Reviewerly/exHarmony huggingface dataset:
A Novel Essential Mutation Method for Evolutionary Algorithms

The mutation is one of the operators that is used by many Evolutionary Algorithms (EA) to diversify the population (solutions). It can enhance the algorithm exploration of the problem search space and improve the evolution process. This paper introduces a novel mutation technique that is based on a recently investigated mutation bias pattern in the Arabidopsis thaliana plant [1]. The proposed mutation technique is called an essential mutation. The proposed method uses the ϵ parameter to control the amount of distance we can be from the parent’s fitness. Three different configurations are studied and the best results are obtained when ϵ=0. It is compared against five well-known mutation techniques which are Boundary, Non-uniform, MPT, and Polynomial on standard benchmark functions. The obtained results show the superiority of the proposed essential mutation in terms of best solution and convergence speed in most of the test functions.
Advanced Information Data-interactive Learning System Effect for Creative Design Project

Compared to the significant approach of project-based learning research, a data-driven design project-based learning has not reached a meaningful consensus regarding the most valid and reliable method for assessing design creativity.This article proposes an advanced information data-interactive learning system for creative design using a service design process that combines a design thinking.We propose a service framework to improve the convergence design process between students and advanced information data analysis, allowing students to participate actively in the data visualization and research using patent data.Solving a design problem by discovery and interpretation process, the Advanced information-interactive learning framework allows the students to verify the creative idea values or to ideate new factors and the associated various feasible solutions.The student can perform the patent data according to a business intelligence platform.Most of the new ideas for solving design projects are evaluated through complete patent data analysis and visualization in the beginning of the service design process.In this article, we propose to adapt advanced information data to educate the service design process, allowing the students to evaluate their own idea and define the problems iteratively until satisfaction.Quantitative evaluation results have shown that the advanced information data-driven learning system approach can improve the design projectbased learning results in terms of design creativity.Our findings can contribute to data-driven project-based learning for advanced information data that play a crucial role in convergence design in related standards and other smart educational fields that are linked.
Reflections on the theatrical nature of a human being: suggestion and its hidden potential

The scientific interest of this work lies in the field of philosophical anthropology — the study of a human being as a complex open self-organizing system, where one of the constituent elements is, presumably, Theatricality. The author believes that theatrical techniques used by a human being in everyday life help him to adapt to the life in society, to others, and to himself. Theatricality appears as a kind of survival mechanism and a way of adapting to external and internal challenges. Being in plain sight, this amazing mechanism of social communication still remains somewhat hidden, studied abundantly but fragmentarily. It is difficult to trace the genesis of the formation of the Homo Theatrical’s phenomenon as well as to understand the mechanism and purpose of his everyday theatrical behavior. The Play and Theatricality as phenomena are closely intertwined, but not equal to each other. The Play does not involve breaking the rules, while the Homo Theatrical breaks the rules when he needs it. Among the elements of the theatrical nature of a human being, one can distinguish pretense, representation, manipulation, artistry, cunning, pragmatism, as well as suggestion. Suggestion appears as a phenomenon that is very common in the social environment. It has a viral potential and represents a kind of «psycho infection» for the brain. A Homo Theatrical strives to use suggestion to achieve pragmatic goals and, therefore, the theatrical nature itself acquires a viral potential. Reflection upon the viral potential of suggestion («psycho infection») contributes to a deeper analysis of the viral potential of the theatrical nature of a human being, which cannot but rely on an irresistible craving for suggestion, understanding the power of the influence of suggestion in achieving the desired.
From undirected dependence to directed causality: A novel Bayesian learning approach

Bayesian network (BN) is one of the most powerful probabilistic models in the field of uncertain knowledge representation and reasoning. During the past decade, numerous approaches have been proposed to build directed acyclic graph (DAG) as the structural specification of BN. However, for most Bayesian network classifiers (BNCs) the directed edges in DAG substantially represent assertions of conditional independence rather than causal relationships although the learned joint probability distributions may fit data well, thus they cannot be applied to causal reasoning. In this paper, conditional entropy is introduced to measure causal uncertainty due to its asymmetry characteristic, and heuristic search strategy is applied to build Bayesian causal tree (BCT) by identifying significant causalities. The resulting highly scalable topology can represent causal relationship in terms of causal science, and corresponding joint probability can fit training data in terms of data science. Then ensemble learning strategy is applied to build Bayesian causal forest (BCF) with a set of BCTs, each taking different attribute as the root node to represent root cause for causality analysis. Extensive experiments performed on 32 public datasets from the UCI machine learning repository show that BCF achieves outstanding classification performance compared to state-of-the-art single-model BNCs (e.g., CFWNB), ensemble BNCs (e.g., WATAN, IWAODE, WAODE-MI and TAODE) and non-Bayesian learners (e.g., SVM, k-NN, LR).
Parallel Message Passing in Dual-space on Graphs

