In the recent years, deep learning has achieved significant results in various areas of machine learning. Deep learning requires a huge amount of data to train a model, and data collection techniques such as web crawling have been developed. However, there is a risk that these data collection techniques may generate incorrect labels. If a deep learning model for image classification is trained on a dataset with noisy labels, the generalization performance significantly decreases. This problem is called Learning with Noisy Labels (LNL). One of the recent researches on LNL, called DivideMix [1], has successfully divided the dataset into samples with clean labels and ones with noisy labels by modeling loss distribution of all training samples with a two-component Mixture Gaussian model (GMM). Then it treats the divided dataset as labeled and unlabeled samples and trains the classification model in a semi-supervised manner. Since the selected samples have lower loss values and are easy to classify, training models are in a risk of overfitting to the simple pattern during training. To train the classification model without overfitting to the simple patterns, we propose to introduce consistency regularization on the selected samples by GMM. The consistency regularization perturbs input images and encourages model to outputs the same value to the perturbed images and the original images. The classification model simultaneously receives the samples selected as clean and their perturbed ones, and it achieves higher generalization performance with less overfitting to the selected samples. We evaluated our method with synthetically generated noisy labels on CIFAR-10 and CIFAR-100 and obtained results that are comparable or better than the state-of-the-art method.

In this paper, by using the properties of the cyclic Hadamard matrices of order 4t, an infinite class of (4t-1)-variable 2-resilient rotation symmetric Boolean functions is constructed, and the nonlinearity of the constructed functions are also studied. To the best of our knowledge, this is the first class of direct constructions of 2-resilient rotation symmetric Boolean functions. The spirit of this method is different from the known methods depending on the solutions of an equation system proposed by Du Jiao, et al. Several situations are examined, as the direct corollaries, three classes of (4t-1)-variable 2-resilient rotation symmetric Boolean functions are proposed based on the corresponding sequences, such as m sequences, Legendre sequences, and twin primes sequences respectively.

In this paper, we propose rate adaptation mechanisms for robust and low-latency video transmissions exploiting multiple access points (Multi-AP) wireless local area networks (WLANs). The Multi-AP video transmissions employ link-level broadcast and packet-level forward error correction (FEC) in order to realize robust and low-latency video transmissions from a WLAN station (STA) to a gateway (GW). The PHY (physical layer) rate and FEC rate play a key role to control trade-off between the achieved reliability and airtime (i.e., occupancy period of the shared channel) for Multi-AP WLANs. In order to finely control this trade-off while improving the transmitted video quality, the proposed rate adaptation controls PHY rate and FEC rate to be employed for Multi-AP transmissions based on the link quality and frame format of conveyed video traffic. With computer simulations, we evaluate and investigate the effectiveness of the proposed rate adaptation in terms of packet delivery rate (PDR), airtime, delay, and peak signal to noise ratio (PSNR). Furthermore, the quality of video is assessed by using the traffic encoded/decoded by the actual video encoder/decoder. All these results show that the proposed rate adaptation controls trade-off between the reliability and airtime well while offering the high-quality and low-latency video transmissions.

The 2020 International Conference on Emerging Technologies for Communications (ICETC2020) was held online on December 2nd—4th, 2020, and 213 research papers were accepted and presented in each session. It is expected that the accepted papers will contribute to the development and extension of research in multiple research areas. In this survey paper, all accepted research papers are classified into four research areas: Physical & Fundamental, Communications, Network, and Information Technology & Application, and then research papers are classified into each research topic. For each research area and topic, this survey paper briefly introduces the presented technologies and methods.

In this paper, we propose a novel centralized control method to handle multi-radio and terminal connections in an 802.11ax wireless LAN (802.11ax) mixed environment. The proposed control method can improve the throughput by applying 802.11ax Spatial Reuse in an environment hosting different terminal standards and mixed terminal communication quality. We evaluate the proposed control method by computer simulations assuming environments with mixed terminal standards, mixed communication quality, and both.

Recently, segment routing, which is a modern forwarding mechanism, and Topology Independent Loop-free Alternate, which is an IP fast-reroute method using segment routing, have been proposed and have begun to be applied to real networks. When a failure occurs in a network, TI-LFA quickly restores packet forwarding without waiting for other nodes to update their routing tables. It does so by using segment routing to forward sections that may cause loops in the rerouting path. However, determining the segment routing sections has a high computational cost because it requires computation for each destination. This paper therefore proposes an algorithm to determine the egress node that is the exit of the segment routing section for all destination nodes with only three shortest-path tree calculations. The evaluation results of the proposed algorithm showed that the average tunnel lengths are at most 2.0 to 2.2 hops regardless of the size of the network. I also showed that the computational complexity of the proposed algorithm is O(Nlog N).

