The human visual system exhibits a characteristic known as the Helmholtz-Kohlrausch (H-K) effect: even if the hue and the lightness retain the same values, the actual lightness (perceived lightness) changes with changes in the color saturation. Quantification of this effect is expected to be useful for the future development and evaluation of high-quality displays. We have been studying the H-K effect in natural images projected by LED projectors, which play important roles in practical uses. To verify the effectiveness of the determinations of the H-K effect for natural images, we have performed a subjective-evaluation experiment by method of adjustment for natural images and compared the experimental values with values calculated from extended form of Nayatani's equation to apply to natural images. In general, we found a high correlation between the two, although there was a low correlation for some images. Therefore, we obtained a correction function derived from the subjective evaluation experiment value of 108 color (hue: 12 × saturation: 3 × lightness: 3) patterns and have applied it to estimate the equation H-K effect.

In the context of Cyber-Physical System (CPS), analyzing the real world data accumulated in cyberspace would improve the efficiency and productivity of various social systems. Towards establishing data-driven society, it is desired to share data safely and smoothly among multiple services. In this paper, we propose a scheme that services authenticate users using information registered on a blockchain. We show that the proposed scheme has resistance to tampering and a spoofing attack.

For the multi-objective time series search problem, Hasegawa and Itoh [Theoretical Computer Science, Vol.78, pp.58-66, 2018] presented the best possible online algorithm balanced price policy for any monotone function f:Rk→R. Specifically the competitive ratio with respect to the monotone function f(c1,...,ck)=(c1+…+ck)/k is referred to as the arithmetic mean component competitive ratio. Hasegawa and Itoh derived the explicit representation of the arithmetic mean component competitive ratio for k=2, but it has not been known for any integer k≥3. In this paper, we derive the explicit representations of the arithmetic mean component competitive ratio for k=3 and k=4, respectively. On the other hand, we show that it is computationally difficult to derive the explicit representation of the arithmetic mean component competitive ratio for arbitrary integer k in a way similar to the cases for k=2, 3, and 4.

It is not easy for a student to present a question or comment to the lecturer and other students in large classes. This paper introduces a new audience presentation system (APS), which creates slide presentations of students' mobile responses in the classroom. Experimental surveys demonstrate the utility of this APS for classroom interactivity.

In this paper, we consider the relationship between human preference and brain activity, especially pulse wave information using NIRS. First of all, we extracted the information of on pulse wave from the Hb changes signal of NIRS. By using the FFT to the Hb signals, we found out the 2-nd peak of power spectrum that is implying the frequency information of the pulse wave. The frequency deviation of 2-nd peak may have some information about the change of brain activity, it is associated with the human preference for viewing the significant image content.

Information-Centric Networking (ICN) can offer rich functionalities to the network, e.g, in-network caching, and name-based forwarding. Incremental deployment of ICN is a key challenge that enable smooth migration from current IP network to ICN. We can say that Network Function Virtualization (NFV) must be one of the key technologies to achieve this deployment because of its flexibility to support new network functions. However, when we consider the ICN deployment with NFV, there exist two performance issues, processing delay of name-based forwarding and computational overhead of virtual machine. In this paper we proposed a NFV infrastructure-assisted ICN packet forwarding by integrating the name look-up to the Open vSwitch. The contributions are twofold: 1) First, we provide the novel name look-up scheme that can forward ICN packets without costly longest prefix match searching. 2) Second, we design the ICN packet forwarding scheme that integrates the partial name look-up into the virtualization infrastructure to mitigate computation overhead.

We propose a method for preventing smartphone theft when the owner dozes off. The owner of the smartphone wears a wristwatch type device that has an acceleration sensor and a vibration mode. This device detects when the owner dozes off. When the acceleration sensor in the smartphone detects an accident while dozing, the device vibrates. We implemented this function and tested its usefulness.

Boolean functions used in the filter model of stream ciphers should have balancedness, large nonlinearity, optimal algebraic immunity and high algebraic degree. Besides, one more criterion called strict avalanche criterion (SAC) can be also considered. During the last fifteen years, much work has been done to construct balanced Boolean functions with optimal algebraic immunity. However, none of them has the SAC property. In this paper, we first present a construction of balanced Boolean functions with SAC property by a slight modification of a known method for constructing Boolean functions with SAC property and consider the cryptographic properties of the constructed functions. Then we propose an infinite class of balanced functions with optimal algebraic immunity and SAC property in odd number of variables. This is the first time that such kind of functions have been constructed. The algebraic degree and nonlinearity of the functions in this class are also determined.

