Reviewerly/exHarmony huggingface dataset:
Detection Method of Marine Biological Objects Based on Image Enhancement and Improved YOLOv5S

Marine biological object detection is of great significance for the exploration and protection of underwater resources. There have been some achievements in visual inspection for specific objects based on machine learning. However, owing to the complex imaging environment, some problems, such as low accuracy and poor real-time performance, have appeared in these object detection methods. To solve these problems, this paper proposes a detection method of marine biological objects based on image enhancement and YOLOv5S. Contrast-limited adaptive histogram equalization is taken to solve the problems of underwater image distortion and blur, and we put forward an improved YOLOv5S to improve accuracy and real-time performance of object detection. Compared with YOLOv5S, coordinate attention and adaptive spatial feature fusion are added in the improved YOLOv5S, which can accurately locate the target of interest and fully fuse the features of different scales. In addition, soft non-maximum suppression is adopted to replace non-maximum suppression for the improvement of the detection ability for overlapping objects. The experimental results show that the contrast-limited adaptive histogram equalization algorithm can effectively improve the underwater image quality and the detection accuracy. Compared with the original model (YOLOv5S), the proposed algorithm has a higher detection accuracy. The detection accuracy AP50 reaches 94.9% and the detection speed is 82 frames per second; therefore, the real-time performance can be said to reach a high level.
Classification of breast cancer histology images using MSMV-PFENet

Deep learning has been used extensively in histopathological image classification, but people in this field are still exploring new neural network architectures for more effective and efficient cancer diagnosis. Here, we propose multi-scale, multi-view progressive feature encoding network (MSMV-PFENet) for effective classification. With respect to the density of cell nuclei, we selected the regions potentially related to carcinogenesis at multiple scales from each view. The progressive feature encoding network then extracted the global and local features from these regions. A bidirectional long short-term memory analyzed the encoding vectors to get a category score, and finally the majority voting method integrated different views to classify the histopathological images. We tested our method on the breast cancer histology dataset from the ICIAR 2018 grand challenge. The proposed MSMV-PFENet achieved 93.0[Formula: see text] and 94.8[Formula: see text] accuracies at the patch and image levels, respectively. This method can potentially benefit the clinical cancer diagnosis.
Breast cancer prediction based on neural networks and extra tree classifier using feature ensemble learning

Cancer prediction has always been a major and difficult matter for doctors and researchers. Early-stage detection of disease can help in the timely diagnosis and prognosis. Several methods for cancer's early prediction have been proposed by various researchers. In this paper, the author proposed a feature called ensemble learning based on neural networks and extra trees for the classification of breast cancer into non-cancerous (i.e. benign) and cancerous (i.e. malignant). Breast Cancer Wisconsin (Diagnostic) medical data sets from the machine learning repository have been used. The performance of the proposed method is evaluated with indices like accuracy in classification, specificity, sensitivity, recall, precision, f-measure, and MCC. Simulation and results have established that the accepted approach is more capable of giving results when different parameters are selected. The prediction results obtained by the proposed approach were very propitious (99.74% true accuracy). In addition, the proposed method, Neural Network and Extra Tree (NN-ET) outperforms other state-of-the-art classifiers in terms of various performance indices. The model suggested has proved to be more efficient and beneficial for breast cancer classification, which is also shown by experimental simulations, empirical results, and statistical analyses. It is also compared to the machine learning models that are already out there in the related literature.
Digital Transformation in Education: Relevant Paradigms and Theories of Teaching and Learning in the Industry 4.0

The study explores the paradigms and theories of education that will help the education sector to move in the right direction in industry 4.0. The study takes issue with the rate and magnitude of disruptions brought by technology as a result application of the right policies and principles in the management, teaching and learning in the education sector could not only enable the sector to minimise the effects of disruptions but better position the sector towards the digital transformation. The study's major point is that due to rapid changes in political, cultural, social, economic, and technological situations, 21st-century society places a high demand on its members, and the situation is exacerbated by the presence of the COVID-19 epidemic. Using conceptual and content analysis of peer-reviewed journals, reports and books, the study found that the application of connectivism theory of learning, engagement theory, and cognitive theory of multimedia learning among others can help to move the education sector in the right direction despite the challenges brought by the digital transformation. The application of the theories and paradigms can help to minimise the effects of disruptions brought by technology in industry 4.0. The study concludes it is important to adopt a constructivism type of teaching to minimise the negative effects of technological advancement than steaking to a behaviourism type of teaching.
GWO-Based Simulated Annealing Approach for Load Balancing in Cloud for Hosting Container as a Service

