Reviewerly/exHarmony huggingface dataset:
Exploration for epithermal Au–Ag deposits in New Zealand: history and strategy

More than 80 Cenozoic low sulphidation Au–Ag epithermal deposits and prospects are known in Northland, the Hauraki Goldfield (Coromandel Volcanic Zone) and the Taupō Volcanic Zone of the North Island. Reconnaissance exploration since the 1970s has included magnetic surveys, and stream sediment and rock-chip geochemical surveys. Prospect-scale exploration over the same period has included rock and soil geochemical surveys, magnetic and resistivity/induced polarisation geophysical surveys and drilling, including the study of hydrothermal alteration mineralogy (by thin section and XRD) plus fluid inclusion systematics, to help reconstruct the former hydrothermal system and identify zoning related to mineralisation. Since the 1990s, digital regional exploration and research data sets have enabled GIS-based targeting of prospective areas. At the prospect scale, multi-element ICP and ICP-MS analyses, hyperspectral short wave infrared spectral analyses and portable XRF geochemical analyses of field samples and drill core have been used to complement assay data for identifying spatial trends in geochemistry and mineralogy that help vector to mineral occurrences. Exploration since the 1970s has discovered new Au and Ag resources in extensions of some previously mined deposits (e.g. Golden Cross and Karangahake) and in new deposits in adjacent areas (e.g. Favona, Trio and Correnso in the Waihi area).
Powering Hands-on Cybersecurity Practices with Cloud Computing

Cybersecurity education and training have gained increasing attention in all sectors due to the prevalence and quick evolution of cyberattacks. A variety of platforms and systems have been proposed and developed to accommodate the growing needs of hands-on cybersecurity practice. However, those systems are either lacking sufficient flexibility (e.g., tied to a specific virtual computing service provider, little customization support) or difficult to scale. In this work, we present a cloud-based platform named EZSetup for hands-on cybersecurity practice at scale and our experience of using it in class. EZSetup is customizable and cloud-agnostic. Users can create labs through an intuitive Web interface and deploy them onto one or multiple clouds. We have used NSF funded Chameleon cloud and our private OpenStack cloud to develop, test and deploy EZSetup. We have developed 14 network and security labs using the tool and included six labs in an undergraduate network security course in spring 2019. Our survey results show that students have very positive feedback on using EZSetup and computing clouds for hands-on cybersecurity practice.
Shallow Embedding of Type Theory is Morally Correct

There are multiple ways to formalise the metatheory of type theory. For some purposes, it is enough to consider specific models of a type theory, but sometimes it is necessary to refer to the syntax, for example in proofs of canonicity and normalisation. One option is to embed the syntax deeply, by using inductive definitions in a proof assistant. However, in this case the handling of definitional equalities becomes technically challenging. Alternatively, we can reuse conversion checking in the metatheory by shallowly embedding the object theory. In this paper, we consider the standard model of a type theoretic object theory in Agda. This model has the property that all of its equalities hold definitionally, and we can use it as a shallow embedding by building expressions from the components of this model. However, if we are to reason soundly about the syntax with this setup, we must ensure that distinguishable syntactic constructs do not become provably equal when shallowly embedded. First, we prove that shallow embedding is injective up to definitional equality, by modelling the embedding as a syntactic translation targeting the metatheory. Second, we use an implementation hiding trick to disallow illegal propositional equality proofs and constructions which do not come from the syntax. We showcase our technique with very short formalisations of canonicity and parametricity for Martin-Löf type theory. Our technique only requires features which are available in all major proof assistants based on dependent type theory.
An Efficient Training Scheme to Acquire Massive CSI in Analog Function Computation Networks

Analog function computation utilizes the superposition property of multi-access channel (MAC) to compute the target function in an efficient way. However, its corresponding transceiver requires global channel state information (CSI) of the network, which incurs large latency. To tackle this challenge, a novel scheme called over-the-air signaling procedure is proposed by exploiting a defined effective CSI in this paper. We derive the training complexity of the proposed scheme and compare it with the conventional design. It is shown that the training complexity of the proposed scheme can be greatly reduced for massive CSI acquisition by avoiding collecting individual CSI. To account for the difference of the desired CSI, a corresponding robust design is further discussed under the worst-case model. The mean-square error (MSE) improvement and training complexity reduction of the proposed design are demonstrated via simulation.
HSDC–Net: Secure Anonymous Messaging in Online Social Networks

