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Advanced Persistent Threat (APT) groups pose significant cybersecurity threats due to their sophisticated and persistent nature. This study introduces a novel methodology to understand their collaborative patterns and shared objectives, which is crucial for developing robust defense mechanisms. We utilize MITRE ATT&CK Techniques, software, target nations, and industries as our primary features to understand the characteristics of APT groups. Since essential information is often buried within the unstructured data of Cyber Threat Intelligence (CTI) reports, we employ Natural Language Processing (NLP) and Named Entity Recognition (NER) to extract relevant data. To analyze and interpret the complex relationships between APT groups, we compute similarity among the features using weighted cosine similarity metrics and Machine Learning (ML) models, enhanced by feature crosses and feature selection strategies. Subsequently, hierarchical clustering is used to group APTs based on their similarity scores, helping to identify common behaviors and uncover deeper relationships. Our methodology demonstrates notable clustering performance, with a silhouette coefficient of 0.76, indicating strong intra-cluster similarity. The Adjusted Rand Index (ARI) of 0.63, though moderate, effectively measures agreement between our clustering and the ground truth. These metrics provide robust validation, surpassing commonly recognized benchmarks for effective clustering in cybersecurity. Our methodology successfully classifies 23 distinct APT groups into six clusters, highlighting the importance of techniques and industry features in the clustering process. Notably, techniques such as T1059 (Command and Scripting Interpreter) and T1036 (Masquerading) are prevalently deployed, observed in 18 out of 23 APT groups across all six clusters.

Wireless Sensor Networks (WSNs) significantly impact reliable data communications in environmental monitoring, health care, and transportation applications. Low-powered and small sensor nodes compose these networks, configured to perform specific tasks like detection, management, prediction, and monitoring. Clustering in WSNs is a reliable method of effectively transferring data within the network through routing. Thus, developing an effective routing algorithm to optimize network functionality is a big concern. Cluster Heads (CHs), selected through a range of metrics, are responsible for data aggregation and transmission to the Base Station (BS). Many clustered routing algorithms have been developed to address various issues like energy efficiency, network lifetime, and hotspot problems. However, several challenges still need to be addressed, including network segmentation, isolated node issues, and data routing failures. This survey offers a comprehensive review of various routing protocols and their different performance metrics. It evaluates the architectural challenges that caused network segmentations and data routing failures in the case of unexpected head node failures during high-stress events, particularly indoor fires. Recommended solutions to the mentioned issues are also explored. A new taxonomy for the CH-selection methods based on the technique used is proposed, along with a comparative review of other CH-selection surveys. Additionally, this survey scrutinizes the challenges and constraints inherent in current fault tolerance routing algorithms, evaluating their efficacy in fire-related incidents. The survey concluded that protocol designers must focus on the development and implementation of fire-adaptive routing protocols, incorporating optimal fault tolerance routing algorithms that adapt seamlessly to unforeseen environmental changes, including fire incidents. Such adaptability is pivotal in preserving network topology and preventing data loss.

As renewable energy sources are integrated into power systems, the complexity of their management increases. For the analysis of modern power systems, clustering algorithms have become indispensable tools. The representation of an electric power transmission system involves conceptualizing it as a graph—a mathematical structure depicting a set of nodes and edges. In the context of power systems, these nodes correspond to buses, and the edges represent branches. Weighting may be assigned to the edges to denote the strength of their connections. In this paper, spectral clustering and hierarchical agglomerative clustering have been employed to analyze the Italian transmission network. Spectral clustering relies on the eigenvalues and eigenvectors derived from the Laplacian matrix associated with the network. The hierarchical agglomerative clustering, based on Thévenin impedance to define the distances, merges the two closest clusters at each step. The primary objective of this research is to identify distinct areas within the network based solely on its topology. In pursuit of this goal, admittances and Thévenin impedances have been employed as weights in the graph representation. By utilizing these approaches, the research aims at uncovering meaningful structures and patterns within the power transmission system based on the inherent connectivity and strength of the network elements. The techniques have been used on two real network models to demonstrate the performance of the algorithms with different grid configurations.

