Reviewerly/exHarmony huggingface dataset:
A BERT-Based Question Representation for Improved Question Retrieval in Community Question Answering Systems

Community question answering (CQA) services have become a prominent place for seeking answers from experts and sharing knowledge in any domain. Retrieving semantically related historical questions for a new query is a critical task in CQA to eliminate duplicate questions and to avoid indefinite waiting time to get responses. One major challenge in question retrieval is the lexical gap between the new question and the question in the archive that is already answered. This problem can be eliminated to a great extent by giving proper embedding for questions. Traditional bag-of-words (BOW) representation for text is being replaced by semantic embedding models like word2vec, Glove, and FastText and the newly released Bidirectional Encoder Representations from Transformers (BERT) model. In this paper, we propose a modified BERT embedding using the topic information obtained by LDA on top of questions preprocessed using the RAKE keyword extraction algorithm. We perform question retrieval based on cosine similarity of question vectors and evaluated the accuracy on the Quora question pair dataset. Results show that the proposed question embedding improves the performance of question retrieval compared with competing models.
Partial least squares regression with compositional response variables and covariates

The common approach for regression analysis with compositional variables is to express compositions in log-ratio coordinates (coefficients) and then perform standard statistical processing in real space. Similar to working in real space, the problem is that the standard least squares regression fails when the number of parts of all compositional covariates is higher than the number of observations. The aim of this study is to analyze in detail the partial least squares (PLS) regression which can deal with this problem. In this paper, we focus on the PLS regression between more than one compositional response variable and more than one compositional covariate. First, we give the PLS regression model with log-ratio coordinates of compositional variables, then we express the PLS model directly in the simplex. We also prove that the PLS model is invariant under the change of coordinate system, such as the ilr coordinates with a different contrast matrix or the clr coefficients. Moreover, we give the estimation and inference for parameters in PLS model. Finally, the PLS model with clr coefficients is used to analyze the relationship between the chemical metabolites of Astragali Radix and the plasma metabolites of rat after giving Astragali Radix.
A New Approach for Momentum Particle Swarm Optimization

In this paper, a new approach to momentum particle swarm optimization is proposed. We design a particle swarm optimizer that converges faster than the currently available momentum particle swarm optimizers. This simulates gradient descent with momentum and is inspired from the back-propagation algorithm with momentum in neural networks. The proposed optimizing algorithm constantly gives faster convergence time in all the available test optimization functions (constrained or unconstrained). This paper contains graphs and results that summarize the algorithm’s performance in contrast with weighted particle swarm optimizer and momentum particle swarm optimizer.
JSCSP: a Novel Policy-Based XSS Defense Mechanism for Browsers

To mitigate cross-site scripting attacks (XSS), the W3C group recommends web service providers to employ a computer security standard called Content Security Policy (CSP). However, less than 3.7 percent of real-world websites are equipped with CSP according to Google’s survey. The low scalability of CSP is incurred by the difficulty of deployment and non-compatibility for state-of-art browsers. To explore the scalability of CSP, in this article, we propose JavaScript based CSP (JSCSP), which is able to support most of real-world browsers but also to generate security policies automatically. Specifically, JSCSP offers a novel self-defined security policy which enforces essential confinements to related items, including JavaScript functions, DOM elements and data access. Meanwhile, JSCSP has an efficient algorithm to automatically generate the policy directives and enforce them in a cascading way, which is more fine-grained and practical than the functionalities provided by CSP. We further implement JSCSP on a Chrome extension, and our evaluation shows that the extension is compatible with popular JavaScript libraries. Our JSCSP extension can detect and block the tested attacking vectors extracted from the prevalent web applications. We state that JSCSP delivers better performance compared to other XSS defense solutions.
Digital Forensic Approaches for Cloud Service Models: A Survey

Cloud computing has become one of the fastest-growing IT infrastructures in the world. Criminals are aggressively expanding the use of digital technologies for illegal activities. As a consequence, the rise of cybercrimes in cloud systems exacerbates the problem of scale for digital forensic practitioners. Traditional digital forensic approaches such as the data acquisition of electronic devices, personal computer forensics, live data forensics, network investigations and forensics, mobile phone forensics, and are not sufficient or only partly applicable for the investigation, acquisition and analysis of evidence from cloud computing platforms. The evaluation of digital forensic techniques for cloud service models is still a challenge due to the lack of efficient criteria. Therefore, in this chapter, we first define the criteria for evaluating existing digital forensic approaches for the three main cloud service models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). After that, we will review, analyse and compare each digital forensic approach in order to display existing gaps that need further solutions.
RTPA-based Software Generation by AI Programming

