In this paper, we construct ideal and probabilistic secret sharing schemes for some multipartite access structures, including the General Hierarchical Access Structure and Compartmented Access Structures. We devise an ideal scheme which implements the general hierarchical access structure. For the compartmented access structures, we consider three special access structures. We propose ideal and probabilistic schemes for these three compartmented access structures by bivariate interpolation.

Online used markets such as eBay, Yahoo Auction, and Craigslist have been popular due to the web services. Compared to the shopping mall websites like eBay or Yahoo Auction, web community-style used markets often expose the private information of sellers. In Korea, the most popular online used market is a website called “Joonggonara” with more than 13 million users, and it uses an informal posting format that does not protect the users' privacy identifiable information. In this work, we examine the privacy leakage from the online used markets in Korea, and show that 45.9% and 74.0% of sample data expose cellular phone numbers and email addresses, respectively. In addition, we demonstrate that the private information can be maliciously exploited to identify a subscriber of the social network service.

The threshold voltage variations for actual size MOSFETs obtained by capacitance measurement are compared with those obtained by the current measurement, and their differences are studied for the first time. It is found that the threshold voltage variations obtained by the capacitance measurement show the similar behavior to those current measurement and the absolute value is less than those obtained by the current measurement. The reason for the difference is partially explained by that the local channel dopant non-uniformity along the current path makes the threshold voltage variation obtained from current measurement larger. It is found that the flat-band voltage variations, which are obtained from the measured C-V curves, are small and not significant to the threshold voltage variation.

This paper empirically validates battery-less sensor activation via wireless energy transmission to release sensors from wires and batteries. To seamlessly extend the coverage and activate sensor nodes distributed in any indoor environment, we proposed multi-point wireless energy transmission with carrier shift diversity. In this scheme, multiple transmitters are employed to compensate path-loss attenuation and orthogonal frequencies are allocated to the multiple transmitters to avoid the destructive interference that occurs when the same frequency is used by all transmitters. In our previous works, the effectiveness of the proposed scheme was validated theoretically and also empirically by using just a spectrum analyzer to measure the received power. In this paper, we develop low-energy battery-less sensor nodes whose consumed power and required received power for activation are respectively 142µW and 400µW. In addition, we conduct indoor experiments in which the received power and activation of battery-less sensor node are simultaneously observed by using the developed battery-less sensor node and a spectrum analyzer. The results show that the coverage of single-point and multi-point wireless energy transmission without carrier shift diversity are, respectively, 84.4% and 83.7%, while the coverage of the proposed scheme is 100%. It can be concluded that the effectiveness of the proposed scheme can be verified by our experiments using real battery-less sensor nodes.

In this paper, we propose a true random number generator (TRNG) exploiting jitter and the chaotic behavior in cross ring oscillators (CROs). We make a further study of the feedback ring architecture and cross-connect the XOR gates and inverters to form an oscillator. The CRO utilizes totally digital logic circuits, and gains a high and robust entropy rate, as the jitter in the CRO can accumulate locally between adjacent stages. Two specific working modes of CRO in which the CRO can work in a consistent state and a free-running state respectively are introduced and analyzed both theoretically and experimentally. Finally, different stage lengths of cross ring true random number generators (CRTRNGs) are tested in different Field Programmable Gate Arrays (FPGAs) and test results are analyzed and compared. Especially, random data achieved from a design of 63-stage CRTRNG in Altera Cyclone IV passes both the NIST and Diehard test suites at a rate as high as 240Mbit/s.

Cyber-attacks and cybersecurity used to be the issues for those who use Internet and computers. The issues, however, are expanding to anyone who does not even use them directly. The society is gradually and heavily depending on networks and computers. They are not closed within a cyberspace anymore and having interaction with our real world with sensors and actuators. Such systems are known as CPS (Cyber Physical Systems), IoT/E (Internet of Things/Everything), Industry 4.0, Industrial Internet, M2M, etc. No matter what they are called, exploitation of any of these systems may cause a serious influence to our real life and appropriate countermeasures must be taken to mitigate the risks. In this paper, cybersecurity in ICS (Industrial Control Systems) is reviewed as a leading example of cyber physical security for critical infrastructures. Then as a future aspect of it, IoT security for consumers is explained.

