Reviewerly/exHarmony huggingface dataset:
Evaluating Methods for Optical Character Recognition on a Mobile Platform : comparing standard computer vision techniques with deep learning in the context of scanning prescription medicine labels

Deep learning has become ubiquitous as part of Optical Character Recognition (OCR), but there are few examples of research into whether the two technologies are feasible for deployment on a mobile ...
Teaching speaking skill of procedural activity by using FaceApp media (practical guidance and implementation)

Speaking is one of essential skill having by English students. Student need to practice every time to exercise their speaking skill. In speaking skill student must have creativity in showing their ideas. One of the ways to ease student to speak is presenting the procedural activity. In this case, students tell and present the step of procedural activity. This procedural activity taken from Faceapp Application that can be used as media in teaching speaking. It is because, Faceapp Application is a popular application that’s used by students. It almost all the students in university have this application in their adroid mobile. Thus, from this, researcher try to utilize FaceApp application taken as media in teaching speaking. One of the topics of speaking activity is presenting and telling the procedural activity by using FaceApp. This research is qualitative descriptive research. The researcher use observation, documentation and interview to collect the data. The data of this research are practical guidance and the transcription of students’ speaking practice as the result of implementation. The object of this research is students of English Education Department which consist 50 students in the class. The result of this research, students can practice their speaking of procedural activity by doing action in the class through faceapp application. With this faceapp media, student have idea to speak and it becomes interesting class activity in speaking class, beside student have fun activity in editing their own picture, they also able to practice their speaking in the class by telling the procedural activity.
The Role Of Mind Mapping Technique On Speaking Ability

This study aimed to determine the role of mind mapping techniques on the speaking ability. The data were collected from journals, the method used in this research was qualitative descriptive method. The research design used in this research was library research or content analysis. The findings from the journals revealed that a significant increase in speaking skills occurred after the mind mapping method was applied. Mind mapping turns a long list of monotonous information or concepts into colourful, easy to remember, and highly organized diagrams that work according to the natural way of  learners brains do things, making it easier for learners to convey information or concepts. Mind mapping plays an important role in improving speaking skills. All journals used as data sources state that the role of mind mapping was very crucial in improving the speaking skills of learners.
Wiﬁ Device Identiﬁcation through Neural Network based on Channel State Information

The tremendous growth of wireless networks has brought a great deal of convenient to our daily lives. But the wireless devices are vulnerable to several attacks such as unauthorized client access. Therefore we want to be able to identify the legitimate and malicious Access Point (AP) for security purpose. The malicious user may attempt to apply attacks such as ARP attack, a method to change the ARP table to get legitimate user’s data, Evil twin attack or Rouge AP, the attacker imitates the SSID as the legitimate Wiﬁ AP to misleading the user to connect to the malicious APs. It will be a challenge to recognize the difference between legitimate AP and malicious AP when they have exactly same SSID and MAC address. This paper proposed a method to identity the Wiﬁ APs through physical characteristics. The experiment is performed as a supervised learning LSTM and RNN based on Channel State Information (CSI). Comparing between LSTM and RNN, the results show that LSTM architecture is able to perform a better classiﬁcation than RNN.
Managing Digital Discovery in Criminal Cases

The burdens and challenges of discovery—especially electronic discovery—are usually associated with civil, not criminal cases. This is beginning to change. Already common in white-collar crime cases, voluminous digital discovery is increasingly a feature of ordinary criminal prosecutions. This Article examines the explosive growth of digital evidence in criminal cases and the efforts to manage its challenges. It then advances three claims about criminal case discovery in the digital age. First, the volume, complexity, and cost of digital discovery will incentivize the prosecution and the defense to cooperate more closely in cases with significant amounts of electronically stored information (ESI). Second, cooperation between the parties will not be sufficient to address the serious challenges that digital discovery presents to the fair and accurate resolution of criminal cases. And third, for that reason, digital discovery in criminal cases needs to be regulated more closely. In crafting such regulation, courts and legislators can build on the civil procedure model, which has grappled with the challenges of electronic discovery for over two decades. The civil procedure experience suggests that cooperation between the parties, active judicial involvement, and more detailed rules are essential to the effective management of digital discovery. The civil litigation model has its limitations, however, and policymakers must chart new ground to address some of the unique demands of criminal cases. Recognizing the significant resource and bargaining disparities in criminal cases, judges need to limit certain negotiated waivers of discovery so as to prevent abuse. Where the interests of justice demand it, courts may also need to help defendants obtain access to digital discovery in detention or gather digital evidence from third parties. These and other measures can help ensure that the cost and complexity of digital discovery do not undermine the fairness and accuracy of criminal proceedings.
Cryptocurrency and Social Justice: a Study of Indian Taxation Laws on Emerging Virtual Challenges