AI programming toward autonomous software generation is not only a highly demanded technology by the software industry, but also a hard challenge to the theories of software engineering and computational intelligence. A methodology and tool for autonomous program generation (APG) are recently developed based on Real-Time Process Algebra (RTPA). This paper demonstrates an experimental result of autonomous code generation on a digital clock system by the APG tool. The experimental results indicate a novel approach towards AI programming for machine-enabled software generation theories and technologies.
Distributed Ledger Technologies

LEDGER TECHNOLOGIES (DLT), of which blockchain is a popular example, are increasingly becoming a popular means to maintain transactional integrity and achieve consensus among competing parties in many modern distributed data exchanges.Indeed, a Gartner survey estimates that by 2020, DLT and blockchain will support the global movement and tracking of $2 trillion of goods and services annually. 1Unlike centralized files and databases, distributed ledgers rely on peering nodes to record, share, and synchronize transactions and data in their individually maintained local ledgers.In the case of blockchain, information is organized into blocks that are securely and transparently chained together.These blocks become immutable global knowledge among all peers using consensus algorithms to achieve data synchronization.The "append-only, globally accepted" transactions supported by blockchain technologies have given rise to both opportunities and challenges compared to traditional data storage systems.One of the challenges faced by current information sharing systems, and a key concept that makes DLT appealing is the support for the creation of large scale systems from nodes and components that do not trust each other.Being able to reach consensus and share a commonly verifiable ledger is a very powerful primitive,
Tracing Website Attackers by Analyzing Onion Routers’ Log Files

The Onion Router (Tor) is one of the major network systems that provide anonymous communication and censorship circumvention.Tor enables its users to surf the Internet, chat, and send messages anonymously; however, cyber attackers also exploit the system for circumventing criminal activity detection.Recently, various approaches that prevent or mitigate abuse of Tor have been proposed in the literature.This paper, which presents one of the approaches, addresses an IP traceback problem.In our model, onion routers that voluntarily participate in attacker tracing detect attack packets (packets carrying an attacker's code or data) recorded in the log files by sharing necessary information with an attacked server over an Ethereum blockchain network.The detection algorithm in this paper uses the statistics of packet travel and relay times and outputs attack-packet candidates.The proposed method attaches a reliability degree to each candidate, which is based on the upper bounds of its Type I and II error rates.A smart contract running on the blockchain network ranks the detection results from onion routers according to the reliability degrees.
Real Time Anomaly detection-Based QoE Feature selection and Ensemble Learning for HTTP Video Services

Using Machine Learning to the user perception prediction has largely increased in the last decade. Although it is difficult to deduce the best category model to address the predicted Quality of Experience QoE in operational networks. Also, sometimes the result obtained by ML is not relevant i.e not expected according to the present conditions. We talk here about problem of anomaly in Machine Learning context. To fill this gap, in this paper, we try to solve the problem of Machine Learning ML anomaly detection in the QoE context for video streaming service in Software Defined Networking SDN environment. We explore in one hand feature selection methodology to extract the most correlated features to the video QoE. In other hand we investigate different ensemble learning approaches to improve the detection of QoE anomalies following the known stacking or super learning model. Results show that the proposed model presents a high performance comparing to other types of ensemble learning and single learning techniques.
Building trust and co-designing a study of trust and co-operation: Observations from a network study in a high-risk, high-security environment

As part of a larger project on trust and co-operation in IT-Security, the author collected network data from an international security team specialized in cyber-incident response, leading to the only dataset of its kind. Collecting these data involved a number of challenges. First, the team in question had recently been established and was based in a large international firm. Thus, the author had to navigate legal and procedural issues, and stay on top of a changing environment. Second, actors in IT-Security are acutely aware of how data can be misused, requiring the author to build trust and demonstrate appropriate data handling. Attaining support from security managers was a key step to build trust with participants and the host organization. Third, precautions were necessary due to the project's security implications, as the study data could be abused by adversaries. Consequentially, it was necessary to engage in a co-design process, negotiating technical and procedural precautions. Not all measures initially agreed upon were feasible in practice.
On the use of blockchain technologies in smart home applications

Abstract The paper presents an implementation of a smart home architecture with two gateways based on collaboration of blockchain technology and Internet of Things (IoT) Message oriented middleware (MOM) architecture for management of internal home appliances. A basic overview of IoT MOM, MQTT (Message Queue Telemetry Transfer) protocol and blockchain architectures is made and the key characteristics and consensus algorithms are presented. Some challenges and the Ethereum smart contracts concept of the blockchain technology are discussed.
Reduction in solar photovoltaic generation due to aerosol pollution in megacities in western China during 2014 to 2018