This paper addresses the challenge of identifying the paths for vessels with operating routes of repetitive paths, partially repetitive paths, and new paths. We propose a spatial clustering approach for labeling the vessel paths by using only position information. We develop a path clustering framework employing two methods: a distance-based path modeling and a likelihood estimation method. The former enhances the accuracy of path clustering through the integration of unsupervised machine learning techniques, while the latter focuses on likelihood-based path modeling and introduces segmentation for a more detailed analysis. The result findings highlight the superior performance and efficiency of the developed approach, as both methods for clustering vessel paths into five clusters achieve a perfect F1-score. The approach aims to offer valuable insights for route planning, ultimately contributing to improving safety and efficiency in maritime transportation.

Extracting similarity and semantic images from images is a hot topic that is used in various semantic retrieval systems. The GP-Tree, a hierarchical clustering tree, is used in the article to retrieve semantic images. The GP-Tree is then used to create a graph of neighboring leaf nodes, resulting in a smaller search list and a reduced chance of missing related similar images. In addition, the neighbor graph is used to build a Self-Organizing Map (SOM) to improve clustering efficiency and image retrieval performance. To improve high-level semantic retrieval efficacy, we also propose extending the current ontology structure. The ontology framework with rich domains represents most of the basic objects. With this ontology, the sets of images are added for the enrichment and can be used for many different sets of images. The SPARQL query is automatically generated from visual words for querying on the ontology. The query result on the ontology is a set of similar images and the definition of its semantics. The proposed method is tested on the WANG and ImageCLEF datasets, and the results are compared to previous publications on the same dataset, demonstrating its efficacy.

Network intrusion detection is an important technology in national cyberspace security strategy and has become a research hotspot in various cyberspace security issues in recent years. The development of effective and efficient intelligent network intrusion detection methods using advanced machine learning algorithms is of great importance for defending against various network intrusions in complex network environments. In this study, a network intrusion detection method based on decision tree twin support vector machine and hierarchical clustering, named HC-DTTWSVM, is proposed, which can effectively detect different categories of network intrusion. First, the hierarchical clustering algorithm is applied to construct the decision tree for network traffic data, where the bottom-up merging approach is used to maximize the separation of the upper nodes of the decision tree, which reduces the error accumulation in the construction of the decision tree. Then, twin support vector machines are embedded in the constructed decision tree to implement the network intrusion detection model, which can effectively detect the network intrusion category in a top-down manner. The detection performance of the proposed HC-DTTWSVM method is evaluated on NSL-KDD and UNSW-NB15 intrusion detection benchmark datasets. Experimental results show that HC-DTTWSVM can effectively detect different categories of network intrusion and achieves comparable detection performance compared to some of the recently proposed network intrusion detection methods.

This paper proposes an evolutionary algorithm with hierarchical clustering based selection for multi-objective optimization. In the proposed algorithm, a hierarchical clustering is employed to design the environmental and mating selections, named local coverage selection and local area selection, respectively, for multi-objective evolutionary algorithm. The local coverage selection strategy aims to preserve well-distributed individuals with good convergence. While, the local area selection strategy is devised to deliver a balanced evolutionary search. This is achieved by encouraging individuals for exploration or exploitation according to the $I_{\epsilon }+$ indicator. In both strategies, a hierarchical clustering method is employed to discover the population structure. Based on the clustering results, in local coverage selection, the individuals of different clusters will be retained according to their coverage areas and crowding distances, such that distributing as evenly as possible in the Pareto front. In local area selection, the individual(s) with the best value of $I_{\epsilon }+$ indicator in each cluster will be selected to perform mating, with the purpose of achieving a balanced exploration and exploitation. The proposed algorithm has been evaluated on 26 bench-mark problems and compared with related methods. The results clearly show the significance of the proposed method.

