Reviewerly/exHarmony huggingface dataset:
How to Visually Represent Structure

How does the compositional arrangement of elements in a complex image, like a diagram, a picture or a map, represent the structural features of its content? In this paper I argue that they do so iconically, through the exploitation of relations of visual similarity and dissimilarity. I develop the general claim that our interpretation of this sort of images is guided by the implicit defeasible assumption that things that are patently related represent things that are relevantly related in a similar way. I also identify the usual intrinsic and extrinsic mechanisms we use to represent structural features and illustrate them with an example from real life: a recent infographic from a popular science magazine.
Representing Formulas of Propositional Logic by Cographs, Permutations and Tables

The paper presents how formation trees, a type of syntax trees for a formula, can be used to represent semantic information about formulas of classical propositional logic in form of graphs, permutations and tables. The three representation types are discussed in terms of the construction process as well as their cognitive potentials and observational advantages.
Hate Speech and Social Media

It has become usual to distinguish between a culture of dignity, with large expression in Europe, and a culture of freedom, dominant in the USA, as the two possible horizons to deal with hate speech issues. This differentiation, besides constituting an expedient that evades the increasing need for a strong affirmation of freedom of expression, is becoming largely irrelevant due to the development of a tendency for hate speech self-regulation by the private entities that own the platforms and infrastructures of social networks. Hate speech self-regulation tends to be shaped by an economic model and is potentially controlled by algorithms that escape democratic controls. Instead, the specific political value of freedom of expression should be highlighted as a more consistent basis in its confrontation with other constitutional rights and interests.
Aspect Shifting in Aristotelian Diagrams

Aristotelian diagrams represent logical relations of opposition and implication between formulas or concepts. In this paper we investigate the cognitive mechanism of Aspect Shifting in order to describe various families of Aristotelian diagrams. Aspect shifting occurs when an ‘ambiguous’ visual representation triggers a perceptual change from one perspective or interpretation to another. In a first part, we consider aspect shifting which takes place on the level of Aristotelian subdiagrams and which switches focus precisely between the oppositional and the implicational perspective. In a second part, aspect shifting is involved in focussing on the ways in which smaller (but complete) Aristotelian diagrams—in particular, Aristotelian squares—are embedded inside bigger diagrams—in particular Aristotelian hexagons. In both parts, special attention is paid to the iterative nature of the aspect shifting.
Political Speech, Freedom of Expression and Fake News

One of the main issues modern societies face nowadays is the proliferation of fake news, which can have a deeply negative impact on public opinion, especially when elections take place. On the other hand, the challenge of protecting freedom of expression in this chaotic online environment arises. Through the lens of the 2018 presidential elections in Brazil, the present article aims to address the role of the Brazilian Electoral Justice in such controversial but fundamental topics.
Freedom of Expression in the Age of Digital Platforms: Change of Paradigm?

This paper discusses some of the challenges presented to protecting of freedom of expression by the emergence of digital platforms and social networks. It is suggested that these challenges imply a rethinking of freedom of expression in the twenty-first century. To support this conclusion, we start by making some introductory remarks about freedom of expression. Afterwards, we address the dangers arising from the digital revolution and from the new “information society” to this freedom. We particularly address the new restricting techniques of freedom of expression, specifically, the curtailing of freedom of expression by on-line platforms and social networks, the use of “flood information” techniques, as well as the phenomenon of “fake news” and, lastly, on-line harassment. Given these dangers, we propose that it is up to the State, as the guardian of fundamental rights, to regulate this new digital reality, safeguarding freedom of expression.
Normatively Determined Propositions

In the present work we provide a logical analysis of normatively determined and non-determined propositions. The normative status of these propositions depends on their relation with another proposition, here named reference proposition. Using a formal language that includes a monadic operator of obligation, we define eight dyadic operators that represent various notions of “being normatively (non-)determined”; then, we group them into two families, each forming an Aristotelian square of opposition. Finally, we show how the two resulting squares can be combined to form an Aristotelian cube of opposition.
Generalizing Aristotelian Relations and Diagrams

The square of opposition is a type of diagram that graphically represents the Aristotelian relations between sentences or formulas. It has been noted in the literature that certain extensions of the square have several Boolean subtypes. However, the traditional Aristotelian relations themselves cannot be used to distinguish these different subtypes. Furthermore, the traditional Aristotelian relations are relations between two individual formulas. In this paper I propose a very natural generalization, resting on elementary set-theoretical notions, of these relations to sets of formulas of arbitrary size. I show that this generalization can be used to construct new diagrams, viz. generalized squares of opposition, that can express information that could not be expressed by the traditional squares. Furthermore, I show that the generalized Aristotelian relations can be used to classify Boolean subtypes of extensions of the square that could not be distinguished by the traditional relations.
Incentive Alignment Through Secure Computations

