Reviewerly/exHarmony huggingface dataset:
Incorporation of incompatible trace elements into molybdenite: Layered PbS precipitates within molybdenite

Abstract Trace elements in molybdenite can provide important information regarding the composition of ore-forming fluid and the evolution and genesis of ore deposits. However, the occurrence states and behavior of relatively incompatible trace elements (e.g., Pb and Os) in natural molybdenite remain ambiguous. Here, we report an abnormally high enrichment of Pb and layered PbS precipitate within molybdenite grains from the Huanglongpu carbonatite-hosted Mo-Pb deposit in the Qinling orogenic belt of Northern China. High-resolution transmission electron microscopy (HRTEM) and related nanobeam techniques were applied to characterize the occurrence states of Pb within molybdenite at the atomic scale. The results show that up to several weight percent of Pb can be incorporated into the molybdenite structure during initial crystallization, which can lead to the formation of screw dislocations and 3R/disordered stacking of S-Mo-S sandwich layers. Observations using a scanning transmission electron microscope also reveal that Pb diffuses from the host molybdenite into the layered PbS precipitates under prolonged electron beam irradiation. Pb-bearing molybdenite tends to transform into a Pb-poor ordered 2H1 polytype upon Pb exsolution during cooling. Pb preferentially exsolves along the (001) plane of molybdenite and is stored in structural defects (e.g., dislocation loops) and grain boundaries, resulting in nano-scale Pb heterogeneities in molybdenite. Further coarsening of the exsolved Pb results in the formation of layered PbS precipitates along the (001) plane of molybdenite. This study provides an example of the consequences of the incorporation and exsolution of incompatible trace elements in molybdenite and demonstrates that careful mineralogical examination is required to interpret geochemical data obtained by in situ analysis techniques.
A robust fuzzy approach for gene expression data clustering

In the big data era, clustering is one of the most popular data mining method. Most clustering algorithms have complications like automatic cluster number determination, poor clustering precision, inconsistent clustering of various datasets and parameter-dependent, etc. A new fuzzy autonomous solution for clustering named Meskat-Mahmudul (MM) clustering algorithm was proposed to overcome the complexity of parameter-free automatic cluster number determination and clustering accuracy. The Meskat-Mahmudul clustering algorithm finds out the exact number of clusters based on the average silhouette method in multivariate mixed attribute dataset, including real-time gene expression dataset and missing values, noise, and outliers. Meskat-Mahmudul Extended K-Means (MMK) clustering algorithm enhances the K-Means algorithm, which serves the purpose of automatic cluster discovery and runtime cluster placement. Several validation methods are used to evaluate clusters and certify optimum cluster partitioning and perfection. Some datasets are used to assess the performance of the proposed algorithms to other algorithms in terms of time complexity and clustering efficiency. Finally, Meskat-Mahmudul clustering and Meskat-Mahmudul Extended K-Means clustering algorithms were found superior over conventional algorithms.
NegMAS: A Platform for Situated Negotiations

Most research in automated negotiation focuses on strategy development in preset scenarios where decisions about what to negotiate about, whom to negotiate with, and on which issues are given to the agents. Moreover, in many cases, the agents' utility functions are predefined, static, and independent of other negotiations. NegMAS (Negotiations Managed by Agent Simulations/Negotiation Multiagent System) was developed to facilitate the research and development of autonomous agents that operate in a rich multiagent system where negotiations are paramount, such as a supply chain. The richness of the setting creates what we call situated negotiations, where negotiations naturally interdepend and agents' utility functions arise endogenously from system dynamics. This paper introduces NegMAS—a platform for autonomous negotiation within a rich simulated multiagent system—and evaluates its use in a sample application.
Backing Backtracking

Non-chronological backtracking was considered an important and necessary feature of conflict-driven clause learning (CDCL). However, a SAT solver combining CDCL with chronological backtracking succeeded in the main track of the SAT Competition 2018. In that solver, multiple invariants considered crucial for CDCL were violated. In particular, decision levels of literals on the trail were not necessarily increasing anymore. The corresponding paper presented at SAT 2018 described the algorithm and provided empirical evidence of its correctness, but a formalization and proofs were missing. Our contribution is to fill this gap. We further generalize the approach, discuss implementation details, and empirically confirm its effectiveness in an independent implementation.
Towards Vulnerability Types Classification Using Pure Self-Attention: A Common Weakness Enumeration Based Approach

