Reviewerly/exHarmony huggingface dataset:
‘Crypto-investment’ in International Economic Law: A First Sketch

Abstract In January 2009, Bitcoin was launched as the first ‘cryptocurrency’, a term used to refer to a ‘digital representation of value’ operating with Distributed Ledger Technology (DLT). Commonly known as ‘Blockchain’, DLT has been an object of study in different legal disciplines, which seek to understand its financial and legal impacts. However, international law literature is still silent regarding the impacts that DLT technology could have on the different legal regimes. This article contextualizes DLT as digital governance ‘infrastructure’ that could affect the way that public authority is exercised; in this sense, cryptocurrencies are the product of such infrastructure. Taking this notion into account, it is further argued here that the concept of ‘crypto-investment’ could be useful in international investment law when certain conditions come into play.
Customer Benefits on Bitcoin as a Medium of Exchange

Cryptocurrency is a digital currency that is powered by blockchain technology. One of it is bitcoin, a system that is digitally created and traded tokens to which value is assigned. The level of adoption of bitcoin has accelerated due to several fiscal crises that leads to financial crashes that have affected the lives of millions of people. This has created a demand for new kinds of niche money. Issues need to be closely discussed before it is fully accepted by customers as a medium of exchange. Even though bitcoin is used as a medium of exchange, there is still no specific guidance and benefits to the users. The issues concernedare whether the customers will get the benefits of privacy, lower transaction costs and freedom in payment. Therefore, the aims of this paper are to identify the relationship between transaction cost, privacy and digital payment as a benefit of bitcoin as a medium of exchange. This research will be conducted in Klang Valley area and the questionnaires will be disseminated directly to respondents. These respondents will be identified using probability simple random sampling. A regression analysis will be conducted comprising 200 observations in Klang Valley, Malaysia. Partial least square-structural equation (PLS-SEM) will be used. In the measurement model, reliability will be assessed by examining the Composite Reliability (CR), while validity will be assessed by convergent validity and discriminant validity. Subsequently, structural model testing with 500 re-samples was applied to test the hypothesized relationships between exogenous variables and endogenous variable. Digital payment and privacy are statistically significant towards the benefit of using bitcoin as a medium of exchange. It was found that the digital payment contributes the highest benefit to the customers followed by privacy. The results provide interesting insight into the determinants for the customers benefit using bitcoin. Although the findings show significant results customers should always decide the good and bad thoroughly before finalizing their decisions on the usage of bitcoin. Therefore, it is hoped that this study will enrich the growing literature on the subject and future research needs to explore on the benefits of using bitcoin among the real users. This study is expected to give guidelines to the policymakers on the implementation of bitcoin as a medium of exchange. It is also expected that the results may provide interesting insight into the determinants of customer benefits using bitcoin. Simultaneously, it will contribute to the elements of industry, innovation and infrastructure.
On the Use of Sampling Weights for Retrospective Medical Record Reviews

Retrospective medical record review is often used to answer the questions that statistical modeling cannot. In addition to its utility as an explanatory tool, it can be used to generate hypotheses using available retrospective data and so is a convenient guide for developing future prospective studies. A recent review of articles that used the retrospective medical record review method listed 10 best practices that ought to be followed. However, an issue that is not listed is the use of sampling weights, which are important when one can only conduct retrospective medical record review for a sample of the target population. Although that review acknowledged the importance of carefully selecting a sampling strategy for such a scenario and indeed had outlined 3 commonly used sampling methods (convenience, simple random, and systematic), the authors say nothing of the use of sampling information at the data analysis stage. This article aims to fill that gap and to demonstrate why the use of sample weights ought to be another best practice to add to the list by reviewing well-known theoretical details and some published data analysis examples.
Kernel Hypothesis Testing with Set-valued Data

We present a general framework for hypothesis testing on distributions of sets of individual examples. Sets may represent many common data sources such as groups of observations in time series, collections of words in text or a batch of images of a given phenomenon. This observation pattern, however, differs from the common assumptions required for hypothesis testing: each set differs in size, may have differing levels of noise, and also may incorporate nuisance variability, irrelevant for the analysis of the phenomenon of interest; all features that bias test decisions if not accounted for. In this paper, we propose to interpret sets as independent samples from a collection of latent probability distributions, and introduce kernel two-sample and independence tests in this latent space of distributions. We prove the consistency of tests and observe them to outperform in a wide range of synthetic experiments. Finally, we showcase their use in practice with experiments of healthcare and climate data, where previously heuristics were needed for feature extraction and testing.
Data-driven models and computational tools for neurolinguistics: a language technology perspective