A democratic form of Govt. is always defined as “by the Public, for the Public and to the Public.” The basic function of a government is to deliver public services to its citizens for which it requires resources to finance its expenditure. Among others, taxation works as a major source to fund public expenditure. Developments and Advancements in the technologies, has enabled the Govt. to identify new ways and opened new platforms to collect taxes. Among them, comes the curious situation of taxation of Cryptocurrency. Unlike the fiat currency, Cryptocurrencies are decentralized, relying on a peer-to-peer network that operates without any third-party intervention like the Central Bank. The purpose of this paper is to explore Cryptocurrency and the prevailing regulatory structure concerning them. It studies the different natures of cryptocurrency and analyses:(1) Scheme of existing domestic laws and relates cryptocurrency, (2) Domestic laws affecting taxation of cryptocurrency related activities and (3) Major economies response to cryptocurrency.
Analysis of Government Procurement Market in Emerging Countries and Implication for Small and Medium Enterprises

The purpose of this study is to derive implications for SMEs through analyzing the openness of the government procurement market in emerging countries the prospects of these markets opening up in the future, and the overall possibility and plans of Korean SMEs to enter these markets. The procurement markets of international organizations and advanced countries are already saturated with competition, and it is difficult to expand in the procurement market of advanced countries such as the United States, Canada and the EU as they are strengthening their preferential purchasing system in line with protectionist trade policies. Therefore, this study aimed to find a market for competitive companies in Korea by comprehensively grasping the current situation, openness, growth potential, and potential market demand of emerging markets. In particular, we focused on analyzing the possibility of Korean SMEs advancing into the government procurement market of emerging countries, and to offer suggestions on the direction of procurement policies and trade policies in order to effectively advance into these emerging markets. This study analyzed six emerging regions which are either considering WTO-GPA membership or negotiating FTA agreements with Korea: China, Southeast Asia and India, Eurasia, the Middle East, Middle Eastern Europe and Latin America.
Determinants of academic staffs participation in research activities in Ethiopian universities

The study aims to promote the knowledge creation activities for the under developing countries like Ethiopia, which is the need of the hour. Faculty members' participation in research not only helps in improving the quality of faculty members but also improves the quality of the institute. In this paper, we use survey data from Wollega University to understand the factors hindering the research participation decision of faculty members at the university level. This survey tries to analyse the determinants of participation of academic staff in Wollega University. The logit model was used for analysing the data. We conclude that availability of publication incentives, work experience, speed of internet, accessibility to research funds and colleague collaboration on research participation all enhance the probability of academic staff's research participation while teaching load is negatively related to the probability of academic staff's research participation in the study area.
Home-style smart medicine box for the elderly

Abstract With the intensification of the aging of our country’s population, the health care of the elderly has become a social problem that must be solved in my country. Elderly people often suffer from chronic diseases and need long-term medication. However, it is difficult for the elderly to take medicines on time and in doses due to deterioration of their physical functions. Based on the above background, the project team researched and designed a smart medicine box that can intelligently remind and automatically dispense medicine. The device is mainly divided into four functional modules: medicine dispensing module, medicine taking module, medicine dispensing module, and control module. It realizes the five basic functions of medicine addition, temporary storage and sealing, medicine absorption, automatic medicine dispensing, and timing reminder and Additional features such as data sharing.
The concept of money in the 4th Industrial Revolution: A legal and economic analysis

This article explores some of the changes that the 4th Industrial Revolution brings to our understanding of money. Our analysis does not suggest that the only valid form of money is that provided o...
Packers party essentials ahead of Sunday's NFC Championship

It's not an NFC Championship watch party unless you've decked out your kitchen table with good snacks and created a signature cocktail for the game.
Treelet Dimension Reduction of Diagnoses Among Intensive Care Unit Admissions

