Reviewerly/exHarmony huggingface dataset:
Assessing similarities between gold ores, concentrates, and tailings with case-based reasoning

Searching for information has always been a challenge for a researcher. Decades ago it was a matter of gaining access to the information, but in this day and age, the problem is more about how to find the most relevant information amongst the ever-growing amount of scientific literature readily available. This research article presents an information retrieval tool that is designed to compare and rank gold raw materials, such as ores, concentrates, and tailings, according to the user’s research interests. The tool was constructed based on interviews with metallurgists specialized in gold and by utilizing a knowledge modeling technique called case-based reasoning. The tool can compare a gold raw material to existing material descriptions based on six attributes: Material type, Ore type, Mineralogy, Chemical assay, Particle size, and Gold content, resulting in a list of scientific articles that utilized similar raw materials. Different features in the software used enable the user to take their preferences into account and modify the effect of each attribute on the end result, i.e., the articles retrieved. The tool’s function was demonstrated with four test queries that ranked the entire case base of 84 gold raw material descriptions in relation to the test queries. While the tool’s default settings were able to retrieve similar materials from the case base, the best results are obtained when the user can modify the basis of retrieval in accordance with their particular research interests.
Training Back Propagation Neural Networks in MapReduce on High-Dimensional Big Datasets With Global Evolution

Owing to its scalability and high fault-tolerance even on a distributed environment built up with personal computers, MapReduce has been introduced to parallelise the training of Back Propagation Neural Networks (BPNNs) on high-dimensional big datasets. Based on the evolution of local BPNNs produced by distributed Map tasks with different data splits, the paper proposes a novel approach to the distributed data-parallel training of BPNNs in MapReduce. The approach provides a reasonable measure to get global convergent BPNN candidates from local BPNNs only convergent on the specific data splits. Further, it not only can reduce the iterations to get the global convergent BPNN, but also shows great advantages in avoiding the training to get trapped into a local optimum on high-dimensional big datasets. To improve the training efficiency further, local BPNNs from the same computing node are merged based on the average of their weight matrices before they act as individuals of the population for the global evolution. Our approach also leverages Random Project based sampling techniques to evaluate the fitness of each individual in order to lower the computation cost in the evolution stage. Experiments show that our proposed approach improves the training efficiency highly compared to the stand-alone or traditional MapReduce BPNN training, and improves model accuracy for larger datasets. The comparison with 23 other popular classification approaches also shows that our proposed approach has big advantages in accuracy.
Anti‐Muslim hate speech and displacement narratives: Case studies from Sri Lanka and Australia

Abstract Muslim communities have frequently been the victim of vilification and discrimination in Australia and the broader Asian region. This article discusses and compares the narratives around Muslim vilification in Australia and Sri Lanka. Despite the fact that these two nations are vastly different in size, demographics and cultural history, I will argue that there are remarkable similarities in how Muslims are subject to attack in both nations. The hate speech occasioned against Muslims in Sri Lanka and Australia is shaped by almost identical narrative structures and is cultivated in similar online environments. In both cases, anti‐Muslim animus is fuelled by a fear of displacement and perceived contamination of what nativists regard as ethnically sovereign territory. I will argue that the underlying sub‐text in both cases is owed to a repulsion to the physical presence of Muslims which can be understood in terms of Mary Douglas' concept of the moral pollutant.
Research On Cone Bucket Target Detection Based On Improved Faster R-CNN Deep Network

To solve the problem of detecting the cone bucket target on both sides of the FSAC racing car, a new target detection method based on Faster R-CNN is proposed. The method uses a deep residual network as a feature extraction network to fully extract sample data features. In the Region Proposal Network (RPN), the anchor boxes are divided into finer parts to enhance the detection effect on the small target cones. Optimize the Non-maximum Suppression (NMS) algorithm to extract the proposal box and reduce the missed detection rate of the adjacent cone bucket target. The target detection network model is obtained by training on the self-made cone bucket dataset. The experimental results show that the improved algorithm based on Faster R-CNN is robust to the detection of cone bucket targets.
Identity-Based Redactable Lamport Signature Scheme

The Redactable Signature Scheme (RSS) is one of the hottest topics in cryptography in these years. We find that even if the revision of the source document is limited to a very small range, the revision still has a high practical value. Therefore, it is necessary to find a solution that allows the trusted user to make the above restricted modifications to the source document. What's more, anyone who needs to verify the security of the modifications can do this. In order to solve this practical problem, we tried to introduce identities in the Lamport signature scheme to establish associations between keys and people, so that let Alice who was the owner of a document and has signed document then can authorize Bob to modify the document and generate new valid signatures. After discussion, we proved that if the identity system we use is secure, then our redactable signature scheme is also secure.
Interferometric and deflectometric flatness metrology with nanometre measurement uncertainties for optics up to one metre at PTB

