Task scheduling is one of the most challenging aspects of cloud computing nowadays, and it plays an important role in improving overall performance in, and services from, the cloud, such as response time, cost, makespan, and throughput. A recent cloud task-scheduling algorithm based on the symbiotic organisms search (SOS) algorithm not only has fewer specific parameters, but also incurs time complexity. SOS is a newly developed metaheuristic optimization technique for solving numerical optimization problems. In this paper, the basic SOS algorithm is reduced, and chaotic local search (CLS) is integrated into the reduced SOS to improve the convergence rate. Simulated annealing (SA) is also added to help the SOS algorithm avoid being trapped in a local minimum. The performance of the proposed SA-CLS-SOS algorithm is evaluated by extensive simulation using the Matlab framework, and is compared with SOS, SA-SOS, and CLS-SOS algorithms. Simulation results show that the improved hybrid SOS performs better than SOS, SA-SOS, and CLS-SOS in terms of convergence speed and makespan.

In this paper, we investigate a buffer-aided wireless powered cooperative communication network (WPCCN), in which the source and relay harvest the energy from a dedicated power beacon via wireless energy transfer, then the source transmits the data to the destination through the relay. Both the source and relay are equipped with an energy buffer to store the harvested energy in the energy transfer stage. In addition, the relay is equipped with a data buffer and can temporarily store the received information. Considering the buffer-aided WPCCN, we propose two buffer-aided relaying protocols, which named as the buffer-aided harvest-then-transmit (HtT) protocol and the buffer-aided joint mode selection and power allocation (JMSPA) protocol, respectively. For the buffer-aided HtT protocol, the time-averaged achievable rate is obtained in closed form. For the buffer-aided JMSPA protocol, the optimal adaptive mode selection scheme and power allocation scheme, which jointly maximize the time-averaged throughput of system, are obtained by employing the Lyapunov optimization theory. Furthermore, we drive the theoretical bounds on the time-averaged achievable rate and time-averaged delay, then present the throughput-delay tradeoff achieved by the joint JMSPA protocol. Simulation results validate the throughput performance gain of the proposed buffer-aided relaying protocols and verify the theoretical analysis.

In the Internet of Things (IoT) concept, devices communicate autonomously with applications in the Internet. A significant aspect of IoT that makes it stand apart from present-day networked devices and applications is a) the very large number of devices, produced by diverse makers and used by an even more diverse group of users; b) the applications residing and functioning in what were very private sanctums of life e.g. the car, home, and the people themselves. Since these diverse devices require high-level security, an operational model for an IoT system is required, which has built-in security. We have proposed the societal model as a simple operational model. The basic concept of the model is borrowed from human society - there will be infants, the weak and the handicapped who need to be protected by guardians. This natural security mechanism works very well for IoT networks which seem to have inherently weak security mechanisms. In this paper, we discuss the requirements of the societal model and examine its feasibility by doing a proof-of-concept implementation.

In this paper, we investigate an individual channel estimation problem for multiple-input multiple-output (MIMO) two-way amplify-and-forward (AF) relay networks. To avoid self-interference during the estimation of the individual MIMO channels, a novel blind interference cancellation (BIC) approach is proposed based on an orthogonal preceding framework, where a pair of orthogonal precoding matrices is utilized at the source nodes. By designing an optimal decoding scheme, we propose to decompose the bidirectional transmission into a pair of unidirectional transmissions. Unlike most existing approaches, we make the practical assumption that the nonreciprocal MIMO channel and the mutual interference of multiple antennas are both taken into consideration. Under the precoding framework, we employ an orthogonal superimposed training strategy to obtain the individual MIMO channels. However, the AF strategy causes the noise at the terminal to be the sum of the local noise and the relay-propagated noise. To remove the relay-propagated noise during the estimation of the second-hop channel, a partial noise-nulling method is designed. We also derive a closed-form expression for the total mean square error (MSE) of the MIMO channel from which we compute the optimal power allocation. The simulation results demonstrate that the analytical and simulated curves match fully.