Graph neural networks (GNNs) have achieved a great success on graph-based tasks. Most existing GNNs pay attention to the message passing process, where features propagate along the topology of the graph. Recently, dimension-wise separable 2-D graph convolution (DSGC) points out that GNNs ignore the effect of interactions between the features, and specially designs a dimension-wise feature affinity matrix via predefined corpuses. Although DSGC further improves the performance, we find that it has two shortcomings. For one thing, it performs poorly on low homophily datasets. For another thing, it tends to be over-smoothing as the number of layers increases, making node embeddings indistinguishable. In our empirical analysis, these two shortcomings are caused by the serial message passing mechanism of topology and features. To solve this problem, we propose a parallel-space graph convolution (PSGC) in this paper. The central idea is that we design a parallel message passing mechanism in dual spaces including a topology space and a feature space, which can adapt to different homophily real-world datasets and alleviate the over-smoothing issue. Besides, we design the dimension-wise feature affinity matrix derived by the original node features instead of corpuses in DSGC to alleviate the severe overfitting in the feature space, where corpuses are not provided by many benchmark datasets. Our extensive experiments on benchmark datasets obviously show that PSGC achieves significant performance gain over state-of-the-art methods for semi-supervised node classification.
Decoupled Representation Network for Skeleton-Based Hand Gesture Recognition

Skeleton-based dynamic hand gesture recognition plays an increasing role in the human-computer interaction field. It is well known that different skeleton representations will have a greater impact on the recognition results, but most methods only use the original skeleton data as input, which hinders the improvement of accuracy to a certain extent. In this paper, we propose a novel decoupled representation network (DR-Net) for skeleton-based dynamic hand gesture recognition, which consists of temporal perception branch and spatial perception branch. For the former, it uses the temporal representation encoder to extract short-term motion features and long-term motion features, which can effectively reflect contextual information of skeleton sequences. Besides, we also design the temporal fusion module (TFM) to capture multi-scale temporal features. For the latter, we use the spatial representation encoder to extract spatial low-frequency features and spatial high-frequency features. Besides, we also design the spatial fusion module (SFM) to enhance important spatial features. Experimental results and ablation studies on two benchmark datasets demonstrate that our proposed DR-Net is competitive with the state-of-the-art methods.
Alleviating Overconfident Failure Predictions via Masking Predictive Logits in Semantic Segmentation

Currently, semantic segmentation is formulated to a classification task as image classification with similar networks and training settings. We observe an excessive overconfidence phenomenon in semantic segmentation regarding the model’s classification scores. Unlike image classification, segmentation networks yield undue-high predictive probabilities for failure predictions, which may carry severe repercussions in safety-sensitive applications. To this end, we propose manually perturbing the predicted probability distribution via masking predictive logits during training that explicitly enforces the model to re-learn potential patterns, based on the pure intuition that meaningful patterns help alleviate overconfident failure predictions. A direct instantiation is presented that randomly zeroes out the model’s predictive logits but keeps their expectations unchanged before computing the loss in the training phase. This instantiation requires no additional computation cost or customized architectures but only a masking function. Empirical results from various network architectures indicate its feasibility and effectiveness of alleviating overconfident failure predictions in semantic segmentation.
Tables as Powerful Representational Tools

Abstract Tables are widely used for storing, retrieving, communicating, and processing information, but in the literature on the study of representations they are still somewhat neglected. The strong structural constraints on tables allow for a clear identification of their characteristic features and the roles these play in the use of tables as representational and cognitive tools. After introducing syntactic, spatial, and semantic features of tables, we give an account of how these affect our perception and cognition on the basis of fundamental principles of Gestalt psychology. Next are discussed the ways in which these features of tables support their uses in providing a global access to information, retrieving information, and visualizing relational structure and patterns. The latter is particularly important, because it shows how tables can contribute to the generation of new knowledge. In addition, tables also provide efficient means for manipulating information in general and in structured notations. In sum, tables are powerful and efficient representational tools.
Troubleshooting the performance of a large Erlang system

This paper discusses our experiences troubleshooting and optimizing Hyper-Q, an enterprise software written in Erlang. Hyper-Q implements database virtualization that translates the SQL queries of an existing application to a new database system and converts the data returned by the database system to the data representation expected by the existing application. Using a real world example, we present effective troubleshooting techniques to identify poor-performing code and general refactorings and optimizations that can improve the performance of other Erlang systems.
Reconstruction of open science information ecosystem in the era of global challenges

