Nowadays, indoor localization systems using IEEE 802.11 have been actively explored for location-based services, since GPS cannot identify floors or rooms in buildings. However, the user-side device is usually large and consumes high energy. In this paper, the authors propose a fingerprint-based indoor localization system using IEEE 802.15.4 that allows the use of a small device with a long-life battery, named FILS15.4. A user carries a small transmitter whose signal is received by multiple receivers simultaneously. The received signal strengths are compared with the fingerprints to find the current location. To address signal fluctuations caused by the low-power narrow-band signal, FILS15.4 limits one room as the localization unit, prepares plural fingerprints for each room, and allocates a sufficient number of receivers in the field. For evaluations, extensive experiments were conducted at #2 Engineering Building in Okayama University and confirmed high detection accuracy with sufficient numbers of receivers and fingerprints.
Nowadays, human indoor localization services inside buildings or on underground streets are in strong demand for various location-based services. Since conventional GPS cannot be used, indoor localization systems using wireless technologies have been extensively studied. Previously, we studied a fingerprint-based indoor localization system using IEEE802.15.4 devices, called FILS15.4, to allow use of inexpensive, tiny, and long-life transmitters. However, due to the narrow channel band and the low transmission power, the link quality indicator (LQI) used for fingerprints easily fluctuates by human movements and other uncontrollable factors. To improve the localization accuracy, FILS15.4 restricts the detection granularity to one room in the field, and adopts multiple fingerprints for one room, considering fluctuated signals, where their values must be properly adjusted. In this paper, we present a fingerprint optimization method for finding the proper fingerprint parameters in FILS15.4 by extending the existing one. As the training phase using the measurement LQI, it iteratively changes fingerprint values to maximize the newly defined score function for the room detecting accuracy. Moreover, it automatically increases the number of fingerprints for a room if the accuracy is not sufficient. For evaluations, we applied the proposed method to the measured LQI data using the FILS15.4 testbed system in the no. 2 Engineering Building at Okayama University. The validation results show that it improves the average detection accuracy (at higher than 97%) by automatically increasing the number of fingerprints and optimizing the values.
With the systematization of cyber threats, the variety of intrusion tools and intrusion methods has greatly reduced the cost of attackers' threats to network security. Due to a large number of colleges and universities, teachers and students are highly educated and the Internet access rate is nearly 100%. The social status makes the university network become the main target of threat. The traditional defense method cannot cope with the current complex network attacks. In order to solve this problem, the threat intelligence sharing platform based on various threat intelligence sharing standards is established, which STIX and TAXII It is a widely used sharing standard in various sharing platforms. This paper analyzes the existing standards of STIX and TAXII, improves the STIX and TAXII standards based on the analysis results, and proposes a new type of STIX and TAXII based on the improved results. The standard design scheme of threat intelligence sharing platform suitable for college network environment features. The experimental results show that the threat intelligence sharing platform designed in this paper can be effectively applied to the network environment of colleges and universities.
Currently, the User-PC computingsystem (UPC) has been studied as a low-cost and high-performance distributed computing platform. It uses idling resources of personal computers (PCs) in a group.The job-worker assignment for minimizing makespan is critical to determine the performance of the UPC system.Some applications need to execute a lot of uniform jobs that use the identical program but with slightly different data, where they take the similar CPU time on a PC. Then, the total CPU time of a worker is almost linear to the number of assigned jobs. In this paper, we propose a static assignment algorithm of uniform jobs to workers in the UPC system, using simultaneous linear equations to find the lower bound on makespan, where every worker requires the same CPU time to complete the assigned jobs. For the evaluations of the proposal, we consider the uniform jobs in three applications. In OpenPose, the CNN-based keypoint estimation program runs with various images of human bodies. In OpenFOAM, the physics simulation program runs with various parameter sets. In code testing, two open-source programs run with various source codes from students for the Android programming learning assistance system (APLAS). Using the proposal, we assigned the jobs to six workers in the testbed UPC system and measured the CPU time. The results show that makespan was reduced by 10% on average, which confirms the effectiveness of the proposal.
Nowadays, digital transformation (DX) is the key concept to change and improve the operations in governments, companies, and schools. Therefore, any data should be digitized for processing by computers. Unfortunately, a lot of data and information are printed and handled on paper, although they may originally come from digital sources. Data on paper can be digitized using an optical character recognition (OCR) software. However, if the paper contains a table, it becomes difficult because of the separated characters by rows and columns there. It is necessary to solve the research question of “how to convert a printed table on paper into an Excel table while keeping the relationships between the cells?” In this paper, we propose a printed table digitization algorithm using image processing techniques and OCR software for it. First, the target paper is scanned into an image file. Second, each table is divided into a collection of cells where the topology information is obtained. Third, the characters in each cell are digitized by OCR software. Finally, the digitalized data are arranged in an Excel file using the topology information. We implement the algorithm on Python using OpenCV for the image processing library and Tesseract for the OCR software. For evaluations, we applied the proposal to 19 scanned and 17 screenshotted table images. The results show that for any image, the Excel file is generated with the correct structure, and some characters are misrecognized by OCR software. The improvement will be in future works.
The Unlink attack is a way of attacking the heap overflow vulnerability under the Linux platform. However, because the heap overflow data seldom directly leads to program control flow hijacking and related protection mechanism limitations, the existing detection technology is difficult to judge whether the program meets the heap overflow attack condition. There are certain inspection measures in the existing unlink mechanism, but with carefully constructing the contents of the heap, you can bypass the inspection measures. The unlink mechanism must be triggered with the free function, and this principle is similar to function-exit of stacks. The paper obtains the inspiration through the canary protection mechanism in the stack, adds it to the chunk structure, encrypts the canary value, and defends the unlink attack from the fundamental structure. The experimental results show that this method can effectively prevent the occurrence of unlink attacks and has the ability to detect common heap overflows.
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