Reviewerly/exHarmony huggingface dataset:
LRCDNeT: A Lightweight and Real-Time Cloud Detection Network for Remote Sensing Images

Most CNN-based cloud detection methods have high computational complexity, large parameter size, and slow inference speed, which limit their practical applications. Furthermore, cloud detection is a challenging task due to the irregular shapes and random sizes of clouds, often leading to inaccurate detection. To overcome these challenges, we propose a lightweight and real-time network (LRCDNet) tailored for cloud detection. By incorporating Short-Term Dense Concatenate (STDC) module, Multi-group Deformable Convolution (DCNv3) and multiple linear self-attention mechanisms, LRCDNet can effectively extract detail information and adaptively build long-term dependencies. In comparison to the state-of-the-art cloud detection and typical real-time semantic segmentation methods, our proposed LRCDNet strikes a better balance between accuracy and computational costs. Specifically, when tested on the GF-1 WHU dataset, LRCDNet achieves an overall accuracy (OA) of 97.37%, a F1-score of 92.42%, and a remarkable inference speed of 122.83 Frames Per Second (FPS) on a RTX A4000 GPU, with only 19.72MB parameters and 10.69G FLOPs calculations.
Deep Learning Powered Non-Local Speckle Filtering of Sentinel-1 Imagery and its Potential for Humanitarian Action

The continuously increasing amount of Synthetic Aperture Radar (SAR) data is becoming an important complementary source of information for humanitarian action especially when the availability of optical remote sensing is limited. The SAR-inherent speckle effect remains an obstruction to SAR image interpretation and automated analysis. Recently, deep learning-based despeckling techniques such as supervised non-local methods have shown excellent speckle reduction for very high resolution (VHR) SAR data. Because the approaches have been mostly applied to VHR commercial SAR datasets, this work transfers, adapts and tests an existing deep learning-based non-local speckle filter on medium-resolution Sentinel-1 data. The results show that adaptations to the model lead to superior filtering results compared to conventional filters while simultaneously yielding better performance when the filtered data is used in a downstream task relevant to humanitarian application - dwelling change detection in forcibly displaced population settlement areas.
Research on the application of artificial intelligence in computer network technology based on the era of big data

With the arrival of the era of big data, the value of artificial intelligence technology has become more and more prominent, and its application in computer network technology has become more extensive. In practice, the application of artificial intelligence in computer network technology can greatly improve the level of security protection of computer networks, and can improve the level of network management, and can better play the advantages of computer network technology. In this regard, this paper firstly introduces big data and artificial intelligence, and analyses the necessity of applying artificial intelligence in computer network technology, and on this basis proposes the specific application of artificial intelligence in computer network technology.
Hyperspectral Image Destriping and Denoising with Spectral Low Rank and Tensor Nuclear Norm

In this paper, we propose a new method for simultaneous hyperspectral image (HSI) destriping and denoising with spectral low-rank and tensor nuclear norm under the tensor framework. Specifically, the tensor nuclear norm is used to model the tensor low-rank property of stripe. Moreover, the nuclear norm is used to model the low-rank property of spectral gradient of HSI. Then, the ADMM algorithm is used to effectively solve the proposed model. Experimental results on simulated HSI dataset and real HSI dataset verify the superiority of the proposed method.
An Overview of Geologic and Environmental Applications of HISUI

Hyperspectral Imager Suite (HISUI) is a Japanese spaceborne imaging spectrometer observing the Earth from International Space Station from 2019. HISUI has acquired more than 200 M km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of hyperspectral images for terrestrial surface. In this presentation, latest examples of geologic and environmental applications of HISUI data such as mineral mapping and methane plume detection.
Integrated Data Cubes for Large-Scale Spatiotemporal Analysis of Remote Sensing Data in ArcGIS

Advancements in sensor technology have led to significant growth in the volume and variety of geospatial data. Optical, radar, lidar, and sonar sensor technologies have presented us with valuable opportunities to understand our changing world. However, a key challenge still lies in developing data models that can efficiently manage diverse types of spatial and temporal data collected from these sensors to enable information extraction. In this paper, we describe the integrated data cubes within ArcGIS that effectively handle spatial and temporal data. Our approach includes a mosaic dataset for managing satellite time series and gridded climate data, a cloud raster format (CRF) file for storing pixel-aligned analysis-ready data, and a trajectory dataset for managing points from altimetry sensors. We present several use cases to demonstrate the analysis capabilities based on these data models.
Remote Viewing: A 1974-2022 Systematic Review and Meta-Analysis

