Reviewerly/exHarmony huggingface dataset:
Identifying variable interaction using mutual information of multiple local optima

Identifying the interaction of search variables of black-box optimization problem is beneficial for optimization task. However, very little research pay attention to the quality of information source, i.e. what information is beneficial for identifying the interactions between variables. In this paper, we propose a new method that utilizes multiple local optima as information sources to identify the interaction between variables. First, a multimodal optimization algorithm is used to search for multiple local optima of the optimization problem. Then, hierarchical clustering is used to cluster and discretize local optima. Finally, the interaction between variables is quantified using the mutual information of local optima. Experimental results on three 12-dimensional multimodal problems show that the proposed method can effectively identify the interactions among decision variables.
Integrating Historical Content with Augmented Reality in an Open Environment

Museums offer visitors a variety of historic content. However, museums still rely on traditional methods of delivering content to visitors through the use of signage to compliment an exhibit. A research study was conducted to determine the feasibility of developing Mobile Augmented Reality experiences for a museum in an open environment. Three examples of AR experiences were investigated. First, the Serpentine Path AR experience is presented, providing contextual AR, showing the development of a historically significant garden pathway with primary source historical documents, and images. The Horse Chestnut Tree AR experience follows, presenting a miniature 3D model of a tree that is a vital part of the outdoor museum grounds. The Main House AR experience illustrates AR over time, showing the contrasting progression of a home and surrounding foliage. Through mobile delivery, users are provided with an AR experience either on the physical site grounds or from a remote offsite location. Approximately two-thirds of the users (68%) considered the AR experience good for the Horse Chestnut Tree experience, with 90% of the users responding that the Serpentine Path AR experience, which included audio, provided access to historical content they would not receive anywhere else. Similarly, 96% of the users felt that the Main House AR experience provides unique access to historical content which was not available elsewhere. Evaluation of the mobile AR experience identified areas where users learned more, based on the information provided to them during their visit, with the identification of areas for future work.
Performative passivity: Lessons on phenomenology and the extended musical mind with the Danish String Quartet

Based on interviews with the Danish String Quartet, this chapter points to limitations in the philosophical literature that construes musical absorption primarily as involving various forms of reflective and pre-reflective self-awareness. It then shows the paradigmatic experience of musical absorption as being expressed in statements such as ‘It wasn’t I who played the music’ or ‘The music played/came by itself’. To understand this type of experience, the chapter presents a new framework of ‘performative passivity’, relying on an analysis of the Husserlian notion of passivity or passive synthesis. Finally, it shows how the body, the performer’s emotions, and the nature of the performed music are centrally involved in performative passivity.
STUDENTS’ PROBLEMS IN TRANSLATING ENGLISH INTO INDONESIAN AT ENGLISH DEPARTMENT OF UIN ANTASARI BANJARMASIN

This research discussed the students’ problems in translating English into Indonesian at English Department of UIN Antasari Banjarmasin.This research aim at identifying and describing the students’ problems in translating English into Indonesian. This is a descriptive with qualitative research. The population of this research was 138 students and the sample was 36 students which taken randomly. The data were collected through test, questionnaire, and interview; and then analyzed by using descriptive qualitative analysis, and concluded inductively. The research found out that there are three problems that faced by the seventh semester students of English Department in translating English into Indonesian at UIN Antasari Banjarmasin. There are: Less of mastering English vocabularies, accuracy in diction, and Less of Understanding meaning both lexical and grammatical. This research also suggested for the lecturer to be creative to manage activities of translation.
Outlier Detection of Internet of Vehicles

With the development of the Internet of Things (IoT) and automobile industry in recent years, the Internet of Vehicle (IoV) has become a future direction of automobile development. Due to the large amount of vehicles, the opening of wireless media, the high-speed movement of vehicles and the impact of the environment, it is inevitable to produce abnormal data in IoVs including data tampering, loss, disorder and so on. However, there are few systematic research results for outlier detection of IoVs. The usability of the existing outlier detection schemes and their performances are not yet evaluated. To this issue, we select six applicable schemes and propose the outlier detection process for IoVs. Then we evaluate the comparison performances of the proposed schemes on real vehicle data collected by a Focus car.
Technology of Educational Electronic Publications’ Quality Evaluation

Electronic publication (EP) is a complex product, which integrates the achievements of modern technology, content of the subject area and teaching methods, as well as design and artistic qualities.The transition from book reviewing to electronic publication reviewing is similar to the exit from the plane into multidimensional space.It's only natural that pedagogical, methodological and creative possibilities are much higher there, but it makes determining the technology and criteria for quality evaluation more difficult.
IMPLEMENTASI SIMPLE MULTI ATTRIBUTE RATING TECHNIQUE UNTUK PENENTUAN TEMPAT PEMBUANGAN AKHIR

