With the rapid advancement of the Internet of Energy (IoE), peer-to-peer energy trading has become an important mechanism for decentralized energy balancing and distribution-network relief. However, prior works have focused mainly on single platforms or isolated communities, offering limited insight into coordinated operation across multiple communities within a unified IoE ecosystem. This work proposes an IoT-enabled hierarchical trading framework that manages both intra- and inter-community energy transactions through decentralized area platforms and a central coordinating platform. A mixed-integer programming model is formulated to maximize the daily profit of energy-trading operators by optimizing household charging and discharging decisions, supported by distributed renewable generation and battery storage. The framework incorporates a dynamic IoT-driven pricing mechanism that adapts buying and selling prices using real-time measurements from smart meters and local IoE communication networks, ensuring responsive and data-driven market operation under varying conditions. Because the optimization problem is NP-complete, a simplified harmony search (SHS) algorithm is adopted to efficiently generate high-quality solutions. Experiments across 20 system configurations show that SHS outperforms several widely used metaheuristic baselines in both solution quality and run-to-run stability. Results further indicate that larger communities yield higher profits, with the maximum observed when each community contains 25 households. Eliminating the central trading platform causes a drastic reduction in operator profit, confirming its essential role in enabling scalable coordination and equitable energy allocation across heterogeneous communities. These findings highlight the effectiveness of the proposed hierarchical IoE architecture and underscore the importance of centralized coordination in next-generation smart energy systems.

The integration of 5G and multi-access edge computing (MEC) technologies has opened new possibilities for enhancing operational efficiency and responsiveness in smart agriculture. However, determining the optimal deployment of the 5G MEC infrastructures in open-field agriculture environments poses considerable challenges due to strict latency, coverage, and energy constraints. This study proposes a tailored deployment framework for 5G-based smart farming systems that incorporate intelligent edge devices (IEDs), MEC nodes, and 5G small cells. A hybrid metaheuristic, combining the exploration capability of Simplified Harmony Search (SHS) with the local intensification of Variable Neighborhood Search (VNS), is designed to solve the resulting placement optimization problem. The approach is evaluated through a smart farm scenario featuring spatially distributed IEDs and diverse communication demands. Results show that the proposed SHSVNS approach achieves superior performance compared to existing approaches in reducing total deployment cost while maintaining coverage and responsiveness. This demonstrates its practical potential for enhancing agricultural digitalization through efficient infrastructure deployment.

Identity verification is a critical component of financial data and systems security and privacy preservation, and is required by regulatory guidelines to ensure compliance with regulatory requirements and to aid in fraud prevention. With advances in artificial intelligence (AI), deep learning, and statistical methods, financial institutions are increasingly adopting multifactor authentication (MFA) that incorporates biometric-based approaches to perform authentication of identities for access to accounts, records or data and for verification of decision making and confirmation of actions.This paper presents a systematic review of current methodologies that utilize facial recognition, voice biometrics, and other data for identity verification in financial institutions. We explore the effectiveness and challenges associated with these approaches, highlighting recent developments in AI-driven models, deep learning architectures, and statistical techniques. In addition, we discuss the integration of multimodal biometric data and the decision and access systems that are developed for MFA approaches to improve security and accuracy. This review offers insights into the future of biometric identity verification in the financial sector.Our findings suggest that the integration of multi-modal data in financial applications could serve as a valuable avenue for future research and practical applications. In addition, investigating the role of biometric authentication in back-end systems is an important area worth further exploration.

Abstract Diamond multi-wire sawing machines are essential in semiconductor manufacturing, especially for slicing hard and brittle third-generation materials such as silicon carbide (SiC) and gallium nitride (GaN). The increased difficulty in processing these materials has highlighted the urgent need for reliable machine health monitoring and anomaly detection systems. While Predictive Maintenance and Prognostics and Health Management (PHM) frameworks have been widely applied across various industries, little research has specifically addressed semiconductor cutting equipment, where operational dynamics and data confidentiality present unique challenges. This study, in collaboration with an industry partner, develops two anomaly detection models tailored for diamond multi-wire sawing machines. The first model is a rule-based approach that utilizes sliding window techniques to extract statistical features and establish dynamic thresholds for anomaly detection. The second model employs a data-driven Univariate Autoencoder (UAE) to perform unsupervised anomaly detection by learning reconstruction errors from normal operating data. Both models are trained and validated using confidential industrial sensor datasets. Experimental results demonstrate that the UAE-based model achieves high detection accuracy with no observed false positives, providing an effective solution for enhancing operational reliability and production efficiency in semiconductor wafer slicing processes.