The COVID-19 crisis has attracted attention worldwide to supply chain disruptions and resilience. Several supply chain risk management approaches have been revisited or reapplied in the literature; however, collaborative resource sharing is less researched. This study aimed to investigate the current academic state of the art and advances in using collaborative resource sharing as a reactive method to facilitate supply chain recovery in the presence of disruptive events. More specifically we considered the role of different collaborative resource-sharing strategies that organizations can adopt to support supply chain functionalities during times of disruption. We conducted a systematic literature review (SLR) to analyze academic articles that were published online from 2000 to 2022. In order to analyze the literature, we adopted a combination of text-mining, automatic and manual categorization of selected articles, and exploratory analyses such as cluster analysis and relational indicators. We also consider the machine learning classification algorithm i.e. agglomerative hierarchical clustering for the categorization of clusters. The findings show that, for disruptive risks, collaborative sharing of labour and material resources is effective for the recovery of supply chains. More so, labour resources tend to contribute more to the recovery of supply chains through the physical and mental recreation of recovery activities and experiences. Whilst information resources and a mix of information and material resources are highly important in reducing the impact of COVID-19 disruptive supply chain risk. In conclusion, collaborating on the three resources, namely labour, material, and information resources can be an effective post-disruption recovery strategy for supply chains.

Adaptive Resonance Theory (ART) is considered as an effective approach for realizing continual learning thanks to its ability to handle the plasticity-stability dilemma. In general, however, the clustering performance of ART-based algorithms strongly depends on the specification of a similarity threshold, i.e., a vigilance parameter, which is data-dependent and specified by hand. This paper proposes an ART-based topological clustering algorithm with a mechanism that automatically estimates a similarity threshold from the distribution of data points. In addition, for improving information extraction performance, a divisive hierarchical clustering algorithm capable of continual learning is proposed by introducing a hierarchical structure to the proposed algorithm. Experimental results demonstrate that the proposed algorithm has high clustering performance comparable with recently-proposed state-of-the-art hierarchical clustering algorithms.

The recent increasing demand of Silicon-on-Chip devices has triggered a significant impact on the industrial processes of leading semiconductor companies. The semiconductor industry is redesigning internal technology processes trying to optimize costs and production yield. To achieve this target a key role is played by the intelligent early wafer defects identification task. The Electrical Wafer Sorting (EWS) stage allows an efficient wafer defects analysis by processing the visual map associated to the wafer. The goal of this contribution is to provide an effective solution to perform automatic evaluation of the EWS defect maps. The proposed solution leverages recent approaches of deep learning both supervised and unsupervised to perform a robust EWS defect patterns classification in different device technologies including Silicon and Silicon Carbide. This method embeds an end-to-end pipeline for supervised EWS defect patterns classification including a hierarchical unsupervised system to assess novel defects in the production line. The implemented “Unsupervised Learning Block” embeds ad-hoc designed Dimensionality Reduction combined with Clustering and a Metrics-driven Classification Sub-Systems. The proposed “Supervised Learning Block” includes a Convolutional Neural Network trained to perform a supervised classification of the Wafer Defect Maps (WDMs). The proposed system has been evaluated on several datasets, showing effective performance in the classification of the defect patterns (average accuracy about 97%).

The temporal graph can represent a temporal relationship widely used in compound synthesis analysis, biological gene analysis, etc. However, the temporal graph would embody vertex updates frequently, high time resolution, and not enumerated rules. The construction and update of some temporal graph models are too dependent on the graph operation sequence, which leads to a lack of an effective model. Simultaneously, the temporal subgraph clustering of the temporal graph with frequent updating for the lack of an effective model leads to low accuracy. Therefore, we propose an efficient and frequently updated temporal graph model as vertex driven and corresponding temporal subgraph clustering method. First, we propose a temporal graph construction algorithm and set two thresholds to divide the temporal graph on a timeline to obtain temporal subgraphs. Next, an enhancement strategy based on the sliding window is proposed to accelerate the construction process. Third, we offer a double-standard temporal subgraph clustering method based on community comparison and temporal distance. The temporal subgraph can be effectively distinguished in temporal and structure dimensions. Lastly, experimental results on both real and synthetic datasets show that the temporal graph model proposed in this work can reduce the time overhead of construction compared to other existing models. The cluster method improves the clustering accuracy of temporal subgraphs. The clustering results show through the hierarchical clustering at the same time.

