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Recently, Internet of Drones (IoD) are issued to utilize the diverse kinds of drones for leisure, education and so on. Researchers study to prevent the situations that drones are disabled by cyber-attackers by embedding malwares into the drones and Ground Control Stations (GCS). Therefore, it is required to protect the malwares considering the diverse kinds of features of the drones and GCSs. Signature-based detection approaches are traditionally utilized. However, given that those approaches only scan files partially, some of malwares are not detected. This paper proposes a novel method for finding the malwares in GCSs that utilizes a fastText model to create lower-dimension vectors than those the vectors by one-hot encoding and a bidirectional LSTM model to analyze the correlation with sequential opcodes. In addition, API function names are utilized to increase the classification accuracy of the sequential opcodes. In the experiments, the Microsoft malware classification challenge dataset was utilized and the malwares in the dataset were classified by family types. The proposed method showed the performance improvement of 1.87% comparing with the performance by a one-hot encoding-based approach. When the proposed method was compared with a similar decision tree-based malware detection approach, the performance of the proposed method was improved by 0.76%.

Recently, with the development of computer technology, deep learning has expanded to the field of art, which requires creativity, which is a unique ability of humans, and an understanding of the human emotions expressed in art to process them as data. The field of art is integrating with various industrial fields, among which artificial intelligence (AI) is being used in stage art, to create visual images. As it is difficult for a computer to process emotions expressed in songs as data, existing stage background images for song performances are human designed. Recently, research has been conducted to enable AI to design stage background images on behalf of humans. However, there is no research on reflecting emotions contained in song lyrics to stage background images. This paper proposes a style transformation method to reflect emotions in stage background images. First, multiple verses and choruses are derived from song lyrics, one at a time, and emotion words included in each verse and chorus are extracted. Second, the probability distribution of the emotion words is calculated for each verse and chorus, and the image with the most similar probability distribution from an image dataset with emotion word tags in advance is selected for each verse and chorus. Finally, for each verse and chorus, the stage background images with the transferred style are outputted. Through an experiment, the similarity between the stage background and the image transferred to the style of the image with similar emotion words probability distribution was 38%, and the similarity between the stage background image and the image transferred to the style of the image with completely different emotion word probability distribution was 8%. The proposed method reduced the total variation loss of change from 1.0777 to 0.1597. The total variation loss is the sum of content loss and style loss based on weights. This shows that the style transferred image is close to edge information about the content of the input image, and the style is close to the target style image.

Signature-based malware detection approaches are inadequate for detecting the increasingly intelligent and large number of malware programs emerging today. Therefore, alternative approaches are required. The effects of malware can be estimated by analyzing the opcodes in its executable files. It can then be classified into families using a long short-term memory (LSTM) network. Vectorizing opcodes and application programming interface (API) function names using one-hot encoding results in high-dimensional vectors because each case is represented using one dimension. Therefore, this paper proposes a word2vec-based LSTM method to analyze opcodes and API function names using fewer dimensions. The results of opcode and API function name classification using the proposed method and one-hot encoding were compared using the Microsoft Malware Classification Challenge dataset. The proposed method showed approximately 0.5% higher performance than the one-hot encoding-based approach.

Three-dimensional virtual environments can be configured as test environments of autonomous things, and remote sensing by 3D point clouds collected by light detection and range (LiDAR) can be used to detect virtual human objects by segmenting collected 3D point clouds in a virtual environment. The use of a traditional encoder-decoder model, such as DeepLabV3, improves the quality of the low-density 3D point clouds of human objects, where the quality is determined by the measurement gap of the LiDAR lasers. However, whenever a human object with a surrounding environment in a 3D point cloud is used by the traditional encoder-decoder model, it is difficult to increase the density fitting of the human object. This paper proposes a DeepLabV3-Refiner model, which is a model that refines the fit of human objects using human objects whose density has been increased through DeepLabV3. An RGB image that has a segmented human object is defined as a dense segmented image. DeepLabV3 is used to make predictions of dense segmented images and 3D point clouds for human objects in 3D point clouds. In the Refiner model, the results of DeepLabV3 are refined to fit human objects, and a dense segmented image fit to human objects is predicted. The dense 3D point cloud is calculated using the dense segmented image provided by the DeepLabV3-Refiner model. The 3D point clouds that were analyzed by the DeepLabV3-Refiner model had a 4-fold increase in density, which was verified experimentally. The proposed method had a 0.6% increase in density accuracy compared to that of DeepLabV3, and a 2.8-fold increase in the density corresponding to the human object. The proposed method was able to provide a 3D point cloud that increased the density to fit the human object. The proposed method can be used to provide an accurate 3D virtual environment by using the improved 3D point clouds.

