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Name disambiguation is a key and also a very tough problem in many online systems such as social search and academic search. Despite considerable research, a critical issue that has not been systematically studied is disambiguation on the fly — to complete the disambiguation in the real-time. This is very challenging, as the disambiguation algorithm must be accurate, efficient, and error tolerance. In this paper, we propose a novel framework — CONNA — to train a matching component and a decision component jointly via reinforcement learning. The matching component is responsible for finding the top matched candidate for the given paper, and the decision component is responsible for deciding on assigning the top matched person or creating a new person. The two components are intertwined and can be bootstrapped via jointly training. Empirically, we evaluate CONNA on two name disambiguation datasets. Experimental results show that the proposed framework can achieve a 1.21-19.84 percent improvement on F1-score using joint training of the matching and the decision components. The proposed CONNA has been successfully deployed on AMiner — a large online academic search system. [ABSTRACT FROM AUTHOR]

A pipeline is the most efficient way to transport water from one place to another. Due to aging, corrosion, and external factors, the pipeline is prone to damage, which causes leaks. Many machine learning (ML) and deep learning (DL) methods are available to address this issue. This paper does an experimental study on available methods in ML and DL for leak detection for the collected data using an acousto-optic sensor. The experimental setup comprises of an acousto-optic sensor made of an erbium-doped fiber laser (EDFL), galvanized iron pipeline, a tank, a pump, and a data acquisition unit. The dimensions of the galvanized pipeline looped with the water tank are a length of 40 m, an inner diameter of 89 mm, and an outer diameter of 90 mm. The diameter of the simulated leak aperture is 5 mm. The methods analyzed in this study are k-means, k-medoids, Naive Bayes (NB), support vector machines (SVM), k-nearest neighbor (KNN), decision tree (DT), categorical boosting (CatBoost), random forest (RF), XGBoost, AdaBoost, and one-dimensional convolutional neural network (1DCNN). ML algorithms need a feature extraction technique because the data collected from the experiment is too large and contains redundant information. Feature extraction reduces the data size by extracting essential information. This paper extracts ten features from raw data. Among the ML algorithms, AdaBoost gives the highest prediction accuracy of 98.02%. This paper also implements eight models of 1DCNN, and Model 1 shows the best prediction accuracy of 98.16%, which is the highest compared with all the other classifiers in ML and DL for one-dimensional time series acousto-optic sensor data. [ABSTRACT FROM AUTHOR]

Time-of-use (TOU) is an effective price-based demand response strategy. A reasonable design of TOU strategy can effectively reduce the peak-valley difference, and then produce a lot of benefits (such as delaying power grid investment, reducing interruption cost, and improving reliability). However, changing peak-valley period has a great influence on the peak-valley difference and power supply reliability of power system. Therefore, this paper aims to investigate the optimal modification of peak-valley period considering reliability loss under multiple TOU schemes. Firstly, this paper presents a clustering model and algorithm of optimal load curve based on a minimum error iteration method. Secondly, an optimal modification of peak-valley period based on a dynamic load point method is proposed, and the traditional peak-valley difference is replaced by the global peak-valley difference to calculate the objective function. Thirdly, this paper establishes a load–reliability relation fitting model based on the back propagation neural network. Finally, the effectiveness and correctness of the proposed method are investigated by the Roy Billinton test system and reliability test system. [ABSTRACT FROM AUTHOR]

One of the most important researches in wireless sensor networks is energy saving. The clustering algorithm can efficiently save energy. However, most of the existing clustering algorithms use a fixed cluster head election algorithm and perform each round of clustering, so that unsuitable nodes are continuously elected as cluster heads, leading to rapid exhaustion of node energy and short network life. In order to solve these problems, this paper proposes an Energy-saving Fuzzy Clustering Routing algorithm (EFCR) for wireless sensor networks,which designs two types of clustering, Clustering Type1 and Clustering Type2, and alternately executes them in different rounds according to the threshold. Clustering Type1 considers the parameters that affect the energy consumption of the node: the distance from the node to the base station, the number of neighbor nodes of the node, and the remaining energy of the node as fuzzy inputs, and calculates the fitness of the node as a cluster head through fuzzy inference to avoid the occurrence of unsuitable nodes being successively elected as cluster heads. Clustering Type2 performs data transmission by trusting the last round of cluster heads, which reduces the number of control data packets in each round of clustering and reduces the overall energy consumption of the network. This paper conducted simulations in four different scenarios, compared other clustering algorithms of the same type, and analyzed the remaining energy of the network, the number of surviving nodes, and the life of the network. The results show that EFCR algorithm is better than others. [ABSTRACT FROM AUTHOR]