If the fault sources can be located in a timely and accurate manner when an unmanned aerial vehicle (UAV) has an early minor fault, a major accident can be avoided. In light of the early fault of the six-rotor UAV motors, this paper proposes a fusion fault diagnosis model based on Conformal Fourier Transform (CFT) and the improved Self-Organizing Feature Map (SOM) neural network. Firstly, to overcome the weakening of UAV fault information caused by external interference and solve the problem that the non-uniform distribution of the collected flight data affects the accuracy of the frequency domain processing, on the basis of changing the processing method of the Fourier integral kernel and using high-order numerical integration, an improved Conformal Fourier Transform (CFT) algorithm optimized by spectrum refinement strategy is proposed. Then this algorithm is used to further intensify the main features and build a clear fault state in a cycle processing method. Secondly, an improved SOM neural network is designed. We use the unweighted average distance (UPGMA) of the agglomerative hierarchical clustering algorithm to detect the clustering areas of each pattern category of the data samples, and the weight vectors of different clustering areas are used to randomly initialize the connection weights of SOM network, thereby improving the clustering speed and convergence effect. On this basis, CFT algorithm is integrated into the improved SOM network to realize the multi-module collaboration strategy and finally form a new fusion fault diagnosis model.

In machine learning, observation features are measured in a metric space to obtain their distance function for optimization. Given similar features that are statistically sufficient as a population, a statistical distance between two probability distributions can be calculated for more precise learning. Provided the observed features are multi-valued, the statistical distance function is still efficient. However, due to its scalar output, it cannot be applied to represent detailed distances between feature elements. To resolve this problem, this paper extends the traditional statistical distance to a matrix form, called a statistical distance matrix. The proposed approach performs well in object recognition tasks and clearly and intuitively represents the dissimilarities between cat and dog images in the CIFAR dataset, even when directly calculated using the image pixels. By using the hierarchical clustering of the statistical distance matrix, the image pixels can be separated into several clusters that are geometrically arranged around a center like a Mandala pattern. The statistical distance matrix with clustering is called the Information Mandala.

Synchrophasor technology improves power grid visibility by installing phasor measurement units (PMUs) over a wide area in the power system. Big data received from PMUs contains important information about grid behavior. This information is useful in monitoring the safety and security of the grid. An extensive state-of-the-art review of big data analytics and its prime applications in power systems are expressed in this paper. It presents, a general background in data analysis techniques such as exploratory data analysis, statistical data analysis, and unsupervised data mining techniques like clustering. Two 400 kV transmission line tripping events are analyzed from the data recorded by the PMUs installed in the western part of the Indian grid i.e., Maharashtra State Electricity Transmission Company Limited (MSETCL) grid. The box plots, Correlogram, and the formation of clusters carried out for the PMU data recorded under ambient and disturbance events. This provides insights on how effective big data helps to make the right decision at right time for effective management of the power grid under normal and contingency conditions.

To obtain independent navigation results for autonomous underwater vehicles (AUVs) and construct high-resolution consistent seabed maps, a particle filter-based bathymetric simultaneous localization and mapping (BSLAM) method with the mean trajectory map representation is proposed. To reduce the computational consumption, particles only keep the current estimated position of the vehicle, while all historical states of the vehicle are stored in the mean trajectory map. Using this set-up, only the weights of the particles which closed to the mean trajectory map are calculated with newly collected bathymetric data. A hierarchical clustering procedure is also discussed to identify invalid loop closures. The performance of the proposed method is validated using both the simulated data and the field data collected from sea trails. The results demonstrate that the proposed method is 50% more accurate and 50% faster than a state-of-the-art particle filter-based BSLAM method, and it has similar accuracy but 30% faster compared with a graph-based BSLAM method.