Background: The objective of this thesis is to transform a large number of ICD-9-CMdiagnosis codes into a reduced number of variables, using treelet dimension reduction, and use thisresulting transformation in the prediction of clinical outcomes of in-hospital mortality, unplannedre-admission, and hospital length of stay.Data: International Classification of Disease, 9th Revision, Clinical Modification (ICD-9-CM) codes and patient demographic data (age, sex, insurance coverage) from the MedicalInformation Mart for Intensive Care III (MIMIC-III) database prospective cohort study of 38,554adults admitted to a single intensive care unit from 2001 to 2012.Methods: We applied treelet dimension reduction to ICD-9-CM diagnosis codes (n=178,>1% prevalence in the analytic cohort) to identify a transformed feature space of patient diagnosesthat we then used, with patient demographic data, to predict in-hospital mortality, unplannedhospital re-admission, and length of hospital stay using logistic and negative binomial regressionmodels.Results: Treelet dimension reduction for ICD-9-CM diagnosis codes identified reducedfeature spaces in prediction of in-hospital mortality, unplanned hospital re-admission, and lengthof stay. The resulting treelet features for each clinical outcome, in addition to patient age, gender,and payment method, demonstrate improved utility in predicting in-hospital mortality(AUC=0.858) but limited accuracy in prediction hospital re-admission (AUC=0.661). Treelet dimension reduction identifies a sparse number of ICD-9-CM diagnosis codes (107 of 178)retained in the treelet features included in modelling of length of stay (RMSE=10.29).Public Health Significance: These analyses leverage a large, public database of criticalcare admissions, generating predictive models of clinical outcomes using only patientdemographic and comorbidity diagnosis information. The presented analysis builds upon previouswork by applying the novel treelet dimension reduction model on diagnosis data in a dataset ofcritical care admissions and demonstrate the utility of diagnosis code data alone in prediction ofclinical outcomes.
Cyclic Exploration-Based Whale Optimization to Linear Discriminant Regression Classification for Face Recognition

Generally, face recognition determines the judgment of whether or not a specific face is “known.” Moreover, the face identification denotes the retrieval of data or information about the “owner” of face. Under this concept, a number of researches are there in progression. Still, the research works are not yet up to the mark as human brain recognition. The intelligence in face recognition should be enhanced with high accuracy rate, and this approach tends to present a novel face recognition pattern with a concept of feature extraction and classification. The features are extracted using Active Appearance Model (AAM). Then the classification is done via linear collaborative Discriminant regression classification (LCDRC) model proposed by Xiaochao Qu. In the LCDRC classifier, the most important evaluation is the projection matrix that might get multiplied to the features while classification. The projection matrix must be optimal, so that the recognition accuracy can be greatly attained. In order to select the optimal projection matrix, this paper presents a Cyclic Exploration-based Whale Optimization model (CEWO), which is the modified form of Whale Optimization Algorithm (WOA). The comparison of the proposed face recognition model is done with the performance across the additional conventional techniques with regard to measures such as Accuracy, Precision, False positive rate (FPR), False negative rate (FNR) of the proposed model is proven.
Decentralized control of robot joints based on neural network observer

In actual work, the system parameters of the robot joints will change in real time or cannot be measured, and the coupling relationship between the various subsystems and the existence of modeling errors make the system model difficult to determine. Based on such problems, a neural network observation is proposed Decentralized control method for robot joints of the robot. In the actual control of the manipulator, firstly, the model of each joint subsystem is established by the decentralized control theory, and the nonlinear function approximation ability of the neural network is used to approach the uncertain part of the manipulator subsystem online through input and output data. The design observer can estimate the state of the system, and use the estimated state to design a sliding mode controller to dynamically estimate and compensate the unknown model dynamics of each independent joint, and realize the self-control of the system when the speed information and model information are unknown. Adaptive control greatly enhances the robustness and adaptability of the robotic arm system. Finally, the stability criterion of the neural network observer and the sliding mode controller is given by the Lyapunov function method, and the simulation results prove the effectiveness of the design method.
Artificial Intelligence: painting the bigger picture for copyright ownership

To receive copyright protection in Australia works must be original, amongst other requirements. The originality standard involves ‘independent intellectual effort’ that originates from an actual person. The reality of today’s creativity domain is that works are not always originating from actual persons. Due to impressive advancements in technology, some works are being created by artificial intelligence and without the involvement of an actual person. These works cannot meet copyright requirements under current law and subsequently do not receive copyright protection. This paper endeavours to answer a two-tiered question posed by the challenges artificial intelligence works have on traditional concepts of copyright. Firstly, should copyright subsist in works created by AI? Secondly, who would possibly be the copyright owner for such works? Answering these questions involves a discussion of utilitarian and natural rights theories and references to US and UK discussions on the conversation around copyright and artificial intelligence.
Generic Mapping Tools and Matplotlib Package of Python for Geospatial Data Analysis in Marine Geology

