Blockchain is a distributed ledger technology used for trading digital assets, such as cryptocurrency, and trail records that need to be audited by third parties. The use cases of blockchain are expanding beyond cryptocurrency management. In particular, the token economy, in which tokenized assets are exchanged across different blockchain ledgers, is gaining popularity. Cross-chain technologies such as atomic swap have emerged as security technologies to realize this new use case of blockchain. However, existing approaches of cross-chain technology have unresolved issues, such as application limitations on different blockchain platforms owing to the incompatibility of the communication interface and crypto algorithm and inability to handle a complex business logic such as the escrow trade. In this study, the ConnectionChain is proposed, which enables the execution of an extended smart contract using abstracted operation on interworking ledgers. Moreover, field experimental results using the system prototype are presented and explained.

In Bitcoin and Ethereum, nodes require a large storage capacity to maintain all of the blockchain data such as transactions. As of September 2021, the storage size of the Bitcoin blockchain has expanded to 355 GB, and it has increased by approximately 50 GB every year over the last five years. This storage requirement is a major hurdle to becoming a block proposer or validator. We propose an architecture called Trail that allows nodes to hold all blocks in a small storage and to generate and validate blocks and transactions. A node in Trail holds all blocks without transactions, UTXOs or account balances. The block size is approximately 8 kB, which is 100 times smaller than that of Bitcoin. On the other hand, a client who issues transactions needs to hold proof of its assets. Thus, compared to traditional blockchains, clients must store additional data. We show that proper data archiving can keep the account device storage size small. Then, we propose a method of executing smart contracts in Trail using a threshold signature. Trail allows more users to be block proposers and validators and improves the decentralization and security of the blockchain.

The Proof of Stake (PoS) protocol is one of the consensus algorithms for blockchain, in which the integrity of a new block is validated according to voting by nodes called validators. However, due to validator-oriented voting, voting results are likely to be false when the number of validators with wrong votes increases. In the PoS protocol, validators are motivated to vote correctly by reward and penalty mechanisms. With such mechanisms, validators who contribute to correct consensuses are rewarded, while those who vote incorrectly are penalized. In this paper, we consider an incentivization mechanism based on the voting profile of a validator, which is estimated from the voting history of the validator. In this mechanism, the stake collected due to the penalties are redistributed to validators who vote correctly, improving the incentive of validators to contribute to the system. We evaluate the performance of the proposed mechanism by computer simulations, investigating the impacts of system parameters on the estimation accuracy of the validator profile and the amount of validator's stake. Numerical results show that the proposed mechanism can estimate the voting profile of a validator accurately even when the voting profile dynamically changes. It is also shown that the proposed mechanism gives more reward to validators who vote correctly with high voting profile.

Sharding is a solution to the blockchain scalability problem. A sharded blockchain divides consensus nodes (validators) into groups called shards and processes transactions separately to improve throughput and latency. In this paper, we analyze the rational behavior of users in account/balance model-based sharded blockchains and identify a phenomenon in which accounts (users' wallets and smart contracts) eventually get concentrated in a few shards, making shard loads unfair. This phenomenon leads to bad user experiences, such as delays in transaction inclusions and increased transaction fees. To solve this problem, we propose two load balancing methods in account/balance model-based sharded blockchains. Both methods perform load balancing by periodically reassigning accounts: in the first method, the blockchain protocol itself performs load balancing and in the second method, wallets perform load balancing. We discuss the pros and cons of the two protocols, and apply the protocols to the execution sharding in Ethereum 2.0, an existing sharding design. Further, we analyze by simulation how the protocols behave to confirm that we can observe smaller transaction delays and fees. As a result, we released the simulation program as “Shargri-La,” a simulator designed for general-purpose user behavior analysis on the execution sharding in Ethereum 2.0.

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.

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.

Bitcoin is one of popular cryptocurrencies widely used over the world, and its blockchain technology has attracted considerable attention. In Bitcoin system, it has been reported that transactions are prioritized according to transaction fees, and that transactions with high priorities are likely to be confirmed faster than those with low priorities. In this paper, we consider performance modeling of Bitcoin-blockchain system in order to characterize the transaction-confirmation time. We first introduce the Bitcoin system, focusing on proof-of-work, the consensus mechanism of Bitcoin blockchain. Then, we show some queueing models and its analytical results, discussing the implications and insights obtained from the queueing models.

