Reviewerly/exHarmony huggingface dataset:
Enhanced Teaching–Learning-Based Optimization Algorithm for the Mobile Robot Path Planning Problem

This research proposes an enhanced teaching–learning based optimization (ETLBO) algorithm to realize an efficient path planning for a mobile robot. Four strategies are introduced to accelerate the teaching–learning based optimization (TLBO) algorithm and optimize the final path. Firstly, a divide-and-conquer design, coupled with the Dijkstra method, is developed to realize the problem transformation so as to pave the way for algorithm deployment. Secondly, the interpolation method is utilized to smooth the traveling route as well as to reduce the problem dimensionality. Thirdly, an opposition-based learning strategy is embedded into the algorithm initialization to create initial solutions with high qualities. Finally, a novel, individual update method is established by hybridizing the TLBO algorithm with differential evolution (DE). Simulations on benchmark functions and MRPP problems are conducted, and the proposed ELTBO is compared with some state-of-the-art algorithms. The results show that, in most cases, the ELTBO algorithm performs better than other algorithms in both optimality and efficiency.
Representation Learning of Enhanced Graphs Using Random Walk Graph Convolutional Network

Nowadays, graph structure data has played a key role in machine learning because of its simple topological structure, and therefore, the graph representation learning methods have attracted great attention. And it turns out that the low-dimensional embedding representation obtained by graph representation learning is extremely useful in various typical tasks, such as node classification and content recommendation. However, most of the existing methods do not further dig out potential structural information on the original graph structure. Here, we propose wGCN, which utilizes random walk to obtain the node-specific mesoscopic structures (high-order local structure) of the graph and utilizes these mesoscopic structures to enhance the graph and organize the characteristic information of the nodes. Our method can effectively generate node embedding for data of previously unknown categories, which has been proven in a series of experiments conducted on many types of graph networks. And compared to baselines, our method shows the best performance on most datasets and achieves competitive results on others. It is believed that combining the mesoscopic structure to further explore the structural information of the graph will greatly improve the learning efficiency of the graph neural network.
Fuzzy parallel dynamical systems on Zadeh operators

In this paper, we describe the orbital structure of homogeneous fuzzy parallel dynamical systems on Zadeh operators. In particular, we show that, when the (global) evolution operator is either the Zadeh conjunction operator AND or the Zadeh disjunction operator OR, all the points of the system are fixed points or eventually fixed points. Specifically, such systems present infinitely many fixed points. On the other hand, when the (global) evolution operator is either the Zadeh operator NOR or the Zadeh operator NAND, the orbital structure consists of an only fixed point and infinitely many 2‐periodic points.
Im2Graph: A Weakly Supervised Approach for Generating Holistic Scene Graphs from Regional Dependencies

Conceptual representations of images involving descriptions of entities and their relations are often represented using scene graphs. Such scene graphs can express relational concepts by using sets of triplets ⟨subject—predicate—object⟩. Instead of building dedicated models for scene graph generation, our model tends to extract the latent relational information implicitly encoded in image captioning models. We explored dependency parsing to build grammatically sound parse trees from captions. We used detection algorithms for the region propositions to generate dense region-based concept graphs. These were optimally combined using the approximate sub-graph isomorphism to create holistic concept graphs for images. The major advantages of this approach are threefold. Firstly, the proposed graph generation module is completely rule-based and, hence, adheres to the principles of explainable artificial intelligence. Secondly, graph generation can be used as plug-and-play along with any region proposition and caption generation framework. Finally, our results showed that we could generate rich concept graphs without explicit graph-based supervision.
Nemesis: Neural Mean Teacher Learning-Based Emotion-Centric Speaker

Image captioning is the multi-modal task of automatically describing a digital image based on its contents and their semantic relationship. This research area has gained increasing popularity over the past few years; however, most of the previous studies have been focused on purely objective content-based descriptions of the image scenes. In this study, efforts have been made to generate more engaging captions by leveraging human-like emotional responses. To achieve this task, a mean teacher learning-based method has been applied to the recently introduced ArtEmis dataset. ArtEmis is the first large-scale dataset for emotion-centric image captioning, containing 455K emotional descriptions of 80K artworks from WikiArt. This method includes a self-distillation relationship between memory-augmented language models with meshed connectivity. These language models are trained in a cross-entropy phase and then fine-tuned in a self-critical sequence training phase. According to various popular natural language processing metrics, such as BLEU, METEOR, ROUGE-L, and CIDEr, our proposed model has obtained a new state of the art on ArtEmis.
The Research on Complex Lithology Identification Based on Well Logs: A Case Study of Lower 1st Member of the Shahejie Formation in Raoyang Sag