In this paper, our focus is the connection and influence of language technologies on the research in neurolinguistics. We present a review of brain imaging-based neurolinguistic studies with a focus on the natural language representations, such as word embeddings and pre-trained language models. Mutual enrichment of neurolinguistics and language technologies leads to development of brain-aware natural language representations. The importance of this research area is emphasized by medical applications.
Powerful processing to three-dimensional facial recognition using triple information

&lt;p&gt;&lt;span&gt;Face Detection plays a crucial role in identifying individuals and criminals in Security, surveillance, and footwork control systems. Face Recognition in the human is superb, and pictures can be easily identified even after years of separation. These abilities also apply to changes in a facial expression such as age, glasses, beard, or little change in the face. This method is based on 150 three-dimensional images using the Bosphorus database of a high range laser scanner in a Bogaziçi University in Turkey. This paper presents powerful processing for face recognition based on a combination of the salient information and features of the face, such as eyes and nose, for the detection of three-dimensional figures identified through analysis of surface curvature. The Trinity of the nose and two eyes were selected for applying principal component analysis algorithm and support vector machine to revealing and classification the difference between face and non-face. The results with different facial expressions and extracted from different angles have indicated the efficiency of our powerful processing.&lt;/span&gt;&lt;/p&gt;
TGView3D: A System for 3-Dimensional Visualization of Theory Graphs

We describe the TGView3D system, an interactive graph viewer optimized for exploring mathematical knowledge as 3D graphs. To exploit all three spatial dimensions, it extends the commonly-used force-directed layout algorithms with hierarchical components that are more suitable for the typical structure of mathematical knowledge. TGView3D can also communicate with OMDoc-based knowledge management tools in order to offer semantic, mathematics-specific interaction with the graphs.
ANALISIS KESULITAN MEMBACA PERMULAAN PADA SISWA KELAS I DI SDN DEMANGAN YOGYAKARTA

The purpose of the research to describe the types of difficulties in reading the beginning, the causes of difficulty reading the beginning, and the efforts of the teacher in the face of difficulty reading beginning in Grade 1 students at SDN Demangan Yogyakarta. This type of research is qualitative descriptive research, with data collection using interviews, observations, and documentation. The results show that there are types of difficulties in reading the beginning of students, namely the lack of student recall, difficulty spelling when the consonant is in the middle, difficulty seeing long distances, mistakes in changing letters, not yet Punctuation and difficulties in letter recognition. Factors for the cause of difficulty reading early physiological, intellectual, psychological, and solutions and efforts that have been done by the teacher in the learning process on the reading activities using Vidio to learn to read, using letters cards and Learning guidance.
An Improvement in Support Vector Machines Algorithm with Imperialism Competitive Algorithm for Text Documents Classification

An Improvement in Support Vector Machines Algorithm with Imperialism Competitive Algorithm for Text Documents Classification
Mitigating Insider Threats on the Edge: A Knowledgebase Approach

Insider Threats, who are cloud internal users, cause very serious problems, which in terns, leads to devastating attacks for both individuals and organizations. Although, most of the attentions, in the real world, is for the outsider attacks, however, the most damaging attacks come from the Insiders. In cloud computing, the problem becomes worst in which the number of insiders are maximized and hence, the amount of data that can be breached and disclosed is also maximized. Consequently, insiders' threats in the cloud ought to be one of the top most issues that should be handled and settled. Classical solutions to defend against insiders’ threats might fail short as it is not easy to track both activities of the insiders as well as the amount of knowledge an insider can accumulate through his/her privileged accesses. Such accumulated knowledge can be used to disclose critical information –which the insider is not privileged to- through expected dependencies that exist among different data items that reside in one or more nodes of the cloud. This paper provides a solution that suits well the specialized nature of the above mentioned problem. This solution takes advantage of knowledge bases by tracking accumulated knowledge of insiders through building Knowledge Graphs (KGs) for each insider. It also takes advantage of Mobile Edge Computing (MEC) by building a fog layer where a mitigation unit -resides on the edge- takes care of the insiders threats in a place that is as close as possible to the place where insiders reside. As a consequence, this gives continuous reactions to the insiders’ threats in real-time, and at the same time, lessens the overhead in the cloud. The MEC model to be presented in this paper utilizes a knowledgebase approach where insiders’ knowledge is tracked and modeled. In case an insider knowledge accumulates to a level that is expected to cause some potential disclosure of private data, an alarm will be raised so that expected actions should be taken to mitigate this risk. The knowledgebase approach involves generating Knowledge Graphs (KGs), Dependency Graphs (DGs) where a Threat Prediction Value (TPV) is evaluated to estimate the risk upon which alarms for potential disclosure are raised. Experimental analysis has been conducted using CloudExp simulator where the results have shown the ability of the proposed model to raise alarms for potential risks from insiders in a real time fashion with accurate precision
Conditional Independences and Causal Relations implied by Sets of Equations

