Reviewerly/exHarmony huggingface dataset:
Image Dehazing Based on CycleGAN with an Enhanced Generator and a Multiscale Discriminator

Haze is a major issue that influences the image quality and impacts accuracy of computer vision tasks. We propose a novel algorithm for single image dehazing based on CycleGAN with an enhanced generator and a multiscale discriminator (DCEM). The proposed method generates dehazed images without relying on physical scattering models. The DCEM is based on CycleGAN and incorporates a pyramid pooling network in the enhanced generator that utilizes multiresolution inputs. The multiscale discriminator assists the generator in paying attention to details in the generated images with different scales. The proposed CycleGAN is evaluated on the NYU-Depth dataset and the RESIDE dataset. The results demonstrate that the proposed DCEM outperforms current methods.
An Efficient Regional Co-location Pattern Mining Algorithm Over Extended Objects Based on Neighborhood Distribution Relation Computation

Regional co-location patterns (RCPs) indicate the feature sets that generally co-occur within sub-regions but rarely co-occur in the whole study area. Spatial features have different densities and neighbor relationships in different regions, which poses great challenges to the two core steps of RCPs—regional partitioning and prevalence computation of RCPs in the sub-regions. Moreover, the existing RCPs mining methods are limited to point instances, while RCPs over extended spatial instances have not yet been explored. To address these challenges, this study develops a feature transaction method to detect RCPs and their prevalent regions. We first draw the radiation range of instances with distance buffers and generate feature transactions at buffer intersections between instances. Second, transactions are categorized into global prevalent and non-global prevalent. The locations of the non-global prevalent feature transactions are used to imply the range of candidate RCPs. In the local range, the distribution relation of the features is calculated to mine RCPs. We denote this process as neighborhood distribution relation computation. Experimental evaluations illustrate our proposed approach can capture more detailed RCPs information over extended instances and has better efficiency than the comparison algorithms.
GENERATING ARTIFICIAL TSUNAMI WAVEFORMS USING STATISTICAL METHOD

To assist the advancement of tsunami risk assessment methods for nuclear power plants, requirements for the generation of artificial tsunami waveforms crucial for close linkage between the probabilistic tsunami hazard analysis and tsunami fragility analysis were first listed. Then, basic studies were conducted to model the phase and amplitude spectra of waveforms observed during the 2011 Tohoku earthquake tsunami. Consequently, the average values of the group delay times and amplitude spectrum for each period band were confirmed to be effective for each modeling. Finally, based on these requirements and results of the basic studies, a technique for generating artificial tsunami waveforms using a statistical method was proposed.
Multiple Integer Divisions with an Invariant Dividend and Monotonically Increasing or Decreasing Divisors

In this paper, we propose an algorithm for multiple integer divisions with an invariant dividend and monotonically increasing or decreasing divisors. In such multiple integer divisions, we show that if the dividend and divisors satisfy a certain condition, then if only one quotient is calculated by division first, the remaining quotients can be obtained by correcting the previously calculated quotients at most once. The proposed algorithm is up to approximately 1.90 and 1.85 times faster than the 64-bit unsigned integer division instruction of the Intel 64 architecture and Intel Short Vector Math Library (SVML) on the Intel Xeon Platinum 8368 processor, respectively.
MDC: An Interpretable GNNs Method Based on Node Motif Degree and Graph Diffusion Convolution

Graph Neural Networks (GNNs) have achieved significant success in various real-world applications, including social networks, recommendation systems, and natural language processing. One of the challenges in GNNs is their black-box nature, which limits their interpretability and hinders their applications in critical areas such as healthcare and finance. However, existing interpretability of GNNs methods mainly focus on first-hop neighbors of a node and ignores multivariate relationship, resulting in low interpretability and inaccurate predictions. To tackle this challenge, we propose a Motif Diffusion Convolution (MDC) framework with a high-order structure feature augmentation mechanism based on graph diffusion convolution. The mechanism is utilizing multivariate relationship by calculating various node motif degree to enhance the feature channel, which obtains the relationship from a higher-order structure than two directly connecting entities. For better interpretability, MDC automatically learns a set of diffusion coefficients for each feature channel via graph diffusion convolution. We conduct experiments on six real-world datasets with three baselines. The experimental results show that MDC framework can build interpretability of GNNs, and improve accuracy by 2 $$\%$$ on node classification benchmarks.
A Preliminary Result of Implementing a Deep Learning-Based Earthquake Early Warning System in Italy