Hiding contents of users' messages has been successfully addressed before, while anonymization of message senders remains a challenge since users do not usually trust ISPs and messaging application providers. To resolve this challenge, several solutions have been proposed so far. Among them, the Dining Cryptographers network protocol (DC-net) provides the strongest anonymity guarantees. However, DC-net suffers from two critical issues that makes it impractical, i.e., (1) collision possibility and (2) vulnerability against disruptions. Apart from that, we noticed a third critical issue during our investigation. (3) DC-net users can be deanonymized after they publish at least three messages. We name this problem the short stability issue and prove that anonymity is provided only for a few cycles of message publishing. As far as we know, this problem has not been identified in the previous research works. In this paper, we propose Harmonized and Stable DC-net (HSDC-net), a self-organizing protocol for anonymous communications. In our protocol design, we first resolve the short stability issue and obtain SDC-net, a stable extension of DC-net. Then, we integrate the Slot Reservation and Disruption Management sub-protocols into SDC-net to overcome the collision and security issues, respectively. The obtained HSDC-net protocol can also be integrated into blockchain-based cryptocurrencies (e.g. Bitcoin) to mix multiple transactions (belonging to different users) into a single transaction in such a way that the source of each payment is unknown. This preserves privacy of blockchain users. Our prototype implementation shows that HSDC-net achieves low latencies that makes it a practical protocol.
Laughter in the Wild: A Study Into DoS Vulnerabilities in YAML Libraries

YAML is a widely used serialisation language for data interchange and application configuration. Since its introduction, remote code execution vulnerabilities have been reported for YAML parsers, and countermeasures have been proposed. Even though denial-of-service (DoS) vulnerabilities affecting parsers for formats such as XML have been extensively studied, a similar investigation for YAML libraries is lacking. In this paper, we systematically study DoS vulnerabilities for 14 libraries for ten popular programming languages and as a result, we have discovered seven previously unknown vulnerabilities, which have been reported and are pending CVE identifiers.
The Complete Cost of Cofactor $$h=1$$

This paper presents optimized software for constant-time variable-base scalar multiplication on prime-order Weierstraß curves using the complete addition and doubling formulas presented by Renes, Costello, and Batina in 2016. Our software targets three different microarchitectures: Intel Sandy Bridge, Intel Haswell, and ARM Cortex-M4. We use a 255-bit elliptic curve over $$\mathbb {F}_{2^{255}-19}$$ that was proposed by Barreto in 2017. The reason for choosing this curve in our software is that it allows most meaningful comparison of our results with optimized software for Curve25519. The goal of this comparison is to get an understanding of the cost of using cofactor-one curves with complete formulas when compared to widely used Montgomery (or twisted Edwards) curves that inherently have a non-trivial cofactor.
Language use nowadays in Russian communication

Abstract This article presents linguistic and communicative transformations in socially significant areas of modern communication in Russia. The authors apply a historical-comparative method as well as diachronic description and interpretation to analyze the processes taking place under the influence of intra- and extra-linguistic factors in various Internet and media genres, such as advertising, business and professional communication. It was found that the fusion of the features of communication and publication, the combination of dialogue and polylogue determine the “clickbait” nature of blogs and make blogging the most influential genre of Internet communication. The growing influence of oral speech and corporate rules in the field of business communication underlies the trend toward agrammatism of phrases. Innovations in the field of advertising manifest themselves on the verbal level of the slogan and its implicit influence on the recipient.
Tags-Aware Recommender Systems: A Systematic Review

Due to the recent growth of available online data on customers and clients as well as social tagging becoming popular, tag-aware recommender systems are attracting more attention. Therefore, there is a need to provide an overview of the current status of the literature on tag-aware recommender systems to sketch future research directions and opportunities. This article systematically reviews the trend of researches conducted from 2011 to 2018 in terms of challenges and problems regarding developing a recommendation system, areas of application, proposed methodologies, evaluations criteria used to assess the performance and limitations and drawbacks that require investigation and improvements. This paper provides a view for those who are interested in this field to understand what has already been conducted and what are the future research opportunities.
Compassionate design: considerations that impact the users’ dignity, empowerment and sense of security

Human-centered design provides a means to help designers create products or systems with ‘people’ as the focus. Compassionate Design (CD) , introduced in this paper, is an approach that addresses niche sensitive needs and involves a way of thinking where designers pay special attention to the users’ sense of dignity, empowerment, and security. These niche needs surfaced as a result of analyses of 12 cases situated in sensitive contexts where the users felt vulnerable, had a high level of emotional engagement and were negatively affected by the situation. The designers described their deep concern for the users in various talks and interviews. This paper explains the conception of CD and its development that resulted from iteratively and qualitatively analyzing these cases in which designers were intuitively focusing on niche user needs. Dignity, empowerment and security form the basis of CD and have been contextualized in Maslow’s hierarchy of needs after they emerged as a result of the analysis of data. This research sets the platform for a design approach that can help designers to consider the often unarticulated user needs of dignity, empowerment and security, in a more intentional manner and not be left to chance .
Improvement of quantum correlations by repetitive quantum error correction

