Reviewerly/exHarmony huggingface dataset:
Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics

One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the exploration-exploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions.
Sentiment Analysis Using Machine Learning Algorithms

This work aims at building a classifier able of predicting the polarity of a comment while using Machine Learning (ML) algorithms. Our work is essentially divided into three tasks: data extraction, processing and modelling. In order to build our model, we use the NLTK dataset. Then, we use text mining techniques to generate and process the variables. Based on a supervised probabilistic machine learning algorithm, we tended to create a classifier to classify our tweets into positive and negative sentiments then we opt for two experiments to evaluate the performance of our model. Compered to previous reported works, we achieve greater precision.
The impact of perceived crisis severity on intention to use voluntary proximity tracing applications

During a crisis such as COVID-19, governments ask citizens to adopt various precautionary behaviours, such as using a voluntary proximity tracing application (PTA) for smartphones.However, the willingness of individual citizens to use such an app is crucial.Crisis decision theory can be used to better understand how individuals assess the severity of the crisis and how they decide whether or not to adopt the precautionary behaviour.We propose a research model to examine the direct influence of perceived crisis severity on intention to use the technology, as well as the indirect impact via PTAs' benefits for citizens.Exploratory and confirmatory factor analyses confirm the two dimensions of the benefits, namely personal and societal benefits.We used PLS-MGA to evaluate our research model.The results confirm the influence of the perceived severity of COVID-19 on the intention to use the PTA, as well as the mediating effects of personal and societal benefits on this relationship.Our findings contribute to the technology adoption literature and showcase the use of crisis decision theory in the field of information systems.
Finding the Number of Latent Topics With Semantic Non-Negative Matrix Factorization

Topic modeling, or identifying the set of topics that occur in a collection of articles, is one of the primary objectives of text mining.Typically, a text corpus is represented as a words-bydocuments matrix, X, where x ij , encodes the i-th word importance score in the j-th document using the Term Frequency-Inverse Document Frequency (TF-IDF) representation.Non-negative Matrix Factorization (NMF) can then be used in order to extract and model the topics in the corpus.NMF approximates X as a product of two low-rank non-negative factors: W , which represents the topics, and H, which specifies the coordinates of each of the documents in the topic-space.Semantic-assisted NMF (SeNMF) improves upon NMF by incorporating word-context and semantic correlations into the model, via adding a regularization term in the NMF minimization based on a Shifted Positive Pointwise Mutual Information (SPPMI) matrix M , which describes the mutual information between words and their context.In this paper, we consider a semantic-assisted NMF topics model, which we call SeNMFk, based on Kullback-Leibler divergence and integrated with a method for determining the number of latent topics.Determining the correct number of topics is extremely important: underestimating the number of topics results in a loss of information, i.e., omission of topics, underfitting, while overestimating leads to noisy and unexplainable topics and overfitting.Our method SeNMFk involves creating a random ensemble of pairs of matrices whose mean is equal to the initial TF-IDF matrix, X, and SPPMI matrix, M , respectively, and jointly factorizing each of these pairs with different number of topics to acquire sets of latent topics that are stable to noise.We demonstrate the performance of our method by identifying the number of topics in several benchmark text corpora, when compared to other state-of-the-art techniques.We also show that the number of document classes in the input text corpus may differ from the number of the extracted latent topics, but these classes can be retrieved by clustering the column-vectors of the matrix H.We demonstrate that our unsupervised method, SeNMFk, not only determines the correct number of topics, but also extracts topics with a high coherence and accurately classifies the documents of the corpus.
Analyzing blight impacts on urban areas: A multi-criteria approach

Often cities are tainted by degraded areas, abandoned and vandalized buildings, and vacant plots, which is commonly called "blight". Blight is a complex problem with varied consequences requiring integrated decision making at both individual and community levels. This is an important issue since residents of blighted areas live in environments that directly influence their quality of life, values and behaviors. This study conducts an economic, social, and environmental analysis regarding impacts, intermediation and eradication of blight. The complexity of blight requires an analysis encompassing both objective and subjective variables. Also, this study addresses limitations of prior research by applying a methodology that combines cognitive mapping and the decision-making trial and evaluation laboratory (DEMATEL) technique. Study results include a multi-criteria analysis system based on the inputs provided by a panel of experts. Although process-oriented, where results generally are not applicable from one application to another, our approach resulted in blight-caused impacts being allocated to four major clusters: governance; construction and real estate market; social dimensions and security; and health and environment. The findings were presented to and validated by a board member of the Instituto de Habitação e da Reabilitação Urbana (Institute of Housing and Urban Renewal of Portugal). Advantages and limitations of our approach are also discussed.
shinyReCoR: A Shiny Application for Automatically Coding Text Responses Using R

