Reviewerly/exHarmony huggingface dataset:
Lithostratigraphy of the Sinqeni Formation, Pongola Supergroup, South Africa

Abstract The Mesoarchaean Sinqeni Formation forms the lowermost unit of the predominantly sedimentary Mozaan Group (Pongola Supergroup) of southern Africa. The formation comprises a dominantly arenaceous succession, which can be subdivided into four members. A laterally discontinuous gold- and uranium-bearing conglomerate package (Denny Dalton Member) is commonly developed at the base of the formation. Overlying the basal conglomerates are two significant quartz arenite packages (Dipka, and Kwaaiman Members) which are separated by a ferruginous shale package (Vlakhoek Member) that locally hosts banded-iron formation. The formation is the most extensively exposed succession of the Mozaan Group, cropping out extensively in the Hartland region, as well as in multiple inliers from Amsterdam in the Mpumalanga to Nkandla in central KwaZulu-Natal, with further exposures in Eswatini. Subeconomic gold and uranium mineralisation occur sporadically within the conglomerates of the Denny Dalton Member, and have previously been mined from multiple occurrences in the White Mfolozi, Mhlatuze and Nkandla Inliers whilst many prospecting trenches are found in the conglomerates of the Hartland and Amsterdam areas. Gold has also briefly been exploited from ferruginous shales and iron formations of the Vlakhoek Member in the Altona area. Litho-correlative equivalents of the formation comprise the Mandeva Formation (White Mfolozi Inlier), Skurwerant Formation (Amsterdam region) and Mkaya Formation (Magudu region).
Estimation of marginal effects for models with alternative variable transformations

margins is a powerful postestimation command that allows the estimation of marginal effects for official and community-contributed commands, with well-defined predicted outcomes (see predict). While the use of factor-variable notation allows one to easily estimate marginal effects when interactions and polynomials are used, estimation of marginal effects when other types of transformations such as splines, logs, or fractional polynomials are used remains a challenge. In this article, I describe how margins‘s capabilities can be extended to analyze other variable transformations using the command f_able.
BOXREC

Over the past few years, automation of outfit composition has gained much attention from the research community. Most of the existing outfit recommendation systems focus on pairwise item compatibility prediction (using visual and text features) to score an outfit combination having several items, followed by recommendation of top-n outfits or a capsule wardrobe having a collection of outfits based on user's fashion taste. However, none of these consider user's preference of price-range for individual clothing types or an overall shopping budget for a set of items. In this paper, we propose a box recommendation framework - BOXREC - which at first, collects user preferences across different item types (namely, top-wear, bottom-wear and foot-wear) including price-range of each type and a maximum shopping budget for a particular shopping session. It then generates a set of preferred outfits by retrieving all types of preferred items from the database (according to user specified preferences including price-ranges), creates all possible combinations of three preferred items (belonging to distinct item types) and verifies each combination using an outfit scoring framework - BOXREC-OSF. Finally, it provides a box full of fashion items, such that different combinations of the items maximize the number of outfits suitable for an occasion while satisfying maximum shopping budget. Empirical results show superior performance of BOXREC-OSF over the baseline methods.
A New Validity Index Based on Fuzzy Energy and Fuzzy Entropy Measures in Fuzzy Clustering Problems

Two well-known drawbacks in fuzzy clustering are the requirement of assigning in advance the number of clusters and random initialization of cluster centers. The quality of the final fuzzy clusters depends heavily on the initial choice of the number of clusters and the initialization of the clusters, then, it is necessary to apply a validity index to measure the compactness and the separability of the final clusters and run the clustering algorithm several times. We propose a new fuzzy C-means algorithm in which a validity index based on the concepts of maximum fuzzy energy and minimum fuzzy entropy is applied to initialize the cluster centers and to find the optimal number of clusters and initial cluster centers in order to obtain a good clustering quality, without increasing time consumption. We test our algorithm on UCI (University of California at Irvine) machine learning classification datasets comparing the results with the ones obtained by using well-known validity indices and variations of fuzzy C-means by using optimization algorithms in the initialization phase. The comparison results show that our algorithm represents an optimal trade-off between the quality of clustering and the time consumption.
Blockchain: Future Facilitator of Asset Information Modelling and Management?