In this paper, we propose a Pyramid Predictive Attention Network (PPAN) for medical image segmentation. In the medical field, the size of dataset generally restricts the performance of deep CNN and deploying the trained network with gross parameters into the terminal device with limited memory is an expectation. Our team aims to the future home medical diagnosis and search for lightweight medical image segmentation network. Therefore, we designed PPAN mainly made of Xception blocks which are modified from DeepLab v3+ and consist of separable depthwise convolutions to speed up the computation and reduce the parameters. Meanwhile, by utilizing pyramid predictions from each dimension stage will guide the network more accessible to optimize the training process towards the final segmentation target without degrading the performance. IoU metric is used for the evaluation on the test dataset. We compared our designed network performance with the current state of the art segmentation networks on our RGB tongue dataset which was captured by the developed TIAS system for tongue diagnosis. Our designed network reduced 80 percentage parameters compared to the most widely used U-Net in medical image segmentation and achieved similar or better performance. Any terminal with limited storage which is needed a segment of RGB image can refer to our designed PPAN.

Network function virtualization (NFV) achieves the flexibility of network service provisioning by using virtualization technology. However, NFV is exposed to a serious security threat known as cross-VM cache timing attacks. In this letter, we look into real security impacts on network virtualization. Specifically, we present two kinds of practical cache timing attacks on virtualized firewalls and routers. We also propose some countermeasures to mitigate such attacks on virtualized network functions.

Traceability links between software artifacts can assist in several software development tasks. There are some automatic traceability recovery methods that help with managing the massive number of software artifacts and their relationships, but they do not work well for software artifacts whose descriptions are different in terms of language or abstraction level. To overcome these weakness, we propose the Connecting Links Method (CLM), which recovers transitive traceability links between two artifacts by intermediating a third artifact. In order to apply CLM for general use without limitation in terms of software artifact type, we have designed a standardized method to calculate the relation score of transitive traceability links using the scores of direct traceability links between three artifacts. Furthermore, we propose an improvement of CLM by considering software version. We evaluated CLM by applying it to three software products and found that it is more effective for software artifacts whose language type or vocabulary are different compared to previous methods using textual similarity.

In order to evaluate the vehicle detection method, it is necessary to know the correct vehicle position considered as “ground truth”. We propose indices considering subjective evaluation in vehicle detection utilizing IoU. Subjective evaluation experiments were carried out with respect to misregistration from ground truth in vehicle detection.

Upcoming mood prediction plays an important role in different topics such as bipolar depression disorder in psychology and quality-of-life and recommendations on health-related quality of life research. The mood in this study is defined as the general emotional state of a user. In contrast to emotions which is more specific and varying within a day, the mood is described as having either a positive or negative valence[1]. We propose an autonomous system that predicts the upcoming user mood based on their online activities over cyber, social and physical spaces without using extra-devices and sensors. Recently, many researchers have relied on online social networks (OSNs) to detect user mood. However, all the existing works focused on inferring the current mood and only few works have focused on predicting the upcoming mood. For this reason, we define a new goal of predicting the upcoming mood. We, first, collected ground truth data during two months from 383 subjects. Then, we studied the correlation between extracted features and user's mood. Finally, we used these features to train two predictive systems: generalized and personalized. The results suggest a statistically significant correlation between tomorrow's mood and today's activities on OSNs, which can be used to develop a decent predictive system with an average accuracy of 70% and a recall of 75% for the correlated users. This performance was increased to an average accuracy of 79% and a recall of 80% for active users who have more than 30 days of history data. Moreover, we showed that, for non-active users, referring to a generalized system can be a solution to compensate the lack of data at the early stage of the system, but when enough data for each user is available, a personalized system is used to individually predict the upcoming mood.

The Gabidulin-based locally repairable code (LRC) construction by Silberstein et al. is an important example of distance optimal (r,δ)-LRCs. Its distance optimality has been further shown to cover the case of multiple (r,δ)-locality, where the (r,δ)-locality constraints are different among different symbols. However, the optimality only holds under the ordered (r,δ) condition, where the parameters of the multiple (r,δ)-locality satisfy a specific ordering condition. In this letter, we show that Gabidulin-based LRCs are still distance optimal even without the ordered (r,δ) condition.