In this paper we present the preliminary results of a study using a deep-learning tool named LSTM (Long Short-Term Memory) network, to classify seismic events as near-source and far-source, with the final purpose of developing efficient earthquake early warning systems. We use a similar approach as in [15], applied to a database, named Instance, containing information about 54,008 earthquakes that occurred in Italy. Although these are preliminary results, the method shows a good ability to detect far-source events with an accuracy of about $$67\%$$ . For near-source events, the method shows an improvable result with an accuracy of $$57\%$$ .
Artificial Bee Colony Algorithm for Feature Selection in Fraud Detection Process

More and more, nowadays, better performance and quality of current classifiers are required when the topic is fraud detection. In this context, processes such as feature selection help to increase the quality of the results obtained by the existing classifiers in the literature, since the high dimensionality of current datasets and redundant information significantly affect the performance of these techniques. This work proposes a wrapper method of feature selection using the ABC algorithm combined with Logistic Regression classification, seeking to obtain better results for fraud detection. Through the tests performed and the results obtained, it is observed that the reduction in the number of features can reduce the database complexity and achieve a higher accuracy in classification when compared to the set classification when using all its attributes. It is also notable the effectiveness of the method as it reaches the proposed objective with as much as quality as other well-known methods while also contributing to optimizing parameters of other feature selection algorithms.
From Selecting Best Algorithm to Explaining Why It is: A General Review, Formal Problem Statement and Guidelines Towards to an Empirical Generalization

It has been observed on solution algorithms for problems as sorting, forecasting, classification, clustering, constraint satisfaction, decision, optimization from several disciplines (computational complexity theory, data mining, artificial intelligence, machine learning, operations research) that algorithm performance is better in certain problem instances than other. This paper describes how has been the way for trying to reach the empirical generalization for this phenomenon existing in the experimental relation problem – algorithm. For each understanding level, research questions, problem description were formulated, using the same Rice’s nomenclature and supplementing it; as well as, influence indexes and analysis approaches were described. A diagram about this long trajectory and a reflection is performed, highlighting contributions and scope. It shows that up to now the problem of explaining formally why an algorithm is the best for solving an instance set had remained open. A formal problem statement for describing this phenomenon and a general framework were proposed as a guide for working in adequate way toward generation of theories; which could contribute to build generalized indexes and self-adaptive algorithms to give the best solution to problems.
Location Recommendations Based on Multi-view Learning and Attention-Enhanced Graph Networks

Personalized location recommendation plays a very important role in location-based social networks, from which both users and service providers can benefit from. In spite of the significant endeavors that have been made towards acquiring knowledge on location attributes and user inclinations, it is still faced with serious data sparsity problems. In this paper, we propose a personalized location recommendation model based on graph neural networks with multi-view learning to obtain effective representations of mobile users and locations from different heterogeneous graphs. We also design an attention-enhanced mechanism to explore the implicit interactions between mobile users and locations themselves. Conducting adequate comparative experiments on two real-world telecom datasets has demonstrated that our model achieves superior performance. Additionally, our model has been proven effective in addressing data sparsity issues.
Enhancing Amazigh Speech Recognition System with MFDWC-SVM

In this research, a new Automatic Speech Recognition (ASR) system is introduced for the Amazigh language, more precisely Tachelhit, spoken in the High Atlas and the South-West of Morocco. The implementation of this system is a challenging task because the availability of Amazigh speech databases is limited, so the first step is to create a database and then to develop the system. Our database contains 33 letters and 10 digits (0 to 9). 120 speakers participated in the recording of the speech corpus: 80 adults (40 males and 40 females) and 40 children (20 boys and 20 girls). Among them, 60 are native speakers and the other 60 are speaking the language for the first time. After creating the database, we used Short-Time Energy (STE) to remove silence and compress the length of the input speech signals. Next, the features are extracted by a combination of two well-known approaches: the Mel-Frequency Cepstral Coefficients (MFCC) and the Discrete Wavelet Transform (DWT). The result is the Mel-Frequency Discrete Wavelet Coefficients (MFDWC) method. Finally, the classification step is conducted using Support Vector Machine (SVM) model. The experimental results prove that the system performs well by achieving an accuracy of 94.42% for the recognition of 10 digits, 90.82% for the recognition of 33 letters and 92.22% for the recognition of 33 letters and 10 digits.
Rethinking the Robustness of Graph Neural Networks

