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Multiobject tracking (MOT) technology can be deployed to the Internet of Things (IoT) devices to enhance the security and reliability of some video analysis applications, such as video surveillance and intelligent security system. However, since the IoT devices with limited computing capacity and storage, most existing MOT methods are difficult to deploy to IoT devices and exhibit poor tracking robustness in scenes with frequent occlusions, severe crowded, and scale variations. To alleviate the aforementioned issues, we propose a regression-based online MOT method. First, an object-aware embedding extraction module (OAEM) is designed to extract preliminary discriminative embedding of the object in the current frame. Then, an embedding aggregation module (EAM) is proposed to obtain high-quality aggregated embedding in temporal. Finally, the temporal embedding combined with the preliminary embedding extracted from the current frame to obtain a refined embedding feature for subsequent position prediction and association. Importantly, we achieve a beneficial interaction between embedding extraction, position prediction and association task. The proposed method does not suffer from significant memory consumption. Therefore, our method is a potential solution for intelligent video analysis on IoT devices. To evaluate the proposed method, we have conducted numerous experiments on the MOT16, MOT17, and MOT20 benchmark data sets. Results demonstrate that the proposed method can provide a more robust tracking performance compared to other optimal methods.

Providing a structured approach for complex product development (PD) is a challenging issue that necessitates utilizing sophisticated modeling tools and analysis techniques. One effective approach for optimizing the complex engineering system development is the cross application of modularization and sequencing analyses on the design structure matrix (DSM), which leads to a cost-effective and time-efficient PD process. In this regard, the current study presents an application-specific method for the simultaneous implementation of modularization and sequencing analyses on DSMs to find the most independent and excluded modules with the minimum feedback loops among them. To this end, we first formulate the total feedback length among the modules by introducing two new modules’ feedback criteria. Second, we propose a novel objective function combining these feedback criteria with the modularity index metric to simultaneously fulfill the sequencing and modularization goals. This objective function makes the proposed method application specific in the sense that the expert is able to adjust the importance weight of each criterion based on the project's characteristics. Third, an efficient metaheuristic algorithm named discrete particle swarm optimization simulated annealing (DPSOSA) is developed to find the optimal solution by minimizing the introduced objective function. Eventually, to evaluate the DPSOSA performance in different applications, we conduct comprehensive experiments on a total of 21 well-known real-world DSMs. The obtained results demonstrate the superiority of the proposed method over previous approaches proving its effectiveness in each of modularization, sequencing, and modularization-sequencing experiments.

In this work, we consider transferring the structure information from large networks to compact ones for dense prediction tasks in computer vision. Previous knowledge distillation strategies used for dense prediction tasks often directly borrow the distillation scheme for image classification and perform knowledge distillation for each pixel separately, leading to sub-optimal performance. Here we propose to distill structured knowledge from large networks to compact networks, taking into account the fact that dense prediction is a structured prediction problem. Specifically, we study two structured distillation schemes: i) pair-wise distillation that distills the pair-wise similarities by building a static graph; and ii) holistic distillation that uses adversarial training to distill holistic knowledge. The effectiveness of our knowledge distillation approaches is demonstrated by experiments on three dense prediction tasks: semantic segmentation, depth estimation and object detection. Code is available at: https://git.io/StructKD, Comment: v1:10 pages cvpr2019 accepted; v2:15 pages for a journal version; Code is available at: https://github.com/irfanICMLL/structure_knowledge_distillation; fix typos

The development of digitized humanity information provides a new perspective on data-oriented studies of history. Many previous studies have ignored uncertainty in the exploration of historical figures and events, which has limited the capability of researchers to capture complex processes associated with historical phenomena. We propose a visual reasoning system to support visual reasoning of uncertainty associated with spatio-temporal events of historical figures based on data from the China Biographical Database Project. We build a knowledge graph of entities extracted from a historical database to capture uncertainty generated by missing data and error. The proposed system uses an overview of chronology, a map view, and an interpersonal relation matrix to describe and analyse heterogeneous information of events. The system also includes uncertainty visualization to identify uncertain events with missing or imprecise spatio-temporal information. Results from case studies and expert evaluations suggest that the visual reasoning system is able to quantify and reduce uncertainty generated by the data.

In this study, machine learning is examined in relation to commercial machine learning's resilience to the COVID-19 pandemic-related crisis. Two approaches are used to assess the pandemic's impact on machine learning risk, as well as a method to prioritize sectors according to the crisis's potential negative consequences. I conducted the study to determine Santander machine learning's resilience. The data mining area offers prospects for COVID-19's future. A total of 13 machine learning demos were selected for its organization. The Hellweg strategy and the technique for order preference by similarity to ideal solution (TOPSIS) technique were utilized as direct request strategies. Parametric assessment of machine learning versatility in business was based on capital sufficiency, liquidity proportion, market benefits, and share in an arrangement of openings with a perceived disability, and affectability of machine learning's credit portfolio to monetary hazard. As a result of the COVID-19 pandemic, these enterprises were ranked according to their threat. Based on the findings of the research, machine learning worked the best for the pandemic. Meanwhile, machine learning suffered the most during the downturn. It can be seen, for example, in conversations about the impact of the pandemic on developing business sector soundness and managing financial framework solidity risk.

