Reviewerly/exHarmony huggingface dataset:
Melting together prediction and inference

In Leo Breiman's influential article "Statistical modeling-the two cultures" he identified two cultures for statistical practices. The data modeling culture (DMC) denotes practices tailored for statistical inference targeting a quantity of interest, [inline-graphic 01]. The algorithmic modeling culture (AMC) refers to practices defining an algorithm, or a machine-learning (ML) procedure, that generates accurate predictions about an outcome of interest, [inline-graphic 02] was the dominant mode, Breiman argued that statisticians should give more attention to AMC. Twenty years later and energized by two revolutions—one in data-science and one in causal inference—a hybrid modeling culture (HMC) is rising. HMC fuses the inferential strength of DMC and the predictive power of AMC with the goal of analyzing cause and effect, and thus, HMC's quantity of interest is causal effect, [inline-graphic 03]. In combining inference and prediction, the result of HMC practices is that the distinction between prediction and inference, taken to its limit, melts away. While this hybrid culture does not occupy the default mode of scientific practices, we argue that it offers an intriguing novel path for applied sciences.
Topic Modeling and Sentiment Analysis of Electric Vehicles of Twitter Data

Twitter is a well-known social media tool for people to communicate their thoughts and feelings about products or services. In this project, I collect electric vehicles related user tweets from Twitter using Twitter API and analyze public perceptions and feelings regarding electric vehicles. After collecting the data, To begin with, as the first step, I built a pre-processed data model based on natural language processing (NLP) methods to select tweets. In the second step, I use topic modeling, word cloud, and EDA to examine several aspects of electric vehicles. By using Latent Dirichlet allocation, do Topic modeling to infer the various topics of electric vehicles. The topic modeling in this study was compared with LSA and LDA, and I found that LDA provides a better insight into topics, as well as better accuracy than LSA.In the third step, the “Valence Aware Dictionary (VADER)” and “sEntiment Reasoner (SONAR)” are used to analyze sentiment of electric vehicles, and its related tweets are either positive, negative, or neutral. In this project, I collected 45000 tweets from Twitter API, related hashtags, user location, and different topics of electric vehicles. Tesla is the top hashtag Twitter users tweeted while sharing tweets related to electric vehicles. Ekero Sweden is the most common location of users related to electric vehicles tweets. Tesla is the most common word in the tweets related to electric vehicles. Elon-musk is the common bi-gram found in the tweets related to electric vehicles. 47.1% of tweets are positive, 42.4% are neutral, and 10.5% are negative as per VADER Finally, I deploy this project work as a fully functional web app.
Entropy-Based Learning of Compositional Models from Data

We investigate learning of belief function compositional models from data using information content and mutual information based on two different definitions of entropy proposed by Jiroušek and Shenoy in 2018 and 2020, respectively. The data consists of 2,310 randomly generated basic assignments of 26 binary variables from a pairwise consistent and decomposable compositional model. We describe results achieved by three simple greedy algorithms for constructing compositional models from the randomly generated low-dimensional basic assignments.
Design Workflows and Algorithm Diagrams Interpretation Method in Software Development

One of the challenges of the modern business process management theory is that it lacks a formal method for analyzing the design workflows and algorithms presented based on the graphic languages, which could have linear computational complexity and take into account the concept of "time". The authors present an automated method for interpreting the design workflows in the software development field. The method is based on the temporal automaton RVTI-grammar, which takes into account the "time" and "clock" concepts. It differs from the existing methods in a linear design workflows interpretation time based on the visual language and allows for a finite time to perform step-by-step workflow analysis, represented in the form of algorithm diagrams. An example of such a diagram in the EPC language is given, its step-by-step interpretation is described.
Non-Interactive and Secure Data Aggregation Scheme for Internet of Things

The popularity of mobile devices in Internet of Things has brought great convenience to the lives of the people. Massive data generated in the IoT are outsourced and stored on cloud platforms so that data aggregation and analysis can be performed on the massive data. However, these data often contain sensitive information of mobile devices, so effective protection of mobile user privacy is the primary condition for further development of IoT. Most of the current data aggregation schemes require a lot of interactions between users, and thus this paper designs a non-interactive secure multidimensional data aggregation scheme. This scheme adopts an additive secret sharing technique to mask the shared data and send it to two non-colluding servers, and then the servers aggregate the ciphertext respectively. Different from the existing schemes, our proposed scheme achieves non-interaction between users, and the aggregation result is kept confidential to the server and supports mobile users offline. Finally, we perform an experimental evaluation which proves the effectiveness of our scheme.
Property Fraud Detection And Prevention Using Blockchain