A pipeline is critical in conveying water, oil, gas, petrochemicals, and slurry. As the pipeline ages and corrodes, it becomes susceptible to deterioration, resulting in wastage and hazardous damages depending on the material it transports. To mitigate these risks, implementing a suitable monitoring system becomes essential, enabling the early identification of damage and minimizing waste and the potential for hazardous incidents. The pipeline monitoring system can be exterior, visual/biological, and computational. This paper surveys state-of-the-art approaches and also performs experimental analyses with a few methods in signal/data-driven approaches within computational methods. More precisely, signal processing-based leak localization methods, artificial intelligence-based leak detection methods, and combined approaches are given. This paper implements five signal processing-based methods and 17 artificial intelligence-based methods. This implementation helps to compare and understand the significance of appropriate noise removal and feature extraction. The data for this analysis is collected using acousto-optic sensors from an experimental setup. After implementation, the highest observed leak localization accuracy is 99.14% with the wavelet packet adaptive independent component analysis-based generalized cross correlation, and the highest leak detection accuracy is 98.32% with the one-dimensional convolutional neural network. [ABSTRACT FROM AUTHOR]

Due to the development of high-throughput technologies for gene analysis, the biclustering method has attracted much attention. However, existing methods have problems with high time and space complexity. This paper proposes a biclustering method, called Row and Column Structure-based Biclustering (RCSBC), with low time and space complexity to find checkerboard patterns within microarray data. First, the paper describes the structure of bicluster by using the structure of rows and columns. Second, the paper chooses the representative rows and columns with two algorithms. Finally, the gene expression data are biclustered on the space spanned by representative rows and columns. To the best of our knowledge, this paper is the first to exploit the relationship between the row/column structure of a gene expression matrix and the structure of biclusters. Both the synthetic datasets and the real-life gene expression datasets are used to validate the effectiveness of our method. It can be seen from the experiment results that the RCSBC outperforms the state-of-the-art algorithms both on clustering accuracy and time/space complexity. This study offers new insights into biclustering the large-scale gene expression data without loading the whole data into memory. [ABSTRACT FROM AUTHOR]

Recognition of aggressive driving behavior helps future research and practices in Intelligent Transport Systems. This paper tries to briefly explain the concepts related to aggressive driving behavior and introduces a system integrated with a novel machine learning algorithm for the recognition of trip-based aggressive driving behavior. The algorithm is a Gaussian Mixture Model (GMM) structured with Factor Analysis (FA), and Hierarchical Clustering (HC): common factors were extracted using FA, which is further applied to HC and GMM in the recognition of trip-based aggressive driving. The system is applied in a case study using data from the Shanghai Naturalistic Driving Study, for simulating data collection using the Advanced Driving Assistance System (ADAS) system in a real-traffic situation. Three behavior types (cautious, regular, and aggressive driving) were successfully clustered. For validity, the real aggressive driving behavior records were extracted based on the video, and the proposed system was compared with existing recognition methods. Results indicate that the accuracy of aggressive driving recognition of the system is higher than others (accuracy = 87%). This paper provides a reference in defining and determining aggressive driving, and a robust system for aggressive driving behavior recognition along with the trained algorithm, which can be used in real-world applications for improving driving safety with the applications in ADAS systems, auto-insurance industry. [ABSTRACT FROM AUTHOR]

Many datasets including social media data and bibliographic data can be modeled as graphs. Clustering such graphs is able to provide useful insights into the structure of the data. To improve the quality of clustering, node attributes can be taken into account, resulting in attributed graphs. Existing attributed graph clustering methods generally consider attribute similarity and structural similarity separately. In this paper, we represent attributed graphs as star-schema heterogeneous graphs, where attributes are modeled as different types of graph nodes. This enables the use of personalized pagerank (PPR) as a unified distance measure that captures both structural and attribute similarities. We employ DBSCAN for clustering, and we update edge weights iteratively to balance the importance of different attributes. The rapidly growing volume of data nowadays challenges traditional clustering algorithms, and thus, a distributed method is required. Hence, we adopt a popular distributed graph computing system Blogel, based on which, we develop four exact and approximate approaches that enable efficient PPR score computation when edge weights are updated. To improve the effectiveness of the clustering, we propose a simple yet effective edge weight update strategy based on entropy. In addition, we present a game theory based method that enables trading efficiency for result quality. Extensive experiments on real-life datasets offer insights into the effectiveness and efficiency of our proposals. [ABSTRACT FROM AUTHOR]

