Reviewerly/exHarmony huggingface dataset:
Scalability of the mineral prospectivity modelling – An orogenic gold case study from northern Finland

One of the key objectives for mineral exploration is to map the most prospective areas for the selected deposit type at a regional scale, subsequently narrowing these down to camp size target areas and finally to individual prospects that can be developed into economic mines. Exploration targeting, at all scales, is constrained by conceptual mineral system models and requires the integration and interpretation of a variety of geophysical, geological and geochemical data. Geographic information system (GIS) applications are commonly used to integrate spatially referenced data sets to produce mineral prospectivity maps in support of the mineral exploration targeting process. A knowledge-driven fuzzy logic method is used in this work for data integration and prospectivity modelling for orogenic gold deposits in northern Finland. The modelling workflow is done in a stepwise manner at three scales. Each step simulates the successive stage used in mineral exploration from selecting the most prospective belt scale domain to the camp size target area and individual prospects. New, higher resolution data sets are added at each stage as is typically the case in mineral exploration. Northern Finland was selected as the test area due to the regional scale coverage of publicly available geoscientific data and its proven orogenic gold potential. The regional scale prospectivity map outlines the central part of the Paleoproterozoic Central Lapland Greenstone belt (CLGB) as the most prospective area for orogenic gold in northern Finland. The belt scale model of the CLGB improves the resolution mapping of several camp sized high prospectivity target areas. The camp scale model of one of these, a past producing mining camp, further narrows down the high prospectivity zones into prospect size targets. Three models have score Area Under Curve (AUC) values between 0.839 and 0.930 from the Receiver Operating Characteristic (ROC) method validation technique, indicating that the models are robust. The stepwise approach presented confirms that prospectivity mapping using the knowledge-driven fuzzy logic method is a scalable, flexible and relatively fast method which can be used for decision making at different stages of the exploration targeting process. Adding new data and rerunning the prospectivity model with the new data is fast and easy. The most important conclusion from the study is that remodelling individual regional and camp scale domains separately is highly recommended for mapping the most prospective target areas even if the data sets used are the same.
Just married: the synergy between feminist criminology and the Tripartite Cybercrime Framework

Abstract This article is a theoretical treatment of feminist epistemology of crime, which advocates the centrality of gender as a theoretical starting point for the investigating of digital crimes. It does so by exploring the synergy between the feminist perspectives and the Tripartite Cybercrime Framework (TCF) (which argues that three possible factors motivate cybercrimes – socioeconomic, psychosocial, and geopolitical) to critique mainstream criminology and the meaning of the term “cybercrime”. Additionally, the article examines gender gaps in online harassment, cyber‐bullying, cyber‐fraud, revenge porn, and cyber‐stalking to demonstrate that who is victimised, why, and to what effect are the critical starting points for the analysis of the connections between gender and crimes. In turn, it uses the lens of intersectionality to acknowledge that, while conceptions of gender and crime interact, they intersect with other categories (e.g., sexuality) to provide additional layers of explanation. To nuance the utilitarian value of the synergy between the TCF and the feminist perspectives, the focus shifts to a recent case study (which compared socioeconomic and psychosocial cybercrimes). The article concludes that, while online and offline lives are inextricably intertwined, the victimisations in psychosocial cybercrimes may be more gendered than in socioeconomic cybercrimes. These contributions align the TCF to the feminist epistemology of crime in their attempt to move gender analysis of digital crimes “from margin to centre”.
Lightweight authentication for MQTT to improve the security of IoT communication

A widely used application layer protocol for IoT communication is Message Queue Telemetry Transport (MQTT) protocol. The provision of security in MQTT protocol is an essential concern in IoT applications. In this paper, we show how the conventional secure MQTT protocol is vulnerable to cipher attack. Then, we present a novel approach to improve the MQTT security by providing lightweight authentication mechanism. The proposed approach is using chaotic algorithm with topic based self-key agreement and block cipher. Moreover, the empirical study is performed to measure the efficiency of proposed approach in Cooja simulated environment.
Evaluating Performance of Software Defect Prediction Models Using Area Under Precision-Recall Curve (AUC-PR)

