Reviewerly/exHarmony huggingface dataset:
Source, Propagation Path, and Site Characteristics Separated from Strong Motion Records during the 2016 Kumamoto, Japan, Earthquake Sequence

2016年熊本地震および一連の地震活動による熊本県および周辺177地点での強震記録に対してスペクトルインバージョン法を適用し, 震源・伝播経路・サイトの各特性を分離した．地震基盤から地表までのS波サイト増幅特性は, 平野や盆地内の観測点では他点に比べ1 Hz付近で大きな値を示した．分離した182地震（MJMA: 3.0-5.5）の震源スペクトルから推定した応力降下量には, 震源深さ依存性が見られた．また, 分離したQs値は, 0.5-10 Hzの周波数帯域でQs=73.5f0.83とモデル化された．
Extraction of Road Damage after an Earthquake using Images Captured by a Vehicle-Mounted Camera

本研究では, 車載カメラから取得した画像から, 地震による道路被害を深層学習によって自動で抽出することを目的としている．あらかじめ目視で被害の有無を分類した教師用画像を使用し, それらを畳み込みニューラルネットワーク（CNN）で深層学習し, 画像判別モデルの作成を行った．この画像判別モデルに学習に使用していない精度評価用画像300枚を判別させたところ, 道路閉塞の判別精度は87%, 無被害の判別精度は90%と高かったが, 道路被害の判別精度は66%とやや低かった．この結果を踏まえて, 地震直後の利用を想定した条件設定を行い, 道路変状の自動抽出シミュレーションを行った．本研究の手法は地震後の道路被害の早期把握に有効と考えられ, 道路管理者の震後対応に貢献できる．
Improving Security and Load Balancing in Cloud Environment

The demand of cloud computing is increasing day by day. The whole scenario is changing due to rise in issues with regard to the speed of access, privacy and security of data. The data repositories are showing a real time shift from general data base management systems to cloud storage. This step in technology has made our data prone to attacks from intruders and moreover, this has also created an increased load on the technology used. The sole aim of this research work is to improve the performance of computing systems and their security in cloud environment. A new technology is proposed to balance the load on cloud computing and to improve the quality of cloud service with PSO is presented in this research work.
Identity-Based Key Agreement

Identity-based public key cryptography was first proposed by Shamir in 1984 [665]. The idea is to avoid the need for public key certificates by making the public key publicly computable from the identification information of the owner. The identification information can include any desired fields such as real name, physical description or identification numbers. Identity-based cryptography avoids the difficulty of having to distribute public keys and thus avoids the need for a public key infrastructure, although parties still need to obtain and manage private keys.
Research on mathematical model and dynamic positioning control algorithm of six degrees of freedom maneuvering in marine ships

In this paper, based on the idea of separation modelling, through the analysis of ship motion and force, a mathematical model of ship motion under the action of external wind, waves and current is established in 6 free direction modelling. According to the force analysis of the ship, the calculatio n model of the 6-degree-of-freedom nude hull hydrodynamics and torque, the 6-DOF fluid dynamics and torque, the 6-DOF external force interference force and the moment, and the thrust and thrust of the pod propeller are established. Computing model. Finally, the computer simulation is carried out, and the fourth-order Runge-Kutta algorithm of numerical analysis method is used to calculate the 6-degree-of-freedom motion state of the ship. Through the experimental analysis and comparison with the related 6-degree-of-freedom literature, the 6-degree-of-freedom mathematical model designed in this paper is basically fulfil requirements. At the same time, according to the characteristics of ship dynamic positioning, the hybrid intelligent control strategy is used to design an adaptive dynamic positioning controller based on hybrid intelligent control. By testing and analysing the designed dynamic positioning controller, the results show that it responds in time and rises. The time is basically within the controllable range, has a certain ability to resist external environment, has good robustness, and the overshoot rate is relatively low, and the control quality is also good.
Research on Automatic Text Summarization Method Based on TF-IDF