Numerical weather prediction (NWP) data possess internal inaccuracies, such as low NWP wind speed corresponding to high actual wind power generation. This study is intended to reduce the negative effects of such inaccuracies by proposing a pure data-selection framework (PDF) to choose useful data prior to modeling, thus improving the accuracy of day-ahead wind power forecasting. Briefly, we convert an entire NWP training dataset into many small subsets and then select the best subset combination via a validation set to build a forecasting model. Although a small subset can increase selection flexibility, it can also produce billions of subset combinations, resulting in computational issues. To address this problem, we incorporated metamodeling and optimization steps into PDF. We then proposed a design and analysis of the computer experiments-based metamodeling algorithm and heuristic-exhaustive search optimization algorithm, respectively.Experimental results demonstrate that (1) it is necessary to select data before constructing a forecasting model; (2) using a smaller subset will likely increase selection flexibility, leading to a more accurate forecasting model; (3) PDF can generate a better training dataset than similarity-based data selection methods (e.g., K-means and support vector classification); and (4) choosing data before building a forecasting model produces a more accurate forecasting model compared with using a machine learning method to construct a model directly.

Data-centric process management supports knowledge workers in performing knowledge-intensive processes in a flexible way. An essential ingredient of data-centric process management are process templates that are manually modified for a specific case to suit the context of that case. Modifying templates results in many different yet related process variants. However, manually modifying a template is time consuming and may lead to errors. This article defines an approach to extract reusable fragments from data-centric process variants. The set of extracted fragments is minimal. By composing the fragments not only the input variants but many more process variants can be derived. We have implemented the approach in a prototype and evaluated it on several business processes. Using the fragment extraction approach, complex data-centric process variants can be designed more efficiently and their quality can improve, since well-known modifications are applied.

All agencies, companies, and public spaces must employ security guards to maintain security. The problem with the security guard's schedule is that there is an imbalance in disciplinary issues, poor performance because there is no seniority that is emulated so that the guard is not optimal which results in the problem of losing employee items, working time is not according to the rules and there is a vacancy in personnel due to personnel not coming to work, suing With the existence of a policy in the placement and distribution of the right employee work schedule, it is hoped that it can synergize all elements in the institution so that the quantity and quality of the security guard's work can increase and be completed on time. One of the techniques in data mining is classification. By applying classification techniques to security guard data and work schedules, the Decision Tree method and C4.5 algorithm are developed. The results of data processing form the root node of the gender tree as the root, that those who get schedule A are men while those who get schedule B with high school and junior level education are women, besides that they get schedule A. The accuracy of all classifications of the correct number is 61, 53%.

The Decision Tree algorithm is a data mining method algorithm that is often applied as a solution to a problem for a classification. The Decision Tree C5.0 algorithm has several weaknesses, including: the C5.0 algorithm and several other decision tree methods are often biased towards modeling whose features have many levels, some problems for the model can occur such as over-fit or under-fit challenges, big changes to decision logic can result in small changes to data training, C5.0 can experience modeling inconvenience, data imbalance causes low accuracy in C5.0 algorithm. The boosting algorithm is an iterative algorithm that gives different weights to the distribution of training data in each iteration. Each iteration of boosting adds weight to examples of misclassification and decreases weight to examples of correct classification, thereby effectively changing the distribution of the training data. One example of a boosting algorithm is adaboost. The purpose of this research is to improve the performance of the Decision Tree C5.0 classification method using adaptive boosting (adaboost) to predict hepatitis disease using the Confusion matrix. Tests that have been carried out with the Confusion Matrix use the Hepatitis dataset in the Decision Tree C5.0 classification which has an accuracy rate of 80.58% with a classification error rate of 19.15%. Whereas in the Decision Tree C5.0 classification Adaboost has a higher accuracy rate of 82.98%, a classification error rate of 17.02%. This difference is caused by the adaboost algorithm, because the adaboost algorithm is able to change a weak classifier into a strong classifier by increasing the weight of the observations, and adaboost is also able to reduce the classifier error rate.

Heterogeneous domain adaptation (HDA) tackles the learning of cross-domain samples with both different probability distributions and feature representations. Most of the existing HDA studies focus on the single-source scenario. In reality, however, it is not uncommon to obtain samples from multiple heterogeneous domains. In this article, we study the multisource HDA problem and propose a conditional weighting adversarial network (CWAN) to address it. The proposed CWAN adversarially learns a feature transformer, a label classifier, and a domain discriminator. To quantify the importance of different source domains, CWAN introduces a sophisticated conditional weighting scheme to calculate the weights of the source domains according to the conditional distribution divergence between the source and target domains. Different from existing weighting schemes, the proposed conditional weighting scheme not only weights the source domains but also implicitly aligns the conditional distributions during the optimization process. Experimental results clearly demonstrate that the proposed CWAN performs much better than several state-of-the-art methods on four real-world datasets.

With the dawn of 5G network, a new set of requirements for site spectrum monitoring, cellular planning are emerging, all of which are relying on fine-grained signal map. Although with significant importance, the traditional signal map construction could be time-consuming and labor-intensive. The state-of-the arts usually employ crowdsourcing scheme and matrix completion algorithm to solve the dilemma. However, the crowdsourcing scheme usually suffers from uneven distributed and inadequate participants. To this end, in this paper, we study how to effectively reconstruct and update the signal map in the case of partially measured signal maps with smaller cost and propose a GAN-based active signal map reconstruction method (GSMAC). Our method is mainly innovative in two parts: GSMC, GAN-based signal map construction, and ACS, an active crowdsourcing scheme. Specifically, GSMC can effectively update the signal map with only a small number of observations while updating the signal map online. ACS consists of a reinforce learning-based active query mechanism which quantitatively evaluates the most valuable measurement site for reconstruction. The simulation results and real implemented data-driven experiments demonstrate the advantages and effectiveness of our approach in both accuracy and cost.