Software defect prediction (SDP) models are used to improve effort and testing estimate of software by identifying defective modules beforehand. Precision, recall/true positive rate and false positive rate have been used to evaluate the performance of models. In literature, area under receiver operating characteristic curve (AUC-ROC) has been used to evaluate the model performance. The standard learning goal of the defect model is to optimize the (AUC-ROC). Use of this measure has also been advocated in numerous benchmarking studies. The literature has discussed the performance bar (or so-called ceiling effect) of AUC-ROC targeted models. The literature has also indicated the use of area under precision recall curve (AUC-PR) as an evaluation parameter for the models. This study investigates if AUC-PR curve gives different information regarding model performance. To this end this study ranks the existing models based on AUC-ROC and AUC-PR and report the change in ranking of these models. The change in ranking gives an opportunity to study if the ceiling effect can be managed and AUC-PR (instead of AUC-ROC) can be considered as a goal for the prediction models. AUC-PR based evaluation of the models can help avoid the extra cost, time, and effort employed to test non-defective modules.
Modulation recognition for radar emitter signals based on convolutional neural network and fusion features

Abstract With the increase of radar signal modulations and the emergence of new system radars, the receiver will intercept radar signals at the same time. In order to accurately estimate and suppress the signals, this paper proposes an accurate recognition system for radar emitter signals. The system can effectively separate multiple signals and accurately recognize Binary Phase Shift Keying (BPSK), Linear Frequency Modulation (LFM), Continuous Wave (CW), Costas, Frank code, and P1 to P4 codes. The separation technique based on fractional Fourier transform is proposed to decompose received signals into multiple components. Furthermore, a transferable GoogleNet is explored to achieve accurate recognition of the first component with better separation effect. Meanwhile, variational mode decomposition is developed to eliminate the noise of the second component; then, the fusion features are extracted to improve the recognition rate of the second component. Finally, the improved particle swarm optimization algorithm is proposed to find best support vector machine parameters. The simulation results show that the recognition rate of single signal and double signals can reach 96.23% and 72%, respectively, when signal‐to‐noise ratio is 0 dB. The system can also bring some inspiration to medical and mechanical signal recognition.
Transformation boosting machines

The broad class of conditional transformation models includes interpretable and simple as well as potentially very complex models for conditional distributions. This makes conditional transformation models attractive for predictive distribution modelling, especially because models featuring interpretable parameters and black-box machines can be understood as extremes in a whole cascade of models. So far, algorithms and corresponding theory was developed for special forms of conditional transformation models only: maximum likelihood inference is available for rather simple models, there exists a tailored boosting algorithm for the estimation of additive conditional transformation models, and a special form of random forests targets the estimation of interaction models. Here, I propose boosting algorithms capable of estimating conditional transformation models of arbitrary complexity, starting from simple shift transformation models featuring linear predictors to essentially unstructured conditional transformation models allowing complex nonlinear interaction functions. A generic form of the likelihood is maximized. Thus, the novel boosting algorithms for conditional transformation models are applicable to all types of univariate response variables, including randomly censored or truncated observations.
Active control as evidence in favor of sense of ownership in the moving Virtual Hand Illusion

The sense of ownership, the feeling that our body belongs to ourselves, relies on multiple sources of sensory information. Among these sources, the contribution of visuomotor information is still debated. We tested the effect of active control in the sense of ownership in the moving Virtual Hand Illusion. Participants reported sense of ownership and sense of agency over a virtual arm in which we manipulated the morphological congruence of the hand and the visuomotor information. We found that congruent active control enhanced and maintained the reported sense of ownership over a hand that appeared detached from the body, but not in a morphological congruent limb. Also, incongruent active control, achieved by adding noise to the trajectory of the movement, decreased both reported sense of agency and ownership. Overall, our results are consistent with a framework in which active control acts as evidence for eliciting a sense of ownership.
Federated Blockchain-Based Tracking and Liability Attribution Framework for Employees and Cyber-Physical Objects in a Smart Workplace

The systematic integration of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) into the supply chain to increase operational efficiency and quality has also introduced new complexities to the threat landscape. The myriad of sensors could increase data collection capabilities for businesses to facilitate process automation aided by Artificial Intelligence (AI) but without adopting an appropriate Security-by-Design framework, threat detection and response are destined to fail. The emerging concept of Smart Workplace incorporates many CPS (e.g. Robots and Drones) to execute tasks alongside Employees both of which can be exploited as Insider Threats. We introduce and discuss forensic-readiness, liability attribution and the ability to track moving Smart SPS Objects to support modern Digital Forensics and Incident Response (DFIR) within a defence-in-depth strategy. We present a framework to facilitate the tracking of object behaviour within Smart Controlled Business Environments (SCBE) to support resilience by enabling proactive insider threat detection. Several components of the framework were piloted in a company to discuss a real-life case study and demonstrate anomaly detection and the emerging of behavioural patterns according to objects' movement with relation to their job role, workspace position and nearest entry or exit. The empirical data was collected from a Bluetooth-based Proximity Monitoring Solution. Furthermore, a key strength of the framework is a federated Blockchain (BC) model to achieve forensic-readiness by establishing a digital Chain-of-Custody (CoC) and a collaborative environment for CPS to qualify as Digital Witnesses (DW) to support post-incident investigations.
A Path Planning Algorithm for Collective Monitoring Using Autonomous Drones

