Information leakage in Wyner's wiretap channel model is usually defined as the mutual information between the secret message and the eavesdropper's received signal. We define a new quantity called “conditional information leakage given the eavesdropper's received signals,” which expresses the amount of information that an eavesdropper gains from his/her received signal. A benefit of introducing this quantity is that we can develop a fast algorithm for computing the conditional information leakage, which has linear complexity in the code length n, while the complexity for computing the usual information leakage is exponential in n. Validity of such a conditional information leakage as a security criterion is confirmed by studying the cases of binary symmetric channels and binary erasure channels.

Aiming at the problems of traditional algorithms that require high prior knowledge and weak timeliness, this paper proposes an emergency communication network topology planning method based on deep reinforcement learning. Based on the characteristics of the emergency communication network, and drawing on chess, we map the node layout and topology planning problems in the network planning to chess game problems; The two factors of network coverage and connectivity are considered to construct the evaluation criteria for network planning; The method of combining Monte Carlo tree search and self-game is used to realize network planning sample data generation, and the network planning strategy network and value network structure based on residual network are designed. On this basis, the model was constructed and trained based on Tensorflow library. Simulation results show that the proposed planning method can effectively implement intelligent planning of network topology, and has excellent timeliness and feasibility.

Bluetooth is a common wireless technology that is widely used as a connection medium between various consumer electronic devices. The receivers mostly adopt the Viterbi algorithm to improve a bit error rate performance but are hampered by heavy hardware complexity and computational load due to a coherent detection and searching for the unknown modulation index. To address these challenges, a non-coherent maximum likelihood estimation detector with an eight-state Viterbi is proposed for Gaussian frequency-shift keying symbol detection against an irrational modulation index, without any knowledge of prior information or assumptions. The simulation results showed an improvement in the performance compared to other ideal approaches.

This paper presents new key correlations of the keystream bytes generated from RC4 and their application to plaintext recovery on WPA-TKIP. We first observe new key correlations between two bytes of the RC4 key pairs and a keystream byte in each round, and provide their proofs. We refer to these correlations as iterated RC4 key correlations since two bytes of the RC4 key pairs are iterated every 16 rounds. We then extend the existing attacks by Isobe et al. at FSE 2013 and AlFardan et al. at USENIX Security 2013, 0and finally propose an efficient attack on WPA-TKIP. We refer to the proposed attack as chosen plaintext recovery attack (CPRA) since it chooses the best approach for each byte from a variety of the existing attacks. In order to recover the first 257 bytes of a plaintext on WPA-TKIP with success probability of at least 90%, CPRA requires approximately 230 ciphertexts, which are approximately half the number of ciphertexts for the existing attack by Paterson et al. at FSE 2014.

Incident ticket classification plays an important role in the complex system maintenance. However, low classification accuracy will result in high maintenance costs. To solve this issue, this paper proposes a fuzzy output support vector machine (FOSVM) based incident ticket classification approach, which can be implemented in the context of both two-class SVMs and multi-class SVMs such as one-versus-one and one-versus-rest. Our purpose is to solve the unclassifiable regions of multi-class SVMs to output reliable and robust results by more fine-grained analysis. Experiments on both benchmark data sets and real-world ticket data demonstrate that our method has better performance than commonly used multi-class SVM and fuzzy SVM methods.

In this study, we investigate fundamental trade-off among identification, secrecy, template, and privacy-leakage rates in biometric identification system. Ignatenko and Willems (2015) studied this system assuming that the channel in the enrollment process of the system is noiseless and they did not consider the template rate. In the enrollment process, however, it is highly considered that noise occurs when bio-data is scanned. In this paper, we impose a noisy channel in the enrollment process and characterize the capacity region of the rate tuples. The capacity region is proved by a novel technique via two auxiliary random variables, which has never been seen in previous studies. As special cases, the obtained result shows that the characterization reduces to the one given by Ignatenko and Willems (2015) where the enrollment channel is noiseless and there is no constraint on the template rate, and it also coincides with the result derived by Günlü and Kramer (2018) where there is only one individual.

We propose a video authentication scheme to verify whether a given video file is recorded by a camera device or touched by a video editing tool. The proposed scheme prepares software characteristics of camera devices and video editing tools in advance, and compares them with the metadata of the given video file. Through practical implementation, we show that the proposed scheme has benefits of fast analysis time, high accuracy and full automation.

With the spread of high-performance sensors and social network services (SNS) and the remarkable advances in machine learning technologies, fake media such as fake videos, spoofed voices, and fake reviews that are generated using high-quality learning data and are very close to the real thing are causing serious social problems. We launched a research project, the Media Clone (MC) project, to protect receivers of replicas of real media called media clones (MCs) skillfully fabricated by means of media processing technologies. Our aim is to achieve a communication system that can defend against MC attacks and help ensure safe and reliable communication. This paper describes the results of research in two of the five themes in the MC project: 1) verification of the capability of generating various types of media clones such as audio, visual, and text derived from fake information and 2) realization of a protection shield for media clones' attacks by recognizing them.

