The effect of provision of “Neither-Good-Nor-Bad” (NGNB) information on the perceived trustworthiness of agents has been investigated in previous studies. The experimental results have revealed several conditions under which the provision of NGNB information works effectively to make users perceive greater trust of agents. However, the experiments in question were carried out in a situation in which a user is able to choose, with the agent's advice, one of a limited number of options. In practical problems, we are often at a loss as to which to choose because there are too many possible options and it is not easy to narrow them down. Furthermore, in the above-mentioned previous studies, it was easy to predict the size of profits that a user would obtain because its pattern was also limited. This prompted us, in this paper, to investigate the effect of provision of NGNB information on the users' trust of agents under conditions where it appears to the users that numerous options are available. Our experimental results reveal that an agent that reliably provides NGNB information tends to gain greater user trust in a situation where it appears to the users that there are numerous options and their consequences, and it is not easy to predict the size of profits. However, in contradiction to the previous study, the results in this paper also reveal that stable provision of NGNB information in the context of numerous options is less effective in a situation where it is harder to obtain larger profits.

In the situation where there are one sender and multiple receivers, a receiver selective opening (RSO) attack for an identity-based encryption (IBE) scheme considers adversaries that can corrupt some of the receivers and get their user secret keys and plaintexts. Security against RSO attacks for an IBE scheme ensures confidentiality of ciphertexts of uncorrupted receivers. In this paper, we formalize a definition of RSO security against chosen ciphertext attacks (RSO-CCA security) for IBE and propose the first RSO-CCA secure IBE schemes. More specifically, we construct an RSO-CCA secure IBE scheme based on an IND-ID-CPA secure IBE scheme and a non-interactive zero-knowledge proof system with unbounded simulation soundness and multi-theorem zero-knowledge. Through our generic construction, we obtain the first pairing-based and lattice-based RSO-CCA secure IBE schemes.

It is known that quasi-cyclic (QC) codes over the finite field Fq correspond to certain Fq[x]-modules. A QC code C is specified by a generator polynomial matrix G whose rows generate C as an Fq[x]-module. The reversed code of C, denoted by R, is the code obtained by reversing all codewords of C while the dual code of C is denoted by C⊥. We call C reversible, self-orthogonal, and self-dual if R = C, C⊥ ⊇ C, and C⊥ = C, respectively. In this study, for a given C, we find an explicit formula for a generator polynomial matrix of R. A necessary and sufficient condition for C to be reversible is derived from this formula. In addition, we reveal the relations among C, R, and C⊥. Specifically, we give conditions on G corresponding to C⊥ ⊇ R, C⊥ ⊆ R, and C = R = C⊥. As an application, we employ these theoretical results to the construction of QC codes with best parameters. Computer search is used to show that there exist various binary reversible self-orthogonal QC codes that achieve the upper bounds on the minimum distance of linear codes.

Pedestrian detection is a significant task in computer vision. In recent years, it is widely used in applications such as intelligent surveillance systems and automated driving systems. Although it has been exhaustively studied in the last decade, the occlusion handling issue still remains unsolved. One convincing idea is to first detect human body parts, and then utilize the parts information to estimate the pedestrians' existence. Many parts-based pedestrian detection approaches have been proposed based on this idea. However, in most of these approaches, the low-quality parts mining and the clumsy part detector combination is a bottleneck that limits the detection performance. To eliminate the bottleneck, we propose Discriminative Part CNN (DP-CNN). Our approach has two main contributions: (1) We propose a high-quality body parts mining method based on both convolutional layer features and body part subclasses. The mined part clusters are not only discriminative but also representative, and can help to construct powerful pedestrian detectors. (2) We propose a novel method to combine multiple part detectors. We convert the part detectors to a middle layer of a CNN and optimize the whole detection pipeline by fine-tuning that CNN. In experiments, it shows astonishing effectiveness of optimization and robustness of occlusion handling.

In ball games, acquiring skills to change the direction becomes necessary. For revealing the mechanism of skill acquisition in terms of the relevant field, it would be necessary to take an approach regarding players' cognition as well as body movements measurable from outside. In the phase of change-of-direction performance that this study focuses on, cognitive factors including the prediction of opposite players' movements and judgements of the situation have significance. The purpose of this study was to reveal cognitive transformation in the skill acquisition process for change-of-direction performance. The survey was conducted for three months from August 29 to November 28, 2020, and those surveyed were seven university freshmen belonging to women's basketball club of M University. The way to analyze verbal reports collected in order to explore the changes in the players' cognition is described in Sect.2. In Sect.3, we made a plot graph showing temporal changes in respective factors based on coding outcomes for verbal reports. Consequently, as cognitive transformation in the skill acquisition process for change-of-direction performance, four items such as (1) goal setting for skill acquisition, (2) experience of change in running direction, (3) experience of speed and acceleration, and (4) experience of the movement of lower extremities such as legs and hip joints were suggested as common cognitive transformation. In addition, cognitive transformation varied by the degree of skill acquisition for change-of-direction performance. It was indicated that paying too much attention to body feelings including the position of and shift in the center of gravity in the body posed an obstacle to the skill acquisition for change-of-direction performance.