The wake of increasing malicious cyberattack cases has aroused people’s attention on cybersecurity and vulnerabilities. Common Vulnerabilities and Exposures (CVE), a famous cybersecurity vulnerability database, is often referenced as a standard in cybersecurity territory for both research and commercial purposes. In the past decade, the development of Common Weakness Enumeration (CWE) has provided useful vulnerability taxonomy on CVE entities. However, the generation process of CWE categories is totally by manual working, which has made cybersecurity professionals suffer from the unpredictable timing waiting for the up to date information to be published. In this study, a new CWE based vulnerability types classification method is introduced with the adoption of the CVE dataset. Our method adopts transformer encoder-decoder architecture and uses pure self-attention mechanism without any convolutions and recurrences. We first encode the CVE input entries to learn representative features and then decode them to perform vulnerability types classification regarding the CWE standards. Fine-tuned deep pre-trained Bidirectional Encoder Representation from Transformers (BERT) is utilized in experiment and performs automatic vulnerability types classification tasks on unlabeled CVE candidates and assigns CWE IDs. The proposed vulnerability types classification method outperforms all classical Natural Language Processing (NLP) baseline algorithms, conducting a high accuracy of 90.74% on the testing dataset. In addition, the well-trained vulnerability types classification model is believed to achieve considerable correctness at industry level when applied to the real-life cyber threat intelligence related articles and reports.
A healthcare text classification system and its performance evaluation: a source of better intelligence by characterizing healthcare text

A machine learning (ML)-based text classification system has several classifiers. The performance evaluation (PE) of the ML system is typically driven by the training data size and the partition protocols used. Such systems lead to low accuracy (ACC) because the text classification systems lack the ability to model the input text data in terms of noise characteristics. This research study proposes a concept of misrepresentation ratio (MRR) on input healthcare text data and models the PE criteria for validating the hypothesis. Further, such a novel system provides a platform to amalgamate several attributes of the ML system such as data size, classifier type, partitioning protocol, and percentage MRR. Our comprehensive data analysis consisted of five types of text datasets [TwitterA, WebKB4, Disease, Reuters (R8), and SMS]; five kinds of classifiers [support vector machine with linear kernel, multilayer perceptron (MLP)-based neural network, AdaBoost, stochastic gradient descent and decision tree]; and five types of training protocols (K2, K4, K5, K10, and JK). Using the decreasing order of MRR, our ML system demonstrates the mean classification accuracies as 70.13%±0.15%, 87.34%±0.06%, 93.73%±0.03%, 94.45%±0.03%, and 97.83%±0.01%, respectively, using all the classifiers and protocols. The corresponding area under the curve is 0.98 for SMS data using MLP-based neural network. Using all the classifiers, the best ACC of 91.84%±0.04% is shown to be of MLP-based neural network and this is 6% better than previously published. Further we observed that as MRR decreases, the system robustness increases and is validated by standard deviations. The overall text system ACC using all data types, classifiers, and protocols is 89%, thereby showing the entire ML system to be novel, robust, and unique. The system is also tested for stability and reliability.
Smart grid

The integration of blockchain technology and the electric grid makes the grid smart and decentralized. The adoption of blockchain technology allows the grid network to decentralize its operations. This means there is no need for a central authority in the smart grid to control, distribute, and manage the electricity. The decision making and transaction flow in the smart grid do not need to be transmitted through the centralized system. All the transactions and operations performed on the grid network are stored on the public ledgers of a blockchain. The buying and selling energy transactions across the network can be done through a computer program by validating and verifying the pre-determined clauses of the transaction. So, blockchain technology helps in setting up real-time energy markets and identity preserving transactions at much lower costs. In this chapter, we have described the role and benefits of blockchain technology in the smart grid in terms of energy management and demand–supply management.
Forest Firefighting Monitoring System Based on UAV Team and Remote Sensing

In this chapter, the authors propose an approach to using a heterogeneous team of unmanned aerial vehicles and remote sensing techniques to perform tactical forest firefighting operations. The authors present the three-level architecture of the multi-UAV-based forest firefighting monitoring system; features of patrolling, confirming, and monitoring missions; as well as functions of UAV in such missions. The authors consider an infrastructure for the UAV ground support and equipment used for the UAVs control. The method of the data integration into a fire-spreading model in a real-time DSS for the forest fire response is proposed. The proposed approach has been tested with the multi-UAV team that included three drones for the patrol missions, one helicopter for the confirmation mission, and one octocopter for the monitoring mission. The performance of such multi-UAV team has been studied in the laboratory conditions. The result of the experiment has shown that the proposed approach provides required credibility and efficiency of fire prediction and response.
Disambiguation Techniques for Freehand Object Manipulations in Virtual Reality