As online social networks (OSNs) have become remarkably active, we often experience explosive user dynamics such as online flaming, which can significantly impact the real world. Since the rapidity with which online user dynamics propagates, countermeasures based on social analyses of the individuals who cause online flaming take too much time that timely measures cannot be taken. To consider immediate solutions without individuals' social analyses, a countermeasure technology for flaming phenomena based on the oscillation model, which describes online user dynamics, has been proposed. In this framework, the strength of damping to prevent online flaming was derived based on the wave equation of networks. However, the assumed damping strength was to be a constant independent of the frequency of user dynamics. Since damping strength may generally depend on frequency, it is necessary to consider such frequency dependence in user dynamics. In this paper, we derive the strength of damping required to prevent online flaming under the general condition that damping strength depends on the frequency of user dynamics. We also investigate the existence range of the Laplacian matrix's eigenvalues representing the OSN structure assumed from the real data of OSNs, and apply it to the countermeasure technology for online flaming.

TCP/IP, the foundation of the current Internet, assumes a sufficiently low packet loss rate for links in communication path. On the other hand, for communication services such as mobile and wireless communications, communication link tends to be disruptive. In this paper, we propose Layer-5 temporally-spliced path protocol (L5-TSPP), which provides disruption-tolerance in the L5 temporally-spliced path (L5-TSP), as one of the communication paths provided by Layer-5 (L5-paths). We design and implement an API for using L5-paths (L5 API). The L5 API is designed and implemented to support not only POSIX systems but also non-POSIX systems. L5 API and L5-TSPP are implemented in the user space in Go language. The measurement results show that L5-TSP achieves lower and more stable connection establishment time and better end-to-end throughput in the presence of disruption than conventional communication paths.

In this paper, we introduce our latest progress in the colloidal quantum dot enhanced color conversion layer for micro LEDs. Different methods of how to deploy colloidal quantum dots can be discussed and reviewed. The necessity of the using color conversion layer can be seen and color conversion efficiency of such layer can be calculated from the measured spectrum. A sub-pixel size of 5 micron of colloidal quantum dot pattern can be demonstrated in array format.

Liquid crystal (LC) device has high tunability with low power consumption and it is important not only in visible region but also in terahertz region. In this study, birefringence and absorption losses of hydrogen-bonded LC was estimated at 2.5 THz. Our results indicate that introduction of alkoxy chain to hydrogen-bonded LC is effective to increase birefringence in terahertz region. These results indicate that hydrogen-bonded LCs are a strong candidate for future terahertz devices because of their excellent properties in the terahertz region.

A mobile crowdsensing system (MCS) utilizes a crowd of users to collect large-scale data using their mobile devices efficiently. The collected data are usually linked with sensitive information, raising the concerns of user privacy leakage. To date, many approaches have been proposed to protect the users' privacy, with the majority relying on a centralized structure, which poses though attack and intrusion vulnerability. Some studies build a distributed platform exploiting a blockchain-type solution, which still requires a fully trusted third party (TTP) to manage a reliable reward distribution in the MCS. Spurred by the deficiencies of current methods, we propose a distributed user privacy protection structure that combines blockchain and a trusted execution environment (TEE). The proposed architecture successfully manages the users' privacy protection and an accurate reward distribution without requiring a TTP. This is because the encryption algorithms ensure data confidentiality and uncouple the correlation between the users' identity and the sensitive information in the collected data. Accordingly, the smart contract signature is used to manage the user deposit and verify the data. Extensive comparative experiments verify the efficiency and effectiveness of the proposed combined blockchain and TEE scheme.

The high energy consumption of current processors causes several problems, including a limited clock frequency, short battery lifetime, and reduced device reliability. It is therefore important to reduce the energy consumption of the processor. Among resources in a processor, the issue queue (IQ) is a large consumer of energy, much of which is consumed by the wakeup logic. Within the wakeup logic, the tag comparison that checks source operand readiness consumes a significant amount of energy. This paper proposes an energy reduction scheme for tag comparison, called double-stage tag comparison. This scheme first compares the lower bits of the tag and then, only if these match, compares the higher bits. Because the energy consumption of tag comparison is roughly proportional to the total number of bits compared, energy is saved by reducing this number. However, this sequential comparison increases the delay of the IQ, thereby increasing the clock cycle time. Although this can be avoided by allocating an extra cycle to the issue operation, this in turn degrades the IPC. To avoid IPC degradation, we reconfigure a small number of entries in the IQ, where several oldest instructions that are likely to have an adverse effect on performance reside, to a single stage for tag comparison. Our evaluation results for SPEC2017 benchmark programs show that the double-stage tag comparison achieves on average a 21% reduction in the energy consumed by the wakeup logic (15% when including the overhead) with only 3.0% performance degradation.