In order to quickly obtain the main information contained in news documents, reduce redundant information and improve the efficiency of finding news with specific content. A Chinese text summarization method based on TF-IDF is proposed. This method uses TF-IDF to calculate the importance of each word in the article, and calculates the TF-IDF of each sentence based on the TF-IDF value of the word. In order to avoid the effect of sentence length on the calculation of sentence TF-IDF value. The sliding window is used to calculate the mean of all words TF-IDF in each sliding window using the given sliding window size. Use the value of the sliding window with the largest mean value in each sentence as the TF-IDF value of the sentence. Combined with other feature of the sentence, the importance of the sentence is calculated. The sentences with the specified length or number of words are intercepted and arranged according to the order of the articles to form a summarization of the article. After comparison experiments, the method is superior to the text summarization scheme based on TextRank method in terms of efficiency and effect.
Social Behavior Recognition in Mouse Video Using Agent Embedding and LSTM Modelling

Growing demands for automated analysis of animal behavior in areas such as neuroscience, psychology, genetics and pharmacology have been witnessed in recent decades. Some progresses have been made, but studies on social behavior analysis, which is more challenging, are rarely seen and almost all of them rely on hand-crafted features. Motivated by the concept of word embedding in NLP and the success of deep learning, we present a method that extracts features for both of the mouse agents involved in social behavior events and the scenario context using embedding networks, then uses an LSTM network to model the behaviors based on the agent and context embeddings. Our method is tested on a novel dataset, RatSI [8]. We find our mouse state embedding method outperforms traditional hand-crafted feature based methods.
The Impact of Stereo Rendering on the Perception of Normal Mapped Geometry in Virtual Reality

This paper investigates the effects of normal mapping on the perception of geometric depth between stereoscopic and non-stereoscopic views. Results show, that in a head-tracked environment, the addition of binocular disparity has no impact on the error rate in the detection of normal-mapped geometry. It does however significantly shorten the detection time.
Faster Real-Time Face Alignment Method on CPU

Face alignment for facial images captured in-the-wild is a challenging and important problem. In this work, we introduced a two-stage face alignment method in order to solve the problem of the normal face alignment method running slowly on the CPU. Using the residual error between ground truth and mean shape as a training label makes the network easier to converge. The joint input of heatmap and original data in the second stage deepens the feature learning of these landmarks, making the minimal network also has suitable performance. The convolution and pooling structure allow the network to be faster and have good learning ability. The test results on open datasets show that our method has a significant improvement in processing performance with real-time CPU speed of 1100 fps while maintaining high accuracy.
Hand Gesture-Based Vocalizer for the Speech Impaired

Communication is a basic form of interaction between two individuals. Humans have been using speech to communicate with each other since the ancient times. But for the speech- and hearing-impaired people, it becomes a real challenge to put up their views due to their disability. This project aims at developing a microcontroller-based vocalizer that converts hand gestures to audible sound. The system is easy to calibrate according to the user’s need, thus removing the complications of different hand sign conventions used around the globe. This system is further developed to work as a home automation system that uses hand gestures to control various appliances like fan or light.
An ontology learning based approach for focused web crawling using combined normalized pointwise mutual information and Resnik algorithm

The precedence of unexplored Uniform Resource Locators (URLs) is calculated in many existing works based on a linear combination of similarities of different texts of the web_page and the specified topic along with their associated weights. These weights, however, are chosen based on various methodologies like Term Frequency-Inverse Document Frequency (TF-IDF), so these weights can immediately create severe deviations from the priorities of unvisited web pages and also it will calulate the similarity only if the word occurs in the web page. It won’t consider the semantic similarity of the word in the web page. To overcome the troubles mentioned above, this article presents a new focused web crawler based on combined Normalized Pointwise Mutual Information (NPMI) and Resnik based semantic similarity algorithm, called as P-crawler. In the P-crawler, the records of an unexplored web page are made up of web page text, anchor text, title text, bold text and heading text of the web page. The experimental findings show that the suggested algorithm increases focused on crawler efficiency. In conclusion, the above technique is efficient and promising for focused web crawlers.
Towards Building a SAR Ontology : Some Ongoing Standardization and Research Efforts in Progress