In this article, we investigate the problem of social link inference in a target location-aware social network (LSN), which aims at predicting the unobserved links between users within the network. This problem is critical for downstream applications, including network completion and friend recommendation. In addition to the network structures commonly used in general link prediction, the studies tailored for social link inference in an LSN leverage user trajectories from the spatial aspect. However, the temporal factor lying in user trajectories is largely overlooked by most of the prior studies, limiting the capabilities of capturing the temporal relevance between users. Moreover, effective user matching by fusing different views, i.e., social, spatial, and temporal factors, remains unresolved, which hinders the potential improvement of link inference. To this end, this article devises a novel multiview matching network (MVMN) by regarding each of the three factors as one view of any target user pair. MVMN enjoys the flexibility and completeness of modeling each factor by developing its suitable matching module: 1) location matching module; 2) time-series matching module; and 3) relation matching module. Each module learns a view-specific representation for matching, and MVMN fuses them for final link inference. Extensive experiments on two real-world data sets demonstrate the superiority of our approach against several competitive baselines for link prediction and sequence matching, validating the contribution of its key components.

Compressed sensing has been identified as a good candidate for user detection in grant-free non-orthogonal multiple access (NOMA) by exploiting the inherent sparsity of user activity. However, most of the existing works do not fully utilize the temporal correlation in NOMA and rely heavily on the unrealistic assumption that the number of active users is known in advance. To address these issues, we propose a temporal correlation enhanced multiuser detection scheme to achieve efficient and practical multiuser detection. Firstly, using 1 bit memory to piggyback the information whether the active users still have data to transmit, the base station can realize whether the active users in current time slot will turn to be silent or remain active. Then, to make explicit use of the temporal correlation of active user sets, a cross validation based adaptive subspace pursuit (CVASP) algorithm, is developed by utilizing the reported information on prior active users. The proposed CVASP is a highly practical algorithm that does not require any prior knowledge of the user sparsity level, as the cross validation technique could properly determine the stopping condition. Extensive simulation results demonstrate that the proposed mechanism could achieve a superier performance without requiring any prior knowledge.

Remaining useful life (RUL) prediction of aircraft engine (AE) is of great importance to improve its reliability and availability, and reduce its maintenance costs. This article proposes a novel deep bidirectional recurrent neural networks (DBRNNs) ensemble method for the RUL prediction of the AEs. In this method, several kinds of DBRNNs with different neuron structures are built to extract hidden features from sensory data. A new customized loss function is designed to evaluate the performance of the DBRNNs, and a series of the RUL values is obtained. Then, these RUL values are reencapsulated into a predicted RUL domain. By updating the weights of elements in the domain, multiple regression decision tree (RDT) models are trained iteratively. These models integrate the predicted results of different DBRNNs to realize the final RUL prognostics with high accuracy. The proposed method is validated by using C-MAPSS datasets from NASA. The experimental results show that the proposed method has achieved more superior performance compared with other existing methods.

Understanding in-network information diffusion is a fundamental problem in many applications and one of the primary challenges is to predict the information cascade size. Most of the existing models rely either on hypothesized point process (e.g., Poisson and Hawkes processes), or simply predict the information propagation via deep neural networks. However, they fail to simultaneously capture the underlying global and local structures of a cascade and the propagation uncertainty in the diffusion, which may result in unsatisfactory prediction performance. To address these, in this work we propose a novel probabilistic cascade prediction framework: Hierarchical Cascade Normalizing Flows (CasFlow). CasFlow allows a non-linear information diffusion inference and models the information diffusion process by learning the latent representation of both the structural and temporal information. It is a pattern-agnostic model leveraging normalizing flows to learn the node-level and cascade-level latent factors in an unsupervised manner. In addition, CasFlow is capable of capturing both the cascade representation uncertainty and node infection uncertainty, while enabling hierarchical pattern learning of information diffusion. Extensive experiments conducted on real-world datasets demonstrate that CasFlow reduces the prediction error to 21.0% by only observing half an hour of cascades, compared to state-of-the-art approaches, while also enabling model interpretability.

Depression is the next level of negative emotions. When a person is in a sad mood or going through a difficult situation and it is not leaving him and giving him pain continuously and he is unable to bear it anymore, that situation is called depression. The last stage of depression occurs in suicide. According to the World Health Organization (WHO), Currently, 4.4% of people in the world are currently suffering from depression. In 2021, fourteen thousand people committed suicide all over the world and the rating of suicide is increasing day by day. So, our study is to find depressed people by their comments, posts, or texts on social media. We collected almost 10,000 data from Facebook posts, comments, and YouTube comments. Data mining and machine learning (ML) algorithms make our work easier and play a big role in easily detecting a person’s emotions. We applied six classifiers to predict depression & non-depression and found the best accuracy on a support vector machine (SVM).

Funder: Cambridge Trust, Funder: China Scholarship Council, Funder: Christ's College, University of Cambridge, Text mining in the optical-materials domain is becoming increasingly important as the number of scientific publications in this area grows rapidly. Language models such as Bidirectional Encoder Representations from Transformers (BERT) have opened up a new era and brought a significant boost to state-of-the-art natural-language-processing (NLP) tasks. In this paper, we present two "materials-aware" text-based language models for optical research, OpticalBERT and OpticalPureBERT, which are trained on a large corpus of scientific literature in the optical-materials domain. These two models outperform BERT and previous state-of-the-art models in a variety of text-mining tasks about optical materials. We also release the first "materials-aware" table-based language model, OpticalTable-SQA. This is a querying facility that solicits answers to questions about optical materials using tabular information that pertains to this scientific domain. The OpticalTable-SQA model was realized by fine-tuning the Tapas-SQA model using a manually annotated OpticalTableQA data set which was curated specifically for this work. While preserving its sequential question-answering performance on general tables, the OpticalTable-SQA model significantly outperforms Tapas-SQA on optical-materials-related tables. All models and data sets are available to the optical-materials-science community.

