Reviewerly/exHarmony huggingface dataset:
Fluid volatile composition associated with orogenic gold mineralization, Otago Schist, New Zealand: Implications of H2 and C2H6 for fluid evolution and gold source

Abstract We provide the first large-scale investigation of gold mineralizing fluids from 19 gold deposits distributed in the Mesozoic Otago Schist belt endowed with approximately 580 tonnes of gold (past production and resources). Fluid inclusions from gold-bearing veins were petrographically studied and analysed by solid-probe mass spectrometry for H2O, CO2, N2, C2H6, He, Ar, CH4, SO2, H2S, and H2. Fluid inclusion assemblages are quite similar for all studied gold deposits, a particular feature for orogenic gold districts. Gold-bearing fluids are dominantly aqueous (H2O > 82.13 mol %) with very low contents of CO2 (
ProMetaUS: A proactive meta-learning uncertainty-based framework to select models for Dynamic Risk Management

Safety managers, practitioners, and researchers can employ different models for estimating and assessing hazards, consequences, likelihoods, risks, and/or mitigation measures in the safety field. The selection of a specific model may depend on the uncertainty associated with its estimation and its impact on the safety-related decision-making process. The recognition of this issue as an example of Algorithm Selection Problem (ASP) allows investigating the applicability of meta-learning principles that are scarcely adopted in the risk and safety literature. Consequently, we propose a novel meta-learning inspired framework to proactively rank a set of candidate models for Dynamic Risk Management (DRM) based on desired uncertainty conditions. We denominate this framework ProMetaUS (Proactive Meta-learning and Uncertainty-based Selection for dynamic risk management). To achieve this purpose, our meta-learning system acquires knowledge that relates the characteristics extracted both directly and indirectly from datasets (e.g. data-based, domain-based, simple and fast uncertainty-based, simple and fast sensitivity-based meta-features) to some performance measures of the models. Performance measures include confidence information, shape measurable quantities, safety decision criteria and threshold limits, and sensitivity analysis outputs. We tested the proposed framework in a case study about Oxygen Deficiency Hazard (ODH) assessment by means of @RISK. For each of the five datasets, single-performance measure rankings and a final ranking of the three models are generated. Such rankings are aggregated to obtain the global recommended ranking.
ILUNA: Single-pass incremental method for uncertain frequent pattern mining without false positives

Nowadays, due to the mass production of uncertain data, numerous methods have been proposed for mining frequent patterns from uncertain data; however, none of them are proper for dynamic data environments. In many real-world applications, transactions are constantly being updated. After incremental updates, the validity of the uncertain patterns changes. The existing static algorithms to handle this state have to rerun the whole mining process from scratch, which is very costly. Incremental-CUF-growth is a method dealing with dynamic data but it generates many false positives and requires an additional time-consuming database scan to filter them. To handle these drawbacks, in this paper, an efficient single-pass method called ILUNA is proposed for incremental mining of uncertain frequent patterns without false positives. It introduces two new data structures namely IUP-List and ICUP-List to efficiently store data which can be increased. Upon receiving each new database, it only updates the lists without having to rebuild them from scratch. This is the first study in which single-pass incremental mining of uncertain frequent patterns is performed. Comprehensive experimental results show that the proposed method dramatically reduces the runtime and enhances the scalability compared to the state-of-the-art methods for dense and sparse incremental datasets.
Making curation algorithms apparent: a case study of ‘Instawareness’ as a means to heighten awareness and understanding of Instagram's algorithm

Despite the increasing prevalence of curation algorithms on everyday social network sites (SNSs), they often are imperceptible and difficult to become knowledgeable about since little insight in the actual working of these algorithms is given. To address this, we developed an online interface as a visual feedback tool to decrease the ignorance about the Instagram curation algorithm, named 'Instawareness' (https://instawareness.ugent.be/). As such, the goal of this article is to validate the actual effectiveness of Instawareness and to demonstrate how people can be made aware of the Instagram curation algorithm using a quasi-experiment. Validating the actual effectiveness of Instawareness allowed us to connect additional findings about the influence of awareness and understanding of Instagram's curation algorithm to our primary validated findings about achieving such awareness. These show that it is not cognitive understanding about Instagram's algorithms but solely awareness about them that appears to be sufficient for people to indicate increased critical concerns towards SNSs. Furthermore, Instawareness proved to be efficient in increasing cognitive media literacy (CML) and in indirectly stimulating critical concerns towards SNSs.
Bias-reduced hindsight experience replay with virtual goal prioritization