This paper presents a novel mission-oriented path planning algorithm for a team of Unmanned Aerial Vehicles (UAVs). In the proposed algorithm, each UAV takes autonomous decisions to find its flight path towards a designated mission area while avoiding collisions to stationary and mobile obstacles. The main distinction with similar algorithms is that the target destination for each UAV is not apriori fixed and the UAVs locate themselves such that they collectively cover a potentially time-varying mission area. One potential application for this algorithm is deploying a team of autonomous drones to collectively cover an evolving forest wildfire and provide virtual reality for fire fighters. We formulated the algorithm based on Reinforcement Learning (RL) with a new method to accommodate continuous state space for adjacent locations. To consider more realistic scenario, we assess the impact of localization errors on the performance of the proposed algorithm. Simulation results show that success probability for this algorithm is about 80% when the observation error variance is as high as 100 (SNR:-6dB).
Using Ethical Leadership to Improve Business-To-Business Salesperson Performance: The Mediating Roles of Trust in Manager and Ethical Ambiguity

Purpose: Given the ever-increasing pressure put on sales organizations to improve performance, behave ethically and establish long-term customer relationships, this study seeks to better comprehend ethical leadership's part in doing so. It proposes that perceived ethical leadership indirectly influences salesperson performance through trust in manager and ethical ambiguity.Methodology/Approach: A survey of business-to-business salespeople was taken. Hypotheses are tested using structural equation modeling.Findings: The results show that perceived ethical leadership influences salesperson performance through the mediating roles of trust in manager and ethical ambiguity. Salespeople's perceptions of their supervisor's ethical leadership behaviors positively impact their trust in manager and negatively influences their ethical ambiguity. In turn, trust in manager positively influences sales performance while ethical ambiguity negatively influences sales performance.Research Implications: The results from testing the hypothesized model support mechanisms by which ethical leadership behavior may affect business-to-business salesperson job performance. It appears that ethical leadership works through ethical ambiguity and trust in manager to impact salesperson behavior performance, rather than directly impacting salesperson performance. Importantly the findings add to the literature an important consequence of ethical leadership, ethical ambiguity. This research likewise adds to the literature on role, and more specifically ethical, ambiguity by finding that reducing salesperson ethical ambiguity has a positive impact on salesperson behavior performance.Practical Implications: This study finds that one important mechanism for reducing ethical ambiguity is for sales supervisors to practice ethical leadership. By reducing ethical ambiguity, sales managers can improve business-to-business salesperson performance. In addition, use of ethical leadership by sales managers can positively influence the business-to-business salesperson's trust in manager, which subsequently leads to greater sales performance.Originality/Value/Contribution: The results of this study add to our knowledge of ethical leadership by further developing its consequences. It also sheds light on a vastly under-researched construct, ethical ambiguity. Finally, it further validates the important role that trust in manager plays in the organization.
Applying deep matching networks to Chinese medical question answering: a study and a dataset

Medical and clinical question answering (QA) is highly concerned by researchers recently. Though there are remarkable advances in this field, the development in Chinese medical domain is relatively backward. It can be attributed to the difficulty of Chinese text processing and the lack of large-scale datasets. To bridge the gap, this paper introduces a Chinese medical QA dataset and proposes effective methods for the task.We first construct a large scale Chinese medical QA dataset. Then we leverage deep matching neural networks to capture semantic interaction between words in questions and answers. Considering that Chinese Word Segmentation (CWS) tools may fail to identify clinical terms, we design a module to merge the word segments and produce a new representation. It learns the common compositions of words or segments by using convolutional kernels and selects the strongest signals by windowed pooling.The best performer among popular CWS tools on our dataset is found. In our experiments, deep matching models substantially outperform existing methods. Results also show that our proposed semantic clustered representation module improves the performance of models by up to 5.5% Precision at 1 and 4.9% Mean Average Precision.In this paper, we introduce a large scale Chinese medical QA dataset and cast the task into a semantic matching problem. We also compare different CWS tools and input units. Among the two state-of-the-art deep matching neural networks, MatchPyramid performs better. Results also show the effectiveness of the proposed semantic clustered representation module.
Quantum Computation in Robotic Science and Applications