The latest research on smart city technologies mainly focuses on utilizing cities’ resources to improve the quality of the lives of citizens. Diverse kinds of control signals from massive systems and devices such as adaptive traffic light systems in smart cities can be collected and utilized. Unfortunately, it is difficult to collect a massive dataset of control signals as doing so in the real-world requires significant effort and time. This paper proposes a deep generative model which integrates a long short-term memory model with generative adversarial network (LSTM-GAN) to generate agent control signals based on the words extracted from newspaper articles to solve the problem of collecting massive signals. The discriminatory network in the LSTM-GAN takes continuous word embedding vectors as inputs generated by a pre-trained Word2Vec model. The agent control signals of sequential actions are simultaneously predicted by the LSTM-GAN in real time. Specifically, to collect the training data of smart city simulations, the LSTM-GAN is trained based on the Corpus of Contemporary American English (COCA) newspaper dataset, which contains 5,317,731 sentences, for a total of 93,626,203 word tokens, from written texts. To verify the proposed method, agent control signals were generated and validated. In the training of the LSTM-GAN, the accuracy of the discriminator converged to 50%. In addition, the losses of the discriminator and the generator converged from 4527.04 and 4527.94 to 2.97 and 1.87, respectively.

Malware detection and classification methods are being actively developed to protect personal information from hackers. Global images of malware (in a program that includes personal information) can be utilized to detect or classify it. This method is efficient, given that small changes in the program can be detected while maintaining the overall structure of the program. However, if any obfuscation approach that encrypts malware code is implemented, it becomes difficult to extract features such as opcodes and application programming interface functions. Given that malware detection and classification are performed differently depending on whether malware is obfuscated or not, methods that can simultaneously detect and classify general and obfuscated malware are required. This paper proposes a method that uses a generative adversarial network (GAN) and global image-based local image to classify unobfuscated and obfuscated malware. Global and local images of unobfuscated malware are generated using pixel and local feature visualizers. The GAN is utilized to visualize local features and generate local images of obfuscated malware by learning global and local images of unobfuscated malware. The local image of unobfuscated malware is merged with the global image generated via the pixel visualizer. To merge the global and local images of unobfuscated and obfuscated malware, the pixels extracted from global and local images are stored in a two-dimensional array, and then merged images are generated. Finally, unobfuscated and obfuscated malware are classified using a convolutional neural network (CNN). The results of experiments conducted on the Microsoft Malware Classification Challenge (BIG 2015) dataset indicate that the proposed method has a malware classification accuracy of 99.65%, which is 2.18% higher than that of the malware classification approach based on only global images and local features.

Point clouds have been widely used in three-dimensional (3D) object classification tasks, i.e., people recognition in unmanned ground vehicles. However, the irregular data format of point clouds and the large number of parameters in deep learning networks affect the performance of object classification. This paper develops a 3D object classification system using a broad learning system (BLS) with a feature extractor called VB-Net. First, raw point clouds are voxelized into voxels. Through this step, irregular point clouds are converted into regular voxels which are easily processed by the feature extractor. Then, a pre-trained VoxNet is employed as a feature extractor to extract features from voxels. Finally, those features are used for object classification by the applied BLS. The proposed system is tested on the ModelNet40 dataset and ModelNet10 dataset. The average recognition accuracy was 83.99% and 90.08%, respectively. Compared to deep learning networks, the time consumption of the proposed system is significantly decreased.