One of the key design problems of wireless sensor networks is to reduce energy consumption and improve node survival. Because the battery in sensor nodes is difficult to replace and the energy is limited, the design of energy-saving routing protocol becomes very important. Aiming at the problems of limiting node energy and shortening network lifetime in wireless sensor networks, a WSNs routing protocol based on improved archimedes optimization algorithm (IAOAR) is proposed in this paper. Firstly, in the cluster establishment phase, the protocol defines different fitness functions according to the energy, number and distance of sensor nodes to select the appropriate initial cluster. Then the fitness value of the node is calculated, and the weight is dynamically updated according to the number of iterations to improve the optimization ability of AOA algorithm. Virtual force is introduced to adjust the selected cluster head position to ensure the selection of optimal cluster heads, reduce the transmission energy consumption of common sensor nodes. In the data transmission stage, combined with the improved ant colony algorithm, the shortest transmission path to the sink node is constructed to reduce the energy consumption of long-distance data transmission at the cluster head. The simulation results showed that compared with LEACH, E-LEACH, LEACH-ANT and MAXLEACH protocols, the network life cycle of the protocol proposed in this paper was improved by 140.55%, 107.09%, 99.24% and 96.76% respectively. It effectively reduced the speed of node death, balances the network energy consumption and prolonged the network lifetime. [ABSTRACT FROM AUTHOR]

Fragility modeling and real-time risk assessment can be widely applied to evaluate and enhances the resilience of the power system to High-Impact and Low Probability events. In previous studies, fragility modeling generally targets extreme weather conditions other than thunderstorm. This paper proposes a fragility model to describe the relationship between the duration of a thunderstorm and the probability of lightning related trip-out. The duration of thunderstorms, which can usually be forecasted from the meteorological department, together with the fragility function expression can help a power company to predict the possibility of lightning related trip-out. Furthermore, this paper proposes a real-time risk assessment model that can dynamically adjust the risk value based on the update of the location, peak current, and subsequent stroke of real-time thunderstorm. A case study conducted on the lightning related trip-out data in Southwest China demonstrates that the average risk of transmission line trip-out in high risk group is about ten times that in low risk group. It clearly demonstrates that real-time risk assessment can efficiently distinguish the trip-out risks of different real-time thunderstorms. [ABSTRACT FROM AUTHOR]

Anomaly detection is critical across domains, from cybersecurity to fraud prevention. Graphs, adept at modeling intricate relationships, offer a flexible framework for capturing complex data structures. This paper proposes a novel anomaly detection approach, combining Graph Convolutional Networks (GCNs) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). GCNs, a specialized deep learning model for graph data, extracts meaningful node and edge representations by incorporating graph topology and attribute information. This facilitates learning expressive node embeddings capturing local and global structural patterns. For anomaly detection, DBSCAN, a density-based clustering algorithm effective in identifying clusters of varying densities amidst noise, is employed. By defining a minimum distance threshold and a minimum number of points within that distance, DBSCAN proficiently distinguishes normal graph elements from anomalies. Our approach involves training a GCN model on a labeled graph dataset, generating appropriately labeled node embeddings. These embeddings serve as input to DBSCAN, identifying clusters and isolating anomalies as noise points. The evaluation on benchmark datasets highlights the superior performance of our approach in anomaly detection compared to traditional methods. The fusion of GCNs and DBSCAN demonstrates a significant potential for accurate and efficient anomaly detection in graphs. This research contributes to advancing graph-based anomaly detection, with promising applications in domains where safeguarding data integrity and security is paramount. [ABSTRACT FROM AUTHOR]

There is a huge content of Arabic text available over online that requires an organization of these texts. As result, here are many applications of natural languages processing (NLP) that concerns with text organization. One of the is text classification (TC). TC helps to make dealing with unorganized text. However, it is easier to classify them into suitable class or labels. This paper is a survey of Arabic text classification. Also, it presents comparison among different methods in the classification of Arabic texts, where Arabic text is represented a complex text due to its vocabularies. Arabic language is one of the richest languages in the world, where it has many linguistic bases. The researche in Arabic language processing is very few compared to English. As a result, these problems represent challenges in the classification, and organization of specific Arabic text. Text classification (TC) helps to access the most documents, or information that has already classified into specific classes, or categories to one or more classes or categories. In addition, classification of documents facilitate search engine to decrease the amount of document to, and then to become easier to search and matching with queries. [ABSTRACT FROM AUTHOR]

