Reviewerly/exHarmony huggingface dataset:
Join query optimisation in the distributed databases using a hybrid harmony search and artificial bee colony algorithm

The query optimiser is a vital part of any distributed database mechanism. Reducing the execution period of the query depends on reaching an ideal query execution plan. Due to this issue's NP-hard nature, a hybrid harmony search and an artificial bee colony algorithm can be useful. The harmony is used to call query plans and signify them by S-dimension real vectors. A harmony memory is a place for creating and storing a primary population of harmony vectors. Then, bees explore harmony memory as a food source. The production of a novel nominate harmony out of all query plans in the harmony memory requires a pitch adjustment principle, a memory consideration one, and a random re-initialisation. Lastly, the new candidate vector replaces the worst harmony vector when it works better. The simulation outcomes have indicated that the introduced method reduces the expenses of evaluating a query compared to the harmony search and bee colony optimisation algorithms. However, this method has a longer execution time.
A Reinforcement-Learning-Based Evolutionary Algorithm Using Solution Space Clustering For Multimodal Optimization Problems

In evolutionary algorithms, how to effectively select interactive solutions for generating offspring is a challenging problem. Though many operators are proposed, most of them select interactive solutions (parents) randomly, having no specificity for the features of landscapes in various problems. To address this issue, this paper proposes a reinforcement-learning-based evolutionary algorithm to select solutions within the approximated basin of attraction. In the algorithm, the solution space is partitioned by the k-dimensional tree, and features of subspaces are approximated with respect to two aspects: objective values and uncertainties. Accordingly, two reinforcement learning (RL) systems are constructed to determine where to search: the objective-based RL exploits basins of attraction (clustered subspaces) and the uncertainty-based RL explores subspaces that have been searched comparatively less. Experiments are conducted on widely used benchmark functions, demonstrating that the algorithm outperforms three other popular multimodal optimization algorithms.
Education in times of crises: the dilemmas of digital teaching and learning in primary and secondary schools during the COVID-19 pandemic

Click to increase image sizeClick to decrease image size Disclosure statementNo potential conflict of interest was reported by the author(s).
Adaptive-Weighted Multiview Deep Basis Matrix Factorization for Multimedia Data Analysis

Feature representation learning is a key issue in artificial intelligence research. Multiview multimedia data can provide rich information, which makes feature representation become one of the current research hotspots in data analysis. Recently, a large number of multiview data feature representation methods have been proposed, among which matrix factorization shows the excellent performance. Therefore, we propose an adaptive-weighted multiview deep basis matrix factorization (AMDBMF) method that integrates matrix factorization, deep learning, and view fusion together. Specifically, we first perform deep basis matrix factorization on data of each view. Then, all views are integrated to complete the procedure of multiview feature learning. Finally, we propose an adaptive weighting strategy to fuse the low-dimensional features of each view so that a unified feature representation can be obtained for multiview multimedia data. We also design an iterative update algorithm to optimize the objective function and justify the convergence of the optimization algorithm through numerical experiments. We conducted clustering experiments on five multiview multimedia datasets and compare the proposed method with several excellent current methods. The experimental results demonstrate that the clustering performance of the proposed method is better than those of the other comparison methods.
Sentence Complexity in Context

We study the influence of context on how humans evaluate the complexity of a sentence in English.We collect a new dataset of sentences, where each sentence is rated for perceived complexity within different contextual windows.We carry out an in-depth analysis to detect which linguistic features correlate more with complexity judgments and with the degree of agreement among annotators.We train several regression models, using either explicit linguistic features or contextualized word embeddings, to predict the mean complexity values assigned to sentences in the different contextual windows, as well as their standard deviation.Results show that models leveraging explicit features capturing morphosyntactic and syntactic phenomena perform always better, especially when they have access to features extracted from all contextual sentences.
Mikro E-Etkileyici Kişi Bağlamında Marka Tutumu Oluşumu

Brand Attitude Creation in the Context of Micro-InfluencerIt is essential to reach a specific target audience with similar characteristics through messages that will meet their expectations for achieving the goals in creating a brand attitude.This study mainly aims; to evaluate the relationship between consumer's attitudes towards brands, which use micro-influencers in their communication campaigns on Instagram.Additionally, it was aimed to determine the consumer's demographic characteristics, their daily Instagram usage frequency, the number of micro-influencers they follow on Instagram, finding micro-influencers reliable, and their purchase status of a product recommended by the micro-influencers.In the present study, following a quantitative approach, a face-to-face questionnaire technique was used to obtain objective results by specifying the figures to be analyzed statistically.According to the research findings, women have a more positive attitude towards brands that use micro-influencers than men.Besides, findings showed that consumers who spend 4 hours or more time on Instagram and following 10 or more micro-influenc-
Learning Task-Distribution Reward Shaping with Meta-Learning

