Reviewerly/exHarmony huggingface dataset:
A Spatial Convexity Descriptor for Object Enlacement

In (Brunetti et al.: Extension of a one-dimensional convexity measure to two dimensions, LNCS 10256 (2017) 105–116) a spatial convexity descriptor is designed which provides a quantitative representation of an object by means of relative positions of its points. The descriptor uses so-called Quadrant-convexity and therefore, it is an immediate two-dimensional convexity descriptor. In this paper we extend the definition to spatial relations between objects and consider complex spatial relations like enlacement and interlacement. This approach permits to easily model these kinds of configurations as highlighted by the examples, and it allows us to define two interlacement descriptors which differ in the normalization. Experiments show a good behavior of them in the studied cases, and compare their performances.
Imaginary Studies

In this paper, I use 'science fiction autoethnography' to reflect on conducting an imaginary, quantitative study. My fictional study is intended to evaluate a real-life artefact: the 'umamimi' robotic horse ears. This physical device provides a backdrop, against which my experiences and self-reflections are used to critique quantitative 'hard science'. My own cognitive bias, rigid attachment to a viewpoint and presumptions (concerning anticipated results) all provide the real story. When I conduct an imaginary study, what does the process and its speculative results say about my autobiographical story and both the object and subject's broader societal and cultural meanings?
Multivariate modeling and two-level scheduling of analytic queries

Analytic queries are typically compiled into execution plans in the form of directed acyclic graphs (DAGs) of MapReduce jobs. Jobs in the DAGs are dispatched to the MapReduce processing engine as soon as their dependencies are satisfied. MapReduce adopts a job-level scheduling policy to strive for a balanced distribution of tasks and effective utilization of resources. However, such simplistic policy is unable to reconcile the dynamics of different jobs in complex analytic queries, resulting in unfair treatment of different queries, low utilization of system resources, prolonged execution time, and low query throughput. Therefore, we introduce a scheduling framework to address these problems systematically. Our framework includes two techniques: multivariate DAG modeling and two-level query scheduling. Cross-layer semantics percolation allows the flow of query semantics and job dependencies in the DAG to the MapReduce scheduler. With richer semantics information, we build a multivariate model that can accurately predict the execution time of individual MapReduce jobs and gauge the changing size of analytics datasets through selectivity approximation. Furthermore, we introduce two-level query scheduling that can maximize the intra-query job-level concurrency, and at the same time speed up the query-level completion time based on the accurate prediction and queuing of queries. At the job level, we focus on detecting query semantics, predicting the query completion time through an online multivariate linear regression model, thereby increasing job-level parallelism and maximizing data sharing across jobs. At the task level, we focus on balanced data distribution, maximal slot utilization, and optimal data locality of task scheduling. Our experimental results on a set of complex query benchmarks demonstrate that our scheduling framework can significantly improve both fairness and throughput of Hive queries. It can improve query response time by up to 43.9% and 72.8% on average, compared to the Hadoop Fair Scheduling and the Hadoop Capacity Scheduling, respectively. In addition, our two-level scheduler can achieve a query fairness that is, on average, 59.8% better than that of the Hadoop Fair Scheduler.
Generative grammar, neural networks, and the implementational mapping problem: Response to Pater

The target article (Pater 2019) proposes to use neural networks to model learning within existing grammatical frameworks. This is easier said than done. There is a fundamental gap to be bridged that does not receive attention in the article: how can we use neural networks to examine whether it is possible to learn some linguistic representation (a tree, for example) when, after learning is finished, we cannot even tell if this is the type of representation that has been learned (all we see is a sequence of numbers)? Drawing a correspondence between an abstract linguistic representational system and an opaque parameter vector that can (or perhaps cannot) be seen as an instance of such a representation is an implementational mapping problem. Rather than relying on existing frameworks that propose partial solutions to this problem, such as harmonic grammar, I suggest that fusional research of the kind proposed needs to directly address how to ‘find’ linguistic representations in neural network representations.
Toward the Generation of Mission Plans for Operation of Autonomous Marine Vehicles in Confined Areas