In this work, we introduce crowdsourcing home healthcare service (CHHS) systems, where caregivers (including nurses, personal care attendants, and housekeepers) from different locations (rather than centralized institutions) offer diverse home healthcare services to caretakers at home. Powered by cloud computing, the CHHS system enables real-time, dynamic, and large-scale matching between caretakers and caregivers based on their preferences and constraints, and determines caregivers’ rostering and routing plans, involving the NP-hard nurse rostering problem (NRP) and the vehicle routing problem (VRP). This work firstly creates a mathematical programming model to jointly roster and route for the CHHS, maximizing the matching scores of caregivers and caretakers based on the preferred features through analytic hierarchy process (AHP), and minimizing caregivers’ overtime and routing costs, under constraints of caregiver skills, regulations, and vehicle routing. The proposed matching score mechanism assigns weights to caretaker preferences, enhancing pairing with preferred caregivers and reducing dissatisfaction. This work proposes a hybrid genetic algorithm with variable neighborhood search (GAVNS), respectively tailored to handle the rostering and routing aspects of CHHS. Simulation indicates that the GAVNS lowers costs by approximately 38% and 26% in rural and city cases, respectively, and outperforms standalone GA and VNS, achieving a 3% additional cost reduction and consistently yielding feasible solutions.

It has been challenging to find the optimal recipe parameters for semiconductor processes to find a balance between budgets and computing efficiency. Therefore, this study focuses on finding the optimal recipe parameters of a semiconductor process using an improves meta Bayesian optimization (MetaBO) method, which can be trained with extremely few samples and historical data so as to quickly find the optimal process parameter combinations for the product. Experimental results show that the improved MetaBO significantly improves overall quality and efficiency in both model training and new task evaluation.

Obstructive sleep apnea (OSA) is a common type of sleep-related breathing disorder, and polysomnography (PSG) remains the gold standard for its diagnosis. However, it takes a significant amount of time to perform PSG in a well-equipped laboratory, and patients typically have to wait a long time for a PSG test. In view of this, over recent years portable and even wearable tools for OSA classification have been developed as a low-cost and easy-to-use alternative to PSG. In this paper, a deep neural network (DNN)-based model was developed to classify OSA severity using peripheral oxygen saturation (SpO2) signals; it showed the following advantages. First, the presented model takes unsegmented SpO2 signals recorded overnight as its input, and OSA severity is then classified as one of four levels as the output. Consequently, there is obviously no need to label segmented signals, and the tremendous amount of effort spent on signal segmentation and then annotation can be completely saved. Second, a high generalization ability is provided since the largest amount of data were used to test the model. This feature gives the model an improved reliability for clinical use. Notably, the outperformance of this work is highlighted in a two-level classification case (with a cutoff apnea–hypopnea index of 5), where the accuracy and the sensitivity increased to above 91% and 95%, respectively.

In smart factories, a variety of automated guided vehicles (AGVs) communicate with cyber-physical systems (CPSs) to autonomously deliver raw materials and workpieces among smart production facilities. In practice, instead of acquiring more costly AGVs to cause congestion in existing working space, most factories develop rule-based and model-based approaches to improve the AGV utilization rate and further the production efficiency. However, these charging strategies require predefined rules or models for estimating the internal information of batteries, so that they lead to huge computational costs and estimation errors. As a consequence, this work creates a Markov decision process problem for real-time charging scheduling of AGVs to fulfill uncertain AGV dispatching requests from the CPS for production lines, in which four bounds for charging heterogeneous AGVs are considered from practical experiences for increasing the AGV utilization rate. This work further improves a feature-based reinforcement learning approach, in which the state and action space can be effectively reduced through approximating the state-value function by five feature functions, including the estimated revenue for improving the utilization time, the total AGV charging cost, the cost of penalizing unfulfilled dispatching requests, the priority of charging newer batteries, and the priority of charging the batteries close to be fully charged, respectively. Experimental results show that the proposed algorithm obtains better benefits than the current practical approach, and improves the AGV utilization rate.

