Reviewerly/exHarmony huggingface dataset:
Making LTER Data FAIR: A workbench using DEIMS datasets and GBIF Tools

DEIMS-SDR (Dynamic Ecological Information Management System - Site and dataset registry, Wohner et al. 2019) is one of the largest repositories of long-term ecological research (LTER) datasets. It provides sophisticated searching tools by metadata elements and identifiers for all the 930 contained datasets, most of them from European sites. Whereas datasets' metadata are highly structured and searchable, datasets themselves have little standardization in terms of content, identifiers or license, making data integration difficult or cumbersome. Adopting the data FAIR guiding principles(Wilkinson et al. 2016) for LTER data would result in better data integration and reutilization to support knowledge discovery and innovation in ecological research. The Global Biodiversity Information Facility (GBIF 2019a). is the largest repository of species distribution data in the world, providing access to more than a billion records from over 43,000 datasets. GBIF is a good example of FAIR principles implementation: GBIF data is highly standardized, using Darwin Core (Wieczorek et al. 2012) for data and ecological metadata language (EML, Fegraus et al. 2005) for metadata, allowing record-level search; and has implemented globally unique and persistent identifiers for datasets and downloads. Relevant in this context is that GBIF has recently introduced a new data format intended for monitoring projects and sampling event protocols (GBIF 2019b). In this presentation, we explore the suitability of GBIF data formats and workflows to serve LTER datasets, and the work it may take to transform typical LTER datasets into these formats. For this exercise, we use some datasets available via the DEIMS platform, corresponding to the same territory, (Sierra Nevada, Spain (e.g. Bonet 2016, Bonet 2018) and transform them into the GBIF's sample-based Event core publish them in the GBIF data network, and then perform an analysis to assess how the standardized datasets work in practice, both among themselves and also with typical “occurrence-based” GBIF datasets. Finally, we discuss our findings and make recommendations for the GBIF and LTER communities.
Edge Acceleration of Deep Neural Networks (demo)

Running deep learning algorithms at the edge is a necessity in many industrial use-cases, especially in applications that use robots and drones in disaster recovery, surveillance, oil & gas operations etc. Current state of the art deep learning algorithms are extremely efficient in analysing image, audio, video and other time-series signals. However, their performance degrades considerably on constrained edge devices. In this demo, we show how standard pretrained CNN (Convolutional Neural Network) models can be partitioned for efficient parallel execution between constrained devices and also achieve real-time response.
Automatic Contrast Enhancement with Differential Evolution for Leukemia Cell Identification

Image enhancement techniques are needed to decrease the negative effects of blur or unwanted noise in image processing. In biomedical images, the quality of images is very important to achieve an adequate identification to detection or diagnosis purposes. This paper addresses the use of contrast enhancement to facilitate the identification of leukemia in blood cell images. Differential evolution algorithm is used to get parameters required to apply contrast enhancement specifically in the interest region in the image, which facilites the posterior identification of leukemic cells. Identification of leukemic cells is accomplished applying an edges extraction and dilatation. From this image, two types of neural networks are used to classify the cells like healthy or leukemic cells. In first experiment, a multilayer perceptron is trained with the backpropagation algorithm using geometric features extracted from image. While in the second, convolutional networks are used. A public dataset of 260 healthy and leukemic cell images, 130 for each type, is used. The proposed contrast enhancement technique shows satisfactory results when obtaining the interest region, facilitating the identification of leukemic cells without additional processing, like image segmentation. This way, computational resources are decreased. On the other hand, to identify the cell type, images are classified using neural networks achieving an average classification accuracy of $$99.83\%$$ .
Contesting the Truth - Intentional Forking in BFT-PoS Blockchains

Byzantine-Fault-Tolerant Proof-of-Stake Blockchains usually make strong guarantees regarding transaction and block finality, which makes them suitable technologies for consortium usage, where fluctuations in state are disturbing to business processes. Proof of Work Consensus however can not make finality guarantees as it employs the longest-chain-rule, where the longest chain dictates the current state, which can always be replaced by another longer chain. In this paper we propose a dispute mechanism for BFT-PoS Blockchains, where intentional forking is used to replace non-optimal transactions for business cases, where the blockchain is used to store only optimal solutions. Based on the longest-chain-rule of PoW algorithms validator nodes agree upon a more optimized chain via their regular consensus to make adjustments to the previously agreed upon state.
On the computational realization of formal ontologies: Formalizing an ontology of instantiation in spacetime using Isabelle/HOL as a case study