Pseudorandom sequences with low autocorrelation magnitude play important roles in various environments. Let N be a prime with N=Mf+1, where M and f are positive integers. A new method to construct M-sequences of period 4N is given. We show that these new sequences have low autocorrelation magnitude.

Recently, local features computed using convolutional neural networks (CNNs) show good performance to image retrieval. The local convolutional features obtained by the CNNs (LC features) are designed to be translation invariant, however, they are inherently sensitive to rotation perturbations. This leads to miss-judgements in retrieval tasks. In this work, our objective is to enhance the robustness of LC features against image rotation. To do this, we conduct a thorough experimental evaluation of three candidate anti-rotation strategies (in-model data augmentation, in-model feature augmentation, and post-model feature augmentation), over two kinds of rotation attack (dataset attack and query attack). In the training procedure, we implement a data augmentation protocol and network augmentation method. In the test procedure, we develop a local transformed convolutional (LTC) feature extraction method, and evaluate it over different network configurations. We end up a series of good practices with steady quantitative supports, which lead to the best strategy for computing LC features with high rotation invariance in image retrieval.

In this paper, a compact microwave push-push oscillator based on a resonant tunneling diode (RTD) has been fabricated and demonstrated. A symmetrical spiral inductor structure has been used in order to reduce a chip area. The designed symmetric inductor is integrated into the InP-based RTD monolithic microwave integrated circuit (MMIC) technology. The circuit occupies a compact active area of 0.088 mm2 by employing symmetric inductor. The fabricated RTD oscillator shows an extremely low DC power consumption of 87 µW at an applied voltage of 0.47 V with good figure-of-merit (FOM) of -191 dBc/Hz at an oscillation frequency of 27 GHz. This is the first implementation as the RTD push-push oscillator with the symmetrical spiral inductor.

There are some technologies like QR codes to obtain digital information from printed matters. Digital watermarking is one of such techniques. Compared with other techniques, digital watermarking is suitable for adding information to images without spoiling their design. For such purposes, digital watermarking methods for printed matters using detection markers or image registration techniques for detecting watermarked areas are proposed. However, the detection markers themselves can damage the appearance such that the advantages of digital watermarking, which do not lose design, are not fully utilized. On the other hand, methods using image registration techniques are not able to work for non-registered images. In this paper, we propose a novel digital watermarking method using deep learning for the detection of watermarked areas instead of using detection markers or image registration. The proposed method introduces a semantic segmentation based on deep learning model for detecting watermarked areas from printed matters. We prepare two datasets for training the deep learning model. One is constituted of geometrically transformed non-watermarked and watermarked images. The number of images in this dataset is relatively large because the images can be generated based on image processing. This dataset is used for pre-training. The other is obtained from actually taken photographs including non-watermarked or watermarked printed matters. The number of this dataset is relatively small because taking the photographs requires a lot of effort and time. However, the existence of pre-training allows a fewer training images. This dataset is used for fine-tuning to improve robustness for print-cam attacks. In the experiments, we investigated the performance of our method by implementing it on smartphones. The experimental results show that our method can carry 96 bits of information with watermarked printed matters.

The phaseless antenna measurement technique is advantageous for high-frequency near-field measurements in which the uncertainty of the measured phase is a problem. In the phaseless measurement, which is expected to be used in the frequency band with a short wavelength, a slight positional deviation error of the probe greatly deteriorates the measurement result. This paper proposes a phase retrieval method that can compensate the measurement errors caused by misalignment of a probe and its jig. And this paper proposes a far-field estimation method by phase resurrection that incorporated the compensation techniques. We find that the positioning errors are due to the random errors occurring at each measurement point because of minute vibrations of the probe; in addition, we determine that the stationary depth errors occurring at each measurement surface as errors caused by improper setting of the probe jig. The random positioning error is eliminated by adding a low-pass filter in wavenumber space, and the depth positioning error is iteratively compensated on the basis of the relative residual obtained in each plane. The validity of the proposed method is demonstrated by estimating the far-field patterns using the results from numerical simulations, and is also demonstrated using measurement data with probe-positioning error. The proposed method can reduce the probe-positioning error and improve the far-field estimation accuracy by more over than 10 dB.

In this paper, we investigate the subcarriers combination selection and the subcarriers activation of OFDM-IM system. Firstly, we propose an algorithm to solve the problem of subcarriers combination selection based on the transmission rate and diversity gain. Secondly, we ropose a more concise algorithm to solve the problem of power allocation and carrier combination activation probability under this combination to improve system capacity. Finally, we verify the robustness of the algorithm and the superiority of the system scheme in the block error rate (BLER) and system capacity by numerical results.