We developed a mantle-cloak antenna by controlling the surface reactance of a dielectric-loaded dipole antenna. First, a mantle-cloak antenna with an assumed ideal metasurface sheet was designed, and band rejection characteristics were obtained by controlling the surface reactance of the mantle cloak. The variable range of the frequency spacing between the operating and stopband frequencies of the antenna was clarified by changing the value of the surface reactance. Next, a mantle-cloak antenna that uses vertical strip conductors was designed to clarify the characteristics and operating principle of the antenna. It was confirmed that the stopband frequency was 1130MHz, and the proposed antenna had a 36.3% bandwidth (|S11| ≤ -10dB) from 700 to 1010MHz. By comparing the |S11| characteristics and the input impedance characteristics of the proposed antenna with those of the dielectric-loaded antenna, the effect of the mantle cloak was confirmed. Finally, a prototype of the mantle-cloak antenna that uses vertical strip conductors was developed and measured to validate the simulation results. The measurement results were consistent with the simulation results.

The growing threat of Hardware Trojans (HT) in the System-on-Chips (SoC) industry has given way to the embedded systems researchers to propose a series of detection methodologies to identify and detect the presence of Trojan circuits or logics inside a host design in the various stages of the chip design and manufacturing process. Many state of the art works propose different techniques for HT detection among which the popular choice remains the Side-Channel Analysis (SCA) based methods that perform differential analysis targeting the difference in consumption of power, change in electromagnetic emanation or the delay in propagation of logic in various paths of the circuit. Even though the effectiveness of these methods are well established, the evaluation is carried out on simplistic models such as AES coprocessors and the analytical approaches used for these methods are limited by some statistical metrics such as direct comparison of EM traces or the T-test coefficients. In this paper, we propose two new detection methodologies based on Machine Learning algorithms. The first method consists in applying the supervised Machine Learning (ML) algorithms on raw EM traces for the classification and detection of HT. It offers a detection rate close to 90% and false negative smaller than 5%. In the second method, we propose an outlier/novelty algorithms based approach. This method combined with the T-test based signal processing technique, when compared with state-of-the-art, offers a better performance with a detection rate close to 100% and a false positive smaller than 1%. In different experiments, the false negative is nearly the same level than the false positive and for that reason the authors only show the false positive value on the results. We have evaluated the performance of our method on a complex target design: RISC-V generic processor. Three HTs with their corresponding sizes: 0.53%, 0.27% and 0.09% of the RISC-V processors are inserted for the experimentation. In this paper we provide elaborative details of our tests and experimental process for reproducibility. The experimental results show that the inserted HTs, though minimalistic, can be successfully detected using our new methodology.

Along with the fast development of blue economy, wireless communication in oceans has received extensive attention in recent years, and opportunistic networks without any aid from fixed infrastructure or centralized management are expected to play an important role in such highly dynamic environments. Here, link prediction can help nodes to select proper links for data forwarding to reduce transmission failure. The existing prediction schemes are mainly based on analytical models with no adaptability, and consider relatively simple and small terrestrial wireless networks. In this paper, we propose a new link prediction algorithm based on machine learning, which is composed of an extractor of convolutional layers and an estimator of long short-term memory to extract useful representations of time-series data and identify effective long-term dependencies. The experiments manifest that the proposed scheme is more effective and flexible compared with the other link prediction schemes.

This paper presents a driver status monitoring (DSM) system with body channel communication (BCC) technology to acquire the driver's physiological condition. Specifically, a conductive thread, the receiving electrode, is sewn to the surface of the seat so that the acquired signal can be continuously detected. As a signal transmission medium, body channel characteristics using the conductive thread electrode were investigated according to the driver's pose and the material of the driver's pants. Based on this, a BCC transceiver was implemented using an analog frequency modulation (FM) scheme to minimize the additional circuitry and system cost. We analyzed the heart rate variability (HRV) from the driver's electrocardiogram (ECG) and displayed the heart rate and Root Mean Square of Successive Differences (RMSSD) values together with the ECG waveform in real-time. A prototype of the DSM system with commercial-off-the-shelf (COTS) technology was implemented and tested. We verified that the proposed approach was robust to the driver's movements, showing the feasibility and validity of the DSM with BCC technology using a conductive thread electrode.