Imbalanced datasets can be found in a number of fields; they are commonly regarded as big data because of their sheer volume and high attribute dimensions. As the name suggests, imbalanced big datasets come with an extremely imbalanced ratio between the amount of major class and minority class samples. Traditional methods: have been attempted but still cannot fully, effectively, and reliably solve the imbalanced class classification problem, especially when the distribution of the classes is exceedingly imbalanced. In this paper, we propose a collection of algorithms to solve the problem of imbalanced datasets in binary data classification. Most traditional methods: rebalance the imbalanced dataset merely by matching the data quantities of the two classes. Our proposed algorithms, which take the form of a suite of variants, focus on guaranteeing the credibility of the classification model and reaching the greatest possible accuracy by dynamically rebalancing the training dataset with multi-objective swarm intelligence optimisation. The new algorithms are extended from those we proposed earlier, which had a single objective – first find a set of solutions that satisfy the Kappa criterion, then search for the solution in the set that offers the highest accuracy. Two main modifications are made in the new algorithms. Multi-objective optimisation is aimed at finding a solution that satisfies several criteria at the same time, such as accuracy and identifying a list of credibility indicators. The other enhancement is the incremental operation of the multi-objective optimisation. Incremental optimisation is imperative for processing data feeds that may arrive in a streaming manner. Instead of waiting for the full data archive to be available before optimisation, incremental optimisation rebalances the data feed segment by segment on the fly. The experimental results from the suite of proposed algorithms show that they can effectively attain better and more stable performances from the classification model and are accompanied by much greater credibility than the other five traditional methods when imbalanced datasets are used as training datasets for inducing a classifier.

This study contributes to knowledge by advancing the transportation management plan (TMP) development efforts for expressway work zones. Using text mining techniques to a large-scale transportation data set that contains descriptively narrated texts, this research analyzes the association between words related to the type of work being performed and the type of lane closure in expressway work zone areas. It found that recurrent everyday tasks and bridge repair works tend to cause shoulder lane closure, while works—such as tunnel repair, night work, pavement, median barrier, road surface repair, and line marking—are more associated with main lane closure. Moreover, the findings further clarify the characteristic patterns shared between the number of closed lanes, and the respective lane position in two- and three-lane expressways. These offer significant insights into the decision-making process for the development of work zone TMPs, which can further be integrated into the various components of TMP to make the plan more effective and, at the same time, ensure an efficient throughput flow throughout the work zone, reduced congestion, and improved safety.

Smart city is an emerging research field nowadays, with emphasis of using big data to enhance citizens' quality of life. One of the prevalent smart city projects is to use big traffic data collected from road users over time, for road planning, traffic light scheduling, traffic jam relief, and public security. In particular, being able to know a road user's current location and predict his/her next move is important in today's intelligent transportation systems. Trajectory prediction has become a prudential research study direction, by which many algorithms have been published before. In this paper, we present a simple probabilistic model which predicts road users' next locations based on the "concept of segments" abstracted from historical trails which the users have taken and accumulated over time in some data archive. Given a trajectory and a current location, the road user's next move in terms of road direction can be predicted at the junction. It is found that each road user would have his/her unique travel pattern hidden in the aggregate big traffic data. These patterns could be modeled from connected segments for simplicity. With the longer the trail and more frequently this trail was travelled, the more accurate that the next turn can be predicted. Simulation experiment was conducted based on summing up the segments from empirical trajectory data that was used in trajectory data mining by Microsoft. The results of our alternative model in contrast to the state of the arts demonstrated good efficacy.

Nowadays, deep learning methods based on a virtual environment are widely applied to research and technology development for autonomous vehicle&rsquo, s smart sensors and devices. Learning various driving environments in advance is important to handle unexpected situations that can exist in the real world and to continue driving without accident. For training smart sensors and devices of an autonomous vehicle well, a virtual simulator should create scenarios of various possible real-world situations. To create reality-based scenarios, data on the real environment must be collected from a real driving vehicle or a scenario analysis process conducted by experts. However, these two approaches increase the period and the cost of scenario generation as more scenarios are created. This paper proposes a scenario generation method based on deep learning to create scenarios automatically for training autonomous vehicle smart sensors and devices. To generate various scenarios, the proposed method extracts multiple events from a video which is taken on a real road by using deep learning and generates the multiple event in a virtual simulator. First, Faster-region based convolution neural network (Faster-RCNN) extracts bounding boxes of each object in a driving video. Second, the high-level event bounding boxes are calculated. Third, long-term recurrent convolution networks (LRCN) classify each type of extracted event. Finally, all multiple event classification results are combined into one scenario. The generated scenarios can be used in an autonomous driving simulator to teach multiple events that occur during real-world driving. To verify the performance of the proposed scenario generation method, experiments using real driving video data and a virtual simulator were conducted. The results for deep learning model show an accuracy of 95.6%, furthermore, multiple high-level events were extracted, and various scenarios were generated in a virtual simulator for smart sensors and devices of an autonomous vehicle.

