) in short-term time periods (i.e. from future 15 to 120 min). Both neural networks have been implemented, trained and validated exploiting a dataset consisting of four years of solar radiation values collected by a real weather station. We also present the experimental results discussing and comparing the accuracy of both neural networks. Then, the resulting GHI forecast is given as input to a Photovoltaic simulator to predict energy production in short-term time periods. Finally, we present the results of this Photovoltaic energy estimation discussing also their accuracy.
Continuous Scale Adaption for Efficient Box-Based Scene Text Detection

Due to the diversity of text size in scene images, the current box-based methods employ a large amount of fixed-size anchors with different scales to match texts, thus leading to high computational cost. In this paper, we propose to learn the scales of texts and adjust the sizes of anchors accordingly, which can largely reduce the numbers of anchors and therefore significantly reduces the time cost. Moreover, compared to discrete scales used in previous methods, the learned scales are continuous and more reliable. Additionally, we propose Anchor convolution to exploit scaled feature for each anchor by dynamically adjusting the sizes of receptive fields according to the learned scales. Experimental results show that the proposed method significantly improves the computational efficiency of box-based framework(reduce the running time from 0.73s to 0.28s) and enhances its robustness against small texts, while achieving competitive performance with other methods.
Intelligent Control Navigation Emerging on Multiple Mobile Robots Applying Social Wound Treatment

In robotics, learning new tasks is a complex solving problem. This learning depends on the environment, the robot configuration, the difficulty of the problem task, even the prior knowledge. Reinforcement learning has been widely employed for learning from scratch and policy search; however, it is very time-consuming. Multi-robots, as collaborative learners, have been proposed to improve the speed of learning in robotics. In this paper, we propose a collaborative intelligent control navigation strategy in robots, including a social wound treatment approach, such that robots can jointly learn how to avoid obstacles and move freely around the environment. This collective learning about social treatment aims to detect unexpected or inefficient behaviors of the robots, allowing them to redirect the right tasks with more agility, as observed in some animals. Experimental results over a multiple homogeneous robot system simulation validated our proposal.
Review on Mechanisms to Ensure Confidentiality and Integrity of the Data in Cloud Storage

The paper reviews few approaches on securely storing data in the cloud storage. Most of the cloud computing models aim towards ensuring that the data confidentiality and integrity prevails although users of the cloud use internet for using the services provided by the cloud computing system. The first mechanism considered involves checking that data integrity has been preserved or not through use of hash-function and metadata approach. The second mechanism involves making use of SHA-3 hash function to provide confidentiality as well as integrity for the data stored in cloud storage through use of the Mobile Cloud Computing (MCC) services.
Automated Assessment of ER Model Using the Domain Knowledge

It is a challenging task to develop a complete and correct Entity Relationship Model (ERM) from the requirements. Quality of artifacts in the later stages of software development (e.g. logical database design, physical database design, and the final product) dependents on the conceptual models. Domain Knowledge (DK) plays a key role while extracting the artifacts from requirements. It is agreed upon that most errors in the early stages of software development are due to the lack of adequate DK. In this paper, we are proposing an automated assessment approach, which focuses on some major issues of ERM such as completeness and correctness. The automatically extracted ERM is used as a reference for the assessment of a manually developed model from the same set of requirements. We trained the Skip-gram model of word2vec for extracting the DK, which is used for assisting in errors detection and ERM’s labels matching. As a case study, we considered models from the business domain. Inputs of this automated technique are reference model, DK, and the model to be evaluated. The output is a list of errors (indicating the sources) and suggestions. The results show the proposed approach is having a noticeable improvement over the existing approaches.
Visual Servoing Control Based on Reconstructed 3D Features

Image-based visual servoing (IBVS) is an automatic control technique which uses image information to control the camera moving to a desired location. To solve the problems of unsmooth trajectories and slow convergence rate caused by classical IBVS during the servo process, a visual servoing method based on reconstructed three-dimensional features is proposed. First, the camera pose was estimated, to obtain the depth information of the target by solving the Perspective-Four-Point (P4P) problem. Then, the new three-dimensional features for visual servoing is constructed. Finally, the visual servoing model is established based on classical IBVS control structure in terms of cuboid. Simulation results show that the proposes method has higher convergence speed and smoother convergence trajectories compared with the classical IBVS method.
A Multi-platform Microanalysis Approach to Unravelling Geological Problems: a Case Study from Olympic Dam South Australia

