Reviewerly/exHarmony huggingface dataset:
Blockchain: A Technology in Search of Legitimacy

Blockchain can potentially disrupt business processes across many industries. Despite substantial hype, blockchain adoption still remains quite low. Research suggests that the success and adoption of a new technology depends on whether it is deemed legitimate by key stakeholders. This is extremely important in the context of blockchain given the controversial reputation that some derivative innovations, such as Bitcoin, have gained over time. This chapter explores and compares how four types of business actors which play a prominent role in the blockchain ecosystem—the media, IT, financial services and consulting firms—attempted to build legitimacy around blockchain and Bitcoin on Twitter. Findings suggest that the messaging strategies employed by firms for blockchain and Bitcoin differ. While no dominant firm-level messaging strategies to build legitimacy around Bitcoin emerge, firms primarily employ three micro-level legitimation strategies to build legitimacy around blockchain. More specifically, communication strategies around blockchain focus on publishing the involvement of influential actors (pragmatic legitimacy), highlighting blockchain ongoing developments and associated market responses (cognitive legitimacy), and presenting technological advancements directly or indirectly related to blockchain (cognitive legitimacy). The findings illustrate how the key actors engage in the blockchain and Bitcoin legitimation discourse to help interpret this complex innovation and mobilise the market to adopt.
Estimation of ASR Parameterization for Interactive System

In this study, the authors explore the integration of speaker-independent automatic Amazigh speech recognition technology into interactive applications to extract data remotely from a distance database. Based on the combined interactive voice response (IVR) and automatic speech recognition (ASR) technologies, the authors built an interactive speech system to allow users to interact with the interactive system through voice commands. The hidden Markov models (HMMs), Gaussian mixture models (GMMs), and Mel frequency spectral coefficients (MFCCs) are used to develop a speech system based on the ten first Amazigh digits and six Amazigh words. The best-obtained performance is 89.64% by using 3 HMMs and 16 GMMs.
Data on the geology and structure of the Copper Cliff embayment and offset dyke, Sudbury Igneous Complex, Canada

This contribution describes maps of the Copper Cliff Embayment (CCE) and Offset (CCO) dyke. The associated study attempts to unravel the mode of melt emplacement and the role of pre-impact faults in the deformation of the southern part of the Sudbury Igneous Complex (SIC). This contribution summarizes field observations (maps and images) and structural measurements. In addition, perspective views of the 3D Move model of the CCE and CCO dyke are provided. This data can be used by researchers and exploration geologists working in the Sudbury mining camp as a basis for future mapping, research and exploration efforts in the Copper Cliff area. This article is a co-submission to the following article: L. Mathieu, U. Riller, L. Gibson, P. Lightfoot (2021) Structural controls on the localization of the mineralized Copper Cliff embayment and the Copper Cliff offset dyke, Sudbury Igneous Complex, Canada, Ore Geol. Rev., https://doi.org/10.1016/j.oregeorev.2021.104071
The Adoption of Technology in the Kenyan Electoral Process: Lessons from the 2013 and 2017 Presidential Election

The use of technology in elections is now part of the Kenyan electoral system, supplementing the human element in dealing with quantitative and qualitative aspects of the electoral law. Its adoption was largely informed by the aftermath of the 2007-2008 post-election violence that led to loss of lives, displacement of people and destruction of property. According to the Independent Review Commission (Kriegler Commission) Report, delays in the transmission of presidential results coupled with various electoral malpractices contributed materially to the explosive political climate in Kenya. The Kriegler Commission recommended amongst other major reforms, the need to adopt technology in the election process to provide efficient, transparent, auditable and credible results. Following the recommendations, the government through legal and institutional framework sought to embed technology in the Kenyan electoral process. Since then, technology has been employed in the 2010 Constitution of Kenya referendum, the 2013 and 2017 general elections. This paper seeks to examine the use of technology in the Kenyan electoral process. It focuses on the mechanisms the Independent Electoral Boundaries Commission (IEBC) put in place to ensure that the rationale of technology in providing credible and legitimate election was met. The Presidential elections in 2013 and 2017 seek to provide an understanding on the circumstances surrounding technology in the electoral process because it is at the presidential level that the use of technology was more pronounced and raised a number of concerns leading to presidential petitions. This paper concludes that indeed the use of technology is a great step towards enhancing credibility of elections and democracy. However, in the absence of political will, trust amongst electoral stakeholders, credibility and independence of the electoral bodies this may remain futile especially in emerging democracies like Kenya.
Information Security Risks in Civil Aviation Network: Classification, Identification and Preventive Strategies