This paper shows in a case study that for the development, the presentation, and the computer-assisted verification of formal ontologies the usage of higher-order languages and associated proof assistant tools is highly beneficial. This case study demonstrates that the expressive power of a higher order logic in conjunction with a well developed infrastructure for theory development and presentation facilitate the development of formal ontologies in a way that is similar to the ways in which modern object oriented programming languages and associated IDEs facilitate the development of complex software. In particular ontology development in such an environment supports (a) the formal verification of the satisfaction of the axioms of a formal ontology in a class of structures that constitute its intended interpretation; (b) the computational instantiation of specific prototypical examples/models that guide the ontology development; and (c) the formal verification of proofs by demonstrating that the claimed theorems are indeed derivable from the axioms of the theory. Parallels to software development can be drawn for two reasons: Firstly, due to the non- or semi-decidability and the complexity of sufficiently expressive languages, the process of theory development, like software development, is computer assisted rather than fully automated. Secondly, the use of a higher order logic supports modularization, object orientation, model building and other features that greatly simplify the development of complex formal ontologies.
Developing System of Concepts in Polytechnic Education in Modern Conditions

Abstract The current world situation connecting with intensive development of mobile technologies actualizes the problem of training of engineering staff. The engineer of the future should not only to know the experience accumulated by previous generations, but also be able to analyze, develop and apply it in new conditions. The problem raises the questions connected with development of students’ system thinking in educational activity. System thinking is the product of educational activity, namely, the activity by consciousness and comprehension of learning concepts in their entirety properties. The main directions of educational process for solving this problem are presented in the article. In particular, it is proposed to broaden out notion about practice-oriented education in the system of higher technical education. As a matter of fact, educational process in the current conditions should represent large-scale practice. The traditional forms of educational process such as lecture, problem solving (practice, seminar), and laboratory work change and become the basic constituents of this educational practice. The main method that allows for educational practice to take place is dialogue.
Transmission of entangled photons studied by quantum tomography: do we need plasmonic resonances?

Abstract We performed quantum tomography to reveal the robustness of quantum correlations of photons entangled in polarisation after their interaction with plasmonic and nonplasmonic environments at normal incidence. The experimental findings clearly show that the visibility of quantum correlations survives the interaction, and that the presence of plasmonic resonances has not any significant influence on the survival of polarisation correlations for transmitted photon pairs. The results indicate that quantum states can be encoded into the multiple motions of a many-body electronic system without demolishing their quantum nature. The plasmonic structures and their resonances only enhance the overall transmission. Thus, they could benefit the pair detection rate, that is the number of coincidences per unit of time, but they do not affect the visibility of quantum correlations. We also performed quantum tomography of the entangled pairs after interaction with the continuous planar gold film as a function of the incidence angle. The latter illustrates the loss of polarization correlations that arises from the partially polarizing properties of the isotropic sample out of normal incidence. Our work shows that plasmonic structures are not needed to exploit quantum entanglement if the rate of coincidence counting is sufficient.
Modification of Optical Compound Microscope to Detect Malaria Blood Parasites Using Wright-Giemsa Staining Process with SURF Based Stitching

Hematology is the study of diseases and disorders causing irregularities in blood cells. Hematology is used to determine the irregularities in the blood cell containing different parasites ranging from mild to severe symptoms. Malaria has been found to be the 9th leading cause of death in the country. Malaria is caused by a bite of an Anopheles mosquito, an infected mosquito carrying a virus called Plasmodium parasite that will then be released onto a person's blood stream. The general objective of this study is to detect blood parasite from sample blood smears. This study is obtrusive and the device requires a blood sample to be tested, the researchers let expert medical doctors and laboratorians to take the blood from the patients. In hope of helping the telemedicine in the Philippines the device is able to capture images from the microscope and designed an algorithm that will process the captured images stitching it into one whole image. In addition, the device can output if the blood sample is positive or negative based on the dominant color seen from the images. The prototype is designed to replace the analytics of a skilled hematologist in rural cases whereas shortage in experts is a major problem.
A Survey on JSON Mapping With XML/RDF

As a data format, JSON is able to store and exchange data. It can be mapped with RDF (resource description framework), which is an ontology technology in the direction of web resources. This chapter replies to the question about which techniques or methods to utilize for mapping XML to JSON and RDF. However, a plethora of methods have been explored. Consequently, the goal of this survey is to give the whole presentation of the currents approaches to map JSON with XML and RDF by providing their differences.
Enriched J-Bit encrypting technique using data compression algorithms in data warehouse