Biomedical event extraction is an important and challenging task in Information Extraction, which plays a key role for medicine research and disease prevention. Most of the existing event detection methods are based on shallow machine learning methods which mainly rely on domain knowledge and elaborately designed features. Another challenge is that some crucial information as well as the interactions among words or arguments may be ignored since most works treat words and sentences equally. Therefore, we employ a Bidirectional Long Short Term Memory (BLSTM) neural network for event extraction, which can skip handcrafted complex feature extraction. Furthermore, we propose a multi-level attention mechanism, including word level attention which determines the importance of words in a sentence, and the sentence level attention which determines the importance of relevant arguments. Finally, we train dependency word embeddings and add sentence vectors to enrich semantic information. The experimental results show that our model achieves an F-score of 59.61% on the commonly used dataset (MLEE) of biomedical event extraction, which outperforms other state-of-the-art methods.

In this paper, we consider the problem of enumerating spanning subgraphs with high edge-connectivity of an input graph. Such subgraphs ensure multiple routes between two vertices. We first present an algorithm that enumerates all the 2-edge-connected spanning subgraphs of a given plane graph with n vertices. The algorithm generates each 2-edge-connected spanning subgraph of the input graph in O(n) time. We next present an algorithm that enumerates all the k-edge-connected spanning subgraphs of a given general graph with m edges. The algorithm generates each k-edge-connected spanning subgraph of the input graph in O(mT) time, where T is the running time to check the k-edge-connectivity of a graph.

In this paper, we propose a new routing protocol for named data networking applied to ad hoc networks. We suppose a type of ad hoc networks that advertise versatile information in public spaces such as shopping mall and museum. In this kind of networks, information providers prepare fixed nodes, and users are equipped with mobile terminals. So, we adopt a hybrid approach where a proactive routing is used in the producer side network and a reactive routing is used in the consumer side network. Another feature of the proposed protocol is that only the name prefix advertisement is focused on in the proactive routing. The result of performance evaluation focusing on the communication overhead shows that our proposal has a moderate overhead both for routing control messages and Interest packets compared with some of conventional NDN based ad hoc routing mechanisms proposed so far.

In this paper, we propose a packet loss detection mechanism called Interest ACKnowledgement (ACK). Interest ACK provides information on the history of successful Interest packet receptions at a repository (i.e., content provider); this information is conveyed to the corresponding entity (i.e., content consumer) via the header of Data packets. Interest ACKs enable the entity to quickly and accurately detect Interest and Data packet losses in the network. We conduct simulations to investigate the effectiveness of Interest ACKs under several scenarios. Our results show that Interest ACKs are effective for improving the adaptability and stability of CCN with window-based flow control and that packet losses at the repository can be reduced by 10%-20%. Moreover, by extending Interest ACK, we propose a lossy link detection mechanism called LLD-IA (Lossy Link Detection with Interest ACKs), which is a mechanism for an entity to estimate the link where the packet was discarded in a network. Also, we show that LLD-IA can effectively detect links where packets were discarded under moderate packet loss ratios through simulation.

Urban area suffers severe multipath effects due to its complex infrastructure environment and sector antenna is a popular choice as a base station antenna in those areas. Within sector antennas, omni cell antenna is utilized as supporting antenna to cover low reception areas between them. This paper proposes a slant 45° dual polarized omnidirectional antenna to operate as the omni cell antenna in those environments. The frequency band covers the IMT band, ranging from 1920MHz to 2170MHz with directivity focusing in horizontal plane. The antenna structure consists of a loop slot antenna array as excitation element which is placed inside a cylindrical slot antenna as parasitic element. Good performance is achieved in both S-parameter and directivity results, with a gain of more than 4 dBi and a gain difference of less than 1.5dB. The measurement results also agree well with the simulation results and the final design confirms that the proposed antenna works effectively as a slant ±45 ° dual polarized omnidirectional antenna.

Accurate segmentation of the region in the iris picture has a crucial influence on the reliability of the recognition system. In this letter, we present an end to end deep neural network based on U-Net. It uses dense connection blocks to replace the original convolutional layer, which can effectively improve the reuse rate of the feature layer. The proposed method takes U-net's skip connections to combine the same-scale feature maps from the upsampling phase and the downsampling phase in the upsampling process (merge layer). In the last layer of downsampling, it uses dilated convolution. The dilated convolution balances the iris region localization accuracy and the iris edge pixel prediction accuracy, further improving network performance. The experiments running on the Casia v4 Interval and IITD datasets, show that the proposed method improves segmentation performance.