Recently, virtual environment-based techniques to train sensor-based autonomous driving models have been widely employed due to their efficiency. However, a simulated virtual environment is required to be highly similar to its real-world counterpart to ensure the applicability of such models to actual autonomous vehicles. Though advances in hardware and three-dimensional graphics engine technology have enabled the creation of realistic virtual driving environments, the myriad of scenarios occurring in the real world can only be simulated up to a limited extent. In this study, a scenario simulation and modeling framework that simulates the behavior of objects that may be encountered while driving is proposed to address this problem. This framework maximizes the number of scenarios, their types, and the driving experience in a virtual environment. Furthermore, a simulator was implemented and employed to evaluate the performance of the proposed framework.

It is possible to design an English vocabulary learning system using a text to speech (TTS) engine and a module which creates push alarms for new words or sentences at a specific time every day. The function of selecting new words and sentences and showing them to the user is a core technology in vocabulary learning systems, and it has a great effect on the systems’ learning effectiveness. This paper proposes a method for creating a system which selects words and sentences. The system collects English words and sentences via social networks, refines them so that they are in a form which is appropriate for user learning, and allows the user to learn words which are related to their current location and weather.

The importance of cybersecurity has recently been increasing. A malware coder writes malware into normal executable files. A computer is more likely to be infected by malware when users have easy access to various executables. Malware is considered as the starting point for cyber-attacks, thus, the timely detection, classification and blocking of malware are important. Malware visualization is a method for detecting or classifying malware. A global image is visualized through binaries extracted from malware. The overall structure and behavior of malware are considered when global images are utilized. However, the visualization of obfuscated malware is tough, owing to the difficulties encountered when extracting local features. This paper proposes a merged image-based malware classification framework that includes local feature visualization, global image-based local feature visualization, and global and local image merging methods. This study introduces a fastText-based local feature visualization method: First, local features such as opcodes and API function names are extracted from the malware, second, important local features in each malware family are selected via the term frequency inverse document frequency algorithm, third, the fastText model embeds the selected local features, finally, the embedded local features are visualized through a normalization process. Malware classification based on the proposed method using the Microsoft Malware Classification Challenge dataset was experimentally verified. The accuracy of the proposed method was approximately 99.65%, which is 2.18% higher than that of another contemporary global image-based approach.

In ubiquitous computing, diverse media technologies have recently been extensively researched and applied to various fields. The goal of media technologies is to improve our daily lives by enabling us to control active media devices such as smart phones, tablets, and TVs. The locations of users are important to providing a variety of services to users. In addition, given that all devices cannot be simultaneously utilized, and only one device is sometimes being used, the location information can be utilized to help determine one of the core active media devices at a specific instant in time. This paper proposes an active media-control interface that is based on location recognition methods using beacons in indoor ubiquitous computing. In the proposed environment, users have a beacon that denotes the location of each user. The one that is the nearest from the beacon is selected from among multiple active media devices for servicing. In the experiments, four access points and one beacon was used to validate the proposed method. The advantage of the proposed method is that it enables us to apply active media technology to indoor environments. By recognizing user locations in indoor environments, several kinds of active media services become available.

The natural user interface (NUI) has been investigated in a variety of fields in application software. This paper proposes an approach to generate virtual agents that can support users for NUI-based applications through human---robot interaction (HRI) learning in a virtual environment. Conventional human---robot interaction (HRI) learning is carried out by repeating processes that are time-consuming, complicated and dangerous because of certain features of robots. Therefore, a method is needed to train virtual agents that interact with virtual humans imitating human movements in a virtual environment. Then the result of this virtual agent can be applied to NUI-based interactive applications after the interaction learning is completed. The proposed method was applied to a model of a typical house in virtual environment with virtual human performing daily-life activities such as washing, eating, and watching TV. The results show that the virtual agent can predict a human's intent, identify actions that are helpful to the human, and can provide services 16 % faster than a virtual agent trained using traditional Q-learning.