The discovered high potential utility itemsets (HPUIs) have significant influence on a variety of areas, such as retail marketing, web click analysis, and biological gene analysis. Thus, in this paper, we propose an algorithm called HPUIM-QGA (Mining high potential utility itemsets based on a quantum genetic algorithm) to mine HPUIs over uncertain datasets based on a quantum genetic algorithm (QGA). The proposed algorithm not only can handle the problem of the non-downward closure property by developing an upper bound of the potential utility (UBPU) (which prunes the unpromising itemsets in the early stage) but can also handle the problem of combinatorial explosion by introducing a QGA, which finds optimal solutions quickly and needs to set only very few parameters. Furthermore, a pruning strategy has been designed to avoid the meaningless and redundant itemsets that are generated in the evolution process of the QGA. As proof of the HPUIM-QGA, a substantial number of experiments are performed on the runtime, memory usage, analysis of the discovered itemsets and the convergence on real-life and synthetic datasets. The results show that our proposed algorithm is reasonable and acceptable for mining meaningful HPUIs from uncertain datasets.

Degree distribution can provide basic information for structural characteristics and internal relationship in social network. It is a critical procedure for social network topology analysis. In this paper, based on the mean-field theory, we study a special type of social network with exponential distribution of time intervals. First of all, in order to improve the accuracy of analysis, we propose a spreading coefficient algorithm based on intimate relationship, which determines the number of the joined members through the intimacy among members. Then, simulation show that the degree distribution of follows the power-law distribution and has small-world characteristics. Finally, we compare the performance of our algorithm with the existing algorithms, and find that our algorithm improves the accuracy of degree distribution as well as reducing the time complexity significantly, which can complete 29.04% higher precision and 40.94% lower implementation time.

This paper presents a Bluetooth and pedestrian dead reckoning (PDR)-based indoor fusion localization approach (BtPDR) using smartphones. A Bluetooth and PDR-based indoor fusion localization approach can localize the initial position of a smartphone with the received signal strength (RSS) of Bluetooth. While a smartphone is moving, BtPDR can track its position by fusing the localization results of PDR and Bluetooth RSS. In addition, BtPDR can adaptively modify the parameters of PDR. The contributions of BtPDR include: a Bluetooth RSS-based Probabilistic Voting (BRPV) localization mechanism, a probabilistic voting-based Bluetooth RSS and PDR fusion method, and a heuristic search approach for reducing the complexity of BRPV. The experiment results in a real scene show that the average positioning error is < 2m, which is considered adequate for indoor location-based service applications. Moreover, compared to the traditional PDR method, BtPDR improves the location accuracy by 42.6%, on average. Compared to state-of-the-art Wireless Local Area Network (WLAN) fingerprint + PDR-based fusion indoor localization approaches, BtPDR has better positioning accuracy and does not need the same offline workload as a fingerprint algorithm.

Wireless sensor networks (WSNs) consist of set of sensor nodes. These sensor nodes are deployed in unattended area which are able to sense, process and transmit data to the base station (BS). One of the primary issues of WSN is energy efficiency. In many existing clustering approaches, initial centroids of cluster heads (CHs) are chosen randomly and they form unbalanced clusters, results more energy consumption. In this paper, an energy efficient clustering protocol to prevent unbalanced clusters based on firefly and midpoint algorithms called EEC-FM has been proposed, where midpoint algorithm is used for initial centroid of CHs selection and firefly is used for cluster formation. Using residual energy and Euclidean distance as the parameters for appropriate cluster formation of the proposed approach produces balanced clusters to eventually balance the load of CHs and improve the network lifetime. Simulation result shows that the proposed method outperforms LEACH-B, BPK-means, Park's approach, Mk-means, and EECPK-means with respect to balancing of clusters, energy efficiency and network lifetime parameters. Simulation result also demonstrate that the proposed approach, EEC-FM protocol is 45% better than LEACH-B, 17.8% better than BPK-means protocol, 12.5% better than Park's approach, 9.1% better than Mk-means, and 5.8% better than EECPK-means protocol with respect to the parameter half energy consumption (HEC).

