Reviewerly/exHarmony huggingface dataset:
Event Classification with Imbalanced and Missing Data for an Air-Handling Unit

Prediction of faults reliably for Air Handling Units (AHU) is a key aspect of correcting errors and eliminating non-optimal functionality. Machine learning classification methods with data sampling are widely utilized to forecast these kinds of events, which, by their nature, seldom occur in equipment. The model proposed in this paper harnesses seven years of data from an air handling unit that contains information about, for example, temperature, humidity, CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> content, and fan speed. This paper contributes to the field of imbalanced classification problems by proposing a novel data undersampling algorithm to enhance the classification model results in the presence of imbalanced and missing data. Moreover, this paper compares several oversampling methods, undersampling methods, probability calibration, and machine learning methods. Then, the paper reports on the proposed final model (proposed undersampling Algorithm 1, Tomek Links, and Logistic Regression) to forecast imperfect heat recovery events in an air handling unit that occur relatively seldom. The precision of the final model was 0.93 for the unseen data; this result was reasonable considering the imbalance of data concurring with missing data sequences.
Context and Boundary Guided Multi-Scale Feature Fusion Network for Semantic Segmentation

Semantic segmentation aims to assign correct category label for every pixel in the image. Pixel-level classification and locality are thus both essential. To this end, previous methods try to fuse the multi-scale features of backbone network, including high-level category distinguishing cues and low-level location details. Other methods are designed to incorporate contextual cues or boundary guidance. However, previous methods are usually designed to refine the multi-scale feature fusion after the last fusion stage under the guidance of context or boundary, resulting in separate processing pipeline. This post-processing strategy is hard to remedy the context inconsistency and blurring boundary within fused features, which inevitably generates object segmentation with inconsistent context and blob-like contour. In this paper, we aim to bridge this gap by seamlessly introducing the context or boundary guidance into the multiple feature fusion operations. In this way, multi-scale features are effectively combined while maintaining context consistency and sharp object boundary, leading to enhanced semantic segment coherence. Experimental results on Cistyscapes and ADE20K datasets show the superiority of the proposed method.
Human Attention Based Movie Summarization: Dataset and Baseline Model

The movie summarization model can automatically edit a condensed and succinct version of the movie by selecting the keyframes. Previous works mainly resort to hand-crafted heuristics and most of them are unsupervised. Supervised movie summarization is a new research field and, there is currently no publicly suitable dataset available. Moreover, existing works only focus on the movies themselves while neglecting the audiences, who have the most say in which part of the movie is more attractive. To deal with the aforementioned limitations, we establish a human attention based movie summarization dataset Movie50. Specifically, we explore the human attention variations when watching videos and have the following findings: (1) The attention of humans is concentrated when watching keyframes. (2) The attention of humans is distracted when watching non-keyframes. Inspired by these findings, we collect the eye fixations of 20 participants when watching 50 movies and propose a novel human attention based annotation pipeline. In addition, we introduce A/V-MSNet, an audiovisual neural network that takes advantage of spatio-temporal visual and auditory information to better model human attention as well as exploit more plentiful information. Extensive experiments demonstrate the superiority of the proposed method.
The System of the Dissemination Characteristics of Internet Public Opinion Big Data Based on Artificial Intelligence

In the era we live in today, the network is often used to analyze a large number of complex systems. With the development of the information society, there are more and more ways to disseminate public information through social networks. Public opinion dissemination refers to the process of disseminating public opinion information through social networks. Because the dissemination of public opinion is the basis for the exchange of ideas among multiple communicators of public opinion, the network community will certainly have an impact on the dissemination and development of public opinion. This article is based on artificial intelligence to study the network public opinion big data dissemination characteristic analysis system, introduces the network public opinion analysis system based on the characteristics of the network public opinion, introduces in detail multiple methods and clustering algorithms for extracting the text information of Internet public opinion, and proposes the Kmeans + Canopy + semantic similarity algorithm, and uses the A event to compare the parameters of the network clustering coefficient, the correlation measure and the degree centrality measure, and the performance of the Kmeans + Canopy algorithm and the Kmeans + Canopy + semantic similarity algorithm. The results of the experiment found that the clustering coefficient of “People’s Daily” in the network dissemination of A event was 0.038, which was the highest among all nodes. It shows that 3.8% of the nodes established by the “People’s Daily” can interact one-to-one to deliver information and intelligence resources. Although the complexity of the algorithm has increased and the time consumed by the system has increased, the accuracy of clustering has been improved, especially for cultural articles, the accuracy rate has been as high as 75%, and entertainment articles can reach up to 70%, and stabilize at around 70%.
Museum Personal Guide: Tackling the Limited Conditions

