Reviewerly/exHarmony huggingface dataset:
3D Modeling and 3D Materialization of Fluid Art that Occurs in Very Short Time

We have been creating artworks called “liquid art” utilizing liquid dynamics phenomena. One of the liquid artworks is “Sound of Ikebana” which is created by giving sound vibration to color paints and shooting the phenomenon by a high-speed camera, which has been evaluated as “the artwork includes Japanese beauty.” To investigate further why it is evaluated in such a way and also to seek the possibility of its application in society, we tried to materialize it into 3D objects. As the phenomenon occurs in a very short time of less than one second, we have developed a specific experimental environment consisting of multiple high-speed cameras surrounding a speaker where the phenomenon occurs. Among various technologies to reconstruct the 3D model from multiple 2D images, we have chosen a method called Phase-Only Correlation and developed a 3D mesh model of a snapshot of “Sound of Ikebana.” Also using a 3D printer we have successfully obtained 3D materialized “Sound of Ikebana.”
Cognitive Maps Across Multiple Social Sectors: Shared and Unique Perceptions on the Quality of Agricultural Soils in Mexico

Incorporating the views and perceptions of local farmers and other actors with stakes in agricultural production is critical for better-informed decision making and tackling pressing issues, such as soil degradation. We conducted a study that sought to integrate and analyze perceptions regarding the quality and degradation of agricultural soils across different social sectors in Mexico, including producers of two annual crops (maize and beans) and two perennial crops (coffee and avocado), members of civil society organizations and members of the Federal Government. We analyzed the community perception using Cognitive Maps and network metrics. Our fully documented method to formally gather and analyze local and regional perceptions can be used in future efforts toward the collective design of sustainable food systems. Our results highlighted common and potentially conflicting aspects among the different perceptions and allowed us to identify and discuss drivers and processes of special interest in different regions in Mexico. This study also contributes to a deeper understanding of the current situation of agricultural soils in Mexico and seeks to inform the decision-making process regarding agricultural management in the country.
The Role of Al-Shifa Medical Complex Administration in Evacuation &amp; Sheltering Planning

The study aimed to Highlight the role of Al-Shifa Medical Complex administration in evacuation and Sheltering Planning, due to the suffering of the Gaza Strip from repeated attacks by the Israeli occupation, and the escalation of such attacks over the past ten years. The researcher used the content analysis method and Descriptive approach to try to collect all the appropriate data for this topic. The researcher relied on several tools: observation (field visits), personal interviews with stakeholders, risk analysis of the Al-Shifa Medical Complex. The results showed that Quick response in implementing evacuation mechanisms is a critical element in the success of the plan and saving the lives, and showed that planning for evacuations and sheltering is among the priorities of Al-Shifa Complex Administration and the General Administration of Hospitals and that Al-Shifa Complex Administration had prepared a comprehensive evacuation plan and it is developed annually. However, the study found that no maneuver was conducted that simulate activation of the plan for all the working staff in the complex, due to several reasons, and also showed that risk analysis contributes to enhancing preparedness for crisis and disasters, and improving response level to any risk that may occur in future. The study recommends the necessity of forming an internal emergency committee specialized in crisis, disasters, and emergency management and activating it permanently to enhance preparedness level, implementing maneuvers that simulate the evacuation, sheltering, and isolation of major hospitals by standard and modern methods, Developing and strengthening of working staff capabilities in emergency and evacuation management.
A Novel Stacked Generalization Ensemble-Based Hybrid PSVM-PMLP-MLR Model for Energy Consumption Prediction of Copper Foil Electrolytic Preparation

At present, the energy consuming during the electrolytic copper foil preparation accounts for more than 75% of the total energy consumption. In real-life production, the process parameters are set by the operator empirically and the system may not work at the operating point with minimum energy consumption. Therefore, it is critical to establish an effective model for predicting electrolysis energy consumption to guide the parameters design. In this paper, a novel hybrid model (named PSVM-PMLP-MLR) based on stacked ensemble learning is proposed. The model is divided into two parts: the base-learning model and the meta-learning model. The support vector machine (SVM) model and multilayer perceptron (MLP) model with different input structures are established by the former first. Then the particle swarm algorithm is employed to determine the optimal value of SVM parameters and the optimal weight of MLP by minimizing the mean absolute percentage error (MAPE). The multiple linear regression (MLR) is finally employed as a meta-learning machine to compute the final predictions. Experimental results show that the regression coefficient of this model reached 0.987, and compared with the traditional SVM and MLP models, the accuracy of the model is improved by 10.29% and 8.28%, respectively.
Predicting Influential Blogger’s by a Novel, Hybrid and Optimized Case Based Reasoning Approach With Balanced Random Forest Using Imbalanced Data