Information security of civil aviation network is an important issue in national cyberspace security governance. Based on the ISO27001 information security management system, the main contribution of this paper is build a classification and identification framework to identify key sources and specific factors of information security risks in civil aviation network through generalization and summary approaches. The detected risk sources mainly include management subject, management process, technology, laws and regulations, and infrastructure. In accordance with the attributes of different risk sources, systematic preventive strategies are proposed in this paper to serve as references for preventing and resolving information security risks in civil aviation network.
DAS-AST: Defending Against Model Stealing Attacks Based on Adaptive Softmax Transformation

Deep Neural Networks (DNNs) have been widely applied to diverse real life applications and dominated in most cases. Considering the hardware consumption for DNN and large amount of labeled training data to support the performance, machine-learning-as-a-service (MLaaS) came into being. However, malicious attacker takes the opportunity to launch possible deep model stealing attacks via black-box access, leading to a great security threat to the interests of the model agency. Addressing to the problem, defensive methods are designed, mainly categorized to truncated-based and perturbation-based, to reduce the stealing efficiency or increase the attack cost, i.e. more queries. Essentially, it is still a challenge to fully defend the deep model stealing attack. In the paper, we propose a novel defense algorithm based on adaptive softmax transformation by introducing posterior probability perturbation, namely DAS-AST. We evaluate the proposed defense against several state-of-the-art attack strategies, and compare the performance with other defense methods. The experiment results show that our defense is effective across a wide range of challenging datasets and performs better than the existing defenses. More specifically, it can degrade the average accuracy of the stolen model at least 30%, without affect the accuracy of target DNN model on original tasks.
Self-Organized Subpopulation Based on Multiple Features in Genetic Programming on GPU

Genetic Programming (GP) is an Evolutionary Computation (EC) algorithm. Controlling genetic diversity in GP is a fundamental requirement to obtain various types of local minima effectively; however, this control is difficult compared to other EC algorithms because of difficulties in measuring the similarity between solutions. In general, common subtrees and the edit distance between solutions is used to evaluate the similarity between solutions. However, there are no clear guidelines regarding the best features to evaluate it. We hypothesized that the combination of multiple features helps to express the specific genetic similarity of each solution. In this study, we propose a self-organized subpopulation model based on similarity in terms of multiple features. To reconstruct subpopulations, we introduce a novel weighted network based on each normalized feature and utilize network clustering techniques. Although we can regard similarity as a correlation network between solutions, the use of multiple features incurs high computational costs, however, calculating the similarity is very suitable for parallelization on GPUs. Therefore, in the proposed method, we use CUDA to reconstruct subpopulations. Using three benchmark problems widely adopted in studies in the literature, we demonstrate that performance improvement can be achieved by reconstructing subpopulations based on a correlation network of solutions, and that the proposed method significantly outperforms typical methods.
Learning to Label with Active Learning and Reinforcement Learning

Training data labelling is financially expensive in domain-specific learning applications, which heavily relies on the intelligence from domain experts. Thus, with budget constraint, it is important to judiciously select high-quality training data for labelling in order to prevent over-fitting. In this paper, we propose a learning-to-label (L2L) framework leveraging active learning and reinforcement learning to iteratively select data to label for Name Entity Recognition (NER) task. Experimental results show that our approach is more effective than strong previous methods using heuristics and reinforcement learning. With the same number of labeled data, our approach improves the accuracy of NER by 11.91%. Our approach is superior to state-of-the-art learning-to-label method, with an improvement of accuracy by 6.49%.
IIE’s Neural Machine Translation Systems for WMT20

In this paper we introduce the systems IIE submitted for the WMT20 shared task on German-French news translation. Our systems are based on the Transformer architecture with some effective improvements. Multiscale collaborative deep architecture, data selection, back translation, knowledge distillation, domain adaptation, model ensemble and re-ranking are employed and proven effective in our experiments. Our German-to-French system achieved 35.0 BLEU and ranked the second among all anonymous submissions, and our French-to-German system achieved 36.6 BLEU and ranked the fourth in all anonymous submissions.
Sum of Squared Difference (SSD) Template Matching Testing on Writing Learning Application

Abstract&#x0D; This research was conducted to determine the accuracy of the Sum of Squared Difference (SSD) Template Matching method in the Application of Learning Numbers Writing Games. This game application is an application created to help early childhood in recognizing Arabic numbers, namely numbers from 0 to 9. In the SSD Template Matching method there are several processes including Preprocessing, thinning, feature extraction, and classification (SSD template matching). In testing the game application involves 10 respondents who were asked to write numbers correctly as requested by the application. For each number writing test, it is tested by 3 times. From the tests conducted, obtained an accuracy of 94.67%.&#x0D; Keyword: Template Matching, Sum of Squared Difference (SSD), Education Game, Optical Character Recognition, Mobile Learning
Overview of NLPTEA-2020 Shared Task for Chinese Grammatical Error Diagnosis