The rate of recidivism by criminals after their release from prison is high, which is harmful to society. Thus, it is socially significant to reduce their recidivism rate. This article uses public data from the state of Iowa in the United States. According to the data characteristics, such as having redundant samples and mixed attributes, we propose the following methods. First, we use a rough set attribute reduction algorithm based on probability distributions to reduce the redundant items. Second, the sample data are clustered with an improved clustering algorithm. Based on the traditional K-prototype clustering algorithm, the clustering algorithm is improved by changing the measurement method of the categorical attributes, changing the initial cluster center selection method, and weighting the attributes based on the information entropy. The clustering experiment results show that the improved clustering algorithm has a better clustering effect and higher clustering accuracy than the traditional K-prototype clustering algorithm. Finally, a back propagation neural network is used to predict the recidivism probability of the sample processed by the above algorithm. The final experimental results show that the two redundant attributes are successfully reduced by rough sets, which greatly reduces the run time of the model. Compared with the traditional K-prototype clustering algorithm, the improved K-prototype clustering algorithm proposed in this paper has a better effect on the various indicators and objective function. Finally, through neural network prediction, the prediction accuracy of this model reached 87.9%. At the same time, a large number of experiments on benchmark datasets verify the effectiveness of our proposed model. [ABSTRACT FROM AUTHOR]

In this article we present our results from the SC19 Student Cluster Competition Reproducibility Challenge. The challenge entails reproducing the article entitled “Computing Planetary Interior Normal Modes with A Highly Parallel Polynomial Filtering Eigensolver” presented at SC’18, which proposes a parallel polynomial filtered Lanczos algorithm to directly calculate the planetary normal modes of heterogeneous planets. The proposed algorithm showed excellent performance with relatively low memory consumption and high parallel efficiency. In this work, we reproduce the scaling tests in that article on a cluster using Intel Cascade Lake architecture and use the proposed algorithm to illustrate specific normal modes of Mars. We compare the results obtained on our cluster with those in the original article. We also design a new metric to better analyze the results. In addition, we use the profiling tool Intel VTune Amplifier to explain our discoveries. Our results demonstrate that the given models show great scalability, which is similar to the original article. The required normal modes of Mars are also successfully calculated and visualized. [ABSTRACT FROM AUTHOR]

With the abundance of data, machine learning applications engaged increased attention in the last decade. An attractive approach to robustify the statistical analysis is to preprocess the data through clustering. This paper develops a low-complexity Riemannian optimization framework for solving optimization problems on the set of positive semidefinite stochastic matrices. The low-complexity feature of the proposed algorithms stems from the factorization of the optimization variable $\mathbf {X}=\mathbf {Y}\mathbf {Y}^{\mathrm{T}}$ X = Y Y T and deriving conditions on the number of columns of $\mathbf {Y}$ Y under which the factorization yields a satisfactory solution. The paper further investigates the embedded and quotient geometries of the resulting Riemannian manifolds. In particular, the paper explicitly derives the tangent space, Riemannian gradients and Hessians, and a retraction operator allowing the design of efficient first and second-order optimization methods for the graph-based clustering applications of interest. The numerical results reveal that the resulting algorithms present a clear complexity advantage as compared with state-of-the-art euclidean and Riemannian approaches for graph clustering applications. [ABSTRACT FROM AUTHOR]

Recently, Energy Harvesting-supported Device-to-Device (EH-D2D) communication receives extensive concerns due to its excellent properties in Energy Efficiency (EE), offloading capability, etc. Researches on EH-D2D mainly focus on resource allocation schemes of available energy. With the popularity of mobile devices and applications, content sharing becomes anytime, anywhere. Multicast is the foundation to realize the content sharing and can reduce transmission consumption because of multi-user service feature. Thus, Mutlicast can further improve EE of EH-D2D in communication mode rather than in resource allocation. However, the heterogeneity and imbalance of available energy, content requests and social ties on the user side will bring great challenges to the deployment of EH-D2D Multicast (EH-D2MD) communication. This paper aims at EE optimization and establishes a two-layer cluster formation model, which solves grouping, cluster head selection and power control jointly. When facing the non-convex of the sum of fractional functions and the product of binary variables, this paper specifically designs a two-step Convex Approximation Algorithm (CDA). CDA skillfully transforms the modeled non-convex Mixed Integer Non-Linear Programming (MINLP) problem into a convex MINLP one, which is easier to solve. Simulation results reveal that CDA can obtain the joint cluster formation results with approximate optimal EE and lower complexity. [ABSTRACT FROM AUTHOR]