In large-scale agriculture, insufficient irrigation water may lead to overpumping of groundwater, increasing the risk of land subsidence. Growing dryland crops can effectively decrease the demand for irrigation water. However, the previous works on annual crop planning (ACP) focused on maximizing the profit through growing wetland crops and consuming much water. For sustainability, in this article, we propose a mathematical programming model for an ACP that allocates a land area for growing dryland and wetland crops to maximize the total profit and minimize the total irrigation water used for multiple cropping, under practical constraints. The simplified swarm optimization (SSO) improves the particle swarm optimization with four probabilities to determine the operations of updating solutions. We further propose dynamic SSO (DSSO) to solve the concerned ACP in which the four probabilities are adjusted dynamically according to the performance of the operations executed. Through simulation on a case study, the proposed DSSO demonstrates high performance over some classical approaches.

Recent empirical works on graph drawing have analyzed users' interpretation ability with user-sketched layouts. However, user-sketched layouts have not been studied with vertex-weighted graphs. We conducted a study that was to extend the previous work to conduct an empirical study with vertex-weighted graphs. In a four-stage experiment, we analyzed characteristics of the final graph drawings, participants' drawing processes and strategies, participants' drawing preferences through questionnaires, and the differences between two groups of participants divided according to different attributes. In this paper, we report on effects of individual difference in terms of user preference, gender and prior drawing experience on user-sketched layouts of vertex-weighted graphs.

In Industry 4.0, various types of IoT sensors which are installed on machines to collect data for predictive maintenance. As the collected data increases, there are more missing values and noisy data. Related studies have already proposed various methods to solve the problems in big data. Among them, most studies focused on either feature selection or instance selection for data preprocessing before training forecast models. Metaheuristic algorithm is one of the mainstream methods in data preprocessing. However, most of these studies rarely considered feature and instance selection simultaneously. In addition, they seldom focused on noisy data. Therefore, this work combines the UCI datasets with noisy data to simulate the real situation. Memetic algorithm (MA) has excellent performance in machine learning of data selection, and variable neighborhood search (VNS) was also proved to be widely applied to the systematic change of local search algorithms. This work proposes a hybrid MA and VNS to find a new subset that maximizes the accuracy of the classifier while preserving the minimum amount of data by feature and instance selection simultaneously. Experimental results show that the proposed method can efficiently reduce the amount of data and the ratio of noisy data. By comparison with other metaheuristic algorithms, the proposed method has good performance by an excellent balance between exploration and exploitation.

Manufacturing is involved with complex job shop scheduling problems (JSP). In smart factories, edge computing supports computing resources at the edge of production in a distributed way to reduce response time of making production decisions. However, most works on JSP did not consider edge computing. Therefore, this paper proposes a smart manufacturing factory framework based on edge computing, and further investigates the JSP under such a framework. With recent success of some AI applications, the deep Q network (DQN), which combines deep learning and reinforcement learning, has showed its great computing power to solve complex problems. Therefore, we adjust the DQN with an edge computing framework to solve the JSP. Different from the classical DQN with only one decision, this paper extends the DQN to address the decisions of multiple edge devices. Simulation results show that the proposed method performs better than the other methods using only one dispatching rule.