A private decentralized e-health environment, empowered by blockchain technology, grants authorized healthcare entities to legitimately access the patient's medical data without relying on a centralized node. Every activity from authorized entities is recorded immutably in the blockchain transactions. In terms of privacy, the e-health system preserves a default privacy option as an initial state for every patient since the patients may frequently customize their medical data over time for several purposes. Moreover, adjustments in the patient's privacy contexts are often solely from the patient's initiative without any doctor or stakeholders' recommendation. Therefore, we design, implement, and evaluate user-defined data privacy utilizing nudge theory for decentralized e-health systems named PDPM to tackle these issues. Patients can determine the privacy of their medical records to be closed to certain parties. Data privacy management is dynamic, which can be executed on the blockchain via the smart contract feature. Tamper-proof user-defined data privacy can resolve the dispute between the e-health entities related to privacy management and adjustments. In short, the authorized entities cannot deny any changes since every activity is recorded in the ledgers. Meanwhile, the nudge theory technique supports providing the best patient privacy recommendations based on their behaviour activities even though the final decision rests on the patient. Finally, we demonstrate how to use PDPM to realize user-defined data privacy management in decentralized e-health environments.

The continuous development of the mobile computing environment has led to the emergence of fintech to enable convenient financial transactions in this environment. Previously proposed financial identity services mostly adopted centralized servers that are prone to single-point-of-failure problems and performance bottlenecks. Blockchain-based self-sovereign identity (SSI), which emerged to address this problem, is a technology that solves centralized problems and allows decentralized identification. However, the verifiable credential (VC), a unit of SSI data transactions, guarantees unlimited right to erasure for self-sovereignty. This does not suit the specificity of the financial transaction network, which requires the restriction of the right to erasure for credit evaluation. This paper proposes a model for VC generation and revocation verification for credit scoring data. The proposed model includes double zero knowledge - succinct non-interactive argument of knowledge (zk-SNARK) proof in the VC generation process between the holder and the issuer. In addition, cross-revocation verification takes place between the holder and the verifier. As a result, the proposed model builds a trust platform among the holder, issuer, and verifier while maintaining the decentralized SSI attributes and focusing on the VC life cycle. The model also improves the way in which credit evaluation data are processed as VCs by granting opt-in and the special right to erasure.

The phenomenon where a speaker mixes two or more languages within the same conversation is called code-switching (CS). Handling CS is challenging for automatic speech recognition (ASR) and text-to-speech (TTS) because it requires coping with multilingual input. Although CS text or speech may be found in social media, the datasets of CS speech and corresponding CS transcriptions are hard to obtain even though they are required for supervised training. This work adopts a deep learning-based machine speech chain to train CS ASR and CS TTS with each other with semisupervised learning. After supervised learning with monolingual data, the machine speech chain is then carried out with unsupervised learning of either the CS text or speech. The results show that the machine speech chain trains ASR and TTS together and improves performance without requiring the pair of CS speech and corresponding CS text. We also integrate language embedding and language identification into the CS machine speech chain in order to handle CS better by giving language information. We demonstrate that our proposed approach can improve the performance on both a single CS language pair and multiple CS language pairs, including the unknown CS excluded from training data.