The Synthetic Aperture Radar (SAR) is a widely used instrument in remote sensing applications due its all weather, daynight imaging capabilities. With emergence of many air borne, space-borne SAR platforms operating in multiple wavelength and polarization aimed at different applications, there is significant additions to SAR vocabulary and terminology. Since terminology forms the key for exchange of knowledge across information systems processing SAR data its standardization forms the key for improved interoperability. In this paper we describe our efforts to develop a framework for SAR ontology through discovery of concepts and relationships from its terminology. Our initial progress indicate that this efforts would greatly help in better standardization and ease in processing/utilizing data across several information systems seamlessly.
A Novel Crowd Counting Method via Deep Convolutional Neural Network

With the rapid growth of population and urbanization, urban management departments pay more and more attention to the monitoring of dense crowd. So it is important to estimate the number of people accurately for scene understanding. In this paper, we propose a new crowd counting framework with deep convolutional neural network. In order to overcome the perspective problem of the camera, the proposed framework uses multi filters with different sizes to enhance the discriminative power of the framework. In the proposed work, end-to-end approach is utilized to estimate the crowd density map. In order to speed up the training of the model and reduce the number of parameters, we processed the data with 1×1 filters, which could reduce the numbers of channels. Finally, the proposed framework combines the feature layers calculated by different sizes of filters to output the density map. In this way, we allow the model to merge different features calculated by different sizes of filters, rather than setting them manually. To evaluate the performance of the algorithm, the proposed model is compared with seven popular algorithms on three open datasets (ShanghaiTech dataset, UCF_CC_50 dataset and WorldExpo'10 dataset). The experimental results show that the proposed algorithm is effective. Specifically, comparing with the existing best-performing methods, we achieve an improvement of 7% the ShanghaiTech dataset and 8% on the most challenging dataset UCF _CC_50 dataset.
Finsterworlds

The Interactions website (interactions.acm.org) hosts a stable of bloggers who share insights and observations on HCI, often challenging current practices. Each issue we'll publish selected posts from some of the leading and emerging voices in the field.
Dictionary Learning and Confidence Map Estimation-Based Tracker for Robot-Assisted Therapy System

In this paper, we propose a new tracker based on dictionary learning and confidence map estimation for a robot-assisted therapy system. We first over-segment the image into superpixel patches, and then employ color and depth cues to estimate the object confidence of each superpixel patch. We build two Bag-of-Word (BoW) models from initial frames to encode foreground/background appearance, and compute object confidence at superpixel level using BoW model in both foreground and background. We further refine target confidence by depth-based statistical features to mitigate noise interference and the uncertainty of visual cues. We derive the global confidence of each target candidate at bag level, and incorporate the confidence estimations to determine the posterior probability of each candidate within the Bayesian framework. Experimental results demonstrate the superior performance of the proposed method, especially in long-term tracking and occlusion handling.
Personalized Travel Recommendation via Multi-view Representation Learning

Personalized travel recommendation has become a significant approach for people to find attractions in line with their interests from explosive information. Existing personalized travel recommendation methods always focus on travel history records but attach limited attention to acquire the high-level representation of user’s travel preferences from multi-view heterogeneous information. In this paper, we present a personalized travel recommendation approach based on multi-view representation learning. In the proposed approach, four-view representation obtained from rating, comment, image and regional popularity of attractions are exploited to acquire user’s travel preferences by deep learning and pair-wise optimization. Specially, the aesthetic features are extracted to describe the visual appeal of image, and the regional popularity is introduced to represent the popularities of attractions in a region for personalized recommendation. Finally, an attention module is utilized to automatically learn the significances of four views to the user, and then the predicted preferences is obtained through a weighted average pooling strategy. Extensive experiments constructed on the real-world dataset we collected from tourism websites have demonstrated that the proposed method based on multi-view representation learning is effective and significantly improves the accuracy of personalized travel recommendation.
Some Results of Designing an IIR Smoothing Filter with P- Splines