As a core technology to manage decentralized systems, blockchain is gaining much popularity to deploy such applications as smart grid and healthcare systems. However, its utilization in resource-constrained mobile devices is limited due to high demands of resources and poor scalability with frequent-intensive transactions. Edge computing can be integrated to facilitate mobile devices in offloading their mining tasks to cloud resources. This integration ensures reliable access, distributed computation and untampered storage for scalable and secure transactions. It is imperative therefore that crucial issues of security, scalability and resources management be addressed to achieve successful integration. Studies have been conducted to explore suitable architectural requirements, and some researchers have applied the integration to deploy some specific applications. Despite these efforts, however, issues of anonymity, adaptability and integrity still need to be investigated further to attain a practical, secure decentralized data storage. We based our study on peer-to-peer and blockchain to achieve an Internet of Things (IoT) design supported by edge computing to acquire security and scalability levels needed for the integration. We investigated existing blockchain and associated technologies to discover solutions that address anonymity, integrity and adaptability issues for successful integration of blockchain in IoT systems. The discovered solutions were then incorporated in our conceptual design of the decentralized application prototype presented for secure storage of IoT data and transactions.

With intelligent big data, a variety of gesture-based recognition systems have been developed to enable intuitive interaction by utilizing machine learning algorithms. Realizing a high gesture recognition accuracy is crucial, and current systems learn extensive gestures in advance to augment their recognition accuracies. However, the process of accurately recognizing gestures relies on identifying and editing numerous gestures collected from the actual end users of the system. This final end-user learning component remains troublesome for most existing gesture recognition systems. This paper proposes a method that facilitates end-user gesture learning and recognition by improving the editing process applied on intelligent big data, which is collected through end-user gestures. The proposed method realizes the recognition of more complex and precise gestures by merging gestures collected from multiple sensors and processing them as a single gesture. To evaluate the proposed method, it was used in a shadow puppet performance that could interact with on-screen animations. An average gesture recognition rate of 90% was achieved in the experimental evaluation, demonstrating the efficacy and intuitiveness of the proposed method for editing visualized learning gestures.

Background An imbalanced dataset is defined as a training dataset that has imbalanced proportions of data in both interesting and uninteresting classes. Often in biomedical applications, samples from the stimulating class are rare in a population, such as medical anomalies, positive clinical tests, and particular diseases. Although the target samples in the primitive dataset are small in number, the induction of a classification model over such training data leads to poor prediction performance due to insufficient training from the minority class. Results In this paper, we use a novel class-balancing method named adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique (ASCB_DmSMOTE) to solve this imbalanced dataset problem, which is common in biomedical applications. The proposed method combines under-sampling and over-sampling into a swarm optimisation algorithm. It adaptively selects suitable parameters for the rebalancing algorithm to find the best solution. Compared with the other versions of the SMOTE algorithm, significant improvements, which include higher accuracy and credibility, are observed with ASCB_DmSMOTE. Conclusions Our proposed method tactfully combines two rebalancing techniques together. It reasonably re-allocates the majority class in the details and dynamically optimises the two parameters of SMOTE to synthesise a reasonable scale of minority class for each clustered sub-imbalanced dataset. The proposed methods ultimately overcome other conventional methods and attains higher credibility with even greater accuracy of the classification model.

Games that use brainwaves via brain–computer interface (BCI) devices, to improve brain functions are known as BCI serious games. Due to the difficulty of developing BCI serious games, various BCI engines and authoring tools are required, and these reduce the development time and cost. However, it is desirable to reduce the amount of technical knowledge of brain functions and BCI devices needed by game developers. Moreover, a systematic BCI serious game development process is required. In this paper, we present a methodology for the development of BCI serious games. We describe an architecture, authoring tools, and development process of the proposed methodology, and apply it to a game development approach for patients with mild cognitive impairment as an example. This application demonstrates that BCI serious games can be developed on the basis of expert-verified theories.