In this paper, we introduce shinyReCoR: a new app that utilizes a cluster-based method for automatically coding open-ended text responses. Reliable coding of text responses from educational or psychological assessments requires substantial organizational and human effort. The coding of natural language in responses to tests depends on the texts’ complexity, corresponding coding guides, and the guides’ quality. Manual coding is thus not only expensive but also error-prone. With shinyReCoR, we provide a more efficient alternative. The use of natural language processing makes texts utilizable for statistical methods. shinyReCoR is a Shiny app deployed as an R-package that allows users with varying technical affinity to create automatic response classifiers through a graphical user interface based on annotated data. The present paper describes the underlying methodology, including machine learning, as well as peculiarities of the processing of language in the assessment context. The app guides users through the workflow with steps like text corpus compilation, semantic space building, preprocessing of the text data, and clustering. Users can adjust each step according to their needs. Finally, users are provided with an automatic response classifier, which can be evaluated and tested within the process.
U-Net convolutional neural network models for detecting and quantifying placer mining disturbances at watershed scales

Placer mining is a mineral extraction method in floodplains that involves the removal of earth material to access mineral-laden sediments, a process that can have significant and long-term impacts on aquatic ecosystems. Given the widespread nature of mining, new tools are required to monitor the potential watershed-scale ecological impacts of placer mining. This study adapted and evaluated a deep learning model – a U-Net convolution neural network, and compared it to a traditional image classification method – random forests (RF) – to detect and quantify the area of post-placer mining disturbance at the watershed scale. Overall, both random forest and U-Net models performed well at classifying digitized image samples where placer disturbances were mapped. Sensitivity in placer classification was high, with both modelling frameworks achieving at least 75% accuracy in the classification of digitized placer samples in 7 out of 12 modelling scenarios. Misclassification of non-placer pixels as placer was highly variable among different models, data configurations, study sites, and time periods. Commission errors (i.e., incorrectly classifying a non-placer pixel as placer) were typically the result of models labelling water areas or forest areas as placer – errors which may have only marginal practical significance. In general, U-Net models performed better in terms of minimizing misclassification errors, whereas RF models performed slightly better in classifying known placer pixels. We conclude with discussions on the advantages of deploying U-Net and RF models for placer detection, challenges that may be encountered in operational systems that employ the models, and identifying outstanding issues which need to be addressed in future placer modelling studies.
Speech is Silver, Silence is Golden: What do ASVspoof-trained Models Really Learn?

We present our analysis of a significant data artifact in the official 2019/2021 ASVspoof Challenge Dataset. We identify an uneven distribution of silence duration in the training and test splits, which tends to correlate with the target prediction label. Bonafide instances tend to have significantly longer leading and trailing silences than spoofed instances. In this paper, we explore this phenomenon and its impact in depth. We compare several types of models trained on a) only the duration of the leading silence and b) only on the duration of leading and trailing silence. Results show that models trained on only the duration of the leading silence perform particularly well, and achieve up to 85% percent accuracy and an equal error rate (EER) of 0.15 (scale between 0 and 1). At the same time, we observe that trimming silence during pre-processing and then training established antispoofing models using signal-based features leads to comparatively worse performance. In that case, EER increases from 0.03 (with silence) to 0.15 (trimmed silence). Our findings suggest that previous work may, in part, have inadvertently learned thespoof/bonafide distinction by relying on the duration of silence as it appears in the official challenge dataset. We discuss the potential consequences that this has for interpreting system scores in the challenge and discuss how the ASV community may further consider this issue.
Standing out in a small crowd: The role of display size in attracting attention