This paper proposes an advanced turbo receiver with joint inter-carrier interference (ICI) self cancellation and channel equalization for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems over rapidly time-varying channel environment. The ICI caused by impairment of local oscillators and carrier frequency offset (CFO) is the major problem for MIMO-OFDM communication systems. The existing schemes (conjugate cancellation (CC) and phase rotated conjugate cancellation (PRCC)) that deal with the ICI cancellation and channel equalization can't provide satisfactory performance over time-varying channels. In term of error rate performance and low computational complexity, ICI self cancellation is the best choice. So, this paper proposes a turbo receiver to deal with the problem of joint ICI self cancellation and channel equalization. We employ the adaptive phase rotations in the receiver to effectively track the CFO variations without feeding back the CFO estimate to the transmitter as required in traditional existing scheme. We also give some simulations to verify the proposed scheme. The proposed schene outperforms the existing schemes.

Text data distribution is often imbalanced. Imbalanced data is one of the challenges in text classification, as it leads to the loss of performance of classifiers. Many studies have been conducted so far in this regard. The proposed solutions are divided into several general categories, include sampling-based and algorithm-based methods. In recent studies, feature selection has also been considered as one of the solutions for the imbalance problem. In this paper, a novel one-sided feature selection known as probabilistic feature selection (PFS) was presented for imbalanced text classification. The PFS is a probabilistic method that is calculated using feature distribution. Compared to the similar methods, the PFS has more parameters. In order to evaluate the performance of the proposed method, the feature selection methods including Gini, MI, FAST and DFS were implemented. To assess the proposed method, the decision tree classifications such as C4.5 and Naive Bayes were used. The results of tests on Reuters-21875 and WebKB figures per F-measure suggested that the proposed feature selection has significantly improved the performance of the classifiers.

As one of the most potential types of low-density parity-check (LDPC) codes, CPM-QC-LDPC code has considerable advantages but there still exist some limitations in practical application, for example, the existing decoding algorithm has a low convergence rate and a high decoding complexity. According to the structural property of this code, we propose a new method based on a CPM-RID decoding algorithm that decodes block-by-block with weights, which are obtained by neural network training. From the simulation results, we can conclude that our proposed method not only improves the bit error rate and frame error rate performance but also increases the convergence rate, when compared with the original CPM-RID decoding algorithm and scaled MSA algorithm.

Social pedestrian groups are the basic elements that constitute a crowd; therefore, detection of such groups is scientifically important for modeling social behavior, as well as practically useful for crowd video understanding. A social group refers to a cluster of members who tend to keep similar motion state for a sustained period of time. One of the main challenges of social group detection arises from the complex dynamic variations of crowd patterns. Therefore, most works model dynamic groups to analysis the crowd behavior, ignoring the existence of stationary groups in crowd scene. However, in this paper, we propose a novel unified framework for detecting social pedestrian groups in crowd videos, including dynamic and stationary pedestrian groups, based on spatiotemporal-oriented energy measurements. Dynamic pedestrian groups are hierarchically clustered based on energy flow similarities and trajectory motion correlations between the atomic groups extracted from principal spatiotemporal-oriented energies. Furthermore, the probability distribution of static spatiotemporal-oriented energies is modeled to detect stationary pedestrian groups. Extensive experiments on challenging datasets demonstrate that our method can achieve superior results for social pedestrian group detection and crowd video classification.

Content-based image retrieval is an approach used to query images based on their semantics. Semantic based retrieval has its application in all fields including medicine, space, computing etc. Semantically generated binary hash codes can improve content-based image retrieval. These semantic labels / binary hash codes can be generated from unlabeled data using convolutional autoencoders. Proposed approach uses semi-supervised deep hashing with semantic learning and binary code generation by minimizing the objective function. Convolutional autoencoders are basis to extract semantic features due to its property of image generation from low level semantic representations. These representations of images are more effective than simple feature extraction and can preserve better semantic information. Proposed activation and loss functions helped to minimize classification error and produce better hash codes. Most widely used datasets have been used for verification of this approach that outperforms the existing methods.

