Reviewerly/exHarmony huggingface dataset:
Enhancing the detection of buried anthropogenic features thought a cloud-computing Python procedure

&amp;lt;p&amp;gt;The necessity of sustainable development for landscapes has emerged as an important theme in recent decades. Moreover, past landscape reconstruction enables a better understanding of human resilience to climatic and environmental changes in different periods and locations, and illustrates examples of sustainable development in the past. Free and open-source (FOSS) datasets of satellite imagery offer considerable opportunities for landscape heritage stakeholders both for recording and monitoring activities. In this research, a completely FOSS-cloud procedure to enhance the detection of palaeo-landscape features is presented. Sentinel - 2 satellite imagery has been retrieved in the Google Earth Engine dataset collection and analysed through a Python script code realized in Google Colaboratory. A multi-temporal approach has been adopted to investigate the potential of satellite imagery to detect buried features along with Spectral Index (i.e., RGB, False Short Wave Infrared Colour and Bare Soil Index) and Spectral Decomposition analysis (i.e., Hue, Saturation and Value, Tasselled Cap Transformation and Principal Component Analysis). This procedure has been tested in the Po Plain (Northern Italy), chosen because it is characterized by human-landscape interaction since the Mid-Holocene. Thanks to its complex settlement and land-management history, the Po Plain represents an ideal laboratory to assess the potentiality of satellite imagery to enhance riverscapes&amp;amp;#8217; palaeo-features. &amp;amp;#160;The outputs obtained can be visualized directly in the Google Colaboratory browser or downloaded via Google Drive for further graphical applications or spatial analysis. The buried features detected have been checked through the available geomorphological and archaeological literature; published case studies interpreting the occurrence of buried features served as a benchmark to validate the script code developed. This research represents one of the first applications of the GEE Python API in landscape studies. The main advantages of this procedure consist of: i) being FOSS, all the software used here are open-licensed; ii) working in cloud, no powerful hardware is necessary to run the script code; iii) high adaptability, changing the ROI is possible to calculate SI and SD outputs for any area of the world; iv) very basic coding skills are required to adapt the code to a ROI with different environmental characteristics. The development of FOSS-cloud procedures could support the identification, conservation and management of cultural and natural heritage anywhere around the world. In remote areas or where local heritage is threatened as a result of political instability, climate change or other factors, FOSS-cloud protocols can facilitate the application of new scientific methods and enable the dissemination of and access to scientific information.&amp;lt;/p&amp;gt;
Trusting records in the cloud

Trusting Records in the Cloud is a compilation of the most recent research generated by the research project of the International Research on Permanent Authentic Records in Electronic Systems (Inte...
Artificial Intelligence Technology to Improve Creativity, Cognitive Ability and Emotional Ability in an Educational Environment

Nowadays, in the high-tech era of informatization, big data, and artificial intelligence, computer technology has developed rapidly, and artificial intelligence technology has gradually penetrated into all walks of life and is fully applied. As education is the top priority in China, the integration of Internet + education and information technology is deepening day by day, and the network and information construction of colleges and universities has gradually moved from digitalization to intelligentization, which has promoted the change of teaching and learning environment. This study adopts the transfer learning method, utilizes the deep learning model of artificial intelligence technology, integrates classroom teaching analysis method, identifies and analyzes students' classroom behavior, and verifies the effectiveness and accuracy of the algorithm through experiments. It can realize students' all-round self-evaluation, promote students' individuality and standardization, and thus improve students' creativity, cognitive ability and emotional ability.
Deformed Statues of Ramses II: a Study of Disruptive Restorations in Egypt in the 21st Century

The research paper discusses the problem of distorting the body proportions in Ramses II statues during the inadequate restoration operations carried out in the 21st century in Egypt. It also discusses the correct artistic body proportions and formation of Ramses II, which must be followed when assembling and restoring statues depicting this figure, at the same time taking into consideration other, unrestored statues, reliefs and paintings of the same subject. In addition, it looks at global restoration technologies that can help to solve the problem of improper restoration in Egypt.
Gaussian Soft Decision Trees for Interpretable Feature-Based Classification

How can we accurately classify feature-based data such that the learned model and results are more interpretable? Interpretability is beneficial in various perspectives, such as in checking for compliance with exiting knowledge and gaining insights from decision processes. To gain in both accuracy and interpretability, we propose a novel tree-structured classifier called Gaussian Soft Decision Trees (GSDT). GSDT is characterized by multi-branched structures, Gaussian mixture-based decisions, and a hinge loss with path regularization. The three key features make it learn short trees where the weight vector of each node is a prototype for data that mapped to the node. We show that GSDT results in the best average accuracy compared to eight baselines. We also perform an ablation study of the various structures of covariance matrix in the Gaussian mixture nodes in GSDT and demonstrate the interpretability of GSDT in a case study of classification in a breast cancer dataset.
How to Recognize and Explain Bidding Strategies in Negotiation Support Systems