Mission planning for autonomous marine vehicle operations normally considers journeys over long periods of time. Due to these long transit times, obstacles and hazards may be treated as independent. However, when docking, or operating in other similarly spatially constrained environments, this model may no longer be appropriate. In particular, the space between obstacles and the resultant constraints on vehicle motion may dominate the required planning model. This becomes important when considering the model of planning complex autonomous actions. Autonomous Surface vessels are increasing in capability to the point where they will be able to independently plan and execute complex tasks including many different spatially related subtasks. For an efficient selection of such tasks to be performed, the ability to explore and evaluate potential future states is required. To allow such exploration, the complex real-world environment must be reduced to a simplified model comprehensive enough to allow effective planning while still maintaining the simplicity required for effective search to be performed on future states. This paper discusses an alternate model for planning using a Delaunay triangulation for the creation of maritime planning domains in spatially constrained environments. Using data and tasks from the 2016 Maritime RobotX competition, this model is evaluated to assess the effectiveness and safety of the generated plans. The use of a Delaunay triangulation within this environment is shown to reliably generate mission plans that cover large and complex obstacle fields while selecting achievable paths and meeting task constraints.
Topic Development to Support Revision Feedback

Revision is important but challenging for novice writers, particularly in post-secondary education where opportunities for personalized feedback are limited. Inexperienced writers typically overlook revision; when they do revise, they focus on surface errors rather than global revisions that enhance meaning and coherence. Writing analytics can automate personalized prompts to guide revision. We use topic modelling LDA as grounds for an analytic to scaffold holistic revision at paragraph and essay levels. The analytic visualizes topic distribution and generates three types of prompts: Introduction, Paragraph and Conclusion. Feedback encourages revisions focusing on sequencing topics, expanding underdeveloped ideas, and making holistic revisions to improve clarity and coherence of paragraphs. Model feedback was evaluated using undergraduate student essays on various topics scored by human evaluators. Model accuracy was strong for all types of feedback. This opens new branches of research to explore generating personalized feedback at paragraph and essay levels.
A semantic pattern for trusted orchestration in IoT edge clouds

Edge cloud architectures provide a layer between centralized clouds and the IoT and sensor space. In this intermediate layer, we find devices of different types that manage the communication with sensors and cloud services, but also process data from sensors locally without access to the cloud. In order to support the flexible creation, placement and usage of data and software resources in this layer, advanced orchestration techniques need to include communication and processing activities. These architectures often stretch cross organizational boundaries, causing trust concerns regarding secure data storage and the management of the identity of sensors and also the origin of data. We present an ontology‐based semantic pattern that semantically defines trusted orchestration for edge clouds by linking a trust ontology to an architecture pattern and that allows reasoning about essential trust properties.
Quantum secure direct communication based on four-particle cluster state grouping

In this paper we present a quantum secure direct communication protocol based on four-particle cluster states. In our protocol both the sender and the receiver keep two particles of the cluster state, and we verify that our protocol can prevent the eavesdropper from intercepting valid messages. Meanwhile, we also analyze our protocol in a lossy channel under the attack of an eavesdropper. We show that both the communication efficiency and the qubit-utilization ratio are improved compared with other existing schemes.
Visibility extension via mirror-edges to cover invisible segments

Given a simple polygon P with n vertices, the visibility polygon (VP) of a point q, or a segment pq‾ inside P can be computed in linear time. We propose a linear time algorithm to extend the VP of a viewer (point or segment), by converting some edges of P into mirrors, such that a given non-visible segment uw‾ can also be seen from the viewer. Various definitions for the visibility of a segment, such as weak, strong, or complete visibility are considered. Our algorithm finds every edge that, when converted to a mirror, makes uw‾ visible to our viewer. We find out exactly which interval of uw‾ becomes visible, by every edge middling as a mirror, all in linear time. In other words, in this article, we present an algorithm that, in linear time, for every edge e of P reveals precisely which part of uw‾ is mirror-visible through e.
Copyright Literacy of LIS Students in the Czech Republic

Librarians help university students and staff work with information on different tasks. Copyright is strongly connected with information processing. Therefore, university education in librarianship should include this topic to prepare future librarians for their work. This paper presents research results of copyright literacy of Czech students of library and information studies and their attitudes toward this topic. This national part of international research identified a number of problems in students’ knowledge (e.g. copyright and databases or unpublished works) but also their strong belief in the importance of the topic for librarians (89.9%) and its inclusion in the LIS curriculum (61.3%). Students also welcomed the extension of their education about copyright mostly in the international context and practical examples.
Fast and efficient contrast‐enhanced super‐resolution without real‐world data using concatenated recursive compressor–decompressor network

The authors propose a novel model called concatenated recursive compressor–decompressor network (CRCDNet) for contrast-enhanced super-resolution. The characteristics of authors’ model can be summarised as follows. First, a compression–decompression process reduces the computational complexity compared with the general fully convolutional model. Second, an internal/external skip-connection is used to preserve information of the preceding layers. Finally, by employing a recursive module, authors’ model has a small number of parameters, yet is a deep and robust network. The authors apply authors’ proposed network to license plate images. As a real application, license plates can provide important evidence for investigation of crimes and for security, but it is very difficult to collect the vast amounts of license plates required for analysis based on a data-driven approach. To solve this problem, the authors generated virtual datasets to train authors’ model, while analysing the performance with real license plate datasets. Authors’ method achieves better performance than the state-of-the-art models on license plate images.
Clustering method based on the elastic energy functional of directed signed weighted graphs

