Reviewerly/exHarmony huggingface dataset:
Diversity-driven Multi-population Particle Swarm Optimization for Dynamic Optimization Problem

Dynamic optimization problem (DOP) is a kind of problem that contains a series of static problems with different problem characteristics. The main idea of the existing dynamic optimization algorithms is to continuously locate and track changing optimal solutions using limited computational resources. Hence, how to strengthen the exploration ability for locating the optimum of the static problem in an environment and how to improve the adaptation ability to the changing optima in different environments are two key issues for efficiently solving DOP. To address these issues, we propose a diversity-driven multi-population particle swarm optimization (DMPSO) algorithm. First, we propose a center information-based update strategy to strengthen the exploration ability of the PSO algorithm in each subpopulation. Second, a stagnant subpopulation activation strategy is proposed to activate the stagnant subpopulations, and a random walk strategy is proposed to improve the optima tracking capability of the best-performing subpopulation. Third, an archive-based initialization strategy is proposed to reinitialize the population. Experimental studies are conducted on the moving peaks benchmark to compare the DMPSO algorithm with some state-of-the-art dynamic optimization algorithms. The experimental results show that the proposed DMPSO algorithm outperforms the contender algorithms which validate the effectiveness of the proposed algorithm.
Evolutionary computation for stochastic problems

tutorial Free Access Share on Evolutionary computation for stochastic problems Authors: Frank Neumann The University of Adelaide, Adelaide, Australia The University of Adelaide, Adelaide, Australia https://orcid.org/0000-0002-2721-3618Search about this author , Aneta Neumann The University of Adelaide, Adelaide, Australia The University of Adelaide, Adelaide, Australia https://orcid.org/0000-0002-0036-4782Search about this author , Hemant Singh University of New South Wales, Canberra, Australia University of New South Wales, Canberra, Australia https://orcid.org/0000-0003-1653-232XSearch about this author Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 1463–1476https://doi.org/10.1145/3583133.3595052Published:24 July 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Randomness in Local Optima Network Sampling

We consider statistical randomness in the construction of local optima networks (LONs) and conduct a preliminary and exploratory study into this. LONs capture a fitness landscape into network format: the nodes are local optima, and edges are heuristic search transitions between them. Problem instances from the benchmark quadratic assignment problem library are used in the analysis. LONs are constructed using an iterated local search (ILS) and several different random seeds. Metrics are computed from the networks and visualised to assess the effect of randomness. Algorithm performance models for ILS runtime are built using metrics of LONs constructed using different seeds and the results compared. The results show that some LON metrics seem consistent across seeds, while others vary substantially. Additionally, the quality of algorithm performance models using LON metrics as predictors can differ depending on randomness. Finally, LON metrics associated with separate seeds can lead to different algorithm configuration recommendations for the same instance.
On the prediction of source code design problems: A systematic mapping study

Context: Nowadays, the prediction of source code design problems plays an essential role in the software development industry, identifying defective architectural modules in advance. For this reason, some studies explored this subject in the last decade. Researchers and practitioners often need to create an overview of such studies considering the predictors of design problems, their main contributions, the used prediction techniques and research methods. Problem: However, the current literature remains scarce of studies proposing a detailed mapping of studies already published. Objective: This article, therefore, focuses on classifying the current literature and pinpointing trends and challenges worth investigating in this research field. Method: A systematic mapping of the literature was designed and performed based on well-established practical guidelines. In total, 35 primary studies were selected, analyzed, and categorized after applying a careful filtering process from a corpus of 894 candidate studies to answer six research questions. Results: The main results are that a majority of the primary studies (1) explore Bloater bad smells, (2) use code complexity and size as predictors, (3) apply machine learning techniques to generate predictions, and (4) present a prediction proposal without an extensive empirical assessment. Conclusions: Predicting design problems is still in its infancy, showing that there is plenty of room for future work. Finally, this study can serve as a starting point for the research community
The Use of Virtual Private Networks to Facilitate Remote Working: A Critical Review of Cyber Security Implications to Develop Successful VPN Systems

A significant expansion in monetary misfortunes gathering due to PC and organization assaults on RuNet objects demonstrates that current strategies for security are not adequately compelling. Examination of data security episodes shows that crooks to accomplish their goals apply different and multidirectional cyber assaults. At the point when the pandemic struck and education went online around the world, colleges needed to go with squeezing choices that decent cyber security against different variables, including well-being and security, convenience, and cost. A few colleges reflexively advanced virtual private organizations used for movements of every sort. However, due to the licences and resources needed for the large number of clients and the high-throughput applications they rely on, such a methodology would not have been feasible at IU. Maybe much more terrible, it would have expanded the possibility that the VPN would be inaccessible during a basic episode or other circumstance in which secure correspondences should be ensured.
Query-aware Explainable Product Search with Reinforcement Knowledge Graph Reasoning