Lithology identification is the basis for sweet spot evaluation, prediction, and precise exploratory deployment and has important guiding significance for areas with low exploration degrees. The lithology of the shale strata, which are composed of fine-grained sediments, is complex and varies regularly in the vertical direction. Identifying complex lithology is a typical nonlinear classification problem, and intelligent algorithms can effectively solve this problem, but different algorithms have advantages and disadvantages. Compared were the three typical algorithms of Fisher discriminant analysis, BP neural network, and classification and regression decision tree (C&amp;RT) on the identification of seven lithologies of shale strata in the lower 1st member of the Shahejie Formation (Es1L) of Raoyang sag. Fisher discriminant analysis method is linear discriminant, the recognition effect is poor, the accuracy is 52.4%; the accuracy of the BP neural network to identify lithology is 82.3%, but it belongs to the black box and can not be visualized; C&amp;RT can accurately identify the complex lithology of Es1L, the accuracy of this method is 85.7%, and it can effectively identify the interlayer and thin interlayer in shale strata.
A Comprehensive Taxonomy for Prediction Models in Software Engineering

Applying prediction models to software engineering is an interesting research area. There have been many related studies which leverage prediction models to achieve good performance in various software engineering tasks. With more and more researches in software engineering leverage prediction models, there is a need to sort out related studies, aiming to summarize which software engineering tasks prediction models can apply to and how to better leverage prediction models in these tasks. This article conducts a comprehensive taxonomy on prediction models applied to software engineering. We review 136 papers from top conference proceedings and journals in the last decade and summarize 11 research topics prediction models can apply to. Based on the papers, we conclude several big challenges and directions. We believe that the comprehensive taxonomy will help us understand the research area deeper and infer several useful and practical implications.
Ethical Design Approaches for Workplace Augmented Reality

Augmented reality (AR) technologies are increasingly being implemented in various workplace contexts; however, they pose a number of ethical design challenges. To discern the ethical implications of workplace AR, this article conducts an analysis of the promotional discourses surrounding a workplace AR system. This analysis demonstrates a tendency to frame AR technologies in terms of a transhumanist evolution in worker agency and organizational efficiency. Such discourses elide applications of workplace AR for purposes of worker surveillance and exploitation. The article concludes by outlining speculative ethical design guidelines that communication designers can take up in their work on workplace AR systems.
Review of "Beyond the Makerspace: Making and Relational Rhetorics by Malaka Friedman" Shivers-McNair, A. (2021) <i>Beyond the Makerspace: Making and Relational Rhetorics.</i> University of Michigan Press.

Beyond the Makerspace: Making and Relational Rhetorics (2021) provides an engaging study of contributions makerspaces provide (both within and outside the making movement) to meaning making through the lens of rhetoric and storytelling. Shivers-McNair situates herself as both a storyteller and an amateur maker in the makerspace she studies and considers applications from these stories for instruction and making knowledge. Situating rhetoric within makerspaces allows Shivers-McNair to create a broad understanding of the relational rhetorics that are "both more than symbolic and more than human" (p. 11). Questioning and evaluating the boundaries between individuals within these spaces allows Shivers-McNair to evaluate conceptualizing meaning making beyond the making movement, an important trend towards reconceptualizing making as an embodied and relational practice that extends to different contexts in society (Gollihue, 2019). Through six chapters, Shivers-McNair provides a concise look into how meaning can be embodied by the individuals of the SoDo Makerspace and how considerations towards making as a relational rhetorical act can extend beyond the scope of one specific makerspace. Readers are invited to consider making as a boundary-marking practice that can speak to the larger nature of how we understand meaning beyond words.
A survey of specific emitter identification

Radio frequency fingerprinting (RFF) refers to the technology of Specific Emitter Identification(SEI) by extracting device signals at the physical layer. By extracting the unique RFF of wireless devices at the physical layer, it is used to identify specific individuals and prevent malicious attacks, and ultimately improve network security. This paper first introduces the principle of RF fingerprint generation, and analyzes the related research from two aspects of transient extraction and steady state extraction by analyzing the process of RFF feature extraction and recognition. Then, by sorting out the latest papers on RF fingerprint recognition in the past three years, the extraction methods are classified and theoretically analyzed. In the RFF identification stage, we analyzed several commonly used classification methods, and summarized the advantages and disadvantages of several commonly used methods. Finally, an outlook is made on the future development prospects of RFF, and the views on issues related to the future development of RFF are put forward.
Intelligent database of face recognition on account of Lingo3G clustering algorithm