A quantum error-correcting code is established in entangled coherent states (CSs) with Markovian and non-Markovian environments. However, the dynamic behavior of these optical states is discussed in terms of quantum correlation measurements, entanglement and discord. By using the correcting codes, these correlations can be as robust as possible against environmental effects. As the number of redundant CSs increases due to the repetitive error correction, the probabilities of success also increase significantly. Based on different optical field parameters, the discord can withstand more than an entanglement. Furthermore, the behavior of quantum discord under decoherence may exhibit sudden death and sudden birth phenomena as functions of dimensionless parameters.
Morphosyntactic Preprocessing Impact on Document Embedding: An Empirical Study on Semantic Similarity

Word embedding technique is among the most widely known and used representations of text documents vocabulary. It serves to capture word context in a document, but in many applications the need is to understand the content of text, which is longer than just a single word, that’s what we call “Document Embedding”. This paper presents an empirical study that evaluates the morphosyntactic data preprocessing impact on document embedding techniques over textual semantic similarity evaluation task, and that by comparing the impact of the most widely known text preprocessing techniques, such as: (1) Cleaning technique containing stop-words removal, lowercase conversion, punctuation and number elimination, (2) Stemming technique using the most known algorithms in the literature: Porter, Snowball and Lancaster stemmer and (3) Lemmatization technique using Wordnet Lemmatizer. Experimental analysis on MSRP (Microsoft Research Paraphrase) dataset reveals that preprocessing techniques improve classifier accuracy, where Stemming methods outperforms other techniques.
Enhancing Writing Skills via Mobile Learning and Wikis

This chapter reports on how a group of advanced EFL students in a private language school in Spain employed their smartphones and wikis to create a learning environment outside their classroom context to enhance their writing skills. Twenty-one participants of C1 level downloaded the Google application on their smartphones with a view to performing their reading and vocabulary tasks prior to writing their essays on a wiki learning platform, with subsequent peer editing and feedback. The findings suggest that the participants' general overview of this novel learning experience was positive and that collaborative work on the wiki fostered negotiation of meaning and sharing knowledge, which can benefit the writing process. The smartphone was deemed a convenient tool for reading and completing vocabulary exercises as its portability permits ubiquitous learning. Peer editing and feedback are crucial for developing linguistic proficiency, thereby improving writing skills.
Reduced-Bias Co-trained Ensembles for Weakly Supervised Cyberbullying Detection

Social media reflects many aspects of society, including social biases against individuals based on sensitive characteristics such as gender, race, religion, physical ability, and sexual orientation. Machine learning algorithms trained on social media data may therefore perpetuate or amplify discriminatory attitudes against various demographic groups, causing unfair decision-making. One important application for machine learning is the automatic detection of cyberbullying. Biases in this context could take the form of bullying detectors that make false detections more frequently on messages by or about certain identity groups. In this paper, we present an approach for training bullying detectors from weak supervision while reducing the degree to which learned models reflect or amplify discriminatory biases in the data. Our goal is to decrease the sensitivity of models to language describing particular social groups. An ideal, fair language-based detector should treat language describing subpopulations of particular social groups equitably. Building on a previously proposed weakly supervised learning algorithm, we penalize the model when discrimination is observed. By penalizing unfairness, we encourage the learning algorithm to avoid unfair behavior in its predictions and achieve equitable treatment for protected subpopulations. We introduce two unfairness penalty terms: one aimed at removal fairness and another at substitutional fairness. We quantitatively and qualitatively evaluate the resulting models' fairness on a synthetic benchmark and data from Twitter comparing against crowdsourced annotation.
CPL: A Combined Framework of Pointwise Prediction and Learning to Rank for top-N Recommendations with Implicit Feedback

Pointwise prediction and Learning to Rank (L2R) are both widely used in recommender systems. Currently, these two types of approaches are often considered independently, and most existing efforts utilize them separately. Unfortunately, pointwise prediction tends to overfit the training data while L2R is more prone to higher variance, and both of them suffer one-class problems using implicit feedback. Therefore, we propose a new framework called CPL, where pointwise prediction and L2R are inherently combined to discriminate user preferences on unobserved items, to improve the performance of top-N recommendations. To verify the effectiveness of CPL, an instantiation of CPL, which is named CPLmg, is introduced. CPLmg is based on two components, i.e., FSLIM (Factorized Sparse LInear Method) and GAPfm (Graded Average Precision factor model), to perform pointwise prediction and L2R, respectively. The low-rank users' and item's latent factor matrices act as a bridge between FSLIM and GAPfm. Moreover, FSLIM dynamically rates an unobserved item for a user based on its similarity with observed items. These pseudo ratings are further utilized with a confidence score to rank items in GAPfm. Extensive experiments on two datasets show that CPLmg significantly outperforms the baselines.
Mental Retrieval of Large-Scale Satellite Images Via Learned Sketch-Image Deep Features