To access Internet services supported in a home network, a mobile node must obtain the right to use an access network, and it must be able to contact a home network gateway to access the Internet in the home network. This means that the device must be authenticated by an AP to use the access network, and it must additionally be authenticated by the home network gateway to access its home network. EAP-PEAP is currently the most commonly used authentication protocol in access networks, and IKEv2 is common security protocol for mutual authentication on the Internet. As the procedures in EAP-PEAP and IKEv2 are quite similar, EAP-PEAP can be replaced by IKEv2. If the access network authentication uses IKEv2-based protocols and the home network authentication also uses IKEv2, the IKEv2 messages exchanged in each authentication become duplicated. However, it should be noted that EAP-IKEv2 is not able to carry EAP exchanges. We propose a hybrid authentication mechanism that can be used to authenticate a mobile node for both networks simultaneously. The proposed mechanism is based on the IKEv2-EAP exchanges instead of the EAP exchanges currently used to authenticate the access network, but our scheme adopts the encapsulation method defined by EAP-IKEv2 to transport the IKEv2 message over IEEE 802.11 so as not to change the current access network authentication architecture and the message format used by the authentication protocols. The scheme authenticates both networks through a single IKEv2 authentication, rather than two authentication procedures - one for the access network and one for the home network. This reduces the number of exchanged messages and authentication time.

In recent years, federated learning has attracted more and more attention as it could collaboratively train a global model without gathering the users' raw data. It has brought many challenges. In this paper, we proposed layer-based federated learning system with privacy preservation. We successfully reduced the communication cost by selecting several layers of the model to upload for global averaging and enhanced the privacy protection by applying local differential privacy. We evaluated our system in non independently and identically distributed scenario on three datasets. Compared with existing works, our solution achieved better performance in both model accuracy and training time.

In this letter, we derive a novel and accurate closed-form bit error rate (BER) approximation of the optical wireless communications (OWC) systems for the sub-carrier intensity modulation (SIM) employing binary phase-shift keying (BPSK) with multiple transmit and single receive apertures over strong atmospheric turbulence channels, which makes it possible to effectively investigate and predict the BER performance for various system configurations. Furthermore, we also derive a concise asymptotic BER formula to quantitatively evaluate the asymptotically achievable error performance (i.e., asymptotic diversity and combining gains) in the high signal-to-noise (SNR) regimes. Some numerical results are provided to corroborate the accuracy and effectiveness of our theoretical expressions.

It has been demonstrated that large-scale labeled datasets facilitate the success of machine learning. However, collecting labeled data is often very costly and error-prone in practice. To cope with this problem, previous studies have considered the use of a complementary label, which specifies a class that an instance does not belong to and can be collected more easily than ordinary labels. However, complementary labels could also be error-prone and thus mitigating the influence of label noise is an important challenge to make complementary-label learning more useful in practice. In this paper, we derive conditions for the loss function such that the learning algorithm is not affected by noise in complementary labels. Experiments on benchmark datasets with noisy complementary labels demonstrate that the loss functions that satisfy our conditions significantly improve the classification performance.

With the increase of IoT devices, P2P-based IoT platforms have been attracting attention because of their capabilities of building and maintaining their networks autonomously in a decentralized way. In particular, Skip Graph, which has a low network rebuilding cost and allows range search, is suitable for the platform. However, when data observed at geographically close points have similar values (i.e. when data have strong spatial autocorrelation), existing types of Skip Graph degrade their search performances. In this paper, we propose a query transfer method that enables efficient search even for spatially autocorrelated data by adaptively using two-types of Skip Graph depending on the key-distance to the target key. Simulation results demonstrate that the proposed method can reduce the query transfer distance compared to the existing method even for spatially autocorrelated data.

Machine learning, especially deep learning, is dramatically changing the methods associated with optical thin-film inverse design. The vast majority of this research has focused on the parameter optimization (layer thickness, and structure size) of optical thin-films. A challenging problem that arises is an automated material search. In this work, we propose a new end-to-end algorithm for optical thin-film inverse design. This method combines the ability of unsupervised learning, reinforcement learning and includes a genetic algorithm to design an optical thin-film without any human intervention. Furthermore, with several concrete examples, we have shown how one can use this technique to optimize the spectra of a multi-layer solar absorber device.

Blockchain is one of the prominent rapidly used technology in the last decade in various applications. In recent years, many researchers explored the capabilities of blockchain in smart IoT to address various security challenges. Integration of IoT and blockchain solves the security problems but scalability still remains a huge challenge. To address this, various AI techniques can be applied in the blockchain IoT framework, thus providing an efficient information system. In this survey, various works pertaining to the domains which integrate AI, IoT and Blockchain has been explored. Also, this article discusses potential industrial use cases on fusion of blockchain, AI and IoT applications and its challenges.

Personalized news recommendation is becoming increasingly important for online news platforms to help users alleviate information overload and improve news reading experience. A key problem in news recommendation is learning accurate user representations to capture their interest. However, most existing news recommendation methods usually learn user representation only from their interacted historical news, while ignoring the clustering features among users. Here we proposed a hierarchical user preference hash network to enhance the representation of users' interest. In the hash part, a series of buckets are generated based on users' historical interactions. Users with similar preferences are assigned into the same buckets automatically. We also learn representations of users from their browsed news in history part. And then, a Route Attention is adopted to combine these two parts (history vector and hash vector) and get the more informative user preference vector. As for news representation, a modified transformer with category embedding is exploited to build news semantic representation. By comparing the hierarchical hash network with multiple news recommendation methods and conducting various experiments on the Microsoft News Dataset (MIND) validate the effectiveness of our approach on news recommendation.