Container-based virtualization has gained significant popularity in recent years because of its simplicity in deployment and adaptability in terms of cloud resource provisioning. Containerization technology is the recent development in cloud computing systems that is more efficient, reliable, and has better overall performance than a traditional virtual machine (VM) based technology. Containerized clouds produce better performance by maximizing host-level resource utilization and using a load-balancing technique. To this end, this article concentrates on distributing the workload among all available servers evenly. In this paper, we propose a Grey Wolf Optimization (GWO) based Simulated Annealing approach to counter the problem of load balancing in the containerized cloud that also considers the deadline miss rate. We have compared our results with the Genetic and Particle Swarm Optimization algorithm and evaluated the proposed algorithms by considering the parameter load variation and makespan. Our experimental result shows that, in most cases, more than 97% of the tasks were meeting their deadline and the Grey Wolf Optimization Algorithm with Simulated Annealing (GWO-SA) performs better than all other approaches in terms of load variation and makespan.
Forme non normative di vocativo in ceco. Accettabilità e valori stilistico-pragmatici

In Czech the vocative case is still obligatory and the set of vocative endings is best preserved amongst Slavic languages. However, in spoken Czech the following irregularities in the usage of the vocative can be observed: a) substitution of the nominative for the vocative on the second part of masculine addressative expressions such as ‘pan + surname’; b) substitution of the nominative for the vocative with single names; c) “new” vocative forms created by dropping final vowels on female first names. The article reports the results of a study on such irregular forms that has been carried out through a questionnaire, and aims to investigate their acceptability as well as to ascertain how they are perceived by native speakers especially with reference to their pragmatic values.
Model Driven Software Product Line Process for Service/Component-Based Applications

The software reuse becomes the key for companies to improve development costs, time-to-market, and software quality. The Software Product Line Engineering (SPLE) and the Model Driven Engineering (MDE) are two new forms of software reuse. Software product lines are recognized as a successful approach to reuse in many domains (cars, printers, phones…) and especially in software development. Software Product Line Engineering proposes a set of methods, techniques and a common set of software artifacts for the production of customized software products as atomic or composite services to be reused in SOA-based applications. The main purpose of MDE is to enhance productivity by maximizing compatibility between software systems and simplifying the process of design via reuse of standardized models and design patterns. This paper presents a process for the construction of software product lines using model driven techniques. This process is based on a combination of SPLE and the MDE throw the use of visual techniques for modeling product lines and model driven techniques. We have validated the approach with a common example of software product line to evaluate the approach (airline travel agency reservation system). We propose a tool that supports some software product line visual techniques and model driven techniques.
Computing Consensus: A Logic for Reasoning About Deliberative Processes Based on Argumentation

Argumentation theory can encode an agent’s assessment of the state of an exchange of points of view. We present a conservative model of multiple agents potentially disagreeing on the views presented during a process of deliberation. We model this process as iteratively adding points of view (arguments), or aspects of points of view. This gives rise to a modal logic, deliberative dynamic logic, which permits us to reason about the possible developments of the deliberative state. The logic we propose applies to all natural semantics of argumentation theory. Furthermore, under a very weak assumption that the consensus considered by a group of agents is faithful to their individual views, we show that model checking these models is feasible, as long as the argumentation frameworks, which may be infinite, does not have infinite branching.
Vocabulário controlado para arquivos: análise de viabilidade e propostas

This research refers to a master's dissertation where it was noticed that the informational increase existing in the organizations demands better forms of informational treatment. For this reason, the archivology feels the need to use instruments that provide subsidies for the organization and retrieval of information. In this informational scenario, the problematic underlying this research questions: how to contribute with the representation of information so that this process is more qualitative? The objective of this work was to show the reasons why archivology can make use of controlled vocabularies in relation to their theories, practices and methodologies. For the development of this research, some Brazilian databases (BRAPCI, SCIELO) were searched in order to base the literature review on the proposed theme. Norms ANSI / NIZO Z 39.19-2005 (R2010) and ISO 25964-2011 were also used to support the normative components of controlled vocabularies. The results indicate that archivology can benefit from the instrumentality of controlled vocabularies, since controlled vocabulary can contribute significantly to the processes of information representation
AMALGAMABLE DIAGRAM SHAPES