Graph neural networks (GNNs) have achieved tremendous success in many fields like social networks, citation networks and recommendation systems due to its powerful expression ability and excellent performance. However, GNNs are vulnerable to adversarial attacks which can significantly reduce its performance. Meanwhile, researches on the robustness of GNNs lack the exploration of its underlying mechanism, leading to the insufficiency of explaining adversarial attack. Thereby, the reasons for the vulnerability of the model and the process of adversarial attack need to be explained, which contributes to designing robust graph neural networks. To achieve this goal, we explore the robustness of GNNs from three viewpoints. Firstly, from the perspective of data pattern, we find that the more regular the data, the worse robustness of the model. Moreover, model factors based on the capacity and architecture are discussed. The results demonstrate that the model with large capacity has stronger robustness under the circumstance of adequate data, and the graph attention mechanism is more robust compared with other model architectures. Finally, in the view of the transferability of adversarial examples, we observe that adversarial examples are easily transferred among models with similar architectures, and thus these models are vulnerable to attacks.
Intelligent Government Decision-Making: A Multidimensional Policy Text Visualization Analysis System

Government decision-making refers to the process of making decisions by government departments in various domains, such as politics, economics, and culture, based on national, ethnic, and public interests, as well as the current realities. The rationality and scientificity of government decisions are essential to social development and people’s well-being. However, the increasing complexity of social and economic conditions requires comprehensive consideration of multiple factors and influences. Traditional decision-making methods, relying solely on expert experience and case studies, are insufficient to support effective government decision-making. They are time-consuming, subjective, and prone to limitations and uncertainties. Therefore, with the advent of the digital age, utilizing a large amount of digitized data has become a new challenge in government decision-making. To overcome these limitations, this article proposes a policy text multi-dimensional visualization analysis system that incorporates natural language processing technology and data visualization technology. The system enables decision-makers from different regions to learn from each other and those in the same region to coordinate at a top-level, avoiding policy overlap and disconnection. This system mines a large-scale historical policy text set from a semantic and temporal-spatial perspective, allowing decision-makers to explore similar policy cases and gain insights. Through analyzing three similar policy cases, the effectiveness of the system in supporting government decision-making is validated.
Meningkatkan Pengetahuan Mitigasi Bencana Pada Anak Usia Sekolah Melalui Media Game Edukasi “Utas-Gana” di Desa Pindahan Baru

This counselling and demonstration were conducted to analyze the improvement of disaster mitigation abilities in school-age children through the educational game media "UTAS-GANA" in the New Transfer Village, Rantau Badauh District, Batola Regency. The method used in this activity uses lecture and demonstration methods. This activity was held on Monday, March 7, 2021, in the Village of New Transfer, Rantau Badauh District, Batola Regency. The counselling and demonstrations show an increase in disaster mitigation abilities in school-age children. This activity uses media in the form of an educational game UTAS-GANA (snake and ladder-disaster alert). The learning process about disasters will be well recorded in students' long-term memory.
Teaching reform and practice of database principle and application

Aiming at the characteristics that the content of database principles and applications is difficult to understand and master, this paper first analyzes the comprehensive and practical characteristics of database principle and application course, and then systematically introduces some innovative practices of the author in textbook construction, teaching content reform and design ability training, including the construction of systematic case teaching content system; The reform of curriculum content system keeping pace with the times; The construction of innovative design ability training system; The practice of curriculum design that is closely integrated with application; The introduction of typical entrepreneurial innovation cases; The guidance of famous sayings and aphorisms compatible with the curriculum. The author's beneficial attempts and innovative practices in the construction of database principle and application courses and the training of applied computer talents are expected to play a certain enlightening role in the teaching of computer majors, teachers and courses in relevant colleges and universities.
Foundations and trends in the darknet-related criminals in the last 10 years: a systematic literature review and bibliometric analysis

Abstract After the Silk Road closure, many studies started focusing on the trend and patterns of darknet-related crimes in the 2010s. This first study combined a systematic literature review and bibliometric analysis in the field. This study clarifies 49 articles in criminology and penology among 1150 publications relating to the darknet on the Web of Science database to review and analyze the research evolutions of this topic in the last decade. The main findings point out (1) almost all leading authors with their most influential papers came from the Global South with predominant contributions; (2) unbalancing publications between regional scholars and their institutions and countries although the darknet-related criminals occurred and operated without border; and (3) some specialized themes have identified to call further extensive research such as policing interventions in the darknet and flows of the cryptocurrency in cryptomarkets, among others.
A Novel Anomaly Detection Method for Strip Steel Based on Multi-Scale Knowledge Distillation and Feature Information Banks Network