The objective of the current study is to present a comparison of techniques for the forecasting of low frequency climate oscillation indices with a focus on the Great Lakes system. A number of time series models have been tested including the traditional Autoregressive Moving Average (ARMA) model, Dynamic Linear model (DLM), Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, as well as the nonstationary oscillation resampling (NSOR) technique. These models were used to forecast the monthly El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) indices which show the most significant teleconnection with the net basin supply (NBS) of the Great Lakes system from a preliminary study. The overall objective is to predict future water levels, ice extent, and temperature, for planning and decision making purposes. The results showed that the DLM and GARCH models are superior for forecasting the monthly ENSO index, while the forecasted values from the traditional ARMA model presented a good agreement with the observed values within a short lead time ahead for the monthly PDO index.

This article addresses the challenges in the application of artificial intelligence methods such as machine learning, computational intelligence and/or soft computing methods in social sciences. The literature review is performed in order to give a review of different approaches and methods that have been applied so far. The most used method in social sciences and management is the SWOT method, for the identification of strengths, weaknesses, opportunities, and threats when making strategic decisions. Two fundamental characteristics of previous approaches are the development of numerical models of utility functions and the possibility of upgrading these models by formalizing the intuition of strategic decision-makers. There are several shortcomings of the existing approaches. The application of computational intelligence and machine learning methods in social sciences is identified as one of the most challenging and promising areas, which could overcome identified shortcomings. The principles of one popular machine learning method, the decision tree, are explained and a demonstration is performed on the case study of churn prediction. Benchmarking data set from the publicly available repository is used to demonstrate the suggested approach Evaluation results measured through model accuracy and reliability gave promising results for further analysis. A developed predictive model could serve as a standalone tool or as support for decision-making in social sciences.

Temporal networks are ubiquitous in nature and society, and tracking the dynamics of networks is fundamental for investigating the mechanisms of systems. Dynamic communities in temporal networks simultaneously reflect the topology of the current snapshot (clustering accuracy) and historical ones (clustering drift). Current algorithms are criticized for their inability to characterize the dynamics of networks at the vertex level, independence of feature extraction and clustering, and high time complexity. In this study, we solve these problems by proposing a novel joint learning model for dynamic community detection in temporal networks (also known as jLMDC) via joining feature extraction and clustering. This model is formulated as a constrained optimization problem. Vertices are classified into dynamic and static groups by exploring the topological structure of temporal networks to fully exploit their dynamics at each time step. Then, jLMDC updates the features of dynamic vertices by preserving features of static ones during optimization. The advantage of jLMDC is that features are extracted under the guidance of clustering, promoting performance, and saving the running time of the algorithm. Finally, we extend jLMDC to detect the overlapping dynamic community in temporal networks. The experimental results on 11 temporal networks demonstrate that jLMDC improves accuracy up to 8.23% and saves 24.89% of running time on average compared to state-of-the-art methods.

Missing data widely exist in the raw or processed data, implying information loss. In many cases, missing values have to be accurately imputed for further use. In this paper, an extreme case, consecutively missing data in large-length and mainly remaining data in small-length, is discussed for time series varying with operating conditions, very universal in industrial processes. Firstly, to fully utilize the information of remaining data, a similar conditions screening scheme is provided, efficient to improve imputation accuracy. Then, multiple receding imputation via Gaussian process regression (GPR) and long short term memory (LSTM) neural network are proposed, deducing generic multiple combination imputation and bidirectional imputation structures. At last, applied for data imputation of extremely missing wind power data, condition-dependent on wind speed, imputation effects of the proposed methods are carefully compared. Simulation results reveal effectiveness of these methods to impute missing data under the extreme case, laying very important foundation for data-driven applications.

Supply lead time constitutes a core parameter in inventory management and plays a critical role in supply chain performance. Yet, how to promote better supply lead time estimations that account for multivariate effects of historical supplier dynamics remains poorly understood. This paper proposes a decision support system that uses a supervised regression strategy with multivariate information for estimating supply lead times. We combine ideas from big data analytics and data mining to explore the effects of different supply-related variables on the dynamics of supply lead time. We design a robust rolling window evaluation scheme to compare both the statistical and inventory performance of different well-known data mining models. Numerical tests with empirical data from a large automotive manufacturer demonstrate that the Random Forest model consistently outperforms other competing models, leading to median decreases of 18%–24% in the mean absolute errors of supply lead time estimations. As a consequence of our results, we also provide insights on how these estimations contribute to the proactive management of safety stocks.

Background: While functional connectivity is widely studied, there has been little work studying functional connectivity at different spatial scales. Likewise, the relationship of functional connec...

During the final two decades, the credit card framework has been broadly utilized as a component to drive the worldwide economy to develop significantly. A credit card supplier has issued millions of credit cards to its clients. Be that as it may, issuing credit cards to off-base clients can be a pivotal calculation of a monetary emergency. The objective of this paper is to mine useful data from the databases and apply data mining algorithms for processing credit card applications based on certain criteria. We have done the credit card application processing through k-clusters and decision trees, thus making the process easy and efficient. The processing takes place by considering the applicant's data in a day and then evaluating them by different data mining algorithms. We have used data classification, query analyzer, k-clustering, decision trees and association rules. Our study shows that by clustering and decision trees the applicants can be classified effectively according to the needed criteria.