Recently, businesses and government bodies in the Architecture, Engineering, and Construction (AEC) sector have faced a key challenge to the delivery of effective project outcomes. Asset Information Modelling (AIM) is a process aimed to serve the asset process once the project is handed over. Considering that Blockchain is a new data management technology that is intended to provide security within a decentralized culture, this could be the foundation for improving asset processes. The research is achieved through a systematic review of the relevant literature. Several studies have been identified in Building Information Modelling (BIM) and Blockchain in the construction domain. However, there is little research conducted in the context of Asset Management Processes (AMP). Skilled people are the main challenge for AIM and Blockchain: findings show that a lack of technological awareness and its link to the adoption of using AIM and Blockchain in the asset management life cycle is the main problem.
An anti‐phishing enterprise environ model using feed‐forward backpropagation and Levenberg‐Marquardt method

Abstract Phishing in an enterprise is serious issue rising in wide scale and complexity, as phishers use email phishing via obfuscated, malicious or phished URLs and continuously adapt or innovate their strategies to lure victims for identity theft for financial benefits. To gain victim's trust and confidence phishers have started using visceral factors and familiarity cues. Phishing is not always money centric; phisher defame the user's goodwill and character. Defamation in enterprise could be much more traumatic than being embarrassed at a social networking site. It is a challenging task to address this issue. It is evident through literature review that single phishing detection filter approaches are insufficient to detect phishing in enterprise environ. Therefore, a novel anti‐phishing model for enterprise using artificial neural network is proposed. In addition, this model effectively identifies whether the phishing email is known phishing or unknown phishing to reduce the trust and familiarity‐based email phishing enterprise environ. The feed‐forward backpropagation and Levenberg‐Marquart methods of ANN are adopted to enhance the URL classification process and with Fuzzy Inference System to get result with imprecise data of social features. The proposed model can accurately classify the known and unknown email phishing via URLs.
Sentiment classification of skewed shoppers' reviews using machine learning techniques, examining the textual features

Abstract With the speedy growth of online shopping, it has become of crucial importance for product makers to analyze, and handle a wealth of products' reviews. However, such a high volume of reviews, along with a wide variety of opinions, makes it hard for manufacturers to know exactly how they can improve their products without having an efficient approach. For this purpose, the results of sentiment classification would help the customers to retrieve the necessary information to choose an appropriate product, and the sellers to effectively collect customer feedback in order to improve their products. Like most of the read‐world problems, the shopping review data being used in this study were imbalanced, being predominately composed of positive with only a small percentage of negative reviews. Machine learning (ML) algorithms do not perform well when data are imbalanced, as they tend to get biased toward the overrepresented data category. The synthetic minority over‐sampling technique (SMOTE) was used to address this class imbalance problem. In this study, three different ML‐based algorithms, namely the Naïve Bayes (NB), Support Vector Machine, and decision tree (DT) were employed. An extensive preprocessing procedure was taken to prepare the text datasets, and details are discussed in the manuscript. The performance analysis indicated that the DT algorithm outperforms the other two methods. As positive reviews account for the majority of the reviews, sparse words removal for the data resulted in the removal of almost all negative reviews' sentiments. Hence, the model training process is here performed on positive and negative reviews separately. A combination of the review titles with their contents, separate tokenization process, applications of various N‐gram, and maintaining stops words (e.g. “not” or “but”) were some other steps considered to improve the performance of the model.
Researching Mobile Phones in the Everyday Life of the “Less Connected”: The Development of a New Diary Method

This article introduces our mobile diary method, a qualitative method for the study of mobile phone practices. Adapted from the diary methods of psychology and media studies audience research, it is designed to foreground tacit and mundane data about everyday mobile phone practices. The diary interview reconstructs details of the social practices of everyday life that make up each participant's "yesterday" and situates mobile practices within this account. To illustrate the method, we provide examples from our study, Izolo, that spanned three distinct South African neighbourhoods in different parts of the country and focused on less-connected people. Participants owned mobile phones and were officially classified as poor or very poor. Grounded in a media practice approach, the method generated rich narratives that foregrounded the experiences of marginalised people. It explicitly addressed technological infrastructures and digital materialities, including devices, platforms, tariff structures, different types of connectivity and computational power. Such holistic narratives are essential to understanding the full spectrum of digital inequality, and allow a movement beyond binary conceptions of digital divides. The method provides the kind of detailed empirical overview of existing media practices that enables careful policy making and innovation in developing countries.
Blockchain-Enabled Security and Privacy for Internet-of-Vehicles