Bloggers possess the capability of understanding and influencing mass psychology to a wide community of fans and followers by posting their online valuable content. Their dominance over audience can be used as a helping hand in the corporate world which desires to disseminate their product or services among diversified people belonging to varying localities, and is always on the lookout for suitable and quick ways to grasp public access. Due to this reason, influential bloggers are preferred in the online market to initiate marketing campaigns which is a thought-provoking task due to loads of blogger communities. The novelty of this paper lies in the proposed Framework for Influential Blogger Prediction based on Blogger and Blog Features (IBP-BBF) using Case-Based Reasoning (CBR) which is not only capable of handling labeled data but also unstructured data (blogs) and imbalanced data in an optimized way. Detailed labelled and unstructured data are collected by online survey of 129 bloggers and text mining of their 32,200 blogs respectively. The classification results are compared and validated with state-of-the-art machine learning techniques by using standard evaluation measures respectively in the context of imbalanced data. The results show that the proposed IBP-BBF framework through CBR modeling outperforms existing techniques in classifying and adapting the influential blogger prediction. The IBP-BBF framework performed better as compared to baseline imbalanced data classification techniques. It is found that the Balanced Random Forest contributes towards the performance of CBR approach than Balanced Bagging Classifier and RUSBoost classifier. By using the CBR approach, baseline techniques can be optimized for influential blogger identification in a better way.
Adaptive e-Learning AI-Powered Chatbot based on Multimedia Indexing

With the rapid evolution of e-learning technology, the multiple sources of information become more and more accessible.However, the availability of a wide range of e-learning offers makes it difficult for learners to find the right content for their training needs.In this context, our paper aims to design an e-learning AI-powered Chatbot allowing interaction with learners and suggesting the e-learning content adapted to their needs.In order to achieve these objectives, we first analysed the e-learning multimedia content to extract the maximum amount of information.Then, using Natural Language Processing (NLP) techniques, we introduced a new approach to extract keywords.After that, we suggest a new approach for multimedia indexing based on extracted keywords.Finally, the Chatbot architecture is realized based on the multimedia indexing and deployed on online messaging platforms.The suggested approach aims to have an efficient way to represent the multimedia content based on keywords.We compare our approach with approaches in literature and we deduce that the use of keywords on our approach result on a better representation and reduce time to construct multimedia indexing.The core of our Chatbot is based on this indexed multimedia content which enables it to look for the information quickly.Then our designed Chatbot reduce response time and meet the learner's need.
The Role of Chatbots in End-To-End Intelligent Automation and Future Employment Dynamics

Employing automation on cloud, on-premise, or hybridly, companies are shifting their workloads to software robots, thus reshaping the old operating models, reducing toil and creating jobs that are better suited for humans. In this context, the messaging platforms are becoming the dominant communication channel worldwide and, coupled with a raising customer demand for self-services, companies are now able to create internal and external communications with zero necessity for a new user interface. With a market size expected to continually increase in the next years, modern chatbots are impressive facilitators of human-to-robot interaction. The technologies underlying chatbots can provide natural language processing (NLP) and intent recognition, enabling them to surpass language ambiguity and jargon. Furthermore, chatbots can be integrated with common messaging applications (e.g. Messenger, WhatsApp, Slack, Viber), connect to RPA robots and other technologies. Chatbots can create visitor profiles and tailored responses, initiate conversations, have a "personality", constantly improve, or seamlessly deflect to a human operator. Time is precious, and chatbots can manage an extensive number of operations in much less than a second, bringing cost efficiency, with better internal and/or external customer experience. Nevertheless, either employing a declarative or a conversational chatbot, businesses must select the proper solution for their current and future needs, in order to achieve an adequate return on investment. In this paper we will explore the context of the chatbot market and technologies, with a case study on the effects of chatbot deployment, thus helping organizations take better digital transformation decisions.
The favored language learning strategies of Islamic university EFL learners