Data compression has the best approach for compressing the data in every field. The size of databases is increasing the time to time, and it needs to compress for storage and retrieval. This research paper has to shrink the dimension of Figures through compression methods consuming various combinational algorithms. We are proposing a new compression algorithm created on J-Bit encrypting methods. This algorithm is planning, enhancing and excruciating the contribution of data by separating into two parts true for ‘1’ and false for ‘0’. Data compression algorithm compresses the data and shrinks the dimension of the data warehouse. A data warehouse has a huge capacity of storing data that can be recycled by J-Bit encrypting for statistics bits. It has to shrink the dimension of the data warehouse and decrease the recovery period. The future algorithm of data compression has named Enriched J-Bit Encrypting (EJBE). The procedure will control folder all separately bit of statistics of and to optimize, statistics after using decoding which data is confidential to lossless to compression.
Contextual Classification of Tumor Growth Patterns in Digital Histology Slides

Patch-based image classification approaches are frequently employed in digital pathology applications with the goal to automatically delineate diagnostically important regions in digital slides. However, patches into which a slide is partitioned are often classified singly and sequentially without additional context. To address this issue, we tested a contextual classification of image patches with soft voting applied to a multi-class classification problem. The context comprised five or nine overlapping patches. The classification is performed using convolutional neural networks (CNNs) trained to recognize four histologically distinct growth patterns of lung adenocarcioma and non-tumor areas. Classification with soft voting outperformed sequential classification of patches yielding higher whole slide classification accuracy. The F1-scores for the four tumor growth patterns improved by 3% and 4.9% when the context consisted of five and nine neighboring patches, respectively. We conclude that the context can improve classification performance of areas sharing the same histological features. Soft voting is a non-trainable approach and therefore straightforward to implement. However, it is computationally more expensive than the classical single patch-based approach.
Intra- and Intergroup Measurement Errors in Anthropometric Studies Carried Out on Face Images

AGraja, Katarzyna@Graja, Katarzyna Lipowicz, Anna@Lipowicz, Anna Bugdol, Monika N.@Bugdol, Monika N. Mitas, Andrzej W.@Mitas, Andrzej W. biometric photo can be a research material allowing to limit the participation of a measured and measuring person, shortening the process of data collection and reducing the measurement error. The aim of the study was to compare anthropometric indices obtained from measurements made by single measurer and team of measurers. Measurements made by single measurer were more accurate, they were characterized by a smaller standard deviation than those performed by team of measurers. Most of the mean values of anthropometric indices obtained by both methods differed significantly. The largest discrepancies were noted for indices based on the measurements of the height of the nose (n-sn) and width of the mandible (go-go), which indicates difficulties in the exact position of these anthropometric points in the photo. Selection of point position performed by single measurer ensures greater accuracy and repeatability of the technique used to apply points to the face image.
Study of Key Issues for the Combat Application of GNSS

The key issues for the usage of GNSS in the information warfare are studied. Four application fields are summarized from the function of the GNSS in operation. The "5W1H" question in the operational planning was proposed, and the importance of When and Where regional issues were analyzed in detail. This paper puts forward the problems of "2E" and "1SnD" in combat operations, as well as the cause and solutions. Finally, the signal jamming and anti-jamming problem are considered as the main factors for the Navigation Warfare, and some measures and solutions are proposed for the increasing of counter ability. The paper can be a reference of the warfighter and management personnel of the satellite navigation system.
Internet Data Extraction and Analysis for Profile Generation

Almost everything is stored on the Internet nowadays, and relying data on the Internet has become usual over the last years, directly increasing the value of data retrieval. Via Internet, data scientist can now find a way to access all the available data that is stored on the Internet, so they can turn that data into useful information. As people rely a lot of data on the Internet, they sometimes ignore the fact that all that data can be easily extracted, even when people think their information is safe or unavailable. In this article, we propose a system in where some data extraction techniques are going to be analysed in order to have an overview of the amount of data of a person that can be extracted from the Internet, and how that data is turned into information with an additional value in order to make data useful. The proposed system is going to be capable of retrieving huge loads of data from a person and process it using Artificial Intelligence, in order to classify its content to generate a personal profile containing all the information once its analysed. This research is based on personal profile generation of people from Spain, but it could be implemented for any other country. The proposed system has been implemented and tested on different people, and the results were quite satisfactory.
Witt: Querying Technology Terms Based on Automated Classification