The increasing popularity of HTTP adaptive video streaming services has dramatically increased bandwidth requirements on operator networks, which attempt to shape their traffic through Deep Packet inspection (DPI). However, Google and certain content providers have started to encrypt their video services. As a result, operators often encounter difficulties in shaping their encrypted video traffic via DPI. This highlights the need for new traffic classification methods for encrypted HTTP adaptive video streaming to enable smart traffic shaping. These new methods will have to effectively estimate the quality representation layer and playout buffer. We present a new machine learning method and show for the first time that video quality representation classification for (YouTube) encrypted HTTP adaptive streaming is possible. The crawler codes and the datasets are provided in [43,44,51]. An extensive empirical evaluation shows that our method is able to independently classify every video segment into one of the quality representation layers with 97% accuracy if the browser is Safari with a Flash Player and 77% accuracy if the browser is Chrome, Explorer, Firefox or Safari with an HTML5 player.

Most of the accidents occur due to drowsiness while driving, avoiding road signs and due to driver's distraction. Driver's distraction depends on various factors which include talking with passengers while driving, mood disorder, nervousness, anger, over-excitement, anxiety, loud music, illness, fatigue and different driver's head rotations due to change in yaw, pitch and roll angle. The contribution of this paper is two-fold. Firstly, a data set is generated for conducting different experiments on driver's distraction. Secondly, novel approaches are presented that use features based on facial points; especially the features computed using motion vectors and interpolation to detect a special type of driver's distraction, i.e., driver's head rotation due to change in yaw angle. These facial points are detected by Active Shape Model (ASM) and Boosted Regression with Markov Networks (BoRMaN). Various types of classifiers are trained and tested on different frames to decide about a driver's distraction. These approaches are also scale invariant. The results show that the approach that uses the novel ideas of motion vectors and interpolation outperforms other approaches in detection of driver's head rotation. We are able to achieve a percentage accuracy of 98.45 using Neural Network.

A novel image completion method is proposed that uses the advantage of planar structural information to fill corrupted portions of an image. First, in estimating parameters of the projection plane, the image is divided into several planes, and their planar structural information is analyzed. Second, in calculating the a priori probability of patch and patch offset regularity, this information is converted into a constraint condition to guide the process of filling the hole. Experimental results show that the proposed algorithm is fast and effective, and ensures the structure continuity of the damaged region and smoothness of the texture.

HTTP-based adaptive streaming (HAS) has recently been widely deployed on the Internet. In the HAS system, a video content is encoded at multiple bitrates and the encoded video content is segmented into small parts of fixed durations. The HAS client requests a video segment and stores it in the playout buffer. The rate adaptation algorithm employed in HAS clients dynamically determines the video bitrate depending on the time-varying bandwidth. Many studies have shown that an efficient rate adaptation algorithm is critical to ensuring quality-of-experience in HAS systems. However, existing algorithms have problems estimating the network bandwidth because bandwidth estimation is performed on the client-side application stack. Without the help of transport layer protocols, it is difficult to achieve accurate bandwidth estimation due to the inherent segment-based transmission of the HAS. In this paper, we propose an alternative approach that utilizes the playout buffer occupancy rather than using bandwidth estimates obtained from the application layer. We start with a queueing analysis of the playout buffer. Then, we present a buffer-aware rate adaptation algorithm that is solely based on the mean buffer occupancy. Our simulation results show that compared to conventional algorithms, the proposed algorithm achieves very smooth video quality while delivering a similar average video bitrate.