This paper, based on differential privacy protecting K-means clustering algorithm, realizes privacy protection by adding data-disturbing Laplace noise to cluster center point. In order to solve the problem of Laplace noise randomness which causes the center point to deviate, especially when poor availability of clustering results appears because of small privacy budget parameters, an improved differential privacy protecting K-means clustering algorithm was raised in this paper. The improved algorithm uses the contour coefficients to quantitatively evaluate the clustering effect of each iteration and add different noise to different clusters. In order to be adapted to the huge number of data, this paper provides an algorithm design in MapReduce Framework. Experimental finding shows that the new algorithm improves the availability of the algorithm clustering results under the condition of ensuring individual privacy without significantly increasing its operating time. [ABSTRACT FROM AUTHOR]

The present paper addresses the problem of writing style patterns in the context of English Academic Writing. Stylometric analysis is used in order to extract the main characteristics obtained from the evaluation of articles written in well-known scientific journals such as Elsevier and Springer. The objective of the paper is to establish a pattern description of articles written in the same domain depending on the gender of the authors. Relevant prior written work upon the current subject reveal different characteristics of writing style of authors from different cultural orientation and gender. The paper describes the main characteristics taken into account for the clustering model when it comes to title, abstract and chapters' construction within the analyzed articles. A short description of the algorithms and tools for clustering and space reduction is presented for further selecting the best combination for the proposed model. An additional statistical layer is added to the current clustering algorithms and space reduction for obtaining statistical proven results of usage. An aggregated structure model is conducted as a result of characteristics selection and processing for future work usage in gender analysis of scientific articles writing. Conclusions and withdrawn along with the future directions extracted from the current work. A database structure is proposed formed out of statistical calculated percentage of papers depending on the author gender. The relevance of the work can be well used as a guide line in writing scientific articles as the main musts in scientific writing are presented. [ABSTRACT FROM AUTHOR]

This paper describes a solution for the image density classification problem (DCP) using an entirely distributed system with only local processing of information named cellular automata (CA). The proposed solution uses two cellular automata's features, density conserving and translation of the information stored in the cellular automata's cells through the lattice, in order to obtain the solution for the density classification problem. The motivation for choosing a bio-inspired technique based on CA for solving the DCP is to investigate the principles of self-organizing decentralized computation and to assess the capabilities of CA to achieve such computation, which is applicable to many real-world decentralized problems that require a decision to be taken by majority voting, such as multi-agent holonic systems, collaborative robots, drones' fleet, image analysis, traffic optimization, forming and then separating clusters with different values. The entire application is coded using the C# programming language, and the obtained results and comparisons between different cellular automata configurations are also discussed in this research. [ABSTRACT FROM AUTHOR]

The paper presents research on mode identification of broad-band oscillation in interconnected power system. A data-driven mode identification (DDMI) method for broad-band oscillation signals is proposed creatively in this paper. Firstly, piecewise aggregation approximation algorithm is improved to achieve effective dimension reduction of oscillation data. Combined with ${k}$ -Shape clustering algorithm, oscillation database is established with historical data, real-time data and simulation data. Then, oscillation mode identification models corresponding to different data categories can be obtained based on random forest algorithm, which can realize fast and automatic matching between broad-band oscillation data and oscillation mode parameters. Finally, the identification results of two simulation oscillation cases and an actual oscillation case show that proposed method can accurately identify the oscillation mode parameters from broad-band oscillation signals and has higher accuracy compared with other methods. [ABSTRACT FROM AUTHOR]

To monitor process and identify the deviation as early as possible, data-driven methods have been applied for process monitoring and fault detection in semiconductor manufacturing. Although various fault detection and classification models had been discussed in the literature, however, little research has been devoted to feature selection from trace data that is important for process monitoring of natural variation. Additionally, the high-mix production mode with different recipes leads to process dynamic of wafer-to-wafer (W2W) variation which should also be identified for safeguarding false alarms and serving as a warning indicator. Therefore, this paper proposes a data-driven framework to identify the key features with respect to the W2W variation. In particular, the self-organizing map is used to annotate the grade of wafer variation among the in-line metrology data. Subsequently, the adaptive boosting (AdaBoost) is adopted to examine the effectiveness of every feature and its processing times, respectively. To validate the proposed framework, an empirical study from a semiconductor fabrication plant is conducted. The experimental results demonstrate that the key feature identification is of critical importance to build highly capable models for process monitoring. Through the dimensionality reduction technique, it has been illustrated that a smaller set of the identified key features are able to pinpoint the W2W variation of different wafer grades more clearly than the whole set of process features. Note to Practitioners— Process monitoring has become more difficult with the shrinking linewidth in semiconductor manufacturing. The challenges of analyzing equipment sensor or raw trace data for process monitoring in high-mix manufacturing processes are to incorporate subject-matter expert knowledge for setting control limit meticulously, to detect the subtle changes by analyzing the whole trace data profile, and to identify W2W variation for reducing false alarms. This paper proposes a data-driven framework for process monitoring by adopting data-driven approaches without recourse to domain judgement. Experimental results demonstrate that the proposed data-driven framework can effectively identify the key features via sensor readings and corresponding processing times, respectively. The engineers can make use of the extracted features to perform a predictive monitoring on metrology data for detection of potential process deterioration. [ABSTRACT FROM AUTHOR]