Learning Museum of Universitas Negeri Malang (Museum UM) is one of the university learning museums in Indonesia. Museum UM comes with a more modern concept by referring to the museum's storyline and utilizing technological developments. Museums cannot be separated from digitalization as an effort to adapt to the disruptive era. Digitalization is important after the Covid-19 outbreak was declared as a world pandemic by WHO. Virtual Augmented Reality or AVMUSEUM application can be an alternative solution to the crisis experienced by the museum. Visitors will be presented with a reconstruction of the Museum UM in a virtual world. A real museum guide was added in the guidance video. The method used to design the AVMUSEUM application is Rapid Application Development (RAD) with requirements planning, design workshop, and implementation stages. The innovation of Museum UM is expected to adapt and answer the challenges of the disruptive era and the Covid-19 Pandemic.
Localized Differential Location Privacy Protection Scheme in Mobile Environment

When users request location services, they are easy to expose their privacy information, and the scheme of using a third-party server for location privacy protection has high requirements for the credibility of the server. To solve these problems, a localized differential privacy protection scheme in mobile environment is proposed, which uses Markov chain model to generate probability transition matrix, and adds Laplace noise to construct a location confusion function that meets differential privacy, Conduct location confusion on the client, construct and upload anonymous areas. Through the analysis of simulation experiments, the scheme can solve the problem of untrusted third-party server, and has high efficiency while ensuring the high availability of the generated anonymous area.
Dynamic Feature Pyramid Networks for Detection

Feature Pyramid Network (FPN) has been a generic feature extractor in computer vision tasks, which utilizes multi-level features to generate discriminative pyramidal representations. However, the way simply using Sum or Concatenate operation on features to integrate multi-scale information is not sufficient to obtain discriminative semantic representations. In this paper, we propose a dynamic feature pyramid network (DyFPN) to merge multi-scale information in both features and weights. DyFPN uses both high-level context features and low-level spatial structural features to obtain dynamic convolution kernel that contains multi-scale information. In this manner, each resolution in the pyramid performs unique and adaptive convolution directly, meanwhile strengthening the information flow. Specially, DyFPN can be regarded as a complementary enhancement to existing feature pyramid networks. We analyze the effective receptive field and attention map of DyFPN. It proves that our method contains more local information and global information compared with merging multi-scale information only on feature level. Benefit from multi-ways of integrating multi-scale information, our method outperforms other existing feature pyramid methods on COCO detection tasks by a large margin.
Spatial-Temporal Alignment via Optimal Transport for Video-Text Retrieval

Video-text retrieval is one of the most popular branches in the cross-modal research area facing the exponential growth of multimedia services. Present methods typically mea-sure cross-modal similarities only by the cosine metric in a common space. However, this function ignores the in-herent discrepancy between videos and texts for reflecting spatial-temporal contents and fails to align them under the weakly supervised setting. To address this issue, we propose a novel Spatial-Temporal Optimal Transport (STOT) frame-work building upon advances in optimal transport. The spatial and temporal characteristics of videos and texts are carefully considered in STOT and associated by calculating a spatial-temporal alignment distance, which can be implemented as a regularizer for existing models to improve retrieval accu-racy. Extensive experiments are conducted on two video-text datasets with two models as base architectures to demonstrate the outperformance of our framework due to the powerful spatial-temporal alignment capability.
General-Use Unsupervised Keyword Extraction Model for Text Analysis

Keyword extraction is the foundation for solving various text mining tasks. However, the literature heavily relies on statistical, linguistic feature-based, or graph-based metrics to gauge corpus-representative keywords, the process of which is sensitive to preprocessing and stopword selection. In this paper, we propose a general-use keyword extraction model designed to work with document groups of various sizes, domains, and readability, as well as the existence of keyword labels. We employ a simple logistic regression model with the least absolute shrinkage and selection operator regularization. The classification-based structure of our approach ensures learning words that distinctively characterize the given document group against the comparison groups, enhancing the representativeness of the extracted keywords. We test our modelâ€™s performance against numerous classic keyword extraction frameworks as baseline models using online customer reviews, news articles, and patent documentation. The results indicate that our proposed method has robust performance in terms of representability and distinctiveness across document groups with varying sizes, number of class labels, levels of readability, and domain. Given its generalizability and simplicity, we believe that our proposed model may serve as an easy-to-use, yet a powerful tool for keyword extraction, especially when working with various groups of documents from different domains.
ICT Diffusion, Renewable Energy Consumption and Co2 Emissions in Sub-Saharan Africa