In recent years FPGA has become popular in CNN acceleration, and many CNN-to-FPGA toolchains are proposed to fast deploy CNN on FPGA. However, for these toolchains, updating CNN network means regeneration of RTL code and re-implementation which is time-consuming and may suffer timing-closure problems. So, we propose HBDCA: a toolchain and corresponding accelerator. The CNN on HBDCA is defined by the content of BRAM. The toolchain integrates UpdateMEM utility of Xilinx, which updates content of BRAM without re-synthesis and re-implementation process. The toolchain also integrates TensorFlow Lite which provides high-accuracy quantization. HBDCA supports 8-bits per-channel quantization of weights and 8-bits per-layer quantization of activations. Upgrading CNN on accelerator means the kernel size of CNN may change. Flexible structure of HBDCA supports kernel-level parallelism with three different sizes (3×3, 5×5, 7×7). HBDCA implements four types of parallelism in convolution layer and two types of parallelism in fully-connected layer. In order to reduce access number to memory, both spatial and temporal data-reuse techniques were applied on convolution layer and fully-connect layer. Especially, temporal reuse is adopted at both row and column level of an Input Feature Map of convolution layer. Data can be just read once from BRAM and reused for the following clock. Experiments show by updating BRAM content with single UpdateMEM command, three CNNs with different kernel size (3×3, 5×5, 7×7) are implemented on HBDCA. Compared with traditional design flow, UpdateMEM reduces development time by 7.6X-9.1X for different synthesis or implementation strategy. For similar CNN which is created by toolchain, HBDCA has smaller latency (9.97µs-50.73µs), and eliminates re-implementation when update CNN. For similar CNN which is created by dedicated design, HBDCA also has the smallest latency 9.97µs, the highest accuracy 99.14% and the lowest power 1.391W. For different CNN which is created by similar toolchain which eliminate re-implementation process, HBDCA achieves higher speedup 120.28X.

We consider an asymptotic stabilization problem for a chain of integrators by using an event-triggered controller. The times required between event-triggered executions and controller updates are uncertain, time-varying, and not necessarily small. We show that the considered system can be asymptotically stabilized by an event-triggered gain-scaling controller. Also, we show that the interexecution times are lower bounded and their lower bounds can be manipulated by a gain-scaling factor. Some future extensions are also discussed. An example is given for illustration.

Blockchain-based voting, including liquid voting, has been extensively studied in recent years. However, it remains challenging to implement liquid voting on blockchain using Ethereum smart contract. The challenge comes from the gas limit, which is that the number of instructions for processing a ballot cannot exceed a certain amount. This restricts the application scenario with respect to algorithms whose time complexity is linear to the number of voters, i.e., O(n). As the blockchain technology can well share and reuse the resources, we study a model of liquid voting on blockchain and propose a fast algorithm, named Flash, to eliminate the restriction. The key idea behind our algorithm is to shift some on-chain process to off-chain. In detail, we first construct a Merkle tree off-chain which contains all voters' properties. Second, we use Merkle proof and interval tree to process each ballot with O(log n) on-chain time complexity. Theoretically, the algorithm can support up to 21000 voters with respect to the current gas limit on Ethereum. Experimentally, the result implies that the consumed gas fee remains at a very low level when the number of voters increases. This means our algorithm makes liquid voting on blockchain practical even for massive voters.

In network function virtualization (NFV) environments, service chaining is an emerging technology that enables network operators to provide network service dynamically and flexibly by using virtual network function (VNF). In the service chaining, a service chain is expected to be constructed based on VNF performances such as dependences among VNFs and traffic changing effects in VNFs. For achieving optimal data transmission services in NFV environments, we focus on the optimal service chain construction based on VNF performances so that both the maximum amount of traffic on links and the total number of VNF instances are decreased. In this paper, at first, an optimization problem is formulated for determining placements of VNFs and a route for each service chain. The service chains can be constructed by solving this optimization problem with an optimization software or meta-heuristic algorithm. Then, for the optimization problem, we propose a heuristic service chain construction algorithm. By using our proposed algorithm, the service chains can be constructed appropriately more quickly. We evaluate the performance of the proposed heuristic algorithm with simulation, and we investigate the effectiveness of the heuristic algorithm from the performance comparison. From some numerical examples, we show that the proposed heuristic algorithm is effective to decrease the amount of traffic and the number of VNF instances. Moreover, it is shown that our proposed heuristic algorithm can construct service chains quickly.

Loading test vectors and unloading test responses in shift mode during scan testing cause many scan flip-flops to switch simultaneously. The resulting shift switching activity around scan flip-flops can cause excessive local IR-drop that can change the states of some scan flip-flops, leading to test data corruption. A common approach solving this problem is partial-shift, in which multiple scan chains are formed and only one group of the scan chains is shifted at a time. However, previous methods based on this approach use random grouping, which may reduce global shift switching activity, but may not be optimized to reduce local shift switching activity, resulting in remaining high risk of test data corruption even when partial-shift is applied. This paper proposes novel algorithms (one optimal and one heuristic) to group scan chains, focusing on reducing local shift switching activity around scan flip-flops, thus reducing the risk of test data corruption. Experimental results on all large ITC'99 benchmark circuits demonstrate the effectiveness of the proposed optimal and heuristic algorithms as well as the scalability of the heuristic algorithm.