A category has the amalgamation property (AP) if every pushout diagram has a cocone, and the joint embedding property (JEP) if every finite coproduct diagram has a cocone. We show that for a finitely generated category $\mathbf I$, the following are equivalent: (i) every $\mathbf I$-shaped diagram in a category with the AP and the JEP has a cocone; (ii) every $\mathbf I$-shaped diagram in the category of sets and injections has a cocone; (iii) a certain canonically defined category $\mathcal{L}(\mathbf{I})$ of "paths" in $\mathbf I$ has only idempotent endomorphisms. When $\mathbf I$ is a finite poset, these are further equivalent to: (iv) every upward-closed subset of $\mathbf I$ is simply-connected; (v) $\mathbf I$ can be built inductively via some simple rules. Our proof also shows that these conditions are decidable for finite $\mathbf I$.
HyperCo: Optimizing Network Performance in ARM-Based Mobile Virtualization

In the ARM-based mobile virtualized environment, optimizing both the network throughput and the latency for network-intensive applications is of great importance. Our experimental studies have shown that the context switch between the host and guest triggered by a hypercall results in CPU overload and thus degrades the I/O performance under an intensive workload. To address this problem, we propose the Adaptive Hypercall Coalescing (HyperCo) algorithm, a software-only approach to optimizing the network I/O performance by reducing the number of hypercalls and achieving a trade-off between throughput and latency. We implement HyperCo by modifying the front-end driver of Virtio-net on the KVM/ARM platform, and we carry out extensive performance evaluations, which indicate that the number of hypercalls and the load on the CPU can be significantly reduced. We show that HyperCo can significantly improve the network throughput under an intensive workload while keeping a low penalty by adapting a coalescing interval dynamically at a low frequency of network I/O requests.
Large scale research data archiving: Training for an inconvenient technology

At small scales, storage is straightforward to afford and to use, but at large scales – from several Terabytes (TB) to many Petabytes (PB) and soon Exabytes (EB) – tradeoffs must be made between cost and convenience, and training for use of such resources needs to take such inconveniences into account. A large scale, long term (over 10 year) institutional research data storage archive is described, focusing on both hardware and software. The technology choices give rise to inconveniences, which in turn not only lead to a crucial requirement for training on the proper use of the archive, but also inform the specifics of that training, as does each individual use case.
THE INFORMATION BASE SYSTEMS FOR MATERIALS RESEARCH

This paper describes a new information model called MIBS-SORITES (materials information base systems with self organizing receptor interconnections), which can handle semantics through dynamic structures of composite information for materials research and development. The system can store, manage and manipulate composite information and can realize induction and learning capabilities to support scientists and engineers in the relevant fields
Optical Profiling Techniques for MEMS Measurement

This chapter reviews the confocal microscopy, a description of the depth-scanning fringe projection system, and an overview about low-coherence interferometric microscopy for the optical profiling of encapsulated Microelectromechanical Systems. The strong depth discrimination of the confocal sensor leads to a good depth resolution. When a depth scan is performed, the peak intensity is measured if the measurement spot falls onto the object surface. The measured confocal intensity signal has to be analyzed relative to a common zero point. The spectrally encoded confocal intensity signal can be used to calculate the topography. The reflected light is focused onto the pinholes of the Nipkow disk, which are also the confocal detection pinholes in this setup. As the microlenses alone cannot do the confocal discrimination, a central pinhole is placed in front of the camera, which can be used for all microlenses in parallel. Confocal microscopes translate the defocus in each position into an intensity signal.
El distanciamiento del emisor frente a su propio mensaje

Speaker’s Distance Towards his own Enunciation This paper approaches linguistic methods either grammatical or lexical, used by Spanish speaking participants of a communication act to express distance to their own enunciation
Crash mode analysis

A project network is a tool for graphically depicting a schedule. This chapter discusses the use of project networks and the critical path method to evaluate options for developing a system in crash mode. Crash mode analysis is a technique for studying the cost/time tradeoff by manipulating the project network. Critical Path Method (CPM) is used to help plan and monitor a project schedule when the activity times are known with reasonable precision. Only by shortening the critical path can the project completion time be improved. The accuracy of the project network is no better than the activity duration estimates, and the accuracy of the cost computations is no better than the cost estimates. A project management technique based on a project network; the focus of CPM is project planning, with the critical path defining those activities into which additional resources might be poured to accelerate the schedule.
How to make a digital currency on a blockchain stable