The purpose of the study is to reveal trends in the open science information ecosystem in the era of global crisis (COVID-19, etc.). Based on the findings, the author argues that the international interaction intensifies, publications and research findings are available in open access, research data dissemination, reproduction, preservation and seamless access are demanded and offered as never before. The examples of the resources made accessible for research and educational purposes are provided. The role of libraries in offering resources and services to the users in the period of restrictions due to COVID-19 pandemic, as well as in fighting fake information and adapting to the new forms of interaction with vendors and resource aggregators, is advocated.
CUP STACKING GAMES: THE CONTRIBUTION IN IMPROVING STUDENTS SPEAKING ABILITY

Cup stacking games allows the equal opportunities to each student to speak up during the classroom. Thus, the present study is to improve the students' speaking ability in by utilizing Cup stacking games. This study applied classroom action research which was conducted in four stages; planning, action, observation, and reflection. A total of 22 students in eleventh grade of SMAN 6 Luwu is the subject of speaking tests and observation.. The results of data analysis showed a significant improvement in students' speaking abilities until the second cycle. speaking test in cycle 2, there are 80 % of students reach 3.1-4.0 score categorized as good. In addition, the observation of cycle 2 showed that there are 75% of students got good criteria in class participation. Both of these criteria indicated that utilizing the cup stacking games can boost the students' speaking ability.
esT5s: A Spanish Model for Text Summarization

Deep Learning models based on the Transformer architecture have revolutionized the state of the art of NLP tasks. As English is the language in which most significant advances are made, languages like Spanish require specific training, but this training has a computational cost so high that only big corporations with servers and GPUs are capable of generating them. This work has explored how to create a model for the Spanish language from a big multilingual model. Specifically, a model aimed at creating text summarization, a very common task in NLP. The results, concerning the quality of the summarization (ROUGE score), point out that these small models, for a specific language, achieve similar results than much bigger models, with a reasonable training in terms of time required and computational power, and are significantly faster at inference.
Modelling Business Agreements in the Multimodal Transportation Domain Through Ontological Smart Contracts

The blockchain technology provides integrity and reliability of the information, thus offering a suitable solution to guarantee trustability in a multi-stakeholder scenario that involves actors defining business agreements. The Ride2Rail project investigated the use of the blockchain to record as smart contracts the agreements between different stakeholders defined in a multimodal transportation domain. Modelling an ontology to represent the smart contracts enables the possibility of having a machine-readable and interoperable representation of the agreements. On one hand, the underlying blockchain ensures trust in the execution of the contracts, on the other hand, their ontological representation facilitates the retrieval of information within the ecosystem. The paper describes the development of the Ride2Rail Ontology for Agreements to showcase how the concept of an ontological smart contract, defined in the OASIS ontology, can be applied to a specific domain. The usage of the designed ontology is discussed by describing the modelling as ontological smart contracts of business agreements defined in a ride-sharing scenario.
TYPES OF INDEPENDENT WORK OF STUDENTS IN THE LESSONS OF MATH IN THE ELEMENTARY CLASSES

The information age, unlike its predecessors, has brought about significant changes in our lives. In modern society, the rapid development of education is important. The article speaks of the close connection between education and life. It was noted that a requirement to meet the growing spiritual and material needs of people. Since the foundation of education is laid in the primary grades, teachers should pay great attention to the creative work of students, because new inventions, technological processing are the result of creative work. In fact, creative work is associated with the independent activity of people, as well as students. From this point of view, junior students should be provided with sufficient space for independent work. Of particular importance is the importance of independent work in mathematics lessons. The opinions of a number of scientists and methodologists on this issue are considered. Attempts have been made to find common ground in their approaches. The classifications of students' independent work are considered. Given that these classifications are based on different principles, the author presents his approach in the form of tables. The approximate schedule of time allocated for independent work has been investigated. It has been shown that this primarily depends on the level of knowledge of the class. Examples of assignments for two forms of independent work in the classroom are given. The differences between these types are explained. At the end, an experiment was carried out, the results of which showed that the applied method was justified. At the end, the didactic significance of the work is emphasized.
Predictive Modelling for Financial Fraud Detection Using Data Analytics