In this paper, we propose a novel method to recover images corrupted by impulse noise. The proposed method uses two stages: noise detection and filtering. In the first stage, we use pixel values, rank-ordered logarithmic difference values, and median values to train a neural-network-based impulse noise detector. After training, we apply the network to detect noisy pixels in images. In the next stage, we use group-based weighted couple sparse representation to filter the noisy pixels. During this second stage, conventional methods generally use only clean pixels to recover corrupted pixels, which can yield unsuccessful dictionary learning if the noise density is high and the number of useful clean pixels is inadequate. Therefore, we use reconstructed pixels to balance the deficiency. Experimental results show that the proposed noise detector has better performance than the conventional noise detectors. Also, with the information of noisy pixel location, the proposed impulse-noise removal method performs better than the conventional methods, through the recovered images resulting in better quality.

As an increasing number of defense methods against control-data attacks are deployed in practice, control-data attacks have become challenging, and non-control-data attacks are on the rise. However, defense methods against non-control-data attacks are still deficient even though these attacks can produce damage as significant as that of control-data attacks. We present a method to defend against non-control-data attacks using influence domain monitoring (IDM). A definition of the data influence domain is first proposed to describe the characteristics of a variable during its life cycle. IDM extracts security-critical non-control data from the target program and then instruments the target for monitoring these variables' influence domains to ensure that corrupted variables will not be used as the attackers intend. Therefore, attackers may be able to modify the value of one security-critical variable by exploiting certain memory corruption vulnerabilities, but they will be prevented from using the variable for nefarious purposes. We evaluate a prototype implementation of IDM and use the experimental results to show that this method can defend against most known non-control-data attacks while imposing a moderate amount of performance overhead.

This paper proposes a novel steganography method to hide secret data during the generation of tangle patterns. Different from the traditional steganography based on modifying natural images, we propose to construct stego images according to the secret messages. We first create a model to group a selected image contour, and define some basic operations to generate various pattern cells. During data hiding, we create a cell library to establish the relationships between cells and secret data. By painting the cell inside the image contour, we create a dense tangle pattern to carry secret data. With the proposed method, a recipient can extract the secret data correctly. Experimental results show that the proposed method has a flexible embedding capacity. The constructed stego tangle image has good visual effects, and is secure against adversaries. Meanwhile, the stego tangle pattern is also robust to JPEG compression.

In Jøsang's trust model, trust evaluation is obtained through operators, but there are problems with the mutuality and asymmetry of trust and the impact of event weight on trust evaluation. Trust evaluation is updated dynamically and continuously with time and the process of interactions, but it has not been reflected in Jøsang's model. Therefore, final trust evaluation is not accurate, and malicious fraud cannot be prevented effectively. This causes the success rate of interaction to be low. To solve these problems, a new dynamic trust model is proposed based on extended subjective logic (DTM-ESL). In DTM-ESL, the event weight and the mutuality of trust are fully considered, the original one-way trust relationship is extended to a two-way trust relationship, discounting and consensus operators are improved, and trust renewal is designed based on event weight. The viability and effectiveness of this new model are verified by simulation experiments.

Network anomaly detection in Software Defined Networking, especially the detection of DDoS attack, has been given great attention in recent years. It is convenient to build the Traffic Matrix from a global view in SDN. However, the monitoring and management of high-volume feature-rich traffic in large networks brings significant challenges. In this paper, we propose a moving window Principal Components Analysis based anomaly detection and mitigation approach to map data onto a low-dimensional subspace and keep monitoring the network state in real-time. Once the anomaly is detected, the controller will install the defense flow table rules onto the corresponding data plane switches to mitigate the attack. Furthermore, we evaluate our approach with experiments. The Receiver Operating Characteristic curves show that our approach performs well in both detection probability and false alarm probability compared with the entropy-based approach. In addition, the mitigation effect is impressive that our approach can prevent most of the attacking traffic. At last, we evaluate the overhead of the system, including the detection delay and utilization of CPU, which is not excessive. Our anomaly detection approach is lightweight and effective.