Manipulating virtual objects using bare hands has been an attractive interaction paradigm in virtual and augmented reality due to its intuitive nature. However, one limitation of freehand input lies in the ambiguous resulting effect of the interaction. The same gesture performed on a virtual object could invoke different operations on the object depending on the context, object properties, and user intention. We present an experimental analysis of a set of disambiguation techniques in a virtual reality environment, comparing three input modalities (head gaze, speech, and foot tap) paired with three different timings in which options appear to resolve ambiguity (before, during, and after an interaction). The results indicate that using head gaze for disambiguation during an interaction with the object achieved the best performance.
Confidence Regularized Label Propagation Based Domain Adaptation

In domain adaptation (DA), label-induced losses generally occupy a dominant position and most previous models regard hard or soft labels as their inputs. However, these two types of labels may mislead the modeling process of label-induced losses since hard label is sensitive to a wrongly-predicted sample while soft label may introduce label noise, thus they may cause negative transfer. To relieve this problem, we propose a novel label learning approach namely confidence regularized label propagation (CRLP) that regularizes the confidence of predicted soft labels with constraints of F-norm or L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</sub> -norm. It is validated that maximizing either one of these two constraints equals to minimizing entropy loss. Specially, we illustrate that L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</sub> -norm is more suitable for DA than F-norm when the dataset contain a large number of categories. Then, we leverage the regularized soft labels produced by CRLP to reformulate some popular label-induced losses that consider feature transferability and discriminability such as class-wise maximum mean discrepancy, intra-class compactness and inter-class dispersion in a probability manner to present a novel DA method ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , CRLP-DA). Comprehensive analysis and experiments on four cross-domain object recognition datasets verify that the proposed CRLP-DA outperforms some state-of-the-art methods, especially 59.5% for Office10+Caltech10 dataset with SURF features. For others to better reproduce, our preliminary Matlab code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/WWLoveTransfer/CRLP-DA/</uri> .
Significance of Biologically Inspired Optimization Techniques in Real-Time Applications

The techniques inspired from the nature based evolution and aggregated nature of social colonies have been promising and shown excellence in handling complicated optimization problems thereby gaining huge popularity recently. These methodologies can be used as an effective problem solving tool thereby acting as an optimizing agent. Such techniques are called Bio inspired computing. Our study surveys the recent advances in biologically inspired swarm optimization methods and Evolutionary methods, which may be applied in various fields. Four real time scenarios are demonstrated in the form of case studies to show the significance of bio inspired algorithms. The techniques that are illustrated here include Differential Evolution, Genetic Search, Particle Swarm optimization and artificial bee Colony optimization. The results inferred by implanting these techniques are highly encouraging.
AVrate Voyager: an open source online testing platform

Subjective testing is an integral part of many research fields considering, e.g., human perception. For this purpose, lab tests are a popular approach to gather ratings for subjective evaluations. However, not in all cases controlled lab tests can be performed, either in cases where no labs are existing, accessible or it may be disallowed to use them. For this reason, online tests, e.g., using crowdsourcing are supposed to be an alternative approach for traditional lab tests. We describe in the following paper a framework to implement such online tests for audio, video, and image-related evaluations or questionnaires. Our framework AVrate Voyager builds upon previously developed frameworks for lab tests including the experience with them. AVrate Voyager uses scalable web technologies to implement a test framework, this ensures that it will be running reliably. In addition, we added strategies for pre-caching to avoid additional influence for play-out, e.g. in the case of video testing. We analyze several conducted tests using the new framework and describe the required steps to modify the provided tool in detail.
Occluded Pedestrian Detection via Distribution-Based Mutual-Supervised Feature Learning

Pedestrian detection is a very important task in intelligent transportation system. State-of-the-art detectors work well on non-occluded pedestrians, but they are still far from satisfactory for heavily occluded ones. Recently, to deal with occlusion problems, the popular two-stage approaches are to build a two-branch architecture with the help of additional visible body annotations. However, these methods still have disadvantages. Either the two branches only use score-level fusion, which cannot guarantee the detectors to learn more robust pedestrian features. Or they only focus on the features of visible part via the attention mechanisms. However, the visible body features of heavily occluded pedestrians are only concentrated in a relatively small area, which may easily lead to missed detections. To alleviate the above issues, we propose a novel Distribution-based Mutual-Supervised Feature Learning Network (DMSFLN), to better deal with occluded pedestrian detection. The key DMSFL module in our network is to learn more discriminative feature representations of pedestrians by minimizing the similarity loss between feature distributions of full body and visible body, which has two advantages: enhancing the feature representations of occluded pedestrians and reducing the intra-class variance in pedestrians. To facilitate the DMSFL module, we also propose a novel two-branch network architecture, which is trained in a mutual-supervised way with both full body and visible body annotations respectively. Extensive experiments are conducted on four challenging pedestrian datasets: Caltech, CityPersons, CrowdHuman and CUHK occlusion. Our approach achieves superior performance compared to other state-of-the-art methods, especially on heavy occlusion subsets.
Inconsistency Measurement