Installed solar photovoltaic generation is expanding fast in western China, with total capacity accounting for &gt;15% of global photovoltaic capacity. However, severe aerosol pollution over western China has weakened the solar radiation reaching the panels. We assessed the impact of aerosol pollution on photovoltaic power generation at the city level in western China from 2014 to 2018. Over northwestern China, severe aerosol pollution regions, aerosols reduced annual average photovoltaic generation by 0.15–0.31 kWh/m 2 /day relative to clean air conditions, a decrease of 4.8–9.0%. The impact had significant seasonal variations. Aerosols can exert an influence even as important as clouds in winter in northwestern China, with the reduction of 11.2–17.4% in December. Photovoltaic generation can be benefited from the improvement of aerosol pollution and can further improve the air quality by providing clean power.
A web-based intelligence platform for diagnosis of malaria in thick blood smear images: a case for a developing country

Malaria is a public health problem which affects developing countries world-wide. Inadequate skilled lab technicians in remote areas of developing countries result in untimely diagnosis of malaria parasites making it hard for effective control of the disease in highly endemic areas. The development of remote systems that can provide fast, accurate and timely diagnosis is thus a necessary innovation. With availability of internet, mobile phones and computers, rapid dissemination and timely reporting of medical image analytics is possible. This study aimed at developing and implementing an automated web-based Malaria diagnostic system for thick blood smear images under light microscopy to identify parasites. We implement an image processing algorithm based on a pre-trained model of Faster Convolutional Neural Network (Faster R-CNN) and then integrate it with web-based technology to allow easy and convenient online identification of parasites by medical practitioners. Experiments carried out on the online system with test images showed that the system could identify pathogens with a mean average precision of 0.9306. The system holds the potential to improve the efficiency and accuracy in malaria diagnosis, especially in remote areas of developing countries that lack adequate skilled labor.
New metric learning model using statistical inference for kinship verification

Kinship verification aims to predict whether there is a kin relationship between a pair of facial images. Previous representative research has proved that learning an appropriate similarity metric plays an important role in this task. Most of the related metric learning methods focus on learning a single Mahalanobis distance metric under the assumption that kin data has a unimodal distribution. Instead, in this paper, we propose a novel nonlinear multi-metric learning (NMML) method to learn a similarity measure where the pairwise difference is drawn from a mixture distribution. Particularly, the similarity measure is derived based on a statistical inference perspective, which can indicate the characteristics of a likelihood ratio that captures the subtle resemblance between identities. To make better use of multiple estimators, we further propose an ensemble NMML (ENMML) method to perform multiple estimators fusion to improve the performance of kinship verification. Extensive experiments on three publicly available kinship datasets demonstrate the feasibility and effectiveness of our proposed methods.
Environmental Visualization: Moving Beyond the Rainbows

Pseudo-coloring is a well-established, fundamental tool for visualizing scientific data. As the size and density of data grows, increasingly more discriminatory power is required to extract optimum feature resolution. The environmental community, in particular, relies heavily on this technology to dissect and interpret a huge variety of visual data. These scientists often turn to traditional rainbow colormaps, despite their well-documented deficiencies in rendering dense detail. A popular default, the desaturated rainbow’s non-monotonically varying luminance range misrepresents data. Despite increasing overall feature resolution, this variance creates hue simultaneity and vibration, introducing false artifacts, hindering exploration of swaths of data and impeding analysis [13, 21, 26]. Drawing on artistic color theory, we hypothesized the desaturated rainbow could be improved by increasing luminance ranges, decreasing saturation, and employing hue-cycling to boost discriminatory power. These adjusted maps exhibit algorithmically corroborated higher feature resolve, a primary objective of all scientists interviewed, without distorting data in discordant false coloring. Our studies indicate that our maps are preferred by these domain scientists, thereby providing a potential alternative for effective, human-centric colormapping.
An entropy analysis of the Cirebon language script using the Ternary Huffman code algorithm