Effective use of negotiation support systems depends on the systems capability of explaining itself to the user. This paper introduces the notion of an explanation matrix and an aberration detection mechanism for bidding strategies. The aberration detection is a mechanism that detects if one of the negotiating parties deviates from their expected behaviour, i.e. when a bid falls outside the range of expected behaviour for a specific strategy. The explanation matrix is used when to explain which aberrations to the user. The idea is that the user, when understanding the aberration, can take effective action to deal with the aberration. We implemented our aberration detection and our explanation mechanisms in the Pocket Negotiator (PN). We evaluated our work experimentally in a task in which participants are asked to identify their opponent’s bidding strategy, under different explanation conditions. As the number of correct guesses increases with explanations, indirectly, these experiments show the effectiveness of our aberration detection mechanism. Our experiments with over 100 participants show that suggesting consistent strategies is more effective than explaining why observed behaviour is inconsistent. An extended abstract of this article can be found in [].
Accelerating Genetic Variant Discovery for DNA Sequencing

Summary form only given, as follows. A complete record of the panel discussion was not made available for publication as part of the conference proceedings. DNA sequencing is the process of determining the precise order of nitrogenous bases within a DNA molecule. It is now an indispensable tool for determining the cause of genetic diseases and for developing associated treatments. Next-generation sequencing (NGS) is currently the fastest sequencing technique and can sequence the short fragments in a massively parallel fashion, achieving orders of magnitude higher throughput than the first-generation sequencing technique. However, as the throughput of NGS growth exponentially, the succeeding data processing and analysis are still excessively time consuming. In this talk, I will present a genetic variant discovery SoC that can perform the entire NGS data analysis workflow. The engine for preprocessing achieves a significant latency reduction compared to the prior art. A dynamic programming processing engine is proposed to perform both inexact matching and variant calling. The chip achieves four orders of magnitude energy efficiency improvement over the GPU solution.
Culture-Creation Component of Architectonics of Humanities Information and Education Spaces: Value Context of Postmodern Era

There is no shortage of research materials intended to meet the teachers’ need for continuing/life-learning professional development as a creator of the social and educational activities in the national public school spaces in terms of Pedagogy and Knowledge Content Efficacy. The purpose of this manuscript is to stand back from the surface detail of formal education of the teacher and to provide an overall perspective on the teacher’s creative mission as a communicator, a person of high language and communication culture, for harmony and psychological balance within intercultural communication created via different devices. Concept and methodology of this research, based in the main on the principles of humanism which reflect on the ideas of axiological and process approaches that concern the concepts of engaging teachers in a process of recurrent professional development. Results of this research include the comprehensive model that consists of three value concepts for a) creating the humanities educational spaces via acculturation; b) teaching the four skills and spoken communication skills via current devices of language teaching and ideological trend of acculturation; c) involving participants into ‘genuine’ communication via bringing social oriented and language learning activities reflected on the creative mission of national public school education that works its way up through culture experience in the classroom. In conclusion this article presents a handful of arguments of the significance of the growth and level of multilingual culture to affect the diversity of the personality’s vital forces, its development providing opportunities for professional and social self-realization, adaptation, and increasing creative potential in the process of critical thinking and creative communication via Technology, Pedagogy, and Knowledge Content Efficacy of current methodology of teaching and learning in the classroom. By the same token we have grounds for clarifying and verifying our vision on the Concept of “Humanities environment/spaces” in the frameworks of the comprehensive model of its architectonics, i.e. a harmony of the culture-creation elements via languages of education, the polyphony of knowledge, technology, spheres of recurrent professional development of a current educator.
AN ANALYSIS OF READING TASK PRESENTED IN ENGLISH TEXTBOOK FOR TENTH GRADE SENIOR HIGH SCHOOL

This research investigated the quality of English textbook and teacher’s perspective toward the textbook entitled “Pathway to English untuk SMA/MA Kelas X Kurikulum 2013 yang Disempurnakan Kelompok Peminatan” published by Erlangga Publisher with the framework of checklist criteria proposed by Pusat Perbukuan (2014). Qualitative method was applied in this research and a case study as the research design. The data were collected through document analysis and interview. The data analysis technique is by analyzing the content appropriateness of reading task in the textbook through checklist criteria proposed by Pusat Perbukuan (2014) and interview with the teacher who used the textbook. The findings of this research revealed that the textbook entitled “Pathway to English untuk SMA/MA Kelas X Kurikulum 2013 yang Disempurnakan Kelompok Peminatan” published by Erlangga Publisher is compatible with the framework of checklist criteria proposed by BSNP rubric assessment. The teacher’s perspective toward the textbook classsified as good. The textbook was classsified as good based on the checklist criteria from Pusat Perbukuan (2014) rubric assessment got 95% based on fulfillment average proposed by BSNP.
WEIR-P: An Information Extraction Pipeline for the Wastewater Domain