Reward shaping is one of the most effective methods to tackle the crucial yet challenging problem of credit assignment and accelerate Reinforcement Learning. However, designing shaping functions usually requires rich expert knowledge and hand-engineering, and the difficulties are further exacerbated given multiple tasks to solve. In this paper, we consider reward shaping on a distribution of tasks that share state spaces but not necessarily action spaces. We provide insights into optimal reward shaping, and propose a novel meta-learning framework to automatically learn such reward shaping to apply on newly sampled tasks. Theoretical analysis and extensive experiments establish us as the state-of-the-art in learning task-distribution reward shaping, outperforming previous such works (Konidaris and Barto 2006; Snel and Whiteson 2014). We further show that our method outperforms learning intrinsic rewards (Yang et al. 2019; Zheng et al. 2020), outperforms Rainbow (Hessel et al. 2018) in complex pixel-based CoinRun games, and is also better than hand-designed reward shaping on grids. While the goal of this paper is to learn reward shaping rather than to propose new general meta-learning algorithms as PEARL (Rakelly et al. 2019) or MQL (Fakoor et al. 2020), our framework based on MAML (Finn, Abbeel, and Levine 2017) also outperforms PEARL / MQL, and could combine with them for further improvement.
Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression

Gaussian Process regression is a popular nonparametric regression method based on Bayesian principles that provides uncertainty estimates for its predictions. However, these estimates are of a Bayesian nature, whereas for some important applications, like learning-based control with safety guarantees, frequentist uncertainty bounds are required. Although such rigorous bounds are available for Gaussian Processes, they are too conservative to be useful in applications. This often leads practitioners to replacing these bounds by heuristics, thus breaking all theoretical guarantees. To address this problem, we introduce new uncertainty bounds that are rigorous, yet practically useful at the same time. In particular, the bounds can be explicitly evaluated and are much less conservative than state of the art results. Furthermore, we show that certain model misspecifications lead to only graceful degradation. We demonstrate these advantages and the usefulness of our results for learning-based control with numerical examples.
CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection

Traditional toxicity detection models have focused on the single utterance level without deeper understanding of context.We introduce CONDA, a new dataset for in-game toxic language detection enabling joint intent classification and slot filling analysis, which is the core task of Natural Language Understanding (NLU).The dataset consists of 45K utterances from 12K conversations from the chat logs of 1.9K completed Dota 2 matches.We propose a robust dual semantic-level toxicity framework, which handles utterance and token-level patterns, and rich contextual chatting history.Accompanying the dataset is a thorough in-game toxicity analysis, which provides comprehensive understanding of context at utterance, token, and dual levels.Inspired by NLU, we also apply its metrics to the toxicity detection tasks for assessing toxicity and game-specific aspects.We evaluate strong NLU models on CONDA, providing fine-grained results for different intent classes and slot classes.Furthermore, we examine the coverage of toxicity nature in our dataset by comparing it with other toxicity datasets.
A Simulated Annealing Approach to Coordinated Motion Planning (CG Challenge)

The third computational geometry challenge was on a coordinated motion planning problem in which a collection of square robots need to move on the integer grid, from their given starting points to their target points, and without collision between robots, or between robots and a set of input obstacles. We designed and implemented an algorithm for this problem, which consists of three parts. First, we computed a feasible solution by placing middle-points outside of the minimum bounding box of the input positions of the robots and the obstacles, and moving each robot from its starting point to its target point through a middle-point. Second, we applied a simple local search approach where we repeatedly delete and insert again a random robot through an optimal path. It improves the quality of the solution, as the robots no longer need to go through the middle-points. Finally, we used simulated annealing to further improve this feasible solution. We used two different types of moves: We either tightened the whole trajectory of a robot, or we stretched it between two points by making the robot move through a third intermediate point generated at random.
Logic-Sensitivity of Aristotelian Diagrams in Non-Normal Modal Logics