Blockchain is a trending topic to explore after the popularity of Bitcoin. Bitcoin can be defined as a digital currency, mainly developed to speed up transactions and to reduce third-party control over any transaction. Blockchain is the underlying technology that is responsible for maintaining the bitcoin transaction ledger. It can be defined as a chain of blocks such that each block holds some information. The information stored is dependent on the type of blockchain and can be referred to as a digital signature. The digital signature of the next block is based on the digital signature of the current block and the digital signature of the next block is based on the previous block signature. Blockchain is a fast-evolving technology that is eventually replacing the current transaction management system. The problems that are being faced by the current transaction system such as the transaction costs, crisis and crashes due to depression and fractional reserve banking, blockchain helps to solve these issues. Blockchain is not limited to the transaction system and can be used in other fields also. The property fraudulent cases are becoming very common these days, the reason being centralized control and no public access to data and absence of a system to cross-check the property for which transaction is being made. In this paper, we focus to discuss on issues like centralized control, how the blockchain technology will benefit and help to overcome the issues in any kind of property fraud cases and what are the major challenges to this technology implementation.
Decentralized Loan Management Application Using Smart Contracts on Block Chain

The most shocking events were was the recent discovery of the fraudulent activities in the Punjab National Bank. This is due to frequent systemic failures that detect human errors. Blockchain technology is the greatest solution for this issue. It is surprisingly common for the information settlement mechanism like SWIFT to be on a isolated ledger from the payment settlement mechanism. If the banks uses a ledger that stores information settlement distributed across all the participants, then the fraudulent user may reflect on all the available participants in the transactions, auditors and regulators. Our Paper is a Decentralised Loan Management Web Application (DApp) built on Ethereum blockchain which targets on preventing such fraudulent attacks on Loans sanctions by decentralising the processes. The security features authentication of the user identity, authentication of bank officials and multiple levels of verification of details are implemented using Public Key Infrastructure (PKI).
Multi-scale Self-supervised Learning for Multi-site Pediatric Brain MR Image Segmentation with Motion/Gibbs Artifacts

Accurate tissue segmentation of large-scale pediatric brain MR images from multiple sites is essential to characterize early brain development. Due to imaging motion/Gibbs artifacts and multi-site issue (or domain shift issue), it remains a challenge to accurately segment brain tissues from multi-site pediatric MR images. In this paper, we present a multi-scale self-supervised learning (M-SSL) framework to accurately segment tissues for multi-site pediatric brain MR images with artifacts. Specifically, we first work on the downsampled images to estimate coarse tissue probabilities and build a global anatomic guidance. We then train another segmentation model based on the original images to estimate fine tissue probabilities, which are further integrated with the global anatomic guidance to refine the segmentation results. In the testing stage, to alleviate the multi-site issue, we propose an iterative self-supervised learning strategy to train a site-specific segmentation model based on a set of reliable training samples automatically generated for a to-be-segmented site. The experimental results on pediatric brain MR images with real artifacts and multi-site subjects from the iSeg2019 challenge demonstrate that our M-SSL method achieves better performance compared with several state-of-the-art methods.
Trade-Off Between Security and Scalability in Blockchain Design: A Dynamic Sharding Approach

Security and scalability are considered as two major issues that are most likely to influence rapid deployment of blockchains in businesses. We believe that the ability to scale up a blockchain lies mainly in improving the underlying technology rather than deploying new hardware. Though recent research works have applied sharding techniques in enhancing scalability of blockchains, they do not cater for addressing the issue of both data security and scalability in blockchains. In this paper, we propose an approach that makes a trade-off between security and scalability when designing blockchain based systems. We propose an efficient replication model, which creates dynamic sharding wherein blocks are stored in a varying number of nodes. The proposed approach shows that the replication of blockchain over peer-to-peer network is minimized as the blockchain’s length evolves according to a replication factor to preserve the security.
Solving Not-Substring Constraint withFlat Abstraction

Not-substring is currently among the least supported types of string constraints, and existing solvers use only relatively crude heuristics. Yet, not-substring occurs relatively often in practical examples and is useful in encoding other types of constraints. In this paper, we propose a systematic way to solve not-substring using based on flat abstraction. In this framework, the domain of string variables is restricted to flat languages and subsequently the whole constraints can be expressed as linear arithmetic formulae. We show that non-substring constraints can be flattened efficiently, and provide experimental evidence that the proposed solution for not-substring is competitive with the state of the art string solvers.
Social-VRNN: One-Shot Multi-modal Trajectory Prediction for Interacting Pedestrians.