We present a game-theoretic approach to analyzing the incentive structure in formal models of inter-organizational businesses processes. In such processes, the choices of each participants influence the outcome of others. A potential participant may be torn between the prospect of a highly preferable outcome on the one hand (e.g., a bonus on timely delivery), and the possibility that another player may make a choice (e.g., reallocation of the fast trucks) which renders that outcome impossible to achieve. We propose (a) an analysis which given the preferences of participants determines if the collaboration is at all meaningful; (b) an algorithm for modifying the incentive structure of such a process using both fines and outcome re-distribution to increase the benefit for all participants; and (c) a practical way of computing this algorithm while concealing the preferences of the collaborators for each other using secure multi-party computation.
Forecasting the Volatility of Cryptocurrencies in the Presence of COVID-19 with the State Space Model and Kalman Filter

During the COVID-19 pandemic, cryptocurrency prices showed abnormal volatility that attracted the participation of many investors. Studying the behaviour of volatility for the prices of cryptocurrency is an interesting problem to be investigated. This research implements the state space model framework for volatility incorporating the Kalman filter. This method directly forecasts the conditional volatility of five cryptocurrency prices (Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Litecoin (LTC) and Bitcoin Cash (BCH)) for 10,000 consecutive hours, i.e., approximately 417 days during the COVID-19 pandemic from 26 February 2020, 00:00 h until 18 April 2021, 00:00 h. The performance of this model is compared to the GARCH (1,1) model and the neural network autoregressive (NNAR) based on root mean square error (RMSE), mean absolute error (MAE) and the volatility plot. The autocorrelation function plot, histogram and the residuals plot are used to examine the model adequacy. Among the three models, the state space model gives the best fit. The state space model gives the narrowest confidence interval of volatility and value-at-risk forecasts among the three models.
A Hoare logic style refinement types formalisation

Refinement types is a lightweight yet expressive tool for specifying and reasoning about programs. The connection between refinement types and Hoare logic has long been recognised but the discussion remains largely informal. In this paper, we present a Hoare triple style Agda formalisation of a refinement type system on a simply-typed λ-calculus restricted to first-order functions. In our formalisation, we interpret the object language as shallow Agda terms and use Agda’s type system as the underlying logic for the type refinement. To deterministically typecheck a program with refinement types, we reduce it to the computation of the weakest precondition and define a verification condition generator which aggregates all the proof obligations that need to be fulfilled to witness the well-typedness of the program.
Exploration of the Problems and Solutions Based on the Translation of Computer Software into Japanese Language

At present, the research on machine translation mainly focuses on English-Chinese translation, while the research on Japanese college students using Japanese Chinese machine translation software is relatively few. In order to solve the above problems in Chinese Japanese bilingual translation, this paper proposes a phrase translation method based on sequence intersection. This method regards sentences as word sequences and aligns the sequence intersection of all source sentences corresponding to the target sentence in the corpus with Chinese and Japanese sentences containing the phrases to be translated. By fully mining the information of sentence alignment bilingual corpus without word alignment resources, we can obtain high-quality phrase translation, syntactic analysis, and dictionary. Then, we focus on the automatic construction of sentence level aligned bilingual corpus and explore the automatic sentence alignment technology of Chinese and Japanese bilinguals. A ten-year alignment model based on combination cues and core extended square matching is proposed. The preprocessing of the computer corpus and the basic construction of the corpus are completed. This paper also puts forward corresponding countermeasures and approaches to the problems encountered in the construction of computer translation.
Research on the Impact of Optimal Configuration of University Library Layout on Students’ Academic Literacy Development Based on 5G