This paper proposes a salient chromagram by removing local trend to improve cover song identification accuracy. The proposed salient chromagram emphasizes tonal contents of music, which are well-preserved between an original song and its cover version, while reducing the effects of timber difference. We apply the proposed salient chromagram to the sequence-alignment based cover song identification. Experiments on two cover song datasets confirm that the proposed salient chromagram improves the cover song identification accuracy.

The transition dynamics of a multistable tunnel-diode oscillator is characterized for modulating amplitude of outputted oscillatory signal. The base oscillator possesses fixed-point and limit-cycle stable points for a unique bias voltage. Switching these two stable points by external signal can render an efficient method for modulation of output amplitude. The time required for state transition is expected to be dominated by the aftereffect of the limiting point. However, it is found that its influence decreases exponentially with respect to the amplitude of external signal. Herein, we first describe numerically the pulse generation scheme with the transition dynamics of the oscillator and then validate it with several time-domain measurements using a test circuit.

The term “flash crowd” describes a situation in which a large number of users access a Web service simultaneously. Flash crowds, in particular, constitute a critical problem in e-commerce applications because of the potential for enormous economic damage as well as difficulty in management. Flash crowds can become more serious depending on users' behavior. When a flash crowd occurs, the delay in server response may cause users to retransmit their requests, thereby adding to the server load. In the present paper, we propose to use the psychological factors of the users for flash crowd mitigation. We aim to analyze changes in the user behavior by presenting feedback information. To evaluate the proposed method, we performed subject experiments and stress tests. Subject experiments showed that, by providing feedback information, the average number of request retransmissions decreased from 1.33 to 0.09, and the subjects that abandoned the service decreased from 81% to 0%. This confirmed that feedback information is effective in influencing user behavior in terms of abandonment and retransmission of requests. Stress tests showed that the average number of retransmissions decreased by 41%, and the proportion of abandonments decreased by 30%. These results revealed that the presentation of feedback information could mitigate the damage caused by flash crowds in real websites, although the effect is limited. The proposed method can be used in conjunction with conventional methods to handle flash crowds.

The hardness in solving the shortest vector problem (SVP) is a fundamental assumption for the security of lattice-based cryptographic algorithms. In 2010, Micciancio and Voulgaris proposed an algorithm named the Gauss Sieve, which is a fast and heuristic algorithm for solving the SVP. Schneider presented another algorithm named the Ideal Gauss Sieve in 2011, which is applicable to a special class of lattices, called ideal lattices. The Ideal Gauss Sieve speeds up the Gauss Sieve by using some properties of the ideal lattices. However, the algorithm is applicable only if the dimension of the ideal lattice n is a power of two or n+1 is a prime. Ishiguro et al. proposed an extension to the Ideal Gauss Sieve algorithm in 2014, which is applicable only if the prime factor of n is 2 or 3. In this paper, we first generalize the dimensions that can be applied to the ideal lattice properties to when the prime factor of n is derived from 2, p or q for two primes p and q. To the best of our knowledge, no algorithm using ideal lattice properties has been proposed so far with dimensions such as: 20, 44, 80, 84, and 92. Then we present an algorithm that speeds up the Gauss Sieve for these dimensions. Our experiments show that our proposed algorithm is 10 times faster than the original Gauss Sieve in solving an 80-dimensional SVP problem. Moreover, we propose a rotation-based Gauss Sieve that is approximately 1.5 times faster than the Ideal Gauss Sieve.

Knowledge distillation approaches can transfer information from a large network (teacher network) to a small network (student network) to compress and accelerate deep neural networks. This paper proposes a novel knowledge distillation approach called multi-knowledge distillation (MKD). MKD consists of two stages. In the first stage, it employs autoencoders to learn compact and precise representations of the feature maps (FM) from the teacher network and the student network, these representations can be treated as the essential of the FM, i.e., EFM. In the second stage, MKD utilizes multiple kinds of knowledge, i.e., the magnitude of individual sample's EFM and the similarity relationships among several samples' EFM to enhance the generalization ability of the student network. Compared with previous approaches that employ FM or the handcrafted features from FM, the EFM learned from autoencoders can be transferred more efficiently and reliably. Furthermore, the rich information provided by the multiple kinds of knowledge guarantees the student network to mimic the teacher network as closely as possible. Experimental results also show that MKD is superior to the-state-of-arts.

We propose a novel framework for integrating beginners' machine operational experiences with those of experts' to obtain a detailed task model. Beginners can provide valuable information for operation guidance and task design; for example, from the operations that are easy or difficult for them, the mistakes they make, and the strategy they tend to choose. However, beginners' experiences often vary widely and are difficult to integrate directly. Thus, we consider an operational experience as a sequence of hand-machine interactions at hotspots. Then, a few experts' experiences and a sufficient number of beginners' experiences are unified using two aggregation steps that align and integrate sequences of interactions. We applied our method to more than 40 experiences of a sewing task. The results demonstrate good potential for modeling and obtaining important properties of the task.