Due to the lack of visualization services and organic combinations between public and private buildings data, the usability of the basic map has remained low. To address this issue, this paper reports on a solution that organically combines public and private data while providing visualization services to general users. For this purpose, factors that can affect building prices first were examined in order to define the related data attributes. To extract the relevant data attributes, this paper presents a method of acquiring public information data and real estate-related information, as provided by private real estate portal sites. The paper also proposes a pretreatment process required for intelligent machine learning. This report goes on to suggest an intelligent machine learning algorithm that predicts buildings' value pricing and future value by using big data regarding buildings' spatial information, as acquired from a database containing building value attributes. The algorithm's availability was tested by establishing a prototype targeting pilot areas, including Suwon, Anyang, and Gunpo in South Korea. Finally, a prototype visualization solution was developed in order to allow general users to effectively use buildings' value ranking and value pricing, as predicted by intelligent machine learning.

In our current information-based society in which knowledge is a fundamental asset to production, the capability to utilize information and produce knowledge with the use of information technology (IT) has become essential to learning. Massive Open Online Courses (MOOCs) have recently been introduced in light of such changes and are recognized as an alternative to open education. MOOCs' capabilities are being acknowledged in lifelong education in terms of reeducation and knowledge sharing, and also in terms of improving teaching quality, and improving university students' levels of creativity and integrated thinking by supporting high-level content and teaching. Therefore, this study presents an extended research model that combines information system (IS) continuance and task-technology fit models. Our study researches previous literature, revealing factors of continuous use after accepting MOOCs from the learner's perspective, and analyzes the model empirically. The ideal environment for MOOCs learners is evaluated, and a strategic approach to the successful settlement and diffusion of MOOCs is presented based on this study's findings.

In recent years, one of the most remarkable 5G technologies is massive multiple-input and multiple-output (MIMO) system which increases spectral efficiency by deploying a large number of transmit-antennas (eg. tens or hundreds transmit-antennas) at base station (BS). However, conventional massive MIMO system using single-polarized (SP) transmit-antennas increases the size of the transmit-array proportionally as the number of transmit-antennas increases. Hence, size reduction of large-scale transmit-array is one of the major concerns of massive MIMO system. To reduce the size of the transmit-array at BS, dual-polarized (DP) transmit-antenna can be the solution to halve the size of the transmit-array since one collocated DP transmit-antenna deploys vertical and horizontal transmit-antennas compared to SP transmit-antennas. Moreover, proposed DP massive MIMO system increases the spectral efficiency by not only in the space domain but also in the polarization domain whereas the conventional SP massive MIMO system increases the spectral efficiency by space domain only. In this paper, the comparative performance of DP and SP massive MIMO systems is analyzed by space division multiple access (SDMA) and space-polarization division multiple access (SPDMA) respectively. To analyze the performance of DP and SP massive MIMO systems, DP and SP spatial channel models (SCMs) are proposed which consider depolarized propagation channels between transmitter and receiver. The simulation results show that the performance of proposed 32 transmitter (Tx) DP massive MIMO system improves the spectral efficiency by about 91% for a large number of user equipments (UEs) compare to 32Tx SP massive MIMO system for identical size of the transmit-array.

In network intrusion detection research, two characteristics are generally considered vital to building efficient intrusion detection systems (IDSs): an optimal feature selection technique and robust classification schemes. However, the emergence of sophisticated network attacks and the advent of big data concepts in intrusion detection domains require two more significant aspects to be addressed: employing an appropriate big data computing framework and utilizing a contemporary dataset to deal with ongoing advancements. As such, we present a comprehensive approach to building an efficient IDS with the aim of strengthening academic anomaly detection research in real-world operational environments. The proposed system has the following four characteristics: (i) it performs optimal feature selection using information gain and branch-and-bound algorithms; (ii) it employs machine learning techniques for classification, namely, Logistic Regression, Naïve Bayes, and Random Forest; (iii) it introduces bulk synchronous parallel processing to handle the computational requirements of large-scale networks; and (iv) it utilizes a real-time contemporary dataset generated by the Information Security Centre of Excellence at the University of Brunswick (ISCX-UNB) to validate its efficacy. Experimental analysis shows the effectiveness of the proposed framework, which is able to achieve high accuracy, low computational cost, and reduced false alarms.