Hindsight Experience Replay (HER) is a multi-goal reinforcement learning algorithm for sparse reward functions. The algorithm treats every failure as a success for an alternative (virtual) goal that has been achieved in the episode. Virtual goals are randomly selected, irrespective of which are most instructive for the agent. In this paper, we present two improvements over the existing HER algorithm. First, we prioritize virtual goals from which the agent will learn more valuable information. We call this property the instructiveness of the virtual goal and define it by a heuristic measure, which expresses how well the agent will be able to generalize from that virtual goal to actual goals. Secondly, we reduce existing bias in HER by the removal of misleading samples. To test our algorithms, we built three challenging environments with sparse reward functions. Our empirical results in both environments show vast improvement in the final success rate and sample efficiency when compared to the original HER algorithm. A video showing experimental results is available at https://youtu.be/xjAiwJiSeLc.
Sentiment Analysis using Neural Network and LSTM

Abstract People put their opinions or views on various events happening in the society or world. Twitter is one of the best social networking sites where a huge amount of data generates on the daily basis. These data can be used to classify their tweets based on various sentiments attached to them. Numerous technologies are applied to analyse the sentiments of users. Sentiment analysis needs a very efficient method to manage long arrangement data and their drawn-out dependencies. In this paper, we have applied a deep learning technique to perform Twitter sentiment analysis. Simple Neural Network, Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) methods are applied for the sentiment analysis and their performances are evaluated. The LSTM is the best among all proposed techniques with the highest accuracy of 87%. We have collected a Twitter dataset from Kaggle to perform our experiment. The future improvement of the proposed research should include REST APIs and web crawling-based solutions to get live tweets to perform real-time analytics. We have analysed 1.6 million tweets in our research work.
Alleviating Class Imbalance in Actuarial Applications Using Generative Adversarial Networks

To build adequate predictive models, a substantial amount of data is desirable. However, when expanding to new or unexplored territories, this required level of information is rarely always available. To build such models, actuaries often have to: procure data from local providers, use limited unsuitable industry and public research, or rely on extrapolations from other better-known markets. Another common pathology when applying machine learning techniques in actuarial domains is the prevalence of imbalanced classes where risk events of interest, such as mortality and fraud, are under-represented in data. In this work, we show how an implicit model using the Generative Adversarial Network (GAN) can alleviate these problems through the generation of adequate quality data from very limited or highly imbalanced samples. We provide an introduction to GANs and how they are used to synthesize data that accurately enhance the data resolution of very infrequent events and improve model robustness. Overall, we show a significant superiority of GANs for boosting predictive models when compared to competing approaches on benchmark data sets. This work offers numerous of contributions to actuaries with applications to inter alia new sample creation, data augmentation, boosting predictive models, anomaly detection, and missing data imputation.
3D reconstruction using Structure From Motion (SFM) algorithm and Multi View Stereo (MVS) based on computer vision

Abstract The development of the Information and Computer Technology (ICT) sector, three-dimensional (3D) technology is also growing rapidly. Currently, the need to visualize 3D objects is widely used in animation and graphic applications, architecture, education, cultural recognition and Virtual Reality. 3D modeling of historic buildings has become a concern in recent years. 3D reconstruction is an attempt to document reconstruction or restoration if the building is destroyed. By using the 3D model reconstruction using Structure from Motion (SFM) and Multi View Stereo (MVS) algorithm based on Computer Vision, it is hoped that the results of this 3D modeling can be utilized as an effort to preserve 3D objects in the Penataran Temple cultural heritage area. This research was conducted by taking as many as 61 images of objects in the Blitar Penataran Temple area. The photos obtained were reconstructed into a 3D model using the Structure From Motion algorithm in the meshroom. This research a trial of the original image with a compressed image for reconstruction is used to compare the 3D reconstruction process from the two input data. From 61 images processed using the Structure Form Motion algorithm, 33 poses of camera pose and 3D points were improved, both original and compressed images. The number of iterations compresses 1.4% less than the original image and takes 43.53% faster than the original image.
Payload Capacity Scheme for Quran Text Watermarking Based on Vowels with Kashida