Vehicular cloud computing (VCC) system coordinates the vehicular cloud (consisting of vehicles' computing resources) and the remote cloud properly to provide in-time services to users. Although pervious works had established the models for resource allocation in the VCC system based on semi-Markov decision processes (SMDPs), few of them discussed heterogeneity of vehicles and influences of roadside units (RSUs). Heterogeneous vehicles made by different manufacturers may be equipped with different amount of computing resources; and furthermore, RSU can enhance the computing capability of VCC. Therefore, this paper creates an SMDP model for VCC resource allocation that additionally considers heterogeneous vehicles and RSUs, and proposes an approach for finding the optimal strategy of VCC resource allocation. The two additional features significantly elaborate the SMDP model, and demonstrate different results from the original model. Simulation shows that the resource allocation in the VCC system can be captured by the proposed model, which performs well in terms of long-term expected values (consisting of consumption costs of power and time), under various parameter settings.

Developing green energy to be applied in green cities has received much attention. The Internet of energy (IoE) effectively improves networking of distributed green energies through extending smart grids with bidirectional transmission of energy and distributed renewable energy facilities. Previous works on the IoE focused on decisions of IoE operators or optimization of the whole system. However, few considered optimal decisions of a single end user in the IoE. Therefore, this work creates a mixed-integer linear programming (MILP) model for a single end user that considers green energy generation, an energy storage, an electric vehicle, and an IoE-based energy trading platform to reduce energy waste. This model considers a complete system of charging control of multiple facilities of a single end user in the IoE, and allows the end user to purchase energy and sell green energy through the IoE, in which the energy prices of the electrical grid and the IoE platform are set by the power company and the energy market, respectively. Because MILP is NP complete and the proposed model involves a large number of variables and constraints, this paper further proposes a genetic algorithm for this problem, in which a repairing scheme is proposed to handle solution infeasibility of all constraints. By simulation, the proposed algorithm is verified to effectively reduce energy waste.

In Industry 4.0, the factories become increasingly smart and efficient through intelligent cyber-physical systems based on the deployment of Internet of Things (IoT), mobile devices, and cloud computing systems. In practice, the cloud computing system in a factory is managed in a centralized way, and hence may not afford heavy computing loads from thousands of IoT devices in the factory. An approach to address this issue is to deploy fog/edge computing resources nearby IoT devices in a distributed way to provide real-time computing responses on sites. This paper investigates deployment of an intelligent computing system consisting of a cloud center, gateways, fog devices, edge devices, and sensors attached to facilities in a logistics center. Except for locations of the cloud center and sensors that have been determined based on the factory layout, this paper establishes an integer programming model for deploying gateways, fog devices, edge devices in their respective potential sites, so that the total installation cost is minimized, under the constraints of maximal demand capacity, maximal latency time, coverage, and maximal capacity of devices. This paper further solves this NP-hard facility location problem by a metaheuristic algorithm that incorporates discrete monkey algorithm to search for good quality solutions and genetic algorithm to increase computational efficiency. Simulation verifies high performance of the proposed algorithm in deployment of intelligent computing systems in moderate-scale instances of intelligent logistics centers.

Data mining technology has been widely studied and applied in recent years. Frequent pattern mining is one important technical field of such research. The frequent pattern mining technique is popular not only in academia but also in the business community. With advances in technology, databases hav e become so large that data mining is impossible because of memory restrictions. In this study, we propose a novel algorithm for Fast mining with Secondary Memory, abbreviated as FSM-Mining, to help improve this situation. FSM-Mining saves a part of the information that is not stored in the memory, and through the use of mixed hard disk and memory mining we are able to complete data mining with limited memory. The results of empirical evaluation under various simulation conditions show that FSM-Mining delivers excellent performance in terms of execution efficiency and scalability.

In the light emitting diode (LED) manufacturing industry, the most expensive and crucial facilities are manufacturing machines. Condition-based maintenance (CBM) for crucial components of a manufacturing machine aims to forecast in advance the precise time when some aging component will be broken and replace it in time, to avoid performing abnormally to manufacture defect products. This study focuses on the CBM for a crucial component called particle filter of a pneumatic conveyor machine in the LED epitaxy plant. Conventional forecasting methods were based on the theory of statistics, which requests a large number of data samples and assumes some probability distribution. With advance of machine technology, however, the data samples of broken particle filters to be collected are very few, such that those conventional methods cannot be applied. As a result, this study proposes a novel hybrid grey forecasting and harmony search approach, in which grey forecasting was shown to perform well for small data samples. In the proposed method, operating conditions of particle filters are monitored and collected by industrial sensors. Then, those data are preprocessed by data filtering and clustering. Finally, a hybrid grey forecasting and harmony search approach is used to fit the curve of the aging condition of a particle filter. Numerical analysis of a real example in an LED epitaxy plant shows that the proposed method performs better than conventional methods.