High-precision topography measurements of nearly flat specimens with nanometre uncertainty are still demanding. They are needed for synchrotron or other high-quality optical mirrors. At the Physikalisch-Technische Bundesanstalt (PTB), we operate an interferometric setup and a small-angle deflectometric setup for flatness measurement. We present both systems, show the measurement uncertainties and discuss the pros and cons of interferometric and gradient based measurement systems. To achieve the nanometre level in flatness metrology, the environmental conditions and the holder of the specimen are very important. We show measurements of topography changes due to delayed elasticity effects. We demonstrate current developments with the small-angle deflectometer, especially to improve the lateral resolution.
Correspondence between Topological and Discrete Connectivities in Hausdorff Discretization

Abstract We consider Hausdorff discretization from a metric space E to a discrete subspace D , which associates to a closed subset F of E any subset S of D minimizing the Hausdorff distance between F and S ; this minimum distance, called the Hausdorff radius of F and written r H ( F ), is bounded by the resolution of D . We call a closed set F separated if it can be partitioned into two non-empty closed subsets F 1 and F 2 whose mutual distances have a strictly positive lower bound. Assuming some minimal topological properties of E and D (satisfied in ℝ n and ℤ n ), we show that given a non-separated closed subset F of E , for any r &gt; r H ( F ), every Hausdorff discretization of F is connected for the graph with edges linking pairs of points of D at distance at most 2 r . When F is connected, this holds for r = r H ( F ), and its greatest Hausdorff discretization belongs to the partial connection generated by the traces on D of the balls of radius r H ( F ). However, when the closed set F is separated, the Hausdorff discretizations are disconnected whenever the resolution of D is small enough. In the particular case where E = ℝ n and D = ℤ n with norm-based distances, we generalize our previous results for n = 2. For a norm invariant under changes of signs of coordinates, the greatest Hausdorff discretization of a connected closed set is axially connected. For the so-called coordinate-homogeneous norms, which include the L p norms, we give an adjacency graph for which all Hausdorff discretizations of a connected closed set are connected.
Visual displays for cyber network defense

Five computer network Defence displays (one Alphanumeric and four graphical displays: Radial Traffic Analyser, Bar Graph, Cube, and Treemap) were evaluated. Two experiments were conducted using different methodological procedures. Participants responded to questions that were structured to approximate various ways in which analysts might need to consider network traffic. Numerous significant effects were obtained and a fairly clear rank ordering of performance for the four graphical displays was obtained across experiments (from best to worst): Bar Graph, Cube, Radial Traffic Analyser, and Treemap. The results are interpreted from the perspective of ecological interface design: the quality of performance is directly related to the quality of semantic mapping between work domain, display, and human constraints. Factors that may have contributed to the poor performance for the Radial Traffic Analyser and Treemap displays are discussed. General implications for display and interface design are provided. Practitioner summary: Proposed displays for computer network Defence are evaluated; the results are interpreted from the perspective of ecological interface design. The associated design principles are applicable to all analogical graphical displays. Abbreviation: CND: cyber network defence; CSE: cognitive systems engineering; EID: ecological interface design; ICMP: internet control message protocol; IP: internet protocol; RTA: radial traffic analyzer; TCP: transmission control protocol; UDP: user datagram protocol.
Iterative Wiener Filtering for Deconvolution with Ringing Artifact Suppression

Sensor and lens blur degrade images acquired by digital cameras. Simple and fast removal of blur using linear filtering, such as Wiener filter, produces results that are not acceptable in most of the cases due to ringing artifacts close to image borders and around edges in the image. More elaborate deconvolution methods with non-smooth regularization, such as total variation, provide superior performance with less artifacts, however at a price of increased computational cost. We consider the alternating directions method of multipliers, which is a popular choice to solve such non-smooth convex problems, and show that individual steps of the method can be decomposed to simple filtering and element-wise operations. Filtering is performed with two sets of filters, called restoration and update filters, which are learned for the given type of blur and noise level with two different learning methods. The proposed deconvolution algorithm is implemented in the spatial domain and can be easily extended to include other restoration tasks such as demosaicing and super-resolution. Experiments demonstrate performance of the algorithm with respect to the size of learned filters, number of iterations, noise level and type of blur.
Adaptive SLA mechanism based on fuzzy system for dynamic cloud environment