Classification is one of the most often employed data mining techniques. It focuses on developing a classification model or function, also known as a classifier, and predicting the class of objects whose class label is unknown. Categorizing applications include pattern recognition, medical diagnosis, identifying weaknesses in organizational systems, and classifying changes in the financial markets. The objectives of this study are to develop a profile of a victim of online fraud and to contrast the approaches frequently used in data mining for classification based on Accuracy, Classification Error, Precision, and Recall. The survey was conducted using Google Forms, which is an online platform. Naive Bayes, Decision Tree, and Random Forest algorithms are popular models for classification in data mining. Based on the sociodemographics of Indonesia's online crime victims, these models are used to classify and predict. The result shows that Naïve Bayes and Decision Tree are slightly superior to the Random Forest Model. Naive Bayes and Decision Tree have an accuracy value of 77.3%, while Random Forest values 76.8%.

In industry 4.0 and digital transformation scenarios, manufacturing companies face the challenges of the exponential growth in the volume of data. A considerable part of the data that enterprises generate in large quantities, at a high speed, and in different forms can be defined as dark data. Companies are not able to extract their potential value for data management, storage, and maintenance issues, and, due to their unstructured form, they become unknown together with their informative value. Some researchers and professionals have addressed the issue of dark data but none of them have focused on the specificity of the manufacturing industry or on the data generated in it. Based on a lack in the literature, in this article, we explore the dark data through a systematic literature review and three focus groups with manufacturing companies. This article fills the gap and provides a valuable support for manufacturing companies to be aware of the presence of dark data in their scenarios, to identify them, and to stimulate initiatives for dark data exploitation. Furthermore, the study provides for the academic audience a reference paper for addressing future exploration in the field of data management in the companies for improving their innovative and operative capabilities.

Accurate prediction of the lithium-ion battery’s future capacity and remaining useful life (RUL) is of great significance to the battery health management. To the best of our knowledge, most of the existing methods only focus on capturing the degradation trend of the battery, which results in an inaccurate prediction because of the occasional capacity regeneration phenomena in practice. To improve the prediction precision, a hybrid approach of the Grey-Markov chain model (GMCM) and the improved Gaussian process is proposed in this paper. Firstly, the historical capacity data is decomposed into two components, namely, the degradation trend and regeneration phenomena, by ensemble empirical mode decomposition. Then, the GMCM method and the multi-output mixture of Gaussian processes are developed for degradation and regeneration prediction, respectively. Lastly, an online calibration strategy is proposed for further prediction accuracy improvement. Extensive experiments are established to validate the proposed GPGM approach, with the results demonstrating that the proposed GPGM consistently outperforms other counterpart methods in terms of one-step and multi-step prediction.

Radio frequency identification technology has been widely used to verify the presence of items in many applications such as warehouse management and supply chain logistics. In these applications, the challenge of how to timely identify the missing tags (namely tag searching or missing tag identification) is a key focus. Existing missing tag identification solutions have not achieved their full potentials because collision slots have not been well explored. In this paper, we propose an approach named collision resolving based missing tag identification (CR-MTI) to break through the performance bottleneck of existing missing tag identification protocols. In CR-MTI, multiple tags are allowed to respond with different binary strings in a collision slot. Then, the reader can verify them together by using the bit tracking technology and particularly designed string, thereby significantly improve the time efficiency. CR-MTI also reduces the number of messages transmitted by the reader using customized coding. We further explore the optimal parameter settings to maximize the performance of our proposed CR-MTI. Extensive simulation results show that our proposed CR-MTI outperforms prior art in terms of time efficiency, total executive time and communication complexity.

Varied edge bundling methods have been used to reduce visual clutter in parallel coordinates plots (PCP). However, existing edge-bundled PCP do not scale well for visual analysis of multidimensional big data and often overplot the bundles in the area near the axes. In this study, we propose a scalable lightweight bundling method to support visual analysis of multidimensional big data in PCP. It helps the users discover trends and detect outliers in the data by bundling the edges between each two adjacent axes independently. We integrate human judgments into the two-dimensional data binning by novel interactions to accelerate the clustering process of the data. We use the frequency-based representation to render the clusters as histogram-like bundles to reveal the distribution of the data and eliminate the overplotting of the bundles. Based on our method, we build a lightweight web-based visual analytics system for exploring multidimensional big data in PCP. The scalability analysis of our method shows that its clustering time increases linearly with the size of the data. Its rendering time is independent of the size of the data. We conduct two case studies and a user study to compare our method with classic PCP and two state-of-the-art edge-bundled PCP.

Continues field monitoring and searching sensor data remains an imminent element emphasizes the influence of the Internet of Things (IoT). Most of the existing systems are concede spatial coordinates or semantic keywords to retrieve the entail data, which are not comprehensive constraints because of sensor cohesion, unique localization haphazardness. To address this issue, we propose deep learning inspired sensor-rank consolidation (DLi-SRC) system that enables 3-set of algorithms. First, sensor cohesion algorithm based on Lyapunov approach to accelerate sensor stability. Second, sensor unique localization algorithm based on rank-inferior measurement index to avoid redundancy data and data loss. Third, a heuristic directive algorithm to improve entail data search efficiency, which returns appropriate ranked sensor results as per searching specifications. We examined thorough simulations to describe the DLi-SRC effectiveness. The outcomes reveal that our approach has significant performance gain, such as search efficiency, service quality, sensor existence rate enhancement by 91%, and sensor energy gain by 49% than benchmark standard approaches.