Journal Article A Multi-platform Microanalysis Approach to Unravelling Geological Problems: a Case Study from Olympic Dam South Australia Get access AKO Netting, AKO Netting Adelaide Microscopy, University of Adelaide, Adelaide, Australia Corresponding author: angus.netting@adelaide.edu.au Search for other works by this author on: Oxford Academic Google Scholar BP Wade, BP Wade Adelaide Microscopy, University of Adelaide, Adelaide, Australia Search for other works by this author on: Oxford Academic Google Scholar MR Verdugo-Ihl, MR Verdugo-Ihl School of Chemical Engineering, University of Adelaide, Adelaide, Australia Search for other works by this author on: Oxford Academic Google Scholar L Courtney-Davies, L Courtney-Davies School of Chemical Engineering, University of Adelaide, Adelaide, Australia Search for other works by this author on: Oxford Academic Google Scholar CL Ciobanu, CL Ciobanu School of Chemical Engineering, University of Adelaide, Adelaide, Australia Search for other works by this author on: Oxford Academic Google Scholar N Cook, N Cook School of Chemical Engineering, University of Adelaide, Adelaide, Australia Search for other works by this author on: Oxford Academic Google Scholar K Ehrig, K Ehrig BHP Olympic Dam, Adelaide, Australia Search for other works by this author on: Oxford Academic Google Scholar A Slattery, A Slattery Adelaide Microscopy, University of Adelaide, Adelaide, Australia Search for other works by this author on: Oxford Academic Google Scholar S Gilbert, S Gilbert Adelaide Microscopy, University of Adelaide, Adelaide, Australia Search for other works by this author on: Oxford Academic Google Scholar K Neubauer K Neubauer Adelaide Microscopy, University of Adelaide, Adelaide, Australia Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 25, Issue S2, 1 August 2019, Pages 2432–2433, https://doi.org/10.1017/S1431927619012893 Published: 01 August 2019
Practical Exploration of Association Rules Mining Algorithm in Open Education

Abstract Open education is a new type of education, which is produced along with the development of modern information technology. It is a teaching model based on modern information technology. With the continuous reform of China’s education system, the autonomy of students in major colleges and universities is increasing. However, because students do not understand the course, it is very difficult to select courses. Therefore, it is especially important to excavate the university teaching platform database. This paper briefly introduces the concept of association rules mining algorithm, mainly studies the application of association rules mining algorithm in the open education learning system, in order to provide meaningful guidance for students’ elective courses, thus improving students’ academic performance.
A New AI Open Problem: WUGU Chess

Game is an early research topic in the field of AI, and it is also a very active and representative research direction. In this paper, we introduce a traditional chess game, WUGU chess, which is popular in Weifang area of Shandong Province, P.R. China. There are obvious differences of WUGU chess compared with other chess games. The special rules often lead to a reversal of the situation. So we introduce WUGU chess as a new artificial intelligence (AI) open problem.
HASHTAGS AS A SPECIAL TYPE OF THE INTERNET DISCOURSE

Internet communication and Internet discourse is the subject of study of many disciplines: sociology, psychology, management, rhetoric, journalism and some others. But the linguistic aspect of Being a universal means of mass and individual communication, Internet discourse enabled the instantaneous transmission of information regardless of distance and geographic location. A huge virtual area of the Internet offers the user a wide range of platforms: media, blogs, information sites, cinema, literature, wikis projects, shops and auctions, advertising, payment and search engines, e-mail, chats, forums, messengers, social networks, radio, television, information portals, etc. Internet communication is characterized by the following features: 1) polyphony; 2) Hypertext and interactive features of the Network; 3) anonymity and distantness. Internet discourse, like any other type of discourse, is characterized by a number of structural and lexical-grammatical features. A hashtag is a label for content. It helps others who are interested in a certain topic, quickly find content on that same topic. People use hashtags in their Instagram and Twitter posts but there is not enough knowledge about their real meaning and what they are used for. According to its structure, a hashtag can be a single word, an abbreviation, an invented combination of letters and numbers, or a phrase. If it is a phrase, there can be no spaces between words. All letters and numbers must run together without spaces in a hashtag. It is not possible to have punctuation or symbols in your hashtag (other than the # symbol at the beginning). Numbers are allowed, but it is necessary to have at least one letter with the numbers — hashtags cannot consist entirely of numbers. According to its meaning, a hashtag can be related to a great variety of topics: private life, personal characteristics, nature, holidays, events, activities, emotions, travelling and many others.
Twitter utilization in application of small area estimation to estimate electability of candidate central java governor

Abstract A survey was designed to estimate electability of a governor candidate in the province of Central Java in 2018. The estimate was planned to be done at the province level. However, it is also interesting to do estimation at the district level. The problem is that the sample size at some district is too small or even zero so that the estimate would have inadequate precision. Using a small area estimation method, this research tried to have good estimation by utilizing information from Twitter and other auxiliary data in the modeling. To evaluate the estimation, we calculated the mean square error (MSE) of the estimated electability using resampling method. We noted that both indirect and direct estimations at the province level have equal precision, but the indirect estimation outperformed the direct estimation at the region level. We also compare our result to the election result of Governor Election.
Mentoring executives and directors. David Megginson and David Clutterbuck. New York: Routledge, 2018, 160 pages, $180 hardcover