This paper presents the NLPTEA 2020 shared task for Chinese Grammatical Error Diagnosis (CGED) which seeks to identify grammatical error types, their range of occurrence and recommended corrections within sentences written by learners of Chinese as a foreign language. We describe the task definition, data preparation, performance metrics, and evaluation results. Of the 30 teams registered for this shared task, 17 teams developed the system and submitted a total of 43 runs. System performances achieved a significant progress, reaching F1 of 91% in detection level, 40% in position level and 28% in correction level. All data sets with gold standards and scoring scripts are made publicly available to researchers.
Utilizing emerging knowledge to support medical argument retrieval

Abstract This article summarizes selected aspects of a dissertation project and prior publications related to the DFG-funded RecomRatio research project. As such, it provides an end-to-end overview of a research project that aims at extracting and utilizing Emerging Knowledge represented by two concepts that we define as Emerging Named Entities and Emerging Argument Entities to support medical argumentation retrieval. We use these two concepts to model novelty in general scientific literature and, in particular, in medical argumentation. Therefore, this paper will provide an overview of Emerging Knowledge and definitions of Emerging Named Entities and Emerging Argument Entities. It includes a review of state-of-the-art and related work. A preparatory study shows that Emerging Argument Entities are in use in the medical literature. Based on the state of the art review and the preparatory study, a conceptual system design based on Emerging Named Entity Recognition and a state-of-the-art Argumentation Mining framework (ArgumenText) is introduced to extract Emerging Argument Entities from medical literature and make them available for Argument Retrieval. The conceptual system design supports two Argument Retrieval use cases: 1.) Ranking of result sets based on Emerging Argument Entities, and 2.) Highlighting Emerging Argument Entities within result sets. A case study for the extraction and visualization of Emerging Named Entities and Emerging Argument Entities is implemented based on the conceptual design. This proof-of-concept system is used to conduct technical evaluations regarding the Emerging Named Entity Recognition. Furthermore, prior results of an expert-based evaluation are presented. The article finishes with a conclusion and brief outlook of future work, e. g., supporting the Argument Interchange Format.
LISUNGIcovid19: Prototype of mobile application to help manage the way out of covid-19 crisis

Coronavirus disease 2019 (Covid-19) has plunged the world into a crisis that will forever be marked in the annals of humanity.Several mobile applications have been proposed, in particular to help limit or even stop the spread of the virus.In this paper, we propose a mobile data collection application, LISUNGIcovid19.This application has been built on the basis of the Open Data Kit (ODK) platform.The proposed solution offers both a mobile application and a data server to store, visualize and analyze the collected data.Although there are several applications created in this context of covid-19 such as STOPcovid or Wiqaytna, our solution is distinguished by the rapid adaptation of its data collection form according to the evolution of lockdown exit or renewed lockdown strategy adopted by the authorities.It will help decision-makers in the evaluation of their strategy and possibly the planning of lockdown exit and thus ensure a good management of the way out of this health crisis.The data collected by LISUNGIcovid19, as well as the analysis it proposes of this data, will enable the competent authorities to take the decision to quarantine, or isolate a city, a neighborhood or a group of individuals; or to start planning a progressive lockdown exit or even a new social isolation if necessary.The case study carried out in Morocco to test LISUNGIcovid19 helped to understand the classification of the cities of Casablanca, Rabat and Salé in zone 2 during the first phase of lockdown exit and then in zone 1 during its second phase; while the cities of Kenitra and Marrakech remained in zone 2 during both phases.
Formation of Competence and Professional Skills of the Future Social Teacher in the Study of Humanitarian Disciplines

This article examines the problems of the potential capabilities of certain academic disciplines in the formation of the professional competence of future social teachers. The author has set the goal to consider the important components of the system of forming the professional competence of future social teachers, which are: forms of organization of classroom work as lectures, conferences, consultations and extracurricular work of students (diploma works, forms of organization of scientific research work, teaching practice, etc.); teaching methods (explanatory-intellectual, reproductive, problem presentation, research, etc.) and education (methods of forming personality consciousness, methods of organizing activities and forming the experience of social behavior, methods of stimulating behavior and activities); means of the system for the formation of professional competence of future social teachers as the State Standard of Higher Professional Education, curricula and curricula, didactic material, computers, computer classes, the Internet, software, etc.
Pembaruan Teknologi Informasi Untuk Sistem Pendukung Keputusan Pemberian Kelayakan Kredit Menerapkan Metode Profile Matching dan Metode Fuzzy Mamdani