Schematic metro maps provide an effective means of simplifying the geographical configuration of public rapid transportation systems. Nonetheless, travelers still find it difficult to identify routes of a specific topology on the maps because it is usually hidden behind the conventional octilinear layout of the entire map. In this paper, we present an approach to designing schematic maps with loop lines, which are drawn as circles together with annotation labels for guiding different traveling purposes. Our idea here is to formulate the aesthetic criteria as mathematical constraints in the mixed-integer programming model, which allows us to either align stations on the loop line at a grid if they are interchange stations or noninterchange stations on a circle otherwise. We then distribute the annotation labels associated with stations on the loop line evenly to the four side boundary of the map domain in order to make full use of the annotation space, while maximally avoiding intersections between leader lines and the metro network by employing a flow network algorithm. Finally, we present several experimental results generated by our prototype system to demonstrate the feasibility of the proposed approach.

The aesthetics for user-sketched layouts of clustered graphs with known clustering information is analyzed empirically. In our experiments, given the adjacency list of a clustered graph and its predefined clustering information, each participant was asked to manually sketch clustered graphs "nicely" from scratch on a tablet system using a stylus. The main feature of this work different from the previous work is to investigate if the user's complex task of sketching clustered graphs "nicely" can be alleviated by providing the cluster partition information. Results show that most participants can draw graphs with clear presence of bridge edges and clustering cohesiveness.

This paper proposes a cultural algorithm for the spatial forest harvest scheduling for maximizing the total harvested timber volume, under the constraints of minimum harvest age, minimum adjacency green-up age, and approximately even volume flow for each period of the schedule. In order to increase the solution-search ability, the cultural algorithm extracts problem-specific information during the evolutionary solution search to update the belief space of a generation, which has cultural influences and guidance on the next generation. The key design of our cultural algorithm is to propose the cultural and evolutionary operators specifically for the problem. Experimental analysis shows that our cultural algorithm performs better than the previous approaches.

Metro maps with thumbnail photographs serve as common travel guides for providing sufficient information to meet the requirements of travelers in the cities. However, conventional methods attempt to minimize the total distance between stations and labels while maximizing the number of the labels rather than further taking into account the overall balance of the spatial distribution of labels. This paper presents an entropy-based approach for effectively annotating large annotation labels sufficiently close to the metro stations. Our idea is to decompose the entire labeling space intro regions bounded by the metro lines, and then further partition each region into Voronoi cells, each of which is reserved for a station to be annotated. This is accomplished by incorporating a new genetic-based optimization, while the fitness of the decomposition is evaluated by the entropy of the relative coverage ratios of such Voronoi cells. We also include several design examples to demonstrate that the proposed approach successfully distributes large labels around the metro network with minimal user intervention.

Social networks are merely a reflection of certain realities among people that have been identified. But in order for people or even computer systems (such as expert systems) to make sense of the social network, it needs to be analyzed with various methods so that the characteristics of the social network can be understood in a meaningful context. This is challenging not only due to the number of people that can be on social networks, but the changes in relationships between people on the social network over time. In this paper, we develop a method to help make sense of dynamic social networks. This is achieved by establishing a hierarchical community structure where each level represents a community partition at a specific granularity level. By organizing each level of the hierarchical community structure by granularity level, a person can essentially “zoom in” to view more detailed (smaller) communities and “zoom out” to view less detailed (larger) communities. Communities consisting of one or more subsets of people having relatively extensive links with other communities are identified and represented as overlapping community structures. Mechanisms are also in place to enable modifications to the social network to be dynamically updated on the hierarchical and overlapping community structure without recreating it in real time for every modification. The experimental results show that the genetic algorithm approach can effectively detect hierarchical and overlapping community structures.