This article proposes a new method of clustering based on minimizing the elastic energy functional (EEF) of directed weighted signed graphs. The new method has three distinctive features: the weights on the edges of the graph are set by the original model of the system (fuzzy cognitive map), and each weight represents a causal relationship between the graph vertices (system factors); a clearly formalized criterion for division into clusters; and the order of the vertices generated by the algorithm reflects the ratio of intra-cluster and extra-cluster energy. The proposed functional of the elastic energy reflects the nature of the factor relationship in a socio-economic system. Minimization of the functional is monotonic and does not require user intervention. The algorithm is computationally efficient.
Promising ways to encode and manipulate quantum information

Quantum computers require controlled encoding to protect computations from environmental noise. Two experiments have achieved such encoding using what are known as infinite-dimensional quantum systems. A major step towards fully fledged quantum error correction.
No-reference quality assessment for contrast-distorted images based on multifaceted statistical representation of structure

In many real-world applications, images are prone to be degraded by contrast distortions during image acquisition. Quality assessment for contrast-distorted images is vital for benchmarking and optimizing the contrast-enhancement algorithms. To this end, this paper proposes a no-reference quality metric for contrast-distorted images based on Multifaceted Statistical representation of Structure (MSS). The “Multifaceted” has two meanings, namely (1) not only the luminance information, but also the chromatic information is used for structure representation. This is inspired by the fact that the chromatic information on the one hand affects the perception of image quality as well, and on the other hand it changes along with the contrast distortions. Therefore, the chromatic information should be integrated with the luminance information for quality assessment of contrast-distorted images, a fact most existing quality metrics overlook; (2) regarding structure representation, three aspects, i.e. spatial intensity, spatial distribution, and orientation of structures are calculated, which is enlightened by the fact that the human visual system (HVS) is sensitive to the three aspects of structures. Specifically, the image is first transformed from RGB to the S-CIELAB color space to obtain a representation that is more consistent with the characteristics of the HVS, as well as to separate the chromatic information from the luminance. Then the statistical structural features are extracted from both luminance and chromatic channels. Finally, the back propagation (BP) neural network is adopted to train a quality prediction model. Experimental results conducted on four public contrast-distorted image databases demonstrate the superiority of the proposed method to the relevant state-of-the-arts.
The oil and gas industry, the Offshore Installation Manager (OIM) and the management of emergencies - Changes necessary to achieve effective competence assessment of OIMs in controlling emergencies

Within the UK offshore oil and gas industry all installations require to be under the charge of a ‘competent’ installation manager in accordance with the Offshore Installations and Pipeline Works (Management and Administration) Regulations (MAR) 1995. The Offshore Installation Manager (OIM) is responsible for the safety of all persons onboard and within 500 m of an installation. Should an incident arise, the OIM performs one of the most critical roles within the offshore industry - management of the response to the emergency to mitigate and make safe the installation to secure a place of safety for persons onboard or organise their safe evacuation. The emergency response also includes limiting the loss of hydrocarbons to the environment. Research at the University of Aberdeen has identified 8 potential barriers to the effective competence assessment of OIMs in controlling emergencies. These underlying barriers are a consequence of current UK legislation and industry practices in the assessment of OIM competence and result from: multiple definitions of competence, competence assessment predominantly by observation, lack of specific and unambiguous competence requirements for trainers and assessors, complexity in UK legislation and complicated interpretation of terms, lack of statutory competence requirements for OIMs in controlling emergencies, lack of ownership of OIM competence and competence assessment, complex interactions across multiple UK Government departments, and lack of regulation and independent audit of the competence assessment process of OIMs in controlling emergencies. The potential barriers were identified by qualitative analysis of key documentation, including legislation, using a critical hermeneutic approach. Hermeneutics has historically been used to analyse texts within the domains of theology, business and law. It arguably, however, represents a powerful approach to the study of documents within the engineering domain and the offshore oil and gas industry. This paper discusses the identified potential barriers, research conclusions and recommendations and represents the culmination of three years of doctoral research investigating the competence assessment of OIMs in controlling emergencies within the offshore oil and gas industry. The research, in particular, is relevant to Duty Holders within the offshore industry, safety engineers and UK Government departments.
Intelligent Signal Classifier for Brain Epileptic EEG Based on Decision Tree, Multilayer Perceptron and Over-Sampling Approach