These days, the Web contains a huge volume of (semi-)structured data, called Linked Data (LD). However, LD suffer in data quality, and this poor data quality brings the need to identify erroneous data. Because manual erroneous data checking is impractical, automatic erroneous data detection is necessary. According to the data publishing guidelines of LD, data should use (already defined) ontology which populates type-annotated LD. Usually, the data type annotation helps in understanding the data. However, in our observation, the data type annotation could be used to identify erroneous data. Therefore, to automatically identify possible erroneous data over the type-annotated LD, we propose a framework that uses a novel nearest-neighbor based error detection technique. We conduct experiments of our framework on DBpedia, a type-annotated LD dataset, and found that our framework shows better performance of error detection in comparison with state-of-the-art framework.

The rapid development of a global navigation satellite system (GNSS) has raised the demand for spacecraft navigation, particularly for lunar spacecraft (LS). First, instead of the traditional approach of combining the united X-band system (UXB) with very-long-baseline interferometry (VLBI) by a terrestrial-based observing station in Chinese deep-space exploration, the spacecraft navigation based on inter-satellite link (ISL) is proposed because the spatial coverage of the GNSS downstream signals is too narrow to be used for LS navigation. Second, the feasibility of LS navigation by using ISL wide beam signals has been analyzed with the following receiving parameters: the geometrical dilution of precision (GDOP) and the carrier-to-noise ratio (C/N0) for satellites autonomously navigation of ISL and LS respectively; the weighting distance root-mean-square (wdrms) for the combination of both navigation modes. Third, to be different from all existing spacecraft ISL and GNSS navigation methods, an ISL annular beam transmitting antenna has been simulated to minimize the wdrms (1.138m) to obtain the optimal beam coverage: 16°-47° on elevation angle. Theoretical calculations and simulations have demonstrated that both ISL autonomous navigation and LS navigation can be satisfied at the same time. Furthermore, an onboard annular wide beam ISL antenna with optimized parameters has been designed to provide a larger channel capacity with a simpler structure than that of the existing GPS ISL spot beam antenna, a better anti-jamming performance than that of the former GPS ISL UHF-band wide band antenna, and a wider effectively operating area than the traditional terrestrial-based measurement. Lastly, the possibility and availability of applying an ISL receiver with an annular wide beam antenna on the Chinese Chang'E-5T (CE-5T) reentry experiment for autonomous navigation are analyzed and verified by simulating and comparing the ISL receiver with the practiced GNSS receiver.

Sentiment analysis of microblogging has become an important classification task because a large amount of user-generated content is published on the Internet. In Twitter, it is common that a user expresses several sentiments in one tweet. Therefore, it is important to classify the polarity not of the whole tweet but of a specific target about which people express their opinions. Moreover, the performance of the machine learning approach greatly depends on the domain of the training data and it is very time-consuming to manually annotate a large set of tweets for a specific domain. In this paper, we propose a method for sentiment classification at the target level by incorporating the on-target sentiment features and user-aware features into the classifier trained automatically from the data createdfor the specific target. An add-on lexicon, extended target list, and competitor list are also constructed as knowledge sources for the sentiment analysis. None of the processes in the proposed framework require manual annotation. The results of our experiment show that our method is effective and improves on the performance of sentiment classification compared to the baselines.

A low-cost prototypic Kinect-based rehabilitation system was developed for recovering balance capability of stroke patients. A total of 16 stroke patients were recruited to participate in the study. After excluding 3 patients who failed to finish all of the rehabilitation sessions, only the data of 13 patients were analyzed. The results exhibited a significant effect in recovering balance function of the patients after 3 weeks of balance training. Additionally, the questionnaire survey revealed that the designed system was perceived as effective and easy in operation.

The recent cyber-attacks utilize various malware as a means of attacks for the attacker's malicious purposes. They are aimed to steal confidential information or seize control over major facilities after infiltrating the network of a target organization. Attackers generally create new malware or many different types of malware by using an automatic malware creation tool which enables remote control over a target system easily and disturbs trace-back of these attacks. The paper proposes a generation method of malware behavior patterns as well as the detection techniques in order to detect the known and even unknown malware efficiently. The behavior patterns of malware are generated with Multiple Sequence Alignment (MSA) of API call sequences of malware. Consequently, we defined these behavior patterns as a “feature-chain” of malware for the analytical purpose. The initial generation of the feature-chain consists of extracting API call sequences with API hooking library, classifying malware samples by the similar behavior, and making the representative sequences from the MSA results. The detection mechanism of numerous malware is performed by measuring similarity between API call sequence of a target process (suspicious executables) and feature-chain of malware. By comparing with other existing methods, we proved the effectiveness of our proposed method based on Longest Common Subsequence (LCS) algorithm. Also we evaluated that our method outperforms other antivirus systems with 2.55 times in detection rate and 1.33 times in accuracy rate for malware detection.