Aristotelian diagrams, such as the square of opposition, are well-known in the context of normal modal logics (i.e., systems of modal logic which can be given a relational semantics in terms of Kripke models). This paper studies Aristotelian diagrams for non-normal systems of modal logic (based on neighborhood semantics, a topologically inspired generalization of relational semantics). In particular, we investigate the phenomenon of logic-sensitivity of Aristotelian diagrams. We distinguish between four different types of logic-sensitivity, viz. with respect to (i) Aristotelian families, (ii) logical equivalence of formulas, (iii) contingency of formulas, and (iv) Boolean subfamilies of a given Aristotelian family. We provide concrete examples of Aristotelian diagrams that illustrate these four types of logic-sensitivity in the realm of normal modal logic. Next, we discuss more subtle examples of Aristotelian diagrams, which are not sensitive with respect to normal modal logics, but which nevertheless turn out to be highly logic-sensitive once we turn to non-normal systems of modal logic.
Building Information Modeling Methods for Post-Earthquake Retrofitting Visualization of Buildings Using Augmented Reality

The post-earthquake retrofitting and repair process of a building is a key factor in improving its seismic capability. A thorough understanding of retrofitting methods and processes will aid in repairing post-earthquake buildings and improving seismic resilience. This study aims to develop a visualization framework for the post-earthquake retrofitting of buildings which builds models based on building information modeling (BIM) and realizes visualization using augmented reality (AR). First, multi-level representation methods and coding criteria are used to process the models for a damaged member. Then, an information collection template is designed for integrating multi-dimensional information, such as damage information, retrofitting methods, technical solutions, and construction measures. Subsequently, a BIM model is presented in three dimensions (3D) using AR. Finally, the visualization process is tested through experiments, which demonstrate the feasibility of using the framework to visualize the post-earthquake retrofitting of a building.
Point Based Forecasting Model of Vehicle Queue with Extreme Learning Machine Method and Correlation Analysis

Traffic is a medium to move from one point to another. Therefore, the role of traffic is very important to support vehicle mobility. If congestion occurs, mobility will be hampered so that it gives influence to other sectors such as financial, air pollution and traffic violations. This study aims to create a model to predict vehicle queue at the traffic lights when its status is red. The prediction is conducted by using Neural Network with Extreme Learning Machine method to predict the length of the vehicle queue, and Correlation Analysis was used to measure the correlation between the connected roads. The conducted experiments use data of the length of the vehicle queue at the traffic lights which was obtained from DISHUB (Transportation Bureau) DI Yogyakarta. Several experiments were carried out to determine the optimum prediction model of vehicle queue length. The experiments found that the optimum model had an average MAPE value of 15.5882% and an average Running Time of 5.2226 seconds.
Assessment of the readability of online texts related to specific learning disorder

The aim of this study was to investigate the readability and content of texts on specific learning disorder published on Turkish websites. In the study, the first three hundred websites which were accessed by writing three main word groups (dyslexia, learning disability, specific learning disorder) to the search engine in July 2019 were evaluated. Of these, chat and forum sites, commercial sales sites, the sites that contained advertising, video and pictures only and less than 10 sentences of information, and news sites that do not contain information about the disorder were excluded. One hundred and two websites remained after the exclusion criteria were applied. Websites are classified according to their makers. The readability values of the texts were calculated using the Ateþman and Bezirci-Yýlmaz formulas. The contents of the text were compared according to the subject headings. There was no significant difference between the groups in terms of readability values. The readability values of the groups were found to be at the level of undergraduate education. As a result, in our study, it was observed that the readability of websites prepared for specific learning disorder was low. It was thought that this result may adversely affect the awareness about the disorder and may reduce the possibility of early diagnosis and treatment in children.
A Training-free and Reference-free Summarization Evaluation Metric via Centrality-weighted Relevance and Self-referenced Redundancy

Wang Chen, Piji Li, Irwin King. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.
QoE-Aware Container Scheduler for Co-located Cloud Environments

Cloud management has traditionally considered Service Level Objectives (SLO) based on QoS metrics. However, QoS-focused metrics have a limited effect on the Quality of Experience (QoE) experienced by the clients. This paper proposes a Kubernetes scheduler extension and resource rescheduling that incorporates QoE metrics into SLOs. As a proof of concept, this work evaluates the architecture using the QoE metric proposed in the ITU P.1203 standard, in the context of video streaming services co-located with other services. Experimental results show that our scheduler improves the average QoE by 50% compared to other schedulers, while resource rescheduling improved the average QoE by 135%. In addition, our architecture eliminated over-provisioning altogether.
UI/UX integrated holistic monitoring of PAUD using the TCSD method