Product search is one of the most effective tools for people to browse and purchase products on e-commerce platforms. Recent advances have mainly focused on ranking products by their likelihood to be purchased through retrieval models. However, they overlook the problem that users may not understand why certain products are retrieved for them. The lack of appropriate explanations can lead to an unsatisfactory user experience and further decrease user trust in the platforms. To address this problem, we propose a Query-aware Explainable Product Search with Reinforcement Knowledge Reasoning, namely <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QEPS</b> , which uses search behaviors related to the current query to reinforce explanations. Specifically, with the aim of retrieving suitable products with explanations, QEPS takes full advantage of the user-product knowledge graph (KG) and develops a reinforcement learning approach, characterized by the demonstration-guided policy network and query-aware rewards, to perform explicit multi-step reasoning on the KG. The reasoning paths between users and products are automatically derived from the current query-related search behavior, which can provide valuable signals as to why the retrieved products are more likely to satisfy the user's search intent. Empirical experiments on four datasets show that our model achieves remarkable performance and is able to generate reasonable explanations for the search results.
CLIPREC: Graph-based Domain Adaptive Network for Zero-Shot Referring Expression Comprehension

Referring expression comprehension (REC) is a cross-modal matching task that aims to localize the target object in an image specified by a text description. Most existing approaches for this task focus on identifying only objects whose categories are covered by training data. This restricts their generalization to unseen categories and practical usage. To address this issue, we propose a domain adaptive network called CLIPREC for zero-shot REC, which integrates the Contrastive Language-Image Pretraining (CLIP) model for graph-based REC. The proposed CLIPREC is composed of a graph collaborative attention module with two directed graphs: one for objects in an image and the other for their corresponding categorical labels. To carry out zero-shot REC, we leverage the strong common image-text feature space from the CLIP model to correlate the two graphs. Furthermore, a multilayer perceptron is introduced to enable feature alignment so that the CLIP model is adapted to the expression representation from the language parser, resulting in effective reasoning from expressions involving both seen and unseen object categories. Extensive experimental and ablation results on several widely-adopted benchmarks show that the proposed approach performs favorably against state-of-the-art approaches for zero-shot REC.
SimCLIC: A Simple Framework for Contrastive Learning of Image Classification

Contrastive learning, a self-supervised learning method, is widely used in image representation learning.The core idea is to close the distance between positive sample pairs and increase the distance between negative sample pairs in the representation space.Siamese networks are the most common structure among various current contrastive learning models.However, contrastive learning using positive and negative sample pairs on large datasets is computationally expensive.In addition, there are cases where positive samples are mislabeled as negative samples.Contrastive learning without negative sample pairs can still learn good representations.In this paper, we propose a simple framework for contrastive learning of image classification (SimCLIC).SimCLIC simplifies the Siamese network and is able to learn the representation of an image without negative sample pairs and momentum encoders.It is mainly by perturbing the image representation generated by the encoder to generate different contrastive views.We apply three representation perturbation methods, namely, history representation, representation dropoput, and representation noise.We conducted experiments on several benchmark datasets to compare with current popular models, using image classification accuracy as a measure, and the results show that our SimCLIC is competitive.Finally, we did ablation experiments to verify the effect of different hyperparameters and structures on the model effectiveness.
CageView: A Smart Food Control and Monitoring System for Phenotypical Research In Vivo

&lt;p&gt;The present work introduces an automated and smart system (named CageView) used to monitor a mouse, detect motion, and control access to food in accordance with experimental schedules. We describe the components of the CageView platform and give a summarized description on how we employed a convolutional neural network to detect and recognize a mouse in real time before presenting the results of a case study. In particular, CageView is a programmable and remotely operable system such that (1) an experimenter at a remote workstation may set up a feeding and fasting schedule that allows feeding and fasting without requiring the physical presence of a staff member, (2) the experimenter can control access to food in real time regardless of the preset schedule, (3) the experimenter has real-time access to a live video feed to assess the mouse, (4) an artificial intelligence system tracks the mouse’s location and physical activity, and (5) a record is kept of activity, which can be displayed as a 2D representation of mouse movement or a histogram showing mouse movement in 15-min blocks for the duration of the experiment.&lt;/p&gt;
Chinese medical entity recognition based on the dual-branch TENER model