Recently there has been a new emerging trend in using the technology of Named Data Networking (NDN) on wireless sensor networks (WSNs) to improve data-centric communications over WSNs. The usage of the NDN notion in WSN for such communications can be advantageous in many respects ranging from ensuring secure data collection without using the nodes’ location and several nodes to possible delivery of sensory data by utilizing the cached data in intermediate components of the network. On the other hand, WSN being a resource-constrained, energy-starved network, the data communication mechanism must be low-overhead in terms of computation and communication overheads. Thus, we propose integrity preserving low overhead query handling over NDN-based WSN. We make the scheme low overhead by judicious use of the NDN data structure Content Store (CS), Pending Interest Table (PIT), Forwarding Information Base (FIB) in a few strategically placed nodes. We also make the data collection secure by applying an existing Light-weight One-way Cryptographic Hash Algorithm (LOCHA) on the collected data. The performance of the scheme is analyzed theoretically in terms of communication, computation, storage overheads. We also simulate the entire query processing scheme including request and response forwarding in Cooja, the Contiki network simulator. Simulation results show that our scheme does not compromise with network performance such as packet loss rate, network lifetime, end-to-end delay, etc. while achieving the design goal of making the scheme energy saving which establishes that the scheme is readily implementable in real life mote e.g., Tmote Sky. Finally, all the results are compared with three competing schemes and the results confirm our scheme’s supremacy in terms of both design performance as well as network performance.

In order to achieve content-based binocular stereoscopic image or video retrieval efficiently, a feature indexing algorithm based on hybrid grid multiple suffix tree and hierarchical clustering is proposed. With the RGB-D image model, the shape features of depth map obtained from the matching of binocular stereoscopic left image and right image and the color features of left image are extracted respectively. The features are quantified and hashed, and the optimized underlying features are sorted as leaf nodes of hierarchical indexing. Then the shape and color feature values of leaf nodes are mapped to the two-dimensional coordinates, and the 2D feature points are put into different grid hash areas respectively by clustering and labeled with multiple suffix tree. Furthermore, to construct the global index, a pointer to an array of clustering grid center point is defined according to the computation of grid area feature values. The experimental results show that compared to the double grid suffix tree and typical stereoscopic image feature indexing structure, the proposed algorithm can effectively reduce the indexing construction complexity While maintaining high recall, it can also greatly improve the query efficiency, which can better realize the feature indexing of binocular stereoscopic images or videos.

Ultra-dense small cell networks (UD-SCNs) will be an integral part of next generation network (NGN). How to deal with serious interference is one of the important challenges in a UD-SCN. In this paper, the cooperative interference management problem for a UD-SCN is explored by allowing small cell base stations (SBSs) to collaborate with their neighbors. The proposed coalitional structure generation among SBSs can mitigate the co-tier interference within a coalition, thus improving the network capacity. Specifically, a cooperative scheme among the neighboring SBSs is formulated as a coalitional structure generation with characteristic forms. Furthermore, the relative sub-channel resources are allocated in the process of cluster generation. Compared with the existing SBS cooperative schemes, the novelty of the proposed SBS cooperation method, which uses a hierarchical clustering algorithm (SC-HCA) to compute the pairs of members, is demonstrated. The computation can enhance the efficiency of the proposed algorithm and is especially suitable for UD-SCN scenarios with tens and even hundreds of cells. The simulation results show that the proposed SC-HCA achieves a 422.13% system data rate improvement relative to that of the non-cooperative scheme in a UD-SCN scenario.

Text Summarization is a process which efficiently retrieves the relevant information from documents. The objective of the proposed, unsupervised approach is to summarize bug reports (software artefacts) with complete content and diversified information. The proposed approach utilizes Rapid Automatic Keyword Extraction and term frequency-inverse document frequency method to extract meaningful keywords and key-phrases with a relevant score. For sentence extraction, fuzzy C-means clustering is used to extracts sentences having high degree of membership from each cluster above a set threshold value. A rule-engine is used for sentence selection. The rules are generated with the domain knowledge and based on the extracted information by the keywords and sentences selected by the clustering method. Cohesive and coherent summary is generated by the proposed method on apache bug reports. For redundancy removal and to re-rank generated summary, hierarchical clustering is presented to enrich the extracted summary. The proposed approach is evaluated on newly constructed Apache project Bug Report Corpus (APBRC) and existing Bug Report Corpus (BRC). The results are compared on the basis of performance metrics such as precision, recall, pyramid precision and F-score. The experimental results depict that our proposed approach attains significant improvement over other baseline approaches such as BRC and LRCA. It also attains significant improvement over existing state-of-art unsupervised approaches such as Hurried, centroid and others. It extracts significant keyword phrases and sentences from each cluster to achieve full coverage and coherent summary. The results evaluated on APBRC corpus attains an average value of 78.22%, 82.18%, 80.10% and 81.66% for precision, recall, f-score and pyramid precision respectively.