The volatile nature of Cloud user’s needs is considered a worthy challenge in formulating the Service Level Agreement (SLA) negotiation. The sterility of the standard SLA templates in handling the diversity in the users’ requirements motives the concept of the dynamic SLA. Establishing the SLA contract is mainly based on converting the user’s QoS requirements into a meaningful Service Level Objectives (SLO) during the negotiation phase, but in case of modification in these QoS requirements especially during the run-time, the current negotiation may be violated and the provider may resort to terminate the current negotiation and re-establish it. This paper presents a novel methodology based on arithmetic and fuzzy logic model to establish a dynamic SLA’s Negotiation system commensurate with the dynamic aspects of the Cloud environment. The proposed negotiation system mainly based on defining pre-conditions and decisions to modify only the required SLO at the run-time without completely hang-up the SLA.
Development of Methods for Integrating Automatic Logical Inference Tools to Generate Knowledge in the Ontological Model

The article is devoted to the development of new knowledge generation methods based on the analysis of natural language texts. To extract knowledge from natural language texts, the method of presenting sentences in the form of binary predicates with new constant-situation is used. For the representation of knowledge in a formal form, we use quantifier-free sentences of predicate logic, as well as the OWL DL language. The generation of new knowledge is realized with the help of reasoners, using pre-defined patterns of inference rules. A software system has been developed that allows users to get answers to specific questions related to given natural language texts. The answers are built in a natural language, using not only the knowledge that is explicitly contained in the document being processed, but also the knowledge generated by the reasoners.
Enriching the Context: Methods of Improving the Non-contextual Assessment of Sentence Credibility

This paper presents several methods of automatic context enrichment of sentences that need to be evaluated, tagged or fact-checked by human judges. We have created a corpus of medical Web articles. Sentences from this corpus have been fact-checked by medical experts in two modes: contextually (reading the entire article and evaluating sentence by sentence) and without context (evaluating sentences from all articles in random order). It is known that non-contextual evaluation is faster, but some sentences are impossible to evaluate without context. We have designed and evaluated several methods of summarizing context that we hypothesized were suitable for supporting evaluation of sentences without reading the entire text. Then, we collected new assessments from medical experts for the sentences with enriched context. The context enrichment methods have been evaluated using two measures: conversion, which calculates how frequently a method allows experts to evaluate sentences that were impossible to evaluate without context, and agreement, which depends on how frequently the new expert evaluations match with evaluations from experts who had read the whole text before rating a sentence. Our results show that the best method achieves a high conversion rate, while providing experts with a condensed context summary. Moreover, the method significantly reduces the time needed to evaluate one sentence, compared to the baseline method (which provides the expert with the entire paragraph surrounding the target sentence). The problem of automatically enhancing the context of a sentence for fast fact-checking or tagging has not appeared in other studies before. We present preliminary results of the research in this area and a framework for testing potential new methods.
Assessing Algorithm Parameter Importance Using Global Sensitivity Analysis

In general, biologically-inspired multi-objective optimization algorithms comprise several parameters which values have to be selected ahead of running the algorithm. In this paper we describe a global sensitivity analysis framework that enables a better understanding of the effects of parameters on algorithm performance. For this work, we tested NSGA-III and MOEA/D on multi-objective optimization testbeds, undertaking our proposed sensitivity analysis techniques on the relevant metrics, namely Generational Distance, Inverted Generational Distance, and Hypervolume. Experimental results show that both algorithms are most sensitive to the cardinality of the population. In all analyses, two clusters of parameter usually appear: (1) the population size (Pop) and (2) the Crossover Distribution Index, Crossover Probability, Mutation Distribution Index and Mutation Probability; where the first cluster, Pop, is the most important (sensitive) parameter with respect to the others. Choosing the correct population size for the tested algorithms has a significant impact on the solution accuracy and algorithm performance. It was already known how important the population of an evolutionary algorithm was, but it was not known its importance compared to the remaining parameters. The distance between the two clusters shows how crucial the size of the population is, compared to the other parameters. Detailed analysis clearly reveals a hierarchy of parameters: on the one hand the size of the population, on the other the remaining parameters that are always grouped together (in a single cluster) without a possible significant distinction. In fact, the other parameters all have the same importance, a secondary relevance for the performance of the algorithms, something which, to date, has not been observed in the evolutionary algorithm literature. The methodology designed in this paper can be adopted to evaluate the importance of the parameters of any algorithm.
Design Prognostic Framework for Scene Classification in Video Processing