Epilepsy is a chronic neurological disease induced by abnormal electrical discharges of brain which tends to irregular seizures. The seizures may cause the patients to lose consciousness and the patients couldn't control their muscles. Epilepsy even possibly endangers one's life. Electroencephalogram (EEG) is a common tool used in the clinical diagnosis and analytics of epilepsy. However, the visual examination of EEG is time-consuming and the diagnostic result is also easily influenced by the viewer's subjective judgement. Therefore, the purpose of this study is to construct an automatic classifier, which could be helpful to analyze, for the epileptic EEG signals. The EEG recordings of patients with intractable epilepsy, which are collected by Boston Children's Hospital, are used in this study. The features of EEG signals in time and frequency domains are collected from results of the Fast Fourier Transform. The Synthetic Minority Oversampling Technique (SMOTE) is used to solve the data imbalance problem. Four machine learning algorithms including C4.5, Classification and Regression Tree (CART), Chi-Square Automatic Interaction Detector (CHAID) and Multilayer Perceptron (MLP) are used to classify the data. As a result, the accuracy rate of the proposed classifier is 99.48%. It might be a clinical assistant tool for doctors to make a more reliable and objective diagnosis.
The Covering Radius of the Reed-Muller Code &lt;i&gt;R&lt;/i&gt;(3, 7) in &lt;i&gt;R&lt;/i&gt;(5, 7) Is 20

We propose a recursive algorithm to reduce the computational complexity of the r-order nonlinearity of n-variable Boolean functions. Applying the algorithm and using the sufficient and necessary condition put forward by [1] to cut the vast majority of useless search branches, we show that the covering radius of the Reed-Muller Code R(3, 7) in R(5, 7) is 20.
Extension of ISO/IEC27001 to Mobile Devices Security Management

Mobile security is more and more important with the fast growth of mobile devices, and people are becoming more dependent on mobile devices in their daily life. Malicious samples in mobile devices are growing in double times each year from 2011 to 2017 in China. ISO/IEC 27000 family of standards helps organizations keep information assets secure, such as financial information, intellectual property, employee details or information entrusted to you by third parties. ISO/IEC 27001 is the best-known standard in the family providing requirements for an information security management system (ISMS). However, ISO/IEC 27001 is not quite adaptable for mobile devices, because these developing mobile information devices lead to new challenges and security risks. This paper analyzes mobile devices security issues, and gives the drawback for 27001 in mobile security. Finally, this paper gives a consideration to these issues under ISO/IEC 27001 information security management system framework.
Educating scientific worldview in the young people at the public libraries

Education of the scientific worldview within the context of national and international regulatory documents is discussed. Based on the study of theoretical concepts and practical experience, the author argues that public libraries contribute to educating and developing scientific worldview in the young people. The need for organizational instruments to build science literacy essential due to conceptual, political, economical, educational transformations in the social life, is substantiated. The author suggests to introduce modern formats, e. g. lectures, master classes, visual presentations, conferences and workshops in technics, the natural and engineering sciences. The libraries, museums, academic and educational institutions have to partner to excite young population's interest toward scientific knowledge to make them taking informed decision when choosing a profession.
Construction of a Neural Network and its Application on Target Classification

With the arrival of big data age, the deep convolutional neural networks (DCNNs) with more hidden layers have a more complex network structure and more powerful ability than traditional machine learning methods for feature learning and feature expression. This paper first proposes a model of the DCNN to discuss the basic structure of model, convolutional feature extraction and learning algorithm of convolutional neural network; then, mainly introduces several aspects, that is, the construction of the typical network structure, the training methods, and the parameter settings of network model to be improved and optimized. Moreover, the network model is applied to the classification and recognition of Antarctic hydrological features and compared with some existing classification methods. The novelty of this paper mainly includes two aspects, i.e., the one is that the design and construction of the structure of deep neural network based on deep learning method are performed, namely, connection, weight, calculator, learning training of network, and other design. The other is classifying hydrological characteristics of Pritz Bay in Antarctica’s images by the DCNN. The results show that the correct recognition rate of the model method constructed by this paper is the highest. Finally, some problems in the current research are briefly summarized and discussed, and the new direction in the future development is forecasted.
The Usability Construct: A Concern for Both Theory and Practice

This commentary responds to the commentaries Appreciative Disagreements by J. Bardzell (Human-Computer Interaction), Usability and the Primacy of Practice by O.W. Bertelsen (Human-Computer Interact...