We present the MeDO project, aimed at developing resources for text mining and information extraction in the wastewater domain. We developed a specific Natural Language Processing (NLP) pipeline named WEIR-P (WastewatEr InfoRmation extraction Platform) which identifies the entities and relations to be extracted from texts, pertaining to information, wastewater treatment, accidents and works, organizations, spatio-temporal information, measures and water quality. We present and evaluate the first version of the NLP system which was developed to automate the extraction of the aforementioned annotation from texts and its integration with existing domain knowledge. The preliminary results obtained on the Montpellier corpus are encouraging and show how a mix of supervised and rule-based techniques can be used to extract useful information and reconstruct the various phases of the extension of a given wastewater network. While the NLP and Information Extraction (IE) methods used are state of the art, the novelty of our work lies in their adaptation to the domain, and in particular in the wastewater management conceptual model, which defines the relations between entities. French resources are less developed in the NLP community than English ones. The datasets obtained in this project are another original aspect of this work.
PERANCANGAN PROGRAM PENGENALAN ISYARAT TANGAN DENGAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

Convolutional Neural Network is one of the Deep Learning Algorithms. CNN itself is developed from Multilayer Peceptron (MLP) method. CNN and MLP are algorithms that focused on processing data in two dimensions form, such as pictures or sounds. CNN is made with the principle of translation invariance. That means CNN is able to recognize objects at various possible positions. There are 150 sign language images that are classified using Alexnet in this system. Alexnet is Krizhevsky's work at developing CNN method as a clessifier. CNN architecture developed by Alex has eight feature extraction layers. The layer consists of five convolution layers and three pooling layers. In its classification layer, Alexnet has two fully connected layers, each of them has 4096 neurons. At the end of the layer, there are classifications into 5 categories using softmax activation. The average accuracy of the classification results even reaches 100%.
DiCyc: Deformation Invariant Cross-Domain Information Fusion for Medical Image Synthesis

Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-domain medical image synthesis tasks particularly due to its ability to deal with unpaired data. However, most CycleGAN-based synthesis methods cannot achieve good alignment between the synthesized images and data from the source domain, even with additional image alignment losses. This is because the CycleGAN generator network can encode the relative deformations and noises associated to different domains. This can be detrimental for the downstream applications that rely on the synthesized images, such as generating pseudo-CT for PET-MR attenuation correction. In this paper, we present a deformation invariant cycle-consistency model that can filter out these domain-specific deformation. The deformation is globally parameterized by thin-plate-spline (TPS), and locally learned by modified deformable convolutional layers. Robustness to domain-specific deformations has been evaluated through experiments on multi-sequence brain MR data and multi-modality abdominal CT and MR data. Experiment results demonstrated that our method can achieve better alignment between the source and target data while maintaining superior image quality of signal compared to several state-of-the-art CycleGAN-based methods.
A Development of Maternity Application Using Cross-Platform Framework

This study focused on the decrease in the obstetric medical hospital in the area. The decrease in the East area in Hokkaido is remarkable, and maternity needs long distance travel to a city hospital. Some advanced medical centers require distances of 160 km. Education for risk and troubleshooting is necessary for maternity that travels long distances. This study summarizes the concept of system design that supports maternity based on 5 years of system development practices. The concept of system design has set three phases. The first phase is a closed integral design. The second phase is a closed modular design. The third phase is an open modular design. The evaluation of the system design concept was implemented by developing a prototype system for smartphone applications. The function of the application makes maternity aware of early hospital visits. As some functions, measured labor time data can be recorded and shared in the cloud. In addition to sharing of data, the cloud can send information on maternity courses by municipalities. The development of the application design is to record the position information of the maternity in the cloud and share the movement trajectory.
Analysis of Effect of Noise Removal and Image Smoothing for Automated White Blood Cells Detection and Counting