Prediction of human motions is key for safe navigation of autonomous robots among humans. In cluttered environments, several motion hypotheses may exist for a pedestrian, due to its interactions with the environment and other pedestrians. Previous works for estimating multiple motion hypotheses require a large number of samples which limits their applicability in real-time motion planning. In this paper, we present a variational learning approach for interaction-aware and multi-modal trajectory prediction based on deep generative neural networks. Our approach can achieve faster convergence and requires significantly fewer samples comparing to state-of-the-art methods. Experimental results on real and simulation data show that our model can effectively learn to infer different trajectories. We compare our method with three baseline approaches and present performance results demonstrating that our generative model can achieve higher accuracy for trajectory prediction by producing diverse trajectories.
Dehazing Effects on Image and Videousing AHE, CLAHE and Dark Channel Prior

The image captured by camera is degraded by various atmospheric parameters for example rain, storm, wind, haze, snow. The removing haze is called dehazing, is naturally done in the physical degradation model that requires a resolution of an ill-posed inverse problem. In this paper discussion and e relative study of Adaptive Histogram Equalization (AHE) as well as Contrast limited adaptive histogram equalization (CLAHE) and dark channel prior (DCP). This article suggest image and video dehazing technique working on DCP method. The DCP is resulted from the characteristics of images taken in outdoor that the value of intensity inside the local window is nearly equal to zero. The DCP system has good haze elimination and color managing potential for the images with various angles of haze. The dehazing is done using following four major steps: atmospheric light estimation, transmission map estimation, transmission map refinement, and image reconstruction. This solution of four step DCP will give solution to ill-posed inverse problem. This dehazing techniques can be used in advanced driverless assisted systems in autonomous cars, satellite imaging, underwater imaging etc.
A preliminary study on the adoption and effectiveness of SameSite cookies as a CSRF defence

The SameSite cookie attribute was introduced to prevent Cross-site Request Forgery (CSRF) attacks. Major browsers support SameSite functionality since 2016. Since 2020, browsers enforce it by default. These developments sometimes have been celebrated as the end of CSRF. In this paper, we have a closer look into the potential of SameSite mechanism to effectively fight CSRF in practice. Our measurements and evaluations over most popular websites indicate that, if properly deployed, the SameSite mechanism can be effective against the major CSRF attack scenario. Still, like any other countermeasure, it is not likely to be a silver bullet to end CSRF, due to various scenarios that require additional protection. We refactor our findings in a set of guidelines for the web community on how to make best use of SameSite and what it is left to do to fight CSRF.
RPA and Artificial Intelligence in Budget Management Based on Multiperspective Recognition Based on Network Communication Integration

Nowadays, RPA robots are increasingly used in daily office tasks such as finance and human resources. They play an increasingly important role in realizing office automation, which can improve work efficiency and reduce labor costs. In order to improve the efficiency of budget management and save human resources, this paper conducts related research based on the multiview recognition technology of network communication integration, combined with RPA in artificial intelligence technology. In the method part, this article introduces the mode of network communication integration and the principles that should be followed, as well as the related processes of RPA. In the algorithm, this paper introduces an integrated algorithm based on ELM. In the experimental part, this article predicts the performance of each model, compares identification functions with different signal-to-voice signals, and compares timing functions on different signal-to-voice signals, periodic transmission mode indicators, recognition rates of different kernel functions, and comparison of average recognition rates and multiview recognition rate comprehensive analysis of these multiple aspects. Under the same conditions, the recognition rate of some angles is lower than other angles; 0 degrees, 18 degrees, 126 degrees, and 180 degrees are slightly lower than other angles, which will affect the average recognition rate of the entire recognition. But for multiview gait features, considering the influence of each angle on the recognition rate, the characteristics of each angle are merged together, so that the recognition rate is significantly higher than the average recognition rate of 11 angles. It can be seen that multiview recognition based on network communication integration does have obvious effects on RPA and artificial intelligence in budget management and can improve the efficiency of budget management. The multiperspective recognition technology designed in this study can realize modernization and digitization in budget management.
Geochemical and mineralogical characterization of phosphatic crusts developed on the basement carbonatites of Sri Lanka: towards a better understanding of the weathering process