Financial fraud remains one of the most discussed topics in literature. The financial scandals of Enron, WorldCom, Qwest, Global Crossing, and Tyco resulted in approximately 460 billion dollars of loss. The detection of financial fraud, therefore, has become a critical task for financial practitioners. Three factors determine the likelihood of fraud occurrence, including pressure, opportunity, and rationalization. The core of these factors lies in people's beliefs and behaviour. Due to the unpredictability and uncertainty in fraudsters' incentives and techniques, fraud detection requires a skill set that encompasses both diligence and judgment. Big data technologies have had a huge impact on a wide variety of industries because they tend to be ubiquitous, starting in the last decade and continuing today.
InvisibiliTee: Angle-Agnostic Cloaking from Person-Tracking Systems with a Tee

After a survey for person-tracking system-induced privacy concerns, we propose a black-box adversarial attack method on state-of-the-art human detection models called InvisibiliTee. The method learns printable adversarial patterns for T-shirts that cloak wearers in the physical world in front of person-tracking systems. We design an angle-agnostic learning scheme which utilizes segmentation of the fashion dataset and a geometric warping process so the adversarial patterns generated are effective in fooling person detectors from all camera angles and for unseen black-box detection models. Empirical results in both digital and physical environments show that with the InvisibiliTee on, person-tracking systems’ ability to detect the wearer drops significantly (Code is available at https://github.com/invisibilitee/invisibilitee ).
Object Detector with Recursive Feature Pyramid and Key Content-Only Attention

To detect objects, human visual perception focuses on the key content of interest and then transmits high-level semantic information through feedback connections, selectively enhancing and suppressing neuronal activation. Inspired by the human visual system, our detector incorporates the recursive feature pyramid in the backbone to integrate the feedback information of the FPN into the backbone network so that the features of the secondary training of the backbone network can be better adapted to the detection task. Furthermore, we propose a key content-only attention mechanism to seek the balance between accuracy and efficiency, which adopts the attention configuration of the key content-only term with deformable convolution to achieve the best accuracy and efficiency trade-off. Both of these can improve our baseline AP by > 4%, and combining them further enhances the performance of our detector. On COCO test dev, our detector achieves 45.1% box AP with ResNet-50.
A Transformer-based Model for Older Adult Behavior Change Detection

The advancements in smart home technologies have created new opportunities for precise monitoring of older adults' daily activities to provide timely care, predict health problems, and promote independent living at home. Many recent studies have investigated abnormality detection in Activities of Daily Living (ADL), and some used deep learning methods to handle the problem. In this paper, we leverage Bi-Directional Encoder Representations from Transformers (BERT), as a state-of-the-art method in machine learning, to analyze older adults' ADL sequences. Due to the fine-tuning capability of transformers, they are a good fit for supervised tasks when a large labeled training dataset is not available. Their architecture also allows for Parallel Computation, which is important for (near) real-time abnormality detection. To the best of our knowledge, this is the first effort to represent the older adult's daily behavior as sequences of ADLs with the goal of applying transformers to the behavior change detection problem. We designed four experiments to illustrate the capability of Transformers in detecting individuals' behavior abnormalities. We conducted a case study on a two-resident ADL dataset to evaluate the model in four experiments. Our results show that a BERT-based classifier can effectively detect behavior abnormalities from sequences of ADLs. Also, transfer learning proved to be helpful when it comes to fine-tuning a pre-trained model for a new resident.
Visualizing the Interpretation of a Criterion-Driven System that Automatically Evaluates the Quality of Health News: An Exploratory Study of Two Approaches

Numerous studies have established the capability of machine learning in detecting health misinformation, but none addresses model interpretability. We propose two approaches to visualization for a criteria-based system that automatically evaluates health news quality. The model's interpretability arises from providing sentence-level evidence to support the document-level automatic criterion evaluation outcome. The two approaches were both implemented and then evaluated on four unseen cases, for one of ten established criteria.
Intuitive Gait Modeling using Mimetic-Words for Gait Description and Generation

Gait is one of the most familiar action for us, that is why we can distinguish slight difference of human gaits and perceive their impressions. However, the relationship has been never explored because of the absence of intuitive labels for the slight differences. In this paper, to solve this problem, we propose a intuitive gait model using Japanese mimetic-words. A mimetic-word has sound-symbolism, which means that there is an association between linguistic sounds and sensory experiences, and the phonemes of a mimetic-word is strongly related to the visual sensation. Thanks to the sound-symbolism, Japanese mimetic-words have a possibility of modeling gaits intuitively. Thus, we have previously proposed a method which describes gait with a mimetic-word. In this paper, in the opposite direction, we propose a method which generates gait from a mimetic-word, and confirm the effectiveness of the proposed intuitive gait model which consists of the phonetic-vector through evaluations of both the generation task and the description task.