Searching targets of interest in large-scale satellite images is an imperative task, which becomes a challenging issue when the targets reside only in the mind of the user as a set of subjective visual patterns. In this paper, we take the advantage of hand-drawn sketches' strong intuition of describing mental target to address the problem of no available exemplar query. We introduce a multi-level-of-detail model to learn a cross-domain representation for bridging the gap between sketches and satellite images. To train the model, we propose a novel method of generating satellite images with corresponding level of details based on generative adversarial network. Experiments on both large-scale satellite images and commonly used RS datasets demonstrate the effectiveness and superiority of our method.
Automated Class Correction and Enrichment in the Semantic Web

The Semantic Web is an effort to interchange unstructured data over the Web into a structured format that is processable not only by human beings but also computers. The key backbones of Semantic Web are ontologies and annotations that provide semantics for data. Ontologies are usually created before actual data is populated. Subsequently, they can be incomplete and they often do not provide all aspects that are required for specific domains of knowledge. Additionally, Semantic Web-based ontologies usually suffer from a considerable amount of faulty facts which are known as Semantic Web data quality issues. Due to the complexity of relationships, Semantic Web data quality issues are continuously growing. This paper follows two main objectives. Firstly, it concentrates on a specific Semantic Web data quality issue that indicates incorrect assignment between instances and classes in the ontology. Secondly, the paper shows how to discover new classes which are not defined in the ontology and how to place them in the hierarchical structure of the ontology. To make ends meet, an entropy-based approach called ACE (Automated Class Corrector and Enricher) is proposed that not only evaluates the correctness and incorrectness of relationships between instances and classes but also generates new classes to enrich ontologies. The contributions of ACE have been also explained throughout the paper. Initial experiments conducted on a Semantic Web dataset demonstrate the effectiveness of the ACE.
NATURAL FORMALIZATION: DERIVING THE CANTOR-BERNSTEIN THEOREM IN ZF

Abstract Natural Formalization proposes a concrete way of expanding proof theory from the meta-mathematical investigation of formal theories to an examination of “the concept of the specifically mathematical proof.” Formal proofs play a role for this examination in as much as they reflect the essential structure and systematic construction of mathematical proofs. We emphasize three crucial features of our formal inference mechanism: (1) the underlying logical calculus is built for reasoning with gaps and for providing strategic directions , (2) the mathematical frame is a definitional extension of Zermelo–Fraenkel set theory and has a hierarchically organized structure of concepts and operations, and (3) the construction of formal proofs is deeply connected to the frame through rules for definitions and lemmas. To bring these general ideas to life, we examine, as a case study, proofs of the Cantor–Bernstein Theorem that do not appeal to the principle of choice. A thorough analysis of the multitude of “different” informal proofs seems to reduce them to exactly one. The natural formalization confirms that there is one proof, but that it comes in two variants due to Dedekind and Zermelo, respectively. In this way it enhances the conceptual understanding of the represented informal proofs. The formal, computational work is carried out with the proof search system AProS that serves as a proof assistant and implements the above inference mechanism; it can be fully inspected at http://www.phil.cmu.edu/legacy/Proof_Site/ . We must—that is my conviction—take the concept of the specifically mathematical proof as an object of investigation. Hilbert 1918
Taxonomy of Package Management in Programming Languages and Operating Systems

Package management is instrumental for programming languages and operating systems, and yet it is neglected by both areas as an implementation detail. For this reason, it lacks the same kind of conceptual organization: we lack terminology to classify them or to reason about their design trade-offs. In this paper, we share our experience in both OS and language-specific package manager development, categorizing families of package managers and discussing their design implications beyond particular implementations. We also identify possibilities in the still largely unexplored area of package manager interoperability.
A novel enhanced intensity‐based automatic registration: Augmented reality for visualization and localization cancer tumors

Abstract The purpose of this study is to replace the manual process (selecting the landmarks on mesh and anchor points on the video) by Intensity‐based Automatic Registration method to reach registration accuracy and low processing time. The proposed system consists of an Enhanced Intensity‐based Automatic Registration (EIbAR) using Modified Zero Normalized Cross Correlation (MZNCC) algorithm. The proposed system was implemented on videos of breast cancer tumors. Results showed that the proposed algorithm—as compared to a reference—improved registration accuracy by an average of 2 mm. In addition, the proposed algorithm—as compared to a reference—reduced the number of pixel matching, thereby reducing processing time on the video by an average of 22 ms/frame. The proposed system can, thus, provide an acceptable accuracy and processing time during scene augmentation of videos, which provides a seamless use of augmented‐reality for surgeons in visualizing cancer tumors.