Reviewerly/exHarmony huggingface dataset:
Understanding CIL and the Role of Dynamic Assemblies

When you are building a full-scale .NET Core application, you will most certainly use C# (or a similar managed language such as Visual Basic), given its inherent productivity and ease of use. However, as you learned in the beginning of this book, the role of a managed compiler is to translate *.cs code files into terms of CIL code, type metadata, and an assembly manifest. As it turns out, CIL is a full-fledged .NET Core programming language, with its own syntax, semantics, and compiler (ilasm.exe).
The Beijing Declaration on Research Data

Abstract CODATA, the Committee on Data of the International Science Council (ISC), wrote The Beijing Declaration on Research Data after discussions regarding current data policy issues during their yearly conference, held in China in September 2019.
Boosting Trees

Bagging trees and random forests base their predictions on an ensemble of trees. In this chapter, we consider another training procedure based on an ensemble of trees, called boosting trees. However, the way the trees are produced and combined differ between random forests (and so bagging trees) and boosting trees.
SSRN and open access for non-institutional scholars

Originally published in the Citizen Technologist (UC Berkeley Center for Technology Society and Policy). Academics and open access advocates expressed concern when Elsevier acquired SSRN, the previously free and open social sciences research network. It appears now that those fears may have come true, as recent copyright takedowns by SSRN indicate a shift away from open access. The loss of a well-established open access research network will be deeply felt in the social sciences research community, including in my own field of law and public policy.
Introduction to Blockchain and Cryptocurrenncy

In recent years blockchain technology has become mainstream research topic because of its decentralized, peer to peer transaction and anonymity properties. There are several applications of blockchain which are secure and easy as compare to the current techniques. One of the applications is a smart contract. Smart contracts are lines of code which are stored on a blockchain and automatically executed when the conditions defined by the it (developer) are met. This smart contract with the addition of blockchain technology can do task fast and with high security. In this paper we have developed a smart contract for a generalized notary application on solidity, Ethereum and the application is tested using the truffle suite. Furthermore, applications and their methodology for notary applications are also mentioned.
Using artificial neural network condensation to facilitate adaption of machine learning in medical settings by reducing computational burden (Preprint)

<sec> <title>BACKGROUND</title> Machine learning applications in the health care domain can have a great impact on people’s lives. At the same time, medical data is usually big, requiring a significant number of computational resources. Although this might not be a problem for the wide adoption of machine learning tools in high-income countries, the availability of computational resources can be limited in low-income countries and on mobile devices. This can limit many people from benefiting from the advancement in machine learning applications in the field of health care. </sec> <sec> <title>OBJECTIVE</title> In this study, we explore three methods to increase the computational efficiency and reduce model sizes of either recurrent neural networks (RNNs) or feedforward deep neural networks (DNNs) without compromising their accuracy. </sec> <sec> <title>METHODS</title> We used inpatient mortality prediction as our case analysis upon review of an intensive care unit dataset. We reduced the size of RNN and DNN by applying pruning of “unused” neurons. Additionally, we modified the RNN structure by adding a hidden layer to the RNN cell but reducing the total number of recurrent layers to accomplish a reduction of the total parameters used in the network. Finally, we implemented quantization on DNN by forcing the weights to be 8 bits instead of 32 bits. </sec> <sec> <title>RESULTS</title> We found that all methods increased implementation efficiency, including training speed, memory size, and inference speed, without reducing the accuracy of mortality prediction. </sec> <sec> <title>CONCLUSIONS</title> Our findings suggest that neural network condensation allows for the implementation of sophisticated neural network algorithms on devices with lower computational resources. </sec>
Opinion Based Memory Access Algorithms using Collaborative Filtering in Recommender Systems

In recent years, the online shopping and the online advertisement businesses is growing in a vast way. The reason behind this growth is, the peoples are not having sufficient time for go for a shop. Without seeing the quality of the product directly, the people are ready to buy the product by seeing the other user recommendation of the particular product. This leads an interest / the need to develop the researcher an innovative recommendation framework. Based on the opinion prediction rule, the huge size of words and the phrases which are presented in the unstructured data is modified as a numerical values. The sale of the particular product in an online shopping is depends on its description of the quality, the review of the customer. Based on the positive and negative polarity, an Inclusive Similarity-based Clustering (ISC) is proposed to cluster the extracted related keywords from the user reviews. To evaluate the strength, weakness of the product, estimate the respective features, as well as the opinions, the Improved Feature Specific Collaborative Filtering (IFSCF) model for the feature with aspect opinion is proposed. Finally the complete feedback of the product is estimated by propose the Novel Product Feature-based Opinion Score Estimation process. The main challenge in this recommendation system is the fault information estimation of the reviews and the unrelated recommendations of the bestselling or the better quality product. To neglect these issues, an Enhanced Feature Specific Collaborative Filtering Model based on temporal (EFCFM) is proposed in the recommendation system. Hence the developed EFCFM method is investigated by comparing along with the existing methods in terms of subsequent parameters, precision, recall, f-measure, MAE and the RMSE. The outcome shows that the developed EFCFM approach predicts the best product and produce the accurate recommendation to the customers.
A lexicon weighted sentiment analysis approach on Twitter