This is the first meta-analysis of all studies related to remote-viewing tasks conducted up to December 2022. After applying our inclusion criteria, we selected 36 studies with a total of 40 effect sizes. Both frequentist and Bayesian meta-analyses revealed a strong average effect size of .34; 95% confidence interval: .22 -.45, after the exclusion of outliers, without signs of publication bias and a minimal decline effect. In terms of raw scores, these average results correspond to a difference in hits score of 19.3%; 95% confidence intervals:13.6%–25%, above the expected chance. Among the meta-analyses of moderators, a small nonstatistical difference emerged between the precognitive and clairvoyance tasks, particularly for those with an outbound agent. A comparison among meta-analyses results observed with other experimental protocols testing extrasensory perception showed the clear superiority of remote viewing. After more than 50 years of investigation into extrasensory perception, remote-viewing experimental protocols appear to be the most efficient for both experimental and practical applications.
ACM SIGEVO Best Dissertation Award 2023

The SIGEVO Best Dissertation Award was created in 2019 to recognize excellent thesis research by doctoral candidates in the field of evolutionary computing. Doctoral dissertation awards are given by other Special Interest Groups of ACM, such as SIGCOMM, SIGKDD, SIGARCH and others. The SIGEVO Best Dissertation Award will be given annually to a maximum of 1 winner and a maximum of 2 honorable mentions. The award presentation will take place at the awards ceremony of GECCO. The award will carry a monetary value, contributed by SIGEVO, of $2,000 to be awarded to the winner and $1,000 to each of the honorable mentions. The award winner and honorable mentions will each receive a beautiful plaque.
STNWeb: A new visualization tool for analyzing optimization algorithms

STNWeb (https://www.stn-analytics.com/) is a new web tool for the visualization of the behavior of optimization algorithms such as metaheuristics. It allows for the graphical analysis of multiple runs of multiple algorithms on the same problem instance and, in this way, it facilitates the understanding of algorithm behavior. It may help, for example, in identifying the reasons for a rather low algorithm performance. This, in turn, can help the algorithm designer to change the algorithm in order to improve its performance. STNWeb is designed to be user-friendly. Moreover, it is offered for free to the research community. An article summarizing STNWeb has been recently published in the Software Impacts Journal.
Lusiana Profil Sarana Dan Prasarana Cabang Olah Raga Renang di PPLOP Jawa Tengah

The purpose of this study was to determine the condition of adequate infrastructure and the effect of infrastructure conditions on the results of the exercise. The research location in this study was carried out directly with PPLOP Central Java. This study used qualitative research methods. Collecting data using descriptive analysis which is the process of describing research. The variables in this research are the facilities and infrastructure in PPLOP Central Java for swimming sports which include swimming pools, waiting rooms, changing rooms, control rooms, staff rooms and warehouses. The results of the research on swimming pool facilities include a swimming pool track, swimming pool lighting, and swimming pool stairs. Swimming pool infrastructure gets the results of the length of the pool, the depth of the pool, the walls of the pool, and the starting place. Swimming pool supporting facilities and infrastructure include changing rooms and engine rooms. The pool facilities and infrastructure in the swimming pool in Jatidiri are standard because they are in accordance with FINA standards, namely having a track line, the track marking line is straight in front of the Start board, giving numbers from all sides, the number of tracks is 9 and the Start board is the Start board.
Manajemen Kolam Renang di Kabupaten Kebumen Tahun 2020

The purpose of this study was to find out how to plan swimming pools in Kebumen Regency in 2020, To find out how to organize swimming pools in Kebumen Regency in 2020, To find out how to move swimming pools in Kebumen Regency in 2020, To find out how to supervise swimming pools in Kebumen Regency in 2020. 2020. This research is a qualitative research, the method used to collect data is interviews. The subjects in this study were the owners, managers and visitors of swimming pools in Kebumen Regency. The data analysis technique used is descriptive analysis in a narrative manner. Planning for swimming pool management in Kebumen Regency is done quite well, each swimming pool has a short, medium and long term plan. The organization of swimming pool management in Kebumen Regency is still not going well, some swimming pools do not have a management organizational structure. The implementation of swimming pool management in Kebumen Regency is going quite well, every employee and employee is working satisfactorily. Supervision carried out by swimming pool owners in Kebumen Regency is carried out by verbal coordination, reporting is carried out only based on bookkeeping.
Indução de Árvore de Decisão utilizando Meta-Aprendizado