Strong evidence supporting the top-down modulation of attention has come from studies in which participants learned to suppress a singleton in a heterogeneous four-item display. These studies have been criticized on the grounds that the displays are so sparse that the singleton is not actually salient. We argue that similar evidence of suppression has been found with substantially larger displays where salience is not in question. Additionally, we examine the results of applying salience models to four-item displays, and find prominent markers of salience at the location of the singleton. We conclude that small heterogeneous displays do not preclude strong salience signals. Beyond that, we reflect on how further basic research on salience may speed resolution of the attentional capture debate.
A Study on the Quality Evaluation of English Teaching Based on the Fuzzy Comprehensive Evaluation of Bat Algorithm and Big Data Analysis

In this paper, the fuzzy comprehensive evaluation model based on the bat algorithm quantifies the qualitative evaluation effectively and provides a feasible and convenient English teaching quality evaluation system by combining quantitative evaluation with objective index data. Firstly, the English teaching quality evaluation model is constructed based on the fuzzy comprehensive evaluation analysis method and the weight values of each factor are calculated; secondly, the three types of data in the model are processed separately. This includes standardizing the data of objective indicators such as students’ course grades and weakening the influence of course difficulty on this indicator. The fuzzy comprehensive evaluation model based on the bat algorithm quantifies the qualitative evaluation to make the calculated comprehensive evaluation of English teaching quality more comprehensive and objective; then the comprehensive calculation of English teaching quality evaluation is completed, and the English teaching quality evaluation model is constructed by extracting keywords based on the qualitative evaluation; finally, a runnable English teaching quality evaluation system is designed and implemented. A fuzzy comprehensive evaluation algorithm based on improved bat algorithm optimization is proposed. The algorithm uses the improved fuzzy comprehensive evaluation algorithm to optimize the initial clustering centers and adopts a new objective function to guide the clustering process, thus improving the clustering quality of the fuzzy comprehensive evaluation algorithm. Comparative analysis through models shows that the improved algorithm improves the clustering accuracy to a certain extent when compared with the traditional fuzzy comprehensive evaluation clustering algorithm for analysis. The bat algorithm is one of the stochastic global optimization models. It can take advantage of the group, integrate global search and local search, and achieve rapid convergence. Therefore, it plays an important role in optimizing the evaluation of English teaching quality. This study enriches the theoretical study of English teaching quality evaluation to a certain extent and can play a role in strengthening and improving English teaching quality evaluation at the present stage.
Hate speech operationalization: a preliminary examination of hate speech indicators and their structure

Abstract Hate speech should be tackled and prosecuted based on how it is operationalized. However, the existing theoretical definitions of hate speech are not sufficiently fleshed out or easily operable. To overcome this inadequacy, and with the help of interdisciplinary experts, we propose an empirical definition of hate speech by providing a list of 10 hate speech indicators and the rationale behind them (the indicators refer to specific, observable, and measurable characteristics that offer a practical definition of hate speech). A preliminary exploratory examination of the structure of hate speech, with the focus on comments related to migrants (one of the most reported grounds of hate speech), revealed that two indicators in particular, denial of human rights and promoting violent behavior, occupy a central role in the network of indicators. Furthermore, we discuss the practical implications of the proposed hate speech indicators—especially (semi-)automatic detection using the latest natural language processing (NLP) and machine learning (ML) methods. Having a set of quantifiable indicators could benefit researchers, human right activists, educators, analysts, and regulators by providing them with a pragmatic approach to hate speech assessment and detection.
CDNet: Centripetal Direction Network for Nuclear Instance Segmentation

Nuclear instance segmentation is a challenging task due to a large number of touching and overlapping nuclei in pathological images. Existing methods cannot effectively recognize the accurate boundary owing to neglecting the relationship between pixels (e.g., direction information). In this paper, we propose a novel Centripetal Direction Net-work (CDNet) for nuclear instance segmentation. Specifically, we define centripetal direction feature as a class of adjacent directions pointing to the nuclear center to rep-resent the spatial relationship between pixels within the nucleus. These direction features are then used to construct a direction difference map to represent the similarity within instances and the differences between instances. Finally, we propose a direction-guided refinement module, which acts as a plug-and-play module to effectively integrate auxiliary tasks and aggregate the features of different branches. Experiments on MoNuSeg and CPM17 datasets show that CDNet is significantly better than the other methods and achieves the state-of-the-art performance. The code is available at https://github.com/honglianghe/CDNet.
Modeling Heterogeneous Graph Network on Fraud Detection