Energy Efficient Hierarchical Clustering (EEHC) method is a distributed random clustering algorithm for residential wireless sensor networks (RWSNs) with the goal of maximizing network lifetime. However, during selection of the cluster head stage, a node declares itself as a cluster head to neighboring nodes with probability p, which does not consider well the topological characteristics of the network. The new energy-efficient hierarchical clustering approach based on neighbor rotation (EEHCN) for RWSN has been proposed in this paper, the new idea of which determines whether a node of RWSN can become a cluster head node by comparing the comprehensive weighting of the degree of the sensor node and the distance between the sensor node and the center of the subarea. When the cluster head is replaced, a member node in the set of neighbor nodes of the current cluster head becomes the new cluster head for a random time slice, and the merged information is transmitted to the nearest sink node in a multi-hop manner according to the routing information. The experimental results demonstrate that the proposed method is more effective in saving network energy, improving data transmission efficiency, and maximizing network lifetime of RWSN. The innovative research work is very useful for the relative application of the RWSN, such as smart home or smart community.

The existing forecast models for PM2.5 concentration can be classified into long term and short term models depending on whether the forecast is performed for the next few hours or days. However, short term forecast models feature narrow forecast time and are thus vulnerable in their sensitivity to soaring variations in air quality, defined as sudden events. The purpose of this work is to investigate the causes behind these sudden events. The PM2.5 data were obtained from monitoring devices deployed in Taichung as a part of the Airbox project. The data were fed into the current short-term forecast model to forecast air quality for the next hour. Event timing was detected by feeding the forecast result as an input to the sudden event detection model. We then combined the filtered timing with factors in environment and human activities. With the application of Hierarchy Clustering, the clustering result was analyzed to find the causes of sudden events. In the springtime and summertime, unexpected changes in rainfall and temperature were critical for forecast models. Moreover, unanticipated changes in the intensity of rainfall and wind are important in the autumn and winter. For human activities, crowds of commuters, tourists, and pilgrims also have influence on unusual air quality. By carefully considering the effects of sudden events, we believe that the response ability of short time forecast can improve significantly in the near future.

Rapid development of single cell RNA sequencing (scRNA-seq) technology has accelerated the exploration in biomedical researches. One of the focal interests in scRNA-seq data analysis is to classify cells into different types, which significantly assists in studying inter-cellular heterogeneity, such as cell types, cell states, and cell lineages, at the resolution of single cells. Although a number of tailored approaches have been developed for scRNA-seq data, their performance varies with different datasets and their clustering accuracy need to be improved. In this paper, we propose a novel ensemble clustering framework for scRNA-seq data called GRACE (GRAph-based Cluster Ensemble approach). First, we construct a highly reliable graph network for single cells by combining the clustering outcomes from five leading scRNA-seq data clustering methods. Then, we remeasure the relationships between cells by exploring the topology structure of network using random walk distance. Finally, we build a hierarchical cell-tree and obtain the clustering labels by cutting the tree structure into an appropriate number of sub-trees. Experimental results on twelve benchmark datasets show that GRACE has the higher clustering accuracy and is more robust among a variety of datasets than the state-of-the-art individual approaches. In addition, the graph structure of the network which is built upon the ensemble clusters is more reliable than the networks which are constructed according to the conventional similarity metrics.

Mixed Datasets with complex interactions between categorical and numerical attributes are common in engineering and business applications. For example, production rates in manufacturing systems are jointly influenced by several categorical and numerical attributes, such as machine and product types and their numerical attributes. This study aims to improve the prediction performance and transparency of mixed datasets with complex interactions using machine learning (ML) methods. The proposed method requires lesser data and computational effort than existing hierarchical or clustering regression methods. Multiple prediction models can be generated by partitioning a dataset into subsets with different categorical attribution combinations. One- and two-stage model selection methods are proposed to use the training and validation datasets in selecting better models among all the prediction models. Numerical results demonstrate the potential of the model selection approach in a mixed dataset from a semiconductor manufacturer. In comparison with regression models, more than 30% reduction in root mean square error is observed using the proposed model selection approach. The cross-validation test results also demonstrated a 10% improvement in accuracy against the properly tuned XGBoost models. Moreover, the proposed model selection approach is compatible with other regression or ML prediction methods and can be used to improve the model's transparency of any existing methods on mixed datasets.