The most sensitive Arabic text available online is the digital Holy Quran. This sacred Islamic religious book is recited by all Muslims worldwide including non-Arabs as part of their worship needs. Thus, it should be protected from any kind of tampering to keep its invaluable meaning intact. Different characteristics of Arabic letters like the vowels (), Kashida (extended letters), and other symbols in the Holy Quran must be secured from alterations. The cover text of the Quran and its watermarked text are different due to the low values of the Peak Signal to Noise Ratio (PSNR) and Embedding Ratio (ER). A watermarking technique with enhanced attributes must, therefore, be designed for the Quran’s text using Arabic vowels with kashida. The gap addressed by this paper is to improve the security of Arabic text in the Holy Quran by using vowels with kashida. The purpose of this paper is to enhance the Quran text watermarking scheme based on a reversing technique. The methodology consists of four phases: The first phase is a pre-processing followed by the second phase-the embedding process phase—which will hide the data after the vowels. That is, if the secret bit is “1”, then the kashida is inserted; however, the kashida is not inserted if the bit is “0”. The third phase is the extraction process and the last phase is to evaluate the performance of the proposed scheme by using PSNR (for the imperceptibility) and ER (for the capacity). The experimental results show that the proposed method of imperceptibility insertion is also optimized with the help of a reversing algorithm. The proposed strategy obtains a 90.5% capacity. Furthermore, the proposed algorithm attained 66.1% which is referred to as imperceptibility.
Secure FaaS orchestration in the fog: how far are we?

Abstract Function-as-a-Service (FaaS) allows developers to define, orchestrate and run modular event-based pieces of code on virtualised resources, without the burden of managing the underlying infrastructure nor the life-cycle of such pieces of code. Indeed, FaaS providers offer resource auto-provisioning, auto-scaling and pay-per-use billing at no costs for idle time. This makes it easy to scale running code and it represents an effective and increasingly adopted way to deliver software. This article aims at offering an overview of the existing literature in the field of next-gen FaaS from three different perspectives: (i) the definition of FaaS orchestrations, (ii) the execution of FaaS orchestrations in Fog computing environments, and (iii) the security of FaaS orchestrations. Our analysis identify trends and gaps in the literature, paving the way to further research on securing FaaS orchestrations in Fog computing landscapes.
The orthogonality speed of two-qubit state interacts locally with spin chain in the presence of Dzyaloshinsky–Moriya interaction

Abstract The orthogonality time is examined for different initial states settings interacting locally with different types of spin interaction: XX , Ising and anisotropic models. It is shown that the number of orthogonality increases, and consequently the time of orthogonality decreases as the environment qubits increase. The shortest time of orthogonality is displayed for the XX chain model, while the largest time is shown for the Ising model. The external field increases the numbers of orthogonality, while Dzyaloshinsky–Moriya interaction decreases the time of orthogonality. The initial state settings together with the external field has a significant effect on decreasing/increasing the time of orthogonality.
ICAS: Two-factor identity-concealed authentication scheme for remote-servers

As the number of users in remote server environments is more prevalent (i.e., in e-payment, e-healthcare), a secure authentication scheme becomes increasingly important for this paradigm. In general, single-factor authentication in remote-systems suffers from several security issues, whereas multi-factor authentication can be considered as an alternative solution where additional factors increase the security level. However, in existing multi-factor authentication schemes, leakage of randomness and identity-concealment are not well considered; these can cause privacy issues in some application scenarios. In this paper, we propose a two-factor-based identity-concealed authentication scheme refer to as ICAS. ICAS ensures secure authentication between the user and remote server even if some intermediate randomness (e.g., Diffie-Hellman exponent) has been exposed to an adversary, prevents users’ identity against adversaries, can resist perpetual leakage of confidential information, and provide a strong security guarantee against device lost attacks. We define a proper security model in the random oracle and prove the security of ICAS under the model. We provide a comprehensive performance evaluation, which shows that ICAS is efficient. Specifically, the proposed scheme reduces the total computation cost by at least 24% and reduces the user’s communication cost by at least 4%; thereby, ICAS is feasible to deploy in the practical environment.
HumaniSE: Approaches to Achieve More Human-Centric Software Engineering

A common problem with many existing software systems and the approaches to engineering them is their lack of the human aspects of their target end users. People are different - with diverse characteristics including age, gender, ethnicity, physical and mental challenges, personality, technical proficiency, emotional reactions to software systems, socio-economic status, educational attainment, language, and so on. In this paper we describe our work at looking to better consider these characteristics by incorporation of human aspects throughout the software engineering lifecycle. We are developing a co-creational living lab approach to better collect human aspects in the software requirements. We are using domain-specific visual languages, themselves a more human-centric modelling approach, to capture these diverse human aspects of target software systems. We are working on incorporating these human aspects into design models to support improved model-driven engineering, and thereby to better support both code generation and run-time adaptation to different end user human characteristics. Finally we are working on better ways to support continuous evaluation of human aspects in the produced software, and to provide improved feedback of user reported defects to developers.
Exploring an Architectural Framework for Human-Building Interaction via a Semi-Immersive Cross-Reality Methodology