White blood platelet recognition and checking is extremely basic platelet cells being wellness marker. A character's health is chosen utilizing all out platelet number. A substance of the platelet specifically the white platelet cells, a crimson platelets and cells layout a realm of the wellbeing. For recognizable proof and treatment of disorders like paleness, leukemia and etc. In research center, blood cell counting is performed with the guide of the utilization of hemocytometer and microscope blood images. This methodology offers flawed and questionable results that rely upon medicinal specialist ability. This endeavor is hard and monotonous. Expectation of the examinations to give a study on pc vision gadget that can find and estimate the wide assortment of white platelet cells inside the platelet test images utilizing images preparing calculations. In this paper considering the image handling for a checking of blood white platelets. image preparing calculations include five steps: input image, preprocessing, image improvement, feature extraction division, highlight extraction and checking calculation. The goal is to take a gander at the exceptional framework of WBC checking and Recognition of the research headings.
A History of Bayesian Inference in Educational Measurement

This chapter surveys the history of Bayesian inference in educational measurement and testing. The mid-century revival of Bayesian inference led the measurement community to look backward and forward: backward to recast earlier developments in Bayesian terms, and forward to advance the field of measurement by way of using Bayesian approaches. In the 1970s, researchers began investigating Bayesian approaches to tackle problems in criterion-referenced testing. This work was closely related to the research on prediction reviewed. Bayesian efforts addressed this principal problem of classifying students with respect to the criterion, as well as problems of determining test length, setting cut scores, evaluating reliability, and marrying probabilistic classifications with utilities in a decision-theoretic frame. Concurrently, researchers such as Embretson were drawing on developments in cognitive psychology to define constructs, construct items, and build measurement models to analyze resulting data. In the 1980s, work in artificial intelligence began to develop in ways that would soon influence educational measurement in several ways.
Augmented Reality Game Development using Unity3D

Mobile augmented reality (mobile AR) enables virtual content such as 3D models, animations and annotations to be placed on top of a real world objects in any context. We applied mobile AR to develop AR action game to expand the AR genre of games in Android. In this game, the player controls a warrior to fight another warrior in a tournament to become the champion. We present the development of the AR game using Unity 3D and Vuforia. Unity 3D provides a simplified way of developing any game by providing all the basic tools and physics system for the game environment. Vuforia plugin of Unity 3D provides the necessary API for detecting planes and object placement in AR environment. A mixed-method usability evaluation on children and university students indicated that especially interaction with AR content and user interface clarity were improved in the Unity 3D version. We expect game engines such as Unity 3D will become essential for developing AR applications in future.
Network Intrusion Detection Method Based on Stacked Denoising Sparse Autoencoder and Extreme Learning Machine

Aiming at the problem of low detection accuracy and high false positive rate caused by noise doping in network data, and at the same time improving detection speed, a network intrusion detection based on stacked denoising sparse autoencoder and extreme learning machine (sDSAE-ELM) is proposed. First, the stacked denoising sparse autoencoder is used to automatically extract the robustness characteristics of the network data, and then the extreme learning machine is used for classification. Experiments on the NSL-KDD dataset show that the network intrusion detection method based on sDSAE-ELM has strong noise robustness when processing high-dimensional noisy data, while shortening the training time. And high detection accuracy and low false positive rate have been achieved.
3D Reconstruction from CT Images Using Free Software Tools

Computed Tomography (CT) is a commonly used imaging modality across a wide variety of diagnostic procedures (World Health Organisation ). By generating contiguous cross-sectional images of a body region, CT has the ability to represent valuable 3D data that enables professionals to easily identify, locate, and accurately describe anatomical landmarks. Based on 3D modeling techniques developed by the field of Computer Graphics, the Region of Interest (ROI) can be extracted from the 2D anatomical slices and used to reconstruct subject-specific 3D models. This chapter describes a 3D reconstruction pipeline that can be used to generate 3D models from CT images and also volume renderings for medical visualization purposes (Ribeiro et al. ). We will provide several examples on how to segment 3D anatomical structures with high-contrast detail, namely skull, mandible, trachea, and colon, relying solely on the following set of free and open-source tools: ITK-SNAP (Yushkevich et al. ) and ParaView (Ahrens et al. ).
A Comprehensive Review on Motion Planning of Bi-differential Wheeled Robots in Cluttered Environment Using Various Artificial Intelligent Techniques

Robotic Mobility in dynamic environments has gained popularity among many researchers over past few years. The environment where human beings perform their normal operations includes narrow spaces, sharp turns, slopes and steps. In addition, some times the field of operation may be found to be chemically hazardous, radioactive or reconnaissance in nature. It is not quite easy to develop intelligent robots with very precise mechanism suitable for specific navigational purposes. The balancing on two wheels and spin on the spot ability with smaller footprints of the bi-differential wheeled mobile robots distinguish them from other multi-wheel counterparts. In this paper some of the basic literatures regarding the motion planning of mobile robots are discussed using different artificial intelligence techniques. The robots can able to reach a respective goal positions by successfully avoiding the intermediate collisions among themselves and with the obstacles using these techniques as control scheme.