The Ceylon Journal of Science is published by the University of Peradeniya, Sri Lanka. Full text available. The journal also has its own website. The Ceylon Journal of Science is a continuation of the Ceylon Journal of Science (Biological Sciences) which is no longer being published as a separate journal. The history of the journal can be found here.From May 2020, Ceylon Journal of Science is indexed in DOAJ.
3D Measurement of Discontinuous Objects with Optimized Dual-frequency Grating Profilometry

Abstract Three-dimensional profilometry tends to be less effective at measuring discontinuous surfaces. To overcome this problem, an optimized profilometry based on fringe projection is proposed in this paper. Due to the limitation of the shooting angle, there are projection blind spots on the surface of discontinuous objects. Since the noises and unwrapping errors are always localized at the projection blind spots, an algorithm is designed to determine the blind spots automatically with the light intensity difference information. Besides, in order to improve the measurement accuracy, a processing scheme is introduced to deal with the local height distortion introduced by the dual-frequency grating profilometry. Lots of measurement tests on various surfaces are carried out to assess the optimized profilometry, and experimental results indicate that the modified profilometry system works more robust with high reliability and accuracy in measuring different kinds of surfaces, especially discontinuous ones.
Benchmarking Modern Named Entity Recognition Techniques for Free-text Health Record Deidentification.

Electronic Health Records (EHRs) have become the primary form of medical data-keeping across the United States. Federal law restricts the sharing of any EHR data that contains protected health information (PHI). De-identification, the process of identifying and removing all PHI, is crucial for making EHR data publicly available for scientific research. This project explores several deep learning-based named entity recognition (NER) methods to determine which method(s) perform better on the de-identification task. We trained and tested our models on the i2b2 training dataset, and qualitatively assessed their performance using EHR data collected from a local hospital. We found that 1) Bi-LSTM-CRF represents the best-performing encoder/decoder combination, 2) character-embeddings tend to improve precision at the price of recall, and 3) transformers alone under-perform as context encoders. Future work focused on structuring medical text may improve the extraction of semantic and syntactic information for the purposes of EHR deidentification.
Dark clouds and silver linings: impact of COVID-19 on internet users’ privacy

Abstract Objectives To examine the impact of coronavirus disease 2019 (COVID-19) pandemic on the extent of potential violations of Internet users’ privacy. Materials and Methods We conducted a longitudinal study of the data sharing practices of the top 1000 websites in the United States between April 9 and August 27, 2020. We fitted a conditional latent growth curve model on the data to examine the longitudinal trajectory of the third-party data sharing over the 21 weeks period of the study and examine how website characteristics affect this trajectory. We denote websites that asked for permission before placing cookies on users’ browsers as “privacy-respecting.” Results As the weekly number of COVID-19 deaths increased by 1000, the average number of third parties increased by 0.26 (95% confidence interval [CI] 0.15–0.37) P &amp;lt; 0.001 units in the next week. This effect was more pronounced for websites with higher traffic as they increased their third parties by an additional 0.41 (95% CI 0.18–0.64); P &amp;lt; 0.001 units per week. However, privacy respecting websites that experienced a surge in traffic reduced their third parties by 1.01 (95% CI −2.01 to 0); P = 0.05 units per week in response to every 1000 COVID-19 deaths in the preceding week. Discussion While in general websites shared their users’ data with more third parties as COVID-19 progressed in the United States, websites’ expected traffic and respect for users’ privacy significantly affect such trajectory. Conclusions Attention should also be paid to the impact of the pandemic on elevating online privacy threats, and the variation in third-party tracking among different types of websites.
A New Term Frequency with Gaussian Technique for Text Classification and Sentiment Analysis

This paper proposes a new term frequency with a Gaussian technique (TF-G) to classify the risk of suicide from Thai clinical notes and to perform sentiment analysis based on Thai customer reviews and English tweets of travelers that use US airline services. This research compared TF-G with term weighting techniques based on Thai text classification methods from previous researches, including the bag-of-words (BoW), term frequency (TF), term frequency-inverse document frequency (TF-IDF), and term frequency-inverse corpus document frequency (TF-ICF) techniques. Suicide risk classification and sentiment analysis were performed with the decision tree (DT), naïve Bayes (NB), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) techniques. The experimental results showed that TF-G is appropriate for feature extraction to classify the risk of suicide and to analyze the sentiments of customer reviews and tweets of travelers. The TF-G technique was more accurate than BoW, TF, TF-IDF and TF-ICF for term weighting in Thai suicide risk classification, for term weighting in sentiment analysis of Thai customer reviews for Burger King, Pizza Hut, and Sizzler restaurants, and for the sentiment analysis of English tweets of travelers using US airline services.