Understanding patterns of the correlation between the geomorphology and geology of the seafloor of the hadal trenches is important for the proper ocean modelling. Current paper focuses on the west Pacific Ocean region with a special case of Mariana Trench, the deepest hadal trench on the planet. Methodology of the research include combination of Generic Mapping Tools (GMT) and Quantum GIS based mapping of the geographic location, bathymetry, geodesy, sediment thickness, geomorphic shape, tectonic and geologic structure of the Mariana Trench area, and statistical analysis by means of Python. A GMT was selected for GIS visualization due to its powerful functionality and effective cartographic solutions. An object-oriented high-level programming language, Python was chosen for the data analysis and scientific plotting. The statistical analysis includes following steps: 1) Data distribution by the box plots; 2) Data sorting and grouping by stem plots; 3) Correlation analysis by 3D comparative plots referred to four tectonic plates; 4) Principal Component Analysis; 5) Analysis of Variance. The statistical analysis of the data set was performed in Matplotlib library and its dependencies: NumPy, SciPy and Pandas. A combination of the powerful methods by GMT with data analysis supported by Python programming language is an important method in geosciences aimed to increase the effectiveness of the data analysis by cartographic mapping, statistical computations and graph plotting. This paper illustrated usage of GMT, QGIS and Python for combined data analysis scheme. The results demonstrated correlation between the sediment thickness, slope steepness, depths and location of the bathymetric profiles crossing adjacent tectonic plates: Philippine, Pacific, Caroline and Mariana.
Perplexity-free Parametric t-SNE

The t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm is a ubiquitously employed dimensionality reduction (DR) method. Its non-parametric nature and impressive efficacy motivated its parametric extension. It is however bounded to a user-defined perplexity parameter, restricting its DR quality compared to recently developed multi-scale perplexity-free approaches. This paper hence proposes a multi-scale parametric t-SNE scheme, relieved from the perplexity tuning and with a deep neural network implementing the mapping. It produces reliable embeddings with out-of-sample extensions, competitive with the best perplexity adjustments in terms of neighborhood preservation on multiple data sets.
Identity work among digital nomads An exploratory study of the connection between a sub-group of digital media entrepreneurs and their felt identity tensions arising from the conditions of COVID19

Identities and identity development are becoming an important topic in research onorganisations and entrepreneurship. However, the connection between digital media entrepreneursand identity work is underresearched, especially among digital nomads. Digital nomads are a subgroup of digital media entrepreneurs that consists of people who work online, travel long andfrequent, and have an uncertain return rate. A prevailing situation that influences how people act asentrepreneurs, how they communicate and evolve –and hence strongly defines possibilities foridentity work– is the current pandemic of COVID19 and the subsequent corona crisis. This researchexplores the struggles involved with identity work, formation, and change as well as ways to solve ortackle this through the frame of COVID19 to ultimately answer the question: How do digital mediaentrepreneurs conceptualise, focus on, and tackle tensions and problems of identity work?Eleven online, semi-structured, in-depth interviews were conducted among digital nomads,with a mix of entrepreneurial experience, nationalities, ages, types of work, and current locations.This data was analysed through a thematic analysis, which resulted in 4 main themes and 19 subthemes.The main contribution of this research is to provide an in-depth understanding of how digitalnomads are facing struggles of identity work. I show that digital nomads conceptualise, focus on, andtackle problems and struggles of identity work by having, developing, and upholding a specificmindset, utilising and discovering social media, finding, associating, and developing relationships,and retaining an opportunistic approach. In doing so, I contribute to academic research on theidentity work in general and around digital nomads in particular. More specifically, the contributionrelates to the effects of COVID19 on identity, reveals the interconnectedness of struggles of identitywork, and supplement previous literature on digital nomadism with research that has COVID19 as aframe. Overall, this research can contribute to being more conscious of our own identity work, andfor entrepreneurs to be able to identify and effectively tackle identity struggles and problems ofidentity work. Additionally, the trend of remote employment could be an opportunity for investingin the co-working industry.
Scattered showers overnight night linger into Thursday, otherwise mainly dry Friday

What a beautiful sunset tonight.  After yesterday's blustery conditions today felt nice.  Highs landing in the mid-40s today, slightly cooler than the average of 48 for this time of year.