Fraud activities in e-commerce, such as spam reviews and fake shopping behaviors, significantly mislead customers' decision making, damage the platforms' reputation, and reduce enterprises' revenue. In recent years, GNN-based models have been widely adopted in fraud detection tasks, which have shown better performance compared to conventional rule-based methods and feature-based models. Most GNN-based models focus on homogeneous graphs, usually including user-to-user, or item-to-item connections. These types of graphs have limitations of eliminating certain types of connections, such as user-item connections. In addition, GNN-based models aggregate neighborhood information based on the assumption that neighbors share the similar structure and content. However, in fraud detection tasks, two major inconsistency issues arise: Severe mixture of structure-inconsistency due to extremely unbalanced positive and negative samples; and mixture of content-inconsistency due to the difference between various item categories. To address the above issues, we propose a Community-based Framework with ATtention mechanism for large-scale Heterogeneous graphs (C-FATH). In order to utilize the entire heterogeneous graph, we directly model on the heterogeneous graph and combine it with homogeneous graphs. The structure-inconsistent nodes are filtered by introducing the community information when constructing neighbors. Content-inconsistent nodes are selected with lower probability by a similarity-based sampling strategy. Further, the model is trained in a multi-task manner that each node type (e.g. user, item, device, order, and review) is associated with a specific loss function. Comprehensive experiments are conducted on two public review datasets and two large-scale datasets from JD.com, and the experimental results demonstrate the effectiveness and scalability of the proposed C-FATH compared to the state-of-the-art approaches.
A GNN-based Recommender System to Assist the Specification of Metamodels and Models

Nowadays, while modeling environments provide users with facilities to specify different kinds of artifacts, e.g., metamodels, models, and transformations, the possibility of learning from previous modeling experiences and being assisted during modeling tasks remains largely unexplored. In this paper, we propose MORGAN, a recommender system based on a graph neural network (GNN) to assist modelers in performing the specification of metamodels and models. The (meta)model being specified, and the training data are encoded in a graph-based format by exploiting natural language processing (NLP) techniques. Afterward, a graph kernel function uses the extracted graphs to provide modelers with relevant recommendations to complete the partially specified (meta)models. We evaluated MORGAN on real-world datasets using various quality metrics, i.e., precision, recall, and F-measure. The experimental results are encouraging and demonstrate the feasibility of our tool to support modelers while specifying metamodels and models.
Blockchain-Enhanced Spatiotemporal Data Aggregation for UAV-Assisted Wireless Sensor Networks

Wireless sensor networks (WSNs) are widely used in the field of monitoring. For data collection of sensor nodes in large-scale monitoring scenarios, unmanned aerial vehicle (UAV)-assisted WSNs have emerged. For the security and validity of data collection, a blockchain-enhanced data collection framework for UAV-assisted WSNs is presented in this article. To reduce data redundancy in WSNs, a sparsity-optimized and compressed sensing-based spatiotemporal data aggregation model is built. A UAV identity authentication mechanism based on a Merkle tree is also designed to ensure the security of data transmission. By combining blockchain building and data aggregation, a disaster semantic blockchain (DSB) based on a data reconstruction-directed consensus mechanism is presented. Disaster semantics are extracted by analyzing the semantic association relationship of disaster, background, event, and sensor data. The experimental results show that the blockchain-enhanced spatiotemporal data aggregation effectively increases the network life cycle and data reconstruction accuracy. The disaster situation can be described accurately through the DSB.
Exploration on the Teaching Reform Measure for Machine Learning Course System of Artificial Intelligence Specialty

Due to the particularity of the artificial intelligence major and the machine learning courses learned, the traditional course teaching model is not suitable for artificial intelligence major machine learning courses. Based on this background, this article proposes a new system based on machine learning curriculum teaching reform. It mainly includes the reform of curriculum teaching mode, curriculum practice reform, and teaching process reform. In order to verify the effect of the proposed new model on the teaching quality of machine learning courses, this article also proposes an evaluation method based on intelligent technology. Firstly, the feasibility of evaluation based on intelligent technology is described. Secondly, it lists the application details of the existing teaching evaluation based on intelligent technology. Finally, a novel teaching quality evaluation system based on intelligent technology is proposed. The system collects student facial expression data and uses classification algorithms to make classification decisions on the data. The result of the decision can give feedback on the quality of classroom teaching. The comparison of experiments based on different intelligent technologies shows that the teaching quality evaluation system proposed in this article is feasible and effective.