Projection techniques are often used to visualize high-dimensional data, allowing users to better understand the overall structure of multi-dimensional spaces on a 2D screen. Although many such methods exist, comparably little work has been done on generalizable methods of inverse-projection -- the process of mapping the projected points, or more generally, the projection space back to the original high-dimensional space. In this paper we present NNInv, a deep learning technique with the ability to approximate the inverse of any projection or mapping. NNInv learns to reconstruct high-dimensional data from any arbitrary point on a 2D projection space, giving users the ability to interact with the learned high-dimensional representation in a visual analytics system. We provide an analysis of the parameter space of NNInv, and offer guidance in selecting these parameters. We extend validation of the effectiveness of NNInv through a series of quantitative and qualitative analyses. We then demonstrate the method's utility by applying it to three visualization tasks: interactive instance interpolation, classifier agreement, and gradient visualization.

Evidence comparing additive 3-dimensional printing (3DP) with subtractive milling for implant-supported frameworks is lacking.The purpose of this systematic review and meta-analysis was to compare the marginal fit and accuracy of complete-arch implant-supported frameworks (CA), implant-retained fixed partial dentures (IRFPDs), single implant crowns (SICs), and interim implant-retained restorations (IIRRs) by using additive manufacturing (AM) and subtractive manufacturing (SM) methods.An electronic search was performed in PubMed, ScienceDirect, Google Scholar, and the Cochrane Library for articles published up to August 2020. The authors acquired the data and evaluated the articles, and the final selection of articles was made according to the inclusion and exclusion criteria. The Methodological Index for Non-Randomized Studies (MINORS) scale was used to evaluate the quality of the included studies. Heterogeneity was evaluated, and meta-analyses with subgroup analyses were performed in the selected studies.The database search resulted in 960 articles. After removing duplicate articles (410 studies), the titles and abstracts were screened in detail, and 10 in vitro studies were selected for qualitative analysis and 9 for quantitative analysis according to the eligibility criteria. Subgroup analyses were performed to compare the 3DP versus the SM technique for different types of implant-supported frameworks (IRFPDs, SICs, IIRRs, and CA). In the IRFPDs analysis, the marginal fit accuracy of the 3DP systems was higher than that with the subtractive manufacturing method (P.001). In the subgroup analysis of SIC frameworks (P=.55) and CA (P=.67) frameworks, no significant difference was observed in the assessed techniques.The marginal fit of implant-supported frameworks manufactured by AM or SM methods is in the clinically acceptable range.

Nonoccurring behavior (NOB) studies have attracted the growing attention of scholars as a crucial part of behavioral science. As an effective method to discover both NOB and occurring behaviors (OB), negative sequential pattern (NSP) mining is successfully used in analyzing medical treatment and abnormal behavior patterns. At this time, NSP mining is still an active and challenging research domain. Most of the algorithms are inefficient in practice. Briefly, the key weaknesses of NSP mining are: 1) an inefficient positive sequential pattern (PSP) mining process, 2) a strict constraint of negative containment, and 3) the lack of an effective Negative Sequential Candidate (NSC) generation method. To address these weaknesses, we propose a highly efficient algorithm with improved techniques, named sc-NSP, to mine NSP efficiently. We first propose an improved PrefixSpan algorithm in the PSP mining process, which connects to a bitmap storage structure instead of the original structure. Second, sc-NSP loosens the frequency constraint and exploits the NSC generation method of positive and negative sequential patterns mining (PNSP) (a classic NSP mining method). Furthermore, a novel pruning strategy is designed to reduce the computational complexity of sc-NSP. Finally, sc-NSP obtains the support of NSC by using the most efficient bitwise-based calculation operation. Theoretical analyses show that sc-NSP performs particularly well on data sets with a large number of elements and items in sequence. Comparison and extensive experiments along with case studies on health data show that sc-NSP is 10 times more efficient than other state-of-the-art methods, and the number of NSPs obtained is 5 times greater than other methods.

Scientific simulations on high-performance computing (HPC) systems can generate large amounts of floating-point data per run. As compared to lossless compressors, lossy compressors, such as SZ and ZFP, can reduce data volume more aggressively while maintaining the usefulness of the data. However, a reduction ratio of more than two orders of magnitude is almost impossible without seriously distorting the data. In deep learning, the autoencoder has shown potential for data compression. Whether the autoencoder can deliver similar performance on scientific data, however, is unknown. In this paper, we for the first time conduct a comprehensive study on the use of autoencoders to compress real-world scientific data and illustrate several key findings on using autoencoders for scientific data reduction. We implement an autoencoder-based compression prototype to reduce floating-point data. Our study shows that the out-of-the-box implementation needs to be further tuned in order to achieve high compression ratios and satisfactory error bounds. Our evaluation results show that, for most test datasets, the tuned autoencoder outperforms SZ by 2 to 4X, and ZFP by 10 to 50X in compression ratios, respectively. Our practices and lessons learned in this work can direct future optimizations for using autoencoders to compress scientific data.