This study analyses the direct effects of information and communication technology (ICT) on environmental quality and its indirect effect through an interaction term with renewable energy consumption for 48 Sub-Saharan African (SSA) countries from 2005 to 2020. To this end, the study adopts panel pooled-OLS, fixed-effects, and random-effects models in conjunction with a more robust system-GMM. The findings reveal that increased energy use, urbanisation, and education dampen environmental quality over the investigated period while trade openness improves it. These outcomes suggest an urgent need to charter a viable route for environmental sustainability. More so, the study alludes that joint interaction between renewable energy and ICT diffusion spill-over improves the quality of the environment in SSA. This implies that the search for environmental sustainability is hidden in R&D-related investment in renewable energy technologies.
Identification of the Adverse Effects of Sports Supplements Through Social Media Text Mining

Introduction: Sports supplements, despite their beneficial effects, are sometimes associated with side effects. Sports supplements mostly are not prescribed by medical doctors and athletes generally share their knowledge and experience regarding sports supplements through the social media. Identification of the side effects of medications is far easier since they are prescribed by physicians, and their effectiveness and adverse effects are recorded in medical record systems. Yet, identification of the adverse effects of sports supplements is more difficult due to arbitrary and non-medical use. Thus, the experience of Twitter users may be a good source to identify these adverse effects. The present study aims to identify the adverse effects of the sports supplements through Twitter using text mining techniques. Materials and Methods: This study is an applied developmental study in which 40466 tweets of Twitter users from 2014 to 2019 have been collected for 10 frequently used sports supplements. In this study reported and known adverse effects for the ten sports supplements compared with the adverse effects that extracted from Tweets. In the present study, K-Means standard algorithm is applied for the clustering of the adverse effects. Silhouette index is used to determine the optimum K value for clustering of the sports supplements. Results: Comparing the extracted list of adverse effects with the known adverse effects revealed 93 new adverse effects that were not reported and could be considered as side effects for investigated sport supplements. Moreover, sports supplements were divided into three groups according to the K-Means standard algorithm; the first group in terms of similarity of adverse effects included six sports supplements (the highest number of sports supplements). Conclusion: Presented research method based on text mining could be used to extract the adverse effects of sports supplements from the texts of tweets. Results of the presents study may be used to update the adverse effects database of sport supplements.
Scalable and Robust Big Data Clustering with Adaptive Local Feature Weighting Based on the Map-Reduce and Hadoop

Fuzzy c-means (FCM) is an effective clustering algorithm, which has been successfully applied on many real-world applications. Although, FCM and its improvements have achieved considerable performance however, most of the FCM-based methods consider equal importance for all features and neglect the feature weights in the clustering processes. To deal with this issue, in this paper, two methods called Local Feature Weighting FCM method (LFWFCM) is proposed and Robust Local Feature Weighting FCM (RLFWFCM) are proposed. RLFWFCM employs a non-Euclidian and robust metric in its process to overcome the presence of noise or outliers in data. Also pure mathematical analyses are provided to show the convergence properties of these methods. Also, these methods have been applied on the Hadoop platform by mapping reduction programming method. To assess the effectiveness of these methods, several experiments are performed on both real-world and synthetic datasets. The obtained results demonstrate the effectiveness of the proposed methods in comparison with some baseline and state-of-the-art clustering methods. Keywords: Big Data, Clustering, Fuzzy c-means, Feature weighting, Robust distance metric, Map-Reduce, Hadoop
Mineralogy and Geochemistry of the Morro Do Engenho Lateritic Nickel Deposit, Goiás Alkaline Province, Brazil