The vision of responsive architecture predicts that human experience can be evoked through the dynamic orchestration of space-defining elements. Whereas recent studies have robotically actuated furniture for functional goals, little is known how this capability can be deployed meaningfully on an architectural scale. We thus evaluated the spatial impact of a responsive wall on the inhabitants of ordinary apartments. To maintain safety during the COVID-19 pandemic, we developed a novel remote, semi-immersive cross-reality simulation evaluation methodology. Based on the orchestration of three space-defining operations, we define a theoretical framework that suggests how the position of a responsive wall can be determined through five distinct architectural qualities. This framework thus proposes how human-building interaction (HBI) could complement its functional goals with augmenting the well-being of occupants in the physical as well as the virtual realm.
Quantum walks defined by digraphs and generalized Hermitian adjacency matrices

We propose a quantum walk defined by digraphs (mixed graphs). This is like Grover walk that is perturbed by a certain complex-valued function defined by digraphs. The discriminant of this quantum walk is a matrix that is a certain normalization of generalized Hermitian adjacency matrices. Furthermore, we give definitions of the positive and negative supports of the transfer matrix, and clarify explicit formulas of their supports of the square. In addition, we give tables by computer on the identification of digraphs by their eigenvalues.
Leveraging Data Augmentation for Service QoS Prediction in Cyber-physical Systems

With the fast-developing domain of cyber-physical systems (CPS), constructing the CPS with high-quality services becomes an imperative task. As one of the effective solutions for information overload in CPS construction, quality of service (QoS)-aware service recommendation has drawn much attention in academia and industry. However, the lack of most QoS values limits the recommendation performance and it is time-consuming for users to get the QoS values by invoking all the services. Therefore, a powerful prediction model is required to predict the unobserved QoS values. Considering the fact that most existing QoS prediction models are unable to effectively address the data-sparsity problem, a novel two-stage framework called AgQ is proposed for QoS prediction. Specifically, a data augmentation strategy is designed in the first stage to enlarge the training set by drawing additional virtual instances. In the second stage, a prediction model is applied that considers both virtual and factual instances during the training procedure. We conduct extensive experiments on the WSDream dataset to demonstrate the effectiveness of the our QoS prediction framework and verify that the data augmentation strategy can indeed alleviate the data-sparsity problem. In terms of mean absolute error, taking the Multilayer Perceptron model as an example, the maximum improvement achieves 5% under 5% sparsity.
Decentralized Parallel SGD With Privacy Preservation in Vehicular Networks

With the prosperity of vehicular networks and intelligent transport systems, vast amount of data can be easily collected by vehicular devices from their users and widely spread in the vehicular networks for the purpose of solving large-scale machine learning problems. Hence how to preserve the data privacy of users during the learning process has become a public concern. To address this concern, under the celebrated framework of differential privacy (DP), we present in this paper a decentralized parallel stochastic gradient descent (D-PSGD) algorithm, called DP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>2</b></sup> -SGD, which can offer protection for privacy of users in vehicular networks. With thorough analysis we show that DP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>2</b></sup> -SGD satisfies (ε,δ)- DP while the learning efficiency is the same as D-PSGD without privacy preservation. We also propose a refined algorithm called EC-SGD by introducing an error-compensate strategy. Extensive experiments show that EC-SGD can further improve the convergence efficiency over DP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>2</b></sup> -SGD in reality.
CYBERSECURITY ARCHITECTURE FOR THE INTERNET OF MEDICAL THINGS AND CONNECTED DEVICES USING BLOCKCHAIN

The internet of medical things (IoMT) has a great role in improving the health around the world. IoMT is having a great impact in our life in which the clinical data of the patient is observed and checked and then can be transferred to the third party for using in the future such as the cloud. IoMT is a huge data system with a continuous developing rate, which implies that we should keep a lot of data secure. We propose a combined security architecture that fuses the standard architecture and new blockchain technology. Blockchain is a temper digital ledger which gives peer-to-peer communication and provides communication between non-trust individuals. Using standard in-depth strategy and blockchain, we are able to develop a method to collect vital signs data from IoMT and connected devices and use blockchain to store and retrieve the collected data in a secure and decentralized fashion within a closed system, suitable for healthcare providers such as private clinics, hospitals, and healthcare organizations were sharing data with each other is required. Right now initially examine the innovation behind Blockchain then propose IoMT-based security architecture utilizing Blockchain to guarantee the security of information transmission between associated nodes. Experimental analysis shows that the proposed scheme presents a non-significant overhead; yet it brings major advantages to meet the standard security and privacy requirements in IoMT.