Abstract Annotating metro maps with thumbnail photographs is a commonly used technique for guiding travelers. However, conventional methods usually suffer from small labeling space around the metro stations especially when they are interchange stations served by two or more metro lines. This paper presents an approach for aesthetically designing schematic metro maps while ensuring effective placement of large annotation labels that are sufficiently close to their corresponding stations. Our idea is to distribute such labels in a well‐balanced manner to labeling regions around the metro network first and then adjust the lengths of metro line and leader line segments, which allows us to fully maximize the space coverage of the entire annotated map. This is accomplished by incorporating additional constraints into the conventional mixed‐integer programming formulation, while we devised a three‐step algorithm for accelerating the overall optimization process. We include several design examples to demonstrate the spatial efficiency of the map layout generated using the proposed approach through minimal user intervention.

According to our studies, the current contention resolution algorithm adapted in wireless ad-hoc networks, binary exponential backoff scheme, does not function well in multi-hop environments due to its several performance issues and technical limitations. For example, unfair channel access, intensive collision, and throughput degradation are several widely known issues. Besides, BEB cannot support multimedia traffic since it does not include any priority mechanism. In this paper, we put forth a simple, fair channel access, priority provision, and well performed contention resolution algorithm for multi-hop wireless ad-hoc networks. Simulations are conducted to evaluate the performance scheme. As it turns out, the results show that the proposed algorithm can effectively alleviate the fairness problem and support multimedia traffic in multi-hop wireless ad-hoc networks.

Aesthetics are often used to judge how good a graph drawing is in terms of specific drawing rules. A direct measurement of overall quality is missing. In this paper, we propose to evaluate quality based on aggregation of individual aesthetics. This measure can be used by visualization designers to quickly compare the quality of drawings at hand at the design stage and make decisions accordingly. We present a user study that validates this measure. The implications of the proposed measure for future research are discussed.

Abstract When providing travel guides for a specific route in a metro network, we often place the route around the center of the map and annotate stations on the route with thumbnail photographs. Nonetheless, existing methods do not offer an effective means of customizing the network layout in order to accommodate such large annotation labels while preserving its planar embedding. This paper presents a new approach for designing the metro map layout in order to annotate stations on a specific travel route with large annotation labels. Our idea is to elongate the travel route to be straight along the centerline of the map so that we can systematically annotate such stations with external labels. This is accomplished by extending the conventional mixed‐integer programming technique for computing octilinear layouts where orientations inherent to the metro line segments are plausibly rearranged. The stations are then connected with external labels through leaders while minimizing intersections with metro lines for enhancing visual clarity. We present several design examples of metro maps and user studies to demonstrate that the proposed aesthetic criteria successfully direct viewers’ attention to specific travel routes.

When visualizing graphs into node-link diagrams, angular resolution is often used as one of the aesthetic criteria measuring the diagram quality in terms of human comprehension. However, angular resolution has not been empirically validated for its relevance to humans. In addition, although many force-directed algorithms have been proposed for automatic graph drawing, performance evaluation of these algorithms has not been conclusive due to the lack of proper methods. To shorten these gaps, this paper 1) validates the aesthetic based on human experimental data and identifies the best of angular resolution measures used in the literature; 2) introduces a force-directed algorithm, forceAR, for improving angular resolution; 3) proposes a new framework for more reliable and thorough evaluation of force-directed algorithms. Finally as a case study, our forceAR algorithm is evaluated using this framework.

Abstract Constructing a 3D papercraft model from its unfolding has been fun for both children and adults since we can reproduce virtual 3D models in the real world. However, facilitating the papercraft construction process is still a challenging problem, especially when the shape of the input model is complex in the sense that it has large variation in its surface curvature. This paper presents a new heuristic approach to unfolding 3D triangular meshes without any shape distortions, so that we can construct the 3D papercraft models through simple atomic operations for gluing boundary edges around the 2D unfoldings. Our approach is inspired by the concept of topological surgery, where the appearance of boundary edges of the unfolded closed surface can be encoded using a symbolic representation. To fully simplify the papercraft construction process, we developed a genetic‐based algorithm for unfolding the 3D mesh into a single connected patch in general, while optimizing the usage of the paper sheet and balance in the shape of that patch. Several examples together with user studies are included to demonstrate that the proposed approach works well for a broad range of 3D triangular meshes.