Network function virtualization (NFV) enables network operators to flexibly provide diverse virtualized functions for services such as Internet of things (IoT) and mobile applications. To meet multiple quality of service (QoS) requirements against time-varying network environments, infrastructure providers must dynamically adjust the amount of computational resources, such as CPU, assigned to virtual network functions (VNFs). To provide agile resource control and adaptiveness, predicting the virtual server load via machine learning technologies is an effective approach to the proactive control of network systems. In this paper, we propose an adjustment mechanism for regressors based on forgetting and dynamic ensemble executed in a shorter time than that of our previous work. The framework includes a reducing training data method based on sparse model regression. By making a short list of training data derived from the sparse regression model, the relearning time can be reduced to about 57% without degrading provisioning accuracy.

Blockchain is a distributed ledger technology for recording transactions. When two or more miners create different versions of the blocks at almost the same time, blockchain forks occur. We model the mining process with forks by a discrete event system and design a supervisor controlling these forks.

A blockchain, which is well known as one of the distributed ledgers, has attracted in many research fields. In this paper, we discuss the effectiveness and limitation of a blockchain in distributed optimization. In distributed optimization, the original problem is decomposed, and the local problems are solved by multiple agents. In this paper, ADMM (Alternating Direction Method of Multipliers) is utilized as one of the powerful methods in distributed optimization. In ADMM, an aggregator is basically required for collecting the computation result in each agent. Using blockchains, the function of an aggregator can be contained in a distributed ledger, and an aggregator may not be required. As a result, tampering from attackers can be prevented. As an application, we consider energy management systems (EMSs). By numerical experiments, the effectiveness and limitation of blockchain-based distributed optimization are clarified.

This paper proposes a voice conversion (VC) method based on a model that links linguistic and acoustic representations via latent phonological distinctive features. Our method, called speech chain VC, is inspired by the concept of the speech chain, where speech communication consists of a chain of events linking the speaker's brain with the listener's brain. We assume that speaker identity information, which appears in the acoustic level, is embedded in two steps — where phonological information is encoded into articulatory movements (linguistic to physiological) and where articulatory movements generate sound waves (physiological to acoustic). Speech chain VC represents these event links by using an adaptive restricted Boltzmann machine (ARBM) introducing phoneme labels and acoustic features as two classes of visible units and latent phonological distinctive features associated with articulatory movements as hidden units. Subjective evaluation experiments showed that intelligibility of the converted speech significantly improved compared with the conventional ARBM-based method. The speaker-identity conversion quality of the proposed method was comparable to that of a Gaussian mixture model (GMM)-based method. Analyses on the representations of the hidden layer of the speech chain VC model supported that some of the hidden units actually correspond to phonological distinctive features. Final part of this paper proposes approaches to achieve one-shot VC by using the speech chain VC model. Subjective evaluation experiments showed that when a target speaker is the same gender as a source speaker, the proposed methods can achieve one-shot VC based on each single source and target speaker's utterance.

Contextual information is a crucial factor in natural language processing tasks such as sequence labeling. Previous studies on contextualized embedding and word embedding have explored the context of word-level tokens in order to obtain useful features of languages. However, unlike it is the case in English, the fundamental task in East Asian languages is related to character-level tokens. In this paper, we propose a contextualized character embedding method using n-gram multi-sequences information with long short-term memory (LSTM). It is hypothesized that contextualized embeddings on multi-sequences in the task help each other deal with long-term contextual information such as the notion of spans and boundaries of segmentation. The analysis shows that the contextualized embedding of bigram character sequences encodes well the notion of spans and boundaries for word segmentation rather than that of unigram character sequences. We find out that the combination of contextualized embeddings from both unigram and bigram character sequences at output layer rather than the input layer of LSTMs improves the performance of word segmentation. The comparison showed that our proposed method outperforms the previous models.