Purpose Online travel reviews platforms have become innovative information systems due to the incorporation of sophisticated gamification elements such as visually appealing badges. This study aims to analyze three features of the review after leveling up a badge: review length (number of words), sentiment scoring and period between two successive reviews (number of days until the next review is written). Design/methodology/approach A total of 77,000 online TripAdvisor reviews written by 100 frequent travelers and contributors are analyzed using a data mining approach. A data-based sensitivity analysis is then conducted to provide an understanding of the data mining trained models. Findings The results show evidence that badges appealing for self-pride (“badge passport”) and for peer-recognition (“badge helpful”) have significant influence across the lifespan of online review, whereas badges simply awarded by counting the contributions have little effect. Originality/value To the best of the authors’ knowledge, this study provides the first analysis of how an experienced traveler is influenced as the badges and points are being awarded. Intrinsic motivational factor to award badges for standard contributions scarcely influence user behavior. Badges need to be designed to reward accomplishments that are not so trivial to be achieved and that do not depend entirely on the user.

The ensemble learning method is considered a meaningful yet challenging task. To enhance the performance of binary classification and predictive analysis, this paper proposes an effective ensemble learning approach by applying multiple models to produce efficient and effective outcomes. In these experimental studies, three base learners, J48, Multilayer Perceptron (MP), and Support Vector Machine (SVM) are being utilized. Moreover, two meta-learners, Bagging and Rotation Forest are being used in this analysis. Firstly, to produce effective results and capture productive data, the base learner, the J48 decision tree is aggregated with the rotation forest. Secondly, machine learning and ensemble learning classification algorithms along with the five UCI Datasets are being applied to progress the robustness of the system. Whereas, the recommended mechanism is evaluated by implementing five performance standards concerning the accuracy, AUC (Area Under Curve), precision, recall and F-measure values. In this regard, extensive strategies and various approaches were being studied and applied to obtain improved results from the current literature; however, they were insufficient to provide successful results. We present experimental results which demonstrate the efficiency of our approach to well-known competitive approaches. This method can be applied to image identification and machine learning problems, such as binary classification.

To utilize a huge amount of observation data based on real-world events, a data assimilation process is needed to estimate the state of the system behind the observed data. The Kalman filter is a very commonly used technique in data assimilation, but it has a problem in terms of practical use from the viewpoint of processing efficiency and estimating the deterioration in precision when applied to particularly large-scale datasets. In this paper, we propose a method that simultaneously addresses these problems and demonstrate its usefulness. The proposed method improves the processing efficiency and suppresses the deterioration in estimation precision by introducing correction processes focusing on the non-negative nature and sparseness of data in wavelet space. We show that the proposed method can accurately estimate population dynamics (MAE ≤ 3, RMSE ≤ 7) on the basis of an evaluation done using population data generated from cellular networks. In addition, the possibility of wide area abnormality detection using the proposed method is shown from a situation analysis of when Category 5 typhoon Hagibis made landfall in Japan. The proposed method has been deployed in a commercial service to estimate real-time population dynamics in Japan.

Every company or organization must have the right strategy to continue business or organizational activities. If not used, product sales data at the company will only become a pile of data; of course, it is regrettable if it is not used properly. The company can use the most product sales data to determine the next marketing strategy. To find out this data (the most sales), Association Rules Analysis is needed on the FP-Growth Algorithm method. This research aims to determine the association rule of Java ATK sales using the FP-Growth algorithm. This algorithm can be used for extensive data sets and to process Big Data. The results of this study are the FP Growth algorithm using association rules, which can be implemented in bookstore sales data with support count and minimum confidence parameters. A decimal value of 0.8 can form a product purchase correlation to increase stationery sales in Java Ateka. The rule obtained from the results of the FP-Growth calculation is that there are three transactions where if you buy item A, you will buy item B.

Pharmacies are shops that sell and mix medicines based on doctors’ prescriptions and trade medical goods. Apart from being a business actor, the pharmacy also plays a role in providing health services that are easily accessible to the public. The problem that often occurs in pharmacies selling drugs is that they are less than optimal in service to consumers. The habit of consumers buying more than one type of drug makes pharmacy staff slow in providing the drug due to the inaccurate layout of the drug. The FP-Growth method in Data Mining is a method that can provide a solution in determining drug sales patterns at pharmacies. The FP-Growth method is a method used in determining the data set that occurs most frequently together (Frequent Itemset). The research objective was to determine drug sales patterns based on drug sales transaction data so that drug layouts could be determined. This research was conducted at the Pharmacy at the Pratama Sehati Husada Clinic in Medan with drug sales transaction data from November 2021 to December 2021. The results of applying the FP-Growth method with a value of ≥ 15% as a minimum support and a value of ≥ 15% as a minimum confidence, a pattern of drug sales in pairs. The application of Data Mining with the FP-Growth method has been implemented at the Pharmacy at the Pratama Sehati Husada Clinic with the desired goals with the final result in the form of a report on the results of determining drug sales patterns.

With the development of online real estate trading platforms, multi-modal housing trading data, including structural information, location, and interior image data, are being accumulated. The accurate appraisal of real estate makes sense for government officials, urban policymakers, real estate sellers, and personal purchasers. In this study, we propose an interpretable multi-modal stacking-based ensemble learning (IMSEL) method that deals with various modalities for real estate appraisals. We crawl the structural and image data of real estate in Chengdu city, China from the nations largest real estate transaction platform with the location information, including public services, within 2 km of the real estate using Baidu map. We then compare the predictive results from IMSEL with those from previous state-of-art methods in the literature in terms of the root mean square error, mean absolute percentage error, mean absolute error, and coefficient of determination (R2). The comparison results show that IMSEL outperformed the other methods. We verified the improvement of introducing a data transformation strategy and deep visual features through a 10-fold cross-validation. We also discuss the managerial implications of our research findings.