Uncertain data objects are objects that can be characterized by either a probability density function (PDF) or with multiple points. Because of existing levels of uncertainty for uncertain data objects, the scatter of this type of objects might be very different than the scatter of certain data objects. Measures of scatter for uncertain objects have not been defined before. In this paper, we define covariance matrix, within scatter matrix, and between scatter matrix as the measures of scatter for uncertain data objects. Also, in this paper, we extend the idea of Fisher linear discriminant analysis for uncertain objects (UFLDA). We also develop kernel Fisher discriminant analysis for uncertain objects (UKFDA). The developed uncertain kernel Fisher discriminants are for two cases: 1) when the uncertain objects are given with PDF and 2) when the uncertain objects are given with multiple points. We compare the performance of our developed uncertain Fisher discriminants (UFLDA and UKFDA) with a few other methods in classification of uncertain data objects through several examples on both simulated and real-world data. We will show in the experiments that our developed uncertain Fisher discriminants outperform other methods in classifying uncertain data objects. [ABSTRACT FROM AUTHOR]

Learning a good graph similarity matrix in data clustering is very crucial. The goal of clustering is to construct a good graph similarity matrix such that the similarity of points between the same classes is largest, and the similarity of points between different classes is smallest. In this paper, a more efficient subspace segmentation approach to learn a similarity matrix with large spectral gap is proposed. In our model, a robust self-representation coefficient matrix is learned by utilizing the Schatten- ${p}$ norm instead of the conventional rank function. Besides, the fast block-diagonal structure of the coefficient representation matrix is enhanced by learning and optimizing the co-association matrix with the soft label of clustering results simultaneously in a unified framework. The affinity graphs constructed in this paper can clearly reveal the intrinsic structures of the data sets. Extensive experiments on the real data sets demonstrate that our proposed method can perform better than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In human–robot coexisting environment, one of the primary objectives is to equip robots with the facilities of detecting and following a human being in front, using various sensors. Within this genre, developing algorithms for vision sensor-based shoe detection and subsequently following is considered an active problem. Considering that the shoe poses, during the pursuit, undergo different transformations, this paper presents how the vision sensor based shoe detection problem can be treated as analogous to template matching under general conditions. This paper first shows how a popular fast randomized template-matching algorithm, called FAsT-Match algorithm, and its contemporary variant for color images, called CFAsT-Match algorithm, can be implemented in real robots with success for detecting shoes in subsequent frames. Then, this paper proposes a new density-based clustering method, called ordering points to identify the clustering structure (OPTICS), based template-matching algorithm that is specifically developed to overcome several implementation problems associated with the FAsT-Match and CFAsT-Match algorithms, in real life. Experimental results and performance evaluations demonstrate the superiority of the proposed OPTICS-based algorithm for shoe detection during human tracking in human–robot coexisting environments. [ABSTRACT FROM AUTHOR]

Image segmentation techniques are challenging to apply to large-size remote sensing imagery. Indeed, if the data to be processed are larger than the computer’s available memory, it must be split into smaller pieces. Without precaution, segmentation errors appear along the edges of these pieces. The goal of this paper is to present a tilewise processing method to overcome this issue for superpixel segmentation, applied in particular to the simple linear iterative clustering algorithm. Incidentally, tilewise methods allow for several pieces of the image to be processed simultaneously, which enables the deployment of these methods in a parallel processing environment. Estimations of the speed-up when using multiple processors are provided. Then, it is demonstrated that the result of the tilewise segmentation is equivalent to the segmentation of the complete image, with respect to a number of global unsupervised segmentation criteria. Finally, experimental results on a large-size Sentinel-2 time series validate the method’s feasibility. [ABSTRACT FROM AUTHOR]