The decision-making by agents in games is commonly based on reinforcement learning. To improve the quality of agents, it is necessary to solve the problems of the time and state space that are required for learning. Such problems can be solved by Macro-Actions, which are defined and executed by a sequence of primitive actions. In this line of research, the learning time is reduced by cutting down the number of policy decisions by agents. Macro-Actions were originally defined as combinations of the same primitive actions. Based on studies that showed the generation of Macro-Actions by learning, Macro-Actions are now thought to consist of diverse kinds of primitive actions. However an enormous amount of learning time and state space are required to generate Macro-Actions. To resolve these issues, we can apply insights from studies on the learning of tasks through Programming by Demonstration (PbD) to generate Macro- Actions that reduce the learning time and state space. In this paper, we propose a method to define and execute Macro-Actions. Macro-Actions are learned from a human subject via PbD and a policy is learned by reinforcement learning. In an experiment, the proposed method was applied to a car simulation to verify the scalability of the proposed method. Data was collected from the driving control of a human subject, and then the Macro- Actions that are required for running a car were generated. Furthermore, the policy that is necessary for driving on a track was learned. The acquisition of Macro-Actions by PbD reduced the driving time by about 16% compared to the case in which Macro-Actions were directly defined by a human subject. In addition, the learning time was also reduced by a faster convergence of the optimum policies.

Collaborative learning for a robot, a software agent, and a human subject in a virtual environment reduces time, labor, and cost compared to real-life approaches.

Video conferencing provides an environment for multiple users linked on a network to have meetings. Since a large quantity of audio and video data are transferred to multiple users in real time, research into reducing the quantity of data to be transferred has been drawing attention. Such methods extract and transfer only the features of a user from video data and then reconstruct a video conference using virtual humans. The disadvantage of such an approach is that only the positions and features of hands and heads are extracted and reconstructed, whilst the other virtual body parts do not follow the user. In order to enable a virtual human to accurately mimic the entire body of the user in a 3D virtual conference, we examined what features should be extracted to express a user more clearly and how they can be reproduced by a virtual human. This 3D video conferencing estimates the user’s pose by comparing predefined images with a photographed user’s image and generates a virtual human that takes the estimated pose. However, this requires predefining a diverse set of images for pose estimation and, moreover, it is difficult to define behaviors that can express poses correctly. This paper proposes a framework to automatically generate the pose-images used to estimate a user’s pose and the behaviors required to present a user using a virtual human in a 3D video conference. The method for applying this framework to a 3D video conference on the basis of the automatically generated data is also described. In the experiment, the framework proposed in this paper was implemented in a mobile device. The generation process of poses and behaviors of virtual human was verified. Finally, by applying programming by demonstration, we developed a system that can automatically collect the various data necessary for a video conference directly without any prior knowledge of the video conference system.

The substantial effort is required to make a virtual character smoothly move like a human being in the virtual environment. Since a virtual character acts on the basis of the actions, it is the most critical to define actions for smooth flow of action. It has been actively studied the programming by demonstration which defines series of actions to be performed by a virtual character based on the actions operated by a person. However, such approaches can't easily draw many sequential actions because they create sequential actions in the same length all the time or restrict the actions used to create such actions. For smooth flow of action, it is required to derive sequential actions as much as possible from the actions of a virtual character and to select the representative set of actions. Accordingly, it is necessary to study how to create sequential actions as reducing diverse limits. This study suggests the approach to select sequential actions suitable for a virtual character by collecting the actions of a character manipulated by a person and deriving a set of actions to be performed by a virtual character. The experiment describes the process to create the actions by applying the approach suggested in this study to the driving game. In accordance with the analysis results, it was found that a set of actions performed by a person was generated without being restricted by a length or a part to divide. Finally, we confirmed that the suggested method selects the best sequential actions, appropriate to virtual character, among more generated actions.

Recently there has been various forms of sexual assault against elementary school children at various locations. In order to prevent such incidents, various educational applications for sexual violence prevention are being developed. As mobile devices are becoming more prevalent, the prevention education applications developed are optimized for the mobile devices. However, a more precise user interface design is required to enhance the efficacy of the education. This research suggests a method to enhance the user interface of an already existing sexual violence prevention application for elementary school students. It also explains a case in which the suggested user interface design method was implemented on an application according to the configuration of the proposed user interface. The results showed the learning experience was effective with the enhancements made on the user interface.