To address the problem of image imbalance in the surface inspection of strip steel, this study proposes a novel anomaly detection method based on multi-scale knowledge distillation (Ms-KD) and a block domain core information module (BDCI) to quickly screen abnormal images. This method utilizes the multi-scale knowledge distillation technique to enable the student network to learn the ability to extract normal image information under the source network pre-trained on ImageNet. At the same time, the optimal storage of block-level features is used to extract low-level and high-level information from intermediate layers and establish a feature bank, which is searched for core subset libraries using a greedy nearest neighbor selection mechanism. By using the Ms-KD module, the student model can understand the abnormal data more comprehensively so as to better capture the information in the data to solve the imbalance of abnormal data. To verify the validity of the proposed method, a completely new dataset called strip steel anomaly detection for few-shot learning (SSAD-FSL) was constructed, which involved image-level and pixel-level annotations of surface defects on cold-rolled and hot-rolled strip steel. By comparing with other state-of-the-art methods, the proposed method performs well on image-level area under the receiver operating characteristic curve (AUROC), reaching a high level of 0.9868, and for pixel-level per region overlap (PRO) indicators, the method also achieves the best score of 0.9896. Through a large number of experiments, the effectiveness of our proposed method in strip steel defect anomaly detection is fully proven.
Increasing efficiency of SVMp+ for handling missing values in healthcare prediction

Missing data presents a challenge for machine learning applications specifically when utilizing electronic health records to develop clinical decision support systems. The lack of these values is due in part to the complex nature of clinical data in which the content is personalized to each patient. Several methods have been developed to handle this issue, such as imputation or complete case analysis, but their limitations restrict the solidity of findings. However, recent studies have explored how using some features as fully available privileged information can increase model performance including in SVM. Building on this insight, we propose a computationally efficient kernel SVM-based framework (l2-SVMp+) that leverages partially available privileged information to guide model construction. Our experiments validated the superiority of l2-SVMp+ over common approaches for handling missingness and previous implementations of SVMp+ in both digit recognition, disease classification and patient readmission prediction tasks. The performance improves as the percentage of available privileged information increases. Our results showcase the capability of l2-SVMp+ to handle incomplete but important features in real-world medical applications, surpassing traditional SVMs that lack privileged information. Additionally, l2-SVMp+ achieves comparable or superior model performance compared to imputed privileged features.
THE INFLUENCE OF LOGISTIC ENGLISH PROFICIENCY TOWARDS WORKING ABILITY OF MARITIME UNIVERSITY ALUMNI

The long-term goal of this research is Logistic English which is needed by maritime students, especially the port management study program, due to they have to master it, especially ESP (English for Special Purpose) Logistics. In addition, many alumni work for foreign shipping companies. This inspires the writer to conduct this research. The study entitled "The Influence of Logistic English Proficiency Towards Working Ability of Maritime University Alumni" was to determine how much influence the Logistics English skills possessed by maritime college students in Semarang. The method used in this study, for the population is maritime school students in Semarang, Indonesia, while the sample is using purposive sampling. This type of research uses causality. Meanwhile, for the instrument testing is the instrument validity test and the reliability test. The data collection techniques used questionnaires, observation, literature study, interviews and documentation. Data processing techniques using editing, scoring and tabulation. The data analysis technique uses multiple regression analysis test and goodness of fit test. While to test the data requirements using the normality test, linearity test and classical assumption test. The main finding in this study is that there is a relationship or influence of “Logistic English Proficiency Towards Working Ability of Maritime University Alumni”. Conclusion There is an influence between AKPELNI, STIMART AMNI, PIP and POLIMARIN, where the result has positive influence.
The Effects of Chinese Perfective Aspect Marker <i>le</i> on the Simple Past Use in English Interlanguage

This study focuses on the effects of Chinese aspect marker le on English simple past use based on 90 English narratives in simple past context, and aims to address three questions: (1) Does Chinese aspect marker le significantly affect the English simple past use for Chinese EFL learners? (2) Does Chinese aspect marker le significantly affect the English simple past use for Chinese EFL learners of different proficiency levels? And (3) Does the effect of le display a balanced development with the increase of the proficiency level of the Chinese EFL learners? Through quantitative analysis of simple past variation, we mainly scrutinized the frequency and distribution features of the simple past, and the influence of Chinese perfective aspect marker le on the simple past forms and marking. The analysis shows that the presence of the aspect marker le can enhance the acquisition of English simple past. And the influence of the aspect marker le displays a balanced development: the influence of le decreases with the increase of the proficiency level of the Chinese EFL learners. This research tries to explain this phenomenon from the perspective of Universal Grammar.