&lt;p&gt;&lt;em&gt;Garbage is a difficult problem to solve. Data in the Environmental sector still uses traditional or manual methods in processing. Data processing needs to be done to get accurate data and spreading the information more effectively and efficiently. Traditional or manual methods are irrelevant. It needs a system that makes data processing more effective, efficient and accurate.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;Simple Multi Attribute Ranking Technique (SMART) is one of various Decision Support System methods that can provide assistance, efficiency and accuracy when processing of data. This method will be implemented in data processing to determine the best Final Disposal Areas (FDA) in the districts and will showing visually using a Geographic Information System (GIS). With the presence of this system, it will provide an overview of the best areas for final disposal areas to be built in the districts. In this case, data processing is carried out in Pemalang District, Central Java Province.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;The fundamental difference between the SMART Method and the other methods is its simplicity and efficiency in processing multi criteria data. Parameters that greatly affect the ideal or are Land Use and Hydrogeology. This Geographic Information System provides the main menu, criteria weight values, relative weight values, alternative values and criteria, assessment and mapping factor values. &lt;/em&gt;&lt;/p&gt;
Partial rigid diffeomorphism for measuring temporal change of pancreatic cancer tumor

The goal of our research is to describe the growth of a tumor region captured in a temporal series of MRI images. The tumor growth description requires registration of the given images in order to compensate the body deformation. It should be noted that one can apply an existing non-rigid registration technique for the compensation but it would deform the tumor region in a source image and the resultant description of the tumor growth would be inaccurate. We hence developed a Large Deformation Diffeomorphic Mapping (LDDMM) method that can non-rigidly registers given images while keeping the shape of the tumor region in a source image. The proposed method was applied to a series of MRI images of a KPC mouse and the results showed our method could successfully described the tumor growth.
Discovering Foreign Keys on Web Tables with the Crowd

Foreign-key relationship is one of the most important constraints between two tables. Previous works focused on detecting inclusion dependencies (INDs) or foreign keys in relational database. To discover foreign-key relationship is obviously helpful for analyzing and integrating data in web tables. However, because of poor quality of web tables, it is difficult to discover foreign keys by existing techniques based on checking basic integrity constraints. In this paper, we propose a hybrid human-machine framework to detect foreign keys on web tables. After discovering candidates and evaluating their confidence of being true foreign keys by machine algorithm, we verify those candidates leveraging the power of the crowd. To reduce the monetary cost, a dynamical task selection technique based on conflict detection and inclusion dependency is proposed, which could eliminate redundant tasks and assign the most valuable tasks to workers. Additionally, to make workers complete tasks more effectively and efficiently, sampling strategy is applied to minimize the number of tuples posed to the crowd. We conducted extensive experiments on real-world datasets and results show that our framework can obviously improve foreign key detection accuracy on web tables with lower monetary cost and time cost.
Improving the Real-Time Searching in the Organizational Memory

The real-time data processing constitutes a critical area when talking about real-time decision making. Strong decisions are based on recommendations for describing the associated course of actions, but the real-time processing gives a very short time for searching them. The Processing Architecture based on Measurement Metadata is a data stream engine oriented to measurement projects, which supports the decision making through an organizational memory. The search space related to the organizational memory is initially in-memory limited using the structure of the measurement projects. Given a project, the related projects are ordered based on a given scoring from its structural definition. Here, a new structural coefficient based on the text similarity, which is computed from the textual definition of each descriptive attribute of a project is introduced. This allows better scoring of the related projects, even when its definitions could be affected by human errors or multiple definitions. The scoring is critical when in a given situation, a project has not specific experience for recommending, in such context, the recommendations from the near projects are served. The pabmm_sh library is outlined and a simulation on its associated processing times for the similarity computing are introduced based on the token definition for a measurement project. The library adds a new alternative perspective in the processing architecture for driving the searches into the organizational memory. It can update 2000 projects less than 1 second, keeping the individual processing time of each project under 1 millisecond.
Correction to: Using Shape Expressions (ShEx) to Share RDF Data Models and to Guide Curation with Rigorous Validation

By mistake this chapter was originally published non open access. This has been corrected.
Neural Machine Translation between