This paper provides an in-depth study and analysis of the relevance of adopting the idea of 5G for the optimal configuration of university library layout to the development of students’ academic literacy. It also analyzes the benefits of 5G on the digital governance of libraries together with the convenience that the application of 5G can provide to smart libraries and helps to realize the research on the service innovation model of smart libraries under 5G. The survey mainly focuses on four indicators: the development of physical space services in university libraries; facilities and equipment for physical space services; the content and form of physical space services; and it presents the current problems of physical space services, including the overall convergence of physical space services in university libraries. When different libraries are co-constructed and shared, there will be incompatibility problems, and if the library is required to develop its own management system, it will waste resources. The library space layout is arranged by highlighting the elements of the cognitive map to improve the lending efficiency; using a more continuous and transparent interface in terms of color and material to increase the experience of spaciousness; using more wood elements and green to add a natural atmosphere to achieve the purpose of relaxing the readers’ heart; and finally increasing the light source in the reading area to complement the illumination in the shaping of the light environment. In terms of spatial functions, more attention is paid to the design of public interaction and leisure spaces so that readers can rejuvenate themselves and devote themselves to the next learning tasks. The interior design is considered in terms of the relationship between arousal theory and task performance, and a moderately complex interface is designed to improve readers’ efficiency in the space. Finally, the paper concludes with a summary of what has not been thoroughly researched and an outlook on the future of university library interior design.
High-resolution atlasing and segmentation of the subcortex: Review and perspective on challenges and opportunities created by machine learning

This paper reviews almost three decades of work on atlasing and segmentation methods for subcortical structures in human brain MRI. In writing this survey, we have three distinct aims. First, to document the evolution of digital subcortical atlases of the human brain, from the early MRI templates published in the nineties, to the complex multi-modal atlases at the subregion level that are available today. Second, to provide a detailed record of related efforts in the automated segmentation front, from earlier atlas-based methods to modern machine learning approaches. And third, to present a perspective on the future of high-resolution atlasing and segmentation of subcortical structures in in vivo human brain MRI, including open challenges and opportunities created by recent developments in machine learning.
Coinduction inductively: mechanizing coinductive proofs in Liquid Haskell

Liquid Haskell is an inductive verifier that cannot reason about codata. In this work we present two alternative approaches, namely indexed and constructive coinduction, to consistently encode coinductive proofs in Liquid Haskell. The intuition is that indices can be used to enforce the base case in the setting of classical logic and the guardedness check in the constructive proofs. We use our encodings to machine check 10 coinductive proofs, about unary and binary predicates on infinite streams and lists, showcasing how an inductive verifier can be used to check coinductive properties of Haskell code.
Cohesive clustering algorithm based on high-dimensional generalized Fermat points

Utilizing high-dimensional generalized Fermat points (Fd-points) as cluster centers, we propose a new method Fd-points Linkage (FL) for calculating intra-cluster and inter-cluster distances. First, we employ the Plant Growth Simulation Algorithm (PGSA) to solve for the Fd-points within clusters. The obtained Fd-points are then used to represent the corresponding clusters in the process of calculating the inter-cluster distance so as to guide the merging of clusters. To verify the effectiveness of the proposed method, we compared it with previous methods in terms of performance on multiple well-known datasets. Fd-points have not been utilized as cluster centers in clustering analysis, although they are theoretically optimal representations of datasets and are able to represent different clusters more accurately. We explore a new direction for refining clustering theory and improving the accuracy of clustering algorithms.
Multistage Cloud-Service Matching and Optimization Based on Hierarchical Decomposition of Design Tasks

In cloud manufacturing systems, the multi-granularity of service resource and design task models leads to the complexity of cloud service matching. In order to satisfy the preference of resource requesters for large-granularity service resources, we propose a multistage cloud-service matching strategy to solve the problem of matching tasks and resources with different granularity sizes. First, a multistage cloud-service matching framework is proposed, and the basic strategy of matching tasks with cloud services is planned. Then, the context-aware task-ontology modeling method is studied, and a context-related task-ontology model is established. Thirdly, a process-decomposition method of design tasks is studied, and the product development process with small granularity tasks is established. Fourthly, a matching strategy of ontology tasks and cloud services is studied, and the preliminary matching is accomplished. Finally, intelligent optimization is carried out, and the optimal cloud service composition is found with the optimal design period as the objective function. With the help of the preceding method, the service matching of maximizing the task granularity is realized on the premise of ensuring the matching success rate, which meets the preference of resource requesters for large-granularity service resources.
A New Unsupervised Technique to Analyze the Centroid and Frequency of Keyphrases from Academic Articles