The development of information technology is currently growing rapidly, with so many positive impacts on people's lives in general. With current developments, there are more and more community needs such as financial needs as well as personal needs. This can be seen from the information technology that provides convenience in many aspects of people's lives, including crediting. Each company usually faces very tough competition, as many credit companies have sprung up to lend funds and BPKB as collateral. DIVA Eletronic and Furniture are engaged in credit financing companies. Credit financing is an institution that provides temporary credit services in the form of guarantees. The company establishes policies in the credit extension process requiring consideration and analysis from the credit party based on regulations and the existence of credit financing policies. PT. DIVA Eletronic and Furniture is a subsidiary of PT. COLOMBUS with the aim of building a network of business units throughout the city that provides the most complete, quality and guaranteed goods. So with the number of consumers who began to increase so that the company experienced difficulties in managing data and in determining creditworthiness. Not infrequently due to errors in determining consumer eligibility, bad credit occurs so that companies have to withdraw electronic goods or furniture from the consumers themselves. So with that it intends to make with the title: Credit Information Technology Renewal for Decision Support System for granting creditworthiness at PT. DIVA Eletronic and Furniture with Profile Matching Method and Fuzzy Method
Wearable and Mobile Technologies Create Innovations in Healthcare

Author Affiliation: Associate Dean for Graduate Programs and Professor, College of Nursing, The University of Alabama in Huntsville. The author reports no conflicts of interest. Correspondence: Karen H. Frith, PhD, RN, NEA-BC, CNE, College of Nursing, The University of Alabama in Huntsville, 301 Sparkman Dr, Huntsville, AL 35899 ([email protected]).
Flakes/Detached Pieces

This chapter reviews conventions for measuring, describing, and classifying flakes or “detached pieces.” These include core-initiation flakes (EAST Typology Group V), core-exploitation flakes (Group VI), core-repair flakes (Group VII), and retouch flakes (Group VIII). The chapter answers common questions about flakes/detached pieces.
Control of an Omnidirectional Robot Based on the Kinematic and Dynamic Model

This work focuses on the proposal for the implementation and evaluation of control algorithms for the monitoring of autonomous trajectories based on the kinematic and dynamic model of an omnidirectional robotic platform in four wheel configuration type mecanum also built for the process of identification and validation of the dynamic parameters obtained from the mathematical model by means of an identification algorithm, the evaluation of these control algorithms is carried out experimentally on the Robotic Platform in real four-wheel omnidirectional configuration with which it also allows to evaluate the operation of the Hardware in the Loop control scheme (HIL), a technique that constitutes the untimely connection of external hardware with computer equipment allowing to simulate in real time the behavior of the robotic system with omnidirectional traction in four-wheel configuration type mecanum. Subsequently the data resulting from the Hardware in the Loop (HIL) control scheme will be compared to the data obtained in the experimental test for corresponding validation.
Biostatistiek versus machine learning: van traditionele predictiemodellen naar geautomatiseerde medische analyse

Biostatistics versus machine learning: from traditional prediction models to automated medical analysis Machine learning is increasingly applied to medical data to develop clinical prediction models. This paper discusses the application of machine learning in comparison with traditional biostatistical methods. Biostatistics is well-suited for structured datasets. The selection of variables for a biostatistical prediction model is primarily knowledge-driven. A similar approach is possible with machine learning. But in addition, machine learning allows for analysis of unstructured datasets, which are e.g. derived from medical imaging and written texts in patient records. In contrast to biostatistics, the selection of variables with machine learning is mainly data-driven. Complex machine learning models are able to detect nonlinear patterns and interactions in data. However, this requires large datasets to prevent overfitting. For both machine learning and biostatistics, external validation of a developed model in a comparable setting is required to evaluate a model’s reproducibility. Machine learning models are not easily implemented in clinical practice, since they are recognized as black boxes (i.e. non-intuitive). For this purpose, research initiatives are ongoing within the field of explainable artificial intelligence. Finally, the application of machine learning for automated imaging analysis and development of clinical decision support systems is discussed.
Enhancement of Region of Interest from a Single Backlit Image with Multiple Features

Backlit images are a combination of dark and bright regions and the objects in the image generally appear to be dark for human perception. The region of interest (ROI) in general confines to the object(s) present in the image or some regions of the image. Such ROI in backlit images have low contrast and it is difficult for visualization. Enhancement of ROI in backlit images is necessary in order to view the contents properly. In this paper, a novel and simple approach for the enhancement of ROI of backlit images is proposed. This approach considers several features including tone mappings, exposedness, gradient, median filtering, etc. and finally, the fusion of the results has been done. The novel contribution in the proposed method, though seems to be trivial, attained best results without applying pyramid based operations namely Laplacian pyramid and Gaussian pyramid. Efficacy of the proposed method is evident from the experimental results which confirm that the proposed approach gives better results both qualitatively (visualization) and quantitatively (objective evaluation) compared to the existing methods.