Among all the chicken meat quality evaluation metrics, color is one of the most significant factors directly related to the freshness of meat inducing the purchase desire. Biochemical tests for evaluating meat quality may contaminate or damage the test samples. Visual rating method is subjectively inefficient and difficult to realize online detection. Colorimeter has the disadvantages of complicated time-consuming operations, high technical requirements and expensive instruments. This paper proposes a low-cost, contactless chicken meat quality evaluation method by examining the color image of chicken meat. Specifically, the meat image is acquired by the camera of a smartphone. To eliminate the chromatic aberration, a pre-defined color card is put beside meat and automatically localized to extract the captured color information for color correction. Finally, the corrected colors of all the experimental meat samples are analyzed by hierarchical clustering to achieve 3 different quality levels.

The brain storm optimization algorithm(BSO) is a population based metaheuristic algorithm inspried by the human conferring process that was proposed in 2010. Since its first implementation, BSO has been widely used in various fields. In this article, we propose an agglomerative greedy brain storm optimization algorithm (AG-BSO) to solve classical traveling salesman problem(TSP). Due to the low accuracy and slow convergence speed of current heuristic algorithms when solving TSP, this article consider four improvement strategies for basic BSO. First, a greedy algorithm is introduced to ensure the diversity of the population. Second, hierarchical clustering is used in place of the k-means clustering algorithm in standard BSO to eliminate the noise sensitivity of the original BSO algorithm when solving TSP. Exchange rules for the individuals in the population individuals were introduced to improve the efficiency of the algorithm. Finally, a heuristic crossover operator is used to update the individuals. In addition, the AG-BSO algorithm is compared with the genetic algorithm (GA), particle swarm optimization (PSO), the simulated annealing(SA) and the ant colony optimization (ACO) on standard TSP data sets for performance testing. We also compare it with a recently improved version of the BSO algorithm. The simulations show the encouraging results that AG-BSO greatly improved the solution accuracy, optimization speed and robustness.

Wireless sensor networks (WSNs) are one of the chief enabling technologies for the Internet of Things. These networks are severely resource-constrained which calls for designing energy-efficient and effective routing techniques. The hierarchical- or clustering-based routing approaches have shown to improve both energy-efficiency and scalability in WSNs. However, when clustering is implemented in mobile WSNs (MWSNs), the mobility of sensor nodes results in high data loss due to possible dis-association of nodes with their cluster heads which negatively affects the data rates and energy consumption. In order to mitigate the impact of node mobility on clustering, we propose two mobility-aware hierarchical clustering algorithms for MWSN based on three-layer clustering hierarchy: mobility-aware centralized clustering algorithm (MCCA) and mobility-aware hybrid clustering algorithm (MHCA). The MCCA algorithm employs centralized gridding at both layers of clustering hierarchy, and the MHCA algorithm employs centralized gridding at the upper layer and distributed clustering at the lower layer. The simulation results show that our proposed algorithms improve network lifetime, reduce energy consumption, stabilize cluster formation, and enhance data rates in mobile sensor networks. We also observe that the centralized clustering approach is superior to the hybrid clustering approach.

Community structure is one of the most important topological properties of complex networks, which can help us to understand the functions and guide the development of networks. In this article, a community detection algorithm is proposed based on local similarity and hierarchical clustering. Local similarity is used to measure link similarities instead of node similarities in order to form a similarity metric. Hierarchical clustering is used to gather all the links to form a hierarchical tree, and then cut the tree with the optimization value of modularity to get the community structure. Experiments on real-world and generated benchmark networks show the significant performance of the algorithm both in accuracy and efficiency.

