Virtual local area network (VLAN) is commonly used to divide a big network into several small network segments. Also, many adopt VLAN for dissecting LANs in order to prevent communications between different VLANs for security and management purposes. It is known that inserting an additional VLAN tag into Ethernet frames, referred to as VLAN hopping attack, can bypass the VLAN-based network separation. There are two preconditions for the attack. The first condition is that a hacker needs to know the destination’s VLAN identification number and the second condition is that the attacking system needs to be connected a switch’s trunk port that is used for connecting a switch. In this study, we propose an SNMP (Simple Network Management Protocol)-based detection method to effectively find a port and an MAC address that meet the second condition before a VLAN hopping attack begins. Since SNMP is implemented by most network components, our method can be easily deployed to the current VLAN networks.
An effective construction safety management system is required for reducing damage caused by construction site accidents. However, construction site safety management systems are mainly operated only at large scale, so there is a lack of safety management system that can be operated at a low cost even in smaller construction sites. In this paper, we propose an Internet of Things (IoT)-based construction site safety management system which can be operated at low cost not only at large construction sites but also at smaller construction sites. A prototype for the proposed system has been developed using a beacon technology, smartphone application, and sensors, Zigbee, WiFi, and LTE to monitor field workers and outsiders approaching the hazard zones at all times. The prototype system also provides danger alarm to safety managers in construction sites and at remote sites. It is expected that the developed system can effectively prevent safety accidents in large-scale and small-scale construction sites.
Existing rehabilitation treatment is experience base of experts, to do a lot of treatment and training. However, in this research, we implemented a rehabilitation support system based on movement and muscle activity data that can support efficient rehabilitation based on more objective data. Implemented system utilizes EMG, acceleration sensor and gyro sensor, it becomes a measurement, so it is possible to accumulate more objective data and plan a treatment when doing rehabilitation treatment. In order to evaluate the performance of the implemented system, we measured EMG data and movement data were measured assuming femoral muscle related rehabilitation exercise situations. As a result of the experiment, four situations classifications were possible and comparative evaluation with commercial systems also confirmed very similar results.
OpenGL (Open Graphics Library) is one of the most widely-used API (application programming interface)-level 3D graphics libraries. Recently, its new safety-critical profile, OpenGL SC (Safety Critical profile) 2.0 is released. To provide these new features, we design a simplified rendering scheme for emulating OpenGL SC 2.0 over OpenGL ES (Embedded System) 2.0. Since OpenGL ES 2.0 is widely used with desktops and mobile devices, our emulation can be used with wide range of graphics devices. Our new emulation scheme shows an efficient architectural way of providing all the rendering features. Prototype implementations are also presented.
This paper proposes a new fractional decimal format and binary- todecimal conversion for highly accurate calculation without error. In many cases, a given fractional constant is the decimal number. After converting the decimal constant to a fractional binary number, any calculation can be performed. Because the given constant is originally the decimal number, the result may contain an error, which degrades the system accuracy. We propose a new format for fractional decimal numbers. The proposed format contains a binary integer and fractional bits, where the fractional decimal number can be provided by the proposed binary bit conversion. In a calculation, instead of using fixed- point numbers, scaled integer numbers are adopted. Then, the result is scaled and converted into the fractional decimal number. Considering practical examples and evaluation results, it is concluded that the proposed method can provide the errorless calculation.
Serious dance students are always looking for ways in which they can improve their technique by practising alone at home or a studio by using a mirror for feedback. The problem these students face is that for many ballet postures it is difficult to analyze one’s own faults. By not having guidance regarding proper positional alignment, dancers risk developing injuries and bad habits. The proposed solution is a system which recognizes the ballet position being performed by a dancer. After recognition, this research aims to work towards providing the necessary correction as feedback. The results for recognition in the system, using a Bag-of-Words approach to a Support Vector Machine classifier, showed an accuracy of 59.6%. Multiple implementations are produced and assessed in this paper. It is clearly found that the approach is feasible, however, work for improving the accuracy is required. Recommendations to improve effective pose recognition for future work are therefore discussed.
Over the years, various ways emerge for evolving the existing applications towards a shared codebase. Among several, Electron is a well-known framework for web developers to build cross-platform desktop applications using familiar web technologies, such as HTML, CSS, and JavaScript. This paper thus presents an approach for transforming web applications created with JavaScript to desktop applications that can run on Windows, MacOS, and Linux. The output desktop application would remain the old set of source code for further development.