Sentiment analysis in social media has grabbed more considerable attention because the results of such studies are highly applicable in social, economic, and political contexts. This study aimed to present an approach for collecting data from Twitter while storing and analysing the data using the hadoop as a big data platform as well as a hybrid trial and error model using the Bayes theorem plus a dictionary of words for the sentiment analysis. This method classifies tweets in two positive and negative classes based on the probability of positive words and negative words. According to the results, the accuracy of the proposed approach boosted from 67% to 71%. Then a new idea was employed in form of a weighted dictionary to achieve a higher accuracy. As such. the accuracy of the proposed approach reached a rate of 78% according to the results of another analysis conducted on the same data.
A Framework and Assessment of Information Security in the Product Design Centre: A Quantifiable Analysis on Information Flow

The present investigation aims to propose an information security system for an item configuration focus of a globally presumed car industry. As the essential commitment of this examination consider, the different arrangements for the verified data stream inside and outside the improvement focus alongside different kinds of assaults are talked about in order to upgrade the general security of the data framework at the middle. A layered compositional model for the data security of the inside is considered to beat the dangers. The security arrangements and instruments are talked about in the entrance control level as well as in accomplishing verified data correspondence level between the various substances The proposed model represents the verified data trade in the trust and protection points of view concerning time and setting of trades in a particular structure, which isn't found in existing proficient measures or scholarly productions. To support the feasibility of this approach and solicitation of these devices to true situations, for instance uncovered a few perilous states in a plan focus data framework approach and distinguished secret data streams in an entrance control arrangement for an enormous server framework.
Implications of Deep Learning-Based Methods for Face Recognition in Online Examination System

In the recent era, the importance of surveillance-related applications is increasing rapidly. In such applications, Face Recognition is becoming an emerging, fast-growing research field in the security authentication systems. Face recognition becomes one of the biometric techniques for identifying individuals face in digital images or in the stored image. It has various applications in biometrics, military, video surveillance and so on. In an earlier age, face recognition techniques implemented using a traditional approach like holistic based, hybrid and feature-based. In the traditional system, there are a number of issues like light illumination, occlusion problem, different facial expressions, and poses of the particular individual. These factors are affecting the accuracy and efficiency of the face recognition system. Nowadays there is an advancement in the technology and methods which are used in the face recognition system. The new methods and techniques of face recognition are devised by deep learning methods. The research focuses on a proposed model developed by using some Deep Learning methods and frameworks for face recognition. This model plays an important role in the authentication of an individual in the online examination system in educational institutes. Multi-level authentication is used for authenticating individual and to have crosschecked over throughout the examination period. The Deep Learning methods and frameworks overcome the issues raised in face recognition by traditional methods. This proposed model used for the authentication of an individual in educational institutes where online examinations are conducted.
Bio-Rad Labs., Inc. v. 10X Genomics Inc. United States Court of Appeals of the Federal Circuit, 2020 967 F.3d 1353

Biotechnology Law ReportVol. 39, No. 6 As presented by Chris HolmanBio-Rad Labs., Inc. v. 10X Genomics Inc. United States Court of Appeals of the Federal Circuit, 2020 967 F.3d 1353Edited for Biotechnology Law Report by CHRISTOPHER M. HOLMAN*Published Online:2 Dec 2020https://doi.org/10.1089/blr.2020.29203.cmhAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 39Issue 6Dec 2020 InformationCopyright 2020, Mary Ann Liebert, Inc., publishersTo cite this article:Bio-Rad Labs., Inc. v. 10X Genomics Inc. United States Court of Appeals of the Federal Circuit, 2020 967 F.3d 1353.Biotechnology Law Report.Dec 2020.479-486.http://doi.org/10.1089/blr.2020.29203.cmhPublished in Volume: 39 Issue 6: December 2, 2020PDF download
The China Academic Digital Associative Library (CADAL)