Requirements for modern digital filters are high and depend on some selective properties of such filters. IIR filters can ensure the narrowest transition width of signal omission and suppression with the same DF order. This study proposes to design and analyze a software-based IIR filter, a digital filter (DF) that is based on simple mathematical basis facilitates subsequent interpretation of data (estimation of derivatives, trends, forecast of time series, etc.). A modified penalty spline has been used as a model for such digital software-based IIR filter. A recurrent form of the P-spline computation has been created for the the IIR spline filter. The new modified recurrent P-spline can be used for stand-alone or group data. The recurrent spline filter has been investigated by the methods of linear dynamic systems. The obtained difference equation of the spline transform corresponds to a linear recursive DF with constant parameters. Frequency and time characteristics of the DF, being the main characteristics system and instrument functions, have been obtained analytically. The system function has been used to investigate the frequency characteristics of the spline filter and it has been found out that the spline filter is low frequency throughout the entire variation range of the spline parameters. Such filter characteristics correspond to a second-order aperiodic segment or a system with delay, which is typical for grouped data processing. The selective properties of the spline filter are clearly illustrated with the width of the instrument function, which depends on the spline parameters. Studies have shown that the passband bandwidth is mostly influenced by the group data size (the number of non-recursive terms of the difference equation). The stability of the recursive spline filter has been also estimated analytically based on the characteristic equation and the Hurwitz criterion. DF is always stable only when the conjugation of recurrent coefficients occurs at the beginning of a spline segment. Frequency characteristics of the spline filter have been confirmed in the time domain by histograms of the remainders of two recovered model functions.
Standard Setting to Relate an English Reading Comprehension Test to the CEFR: A Comparison of Modified Angoff and Bookmark Methods

본 연구는 국내에서 개발된 대규모 영어 능력 시험에 포함된 독해 시험을 유럽 언어 공통기준(Common European Framework of Reference for Languages)의 읽기 척도와 연계하기 위해 전문가 패널을 구성하고 수정된 앙고프 방법과 북마크 방법을 이용하여 분할점수를 산출하였다. 유럽 언어 공통기준의 6개 능력 수준을 나누는 독해 시험의 분할점수를 산출하였고, 그 결과에 대해 내적, 외적, 절차적 준거에 따라 평가하여 타당성을 검토하였다. 수집된 점수의 분석 결과, 전문가 패널 구성원이 판단한 분할점수의 표준편차가 라운드를 거칠수록 작아지는 경향을 보였고, 수정된 앙고프 방법과 북마크 방법을 통해 얻은 분할점수 결과는 매우 유사했으며, 수준설정 이후 패널 구성원의 대다수가 최종 분할점수에 확신을 갖는다고 응답했다. 또한 대부분의 패널 구성원들이 수준설정 준비 및 진행과정, 설명이 명확하였고 판단을 내릴 때 도움이 되었다고 평가하였다.The purpose of this study is to link a reading comprehension subtest in a large-scale English proficiency test to the Common European Framework of Reference for Languages(CEFR). A panel consisting of 13 members made judgments using the modified Angoff and bookmark methods. Based on these judgments, cut scores that divide test takers into the six levels of the CEFR were derived, and the validity of the cut scores was evaluated using internal, external, and procedural criteria. Analysis showed that the standard deviation of the judgments decreased across rounds; the cut scores derived from the two methods were similar; and almost all of the panel members expressed confidence in the final cut scores on the post-standard-setting survey. Furthermore, most panel members responded that they found the preparation materials, the standard setting process, and the facilitators’ explanations to be clear and helpful when making judgments.
Error types of machine translation of popular science text

This paper summarizes and categorizes the common error types of machine translation of popular science text, which includes mistranslation of technical terms, omission, over-translation, mistranslation of clauses, mistranslation of attributive sequences, and mistranslation caused by rigidly converting English special sentence structures. The categorization of these common errors in machine translation might be helpful as a reference for machine translation program improvement.
Safety Management in Smart Ships