In boundary labeling, each point site is uniquely connected to a label placed on the boundary of an enclosing rectangle by a leader, which may be a rectilinear or straight line segment. Most of the results reported in the literature for boundary labeling deal with the so-called one-to-one boundary labeling, i.e., different sites are labelled differently. In certain applications of boundary labeling, however, more than one site may be required to be connected to a common label. In this case, the presence of crossings among leaders often becomes inevitable such that the labeling often has a high degree of confusion in visualization. In this paper, for multi-site-to-one-label boundary labeling, crossings among leaders are avoided by substituting hyperleaders for leaders and by applying dummy labels (i.e., copies/duplicates of labels). Minimizing the number of dummy labels becomes a critical design issue as dummy labels are not required in the initial setting. Therefore, we consider the problem of minimizing the number of dummy labels for multi-site-to-one-label boundary labeling, i.e., finding the placements of labels and hyperleaders such that the total number of dummy labels is minimized and there are no crossings among hyperleaders. Furthermore, after the number of dummy labels is determined, minimizing the total hyperleader length as well as the bends of hyperleaders is also concerned in postprocessing procedure. In this paper, we present polynomial time algorithms for the above one-side and two-side labeling schemes, and show their correctness from a theoretical point of view. In addition, we provide a simulated annealing algorithm for the four-side labeling schemes with objective to minimize the total number of dummy labels as well as the total leader length. Experimental results show that our four-side solutions look promising, as compared to the optimal solutions.

As wireless networks have been widely deployed for public mobile services, predicting the location of a mobile user in wireless networks became an interesting and challenging problem. If we can predict the next cell which the mobile users are going to correctly, the performance of wireless applications, such as call admission control, QoS and mobility management, can be improved as well. In this paper, we propose a mobility prediction algorithm based on dividing sensitive ranges. The division is in accordance with the cell transform probability. Then different prediction methods are applied according to the sensitivity of the range to gain high precision. Simulations are conducted to evaluate the performance of the proposed scheme. As it turns out, the simulation results show that the proposed scheme can accurately predict the location for mobile users even in the situation of lacking location history.

Many automatic graph drawing algorithms implement only one or two aesthetic criteria since most aesthetics conflict with each other. Empirical research has shown that although those algorithms are based on different aesthetics, drawings produced by them have comparable effectiveness. The comparable effectiveness raises a question about necessity of choosing one algorithm against another for drawing graphs when human performance is a main concern. In this paper, we argue that effectiveness can be improved when algorithms are designed by making compromises between aesthetics, rather than trying to satisfy one or two of them to the fullest. In particular, this paper presents a user study. The study compares effectiveness of drawings produced by two different force-directed methods, Classical spring algorithm and BIGANGLE. BIGANGLE produces drawings with a few aesthetics being improved at the same time. The experimental results indicate that BIGANGLE induces significantly better performance of humans in perceiving shortest paths between two nodes.

The text annotation system of a word processor software provides the user the function of memorandums in editing a document. In the visualization interface of the annotation system, each marked word is connected to a text comment label on the right side of the document by a polygonal line. Such a visualization interface can be viewed as a one-side boundary labeling, in which each point site is uniquely connected to a label placed on the right side of an enclosing rectangle by a leader, which may be a rectilinear or straight line segment. In the literature, there have existed some applications and some theoretical results for the boundary labeling. In this paper, we investigate the boundary labeling from the application on the annotation system. For this kind of labeling, if the number of labels on the right side is large, the leaders may be drawn too densely to be recognized easily. Therefore, in this paper, we propose a polynomial time algorithm for the so-called 1.5-side boundary labeling for the annotation system, in which, in addition to being connected to the right side directly, leaders can be routed to the left side temporarily and then finally to the right side. In addition, we investigate a problem for two-side boundary labeling (for the annotation system) that was not discussed previously. We show the problem to be NP-complete, and then proposed a heuristic based on the genetic algorithm to solve it. The experimental results reveal that our approach performs well.