Real-world systems are often modelled by sets of equations with exogenous random variables. What can we say about the probabilistic and causal aspects of variables that appear in these equations without explicitly solving for them? We prove that, under a solvability assumption, we can construct a Markov ordering graph that implies conditional independences and a causal ordering graph that encodes the effects of soft and perfect interventions by making use of Simon's causal ordering algorithm. Our results shed new light on discussions in causal discovery about the justification of using graphs to simultaneously represent conditional independences and causal relations in models with feedback.
EduBot: An Unsupervised Domain-Specific Chatbot for Educational Institutions

In the present time Chatbot is an essential tool used by many organizations to provide services to their targeted customers round the clock. This research focuses on a domain-specific Chatbot that can be helpful for educational institutes. This Chatbot will be a virtual (representation) to the admission seekers. It will provide answers regarding the university, its departments, admission fees and other admission related FAQ. For the sake of the research, frequently asked questions of a university were collected and an unsupervised learning model along with natural language processing techniques was deployed to answer the questions of the admission candidates. Tokenization, stop words removal followed by vectorization were implemented for preprocessing the training data. User’s inputs were similarly processed and then tf-idf based cosine similarity applied to retrieve the best answer. Later, a user-centric evaluation metric was used to evaluate the model and as per the metric, our current model showed approximately 80% accuracy.
Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier

Long-tail recognition tackles the natural non-uniformly distributed data in real-world scenarios. While modern classifiers perform well on populated classes, its performance degrades significantly on tail classes. Humans, however, are less affected by this since, when confronted with uncertain examples, they simply opt to provide coarser predictions. Motivated by this, a deep realistic taxonomic classifier (Deep-RTC) is proposed as a new solution to the long-tail problem, combining realism with hierarchical predictions. The model has the option to reject classifying samples at different levels of the taxonomy, once it cannot guarantee the desired performance. Deep-RTC is implemented with a stochastic tree sampling during training to simulate all possible classification conditions at finer or coarser levels and a rejection mechanism at inference time. Experiments on the long-tailed version of four datasets, CIFAR100, AWA2, Imagenet, and iNaturalist, demonstrate that the proposed approach preserves more information on all classes with different popularity levels. Deep-RTC also outperforms the state-of-the-art methods in longtailed recognition, hierarchical classification, and learning with rejection literature using the proposed correctly predicted bits (CPB) metric.
Measurement Error Models: From Nonparametric Methods to Deep Neural Networks

The success of deep learning has inspired a lot of recent interests in exploiting neural network structures for statistical inference and learning. In this paper, we review some popular deep neural network structures and techniques under the framework of nonparametric regression with measurement errors. In particular, we demonstrate how to use a fully connected feed-forward neural network to approximate the regression function f(x), explain how to use a normalizing flow to approximate the prior distribution of X, and detail how to construct an inference network to generate approximate posterior samples of X. After reviewing recent advances in variational inference for deep neural networks, such as the importance weighted autoencoder, doubly reparametrized gradient estimator, and nonlinear independent components estimation, we describe an inference procedure built upon these advances. An extensive numerical study is presented to compare the neural network approach with classical nonparametric methods, which suggests that the neural network approach is more flexible in accommodating different classes of regression functions and performs superior or comparable to the best available method in many settings.
Quasi-Duality of Width and Depth of Neural Networks

Here we report that the width and depth of a neural network are quasi-dual to each other; i.e., they are intrinsically connected and can be to a good degree converted from one to the other. First, inspired by the De Morgan law, we formulate a transformation from a general ReLU network to a wide network and a deep network respectively without compromising the function of the original network, thereby elaborating a quasi-duality of the width and depth. Then, we estimate the width and depth of a network to represent a partially separable function, and reveal an interchangeability between the width and depth. Additionally, effects of width and depth on optimization and generalization are also discussed
Smart Techie: An Online Examination System with Improved Use of Descriptive Answering