Abstract Entropy is a statistical parameter that measures how much average information is generated for each symbol in a text. Each language usually has several important features that are hidden statistically and certain redundancy. These features can be utilized to form appropriate text compression tools for optimal use of resources. This study proposes an analysis of the entropy of the Cirebon language text for text compression using the Ternary Huffman Code algorithm. This entropy value then becomes the reference level of the Cirebon script compression level. The probability of each symbol in the Cirebon Regional Text is used to calculate the entropy value. The result shows the entropy of the Cirebon language script was 2.508 bits per symbol, with an expected code length of 2.565 bits per symbol. Estimated compression efficiency with Ternary Huffman Code is 97.77% and compression rate is 0.51308.
Same Same but Different: Augmentation of Tiny Industrial Datasets using Generative Adversarial Networks

The evolution of generative adversarial networks has permitted the generation of realistic fake images which, in some cases, are indistinguishable from the real ones. Many recent works in image generation focus on learning internal image statistics via training only on a single natural image. While natural images exhibit a variability in their attributes, industrial images are often acquired in a controlled environment following a specific structure. In this work we utilize the cutting-edge results of single image generation on the structured case of industrial images. Deep Learning plays an important role in Industry 4.0 manufacturing lines and multiple ML-based image processing products are currently on the market. To be able to tackle a variety of problems where image acquisition is costly and time-consuming data generation is a promising approach. The proposed method only requires a handful of images for training, making it an ideal candidate for industrial application where data is scarce and confidential. It provides the foundation for a variety of use cases in the field of industrial Deep Learning.
Parametric Statistical Inference for Comparing Means and Variances

The purpose of this tutorial is to provide visual scientists with various approaches for comparing two or more groups of data using parametric statistical tests, which require that the distribution of data within each group is normal (Gaussian). Non-parametric tests are used for inference when the sample data are not normally distributed or the sample is too small to assess its true distribution.Methods are reviewed using retinal thickness, as measured by optical coherence tomography (OCT), as an example for comparing two or more group means. The following parametric statistical approaches are presented for different situations: two-sample t-test, Analysis of Variance (ANOVA), paired t-test, and the analysis of repeated measures data using a linear mixed-effects model approach.Analyzing differences between means using various approaches is demonstrated, and follow-up procedures to analyze pairwise differences between means when there are more than two comparison groups are discussed. The assumption of equal variance between groups and methods to test for equal variances are examined. Examples of repeated measures analysis for right and left eyes on subjects, across spatial segments within the same eye (e.g. quadrants of each retina), and over time are given.This tutorial outlines parametric inference tests for comparing means of two or more groups and discusses how to interpret the output from statistical software packages. Critical assumptions made by the tests and ways of checking these assumptions are discussed. Efficient study designs increase the likelihood of detecting differences between groups if such differences exist. Situations commonly encountered by vision scientists involve repeated measures from the same subject over time, measurements on both right and left eyes from the same subject, and measurements from different locations within the same eye. Repeated measurements are usually correlated, and the statistical analysis needs to account for the correlation. Doing this the right way helps to ensure rigor so that the results can be repeated and validated.
Design and Implementation of REST API for Academic Information System

Abstract With the increased number of information systems used in an organization, there is also an increased importance of data exchange between these systems. This research deals with prototype development and performance analysis of Rest API for academic information system. Rest API was developed using two different server technologies, NodeJS and PHP. The prototype was developed on top of a database server using one sample table that represents employee in a higher education institution. For each of the Rest API developed, there were 2 types of endpoint created. Experiment was set with one database containing a single table and utilized Apache Jmeter to simulate up to 1000 concurrent requests. The results of the experiment show NodeJS implementation of REST API consistently has better performance compared to PHP based REST API implementation. NodeJS implementation reached 100% throughput for up to 1000 concurrent requests, while PHP reached 48.70% throughput when serving the same number of concurrent requests.
Semisupervised Learning on Graphs With an Alternating Diffusion Process

Graph-based semisupervised learning is of great importance in many effective learning systems, particularly in agnostic settings where no parametric information or other prior knowledge about the data distribution is available. It leverages the graph structure to propagate labels from labeled nodes to unlabeled ones. Two separate stages are usually involved: constructing an affinity graph and propagating labels on the graph for transductive inference. It is suboptimal to manage them independently, as the correlation between the affinity graph and the labels would not be fully exploited. In this article, we integrate these two stages into one unified framework by formulating the graph construction as a regularized function estimation problem, similar to label propagation. We then propose an alternating diffusion process to solve them alternately, which allows us to learn the graph and unknown labels in an iterative fashion. With the proposed framework, we can construct a dynamic graph adapted to the given and predicted labels iteratively, resulting in more accurate and robust label propagation performance. Extensive experiments on synthetic data and various real-world data have demonstrated the superiority of the proposed method compared with other state-of-the-art methods.