Named Entity Recognition (NER) is a long-standing fundamental problem in various research fields of Natural Language Processing (NLP) and has been practiced in many application scenarios. However, the application results of NER methods in Chinese electronic medical records (EMRs) are not satisfactory, mainly due to the following two problems: (1) Existing methods do not take into account the impact of medical terminology on model recognition performance, resulting in poor model performance. (2) Existing methods do not fully utilize the Chinese language features contained in EMR, resulting in poor model robustness. Therefore, it is imminent to solve these two problems regarding the performance of the NER model for EMRs.In this paper, a TENER-based radical feature and entity augmentation model for NER in Chinese EMRs is proposed. The TENER model is first used in the pre-training stage to extract deep semantic information from each layer of the feature extractor. In the decoder part, the recognition of medical entity boundary and entity category are divided into two branch tasks.We compare the overall performance of the proposed model with existing models on different datasets using the computed F1 score evaluation metric. The experimental results show that our model achieves the best F1 score of 82.67%, 74.37%, 70.16% on the CCKS2019, ERTCMM, and CEMR data sets. Meanwhile, in the CMeEE challenge, our model surpassed the top-3 with the F1 score of 68.39%.Our proposed model is the first to divide the NER task into a two-branch tasks, entity boundary and types recognition. Firstly, the medical entity dictionary information is integrated into TENER to obtain the feature information of professional terms in Chinese EMRs. Secondly, the features of Chinese radicals in Chinese EMRs extracted by CNN are added to the entity category recognition task. Finally, the effectiveness of the model is validated on four datasets and competitive results are achieved.
Design by Contract Specification Languages

In the previous chapter, we saw different techniques to validate properties about software. However, if software uses complicated data structures that we wish to provide guarantees about, these techniques are limited in what they can achieve. This chapter introduces specifications using the Design by Contract approach, which allows one to specify precisely the intended behaviour of software that involve complex data structures and have unbounded state spaces. Chapter 8 discusses how to combine Design by Contract with abstraction. Chapters 9 and 10 discuss techniques to validate an implementation w.r.t. such a specification, using runtime and static verification techniques.
Runtime Annotation Checking

In the previous chapters, behavioural interface specification languages such as Acsl and Jml were introduced. Now that we have seen how we can specify code directly using such a specification language, the next question is how to validate that a program indeed respects its specification. We look at different techniques for this. This chapter discusses how a program can be instrumented in such a way that during execution, validity of the specifications can be checked. The last section of this chapter illustrates how this runtime annotation checking approach can be used to mimic monitoring of behavioural properties as discussed in Sect. 6.2 . Chapter 10 discusses an alternative approach to check the validity of a program w.r.t. its specification, namely statically at compile time, without actually executing the program.
Grado de aceptación de los sistemas de evaluación digitales adaptados al uso de recursos tecnológicos educativos basados en Realidad Aumentada

The use of immersive technologies, especially Augmented Reality (AR), is presently one of the main technological trends in the field of education. Some studies carried out in recent years in this field highlight that traditional evaluation systems continue to be used and raise the possibility of using adapted evaluation systems. This research, based on primary sources of information, seeks empirical evidence regarding the need to use digital evaluation systems adapted to technological resources in Science subjects and in stages of Compulsory Secondary Education. The methodology used is quantitative research based on the design and statistical analysis of the responses given by students to a questionnaire created ad hoc, administered to assess an AR educational resource used in class to explain a key concept in connection with the subject. The results obtained through the exploratory factorial analysis of the students’ answers reveal, as a latent construct, a high level of acceptance of the digital evaluation system used. The analysis of the data obtained in this study allows us to establish the hypothesis that a digital evaluation system adapted to the use of an immersive AR technological resource can generate a positive impact on the students' learning process.
Kétnyelvűséghez fűződő attitűdök vizsgálata többnyelvű környezetben élő főiskolai filológus hallgatók körében

The aim of this study was to investigate the attitudes of prospective language teachers toward bilingualism and language learning in a multilingual context.An online attitude survey was used to collect data from 185 students majoring in English, Hungarian, Ukrainian, and German language and literature at the Ferenc Rákóczi II Transcarpathian Hungarian College of Higher Education.Based on the results, college students are aware of the peculiarities of the Transcarpathian variety of the Hungarian language, even themselves use loanwords and codeswitching in their everyday communication.The respondents demonstrated positive attitudes toward bilingualism and foreign language learning, but the meaning of additive bilingualism is largely unknown to them, even though it is one of the basic principles of first and second language teaching in institutional contexts (Beregszászi 2012).In addition, most philology students considered only those people to be bilingual who are capable of controlling at least two languages at a native level (Bloomfield 1933), instead of the much more widely accepted functional approach (Bartha 2000).
Development of Kodular Based Interactive Teaching Materials in Meeting Organizing Materials in SMK Negeri 2 Buduran