Out-of-order superscalar processors have high performance but consume a large amount of energy for dynamic instruction scheduling. We propose a front-end execution architecture (FXA) for improving the energy efficiency of out-of-order superscalar processors. FXA has two execution units: an out-of-order execution unit (OXU) and an in-order execution unit (IXU). The OXU is the execution core of a common out-of-order superscalar processor. In contrast, the IXU consists only of functional units and a bypass network only. The IXU is placed at the processor front end and executes instructions in order. The IXU functions as a filter for the OXU. Fetched instructions are first fed to the IXU, and the instructions are executed in order if they are ready to execute. The instructions executed in the IXU are removed from the instruction pipeline and are not executed in the OXU. The IXU does not include dynamic scheduling logic, and thus its energy consumption is low. Evaluation results show that FXA can execute more than 50% of the instructions by using IXU, thereby making it possible to shrink the energy-consuming OXU without incurring performance degradation. As a result, FXA achieves both high performance and low energy consumption. We evaluated FXA and compared it with conventional out-of-order/in-order superscalar processors after ARM big.LITTLE architecture. The results show that FXA achieves performance improvements of 7.4% on geometric mean in SPECCPU INT 2006 benchmark suite relative to a conventional superscalar processor (big), while reducing the energy consumption by 17% in the entire processor. The performance/energy ratio (the inverse of the energy-delay product) of FXA is 25% higher than that of a conventional superscalar processor (big) and 27% higher than that of a conventional in-order superscalar processor (LITTLE).

The recent increase in the use of intelligent devices such as smartphones has enhanced the relationship between daily human behavior sensing and useful applications in ubiquitous computing. This paper proposes a novel method inspired by personal sensing technologies for collecting and visualizing road accessibility at lower cost than traditional data collection methods. To evaluate the methodology, we recorded outdoor activities of nine wheelchair users for approximately one hour each by using an accelerometer on an iPod touch and a camcorder, gathered the supervised data from the video by hand, and estimated the wheelchair actions as a measure of street level accessibility in Tokyo. The system detected curb climbing, moving on tactile indicators, moving on slopes, and stopping, with F-scores of 0.63, 0.65, 0.50, and 0.91, respectively. In addition, we conducted experiments with an artificially limited number of training data to investigate the number of samples required to estimate the target.

Distributed compressed video sensing (DCVS), combining advantages of compressed sensing and distributed video coding, is developed as a novel and powerful system to get an encoder with low complexity. Nevertheless, it is still unclear how to explore the method to achieve an effective video recovery through utilizing realistic signal characteristics as much as possible. Based on this, we present a novel spatiotemporal dictionary learning (DL) based reconstruction method for DCVS, where both the DL model and the l1-analysis based recovery with correlation constraints are included in the minimization problem to achieve the joint optimization of sparse representation and signal reconstruction. Besides, an alternating direction method with multipliers (ADMM) based numerical algorithm is outlined for solving the underlying optimization problem. Simulation results demonstrate that the proposed method outperforms other methods, with 0.03-4.14 dB increases in PSNR and a 0.13-15.31 dB gain for non-key frames.

Local spatio-temporal features are popular in the human action recognition task. In practice, they are usually coupled with a feature encoding approach, which helps to obtain the video-level vector representations that can be used in learning and recognition. In this paper, we present an efficient local feature encoding approach, which is called Approximate Sparse Coding (ASC). ASC computes the sparse codes for a large collection of prototype local feature descriptors in the off-line learning phase using Sparse Coding (SC) and look up the nearest prototype's precomputed sparse code for each to-be-encoded local feature in the encoding phase using Approximate Nearest Neighbour (ANN) search. It shares the low dimensionality of SC and the high speed of ANN, which are both desired properties for a local feature encoding approach. ASC has been excessively evaluated on the KTH dataset and the HMDB51 dataset. We confirmed that it is able to encode large quantity of local video features into discriminative low dimensional representations efficiently.