Symbolic learning models stand out within the Machine Learning area because their representations are interpretable by humans. A feature of this model is very responsive to the set of examples used, which can result in a significant change in the model if there are small variations in the training set. The model combination strategy (ensembles) is presented as an alternative to improve the accuracy and stability of the models. The strategy consists of generating different models from the same training set and combining them into a single final model, usually through a voting process. An undesirable characteristic of the set strategy is the complexity of the final model, since it is formed by a set of models. In this research, an approach is proposed to induce a decision meta-tree based on the combination of decision trees from one (Random Forest). The experiments were performed on 150 datasets from different domains. The proposed approach applied to 43 sets of categorical data out of 150 analyzed, obtained a performance as good as a forest with 128 trees without statistically significant differences. This is an important result, considering the interpretability provided by a single decision tree as the resulting model.
A multiturn recommender system with explanations

Recommendations engines use interactions between users and items to predict the preferences of the users and generate recommendations for them. However, because they rely on historical data, the user’s interest at the moment may not be captured. In this context, Conversational Recommender Systems (CRSs) have been proposed in order to provide recommendations that provide suggestions based on the user’s current interests by eliciting information from the user in turns in which the system can ask for the user to understand more about the current interest in the time or recommend. In that regard, we propose CRSs for cold-start users as a Knowledge Graph search. The system also employs a Natural Language Processing module to explain the recommendations, bringing transparency to the recommendation algorithm.
Embedding Propagation over Heterogeneous Information Networks

Heterogeneous Information Networks (HINs) play a crucial role in modeling and analyzing multimedia systems and heterogeneous data. They provide a comprehensive understanding of entities and relationships within complex data structures. However, integrating HINs with machine learning tasks poses challenges that require specific models or vector space representation. This paper proposes an innovative embedding propagation graph method for HINs with textual data. By leveraging language models like BERT, our method propagates contextual text embeddings, combining the network’s topological information and the semantic information of textual objects, which are then propagated to non-textual objects within the network. The method facilitates the integration of machine learning techniques with various modeling approaches, enhancing analysis capabilities in multimedia and heterogeneous data domains. Through robust experimental evaluations on different datasets and in three application domains, our method demonstrates competitive performance, enabling direct comparison of entities and relationships within a unified latent space. This research highlights the potential of HINs for intelligent analysis and information retrieval in multimedia systems and heterogeneous data contexts.
TF-MVSA: Multimodal Video Sentiment Analysis Tool using Transfer Learning

Existing methods for sentiment analysis in videos rely on extensive training on large labeled datasets, making them expensive and impractical for real-world applications. This challenge becomes even more complex when dealing with labeled data in different modalities. To address these limitations, we proposed a transfer learning method and a computational tool that leverage pre-trained models for each modality and employ modality consensus to automatically annotate video segments. Our tool implements neural networks with attention mechanisms to learn the significance of each modality during the learning process. The experimental results demonstrate that our tool surpasses unimodal methods and remains competitive with multimodal approaches, even when labeled data for analyzing new videos are unavailable. Moreover, the tool is publicly available, thereby serving as a competitive baseline for similar multimodal sentiment analysis methods.
Task scheduling optimization considering SLA for big data analytics-as-a-service in cloud computing services

Big Data Analytics-as-a-Service (BDAaaS) is an emerging cloud computing service designed to provide big data analytics. BDAaaS offers approximate query services to meet the requirements of fast response but without high accuracy by users. So, service response in Service Agreement Level (SLA) are more stringent and BDAaaS faces greater risk of penalty payout. In this paper, we proposed a task scheduling model that takes SLA into account for the problem of sequential dependencies of some tasks. And a greedy strategy was used to improve the genetic algorithm to solve the model efficiently. Simulation experimental results show that the method in this paper can obtain task scheduling schemes with lower cost and SLA violation rate under limited resources.
The Implementation of the Hidden Object Interactive Game Using Powerpoint in Improving Students' Vocabulary Mastery