With the development of integrated circuit design technology, soft errors have become an important threat to system reliability, and software-based fault-tolerant techniques are gradually attracting people's attention. In many cases, researchers use fault injection techniques that are less observable and less controllable to verify system reliability, and at this point, analysing where soft errors occur requires considerable work. In this paper, we present EBSCN, an error backtracking method. The EBSCN method sorts functions by suspiciousness by analysing the erroneous output results, which will help researchers reduce the amount of work required for analysis. The EBSCN method includes a feature extraction method based on clustering and a feature analysis method based on a deep neural network. This paper introduces the principle of the two methods as well as methods to improve and extend them with program-related information. We discuss the effect of the scale of the output result and the severity of the error on the EBSCN method through experiments and verify the effect of the EBSCN method. The results showed that the proportion of the function in which the soft error actually occurs in the ranking of the top 25% of the suspiciousness sequence is no less than 82%, and the proportion ranked in the top 50% is no less than 97%.

Community structure discovery can help us better understand the capabilities and functions of the network. However, many existing methods have failed to identify nodes in communities accurately. In this paper, we proposed a heuristic community detection method based on node similarities that are computed by assigning different edge weight influence factors based on different neighbor types of nodes. Concretely, by arbitrarily choosing a pair of nodes, we firstly found out the common neighbor nodes of the node pair and their corresponding neighbor nodes. Then, different edge weight influence factors are assigned according to the impact of different types of neighbor nodes on node similarity. Finally, the similarities between a pair of nodes are calculated by the proportion of various edge weight influence factors related to the node pair. Along the direction, a hash table based data storage and retrieval strategy with a lower conflict rate is introduced to hash the edge information into a ternary bucket structure that can be merged according to the same starting node. This operation can reduce the time complexity of the data query to a constant level, and realize the parallel computing of node similarity. When obtaining similarity of node pair, we merged nodes into communities by a heuristic hierarchical clustering. And, the resulting community structure is detected until all node similarities are calculated. With the help of the comparison tests of different methods based on the benchmark networks that have ground-truth communities, the proposed method for community detection provides better performance in both identification accuracy and time efficiency.

This paper presents an original study with the aim of improving users' performance in completing large questionnaires through adaptability in web forms. Such adaptability is based on the application of machine-learning procedures and an A/B testing approach. To detect the user preferences, behavior, and the optimal version of the forms for all kinds of users, researchers built predictive models using machine-learning algorithms (trained with data from more than 3000 users who participated previously in the questionnaires), extracting the most relevant factors that describe the models, and clustering the users based on their similar characteristics and these factors. Based on these groups and their performance in the system, the researchers generated heuristic rules between the different versions of the web forms to guide users to the most adequate version (modifying the user interface and user experience) for them. To validate the approach and confirm the improvements, the authors tested these redirection rules on a group of more than 1000 users. The results with this cohort of users were better than those achieved without redirection rules at the initial stage. Besides these promising results, the paper proposes a future study that would enhance the process (or automate it) as well as push its application to other fields.

An imprecise region is referred to as a geographical area without a clearly defined boundary in the literature. Previous clustering-based approaches exploit spatial information to find such regions. However, the prior studies suffer from the following two problems: the subjectivity in selecting clustering parameters and the inclusion of a large portion of the undesirable region (i.e., a large number of noise points). To overcome these problems, we present DIR-ST2, a novel framework for delineating an imprecise region by iteratively performing density-based clustering of applications with noise (DBSCAN) along with not only spatio-textual information but also temporal information on social media. Specifically, we aim at finding a proper radius of a circle used in the iterative DBSCAN process by gradually reducing the radius for each iteration in which the temporal information acquired from all resulting clusters is leveraged. Then, we propose an efficient and automated algorithm delineating the imprecise region via hierarchical clustering. Experimental results show that by virtue of the significant noise reduction in the region, our DIR-ST2 method outperforms the state-of-the-art approach employing one-class support vector machine in terms of the F1 score from comparison with precisely defined regions regarded as a ground truth, and returns apparently a better delineation of imprecise regions. The computational complexity of DIR-ST2 is also analytically and numerically shown.