The implementation of health in Indonesia is still marked by problems in managing the workforce, especially among honorary health workers. The State Personnel Agency seeks to improve service quality by selecting good human resources to enhance community services. Several Ministries agreed to change the team member recruitment system from Prospective Civil Servants to Government Employees with Work Agreements. In the field, in the Government Employees with Work Agreements acceptance process, there are still pros and cons, both from the rules and the appointment process. The community hopes that the appointment process can be objective and open so that no group is disadvantaged. To achieve these expectations, researchers used data mining to classify health workers who would become Government Employees with Work Agreements. The data mining process uses probability with the Naive Bayes Classifier algorithm from historical data on Government Employees with Work Agreements receipts for 2021. Data on the history of Government Employees with Work Agreements acceptance of health workers as many as 1078 data have been filtered and cleaned. The results of testing the data are 0.00012 for the worthy assumptions and 0.0032 for the unworthy assumptions. Data processing results will be visually displayed using bubble diagrams and Python programming. The researcher concluded that the process of classifying prospective first-aid team members for medical personnel could be done by data mining using the Naïve Bayes algorithm. The results of this classification can be used as a reference for the following year's Government Employees with Work Agreements revenue classification process.

Industrial cyber-physical systems (ICPSs) are deployed in many high-value facilities recently, and the monitoring of ICPS is more and more important. However, the prerequisite of ICPS monitoring is how to obtain an accurate network structure. In addition, the structure of ICPS may change over time and the observations data are limited and noisy. These situations make the ICPS network structure identification more difficult. In this article, we proposed the algorithm of temporal network identification from sparse data (ATNISD) to address these two issues simultaneously. First, we established the temporal network analysis model from the aspect of state equation and observation equation. Then, we analyze the characteristics of temporal networks in both time domain and space domain and propose a general framework of temporal networks structure identification, which is a combinatorial optimization problem. To improve the accuracy and alleviate the computational complexity, we decompose the combinatorial problem into small independent simple problems, which can be solved efficiently. The performance of the proposed algorithm is verified on synthetic evolutionary game dynamics on both homogeneous and heterogeneous temporal networks. The experimental results show that the proposed method can efficiently solve the problem of temporal networks structure identification from sparse data.

Privacy preserving data mining has much importance in the data analysis because it maintains confidential or highly sensitive data in the purpose of data analysis. Many perturbation techniques are developed, but do not perform well. There are used for different purposes in various techniques. Though, the reconstruction of original data can be done from the samples of perturbed data. It does not always preserve the distance that exists in the original data samples. During reconstruction of the original data, some of the information is loosed which is again a complex and time consuming task for traditional data mining algorithm. Therefore, a novel hybrid data perturbation technique is introduced that overcomes the above addressed issues. The proposed paper includes a novel data perturbation technique to hybridize the traditional Geometric Data Perturbation Technique along with the Euclidean distance method to preserve privacy. It obtains the distorted or perturbed data which is sampled at different intervals depending on the levels of privacy. The proposed approach attempts to reduce the tradeoffs that persists between privacy levels, accuracy and information loss. The experimental results for this approach is implemented using real world bench mark datasets downloaded from UCI Repositories using three different data mining clustering algorithms. It is shown that clustering accuracy is similar and fairly high for the perturbed data obtained from the proposed approach when matched with the original data set clustering algorithms. The analysis were shown for different privacy levels on different data samples.

Passenger-flow anomaly detection and prediction are essential tasks for intelligent operation of the metro system. Accurate passenger-flow representation is the foundation of them. However, spatiotemporal dependencies, complex dynamic changes, and anomalies of passenger-flow data bring great challenges to data representation. Taking advantage of the time-varying characteristics of data, we propose a novel passenger-flow representation model based on low-rank dynamic mode decomposition (DMD), which also integrates the global low-rank nature and sparsity to explore the spatiotemporal consistency of data and depict abrupt data, respectively. The model can detect anomalies and predict short-term passenger flow conveniently and flexibly. For anomaly detection, we further introduce a strong temporal Toeplitz regularization to characterize the temporal periodic change of data, so as to more accurately detect anomalies. We conduct experiments with smart card transaction data from the Beijing metro system to assess the performance of the model in two use cases. In terms of anomaly detection, the experimental results demonstrate that our method can detect anomalies efficiently, especially for time sequence anomalies. As for short-term prediction, our model is superior to other methods in most cases.

Testing is the fundamental activity intentional at detecting and solving technical issues in the software source code. In software development, test suite plays a major role. A test suite is a collection of test case which describes actual input, action and an expected response to verify whether a system under test satisfies requirements correctly or not. A huge number of test cases are required for testing the software. The test cases may be redundant. So, the execution of all these test cases entails lot of effort, time and cost. So, it is necessary to make enhancement in the test suite that outcome in good coverage with less effort, time and cost. This work proposes a hybrid technique for test suite enhancement using data mining techniques. The hybrid approach is performed in three ways i) Classification based Clustering ii) Classification based Association iii) Classification based RTM approach used to make enhancement in the test suite and to improve accuracy.

With vast usage of Spatio-temporal data usage in various domains and capturing data as remote sensing, GPS, and ST data. STDM is critical for various domains such as transportation, public safety, medical and environmental statistics. In this article, ST characteristics are explained in seven different types and followed by models for STDM in different domains including medical, mobile usage, sensors, industrial area, weather analysis, data querying, traffic control. Finally, the research and future direction.