This work explores neural machine translation between Myanmar (Burmese) and Rakhine (Arakanese). Rakhine is a language closely related to Myanmar, often considered a dialect. We implemented three prominent neural machine translation (NMT) systems: recurrent neural networks (RNN), transformer, and convolutional neural networks (CNN). The systems were evaluated on a Myanmar-Rakhine parallel text corpus developed by us. In addition, two types of word segmentation schemes for word embeddings were studied: Word-BPE and Syllable-BPE segmentation. Our experimental results clearly show that the highest quality NMT and statistical machine translation (SMT) performances are obtained with Syllable-BPE segmentation for both types of translations. If we focus on NMT, we find that the transformer with Word-BPE segmentation outperforms CNN and RNN for both Myanmar-Rakhine and Rakhine-Myanmar translation. However, CNN with Syllable-BPE segmentation obtains a higher score than the RNN and transformer.
A Visualisation of 3D Lung Anatomy with Augmented Reality as Interactive Medical Learning

Abstract Breathing is the process of breathing air or the movement of oxygenated (O 2 ) external air into the body or lungs and exhaling air containing carbon dioxide (CO 2 ) as residual oxidation out of the body. There are several organs that act as the respiratory system organs such as the nose, oesophagus, pharynx, larynx, trachea, bronchus, and lungs. Augmented reality can be utilized as a learning medium for the recognition of anatomy of the respiratory system organs presented in 3D with animation. In this research, to build the object to be used, researchers use blender software for object modelling process. To be able to display 3D objects, researchers apply target image techniques, namely by using a marker obtained from the book Sobotta Human Atlas Anatomy is adapted to the needs of preclinical medical students. When AR Camera captures the marker, the camera detects the pattern contained in the marker, and then matches the data stored in the database. When the pattern is found and there is conformity to that stored in the database, the application will display the 3D objects of the organ of the respiratory system with Augmented Reality technology. The optimal distance of marker detection is 15 - 35 cm, with a 30° – 90° inclination angles.
The BIALL Irish Group

Abstract In the article, Jennefer Aston reflects on the involvement in BIALL of law librarians from Ireland. She writes about the development of the BIALL Irish Group, some of the challenges that Irish law librarians have faced, notes how the legal sector in Ireland grew in the 1980s and 1990s and describes the collaboration and the activities of law librarians in Ireland. She also invites former Chair of the Association, John Furlong, and current President, Renate ní Uigín, to give some thoughts too.
Comment: Bayes, Oracle Bayes and Empirical Bayes

Empirical Bayes methods are intriguing, and have gained in significance by present day big data applications.Despite their early introduction, they are still not fully understood.It is a pleasure to read the review by a "statistical rock star" [13], who stood at the beginning of the methods and more recently opened our eyes to their importance for large scale inference.Empirical Bayes combines Bayesian ways of thinking about data and what some call "frequentist" methods, often maximum likelihood.The main point of my discussion is to highlight connections to nonparametric and high-dimensional Bayesian methods, which have seen a big development in the past 20 years.In the second paragraph of Section 6, Efron writes: "which is to say that standard Bayes is finite Bayes with N = ∞" and goes on to describe a fully Bayesian approach (consisting of a hyperprior h(g) on the density of the parameters θ i ) as an "uncertain task".I may not be full Bayes enough to say this with absolute certainty, but would think that nowadays most Bayesians would politely disagree and consider the setting a standard one, with a Dirichlet process prior as a "default" choice [17,18,1,20].Then the setting is described by the hierarchy:This is the model of Efron's Sections 1-4 augmented with a prior on G, and could still be preceded by extra levels to construct the parameter α (a finite distribution) of the Dirichlet process DP(α), in particular its total mass (called "prior precision").We restrict to the case that the observations in step three are Gaussian; it would be worth while to extend our discussion to Poisson observations, as in Efron's Section 5.
A data-driven compression method for transient rendering

Monte Carlo methods for transient rendering have become a powerful instrument to generate reliable data in transient imaging applications, either for benchmarking, analysis, or as a source for data-driven approaches. However, due to the increased dimensionality of time-resolved renders, storage and data bandwidth are significant limiting constraints, where a single time-resolved render of a scene can take several hundreds of megabytes. In this work we propose a learning-based approach that makes use of deep encoder-decoder architectures to learn lower-dimensional feature vectors of time-resolved pixels. We demonstrate how our method is capable of compressing transient renders up to a factor of 32, and recover the full transient profile making use of a decoder. Additionally, we show how our learned features significantly mitigate variance on the recovered signal, addressing one of the pathological problems in transient rendering.
Commercialization of multimodal systems

research-article Commercialization of multimodal systems Share on Authors: Philip R. Cohen View Profile , Raj Tumuluri View Profile Authors Info & Claims The Handbook of Multimodal-Multisensor Interfaces: Language Processing, Software, Commercialization, and Emerging DirectionsJuly 2019 Pages 621–658https://doi.org/10.1145/3233795.3233812Published:01 July 2019 1citation106DownloadsMetricsTotal Citations1Total Downloads106Last 12 Months9Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Study on the Application of Blockchain Technology in International Trade Settlement