Despite the existence of many studies about language learning strategies (LLS) around the world, little work has been reported on the LLS use of Islamic university students from a qualitative perspective. Thus, to fill this empirical gap, this study is aimed to explore the LLS use and choice by learners studying at an Islamic university in Indonesia. This study employed a qualitative approach by interviewing 18 learners who would become teachers of English as a Foreign Language (EFL). They have been interviewed about the ways or strategies when they are learning English. The data gained from the interviews were analyzed by thematic analysis. The findings demonstrated that learners reported using the strategies which are classified into six LLS categories (memory, cognitive, compensation, metacognitive, affective, and social strategies) in learning English. However, most strategies reported by the learners were categorized into metacognitive strategies. Another significant finding is that several strategies such as practicing strategies (practicing four language skills; listening, speaking, reading, and writing), watching TV/ English Movies, memorizing, and using the internet were mostly reported by learners in improving their English skills. This study is beneficial for the classroom practice of teachers in enhancing their teaching methodology by knowing learners’ learning strategies to make it easier for the teachers to design the learning activities. Furthermore, this study is also beneficial for novice EFL learners in which the strategies in this study could be models for them.
Optimization of Computer-Assisted Vocabulary Assessment System in International Chinese Language Teaching

The mining and application of data in sports training through intelligent computer-aided research, this paper proposes a system development and design method to improve the development and design efficiency of the data visualization system and reduce the development cost by using the Python language to handle the high efficiency of system programming and the convenience of charts.Thus, it further improves the efficiency and interactivity of the information management of training data and the convenience and stability of managing training data.This system is a preliminary exploration of Python language and Echarte technology in the management of training data of sports training programs.Because of the integration of training programs with computer application technology and data visualization technology, the system still needs to be strengthened in terms of data processing methods and the diversity of chart styles generated.In the future, the use of artificial intelligence, big data analysis, and other technologies in sports training data management and analysis has more room for development.The model only needs to measure 14 indicators of bipedal closed-eye standing and single-leg closed-eye standing movements to achieve the ability to predict the balance of motor training, and its accuracy meets the clinical requirements.This method has a shorter measurement time and fewer metrics than traditional methods.
Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target

Calibration between multiple sensors is a fundamental procedure for data fusion. To address the problems of large errors and tedious operation, we present a novel method to conduct the calibration between light detection and ranging (LiDAR) and camera. We invent a calibration target, which is an arbitrary triangular pyramid with three chessboard patterns on its three planes. The target contains both 3D information and 2D information, which can be utilized to obtain intrinsic parameters of the camera and extrinsic parameters of the system. In the proposed method, the world coordinate system is established through the triangular pyramid. We extract the equations of triangular pyramid planes to find the relative transformation between two sensors. One capture of camera and LiDAR is sufficient for calibration, and errors are reduced by minimizing the distance between points and planes. Furthermore, the accuracy can be increased by more captures. We carried out experiments on simulated data with varying degrees of noise and numbers of frames. Finally, the calibration results were verified by real data through incremental validation and analyzing the root mean square error (RMSE), demonstrating that our calibration method is robust and provides state-of-the-art performance.
Text Semantic Classification of Long Discourses Based on Neural Networks with Improved Focal Loss

Semantic classification of Chinese long discourses is an important and challenging task. Discourse text is high-dimensional and sparse. Furthermore, when the number of classes of dataset is large, the data distribution will be seriously imbalanced. In solving these problems, we propose a novel end-to-end model called CRAFL, which is based on the convolutional layer with attention mechanism, recurrent neural networks, and improved focal loss function. First, the residual network (ResNet) extracts phrase semantic representations from word embedding vectors and reduces the dimensionality of the input matrix. Then, the attention mechanism differentiates the focus on the output of ResNet, and the long short-term memory layer learns the features of the sequences. Lastly but most significantly, we apply an improved focal loss function to mitigate the problem of data class imbalance. Our model is compared with other state-of-the-art models on the long discourse dataset, and CRAFL model has proven be more efficient for this task.
Multi-Mask Camera Model for Compressed Acquisition of Light Fields