This research aims to find out whether the implementation of the Hidden Object interactive game using PowerPoint can improve students' vocabulary mastery or not. A quantitative pre-experimental research type was used in this study. All seventh-grade students at SMP Negeri 1 Watansoppeng made up the population of this study. The sample was taken by selecting one class randomly as a representative of a cluster (cluster random sampling) of 25 students. The instrument used was the vocabulary test while data collection was carried out using pretest, treatment, and posttest techniques. Descriptive statistical analysis was the technique of data analysis that was applied where the average pretest score of the 25 students in this study was 72.84, and the average posttest score was 87.84. In addition, it is evident that the two-tailed significance value in the pretest and posttest results was 0.000 which denotes that the two-tailed value is less than 0.05 (significant level), and based on decision-making, this indicates that H0 is rejected while H1 is accepted. In addition, the analysis results obtained from each aspect of vocabulary, namely: meaning has a mean pretest and posttest difference of 19.40, spelling, 11.72, pronunciation 3.32, word classes, 1.52 and word use has a mean difference of 43.04. Other results also show that there were four aspects of vocabulary, namely meaning, spelling, pronunciation, and word use which can show that the two-tailed value was less than 0.05 while the value for the two-tailed aspect of word classes was more than 0.05. It is stated that the four aspects of vocabulary other than Word Classes reject H0 while H1 is accepted. This means that there is a significant difference between student scores before and after implementing the Hidden Object interactive game using PowerPoint. Thus, it can be inferred that in general the implementation of the Hidden Object interactive games using PowerPoint significantly improves students' vocabulary mastery. But for each aspect of vocabulary, only word classes had the smallest mean difference and did not have a significant improvement in students' vocabulary while the biggest mean difference was in the word use aspect.
An Adaptive Mixup Hard Negative Sampling for Zero-Shot Entity Linking

Recently, the focus of entity linking research has centered on the zero-shot scenario, where the entity purposed to be labeled at the time of testing was never observed during the training phase, or it may belong to a different domain than the source domain. Current studies have used BERT as the base encoder, as it effectively establishes distributional links between source and target domains. The currently available negative sampling methods all use an extractive approach, which makes it difficult for the models to learn diverse and more challenging negative samples. To address this problem, we propose a generative negative sampling method, adaptive_mixup_hard, which generates more difficult negative entities by fusing the features of both positive and negative samples on top of hard negative sampling and introduces a transformable adaptive parameter, W, to increase the diversity of negative samples. Next, we fuse our method with the Biencoder architecture and evaluate its performance under three different score functions. Ultimately, experimental results on the standard benchmark dataset, Zeshel, demonstrate the effectiveness of our method.
Legal Implications of Use of Emojis: A Critical Analysis

The rise of emojis in our communication landscape has brought both their widespread adoption and the pervasive challenge of misinterpretation. These small, expressive symbols are ubiquitous in social media, and their meanings exhibit significant variations influenced by age, culture, and gender. While emojis add a lighthearted dimension to our interactions, their growing influence prompts contemplation of their potential legal implications. The misapplication or misreading of emojis can potentially result in legal consequences, especially in everyday life where they are used for humor, offense, or even in business contexts. Emojis, once considered mere embellishments to our digital conversations, now occupy a more substantial role, blurring the lines between informal discourse and the legal domain.
Modified Meerkat Clan Algorithm for Association Rules Mining

Association Rules Mining (ARM) forms one of the important data mining techniques. The classical methods that were previously worked on by researchers have become ineffective to deal with the steady growth of databases, which prompted us to use the mining process for association rules based on metahuristic, and in our work all the correct rules will extracted, and mining is not limited to high-quality rules. Swarm intelligence based is one of these methods. In this paper, Modified Meerkat Clan for Association Rules Mining (MCC-ARM) has been proposed. Basically, the proposed algorithm depends on Meerkat Clan Algorithm (MCA). The greatest benefit is the diversity of candidate solutions in MCA. In our work the rules will represented using two methods which are borrowed from the genetic algorithm; in the first one each group of rules refers to object in society which is called Pittsburgh; while the second one each rule refers to an object in society which is called Michigan. The proposed algorithm aims to inspect for the maximum possible number of correct association rules. The so-called algorithm follows the approach of defining the effective search area, which depends on a main random mechanism to lead the algorithm in extracting alternative rules and avoiding total solutions from being guided by the same rule, and this led to a great deal of diversity. In addition, the MCC-ARM uses condensation method in the adjacency search process to prevent the algorithm from falling into the local mode. In order to prove their efficiency, it should be applied on four reliable datasets (i.e. Zoo, German Credit, Primary Tumor and Chess). The enhancement brought about by the proposed algorithm has obtained two crucial factors, namely on the number of correct rules and quality fitness value.