Android operating system occupies a high share in the mobile terminal market. It promotes the rapid development of Android applications (apps). However, the emergence of Android malware greatly endangers the security of Android smartphone users. Existing research works have proposed a lot of methods for Android malware detection, but they did not make the utilization of apps' functional category information so that the strong similarity between benign apps in the same functional category is ignored. In this paper, we propose an Android malware detection scheme based on the functional classification. The benign apps in the same functional category are more similar to each other, so we can use less features to detect malware and improve the detection accuracy in the same functional category. The aim of our scheme is to provide an automatic application functional classification method with high accuracy. We design an Android application functional classification method inspired by the hyperlink induced topic search (HITS) algorithm. Using the results of automatic classification, we further design a malware detection method based on app similarity in the same functional category. We use benign apps from the Google Play Store and use malware apps from the Drebin malware set to evaluate our scheme. The experimental results show that our method can effectively improve the accuracy of malware detection.

The usefulness and usability of existing knowledge graphs (KGs) are mostly limited because of the incompleteness of knowledge compared to the growing number of facts about the real world. Most existing ontology-based KG completion methods are based on the closed-world assumption, where KGs are fixed. In these methods, entities and relations are defined, and new entity information cannot be easily added. In contrast, in open-world assumptions, entities and relations are not previously defined. Thus there is a vast scope to find new entity information. Despite this, knowledge acquisition under the open-world assumption is challenging because most available knowledge is in a noisy unstructured text format. Nevertheless, Open Information Extraction (OpenIE) systems can extract triples, namely (head text; relation text; tail text), from raw text without any prespecified vocabulary. Such triples contain noisy information that is not essential for KGs. Therefore, to use such triples for the KG completion task, it is necessary to identify competent triples for KGs from the extracted triple set. Here, competent triples are the triples that can contribute to add new information to the existing KGs. In this paper, we propose the Competent Triple Identification (CTID) model for KGs. We also propose two types of feature, namely syntax- and semantic-based features, to identify competent triples from a triple set extracted by a state-of-the-art OpenIE system. We investigate both types of feature and test their effectiveness. It is found that the performance of the proposed features is about 20% better compared to that of the ReVerb system in identifying competent triples.

Generalized Feistel Network (GFN) is widely used in block ciphers. CLEFIA is one of the GFN type-2 block ciphers. CLEFIA employs Diffusion Switching Mechanism (DSM) in its diffusion layer. DSM improves CLEFIA's security by increasing its number of active S-boxes, which is an indicator of security against differential and linear cryptanalyses. However, two matrices in DSM increase implementational cost. In this paper, we pursue the research question whether it is possible to achieve the same security as original CLEFIA with only one matrix without overhead in hardware. Our idea to answer the research question is applying byte-shuffling technique to CLEFIA. Byte-shuffling is an operation to shuffle 8-bit bytes. On the other hand, traditional GFN ciphers rotate 32-bit or larger words in their permutation layer. Since implementation of byte-shuffling is considered as cost-free in hardware, it adds no overhead in comparison with word rotation. Byte-shuffling has numerous shuffle patterns whereas word rotation has a few patterns. In addition, security property varies among the shuffle patterns. So, we have to find the optimal shuffle pattern(s) on the way to pursue the research question. Although one way to find the optimal shuffle pattern is evaluating all possible shuffle patterns, it is impractical to evaluate them since the evaluation needs much time and computation. We utilize even-odd byte-shuffling technique to narrow the number of shuffle patterns to be searched. Among numerous shuffle patterns, we found 168 shuffle patterns as the optimal shuffle patterns. They achieved full diffusion in 5 rounds. This is the same security as original CLEFIA. They achieved enough security against differential and linear cryptanalyses at 13th and 14th round, respectively, by active S-box evaluations. It is just one and two rounds longer than original CLEFIA. However, it is three and two rounds earlier than CLEFIA without DSM.

Environmental power generation devices such as solar cells are used as power sources for IoT devices. Due to the large internal resistance of such power source, LSIs in the IoT devices may malfunction when the LSI operates at high speed, a large current flows, and the voltage drops. In this paper, a standard cell library of stacked structured cells is proposed to increase the delay of logic circuits within the range not exceeding the clock cycle, thereby reducing the maximum current of the LSIs. We show that the maximum power consumption of LSIs can be reduced without increasing the energy consumption of the LSIs.

To safeguard critical services and assets in a distributed environment, collaborative intrusion detection systems (CIDSs) are usually adopted to share necessary data and information among various nodes, and enhance the detection capability. For simplifying the network management, software defined networking (SDN) is an emerging platform that decouples the controller plane from the data plane. Intuitively, SDN can help lighten the management complexity in CIDSs, and a CIDS can protect the security of SDN. In practical implementation, trust management is an important approach to help identify insider attacks (or malicious nodes) in CIDSs, but the challenge is how to ensure the data integrity when evaluating the reputation of a node. Motivated by the recent development of blockchain technology, in this work, we design BlockCSDN — a framework of blockchain-based collaborative intrusion detection in SDN, and take the challenge-based CIDS as a study. The experimental results under both external and internal attacks indicate that using blockchain technology can benefit the robustness and security of CIDSs and SDN.