This chapter proposes a method that controls User Interfaces by changing properties of UIs after analyzing the patters of events invoked by users. The events and criteria are defined and handled to determine the properties of UIs. In the experiment, our proposed method was applied to an education authoring tool. The changes of UIs after the analysis of user events were validated.

This chapter proposes a two-behavior networks-based method to automatically detect whether a situation is risky or not. Behavior network is used to analyze measured values from the sensors of a smart phone. Bayesian probability is also used for implementing such a behavior network. An experiment was conducted to validate that the speed of recognizing risk situations was improved by learning such situations iteratively through behavior networks with Bayesian probability.

Given that robots provide services in any locations after they move toward humans, the pervasive sensing environment can provide diverse kinds of services through the robots not depending on the locations of humans. For various services, robots need to learn accurate motor primitives such as walking and grabbing objects. However, learning motor primitives in a pervasive sensing environment are very time consuming. Several previous studies have considered robots learning motor primitives and interacting with humans in virtual environments. Given that a robot learns motor primitives based on observations, a disadvantage is that there is no way of defining motor primitives that cannot be observed by a robot. In this paper, we develop a novel interaction learning approach based on a virtual environment. The motor primitives are defined by manipulating a robot directly using demonstration-based learning. In addition, a robot can apply Q-learning to learn interactions with humans. In an experiment, using the proposed method, the motor primitives were generated intuitively and the amount of movement required by a virtual human in one of the experiments was reduced by about 25% after applying the generated motor primitives.

Emerging smart space applications are increasingly relying on capabilities for recognizing human activities. Activity recognition research is however challenged and slowed by the lack of data necessary for testing and validation. Collecting data through live-in trials in real world deployments is often very expensive and complicated. Legitimate limitations on the use of human subjects also renders a much smaller dataset than desired to be collected. To address this challenge, we propose a scenario generation approach in which a small set of scenarios is used to generate new relevant and realistic scenarios, and hence increase the base of testing data needed for activity recognition validation. Unlike existing methods for generating scenarios, which usually focus on scenario structure and complexity, we propose a Bayesian-based approach that learns the stochastic characteristics of a small number of collected datasets to generate additional scenarios of similar characteristics. Our approach is prolific and can generate enormous datasets with high degree of realism at affordable cost. The proposed approach is validated using a Viterbi-based algorithm and a real dataset case study. The validation experiment confirms that the generated dataset has highly similar stochastic characteristics as that of the real dataset.

Given that entropy-based IT technology has been applied in homes, office buildings and elsewhere for IT security systems, diverse kinds of intelligent services are currently provided. In particular, IT security systems have become more robust and varied. However, access control systems still depend on tags held by building entrants. Since tags can be obtained by intruders, an approach to counter the disadvantages of tags is required. For example, it is possible to track the movement of tags in intelligent buildings in order to detect intruders. Therefore, each tag owner can be judged by analyzing the movements of their tags. This paper proposes a security approach based on the received signal strength indicators (RSSIs) of beacon-based tags to detect intruders. The normal RSSI patterns of moving entrants are obtained and analyzed. Intruders can be detected when abnormal RSSIs are measured in comparison to normal RSSI patterns. In the experiments, one normal and one abnormal scenario are defined for collecting the RSSIs of a Bluetooth-based beacon in order to validate the proposed method. When the RSSIs of both scenarios are compared to pre-collected RSSIs, the RSSIs of the abnormal scenario are about 61% more different compared to the RSSIs of the normal scenario. Therefore, intruders in buildings can be detected by considering RSSI differences.