In 2000, a collaboration project called the China-America Digital Academic Library (CADAL) was launched by Chinese and U.S. computer scientists and supported by the U.S. National Science Foundation (NSF) and the Chinese Ministry of Education. The leading parties, Carnegie Mellon University and Zhejiang University, pioneered the construction of massive, digitized resources across the world. In 2002, CADAL merged with the China Academic Library and Information System into the China Academic Digital Library and Information System, emerging as one of the most important information infrastructures in the nation. In August 2009, the China Academic Digital Associative Library (new CADAL) was approved by the Ministry of Education, initiating CADAL Project Phase II. CADAL has promoted Chinese university libraries from the role of information-sharing institutions to partners of resource construction and sharing, achieving the world's largest non-profit digital library with more than 2.5 million Chinese and English e-books, a resource that has been exclusively “made in China.”
DeepQuake -- An application of CNN for seismo-acoustic event classification in The Netherlands

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Geophysical Research Letters. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing an older version [v1]Go to new versionDeepQuake -- An application of CNN for seismo-acoustic event classification in The NetherlandsAuthorsLucaTraniiDGiuliano AndreaPaganiJoão PauloPereira ZanettiiDCamilleChapelandLäslo GerardusEversiDSee all authors Luca TraniiDCorresponding Author• Submitting AuthorKNMIiDhttps://orcid.org/0000-0001-7750-7254view email addressThe email was not providedcopy email addressGiuliano Andrea PaganiKNMIview email addressThe email was not providedcopy email addressJoão Paulo Pereira ZanettiiDKNMIiDhttps://orcid.org/0000-0002-9955-7751view email addressThe email was not providedcopy email addressCamille ChapelandTU Delftview email addressThe email was not providedcopy email addressLäslo Gerardus EversiDKNMITU DelftiDhttps://orcid.org/0000-0003-2825-6211view email addressThe email was not providedcopy email address
1st International Conference on Advance and Scientific Innovation (ICASI)

We are delighted to introduce the proceedings of the first edition of Joint Workshop KO2PI and International Conference on Advance and Scientific Innovation (ICASI) 2018 with theme "Empowering Digital Society through Integration of Multidisciplinarity Aspect". The technical program has brought researchers and practitioners around the world to a good forum for discussing, leveraging and developing all scientific and technological aspects that are relevant to digital society. This Joint Workshop KO2PI and ICASI 2018 acquired 388 full papers with 15 Categories, with most papers from Computer Science/Engineering, Information Technology and Education and also authors from almost 25 countries such as Malaysia, Thailand, Laos, South Korea, USA and many more. This Joint Workshop was very special because it involved the collaboration of publications from six other conferences. Thank you especially to:
UV/VIS Characterization and FT-IR Analysis of Harmattan Dust across Sub-Sahara Region of Africa

Abstract: The suspended dust particles that blow across Nigeria as a result of north-east trade wind emanating from the Sahara desert annually are locally referred to as Harmattan. Suspended dust samples were collected across ten different stations in Nigeria; namely: Maiduguri (11º49'N, 13º09'E), Potiskum (11º43'N, 11º02'E), Bauchi (10º17'N, 09º 48'E), Jos (9º55'N, 8º55'E), Lafia (08º49'N, 07º50'E), Abuja (09º09'N, 07º11'E), Minna (9º36'N, 06º35'E), Ilorin (8º36'N, 4º 35'E), Oyo (8º12'N, 3º42'E) and Iwo (7º63'N, 4º19'E) and analyzed using Fourier Transform Infrared (FT-IR) and UV-Visible spectroscopy. The quantitative, qualitative and optical characterization analyses were carried out on all the samples collected across all the stations considered, in order to obtain the functional groups and some elements present in the samples. The FT-IR spectra peaks show that the transmittance waveband from 3337.8 cm-1 to 3291.2 cm-1 corresponds to the Hydroxyl group, H-bonded OH stretch in plane and out of plane bonds for all the stations considered. These bonds represent the presence of transition metals and unsaturated bonds found in the samples, which include K, Ca, Ti, Mn, Fe, Ni, Cu, Zn, Mo, As, Zr, Pb, V, Sr, Cr and Ce. The samples collected across all stations have maximum UV absorbance peaks at around 210 nm waveband and weak visible light absorbance peaks (orange – red spectra) around 620 nm and 700 nm. The study concluded that the transmittance waveband, the waveband absorbance peaks and the elemental composition of the dust samples analyzed vary from station to station. This study will however recommend that further study be made for the purpose of environmental awareness. Keywords: Harmattan, Particles, FT-IR, UV, Dust.
Early Childhood Literacy Skills: The Effect of Socioeconomic Status, Home Literacy Environment, and Social Skills