Witt is a tool that systematically and automatically categorizes software technologies using original information extraction algorithms applied to Stack Overflow and Wikipedia. Witt takes as input a term, such as "django", and returns one or more categories that describe it (e.g., "framework"), along with attributes that further qualify it (e.g., "web-application"). Our comparative evaluation of Witt against six independent taxonomy tools showed that, when applied to software terms, Witt has better coverage than alternative solutions, without a corresponding degradation in the number of spurious results. The information extracted by Witt is available through the Witt Web Application, which allows users to query and explore Witt's categorization of software technologies by both obtaining the category for a term, and all the terms in a given category. On-line Portal: https://cs.mcgill.ca/~swevo/witt-web Video: https://www.youtube.com/watch?v=tPsp1M4Ua3w.
Rapid prototyping of cognitive agent simulations using C-BML transformations

Simulating battle management is an essential technique used in planning and mission rehearsal as well as training. Simulation development costs tend to be high due to the complexity of cognitive system architectures in such applications. Due to this complexity, it takes significant effort for a simulation developer to comprehend the problem domain enough to capture accurately in a simulation code. Domain-specific languages (DSL) play an important role in narrowing the communication gap between the domain user and the developer and hence facilitate rapid development. In command and control (C2) applications, the coalition battle management language (C-BML) serves as a DSL for exchanging battle information among C2 systems, simulations, and autonomous elements. In this article, we use a rapid prototyping framework for cognitive agents and demonstrate deployment of agent systems by adopting the model driven engineering approach. To this end, we extend the use of C-BML and automatically transform it in a cognitive agent model, which is then used for adaptive decision making at runtime. As a result, during a simulation run, it is possible to initialize and modify an agent’s goal reasoning model. The cognitive agent models are based on the deliberative coherence theory, which provides a goal reasoning system in terms of coherence-driven agents.
Literacy Village: A Breakthrough in Creating a Literate Culture

The low level of quality of Human Resources (SDM) is one of the concerns of Indonesia. This is caused by the level of reading interest of the Indonesian people that is very low. According to the census results of Central Bureau of Statistics (BPS) in 2015, the number of illiterates in Indonesia is about 5,984,075 people or 3.70%. Based on the results of the survey, it shows the critical issue of reading and literacy interests in Indonesia. Based on these problems, cooperation between society and the government is needed. One form of cooperations can be actualized by creating literacy villages as an effort to create a culturally literate society. The method used in this research is qualitative method. Through this approach, the researcher makes a complex picture, examines words, detailed reports from the respondents' views, and conducts studies in natural situations. The results showed that the community in Cipadung Village, Bandung participated in reducing the number of illiteracy through the literacy village movement. This activity is expected to be able to increase the reading interest of the people in Cipadung Village, Bandung towards literate society.
Data Loss Prevention and Data Protection in Cloud Environments Based on Authentication Tokens

Data leek issues represents a very high or critical importance within the cloud developers. Given the tremendous growth of cloud computing in the past 3 years the authors of this paper believe that an analysis of the implementation of modern security solutions for data leaks and data protection is of the utmost importance. Authentication tokens, provided by several different cloud providers and third parties, are one of the most common and useful tools that developers have in order to secure their applications. The goal of this paper is to analyze how these tokens are currently used and to provide a solution in order to improve their implementation by reviewing their compatibility with novel security concepts or challenges such as data tagging, hybrid data security algorithms, microservices deployment using Docker or serverless applications.
High-Throughput Low-Latency Encoder and Decoder for a Class of Generalized Reed–Solomon Codes for Short-Reach Optical Communications

Forward error correction is imperative in most communication systems. Applications like short-reach optical communications have a limited power and area occupation budget for encoder and decoder implementation. In this brief, we propose high-throughput low-latency Reed Solomon (RS) encoder and decoder architectures, compatible with a recently proposed class of generalized RS codes. The architectures are synthesized in TSMC 65 nm CMOS technology, showing a significant performance improvement with moderate hardware cost and power consumption. Throughput is increased by ×11.5 for the encoder, with respect to a conventional architecture, while the latency is reduced by ×10.3. These improvements respectively amount to ×14.7 and ×7.5 for the decoder.
GWU NLP Lab at SemEval-2019 Task 3 : EmoContext: Effectiveness ofContextual Information in Models for Emotion Detection inSentence-level at Multi-genre Corpus

In this paper we present an emotion classifier models that submitted to the SemEval-2019 Task 3 : EmoContext.The task objective is to classify emotion (i.e.happy, sad, angry) in a 3-turn conversational data set.We formulate the task as a classification problem and introduce a Gated Recurrent Neural Network (GRU) model with attention layer, which is bootstrapped with contextual information and trained with a multigenre corpus.We utilize different word embeddings to empirically select the most suited one to represent our features.We train the model with a multigenre emotion corpus to leverage using all available training sets to bootstrap the results.We achieved overall %56.05 f1-score and placed 144.