무역거래는 서로 상이한 국가에 위치한 매매당사자간의 거래로 물품의 흐름과 서류의 흐름이 분리되어 이루어진다. 특히, 물품의 권리를 표창하는 선하증권은 무역대금결제과정에서 매우 중요한 역할을 한다. 그러나 선하증권의 도착지연으로 인한 물품인도의 지연을 초래하거나 관련 서류들간의 내용상 불일치로 인하여 분쟁이 발생하고 있다. 이러한 문제점을 해결하기 위해 거래 당사자들과 국제기구들은 많은 노력을 해 왔다. 특히, 거래의 중개자로 은행의 개입은 무역과 관련된 문제를 해결하는데 많은 도움을 주고 있으나, 이로 인한 거래비용의 증가와 복잡한 절차, 종이서류 위주의 거래 관행은 오히려 무역 발전을 저해하는 요인이 되기도 한다. 따라서 본 연구에서는 블록체인 기술에 관한 기본적인 내용에 대해 살펴본 후, 현행 무역결제 프로세스에서 발생하는 문제점을 해결하기 위해 블록체인 기술의 활용 필요성을 검토하고, 블록체인 기술을 활용한 무역결제 프로세스 모델을 제시한다. 그리고 이러한 블록체인 기반 무역 결제 프로세스 적용에 따른 영향 및 기대효과에 대해 살펴본 후, 블록체인 기술의 활용을 위한 향후 과제를 제시하고자 한다.Purpose : This study analyzes the problems of the trade settlement system, verifies the necessity of applying the blockchain technology in trade settlement process in order to solve these kinds of problems, and then suggests an application model of blockchain technology. Research design, data, methodology : This study was conducted based on various literature related to trade settlement and blockchain technology. This study consisted of 5 sections. Section I is an introduction, and in section II, the basic contents of the blockchain technology are reviewed. In section III and IV of this study, the problems of the current trade settlement process are analyzed and the application of the blockchain technology in the trade settlement process is considered respectively. Finally, conclusions are presented in section V. Results : In this study, a trade settlement model using blockchain technology is proposed. Through using this blockchain based trade settlement model, it is expected to solve problems related to existing trade settlement such as clarification of trade terms, real-time tracking of documents and goods, solution of double payment problems and conflict prevention. Conclusions : Current paper based trade settlement processes have many problems, such as process complexity, excessive costs, delays in document arrival, inconsistency between shipping documents, and fraud by forgery and alteration of shipping documents. As a solution for solving these problems and facilitating trade settlement, this study proposed a trade settlement model using blockchain technology, and examined future works to apply them.
Spline adjustment for modelling solar intermittences

One of the reasons why photovoltaic technology is not massively installed is its variation in production. This variation is due to intermittences in the solar resource. Based on real data from the microgrid of the Renewable Energy Development Center (CEDER, Spain) and another scenario in Xalapa (México), the study determines the solar intermittences produced and grouped monthly. The period of data acquisition, in the first study, was from May 30th, 2012 to March 3rd, 2015 with the help of a Baseline Surface Radiation Network (BSNR) team; in the second, 2014 measurements were obtained from a meteorological station certified by the National Meteorological System (SMN). The analysis is based on the determination of monthly frames of reference for radiation by third-degree spline adjustments with smoothing, using the JUMP statistical application software (JMP © 2009, SAS Institute, version 8.0.2). The results of the analyses have provided important information to understand the unstable appearance of solar radiation and, in turn, will be the basis of a control system to optimize photovoltaic production.
Digital Image Watermarking in Special and Frequency Domain

Requirement of proficient and valuable copyright protection approaches is needed because of the prompt and fabulous evolution of multimedia and the outspread use of the Internet. For countering the copying, forgery and unauthorized handling of images and video system are required to tackle this issue. Due to lack of such methods, storing images over a public network is prime concern.  In this paper, digital watermarking is implemented in special domain (LSB) and frequency domain (DWT) which reveals that DWT perform better than LSB. Two examples of each of digital image watermarking are taken under consideration and obtained results are compared. This comparison reveals that the DWT based digital image watermarking outperform the corresponding LSB based watermarking technique.
NukaBot: Research and Design of a Human-Microbe Interaction Model

We describe the design, implementation and on going evaluation processes of “NukaBot”, a system built to realize Human to Microbe Interaction, aimed to assist production of fermented food. Our syst...