Personnel PsychologyVolume 72, Issue 3 p. 482-484 BOOK REVIEW SECTION Mentoring executives and directors. David Megginson and David Clutterbuck. New York: Routledge, 2018, 160 pages, $180 hardcover Rik Nemanick, Rik Nemanick The Leadership Effect, Washington University in St. Louis, St. Louis, MissouriSearch for more papers by this author Rik Nemanick, Rik Nemanick The Leadership Effect, Washington University in St. Louis, St. Louis, MissouriSearch for more papers by this author First published: 01 August 2019 https://doi.org/10.1111/peps.12350Read 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 onFacebookTwitterLinkedInRedditWechat Volume72, Issue3Autumn (Fall) 2019Pages 482-484 RelatedInformation
Optimization of 3D Object Placement in Augmented Reality Settings in Museum Contexts

Augmented Reality (AR) is a technology that can be used to provide personalized and contextualized information regarding physical objects in form of digital overlays. We use this technology in our research project Ambient Learning Spaces (ALS) to provide museum visitors with specific additional digital 3D information regarding the exhibits presented. With this technology, we enable museum curators to use a new form of transporting contextualized information without the need for additional physical space. However, the use of AR brings up new challenges for the creation and placement of digital contents into the museum space. In this context, we ran an anonymous survey on the use of AR in museums throughout Germany and studied responses of (N = 133) museum professionals. The results indicate that, although many museum professionals are interested in using AR technology, currently the integration is very costly and complex. This paper proposes a system we developed in a user-centered design process with a museum. This system provides an interface that helps museum professionals to cope with the complexity when placing and aligning digital 3D objects in their exhibition using mobile devices. Through this solution, visitors have the chance to experience the virtual objects spatially embedded in the exhibition by the curators themselves. In multiple user studies during the development phase we measured the usability of the interface. The findings show that the system provides a high degree of usability and can be applied effectively by museum professionals.
Temporal Segment Convolutional Kernel Networks for Sequence Modeling of Videos

Sequence modeling is crucial for video action recognition. In this paper, we propose temporal segment convolutional kernel networks (TS-CKN), where we take advantage of convolutional neural networks to facilitate the extraction of appearance features, while time sequence is modeled with deep kernel networks. We employ the kernel methods to capture time-varying information of videos and propose a training method for kernel map approximation by matrix backpropagation. This leads to the model named deep kernel networks which can be easily integrated with existing deep learning models such as Resnet. Our approach also samples several video clips sparsely in the video and unifies class predictions from all clips. More importantly, all parameters of our model can be learned by stochastic optimization in an end-to-end manner. We evaluate our method on two standard action recognition datasets including HMDB-51 and UCF-101, achieving the state-of-the-art results.
A Collaborative Filtering System Using Clustering and Genetic Algorithms

Recommender systems have been essential these days to assist online customers to acquire useful information. However, one of the popular types of the systems called memory-based collaborative filtering suffers from several fundamental problems in spite of its main advantages such as simplicity and efficiency. This study addresses the scalability problem which is one of major problems of the system. We employ a clustering technique to handle the problem and propose a novel idea using the genetic algorithm to enhance the performance of the system in terms of prediction accuracy, not to mention scalability. Experimental results demonstrated successful performance achievements of the proposed method under various data conditions.
Adaptasi dan Validasi Skala Kecanduan Game Online untuk Siswa SMK

For most teenagers, the game is a fun entertainment activity. On the other hand, many conflicting views claim that modern online games can cause addictions which in turn leads to depression, isolation and dependence. The purpose of this research is to validate the online game addiction scale in SMK Negeri 2 Cilacap. Validity and reliability test results show that the scale consists of eight dimensions, namely online game usage, psychological dependence, compulsive usage, withdrawal, school or work related issues, sleep problems, family-related issues, and time-management issues. Subjects in this study amounted to 30 students of SMK. The results of statistical analysis show that the online game addiction scale is a valid and reliable instrument for measuring online game addiction that will be used school counselor to help measure the online game addicted to students in SMK Negeri 2 Cilacap. Keywords: Validity, Online Game Addiction.
Analysis of Several Difficult Problems in Assembly Language Programming

In assembly language teaching process, Some problems in assembly language programming are difficult to explain.In order to improve the quality of teaching, the best way is to verify them by experiments.This paper is an experimental verification of several main problems encountered in the teaching process, and gives a detailed explanation.This paper includes the specific allocation of memory in runtime, the calculation of transfer address in transfer instruction, the differences between hard interrupt and soft interrupt.The purpose of this paper is to provide a clear understanding of the difficult problems in assembly language programming.