User interface (UI)/user experience (UX) is one part of the stages in the development of the system to produce interactive and attractive web-based application layouts so that it is easy to understand and use by users. In this research, a case study was conducted on early childhood in PAUD Kuntum Mekar. The design of the UI/UX model for holistic integrative PAUD monitoring becomes one of the solutions to help parents and teachers. The method used to design UI/UX is the task centered system design (TCSD) approach starting from the stages; 1) identification scope of use, 2) user centered requirement analysis, 3) design and scenario, and 4) walkthrough evaluate, the method used for system testing is user satisfaction, and heuristic usability. The purpose of this study is the UI/UX design with TCSD can provide valid data needs of each actor based on the assignment and design of the story board of the developed system. The results of this research are UI/UX model design for integrative holistic PAUD monitoring application.
A Systematic Review on Image Encryption Techniques

In the form of text, audio, videos and photographs, the Internet is a commonly used tool to exchange details. The long-distance exchange of information on a wide network needs encryption to secure the information from unauthorized access. The reliability of the network plays an important role in protecting data on an insecure network. Many encryption solutions have been discovered to protect the data on the network, and recent creative encryption schemes have been in demand since e-commerce e-banking and multimedia technologies are viewed on a regular basis on the internet. Cryptographic coding methods have lately been used primarily to protect unauthorized access to knowledge on an insecure network. The researchers also established many cryptographic strategies for protecting and efficiently transmitting knowledge on an insecure network. Image encryption techniques are commonly used by all cryptographic techniques to transmit photos on an insecure network. The purpose of this paper is to demonstrate the few encryption techniques that are used on an insecure network to encrypt the image. To encrypt the image, this paper proposes a new encryption technique. The suggested approach encrypts the image using Wavelet Transform, the Chaotic Mechanism, along with the production of the image fingerprint using the Hash function to be sent to the recipient. The suggested procedure, along with anonymity, often protects the image's encryption.
Changes in Core Competencies among Korean University Students Due to Remote Learning during the COVID-19 Pandemic

This study aimed to determine the academic performance and learning skills of students who studied through remote teaching methods during the coronavirus disease 2019 (COVID-19) pandemic. It was conducted in February 2021 with 398 university students in South Korea. Data were collected through online surveys. Generalized estimating equations (GEEs) with an autoregressive correlation structure were employed to distinguish differences in core competencies, academic performance, satisfaction, and usefulness of teaching methods before 2019 and after the COVID-19 pandemic. The findings revealed that the overall core competencies of participants were significantly lower in 2020 than before the COVID-19 pandemic. Furthermore, knowledge construction, responsibility practice, and socialization were significantly low during the COVID-19 pandemic, whereas information management and identity value did not show a significant difference. However, problem-solving was higher during the COVID-19 pandemic. Enhancing the core competencies of university students is integral in the new learning environment of the post-COVID-19 era. It is necessary to devise approaches that improve the effectiveness of remote teaching methods and simultaneously augment student satisfaction.
New Vector-Space Embeddings for Recommender Systems

In this work, we propose a novel recommender system model based on a technology commonly used in natural language processing called word vector embedding. In this technology, a word is represented by a vector that is embedded in an n-dimensional space. The distance between two vectors expresses the level of similarity/dissimilarity of their underlying words. Since item similarities and user similarities are the basis of designing a successful collaborative filtering, vector embedding seems to be a good candidate. As opposed to words, we propose a vector embedding approach for learning vectors for items and users. There have been very few recent applications of vector embeddings in recommender systems, but they have limitations in the type of formulations that are applicable. We propose a novel vector embedding that is versatile, in the sense that it is applicable for the prediction of ratings and for the recommendation of top items that are likely to appeal to users. It could also possibly take into account content-based features and demographic information. The approach is a simple relaxation algorithm that optimizes an objective function, defined based on target users’, items’ or joint user–item’s similarities in their respective vector spaces. The proposed approach is evaluated using real life datasets such as “MovieLens”, “ModCloth”, “Amazon: Magazine_Subscriptions” and “Online Retail”. The obtained results are compared with some of the leading benchmark methods, and they show a competitive performance.