With the continuous progress of mobile network and the acceleration of mobile education, the popularity of smart devices is accelerating, and the utilization rate is increasing day by day. More and more people develop application software to assist learning. In the context of the rapid development of the Internet, students can use more and more information learning resources, using these Internet resources can improve their studies more efficiently, but the Internet is two-sided, the Internet will destroy good academic learning habits and life style in the bad aspect. The development of a face recognition system for the university in particular to identify students using the Internet is good and bad to identify, distinguish, for improving students&apos; studies has a significant effect. This paper studies the construction of intelligent database of face recognition based on Lingo3G clustering algorithm, mainly introduces the function and construction of intelligent database of face recognition and other related content. The test shows that the intelligent database construction of face recognition based on Lingo3G clustering algorithm greatly improves the accuracy of the database in face recognition.
Data mining in smart classroom management system

The rapid development of society shows that knowledge-based talents are no longer the main force to promote social progress. Instead, there is a demand for innovative talents. This demand makes us realize that we need to carry out new classroom reforms, so smart classroom education has emerged. Smart classrooms use modern Internet technology to make education intelligent, improve teaching quality and learning effect. This paper designs a smart classroom management system based on the B/S model and SSH framework. Using this system for teaching can realize various functions such as teaching interaction, sign-in recognition, and performance evaluation. Through data mining technology and the introduction of K-means algorithm into the system to cluster students&apos; grades, the results show that the more students who are clustered at a certain level, the greater the clustering error.
Experimental research on the colour interpolation algorithm of the full colour image

With the rapid development of multimedia technology, images have become the main data form of people's daily communication and information expression. In order to ensure fast data transmission, much effort has been made to compress the three-channel color image into the 2D matrix and then recover the image from the 2D matrix through interpolation algorithms. However, there usually exists different degrees of edge blurring or noise generation when reconstructing images, which significantly affects the accuracy of reconstruction. More importantly, we find that different interpolation algorithms may have drastic performance differences when dealing with different scene images. All of these aspects inspire us to explore the application boundary of interpolation algorithms. In this paper, we compare the representative bilinear interpolation, cok interpolation and Hibbard interpolation from the degree of edge blurring, the amount of noise generated and the processing speed. With extensive experiments, both visual and quantitative analysis shows an interesting but meaningful result. Three algorithms obtain a similar accuracy for the image with a single color or simple lines, while the Hibbard algorithm could restore the most realistic image when the image structure is complex.
Evaluation of Online Teaching based on SPSS Data Analysis

In order to study the overall situation of college English online teaching and students' online learning satisfaction during the COVID-19 epidemic, this paper made a questionnaire from four aspects: learning environment, teachers' teaching activities, students' learning activities, and online learning effect. Through SPSS23.0 software, the questionnaire data were analyzed by reliability analysis, validity analysis, principal component analysis, regression analysis and other methods, and the impacts of learning environment, teaching activities, learning activities, learning evaluation on students' learning satisfaction were studied.
Paraphrase Generator for Indonesian Language: Basic Method for Generating Question on Interviewbot Engine

An interview is a stage to dig up information for participants who want to know the profile and matching to the interviewer's needs. The development of interviews using bots has undergone many developments, one of which is by creating more natural conversational interactions. In the process of extracting information, sometimes bots don't get much information if participants only answer in a limited. Therefore, this study proposes a basic technique for generating questions to dig deeper into the information during the interview process. The basic technique used is to use a paraphrase. The proposed paraphrase is to use a structure change based on a predefined structure. The results of this study were carried out in the Indonesian language (Bahasa) and the sentence structure used was referring to the rules in the Indonesian language. The basic method used is using NLP (Natural Language Processing) and Rule Based Structure. The results of this research can be used as a basis for developing questions that will automatically be generated by bots for interview purposes.
Rational of Study: Speed up Production Development By DevOps Culture