This paper proposes an efficient method for evaluation of the fault severity in bearing using the discrete wavelet packet transform (DWPT) and the envelope analysis. The acoustic emission (AE) signals for each defect are first decomposed to the sub-band signals. The envelope power spectrum analysis is performed on each sub-band to detect the frequency periodic impulses showing the abnormal symptoms of bearing defects. It is essential to select an optimal sub-band for reliable assessment of the fault severity in bearing. A ratio of defect spectral component to residual spectral component (RDR) is calculated from their envelope power spectrum using the Gaussian window for an optimal sub-band selection which shows clearly information about failures. As a result, the severe degree of bearing defects is assessed based on the RDR calculation. The effectiveness of the proposed scheme is validated through experimental results of evaluating the different fault conditions under variable crack size in bearing.
This article discusses the possibility of verifying whether the formalism of Petri nets suitable for the optimization of transport systems. This work discusses the optimization of automotive traffic using the saturation flow method. The main aim of the article is to propose and validate a methodology for optimizing transport systems with saturation flow methods. There are discussed several optimization methods that are used for this purpose. The results of this search section were summarized and then a suitable model and an optimization method were chosen according to established criteria. Based on them we have proposed methodology that was verified pursuant to the application of the chosen procedures. One of Ostrava’s most interrupted crossroads was chosen as a model for optimization and simulation. Results of the experimental study are summarized in the conclusion. From the results of the experimental study, it can be stated that the static signaling model has been optimized in terms of time.
Modeling occupies an important place in microservice solutions. However, as far as approaches covering whole development cycle are concerned, either their modeling languages are too simple, or modeling processes are incomplete. This paper presents an approach to modeling microservice solutions based on CBDI SAE metamodel for SOA 3, which has gotten the widely attention of academic and industrial circles. The paper discusses which modeling activities can output which models and how to build and describe the models, and prescribes the relations between the models.
This study examines features of information technology that can provide new information for viewers of sportscasts as a means of spectator sports, whose demands are increasing as a leisure activity. Additionally, expected effects of such features are discussed. As information and communication technology advances, it is expected that checking psychological conditions in sports will be possible in the near future. This study designs a system that monitors players’ psychological conditions and resulting changes in their motor functions by means of application technology. Such technology applications will be of significant values as new media contents as well as basic materials for players’ performance enhancement.
Conventional enterprise application design methodologies emphasize performance, scalability, and development/maintenance costs. Often such applications deal with access to confidential data (e-commerce, health, etc.). A single flaw in the application may lead to a compromise, exposing computational resources and sensitive data, such as private information, trade secrets, etc. Traditionally, security for enterprise applications focused on prevention; however, recent experience demonstrates that exploitation of infrastructure, operating systems, libraries, frameworks, personnel, etc. are almost unavoidable. While prevention should certainly remain the first line of defense, system architects must also incorporate designs to enable breach containment and response. In this paper, we survey related research on software application design that targets isolation, where the compromise of a single module presents a knowable and scope-limited worst-case impact.
In the application of the Internet of Things (IoT), all data is stored in the cloud, that causes the long distance of the network logic between the cloud and the device side or client side, this might leads to network delay or slow response time. A challenging issue is how to increase the speed of response time in the cloud computing and the IoT environment for clients. In this paper, we propose a complete set of Edge Computing architecture. There are three layers, namely, Cloud side, Edge side, and Device side. Cloud side mainly deals with more complicated operations and data backup. For overall system infrastructure, we deployed Kubernetes cluster on an OpenStack platform. Edge side optimizes the service of cloud computing systems by performing data processing at the edge of the network. In this phase, we created an Edge Gateway to increase the capacity and performance and reduce the communications bandwidth needed between sensors and the central data.
The paper proposes a new approach to software evaluation, which takes into consideration cultural factors. We use Profile Theory to develop a model that captures the essential technical and cultural characteristics of contextually effective software evaluators. These evaluator-defining characteristics include cultural, organizational, technical, and individual attributes, and relationships among them. We used surveys and literature to identify the prevalent characteristics and then defined a formal model. An illustrative example is elaborated to show how our model can be integrated in a CASE tool. We surmise that identifying the profile of software evaluators is a necessary step to ensure the effectiveness and validity of the evaluation of software systems.