Smart Ships represent the next-generation of ships and they use ICT to connect all the devices on board to support integrated monitoring and safe management. In such cyber-physical systems, software has the responsibility of bridging the physical components and creating smart functions. Safety is a critical concern in such kind of systems whose malfunctioning may result in damage to equipment and injury to people. In this paper, we deal with this aspect, by identifying two interconnected sub-systems: shipboard power system and emergency management. The proposed architecture is developed through the H-entity multi-paradigm approach, in which heterogeneous technologies are interconnected. We propose to extend the MOISE+ organisational model to deal with systems of H-entities.
Design of Home Power Energy Management System Using Mixed Integer Linear Programming (MILP) Based on Extreme Learning Machine (ELM) Dynamic Pricing

Indonesia's electricity consumption has grown at an average of 4.2% per year over the past 15 years. This causes several problems such as environmental pollution and depletion of fossil energy sources. In order to overcome these problems, one of the things that can be done is saving electricity consumption. In this paper, home power management system (HPMS) application is developed with the goal for saving electricity consumption in order to minimize the daily operation cost. The HPMS system consists of smart electrical appliances, power units (grid and photovoltaic systems with energy storage), main controllers, web applications and communication networks. At the beginning of the day, the main controller will collect electricity equipment operation schedules and electricity prices from the grid and PV. Moreover, MILP, which is formulated with the operating constraints for electrical equipment, electricity prices (obtained from ELM predictions), and power resources, is used for solving the problem and will recommend the schedule of electricity resources with the most optimal costs for consumers. The simulation has been successfully tested with the results that HPMS can reduce electricity costs by more than 30%.
Identifying Fake Account in Facebook Using Machine Learning

Nowadays people rely vigorously on online social networks (OSNs) that have attracted cyber criminals’ interest in performing malicious acts. Furthermore, with the existence of illicit businesses that provide transactions of fake account services. This study focuses on identifying fake accounts in Facebook which is the most widely used in OSN. The methodology of this study is started with data collection, features identification and learning classifiers. The first process is to collect information on true and fake Facebook accounts. The second process is the use of Facebook user feed data to comprehend user profile activity and to identify a comprehensive collection of 5 characteristics that play a critical role in discriminating against fake users with true users on Facebook. Lastly, we use these characteristics and the identification of main classifiers based on machine learning that perform well in the assignment of identification out of a total of 3 classifiers namely K-nearest neighbour (KNN), support vector machine (SVM) and neural network (NN). The result shows that KNN generate 82% of the highest performing classifiers with classification precision. The findings have revealed that “likes” and “remarks” add well to the job of detection. However, although the precision value is not highly perfect, the findings of this study shows that most fake accounts are able to imitate actual users.
Nutrition Education and Psychosocial Stimulation Improves Child Development in Rural Early Childhood Education in Indonesia

Early childhood education (Pendidikan Anak Usia Dini - PAUD) in Indonesia is an educational effort intended to preschool-aged children through stimulation to support childrenâs growth and development. The aim of this study was to investigate the effects of nutrition and psychosocial stimulation to the child development in rural PAUD. A quasi-experimental design consisted baseline and end-line data collections of control (baseline: n=68; end-line: n=61) and intervention (baseline: n=69; end-line: n=63) groups of children under five years and their mothers were investigated for 12 months following the nutrition education and psychosocial stimulation. In addition, the investigation also carried out to the teachers at PAUDs. The study was conducted at 6 PAUDs in Dramaga Sub-district (control) and 4 PAUDs in Tamansari Sub-district (intervention) in the City of Bogor, West Java, Indonesia. The outcomes included characteristics of study participants, mothersâ nutritional knowledge-attitude-practice, mothersâ and teachersâ psychosocial stimulation knowledge and practice, childrenâs nutritional status and development. Nutrition education increase mothersâ nutritional knowledge score of 4.7 points (p The intervention improves mothersâ nutritional knowledge and practice, mothersâ and teacherâs psychosocial knowledge and practice, and child development. The intervention can be successfully implemented by mothers and teachers in rural PAUD.