This paper proposes a clustering-based heuristic, named average Chebyshev linkage directed search (ACLDS), to optimize the nozzle and feeder assignments in a single spin-head gantry-type collect-and-place (CAP) surface-mount technology machine. The CAP machine is widely used in the printed circuit board assembly (PCBA) of consumer electronic products, but still a challenging application field from an operations research perspective. The PCBA optimization of a single machine is decomposed into interrelated nozzle assignment, feeder assignment, and CAP sequence subproblems, which is treated as a special case of the capacitated location routing problem. Because of the NP-hard nature of this problem, the ACLDS is proposed to solve it efficiently, which is a hierarchical heuristic to obtain the optimal nozzle assignment and then optimize feeder assignment and CAP sequence iteratively. A clustering technique is applied in the ACLDS to group components based on their nozzle and component types in the consideration of the optimal CAP sequence. To investigate the efficiency of the proposed algorithm, 13 industrial PCB samples and 40 artificial samples are used for experiments. Compared with the adaptive simulated annealing algorithm, the large clusters of operations algorithm, the hybrid genetic algorithm, industrial package, and the adaptive nearest neighbor tabu search algorithm, the proposed algorithm demonstrates its efficiency by testing through both the industrial and artificial PCB samples. Note to Practitioners—The production efficiency of the collect-and-place (CAP) surface-mount technology machine is critical to the electronic manufacturing. This paper is motivated by an optimization project cooperated with a spin-head gantry-type CAP machine manufacturer. To minimize the CAP cost, this paper proposed a clustering-based heuristic, named average Chebyshev linkage directed search (ACLDS), to optimize the nozzle assignment, feeder assignment, and CAP sequence. Based on the experimental results, the single-solution-based ACLDS outperforms other population-based heuristics, for instance, genetic algorithm, in terms of the solution quality and computational expense. Because the number of nozzle types is typically no more than five in high-speed machines, the enumeration method can be applied to obtain the optimal nozzle assignment in the ACLDS, which has been proven to be significant for the printed circuit board assembly (PCBA) optimization. The proposed heuristic can be applied to both the rotary-head and revolver-head gantry-type CAP machines. It can be extended to solve the optimization problems of dual-gantry operation or line balance in the PCBA. This paper assumes the mass production situation in the PCBA, which is not suitable for high-mix and low-volume situations. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamic access point (AP) clustering and precoding problem in the downlink of user-centric virtual cell networks. The goal is to maximize the weighted sum spectral efficiency (SE) while satisfying the power constraints and AP clustering constraints in adjacent time slots (TSs). By adopting the random walk mobility to model the mobile user equipments’ movement behaviors, we consider dynamic and time-varying channel conditions. Therefore, the weighted sum SE maximization programming takes the form of discrete-time sequence of mixed-integer non-convex optimization problems. In this paper, we propose to solve this sequential problem in two stages. In the first stage, a dynamic AP clustering approach based on discrete particle swarm optimization is developed. This approach takes the advantage of the channel correlation by exploiting the relationship between AP clustering solutions in adjacent TSs to improve the SE performance and reduce complexity. In the second stage, given the AP clustering solution obtained in the first stage, a distributed precoding algorithm is devised via applying the weighted minimum mean square error method. By combining these two stages, we propose a novel dynamic AP clustering and precoding algorithm (DAPC-Pre). The effectiveness of the proposed DAPC-Pre algorithm is verified by the simulation results. In particular, the proposed algorithm converges fast and significantly outperforms benchmark algorithms in terms of sum SE under different dynamic environments. [ABSTRACT FROM AUTHOR]

We address the problem of reducing the size of Craig’s interpolants used in SAT-based model checking. Craig’s interpolants are AND-OR circuits, generated by post-processing refutation proofs of SAT solvers. Being highly redundant, their compaction is typically tackled by reducing the proof graph and/or by exploiting standard logic synthesis techniques. In this paper, we propose a set of ad-hoc logic synthesis functions that, revisiting known logic synthesis approaches, specifically address speed and scalability. Though general and not restricted to interpolants, these techniques target the main sources of redundancy in combinational circuits. This paper includes an experimental evaluation, showing the benefits of the proposed techniques, on a set of benchmark interpolants arising from hardware model checking problems. [ABSTRACT FROM AUTHOR]

Development of smart grid technologies has created a promising atmosphere for smart cities and energy trading markets. Especially, traditional electricity consumers evolve into prosumers who produce as well as consume electricity in modern power electric systems. In this evolution, the electric power industry has tried to introduce the notion of local energy markets for prosumers. In the local energy market, prosumers purchase electricity from distributed energy generators or the other prosumers with surplus electricity via a local power exchange center. For this purpose, this paper proposes joint geometric clustering and truthful auction schemes in the local energy markets. The proposed clustering scheme is designed for distribution fairness of the distributed energy generator for serving prosumers, where the scheme is inspired by expectation and maximization based unsupervised learning. Moreover, this paper proposes an auction mechanism for truthful electricity trading in a local energy market. In order to guarantee truthful electricity trading, the proposed auction mechanism is constructed based on the Vickrey–Clarke–Groves auction, which was proven to guarantee truthful operations. The Hungarian method is also considered in addition to the auction. The simulation results for the auction verify that the utilities of local market energy entities are maximized when the prosumers are truthful. [ABSTRACT FROM AUTHOR]