The evolution of Internet-of-Vehicles (IoV) has promised improvement in traffic management and road safety. However, with continuously increasing number of vehicles on road, there are numerous challenges associated with IoV. Communication among vehicles is needed to be secure and bandwidth efficient. Messages exchanged between vehicles must be authentic so as to maintain trust among the network. On the other hand, blockchain is a rapidly emerging technology for various IoT related applications. It ensures security by maintaining transaction history in the form of an encrypted and immutable ledger. Therefore, blockchain-based communications can potentially solve various challenges of IoV. However, there are certain constraints which make the practical implementation of blockchain in IoV difficult. For example, one of the most common consensus algorithms of blockchain, Proof-of-Work, is energy inefficient and time consuming. Various other consensus algorithms have been developed but they compromise security. This chapter conceptualises the implementation of blockchain in IoV, particularly useful in emergency situations, such as accident. We discuss some consensus algorithms of blockchains and their suitability in IoV, ultimately leading to a formation of voting based consensus integrated with a relay selection mechanism. Furthermore, we present an economic model to incentivise vehicles for safe driving and cooperation through blockchain-enabled transaction of rewards. The security capacity of proposed blockchain against collusion of relay nodes is analysed by game theory. Simulation results of the proposed approach demonstrate its efficiency in terms of latency and success rate in message transmission as compared to other blockchain-based solutions for IoV.
From interactive teaching to immersive learning: Higher Education 4.0 via 360-degree videos and virtual reality in Malaysia

Abstract Higher Education 4.0 deals with the spread of technologies and espouses the notion that today’s learners need to be trained and not merely taught by rote and memorisation drills. The present is a period whereby data and information must be made accessible and open 24/7 so that learners can learn about how to find knowledge rather than to wait to be spoon-fed from their instructor’s textbooks. Higher Education 4.0 is contingent upon the concept of ‘immersive learning’ to afford learners with an educational atmosphere that is exceedingly interactive at the physical and virtual levels. Hence, immersive learning experiences could replicate possible scenarios that learners will face in real life and impart specific techniques or skills based on their different academic syllabuses. Employing immersive learning experiences that incorporate 360-degree real life videos and VR technologies to remote teach advanced communication skills to technical and vocational students at several public universities in Malaysia, this research paper highlights the fact that novel learning technologies and immersive learning experiences are not merely fads of Higher Education 4.0. Rather, they are glimpses of tomorrow’s world, based on quantitative and qualitative data collected from end-users (Malaysian university students) of a learning innovation initiative called ‘ELSA 360°-Videos’.
Effect of Timer, Top Score and Leaderboard on Performance and Motivation in a Human Computing Game

The development of human computing games requires the implementation of different game mechanics to make them challenging, interesting and motivating. These mechanics are often borrowed from popular video games, but their outcomes on the quality of solutions obtained and player motivation are not fully understood. We analyze the effect of showing a timer, an achievable (top) score and a live leaderboard on players' scores, puzzle completion time and motivation using different versions of a human computing game. We show that presenting a top score on a puzzle results in better solutions, but at the expense of completion time, whereas the presence of a timer has the opposite outcome. As for the live leaderboard, we have observed an almost significant interaction effect with the timer. This work offers guidance for human computing game developers about what to expect from these different game mechanics, and how players react to them.
A Testing Environment for Continuous Colormaps

Many computer science disciplines (e.g., combinatorial optimization, natural language processing, and information retrieval) use standard or established test suites for evaluating algorithms. In visualization, similar approaches have been adopted in some areas (e.g., volume visualization), while user testimonies and empirical studies have been the dominant means of evaluation in most other areas, such as designing colormaps. In this paper, we propose to establish a test suite for evaluating the design of colormaps. With such a suite, the users can observe the effects when different continuous colormaps are applied to planar scalar fields that may exhibit various characteristic features, such as jumps, local extrema, ridge or valley lines, different distributions of scalar values, different gradients, different signal frequencies, different levels of noise, and so on. The suite also includes an expansible collection of real-world data sets including the most popular data for colormap testing in the visualization literature. The test suite has been integrated into a web-based application for creating continuous colormaps (https://ccctool.com/), facilitating close inter-operation between design and evaluation processes. This new facility complements traditional evaluation methods such as user testimonies and empirical studies.
Adaptive Adversarial Attack on Graph Embedding via GAN

Graph embedding method learns the low-dimensional representation of graph data, which facilitates downstream graph analysis tasks, such as node classification, graph classification, link prediction and community detection. With the in-depth study of graph analysis tasks, the issues of excessive data mining by graph embedding methods have become increasingly prominent, a number of graph embedding attack methods have been put forward. Inspired by promising performance of generative adversarial network, this paper proposes an adaptive graph adversarial attack framework based on generative adversarial network (AGA-GAN). We use the game between a generator and two discriminators with different functions to iteratively generate the adversarial graph. Specifically, AGA-GAN generates the adversarial subgraph according to different attack strategies to rewire the corresponding parts in the original graph, and finally form the whole adversarial graph. To address the scalability problem of existing graph embedding attack methods, we consider the adaptively selected K-hop neighbor subgraph as the attack target instead of the original graph. Experimental study on real graph datasets verifies that the AGA-GAN can achieve state-of-the-art attack performance in most node classifications.
HGED: A Hybrid Search Algorithm for Efficient Parallel Graph Edit Distance Computation