Using the effects of quantum mechanics for computing challenges has been an often discussed topic for decades. The frequent successes and early products in this area, which we have seen in recent years, indicate that we are currently entering a new era of computing. This paradigm shift will also impact the work of robotic scientists and the applications of robotics. New possibilities as well as new approaches to known problems will enable the creation of even more powerful and intelligent robots that make use of quantum computing cloud services or co-processors. In this position paper, we discuss potential application areas and also point out open research topics in quantum computing for robotics. We go into detail on the impact of quantum computing in artificial intelligence and machine learning, sensing and perception, kinematics as well as system diagnosis. For each topic we point out where quantum computing could be applied based on results from current research.
An Ontology-based Approach to Support for Requirements Traceability in Agile Development

The paper proposes an ontology-based approach to support for requirements traceability in Agile development. The task of supporting the requirements traceability in a software development project is considered as a part of requirements engineering process. A brief overview of the benefits of requirements traceability usage is given. The expediency of using the ontology-based approach to support for requirements engineering, requirements traceability in particular, is shown through the analysis of scientific publications. OWL ontology is chosen to support the requirements engineering process. The ontology is implemented in the Protégé environment. The developed ontology takes into account particular qualities of working with the requirements in Agile projects, accumulates knowledge about the requirements types and requirements artefacts, enables tracing the relations between them.
Discriminative Adaptation Regularization Framework-Based Transfer Learning for Ship Classification in SAR Images

Ship classification in synthetic-aperture radar (SAR) images is of great significance for dealing with various marine matters. Although traditional supervised learning methods have recently achieved dramatic successes, but they are limited by the insufficient labeled training data. This letter presents a novel unsupervised domain adaptation (DA) method, termed as discriminative adaptation regularization framework-based transfer learning (D-ARTL), to address the problem in case that there is no labeled training data available at all in the SAR image domain, i.e., target domain (TD). D-ARTL improves the original ARTL by adding a novel source discriminative information preservation (SDIP) regularization term. This improvement achieves an efficient transfer of interclass discriminative ability from source domain (SD) to TD, while achieving the alignment of cross-domain distributions. Extensive experiments have verified that D-ARTL outperforms state-of-the-art methods on the task of ship classification in SAR images by transferring the automatic identification system (AIS) information.
Gaze-Guided Narratives

Exploring a city panorama from a vantage point is a popular tourist activity. Typical audio guides that support this activity are limited by their lack of responsiveness to user behavior and by the difficulty of matching audio descriptions to the panorama. These limitations can inhibit the acquisition of information and negatively affect user experience. This paper proposes Gaze-Guided Narratives as a novel interaction concept that helps tourists find specific features in the panorama (gaze guidance) while adapting the audio content to what has been previously looked at (content adaptation). Results from a controlled study in a virtual environment (n=60) revealed that a system featuring both gaze guidance and content adaptation obtained better user experience, lower cognitive load, and led to better performance in a mapping task compared to a classic audio guide. A second study with tourists situated at a vantage point (n=16) further demonstrated the feasibility of this approach in the real world.
Neighborhood Perception in Bar Charts

In this paper, we report three user experiments that investigate in how far the perception of a bar in a bar chart changes based on the height of its neighboring bars. We hypothesized that the perception of the very same bar, for instance, might differ when it is surrounded by the top highest vs. the top lowest bars. Our results show that such neighborhood effects exist: a target bar surrounded by high neighbor bars, is perceived to be lower as the same bar surrounded with low neighbors. Yet, the effect size of this neighborhood effect is small compared to other data-inherent effects: the judgment accuracy largely depends on the target bar rank, number of data items, and other data characteristics of the dataset. Based on the findings, we discuss design implications for perceptually optimizing bar charts.
Actively Seeking and Learning From Live Data

One of the key limitations of traditional machine learning methods is their requirement for training data that exemplifies all the information to be learned. This is a particular problem for visual question answering methods, which may be asked questions about virtually anything. The approach we propose is a step toward overcoming this limitation by searching for the information required at test time. The resulting method dynamically utilizes data from an external source, such as a large set of questions/answers or images/captions. Concretely, we learn a set of base weights for a simple VQA model, that are specifically adapted to a given question with the information specifically retrieved for this question. The adaptation process leverages recent advances in gradient-based meta learning and contributions for efficient retrieval and cross-domain adaptation. We surpass the state-of-the-art on the VQA-CP v2 benchmark and demonstrate our approach to be intrinsically more robust to out-of-distribution test data. We demonstrate the use of external non-VQA data using the MS COCO captioning dataset to support the answering process. This approach opens a new avenue for open-domain VQA systems that interface with diverse sources of data.