Learning a prototype from a set of given objects is a core problem in machine learning and pattern recognition. A commonly used approach for consensus learning is to formulate it as an optimization problem in terms of generalized median computation. Recently, a prototype-embedding approach has been proposed to transform the objects into a vector space, compute the geometric median, and then inversely transform back into the original space. This vector space embedding approach has been successfully applied in several domains, where the generalized median problem has inherent high-computational complexity (typically $\mathcal {NP}$ -hard) and thus approximate solutions are required. Generally, it can be expected that the embedding should be done in a distance-preserving manner. However, the previous work based on the prototype-embedding approach did not take this embedding aspect into account. In this paper, we discuss the drawbacks of the current prototype-embedding approach and present an extensive empirical study that provides strong evidence of significantly improved quality of generalized median computation using distance-preserving embedding (DPE) methods. We also give concrete suggestions about suitable DPE methods. Moreover, we show that this framework can be used to effectively compute other consensus objects like the closest string. Finally, a MATLAB toolbox resulting from this paper is made publically available in order to encourage other researchers to explore the embedding-based consensus computation. [ABSTRACT FROM AUTHOR]

Automotive imaging is a recent trend in research to assist drivers and is finally moving forward to achieve the goal of designing a driverless car. Along with a state-of-the-art algorithm, a state-of-the-art validation framework is also a requirement to ensure the quality of the system. This paper proposes an enhancement of the ground truth determination for automated lane detection system. The approach of time slicing has been built up on the binary framework. However, the classical binarization algorithms are not found to be good enough to address the particular domain of lane detection in an unconstrained environment and varied scenarios of lane structures, including curvy and dashed lane marks. This paper proposes a novel binarization algorithm based on min-between-max thresholding (MBMT). The adaptive binarization addresses the issue of outlier rejection in an efficient way and handles the effect of shadow, illumination variation, and other factors in time-sliced images in an automated manner. Additionally, this paper identifies the limitation of the classical time-slice-based approach even with time MBMT for ground truth determination and addresses the same through the second level of adaptation by spatial MBMT. Finally, a complete mathematical model is presented to validate any arbitrary lane detection algorithm with respect to the ground truth determined through the said method of hybrid or modified MBMT or \textM^2\textBMT. [ABSTRACT FROM PUBLISHER]

The transition from the current energy architecture to a new model is evident and inevitable. The coming future promises innovative and increasingly rigorous projects and challenges for everyone involved in this value chain. Technological developments have allowed the emergence of new concepts, such as renewable energy communities, decentralized renewable energy production, and even energy storage. These factors have incited consumers to play a more active role in the electricity sector and contribute considerably to the achievement of environmental objectives. With the introduction of renewable energy communities, the need to develop new management and optimization tools, mainly in generation and load management, arises. Thus, this paper proposes a platform capable of clustering consumers and prosumers according to their energy and geographical characteristics to create renewable energy communities. Thus, this paper proposes a platform capable of clustering consumers and prosumers according to their energy and geographical characteristics to create renewable energy communities. Moreover, through this platform, the identification (homogeneous energy communities, mixed energy communities, and self-sufficient energy communities) and the size of each community are also obtained. Three algorithms are considered to achieve this purpose: K-means, density-based spatial clustering of applications with noise, and linkage algorithms (single-link, complete-link, average-link, and Wards' method). With this work, it is possible to verify each algorithm's behavior and effectiveness in clustering the players into communities. A total of 233 members from 9 cities in the northern region of Portugal (Porto District) were considered to demonstrate the application of the proposed platform. The results demonstrate that the linkage algorithms presented the best classification performance, achieving 0.631 by complete-ink in the Silhouette score, 2124.174 by Ward's method in the Calinski-Harabasz index, and 0.329 by single-link on the Davies-Bouldin index. Additionally, the developed platform demonstrated adequacy, versatility, and robustness concerning the classification and sizing of renewable energy communities. [ABSTRACT FROM AUTHOR]

This paper investigates the productivity and collaborations patterns in the publications of IEEE Transactions on Intelligent Transportation Systems (T-ITS) between 2010 and 2013. Our findings show that industry is playing a more important role in research cooperation. Such a trend is particularly beneficial for expediting the commercialization of scientific and technological advances. In addition, growing international collaborations have become a norm as researchers in the U.S., China, and Europe are increasingly involved in joint research endeavors. [ABSTRACT FROM PUBLISHER]