We present an all-in-one camera model that encompasses the architectures of most existing compressive-sensing light-field cameras, equipped with a single lens and multiple amplitude coded masks that can be placed at different positions between the lens and the sensor. The proposed model, named the equivalent multi-mask camera (EMMC) model, enables the comparison between different camera designs, e.g using monochrome or CFA-based sensors, single or multiple acquisitions, or varying pixel sizes, via a simple adaptation of the sampling operator. In particular, in the case of a camera equipped with a CFA-based sensor and a coded mask, this model allows us to jointly perform color demosaicing and light field spatio-angular reconstruction. In the case of variable pixel size, it allows to perform spatial super-resolution in addition to angular reconstruction. While the EMMC model is generic and can be used with any reconstruction algorithm, we validate the proposed model with a dictionary-based reconstruction algorithm and a regularization-based reconstruction algorithm using a 4D Total-Variation-based regularizer for light field data. Experimental results with different reconstruction algorithms show that the proposed model can flexibly adapt to various sensing schemes. They also show the advantage of using an in-built CFA sensor with respect to monochrome sensors classically used in the literature.
A Novel Hybrid Approach for Access Control in Cloud Computing

Access control is essential to protect data and system resources by preventing unauthorized access.Cloud computing ensures that users get benefit from such resources by following their requests.Hence, users can access applications, programs, and storage.Data security is a significant concern to be dealt with in the cloud computing area.The dispersion of data across multiple storage devices in cloud computing and abundant access from heterogeneous devices pose serious security threats.Therefore, the protection of data and resources using proper access control mechanisms is a critical task for cloud service providers.Various access control approaches and models for cloud computing have been suggested in existing research , however; security breaches in these approaches cause great threats for cloud service providers.Based on the analysis of existing works, we proposed that the combination of multiple approaches is the best solution to ensure access control according to users' requirements and to the network conditions.Hence, concerning the analysis conducted on related works, we propose a hybrid approach based on the Attribute-based-Access-control and Role-Based-Access-Control models.Our approach combines the best features of these models and ensures both security and flexibility.Compared to the conventional models, the proposed approach defines different security levels via the assignment of a different number of attributes for each role by guaranteeing the least privilege concept.
graphkit-learn: A Python library for graph kernels based on linear patterns

This paper presents graphkit-learn, the first Python library for efficient computation of graph kernels based on linear patterns, able to address various types of graphs. Graph kernels based on linear patterns are thoroughly implemented, each with specific computing methods, as well as two well-known graph kernels based on non-linear patterns for comparative analysis. Since computational complexity is an Achilles' heel of graph kernels, we provide several strategies to address this critical issue, including parallelization, the trie data structure, and the FCSP method that we extend to other kernels and edge comparison. All proposed strategies save orders of magnitudes of computing time and memory usage. Moreover, all the graph kernels can be simply computed with a single Python statement, thus are appealing to researchers and practitioners. For the convenience of use, an advanced model selection procedure is provided for both regression and classification problems. Experiments on synthesized datasets and 11 real-world benchmark datasets show the relevance of the proposed library.
Strong Explanations in Abstract Argumentation

Abstract argumentation constitutes both a major research strand and a key approach that provides the core reasoning engine for a multitude of formalisms in computational argumentation in AI. Reasoning in abstract argumentation is carried out by viewing arguments and their relationships as abstract entities, with argumentation frameworks (AFs) being the most commonly used abstract formalism. Argumentation semantics then drive the reasoning by specifying formal criteria on which sets of arguments, called extensions, can be deemed as jointly acceptable. Such extensions provide a basic way of explaining argumentative acceptance. Inspired by recent research, we present a more general class of explanations: in this paper we propose and study so-called strong explanations for explaining argumentative acceptance in AFs. A strong explanation is a set of arguments such that a target set of arguments is acceptable in each subframework containing the explaining set. We formally show that strong explanations form a larger class than extensions, in particular giving the possibility of having smaller explanations. Moreover, assuming basic properties, we show that any explanation strategy, broadly construed, is a strong explanation. We show that the increase in variety of strong explanations comes with a computational trade-off: we provide an in-depth analysis of the associated complexity, showing a jump in the polynomial hierarchy compared to extensions.
Advanced practices: web technologies in the educational process and science

Advanced technologies for remote learning and science are very actual, especially in the case of epidemic or pandemic. Videoconference is an area of information technology that provides simultaneous two-way transmission, processing, conversion and presentation of interactive information at a distance in real time using hardware and software of computer technology. Using a web conference allows you to expand the capabilities of the communication form in which several computer users connected to the Internet constantly see the same screen in their web browsers. Some web conferencing systems include features such as text messaging, video recording, voice over Internet protocol (VoIP), and full video. However, despite all the advantages of this technology, there are also disadvantages to using the product.