This study describes the development of kodular-based interactive teaching materials in the essential competencies of holding meetings and making notes, analyzing the feasibility of teaching materials, analyzing student responses, and analyzing the effectiveness of using teaching materials to improve student learning outcomes at SMK Negeri 2 Buduran. This research is development research that refers to the 4D model: define, design, develop, and disseminate. Students from class XII OTKP 1 as the experimental class and class XII OTKP 2 as the control class were used as subjects and samples in this study. Material expert validation sheets, language, graphs, student response questionnaires, and student learning outcomes tests were all used as part of the data collection instrument. Expert validation of questions and materials, language, graphics, item analysis, N-Gain analysis, completeness of student learning outcomes, homogeneity test, normality test, and hypothesis testing are data analysis techniques used. The developed interactive teaching materials were scored in the appropriate category, with 87.6% material experts, 90.6 % linguists, and 94.2 % graphic experts, with the overall criteria assessed in the very decent range. Meanwhile, students' responses to the developed interactive teaching materials got a %age of 90.26% with excellent criteria. It can be concluded that the interactive teaching materials developed are effective as supporting teaching materials to improve student learning outcomes of SMK Negeri 2 Buduran in class XII OTKP.
Science Mapping of Library and Information Science (LIS) and Library Technology Studies in Indonesia

This study aims to map the knowledge of the Library and Information Science (LIS) study and its technology in Indonesia. Data were obtained from the Scopus database by entering keywords in the title: library; library science; technology libraries; and Indonesia, during 1974-2022 and received 87 documents. The method used was bibliometric, with Bibliometric and CiteSpace software. The results showed that the annual growth of LIS studies in Indonesia was still relatively low at 3.41%, and the highest average citations occurred in 2013. Indonesia dominated authors with the highest citations, and the most relevant sources were dominated by proceedings showing that the LIS study was still in its infancy, a less scientific research stage. Most cited journal articles in references. Indonesia was the country with the most frequency of collaboration, namely 21 collaborations. The keyword library was the most commonly used 349 times, then the technical terms that emerged included information system (5%), search engine (2%), automation, and big data, respectively (1%). Cluster #0 public library was the largest, and this cluster includes a smart public library, smart library, and smart environment in its cluster members.
PENERAPAN SISTEM INFORMASI AKUNTANSI DAN AKUNTABILITAS DALAM PENGELOLAAN DANA BANTUAN OPERASIONAL SEKOLAH (STUDI KASUS DI SMP NEGERI 4 KOTA MOJOKERTO)

UPT SMP NEGERI 4 is one of the educational units with a junior high school level which is addressed at Kel. Kranggan, Kec. Kranggan, Mojokerto City, East Java. In carrying out its activities, UPT SMP NEGERI 4 is under the auspices of the Ministry of Education and Culture. This study aims to analyze the extent to which accountability information systems are implemented in the management of school operational assistance funds at SMP Negeri 4 Mojokerto City. The method used is a qualitative method with a descriptive approach. Data collection techniques used are interviews and observation. The data analysis techniques used are: 1) data collection, 2) data reduction, 3) data display, 4) inference and verification. The results showed that the school's accounting information system and division of tasks had not been effective. Accountability for the management of BOS funds is good enough. Although some RKAS budgets have not been allocated properly. However, the procedure for reporting BOS funds complies with the Technical Instructions
Efficacy of English Phonics Instruction in Helping EFL Students to Decode Vowel Digraph Letters

The current study aimed to disclose how effective Phonics instruction is in increasing speech accuracy and glimpsing at students' perspectives on phonics learning in the classroom. This study applied quasi-experimental research involving 30 Department of Literature students learning English as a foreign language. The researcher also used the questionnaire to glimpse their perspective on phonics instruction. This study discovered that phonics learning significantly improved pronunciation accuracy and the ability to decode vowel digraph &lt;ai&gt;. In addition, students also have a positive view of phonics learning, which is one of the important materials at the beginning of the introduction of foreign language systems which helps them produce accurate utterances and helps improve literacy. The current study considers that phonics instruction needs to get important attention to become a solution to student learning problems.