The Morro do Engenho lateritic nickel deposit results from the action of weathering processes on the alkaline mafic-ultramafic rocks that make up the Cretaceous zoned intrusive body belonging to the Goiás Alkaline Province. Historically, the deposit is classified as a silicate one, and mineralization is present especially in phyllosilicates such as serpentine and chlorite. By identifying Ni-bearing phases and providing the lithogeochemical characterization of alteration profiles, this study aims to provide further insights into the metallogenetic processes and controls acting on the mineralization of the Morro do Engenho lateritic Ni deposit. The following methods were used: petrographic studies, SEM/EDS, XRD and lithogeochemical characterization of the alteration profiles developed on each of the parent rocks that make up the body. The Morro do Engenho intrusive massif is formed by a dunite core, surrounded by a peridotite/pyroxenite zone, an alkaline gabbro zone and more externally by a syenite-nepheline zone. The lateritic profile contained eight alteration horizons, stacked one upon another from the bedrock namely: (i) saprock, (ii) lower saprolite, (iii) ferruginous saprolite, (iv) ocher saprolite, (v) plasma zone, (vi) lateritic duriccrust, (vii) silcrete and (viii) topsoil. Most Ni mineralization occurs in the lower saprolite, ferruginous saprolite and ocher saprolite zones. The findings pointed to the presence of Ni in silicate mineral phases - represented by serpentine, chlorite and smectite - as well as in oxide phases - concentrated in Fe and Mn oxyhydroxides. The depressed area, located between the two major topographic elevations, has the highest Ni enrichments, which can account for up to 40%. Hydrodynamic factors, by dissolution/precipitation of silicates in saprolite horizons of dunite core, are suggested as possible agents in the remobilization of Ni - among other mobile elements - to topographically flat zones. The geometry of the Morro do Engenho deposit show that lateral redistribution by hydrodynamic factors could also account for the mineralization of areas peripheral to the dunite core, composed of pyroxenite and gabbro-derived lateritic profiles. The direction of regional groundwater flow, strongly influenced by topography, would also be an important factor in the remobilization of Ni, as well as the mechanical transport of lateritic materials. Silicate mineralization predominates in the Morro do Engenho deposit, concentrated in Mg hydrosilicates and clay minerals; oxide mineralization also occurs, mostly in Fe and Mn oxyhydroxides. The findings discussed in this paper can broaden the understanding of metallogenetic processes involved in the genesis of lateritic nickel deposits from mafic-ultramafic bodies and provide valuable information for scientific expeditions aiming to explore similar deposits.
Class-Balanced Loss Based on Example Forgetting for Long-Tailed Recognition

Many of the successes of deep learning have been based on datasets with a balanced number of samples per class, but the effects of such methods will be greatly reduced when faced with real-world imbalanced datasets. We propose Class-Balanced Loss Based on Example Forgetting, which draws lessons from hard example mining, uses the forgetting information to fit the total information volume of class, and constructs the weight factor based on the effective number of samples. We innovatively propose a formula to calculate the effective number of samples in the weighting factor, which reflects the idea that samples with high forgetting scores contribute more to the total information volume of the class. The experimental results show that FCB loss can significantly improve the accuracy of the long-tailed image recognition task, and the more prominent the long-tailed phenomenon is, the better the results are.
A New Focused Crawler Using Improved Tabu Search Algorithm Incorporating Ontology and Host Information

In order to solve the problems of incomplete topic description and repetitive crawling of visited hyperlinks in the traditional focused crawling methods, in this paper we propose a novel focused crawler using improved tabu search algorithm with domain ontology and host information (FCITS_OH), where domain ontology is constructed by the formal concept analysis (FCA) to describe topic at the semantic and knowledge level. In order to avoid crawling visited hyperlinks and expand the search range, we present an improved tabu search (ITS) algorithm and the strategy of host information memory. In addition, a comprehensive priority evaluation method based on Web text and link structure is designed to improve the assessment of topic relevance for unvisited hyperlinks. The experimental results on both tourism domain and rainstorm disaster domain show that the proposed focused crawlers overmatch the traditional focused crawlers in the literature for different performance metric indices.
Frame-Wise Graph Contextual Learning for Continuous Sign Language Recognition

The continuous sign language recognition task aims to recognize an series of signs from a sequence of complex visual cues in a weakly supervised manner. Most recent studies, which utilized recurrent neural networks or 1-dimensional convolution neural networks, had difficulty classifying alternating signs and consumed excessive computational resources because they indiscriminately ex- tracted temporal features even though certain frames were more important. In this study, we propose a graph contextual module combined with an auxil- iary selector module that reduces the computational cost while maintaining the model’s performance. The graph contextual module dynamically extracts tem- poral features by constructing a frame-wise graph, and the auxiliary selector module makes predictions about the important frames during graph construc- tion to enhance the alignment between the convolution neural network and the graph contextual module. To evaluate our proposed network, we conducted an experiment that compared the proposed model’s processing time and perfor- mance with the state-of-the-art networks on the a benchmark dataset.
Three Modules for Introducing Students to Cryptocurrency Risks and Returns

We present three modules that can be used in finance and investment courses to introduce undergraduate students to cryptocurrency risks and returns. Detailed instructions include learning objectives, assignment instructions, and links to free online resources. In Module 1, students are provided with free online resources to understand cryptocurrency terminology, recent innovations, and risks, including exchange rate volatility and potential financial loss, fraud, hacking, and losing your wallet fraud. In Module 2, students use free online data and statistical analysis to compare bitcoin’s risk-return profile with several stock index ETFs. The analyses include comparison of maximum drawdown risk, which is used increasingly by practitioners but often missing in undergraduate finance and investment textbooks. In Module 3, students close the loop, applying what they learn in Modules 1 and 2 to debate the pros and cons of taking salaries in cryptocurrencies.