Literacy is an important skill that must be developed by a child for the next level of education, so it must be expected that parents help their children to develop reading habits at an early age. A family has a responsibility to provide rich experiences inside and outside the home that are very important for the development of children's literacy. This study aims to investigate the socio-economic status, home literacy environment, and social skills in early childhood literacy skills. The method used in this study is survey. A number of 120 students from first grade elementary school were recruited as samples. Parents of these children filled out questionnaires in the column of education and income of parents. Home literacy environment consists of literacy fasilities and parents-children activities at home, while social skills are obtained by measuring the ability to improve cooperative relationships, social sensitivity, adaptability, and communication. On the other hand, children's literacy skills are directly assessed by using tests. The research data was collected through questionnaires designed in the form of Likert scale and rating scale. The data is analyzed by using path analysis. The results of the research reveal a significant correlation between the socioeconomic status, home literacy environment, and social skills in literacy. The effect of socioeconomic status, home literacy environment, and social skills give direct positive influence on early childhood literacy skills.
Research Trends on Deep Transformation Neural Models for Text Analysis in NLP Applications

In the recent few years, text analyses with neural models have become more popular due its versatile usages in different software applications. In order to improve the performance of text analytics, there is a huge collection of methods that have been identified and justified by the researchers. Most of these techniques have been efficiently used for text categorization, text generation, text summarization, query formulation, query answering, sentiment analysis and etc. In this review paper, we consolidate a recent literature along with the technical survey on different neural models such as Neural Language Model (NLM), sequence to sequence model (seq2seq), text generation, Bidirectional Encoder Representations from Transformers (BERT), machine translation model (MT), transformation model, attention model from the perception of applying deep machine learning algorithms for text analysis. Applied extensive experiments were conducted on the deep learning model such as Recurrent Neural Network (RNN) / Long Short-Term Memory (LSTM) / Convolutional Neural Network (CNN) and Attentive Transformation model to examine the efficacy of different neural models with the implementation using tensor flow and keras.
A privacy-preserving recommendation method based on multi-objective optimisation for mobile users

Recommender systems have proven to be an effective technique to deal with information overload and mislead problems by helping users get useful and valuable information or objects from massive data. However, exploiting users' preferences with recommendation algorithms lead to serious privacy risks, especially when recommender service providers are unreliable. An ideal recommender system should be both accurate, diverse and security. In this paper, we propose a private recommendation method which consists of a private collaborative filtering algorithm and a multi-objective evolutionary algorithm for mobile users. Experimental results demonstrate that even though the mobile users' preferences are significantly obfuscated, our method is effective in terms of recommendation accuracy and diversity.
A Novel Multi-Parameter Tuned Optimizer for Information Retrieval Based on Particle Swarm Optimization

Tuning multi-parameter and parameter optimization in Information Retrieval has been a huge area of research and development, especially with BM25F scoring functions having a 2F+1 feature with F fields in the documents. The scoring and ranking function conventionally uses multiple input parameters, to augment the quality of results even at the value of huge calculation time. The searching and ranking documents in the medical literature encompass high recall rates, which are difficult to satisfy with multiple input parameters. The performance of the BM25F depends upon the choice of these F parameters. Particle Swarm Optimization (PSO) searches through the solution- space independently and discovers an optimal solution as opposed to improving and optimizing the gradient; henceforth it can straightforward optimize Mean Average Precision (MAP) a non-differentiable function. In this paper, the usage of PSO to tune multi-parameters is proposed to deal with the gaps in BM25Fscoring function. Also, the advantage of the proposed technique by directly optimizing the MAP has been discussed. Experimental results of quantitative performance metrics MAP and Mean Reciprocal Rank of the proposed PSO-optimized BM25F and most recent ranking algorithms have been compared. The performance measure results demonstrate that the proposed PSO-optimized BM25F performance measure outclasses the standard ranking methods for the OHSUMED data set.
Applications of blockchain technology in the Brazilian government

Blockchain technology has emerged as a trend that can influence business, government, and society. By validating the exact time an action is taken without the need for an intermediary, the technology has the potential to provide profound modifications in the public service. This paper proposes an exploratory study built from the perspective of institutional logics whose research question is: how are the institutional logics present in the Brazilian public sector responding to the incorporation of blockchain technology? A qualitative approach was used to understand initial perceptions and to learn about the motivations and mechanisms of government initiatives that employ blockchain technology. Our research has found that, despite its disruptive character, the blockchain has entered the Brazilian public service scenario through an already institutionalised structure consisting of a hybrid logic of different approaches to public administration. Changes in this logic will require greater popularisation and regulation of the technology.