This paper provides the technique over selecting phenomenal frames extraction structure using SBD, KFE, and Semantic mechanism which quite easy to extract the valuable scene from the entire video. The proposed system will concentrates on findings the accuracy, precision and recall measure of inputted video from TRACEVID dataset. Here the proposed system signifies the use of thresholding technique and TBD of individual frame and calculate the various parameters like standard deviation, Mean etc. In order to legalize this claim, content based video reclamation systems were furnished using color histogram, features extraction and different approaches are applied for the supervision of the semantic temperament of each frame in the video. Also it gives major idea about the classification of the scene and algorithm which generates significant result.
Construction and Analysis of Knowledge Network: On the Example of Educational Technology Syllabus

The syllabus is the guiding document for subject teaching and the main basis for teacher teaching.The mastery of the teacher's knowledge structure of the syllabus marks the quality of teaching.Teachers should not only understand the basic content of the syllabus, but also be familiar with the overall structural characteristics of the syllabus.In this paper, we mainly applies the social network analysis method to research the structural characteristics of the syllabus.Firstly, according to the Educational Technology syllabus, its knowledge structure is analyzed.Then the concept map is used to construct its knowledge structured model, and the adjacency matrix is used to establish the knowledge network model.Secondly, through the analysis of the content of the Educational Technology syllabus, the relative knowledge nodes and knowledge relationships are ascertained, and the UCINET software is used to construct the Educational Technology knowledge network.Thirdly, the node centrality of the Educational Technology knowledge network is analyzed, three indicators of node centrality (degree centrality, betweenness centrality and closeness centrality) are used to respectively explain the importance, connectivity and difficulty of the knowledge node in the Educational Technology knowledge network.Finally, through the calculation and analysis of the central indicators, it can get that the centrality of the knowledge node is greater, this shows its position is more important, its connectivity is closer and its learning difficulty is smaller.And combining the analyzed results with the actual teaching, it can provide a new way to optimize the teaching.
Ranked Keyword Search Result Verification to Detect Misbehaving Cloud Servers

Nowadays, so many people outward their delicate information to the cloud in an encrypted format to maintain its privacy. On this basis, financial cloud computing becomes more efficient and useful. Protected keyword go through encrypted cloud information has fascinated many scholars for performing an effective data utilization. Current research is based on the impression that a cloud server is curious but truthful and therefore the search outcomes are not proved. But in real-world, sometimes cloud server might compromise and behave untruthfully. To maintain the reliability and secrecy, the outsourced delicate information should remain in the encrypted form. It is a task to go through the encoded data. There is a search facility to deliberate a ranked keyword search and there will be various data users and important files imported into the cloud. Hence, we propose a Ranked keyword search result verification for the detection of misbehaving cloud servers. We demonstrate two misbehaving scenarios of cloud server that will be detected by data user by using the relevance score of each file. In the whole process cloud server operates on the encrypted data.
Invited Paper: On the Characterization of Blockchain Consensus Under Incentives

One of the novel aspects of blockchains is the intertwining of consensus properties and incentives. An incentive model determines participant behaviors and then the possibility to reach consensus. In this paper we propose a methodological approach to characterize an incentive model for blockchain consensus. An incentive model is defined through the characterization of an oracle, along with its failure model, and blockchain participants behaviors. The oracle assures Safety properties at the expense of Liveness, since Liveness is in the hands of participants that can behave obediently, strategically or in an adversarial way. We then apply the proposed methodology to define and analyze incentive models of popular blockchain solutions. The paper concludes on future research directions that can take advantage of the proposed characterization.
Gestures Controlled Audio Assistive Software for the Voice Impaired and Paralysis Patients

Abstract Voice impairment is a barrier to the treatment of old age neurodegenerative disorders and paralysis where patients fail to convey their thoughts to doctors and caretaker. Though voice enhancers are available it’s very costly. The aim of our research work is to develop a ‘Gestures Controlled Audio Assistive Software’ (GECAS) which make the subject’s life easier and available free of cost to the people. The objective is to design software which enables severe cerebral palsy and also the voice impaired people to vocalize their basic needs like water, food, doctor and lavatory through a facial and body gesture. Our designed software is compartmentalized into two modules; one detecting subject’s gestures using Camera mouse and the other responding in audio output. It is created using Microsoft Visual Studio 2015 (a freemium platform) which open sourced. The designed software is implemented and tested on 32 subjects and the result was analyzed using statistical tool Minitab. The conclusion of the result showed impaired patients can operate the software as effectively and efficiently as a normal person which may help the subject to live more independent life and make their treatment easier.