The recent popularization of social network services (SNSs), such as YouTube, Dailymotion, and Facebook, enables people to easily publish their personal videos taken with mobile cameras. However, at the same time, such popularity has raised a new problem: video privacy. In such social videos, the privacy of people, i.e., their appearances, must be protected, but naively obscuring all people might spoil the video content. To address this problem, we focus on videographers' capture intentions. In a social video, some persons are usually essential for the video content. They are intentionally captured by the videographers, called intentionally captured persons (ICPs), and the others are accidentally framed-in (non-ICPs). Videos containing the appearances of the non-ICPs might violate their privacy. In this paper, we developed a system called BEPS, which adopts a novel conditional random field (CRF)-based method for ICP detection, as well as a novel approach to obscure non-ICPs and preserve ICPs using background estimation. BEPS reduces the burden of manually obscuring the appearances of the non-ICPs before uploading the video to SNSs. Compared with conventional systems, the following are the main advantages of BEPS: (i) it maintains the video content, and (ii) it is immune to the failure of person detection; false positives in person detection do not violate privacy. Our experimental results successfully validated these two advantages.

In this letter, we propose a novel wear leveling technique we call Hidden cold block-aware Wear Leveling (HaWL) using a bit-set threshold. HaWL prolongs the lifetime of flash memory devices by using a bit array table in wear leveling. The bit array table saves the histories of block erasures for a period and distinguishes cold blocks from all blocks. In addition, HaWL can reduce the size of the bit array table by using a one-to-many mode, where one bit is related to many blocks. Moreover, to prevent degradation of wear leveling in the one-to-many mode, HaWL uses bit-set threshold (BST) and increases the accuracy of the cold block information. The performance results illustrate that HaWL prolongs the lifetime of flash memory by up to 48% compared with previous wear leveling techniques in our experiments.

Storage-Class Memory (SCM) and NAND flash hybrid Solid-State Drive (SSD) has advantages of high performance and low power consumption compared with NAND flash only SSD. In this paper, first, three SSD configurations are investigated. Three different SCMs are used with 0.1 µs, 1 µs and 10 µs read/write latencies, respectively, and the required SCM/NAND flash capacity ratios are analyzed to maintain the same SSD performance. Next, by using the three SSD configurations, the variation of SSD reliability, performance and cost are analyzed by changing error correction strengths. The SSD reliability of acceptable SCM and NAND flash Bit Error Rates (BERs) is limited by achieving specified SSD performance with error correction, and/or limited by SCM and NAND flash parity size and SSD cost. Lastly, the SSD replacement cost is also analyzed by considering the limitation of NAND flash write/erase cycles. The purpose of this paper is to provide a design guideline for obtaining high performance, highly reliable and cost-effective SCM/NAND hybrid structure SSD with ECC.

It is crucial to promote interdisciplinary research and recommend collaborators from different research fields via academic database analysis. This paper addresses a problem to characterize researchers' interests with a set of diverse research topics found in a large-scale academic database. Specifically, we first use latent Dirichlet allocation to extract topics as distributions over words from a training dataset. Then, we convert the textual features of a researcher's publications to topic vectors, and calculate the centroid of these vectors to summarize the researcher's interest as a single vector. In experiments conducted on CiNii Articles, which is the largest academic database in Japan, we show that the extracted topics reflect the diversity of the research fields in the database. The experiment results also indicate the applicability of the proposed topic representation to the author disambiguation problem.

Safety is the foremost requirement of avionics systems on aircraft. So far, avionics systems have evolved into an integrated system, i.e., integrated avionics system, and the derivative functions occur when the avionics systems are upgraded. However, the traditional safety analysis method is insufficient to be utilized in upgraded avionics systems due to these derivative functions. In this letter, a safety evaluation scheme is proposed to quantitatively evaluate the safety of the upgraded avionics systems. All the functions including the derivative functions can be traced and covered. Meanwhile, a set of safety issues based on different views is established to evaluate the safety capability from three layers, i.e., the mission layer, function layer and resource layer. The proposed scheme can be considered as an efficient scheme in the safety validation and verification in the upgraded avionics systems.