The field of Inconsistency Measurement is concerned with the development of principles and approaches to quantitatively assess the severity of inconsistency in knowledge bases. In this survey, we give a broad overview on this field by outlining its basic motivation and discussing some of these core principles and approaches. We focus on the work that has been done for classical propositional logic but also give some pointers to applications on other logical formalisms.
FACER: An API usage-based code-example recommender for opportunistic reuse

To save time, developers often search for code examples that implement their desired software features. Existing code search techniques typically focus on finding code snippets for a single given query, which means that developers need to perform a separate search for each desired functionality. In this paper, we propose FACER (F eature-driven A PI usage-based C ode E xamples R ecommender), a technique that avoids repeated searches through opportunistic reuse. Specifically, given the selected code snippet that matches the initial search query, FACER finds and suggests related code snippets that represent features that the developer may want to implement next. FACER first constructs a code fact repository by parsing the source code of open-source Java projects to obtain methods’ textual information, call graphs, and Application Programming Interface (API) usages. It then detects unique features by clustering methods based on similar API usages, where each cluster represents a feature or functionality. Finally, it detects frequently co-occurring features across projects using frequent pattern mining and recommends related methods from the mined patterns. To evaluate FACER, we run it on 120 Java Android apps from GitHub. We first manually validate that the detected method clusters represent methods with similar functionality. We then perform an automated evaluation to determine the best parameters (e.g., similarity threshold) for FACER. We recruit 10 professional developers along with 39 experienced students to judge FACER’s recommendation of related methods. Our results show that, on average, FACER’s recommendations are 80% precise. We also survey a total of 20 professional Android and Java developers to understand their code search and reuse experiences, and also to obtain their feedback on the usability and usefulness of FACER. The survey results show that 95% of our surveyed professional developers find the idea of related method recommendations useful during code reuse.
The Dark Side of Interaction Design

This panel will provoke the audience into reflecting on the dark side of interaction design. It will ask what role the HCI community has played in the inception and rise of digital addiction, digital persuasion, data exploitation and dark patterns and what to do about this state of affairs. The panelists will present their views about what we have unleashed. They will examine how 'stickiness' came about and how we might give users control over their data that is sucked up in this process. Finally, they will be asked to consider the merits and prospects of an alternative agenda, that pushes for interaction design to be fairer, more ethically-grounded and more transparent, while at the same time addressing head-on the dark side of interaction design.
Observer dependence of photon bunching: The influence of the relativistic redshift on Hong-Ou-Mandel interference

We study the influence of the relativistic redshift on Hong-Ou-Mandel (HOM) interference, and present a genuine quantum test of general relativity. We use Glauber's theory of quantum coherence to predict the coincidence probability of realistic broadband photons in HOM-experiments in a non-relativistic setting. We extend the quantum field theoretical framework previously developed to describe the deformation of the spectral profile of single photons in curved spacetimes to a multi-photon framework, which is exact for inertial observers in a flat spacetime and an approximation when observers are located in a curved spacetime. We find that, in case of frequency entangled photons, a mutual redshift between the sender and the receiver can change the coincidence statistics from photon bunching to photon anti-bunching, and vice versa. This implies that the (anti-) symmetry of the photonic spectral wave function is an observer dependent notion, and that this can be probed via HOM-experiments in a relativistic setting.
Future teachers facing the use of technology for inclusion: A view from the digital competence

Technologies provide a differential value to the training process, allowing for the generation of new environments, methodologies and resources that make it possible to attend to students in a more appropriate way. This potential is especially relevant in matters of inclusion, where technology is sometimes an indispensable element for learning. In this paper we explore the main advantages of the use of technology for the attention to diversity, taking into consideration the level of digital competence of future teachers and their perceptions regarding its use for the implementation of inclusive strategies. The results suggest that participants have an intermediate level of digital competence, with differences according to gender, age and degree. It is also remarkable that they perceive inclusion as one of the main challenges of the education system and that technology can contribute to making teaching practice more inclusive, allowing it to be adapted to specific needs and highlighting the importance of teacher training in both digital competence and inclusion as an educational principle.