The objective of this paper is to forecast quarterly GDP in Macao using different neural network models. It is a challenge task due to the scarcity of determinant economic indicators and the scarcity of economic data. We compared the forecast errors of three different neural network models including Back Propagation (BP), Elman and Radial Basis Function (RBF). Elman has never been used in the GDP forecasting in literature, however in our results, Elman has the least forecasting error due to its recurrent network topology which can remember the history economic data.
The main aim of this article is to show what is possible to use mobile devices with verification of adaptivity. We focused on creating applications for the control of robots Lego Mindstorms EV3 verification adaptivity, which uses fuzzy approach. We have used classical fuzzy rules of if-then type. The antecedent contains the measured values from infrared sensors and the consequent contains action response of individual engines of the robot. With this application, it will be able to control the robot via Wi-Fi while, there will be a possibility to bring the robot mode that will move through the maze without bumping into some of the walls. An integral part of the work is also a theoretical basis for adaptive robot control and autonomy. Part of this article describe of creating of applications, their comparison to other existing applications and experiments with the resulting application. The application perfectly functioned on the created experimental environments, including the adaptive mode.
The increasing spread and adoption of the Internet of Things allows for novel methods to gather information about a user's context, which can be used for enhanced authentication. In this article, we focus on context-aware authentication using information about Wi-Fi networks from a user's wearables or nearables. We propose an additional factor for multi-factor authentication based on the other devices present on the same Wi-Fi network. Devices periodically discover all available peer MAC addresses. During subsequent authentication attempts, the network state is compared to previous network states saved under functionally similar conditions. If the devices on the network change significantly, a flag is raised and further action can be triggered. We also demonstrate the solution as a proof of concept.
IoT platforms are the key solution to provide context data network using the sensor devices, and support backend applications that make sense of the mass of data generated by thousands of sensors. The global IoT platform market continues to rise significantly. To enable the interoperability among heterogeneity of IoT platforms becomes a big challenge for the development trend of the IoT nowadays. This paper presents an open framework based IoT interoperability architecture. This architecture offers the required functionalities for integrating with heterogeneous IoT platforms using open framework based on RESTful. Proposed open framework was designed to facilitate the integration of multiple IoT platforms in different standards. The result of our work indicates that the proposed architecture assists the development of interoperable IoT ecosystems.
Many varieties of internet attack make use of the Domain Name System (DNS) at some point. Response Policy Zones (RPZ) is a reputation-based DNS firewall technology intended to obstruct the use of the DNS by malicious actors. We report on a study that compared the blocking behavior of a freely available RPZ-enabled DNS service with the blocking behavior of other DNS services available in the United States. We were surprised to find that only the RPZ-enabled server was doing any significant blocking of malicious domains. Since our study was carried out, this free RPZ-enabled service has been made more widely available as Quad9 (https://quad9.net/).
The setting hyperparameters in the support vector machine (SVM) is very important with regard to its accuracy and efficiency. In this paper, we employ a novel definition of the reinforcement learning state, actions and reward function that allows a deep Q-network (DQN) to learn to control the optimization hyperparameters for the SVM deep neural networks by supervised Big-Data. In this framework, the DQN algorithm with experience replay is based on the off-policy reinforcement learning for the expected discounted return of rewards, or q-values, connected to the actions of adjusting the hyperprameters in the SVM. We propose the two deep neural networks, one with the SVM and the other with Q-network (DQN). The SVM deep neural networks learns a policy for the optimization hyperparameters, but differ in the number of allowed actions. The SVM deep neural networks trains the hyperparameters of the SVM simultaneously such as the Lagrangian multiplier. The proposed algorithm is called a Hybrid DQN combined with SVM deep neural networks. This algorithm could be considered as the classifier in the real-world domains such as network anomalies in the distributed server loads, because the SVM is suitable for the application in a classification, especially for the one-against-the others. Algorithm comparisons show that our proposed algorithm leads to good optimization of the Lagrangian multiplier and can prevent overfitting to a certain extent automatically without human system designers. In terms of the classification performance of the proposed algorithm can be compared to the original LIBSVM with no controls of the hyperparameters.