The modern computerized system is developed with the aim to overcome the drawback of existing system.This paper proposes a web application for Job Searching and Job Posting.The main advantage is that any person anywhere from the world can register into the site for search and find the job.The recruitment is provided on the basis of a test.The test includes questions from various categories.Based on the result of the test, recruitment will be provided for the candidates.In the case of job recruitment, the module helps in registration of the prospective candidates with the system.The candidate has to register with the site to get a login, once login is available they can attend the test.The system contains a list of companies.The test is based on Latent Semantic Analysis.In general LSA can be considered as an excellent information retrieval technique.But for this specific task Descriptive Answer Assessment (DAA) is also used.That means answers can be either objective or descriptive.In case of descriptive answers the candidate can answer either by writing or by explaining the answer in his/her own voice.LSA is mainly used for short answers and DAA for large explanations.The companies can add jobs according to the category.They can view their profile and if needed, can update the details.
Identification of Ischemic Stroke by Marker Controlled Watershed Segmentation and Fearture Extraction

In this paper, we will describe a method that distinguishes the ischemic stroke from Computed Tomography (CT) brain images by extracting the statistical and textural features. First, preprocessing of the CT images is done followed by image enhancement. Segmentation of the CT images is performed by Marker Controlled Watershed. After the segmentation, we get the Grey Level Co-occurrence matrix (GLCM) and extract the textural and statistical features. The disadvantage of watershed is the over-segmentation caused by noise and solved by Marker Controlled Watershed as shown experimentally. The features extracted are contrast, correlation, standard deviation, variance, homogeneity, energy and mean. We noticed in our results that the values of homogeneity, energy and mean are bigger in normal CT images than in abnormal CT images where the contrast, correlation, standard deviation and variance of normal CT images are less than those of abnormal CT images (Ischemic Stroke)
Pengembangan Media Pebelajaran Audio Visual Berbantuan Adobe Flash dan Android Pada Kompetensi Dasar Mengelola Arsip Elektronik untuk Kelas X OTKP1 SMK Negeri 2 Blitar

This development research aims to describe the process of developing Audio Visual learning media assisted by Adobe Flash and Android in Basic Competence in Managing Electronic Archives for Class X OTKP 1 SMK NEGERI 2 Blitar, analyzing the feasibility of developed learning media and analyzing students' responses to learning media that have been developed. This research uses research and development methods and uses the 4-D development model. The sample used for the limited trial was 20 students in class X OTKP 1 SMK NEGERI 2 Blitar. The data collection instruments used material expert validation sheets, media expert validation sheets and student response sheets. All validation sheets and student response sheets use a Likert scale. The product produced in this research development is in the form of android based audio visual learning media which contains material, voice narration, multiple choice questions with automatic assessment, and practical assignments to store records electronically in accordance with the basic competencies in managing electronic records. The results of the assessment by material validation experts were 86% with "very proper" interpretations, and media experts by 81% with "very decent" interpretations. assessment obtained from trials is limited to students by 85% with the interpretation "very feasible". These results indicate that the audio visual learning media assisted by Adobe Flash and Android on the basic competence of Managing Electronic Archives is declared suitable for use in the learning process in class X OTKP 1 SMK NEGERI 2 Blitar.
Geographic Information System (GIS) for Natural Resources Management in Rural Areas:- A Case Study of Village Jeeda, Block Goniana, District Bathinda (Punjab)

Geographic Information System is a computer based tool for marking specific Places on maps. It is a collection of map systems, geographic datum and human knowledge makes it possible to present the geography around us with the aid of digital technology. We have collected locations of Work Sites using Google Earth Android Application and Sharing Locations on WhatApp Messenger. We have selected Village Jeeda in District Bathinda(Punjab) for GIS planning of Works which are to be executed under Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS). Works related to Natural Resource Management such as Plantation, Renovation of Community Ponds, Maintenance of existing plantation, Irrigation Water Channels Maintenance etc. Block plantation, Ponds are marked with Polygons attributes and Line Plantation, Irrigation Water Channels are marked with paths attributes. GIS has a wide range of applications. It is equally important in Natural Resource Management. The GIS in Natural Resource Management is a resourceful technique in measuring natural resource assets.