Neural network-based three-dimensional (3D) reconstruction methods have produced promising results. However, they do not pay particular attention to reconstructing detailed parts of objects. This occurs because the network is not designed to capture the fine details of objects. In this paper, we propose a network designed to capture both the coarse and fine details of objects to improve the reconstruction of the fine parts of objects.

Construction of multiple output functions is one of the most important problems in the design and analysis of stream ciphers. Generally, such a function has to be satisfied with several criteria, such as high nonlinearity, resiliency and high algebraic degree. But there are mutual restraints among the cryptographic parameters. Finding a way to achieve the optimization is always regarded as a hard task. In this paper, by using the disjoint linear codes and disjoint spectral functions, two classes of resilient multiple output functions are obtained. It has been proved that the obtained functions have high nonlinearity and high algebraic degree.

Object contour detection is a task of extracting the shape created by the boundaries between objects in an image. Conventional methods limit the detection targets to specific categories, or miss-detect edges of patterns inside an object. We propose a new method to represent a contour image where the pixel value is the distance to the boundary. Contour detection becomes a regression problem that estimates this contour image. A deep convolutional network for contour estimation is combined with stereo vision to detect unspecified object contours. Furthermore, thanks to similar inference targets and common network structure, we propose a network that simultaneously estimates both contour and disparity with fully shared weights. As a result of experiments, the multi-tasking network drew a good precision-recall curve, and F-measure was about 0.833 for FlyingThings3D dataset. L1 loss of disparity estimation for the dataset was 2.571. This network reduces the amount of calculation and memory capacity by half, and accuracy drop compared to the dedicated networks is slight. Then we quantize both weights and activations of the network to 3-bit. We devise a dedicated hardware architecture for the quantized CNN and implement it on an FPGA. This circuit uses only internal memory to perform forward propagation calculations, that eliminates high-power external memory accesses. This circuit is a stall-free pixel-by-pixel pipeline, and performs 8 rows, 16 input channels, 16 output channels, 3 by 3 pixels convolution calculations in parallel. The convolution calculation performance at the operating frequency of 250 MHz is 9 TOPs/s.

Exponential growth in data volumes has promoted widespread interest in data-selective adaptive algorithms. In a pioneering work, Diniz developed the data-selective least mean square (DS-LMS) algorithm, which is able to reduce specific quantities of computation data without compromising performance. Note however that the existing framework fails to consider the issue of impulse noise (IN), which can greatly undermine the benefits of reduced computation. In this letter, we present an error-based IN detection algorithm for implementation in conjunction with the DS-LMS algorithm. Numerical evaluations confirm the effectiveness of our proposed IN-tolerant DS-LMS algorithm.

Traffic congestion in road networks has been studied as the congestion game in game theory. In the existing work, the road usage by each agent was assumed to be static during the whole time horizon of the agent's travel, as in the classical congestion game. This assumption, however, should be reconsidered because each agent sequentially uses roads composing the route. In this paper, we propose a multi-agent distributed route selection scheme based on a gradient descent method considering the time-dependency among agents' road usage for vehicular networks. The proposed scheme first estimates the time-dependent flow on each road by considering the agents' probabilistic occupation under the first-in-first-out (FIFO) policy. Then, it calculates the optimal route choice probability of each route candidate using the gradient descent method and the estimated time-dependent flow. Each agent finally selects one route according to the optimal route choice probabilities. We first prove that the proposed scheme can exponentially converge to the steady-state at the convergence rate inversely proportional to the product of the number of agents and that of individual route candidates. Through simulations under a grid-like network and a real road network, we show that the proposed scheme can improve the actual travel time by 5.1% and 2.5% compared with the conventional static-flow based approach, respectively. In addition, we demonstrate that the proposed scheme is robust against incomplete information sharing among agents, which would be caused by its low penetration ratio or limited transmission range of wireless communications.

There exists a great demand for automatic anomaly detection in industrial world. The anomaly has been defined as a group of samples that rarely or never appears. Given a type of products, one has to collect numerous samples and train an anomaly detector. When one diverts a model trained with old types of products with sufficient inventory to the new type, one can detect anomalies of the new type before a production line is established. However, because of the definition of the anomaly, a typical anomaly detector considers the new type of products anomalous even if it is consistent with the standard. Given the above practical demand, this study propose a novel problem setting, few-shot anomaly detection, where an anomaly detector trained in source domains is adapted to a small set of target samples without full retraining. Then, we tackle this problem using a hierarchical probabilistic model based on deep learning. Our empirical results on toy and real-world datasets demonstrate that the proposed model detects anomalies in a small set of target samples successfully.