In transfusion medicine, the process of preparing or separating blood components from the whole blood is essential because the indication for the use of unfractionated whole blood almost does not exist nowadays. Since blood is uneasily-collected and easily-perished, a blood center or a hospital blood bank might as well aggressively manage the volume of each blood component, so as to decrease any waste. We assume that the process of blood component preparation can be underlaid by a so-called blood component tree, where each vertex representing a blood component with a certain value is derived from its parent vertex. Initially given a certain amount of the root blood component in a blood component tree (noticing that the amount of every other blood component is zero initially), the blood component preparation problem is concerned with finding the assignment of amount of each blood component such that the total value is maximized while satisfying the demand limit of every blood component. In this paper, we propose a linear time algorithm (in the size of vertices) for efficiently coping with the concerned problem, which also can be modeled as a linear program. Some theoretical analyses are included in this paper.

The majority of program editors available on the market support the view of a directory-explorer style to display only those code lines of interest. Among them, the fisheye and the fractal views of source code (in which each line has a value reflecting the degree of interest and importance) have received a lot of attention in the literature. In information visualization, drawing trees based on fractal theory also plays an interesting role as the so-called balloon drawing of hierarchical data includes two models: the fractal and the SNS (subtrees with nonuniform sizes) models. It is therefore natural to consider a new source code visualization style based on the SNS model of balloon drawing. A main feature of the SNS view is that the value of each line reflects the number of its descendants when the source code is viewed as a tree structure. Unlike the view of a directory- explorer style, the multiscalable font mode (which was originally utilized in the fractal view of source code) displays all the lines in such a way that each line has the font size proportional to its value. In this paper, we investigate various issues concerning the multiscalable font modes of the fish- eye, the fractal, and the SNS views of source code, in hope of providing guidelines for the programmer to better comprehend the program code in practice.

Home delivery is one of the most important cost drivers in the e-commerce industry, and a recent study concluded that over 40% cost reduction for the dotcom companies can be achieved by offering home delivery system. This paper considers the home delivery system to cope with the three-dimensional container packing problem (3DCPP), which is a crucial issue among logistics operations to pack a number of rectangular items (cargos) orthogonally onto a rectangular container so that the utilization rate of the container space is maximized. In our framework of home delivery, we assume that the routing of a consignment is given, and hence there is an order of unloading items with respect to a problem. If loading items doesn't take the unloading order into account, then it may lead to huge unloading costs (i.e., unloading and reloading other items many times). In this paper, the unloading cost with respect to a packing pattern is precisely defined according to the invisible and untouchable rule and an iterative heuristic algorithm based on the sub-volume scheme is proposed. Our approach is compared with the previous approaches by using standard benchmark data set, and our experimental results suggest our approach to be promising, as it can generate the packing patterns without unloading cost, which has a high utilization ratio, and the benchmark problems can be executed efficiently.

The conventional force-directed methods for drawing undirected graphs are based on either vertex-vertex repulsion or vertex-edge repulsion. In this paper, we propose a new force-directed method based on edge-edge repulsion to draw graphs. In our framework, edges are modelled as charged springs, and a final drawing can be generated by adjusting positions of vertices according to spring forces and the repulsive forces, derived from potential fields, among edges. Different from the previous methods, our new framework has the advantage of overcoming the problem of zero angular resolution, guaranteeing the absence of any overlapping of edges incident to the common vertex. Given graph layouts probably generated by classical algorithms as the inputs to our algorithm, experimental results reveal that our approach produces promising drawings (especially for trees and hypercubes) not only preserving the original properties of a high degree of symmetry and uniform edge length, but also preventing zero angular resolution. By allowing vertex-vertex overlapping, our algorithm also results in more symmetrical drawings.