Speed, quality, and frequency of the product are important that we must always improve so that can deliver the best service to be provided. Software processes affected by quality and time in all stages, one of the solutions is implement a DevOps culture to encourage of the improvement. Many option to improve the deployment, however some of the company especially in Indonesia does not implement the DevOps culture as suggested. This paper will validate and criticizes how effective the DevOps can affect the deployment speed. Context: The other way of thinking in domain of Software Engineer is DevOps which is the combination Developer and Operations that currently to be concerned. Objective: This study aims to find the other solution option specially to improve the product delivery, and maintain the performance and service availability in Software domain with a few of the tools that we use in DevOps. Impact on Society: This research will help the software engineer to improve deployment by implementing the DevOps culture with some of the tools
An efficient automatic scoring algorithm based on SQL code

Aiming at the nonstandard and imprecise evaluation of SQL (Structured Query Language) code in database teaching, an automatic scoring model of SQL code is established. Firstly, the model preprocesses the SQL code, analyzes the semantic structure, extracts the characteristics of the code, and performs word segmentation at the same time; Then build the synonym Library of SQL code, and convert the code fragments after word segmentation according to the synonym library; Then the longest common substring algorithm is used to match the similarity of code fragments, and then the influence factors of each fragment are given to obtain the total similarity of code; Finally, analyze the manual scoring data and formulate the corresponding automatic scoring strategy, so as to evaluate the score of SQL code. The model constructs a synonym database for the first time, which effectively improves the scoring efficiency of SQL code and can reach a more accurate level. Circular matching can also score long codes in SQL.
Design and Implementation of Self-Regulated Learning System for Equipment Theoretical Knowledge

According to the characteristics of the current equipment theory courses in the military academy and the needs of cadet, this paper studies and designs a set of self-regulated learning system for equipment theoretical knowledge whose operation function is based on Tabu Search-Parallel Genetic Algorithms (TPGA). The basic framework and functional composition of the system are constructed, and the system operation process and software interface are designed. It has been verified that the search and matching efficiency of the new algorithm has been greatly improved so that it can meet the requirements of the system which can provide technical support and example reference for further research and development of other learning systems.
Knowledge graph construction of multi-source heterogeneous academic resources based on big data technology

In the big data environment, academic communication resources suffer from multi-source heterogeneity and information overload, which makes it more difficult to integrate, manage, and share. Therefore, a knowledge graph architecture is proposed for academic communication resources based on the hierarchy of “Ontology-Resource-Description-Intelligence”. The key technologies for knowledge graph construction, including academic communication resources acquisition, knowledge fusion, and knowledge storage, are designed and implemented, in which it has more than 80,000 entities and 120,000 relationships. And on this basis, its future application and development are analyzed and discussed.
USED CAR CLUSTERING IN BANDUNG CITY USING K-PROTOTYPES ALGORITHMS

Since the case of the 2019 Coronavirus Disease pandemic or commonly referred to as Covid-19, the use of public transportation has slowly begun to become an option as transportation to reduce the spread of the corona virus cluster, therefore some people prefer to buy private vehicles. However, due to the increasing price of cars, some people prefer to buy used cars. On the used car buying and selling platform, OLX Autos Indonesia, the demand for used cars increased by 15% to 20%. Therefore, this study was conducted to determine the characteristics of the cluster formed from the used car sales dataset taken from AtapData (atapdata.ai). AtapData is an open data site in Indonesia that can be used for research related to Data Science. This cluster model was created using the K-Prototypes algorithm, Silhouette Score and Davies Bouldin Index to evaluate the resulting cluster results. This clustering model will produce three clusters. The results of the three clusters will have one thing in common, namely brands that dominate sales, including Toyota, Honda, Daihatsu, Nissan, and Mitsubishi. Clustering evaluation using the Silhouette Score method produces a value of 0.7744140503593034. And for the evaluation of the Davies-Bouldin Index it produces a value of 0.4999221950856398.
An entity linking method based on graph feature and electricity domain knowledge base

Entity linking refers to the process of mapping the entity reference in the text to the entity in the knowledge base, which is one of the key steps in the field of knowledge graph and knowledge fusion. We propose a joint graph feature entity linking method based on electrical knowledge base. Firstly, we construct a knowledge base with relatively rich entity information and professional characteristics of electricity domain, then identify the named entity mentions in the text. Based on the semantic similarity of multi features such as joint topic, context and metadata, the referenced linked entities are selected by restart random walk and joint disambiguation in the extended graph model. The experimental results show that the graph based joint feature entity linking method effectively improves the entity linking effect, especially in electricity domain.