Bitcoin and other similar digital currencies on blockchains are not ideal means for payment, because their prices tend to go up in the long term (thus people are incentivized to hoard those currencies), and to fluctuate widely in the short term (thus people would want to avoid risks of losing values). The reason why those blockchain currencies based on proof of work are unstable may be found in their designs that the supplies of currencies do not respond to their positive and negative demand shocks, as the authors have formulated in our past work. Continuing from our past work, this paper proposes minimal changes to the design of blockchain currencies so that their market prices are automatically stabilized, absorbing both positive and negative demand shocks of the currencies by autonomously controlling their supplies. Those changes are: 1) limiting re-adjustment of proof-of-work targets, 2) making mining rewards variable according to the observed over-threshold changes of block intervals, and 3) enforcing negative interests to remove old coins in circulation. We have made basic design checks and evaluations of these measures through simple simulations. In addition to stabilization of prices, the proposed measures may have effects of making those currencies preferred means for payment by disincentivizing hoarding, and improving sustainability of the currency systems by making rewards to miners perpetual.
Universal compressed text indexing

The rise of repetitive datasets has lately generated a lot of interest in compressed self-indexes based on dictionary compression, a rich and heterogeneous family of techniques that exploits text repetitions in different ways. For each such compression scheme, several different indexing solutions have been proposed in the last two decades. To date, the fastest indexes for repetitive texts are based on the run-length compressed Burrows–Wheeler transform (BWT) and on the Compact Directed Acyclic Word Graph (CDAWG). The most space-efficient indexes, on the other hand, are based on the Lempel–Ziv parsing and on grammar compression. Indexes for more universal schemes such as collage systems and macro schemes have not yet been proposed. Very recently, Kempa and Prezza [STOC 2018] showed that all dictionary compressors can be interpreted as approximation algorithms for the smallest string attractor, that is, a set of text positions capturing all distinct substrings. Starting from this observation, in this paper we develop the first universal compressed self-index, that is, the first indexing data structure based on string attractors, which can therefore be built on top of any dictionary-compressed text representation. Let γ be the size of a string attractor for a text of length n. From known reductions, γ can be chosen to be asymptotically equal to any repetitiveness measure: number of runs in the BWT, size of the CDAWG, number of Lempel–Ziv phrases, number of rules in a grammar or collage system, size of a macro scheme. Our index takes O(γlg⁡(n/γ)) words of space and supports locating the occ occurrences of any pattern of length m in O(mlg⁡n+occlgϵ⁡n) time, for any constant ϵ>0. This is, in particular, the first index for general macro schemes and collage systems. Our result shows that the relation between indexing and compression is much deeper than what was previously thought: the simple property standing at the core of all dictionary compressors is sufficient to support fast indexed queries.
Fuzzy Rule-Based Domain Adaptation in Homogeneous and Heterogeneous Spaces

Domain adaptation aims to leverage knowledge acquired from a related domain (called a source domain) to improve the efficiency of completing a prediction task (classification or regression) in the current domain (called the target domain), which has a different probability distribution from the source domain. Although domain adaptation has been widely studied, most existing research has focused on homogeneous domain adaptation, where both domains have identical feature spaces. Recently, a new challenge proposed in this area is heterogeneous domain adaptation where both the probability distributions and the feature spaces are different. Moreover, in both homogeneous and heterogeneous domain adaptation, the greatest efforts and major achievements have been made with classification tasks, while successful solutions for tackling regression problems are limited. This paper proposes two innovative fuzzy rule-based methods to deal with regression problems. The first method, called fuzzy homogeneous domain adaptation, handles homogeneous spaces while the second method, called fuzzy heterogeneous domain adaptation, handles heterogeneous spaces. Fuzzy rules are first generated from the source domain through a learning process; these rules, also known as knowledge, are then transferred to the target domain by establishing a latent feature space to minimize the gap between the feature spaces of the two domains. Through experiments on synthetic datasets, we demonstrate the effectiveness of both methods and discuss the impact of some of the significant parameters that affect performance. Experiments on real-world datasets also show that the proposed methods improve the performance of the target model over an existing source model or a model built using a small amount of target data.