Software Defined Networking (SDN) has brought many changes in terms of the interaction processes between systems and humans. It has become the key enabler of software defined architecture, which allows enterprises to build a highly agile Information Technology (IT) infrastructure. For Future Sustainability Computing (FSC), SDN needs to deliver on many information technology commitments—more automation, simplified design, increased agility, policy-based management, and network management bond to more liberal IT workflow systems. To address the sustainability problems, SDN needs to provide greater collaboration and tighter integration with networks, servers, and security teams that will have an impact on how enterprises design, plan, deploy and manage networks. In this paper, we propose FS-OpenSecurity, which is a new and pragmatic security architecture model. It consists of two novel methodologies, Software Defined Orchestrator (SDO) and SQUEAK, which offer a robust and secure architecture. The secure architecture is required for protection from diverse threats. Usually, security administrators need to handle each threat individually. However, handling threats automatically by adapting to the threat landscape is a critical demand. Therefore, the architecture must handle defensive processes automatically that are collaboratively based on intelligent external and internal information.

In this paper, we propose a method to reduce the learning time of Q-learning by combining the method of updating even to Q-values of unexecuted actions with the method of adding a terminal reward to unvisited Q-values. To verify the method, its performance was compared to that of conventional Q-learning. The proposed approach showed the same performance as conventional Q-learning, with only 27 % of the learning episodes required for conventional Q-learning. Accordingly, we verified that the proposed method reduced learning time by updating more Q-values in the early stage of learning and distributing a terminal reward to more Q-values.

The technical development of service robots has enhanced the variety of services provided by them to human beings. Service robots need to interact with human beings; hence, they require considerable learning time. The learning time can be reduced by adopting a learning approach in a virtual environment. To this end, it is necessary to describe a human being’s movements in the virtual environment. In this paper, we propose a method to generate an action model of a virtual character by calculating the probability of human movements using Bayesian probability. The virtual character selects actions based on the action model, and it executes these actions. Using the proposed method, the path of a virtual character was decreased by around 74 %, as compared to related methods based on Bayesian probability.

For smart home researchers, it is essential to test activity recognition algorithms with various sets of sensory data. However, diverse sensory datasets are not always available due to several constraints, including limited budgets. Consequently, smart home simulators have recently grown in importance. However, there is still a need for realistic synthetic sensory data. This paper presents a simulator, i®Persim 3D', which relies on automatic scenario generation to create realistic sensory data. Persim 3D provides a 3D graphical user interface to help users' spatial perception, sensors that operate in real-time, similar to actual sensors, and a virtual character that lives in a virtual environment. These features allow users to generate longitudinal simulation data and eventually contribute to activity recognition research.

Q-learning is a reinforcement learning widely used for automatic learning in the game environment. Before applying Q-learning, the many states of environment that an agent may come in contact with is defined. The weak point of Q-learning is the time it takes to learn these states as states become larger. In this paper, the Q- learning mechanism using an influence map (QIM) is proposed to reduce the time needed for learning. By using an influence map and the learning result, a medium Q- value, which is not yet learnt, will be generated. Generally, when learning is finished, it is difficult to improve the performances. If QIM is used, however, the performance could be improved. Although the Q-table in QIM has been defined with small states, QIM obtains nearly the same learning result.

Video conferencing is a communication technology that allows multiple users to communicate with each other by both images and sound signals. As the performance of wireless network has improved, the data are transmitted in real time to mobile devices with the wireless network. However, there is the limit of the amount of the data to be transmitted. Therefore it is essential to devise a method to reduce data traffic. There are two general methods to reduce data rates: extraction of the user's image shape and the use of virtual humans in video conferencing. However, data rates in a wireless network remain high even if only the user's image shape is transferred. With the latter method, the virtual human may express a user's movement erroneously with insufficient information of body language or gestures. Hence, to conduct a video conference on a wireless network, a method to compensate for such erroneous actions is required. In this article, a virtual human-based video conference framework is proposed. To reduce data traffic, only the user's pose data are extracted from photographed images using an improved binary decision tree, after which they are transmitted to other users by using the markup language. Moreover, a virtual human executes behaviors to express a user's movement accurately by an improved behavior network according to the transmitted pose data. In an experiment, the proposed method is implemented in a mobile device. A 3-min video conference between two users was then analyzed, and the video conferencing process was described. Photographed images were converted into text-based markup language. Therefore, the transmitted amount of data could effectively be reduced. By using an improved decision tree, the user's pose can be estimated by an average of 5.1 comparisons among 63 photographed images carried out four times a second. An improved behavior network makes virtual human to execute diverse behaviors.