Automated keyphrase extraction is crucial for extracting and summarizing relevant information from a variety of publications in multiple domains. However, the extraction of good-quality keyphrases and the summarising of information to a good standard have become extremely challenging in recent research because of the advancement of technology and the exponential development of digital sources and textual information. Because of this, the usage of keyphrase features for keyphrase extraction techniques has recently gained tremendous popularity. This paper proposed a new unsupervised region-based keyphrase centroid and frequency analysis technique, named the KCFA technique, for keyphrase extraction as a feature. Data/datasets collection, data pre-processing, statistical methodologies, curve plotting analysis, and curve fitting technique are the five main processes in the proposed technique. To begin, the technique collects multiple datasets from diverse sources, which are then input into the data pre-processing step by utilizing some text pre-processing processes. Afterward, the region-based statistical methodologies receive the pre-processed data, followed by the curve plotting examination and, lastly, the curve fitting technique. The proposed technique is then tested and evaluated using ten (10) best-accessible benchmark datasets from various disciplines. The proposed approach is then compared to our available methods to demonstrate its efficacy, advantages, and importance. Lastly, the results of the experiment show that the proposed method works well to analyze the centroid and frequency of keyphrases from academic articles. It provides a centroid of 706.66 and a frequency of 38.95% in the first region, 2454.21 and 7.98% in the second region, for a total frequency of 68.11%.
A new point-infilling and level-setting algorithm enhanced by heuristic searching and incremental clustering to solve global optimization problems

A new global algorithm of point-infilling and level-setting integrating heuristic searching and incremental clustering is proposed for multi-modal function optimization. This algorithm has an iteration body consisting of point infilling, level-setting, heuristic searching and incremental clustering. Its prime idea is to use continuous multiple rounds of point-infilled sampling plus heuristic search to find the global optimum. In each iteration level-set operations are executed after point-infilled sampling to remove some bad points. Heuristic searching by particle swarm optimization (PSO) or genetic algorithm (GA) are further executed to heighten the precision and meanwhile to find more new possible global optima. Clustering operations, with spectral clustering analysis to determine the cluster number of the initial points set and then performed by adaptive incremental clustering, are further used to classify points close to different optima into different clusters. The prime frame of the proposed algorithm is first presented, its detailed realization is further demonstrated by optimizing Rastrigin’s function, and the theoretical proving is further presented. The experiments of optimizing ten typical multi-modal functions, a combined CEC benchmark set and an engineering problem are done. The statistical comparisons with other eight algorithms are completed and the results indicate that the proposed algorithm has better effect.
The Construction of Knowledge Graph in Reservoir Geology and Its Application in Identifying Hydrocarbon Pay Zone

This paper presents an innovative method to construct a reservoir geology knowledge graph (KG) by combining theories in reservoir geology and oil & gas knowledge from experts, which incorporates the recent advances in KG and natural language processing (NLP) technologies. The reservoir geology ontology was built by extracting knowledge from professional books and hydrocarbon dictionaries in the oil & gas industry, which was used to guide the construction of a reservoir geology KG. The triplets of KG was extracted from the well logs, well descriptions, well formations, and well tests data of over 1,500 wells and contains more than 300,000 entities and 600,000 relations. By encoding the entities, relations and attributes in the knowledge graph into vectors, the authors built a knowledge-driven neural formation evaluation model for predicting different formation types. The model was applied to J pilot block in northwest C oilfield and discovered a new pay zone, which corrects the original interpretation of this formation done by experts. The exploration includes the following steps. Firstly the authors established a set of hydrocarbon geological knowledge classification criterion, then constructed a reservoir geological KG and building machine learning models for intelligence formation identification in the base of the aforementioned KG, together with relevant geological parameters, finally applied the models in two oilfield datasets. This work builds the first reservoir geology KG in the oil & gas domain. It is also the first attempt to combine knowledge-driven and data-driven approach for formation evaluation modeling. As the first intelligent model applied in practice, the formation evaluation model achieves significant outcomes in an oilfield, setting a successful precedent for other areas.
An inverse classification framework with limited budget and maximum number of perturbed samples

Most recent machine learning research focuses on developing new classifiers for the sake of improving classification accuracy. With many well-performing state-of-the-art classifiers available, there is a growing need for understanding interpretability of a classifier necessitated by practical purposes such as to find the best diet recommendation for a diabetes patient. Inverse classification is a post modeling process to find changes in input features of samples to alter the initially predicted class. It is useful in many business applications to determine how to adjust a sample input data such that the classifier predicts it to be in a desired class. In real world applications, a budget on perturbations of samples corresponding to customers or patients is usually considered, and in this setting, the number of successfully perturbed samples is key to increase benefits. In this study, we propose a new framework to solve inverse classification that maximizes the number of perturbed samples subject to a per-feature-budget limits and favorable classification classes of the perturbed samples. We design algorithms to solve this optimization problem based on gradient methods, stochastic processes, Lagrangian relaxations, and the Gumbel trick. In experiments, we find that our algorithms based on stochastic processes exhibit an excellent performance in different budget settings and they scale well. The relative improvement of the proposed stochastic algorithms over an existing method with a traditional formulation is 15% in the real-world dataset and 21% in two public datasets on average.