Graph edit distance (GED) is a measure for quantifying the similarity between two graphs. Because of its flexibility and versatility, GED is widely used in many real applications. However, the main disadvantage of GED is its high computational cost. Many solutions have been proposed to speed up GED computation, but most of them focus on developing serial algorithms and only very few solutions consider parallel computing. In this paper, we study a parallel GED computation to elaborate a fast and precise algorithm. Unlike existing solutions that utilize either a depth-first or a best-first search, we propose a hybrid approach that combines depth-first and best-first search paradigms. Our approach can quickly find tighter GED upper bounds, and effectively prune the search space using the upper bounds. Based on the approach, we develop an efficient parallel GED computation algorithm named HGED. To maximize thread utility, HGED is also equipped with a novel dynamic load balancing scheme whose main focus is on reducing the overhead of thread synchronization. Experimental results on widely used real datasets show that, on average, HGED outperforms the state-of-the art serial algorithm AStar <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> -LSa by 6 times and the state-of-the parallel algorithm PGED by 4 times.
Smart Contracts Vulnerability Auditing with Multi-semantics

Smart contracts vulnerability auditing is vitally critical to ensure transaction execution in normal on blockchain. The current data-driven approaches normally tokenize smart contracts into a series of sequences according to only one tokenization standard for vulnerability detection purpose, resulting some of the semantic contexts could not be reflected within restricted sequence length. To address this limitation, we generate sequences from smart contracts in three tokenization standards for which we utilize n-gram language model to capture semantic contexts respectively, and finally exploiting our effective combination strategy of Intersection or Union to integrate the audited results from multiple semantic contexts. In order to evaluate the proposed approach, we applied it on over 7200 Ethereum smart contract samples. Experimental result shows our proposed method is capable of detecting vulnerabilities and competitive with the baseline in test sets, with improved precision of over 44% when Intersection is applied in their results, as well as improved Recall measure up by over 300% and F-measure up by 220% when Union is applied. Our proposed method for smart contract vulnerability detection, an important tool for developing quality decentralized software applications, is able to analyze multiple semantic contexts and successfully detects more true vulnerabilities with high precision, outperforming that of the baseline approaches.
MIRARTS: A mixed reality application to support postural rehabilitation

The computational power of new technologies has increased in recent years, and new devices for Augmented Reality (AR) and Mixed Reality (MR), such as Microsoft Hololens, have been proposed. We describe here how through MR we enable the therapist to see in real-time a quantitative evaluation of a rehabilitation exercise, in terms of adequate metrics reported both as text and as graphs, while he/she is looking at it. This would allow to combine the qualitative experience of the therapist with quantitative data to enable the therapist to make the most adequate decisions in terms of modification of the rehabilitation scheduling and of the parameters. Moreover, it also allows to verify in real-time the effect of the modifications of the parameters and further tune them. Preliminary results have highlighted some limitations in prolonged use, but have indeed supported the power of this approach inside the rehabilitation field.
Generalized Net Representations of the Causal Structure of a Queuing System

Queuing systems are an important part of virtually all service networks. The problem of determining how the Quality of Service (QoS) of a queuing system depends on the qualities of the buffer and the server is not well studied. Two causal decompositions of the services in a queuing system are considered which use the elements of Service Systems Theory. For each of them, a Generalized Net (GN) representation is proposed. The GN models can be used in the study of the QoS composition in overall telecommunication networks.
Toward a Non-Intrusive, Affordable Platform for Elderly Assistance and Health Monitoring

Ambient Assisted Living (AAL) in general and Activity Recognition (AR) in particular are active fields of research that aim at assisting people in their Activities of Daily Living (ADL). In recent years, we have seen an increased interest in their applicability to the rural seniors who are slowly losing their autonomy due to aging and chronic diseases. One research venue is to aggregate and seek for correlations between the physiological data that serves to monitor the health of the elderly, their ADLs, their movements and any other data that may be collected about their immediate environment. In this paper, we are tackling the possibility of developing a non-intrusive and affordable system based on embedded health, movement, activity and location sensors. Furthermore, we discuss the main concepts behind the creation of a layered, flexible and highly modular architecture that focuses on how the integration of newly combined sensor data can be achieved. Using a mobile phone application prototype, our work has shown that we can integrate two non-invasive technologies that are not necessarily the newest, but the most affordable, scalable and ready to be deployed in real life settings.