Recently, the most frequently studied topics related to the fourth industrial revolution (FIR) are Big data, AI, Cloud Computing and Internet of Things- these four components are collectively known as the main components of the FIR (henceforth MCs). The MCs have a wide range of effects on workers' performance. As such it is imperative that these components are properly understood. This understanding will lead to a proper recognition of the attitudes that workers need to adopt to the MCs. Specifically, the attitudes of workers to several variables need to be examined, including importance, intention to use, belief in improvement, efficacy to use, and negative cognition. Each of these variables plays a role in determining how worker's performance in the FIR era will change. The performance-related variables such as self-efficacy, expectations, and acceptance of change are also crucial. These variables are related to creation of new opportunities, and can greatly influence performance in the FIR era. This study explains how specific attitudes to MCs improve performance-related factors for FIR. The adoption of these attitudes will ultimately lead to more successful adaption to the FIR era.

The software centered world following the fourth industrial revolution is rapidly approaching us. Countries around the world attach importance to software's ability as one of the key elements for training future human resources. In order to train software centered human resources, each university has designated Software Education as an essential curriculum for not only major but also non-majors. In the past Software Education was an education for a major, but recent Software Education was changed to the essential education that is necessary for all living in the software centered world. In the past the curriculum was focused on software development and implementation-oriented education, but recent curriculum emphasizes sequential arranging and thinking of problem solving. In order to reflect trends in recent Software Education in detail, we integrate Software Education with major concept of Computational Thinking. In this paper, we analyzed the effect of the main concept of Computational Thinking on Software Education for non-majored learners who received Software Education based on Computational Thinking (here refers to learners who major in humanities, social sciences and arts). In addition, research models of satisfaction, self-efficacy, and occupational change was established as the elements of Software Education, and it was found that there was a relation between Computational Thinking and Software Education.

This paper introduces a scheme for optimizing the material properties of mass-spring systems of different resolutions to provide coherent behavior for reduced level-of-detail in MSS(Mass-Spring System) meshes. The global optimal material coefficients are derived to match the behavior of provided reference mesh. The proposed method also gives us insight into levels of reduction that we can achieve in the systematic behavioral coherency among the different resolution of MSS meshes. We obtain visually acceptable coherent behaviors for cloth models based on our proposed error metric and identify that this method can significantly reduce the resolution levels of simulated objects. In addition, we have confirmed coherent behaviors with different resolutions through various experimental validation tests. We analyzed spring force estimations through triangular Barycentric coordinates based from the reference MSS that uses a Gaussian kernel based distribution. Experimental results show that the displacement difference ratio of the node positions is less than 10% even if the number of nodes of $MSS^{sim}$ decreases by more than 50% compared with $MSS^{ref}$. Therefore, we believe that it can be applied to various fields that are requiring the real-time simulation technology such as VR, AR, surgical simulation, mobile game, and numerous other application domains.

After emerging online communications, text mining and sentiment analysis has been frequently applied into analyzing electronic word-of-mouth. This study aims to develop a domain-specific lexicon of sentiment analysis to predict box office success in Korea film market and validate the feasibility of the lexicon. Natural language processing, a machine learning algorithm, and a lexicon-based sentiment classification method are employed. To create a movie domain sentiment lexicon, 233,631 reviews of 147 movies with popularity ratings is collected by a XML crawling package in R program. We accomplished 81.69% accuracy in sentiment classification by the Korean sentiment dictionary including 706 negative words and 617 positive words. The result showed a stronger positive relationship with box office success and consumers' sentiment as well as a significant positive effect in the linear regression for the predicting model. In addition, it reveals emotion in the user-generated content can be a more accurate clue to predict business success.